E-Infrastructures and Technologies for Lifelong Learning: Next Generation Environments George Magoulas University of London, UK
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Library of Congress Cataloging-in-Publication Data
E-infrastructures and technologies for lifelong learning: next generation environments / George Magoulas, editor. p. cm. Includes bibliographical references and index. Summary: “This book provides a comprehensive review of state-of-the-art technologies for e-learning and lifelong learning, examining theoretical approaches, models, architectures, systems and applications”-- Provided by publisher. ISBN 978-1-61520-983-5 (hardcover) -- ISBN 978-1-61520-984-2 (ebook) 1. Continuing education--Technological innovations. 2. Continuing education-Computer-assisted instruction. I. Magoulas, George D. LC5225.D38E3 2011 374’.2--dc22 2011007463
British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.
Editorial Advisory Board Sara de Freitas, Coventry University, UK Yannis Dimitriadis, University of Valladolid, Spain Claude Frasson, Université de Montréal, Canada Michael J. Jacobson, University of Sydney, Australia Nancy Law, University of Hong Kong, HK Robert D. Macredie, Brunel University, UK Toshio Okamoto, The University of Electro-Communications, Japan Hokyoung Ryu, Massey University, New Zealand Demetrios Sampson, University of Piraeus, Greece Tamara Sumner, University of Colorado at Boulder, USA Tsutomu Terada, Kobe University, Japan
List of Reviewers Ellen Barbosa, Universidade de São Paulo, Brazil Jeremy Birnholtz, Cornell University, USA Santi Caballé, Universitat Oberta de Catalunya, Spain Patricia Charlton, Birkbeck College, University of London, UK Ines Di Loreto, Université Montpellier II, France Michael J. Jacobson, University of Sydney, Australia Jelena Jovanović, University of Belgrade, Serbia Thanasis Daradoumis, University of the Aegean, Greece Dionisis Dimakopoulos, Birkbeck College, University of London, UK Nicoletta Di Blas, Politecnico di Milano, Italy Maria Kordaki, University of Patras. Greece Eugenijus Kurilovas, Vilnius University Institute of Mathematics and Informatics, Lithuania Diana Laurillard, Institute of Education, UK Fotis Liarokapis, Coventry University, UK Manolis Mavrikis, Institute of Education, UK Russell T. Osguthorpe, Brigham Young University, USA Spyros Papadakis, Hellenic Open University, Greece
Alexandros Paramythis, Johannes Kepler Universität Linz, Austria Mary Pringle, Athabasca University, Canada Marco Ronchetti, Università degli Studi di Trento, Italy Hokyoung Ryu, Massey University, New Zealand Olga Santos, Universidad Nacional de Educación a Distancia, Spain Lucas Zamboulis, Birkbeck College, University of London, UK
Table of Contents
Preface................................................................................................................................................... xv Acknowledgment................................................................................................................................ xxii Section 1 Systems, Models and Architectures Chapter 1 Techniques, Technologies and Patents Related to Intelligent Educational Systems................................ 1 Jim Prentzas, Democritus University of Thrace, Greece Ioannis Hatzilygeroudis, University of Patras, Greece Chapter 2 A General Framework for Inclusive Lifelong Learning in Higher Education Institutions with Adaptive Web-Based Services that Support Standards.......................................................................... 29 Olga C. Santos, aDeNu Research Group, Computer Science School, UNED, Spain Jesus G. Boticario, aDeNu Research Group, Computer Science School, UNED, Spain Chapter 3 Diplek: An Open LMS that Supports Fast Composition of Educational Services................................ 59 C. T. Rodosthenous, University of Patras, Greece A. D. Kameas, Hellenic Open University, Greece P. E. Pintelas, University of Patras, Greece Chapter 4 Social, Personalized Lifelong Learning................................................................................................. 90 Alexandra Cristea, University of Warwick, UK Fawaz Ghali, University of Warwick, UK Mike Joy, University of Warwick, UK
Chapter 5 Mash-Up Personal Learning Environments......................................................................................... 126 Fridolin Wild, Open University, UK Felix Mödritscher, Vienna University of Economics and Business, Austria Steinn Sigurdarson, Open University, The Netherlands Chapter 6 Technological Evaluation and Optimization of E-Learning Systems Components............................. 150 Eugenijus Kurilovas, Institute of Mathematics and Informatics, Lithuania Valentina Dagiene, Institute of Mathematics and Informatics, Lithuania Section 2 Managing Digital Educational Content and Resources Chapter 7 Collaborative Development of Educational Modules: A Need for Lifelong Learning........................ 175 Ellen Francine Barbosa, University of São Paulo – ICMC/USP, Brazil José Carlos Maldonado, University of São Paulo – ICMC/USP, Brazil Chapter 8 Collaborative Learning Design within Open Source E-Learning Systems: Lessons Learned from an Empirical Study.......................................................................................... 212 Maria Kordaki, Patras University, Greece Haris Siempos, Patras University, Greece Thanasis Daradoumis, Open University of Catalonia, Spain Chapter 9 Building Bridges: Combining Webcasting and Videoconferencing in a Multi-Campus University Course................................................................................................................................ 234 Jeremy Birnholtz, Cornell University, USA & University of Toronto, Canada Ron Baecker, University of Toronto, Canada Simone Laughton, University of Toronto at Mississauga, Canada Clarissa Mak, University of Toronto, Canada Rhys Causey, University of Toronto, Canada Kelly Rankin, University of Toronto, Canada Chapter 10 Video-Lectures over Internet: The Impact on Education..................................................................... 253 Marco Ronchetti, Università degli Studi di Trento, Italy Chapter 11 Adaptive Sequencing of Information for Lifelong Learners............................................................... 271 Sergio Gutierrez-Santos, London Knowledge Lab, Birkbeck College, UK
Section 3 Developing and Accrediting Skills and Competences Chapter 12 Learning, Unlearning, and Relearning: Using Web 2.0 Technologies to Support the Development of Lifelong Learning Skills .......................................................................................... 292 Joanna C. Dunlap, University of Colorado Denver, USA Patrick R. Lowenthal, University of Colorado Denver, USA Chapter 13 Technological Aids to the Efficient Assessment of Prior Learning .................................................... 316 Desirée Joosten–ten Brinke, Open University of the Netherlands, The Netherlands Marcel Van der Klink, Open University of the Netherlands, The Netherlands Wendy Kicken, Open University of the Netherlands, The Netherlands Peter B. Sloep, Open University of the Netherlands, The Netherlands Chapter 14 Game Based Lifelong Learning .......................................................................................................... 337 Sebastian Kelle, Open University of the Netherlands, The Netherlands Steinn E. Sigurðarson, Open University of the Netherlands, The Netherlands Wim Westera, Open University of the Netherlands, The Netherlands Marcus Specht, Open University of the Netherlands, The Netherlands Chapter 15 Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context . ....................................................................................................................... 350 Ni Chang, Indiana University South Bend, USA Compilation of References................................................................................................................ 371 About the Contributors..................................................................................................................... 413 Index.................................................................................................................................................... 422
Detailed Table of Contents
Preface................................................................................................................................................... xv Acknowledgment................................................................................................................................ xxii Section 1 Systems, Models and Architectures Chapter 1 Techniques, Technologies and Patents Related to Intelligent Educational Systems .............................. 1 Jim Prentzas, Democritus University of Thrace, Greece Ioannis Hatzilygeroudis, University of Patras, Greece “Techniques, Technologies and Patents Related to Intelligent Educational Systems” by Prentzas and Hatzilygeroudis presents state-of-the-art technologies/techniques of a new generation of educational systems, called Intelligent Educational Systems-IESs. IESs can be used effectively in different contexts. They can accommodate a diverse set of lifelong learning requirements, possessing the capability to personalize instruction to the needs and skills of lifelong learners, and can be integrated into workplace learning and personal development. The chapter reviews technologies and techniques used in IESs and surveys corresponding patents. It covers open issues and problems and discusses solutions that build on Artificial Intelligence methods and recent patents. Chapter 2 A General Framework for Inclusive Lifelong Learning in Higher Education Institutions with Adaptive Web-Based Services that Support Standards ........................................................................ 29 Olga C. Santos, aDeNu Research Group, Computer Science School, UNED, Spain Jesus G. Boticario, aDeNu Research Group, Computer Science School, UNED, Spain Santos and Boticario in “A General Framework for Inclusive Lifelong Learning in Higher Education Institutions with Adaptive Web-Based Services that Support Standards” present a service-oriented framework to support full participation of disabled students in the learning process. Their approach, which combines universal design principles with personalization technologies, follows the complete life cycle of service adaptation and is designed for higher education institutions. It takes into account standards and specifications that try to cover a wide range of possible user needs, and provides inclusive
personalization support through dynamic contextual recommendations. The authors explore aspects of this framework in a case study that concerns two European universities specialized in distance learning. Chapter 3 Diplek: An Open LMS that Supports Fast Composition of Educational Services ............................... 59 C. T. Rodosthenous, University of Patras, Greece A. D. Kameas, Hellenic Open University, Greece P. E. Pintelas, University of Patras, Greece Rodosthenous, Kameas and Pintelas in “Diplek: An Open LMS that Supports Fast Composition of Educational Services” investigate the domain of open-source educational systems. Their chapter describes Diplek, a platform developed using service oriented architecture to enable easy access to educational content and activities for novice learners and instructors with limited IT skills. The authors describe the design and development of the software, which is intended to be used in small educational settings, where computer experts are hard to reach and the need for an easy to use LMS is prominent. Diplek offers a set of special tools and can be operated even without an Internet connection or a Web browser, e.g. in situations where computing equipment is old and the connection between workstations is limited to a LAN. Chapter 4 Social, Personalized Lifelong Learning . .............................................................................................. 90 Alexandra Cristea, University of Warwick, UK Fawaz Ghali, University of Warwick, UK Mike Joy, University of Warwick, UK In “Social, Personalized Lifelong Learning”, Cristea, Ghali and Joy look at how the social Web is changing the way in which instructors and students communicate with each other, as well as the methods of creating and sharing knowledge. The authors’ aim is not only to create personalized learning content but to use content creation and delivery as a means to provide new ways of learning and better learning experiences, customized to a specific learner or group of learners. The authors discuss some well-known models and frameworks for designing personalized e-learning, and show how an existing framework can be extended in order to cover the more complex relationships that emerge when social aspects are considered in lifelong learning and e-learning systems. Their approach allows students to contribute to the authoring phase with different sets of privileges, and differentiates between collaborative authoring (e.g. editing other users’ content, tagging, rating), and authoring for collaboration. The chapter presents the theoretical fundamentals for the framework extension, as well as an implementation and a prototype evaluation. Their findings support the need for an approach that would blend individualized/personalized learning with social learning experiences in future learning environments. Chapter 5 Mash-Up Personal Learning Environments . ...................................................................................... 126 Fridolin Wild, Open University, UK Felix Mödritscher, Vienna University of Economics and Business, Austria Steinn Sigurdarson, Open University, The Netherlands
“Mash-Up Personal Learning Environments” by Wild, Mödritscher and Sigurdarson exploits previous research in personal learning environments and end-user development to propose a new concept in educational systems design, named mash-up personal learning environment. Mash-up personal learning environments enable learners to utilize an open, heterogeneous set of tools to communicate with each other, access content and tools into a learning network, and collaborate with others on different learning activities. The chapter examines the new concept in relation to existing models for personalized adaptive learning and explains the adaptation mechanisms for learning environment’s construction and maintenance. It demonstrates this approach with a prototypical implementation and describes its use in a lifelong learning scenario. Chapter 6 Technological Evaluation and Optimization of E-Learning Systems Components ........................... 150 Eugenijus Kurilovas, Institute of Mathematics and Informatics, Lithuania Valentina Dagiene, Vilnius University Institute of Mathematics and Informatics, Lithuania Kurilovas and Dagiene in “Technological Evaluation and Optimization of E-Learning Systems Components” examine the technological quality of e-learning systems components, i.e. learning objects, learning object repositories, and virtual learning environments. The authors analyze existing technological quality evaluation models and methods, and propose a form of evaluation that is organized along two dimensions: learning software “internal quality” and “quality in use.” The first one, “internal quality”, is a descriptive characteristic that describes the quality of software irrespective of the context of use, whilst the second one, “quality in use”, is an evaluative software characteristic that is obtained by making a judgment based on criteria that determine the value of a software for a particular project or user/group. The proposed approach is further investigated in terms of optimal parameters, and an additive utility function based on multiple criteria, ratings, and weights, is formulated to optimize the learning software characteristics with respect to learners’ needs. Section 2 Managing Digital Educational Content and Resources Chapter 7 Collaborative Development of Educational Modules: A Need for Lifelong Learning ....................... 175 Ellen Francine Barbosa, University of São Paulo – ICMC/USP, Brazil José Carlos Maldonado, University of São Paulo – ICMC/USP, Brazil Barbosa and Maldonado in “Collaborative Development of Educational Modules: A Need for Lifelong Learning” acknowledge that lifelong learning has to accommodate needs of a diverse student population. Their work emphasizes the development of “open and collaborative learning materials” through systematic and innovative mechanisms for creating educational content. The authors focus on issues of content modeling aiming at helping developers to determine the relevant parts of a knowledge domain and to structure concepts and related information of a module. Their aim is to create educational modules that can be easily transferred to different institutions and learning environments and can effectively support non-traditional environments, motivating the transition from lecture-based to self-directed and lifelong learning. The produced modules promote better the development of lifelong competences and
expertise in several related knowledge domains, engaging learners and teachers in an empowering way. The authors illustrate the applicability of their approach with an example of collaborative development for an educational module in the “software testing” domain. Chapter 8 Collaborative Learning Design within Open Source E-Learning Systems: Lessons Learned from an Empirical Study . ....................................................................................... 212 Maria Kordaki, Patras University, Greece Haris Siempos, Patras University, Greece Thanasis Daradoumis, Open University of Catalonia, Spain In “Collaborative Learning Design within Open Source E-Learning Systems: Lessons Learned from an Empirical Study”, Kordaki, Siempos, and Daradoumis investigate collaborative learning design from the instructors’ perspective. They conduct a study where a group of prospective computer technology professionals are asked to design collaborative learning courses on Moodle-a well known open source Learning Management System-for students studying computing. The authors’ study reveals a number of serious problems faced by the participants, especially those with limited experience in learning design, such as lack of aligning collaborative tasks with appropriate assessment methods. The authors propose the design and development of a set of computer-based collaborative learning patterns that reflect a variety of collaboration methods in order to support instructors. These learning design patterns are content free and could be used as scaffolding elements for the design of collaborative learning activities for online and blended courses. Lastly, the chapter provides some examples of designs using patterns within LAMS. Chapter 9 Building Bridges: Combining Webcasting and Videoconferencing in a Multi-Campus University Course ............................................................................................................................... 234 Jeremy Birnholtz, Cornell University, USA & University of Toronto, Canada Ron Baecker, University of Toronto, Canada Simone Laughton, University of Toronto at Mississauga, Canada Clarissa Mak, University of Toronto, Canada Rhys Causey, University of Toronto, Canada Kelly Rankin, University of Toronto, Canada Birnholtz, Baecker, Laughton, Mak, Causey, and Rankin in “Building Bridges: Combining Webcasting and Videoconferencing in a Multi-Campus University Course” investigate lifelong learning scenarios where participants are geographically distributed. This creates the need for designers to support meaningful learning and instruction using flexible system configurations for content delivery. The authors present a novel system that bridges videoconferencing and webcasting technologies and a study of its use for the delivery of a multi-campus university course. Their system uses webcasting to deliver content and allows webcast viewers to periodically participate more actively via on-demand, temporary two-way videoconferencing links that become a part of the streamed webcast that is visible and audible to all. The authors present an analysis of interaction and awareness in distance learning contexts. Their results suggest that designers should consider issues of awareness and interaction when
making educational materials available to lifelong learners via distance learning technologies. Lastly, the chapter presents a list of design guidelines for developing these technologies further. Chapter 10 Video-Lectures over Internet: The Impact on Education . .................................................................. 253 Marco Ronchetti, Università degli Studi di Trento, Italy In “Video-Lectures over Internet: The Impact on Education,” Ronchetti looks at how multimedia content in the form of video is used for delivering online lectures. The chapter reviews the various directions that research has taken over the last 10 years to support and enhance this technology for educational purposes, and discusses the pedagogical soundness of the idea of using videos over Internet for teaching and learning. It presents overwhelming evidence from the literature in favor of this technology as a creative way to change the didactic paradigm of teaching and create resources that can be eminently suitable for informal lifelong learning or self-study for students, who cannot physically attend classroom lectures. The author also discusses ways to increase the value of video-lectures for e-learning and lifelong learning through the use of advanced functionalities/features. Chapter 11 Adaptive Sequencing of Information for Lifelong Learners .............................................................. 271 Sergio Gutierrez-Santos, London Knowledge Lab, Birkbeck College, UK Gutierrez-Santos in “Adaptive Sequencing of Information for Lifelong Learners” investigates issues of reusing learning materials from different sources, and of adapting educational resources to different learners instead of using a one-size-fits-all strategy. The author focuses on the problem of combining and sequencing resources, exploring how to adapt sequences, and how to share them among systems by using the semantics of the well-known IMS Learning Design (LD) standard. The chapter suggests an approach, based on graphs, that is designed for scalability and flexible enough to allow defining cycles in the sequencing process. It relies on hierarchy and compartmentalization to facilitate reuse. The author explores the possibility of using IMS LD as a medium to interchange adaptive sequencings and explains the main challenges involved in this task, providing lessons learnt in the process about the limits of the IMS LD. Section 3 Developing and Accrediting Skills and Competences Chapter 12 Learning, Unlearning, and Relearning: Using Web 2.0 Technologies to Support the Development of Lifelong Learning Skills .......................................................................................... 292 Joanna C. Dunlap, University of Colorado Denver, USA Patrick R. Lowenthal, University of Colorado Denver, USA Dunlap and Lowenthal in “Learning, Unlearning, and Relearning: Using Web 2.0 Technologies to Support the Development of Lifelong Learning Skills” analyse Web 2.0 technologies, such as blogging, social networking, document co-creation, and resource sharing tools, with respect to their potential
for helping educators to achieve teaching objectives, and students to develop lifelong learning skills in changing the social and the professional contexts. The authors explain how educators can use Web 2.0 technologies to create learning experiences that have the potential to help students develop skills and dispositions, e.g. autonomy, reflection, and collaboration, which are needed to become effective lifelong learners. They share several ideas for using Web 2.0 technologies and make recommendations for instructional designers and decision makers in educational institutions. Chapter 13 Technological Aids to the Efficient Assessment of Prior Learning .................................................... 316 Desirée Joosten–ten Brinke, Open University of the Netherlands, The Netherlands Marcel Van der Klink, Open University of the Netherlands, The Netherlands Wendy Kicken, Open University of the Netherlands, The Netherlands Peter B. Sloep, Open University of the Netherlands, The Netherlands In “Technological Aids to the Efficient Assessment of Prior Learning”, Joosten–ten Brinke, Van der Klink, Kicken, and Sloep examine lifelong learning Assessment of Prior Learning (APL). The authors emphasize the importance of taking into account prior learning experiences and competences when designing lifelong learning courses in order to enhance employees’ further professional development and learning. The chapter discusses the major components of the APL procedure, including the current possibilities for exchange and operability (e.g. the IMS QTI and the IMS ePortfolio). It describes the workflow of APL and the instruments needed in this procedure. The authors explain how re-use of instruments, such as competence profiles, e-portfolios, and rubrics specifications, can reduce costs of conducting APL, and support assessors in their tasks. However, they identify several challenges involved, such as the need for more advanced metadata specifications, and for tools for indexing and browsing structures together with techniques for visualization of competence information objects. Despite the availability of some technical solutions, there is still absence of generally accepted competence profiles, which leads to serious interoperability problems. To alleviate this situation, the authors elaborate a new model of assessment for APL and validate its effectiveness. Chapter 14 Game Based Lifelong Learning .......................................................................................................... 337 Sebastian Kelle, Open University of the Netherlands, The Netherlands Steinn E. Sigurðarson, Open University of the Netherlands, The Netherlands Wim Westera, Open University of the Netherlands, The Netherlands Marcus Specht, Open University of the Netherlands, The Netherlands In “Game Based Lifelong Learning,” Kelle, Sigurðarson, Westera, and Specht explore how game design patterns could be used for developing skills in lifelong learning scenarios. The authors start by examining digital games as a means of learning and provide an overview of the current state of the art in this area. They argue that transforming learning activities into games can benefit both learners and course developers, particularly in lifelong learning courses that incorporate elements of distance learning or are run in mixed-mode. Learners’ self-motivation and a sense of “ownership of learning” have been acknowledged as critical factors for learning throughout life, and the authors demonstrate with some simple examples how game design patterns could be used to design learning activities.
Chapter 15 Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context . ....................................................................................................................... 350 Ni Chang, Indiana University South Bend, USA Chang in “Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context” focuses on the importance of constructive and beneficial feedback through assessment in an e-text-based context for the purpose of lifelong learning. Lifelong learners not only need individualized support, but also have high expectations from instructors in terms of timely and quality feedback. The author investigates issues of interaction between instructors and students and discusses teacher immediacy cues. The chapter also suggests practical strategies to exploit online communication tools and encourage students to engage in revisions of their work. Lastly, the author discusses how instructor’s technological skills can be underpinned by theoretical knowledge of the importance of feedback in delivering effective learning and lifelong learning experiences and an understanding of instructors’ role and behavior in this context. Compilation of References................................................................................................................ 371 About the Contributors..................................................................................................................... 413 Index.................................................................................................................................................... 422
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Preface
AN OVERVIEW OF THE SUBJECT AREA Knowledge society has placed new requirements on the education sector with respect to the level of support needed by individuals and organizations, and raised widespread awareness of an educational system that is not limited to childhood and adolescence, but supports learning as a lifelong activity. In this vein, educational institutions have recently undergone a series of transformations changing their organizational structures and teaching practices as well as the way they are serving local communities and the public in general. For example, the educational system is moving from the early notion of access based on defined routes and discrete courses to one more centrally concerned with the “accessibility” of institutions throughout life and the suitability and flexibility of their curricula for a diverse student audience. Digital technologies can support each stage of the learners’ journey. The typical approach to integrating technology in education begins with the context in which technologies operate. Often, the approach is content-driven as software tools aim to support student progression in particular subject matter areas. Learning management systems, multimedia software, and virtual learning environments are examples of technologies that follow this approach. In the context of lifelong learning, however, the learning process has become more complex as lifelong learners have heterogeneous backgrounds and differ in traits such as skills, aptitudes and preferences for processing information, constructing meaning from information, and applying it to real-world situations. It is, therefore, imperative to focus on new approaches to manage the complexity of the learning experience and accommodate learner’s needs in order to maximize the effectiveness of e-learning and lifelong learning. Particularly in lifelong learning, technology is needed to assist learners to access, compose, and manage their learning under varying circumstances and settings, such as institutional, informal, and work-based. Moreover, in this context, e-learning is not only centered on the individual learner but also becomes a collaborative and community-based process. Thus, it is essential to ensure that learners are experiencing an appropriately increased learning challenge and autonomy as well as independence by becoming more aware of their own studying and thinking processes. This demands that tools and guidance are provided to support this process of planning a lifelong learning pathway through the various courses, levels, and stages of a formal educational system, as well as in informal learning contexts. At the same time, available tools should go beyond self-directed learning, towards the autonomous and dynamic creation of lifelong learning communities and distributed e-learning services. Accordingly, learning technologies are moving away from typical computer-assisted learning tools, e.g. educational multimedia software, or even Virtual Learning Environments and Learning Management Systems, such as Blackboard and Moodle that strictly operate within a formal learning context. To address these challenges, the integration of: innovative models, methods and technologies for the creation, storage, use, and exchange of knowledge resources and user-generated content, design tools for
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learning activities and units of learning, competence development programs, and networks for lifelong learning is investigated world wide. It is expected that a successful integration of advanced technologies could enrich educational approaches along several dimensions: (i) advanced networking architectures, knowledge building tools, and groupware can support building of learning communities and collaboration among teachers, lifelong learners and communities; (ii) modeling tools, online simulators, Web-based planning tools, e-portfolio systems, and Web-based, information rich open learner models can foster learner engagement with the lifelong learning process, promote reflection, and lead to the development of further lifelong learning skills; (iii) authoring systems, learning design tools, assessment tools, and personalized learning environments can transform teaching, learning, and assessment by bringing together instructional activities, learners and technological standards. For example, advanced technologies that employ decentralized solutions have been developed: online communities created by online collaborative tool, blogs, wikis, webcasts, webinars and social networking applications have been adopted in the teaching practice. Moreover, several consortia and initiatives around the world have been established with the aim to support the development of a new einfrastructures and services for lifelong learning, such as the E-Learning Framework (www.elframework. org) promoted by JISC (UK) and DEST (AUS), the IMS Abstract Framework (www.imsglobal.org/af/), the Open Knowledge Initiative (www.okiproject.org), the TENCompetence Project and the European Learning Grid Infrastructure Project (www.ELeGI.org). This lifelong learning e-infrastructure is being composed mostly of open-source, standards-based, sustainable and innovative technologies, and is expected to provide easy access to facilities that enable the lifelong development of competencies and expertise in the various occupations and fields of knowledge. In this context, advanced technologies make possible to integrate in-house and open source components with commercial applications by agreeing upon common service definitions, behaviors, data and user models, and protocols within educational organizations and federations. This way permits the development of modular and flexible distributed systems, where components can be added, removed, or replaced more easily than in traditional models of e-learning systems, and where new applications or systems can be composed from collections of available services. It is, of course, necessary that the use of this technology is grounded in broader instructional theories to be effective. To this end, technologies should be seamlessly integrated with pedagogy and embedded in the organizational practice in order to deliver lifelong learning and engage learners in an empowering way.
WHY THIS BOOK IS NEEDED Lifelong learning is recognized as a critical educational objective in order to meet ever-changing societal and professional demands. Educational institutions operating in an evolving educational landscape require informed decision-making and planning through successfully matching technologies, curriculum targets, lifelong learners’ needs, and organizations’ level of learning technology integration. This book brings together a wide range of contributions about the development of infrastructures for e-learning and lifelong learning and on how these can be integrated in teaching practice. It complements available literature on lifelong learning that concentrates on relevant policy matters, and societal, pedagogical, and economical issues relating to or affecting lifelong learning. The book provides a comprehensive overview of the state-of-the-art in relevant technologies covering theoretical approaches, models, architectures, systems, and applications in real world settings. In par-
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ticular, it explores advanced architectures and platforms, and presents studies of technologies adoption, development, and use for lifelong learning, showing experimental results from systems and applications that employ advanced e-learning and lifelong learning technologies to support real practices.
WHAT READERS CAN EXPECT FROM THIS BOOK The book is a collection of original research and development work in technology for lifelong learning, and could be used as a one-stop reference for those working in the area providing insight into the future of lifelong learning technologies. It may benefit readers from different disciplines helping them to gain global understanding of the various issues involved in designing, developing, and deploying technologies for lifelong learning. Book chapters are self-contained and cover various aspects of lifelong learning systems, including creating, managing and modeling content and technologies, integration with teaching practice, instructional design, e-learning and lifelong learning delivery, open problems, and research issues. This volume can make a valuable contribution assisting readers in the analysis and implementation of e-learning technologies, supporting professional development in educational organizations, and the implementation of advanced e-learning and lifelong learning technologies.
HOW THIS BOOK IS ORGANIZED The book is organized in three sections. In Section, “Systems, Models and Architectures,” technical contributions that aim to address user and organizational needs through new systems, models and architectures for the development of institutional technological infrastructures for lifelong learning, are presented. Section 2, entitled “Managing Digital Educational Content and Resources,” includes contributions that focus on technologies that support creation, organization, delivery, and use of digital content and educational resources, which are a vital element of modern institutional infrastructures for the delivery of engaging lifelong learning experiences. Lastly, Section 3, entitled “Developing and Accrediting Skills and Competences,” directs attention to the use of technologies for skills development and support of lifelong learners. In what follows, an overview of the book chapters is presented.
Section 1: Systems, Models and Architectures “Techniques, Technologies and Patents Related to Intelligent Educational Systems” by Prentzas and Hatzilygeroudis presents state-of-the-art technologies/techniques of a new generation of educational systems, called Intelligent Educational Systems-IESs. IESs can be used effectively in different contexts. They can accommodate a diverse set of lifelong learning requirements, possessing the capability to personalize instruction to the needs and skills of lifelong learners, and can be integrated into workplace learning and personal development. The chapter reviews technologies and techniques used in IESs and surveys corresponding patents. It covers open issues and problems and discusses solutions that build on Artificial Intelligence methods and recent patents. Santos and Boticario in “A General Framework for Inclusive Lifelong Learning in Higher Education Institutions with Adaptive Web-Based Services that Support Standards” present a service-oriented
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framework to support full participation of disabled students in the learning process. Their approach, which combines universal design principles with personalization technologies, follows the complete life cycle of service adaptation and is designed for higher education institutions. It takes into account standards and specifications that try to cover a wide range of possible user needs, and provides inclusive personalization support through dynamic contextual recommendations. The authors explore aspects of this framework in a case study that concerns two European universities specialized in distance learning. Rodosthenous, Kameas and Pintelas in “Diplek: An Open LMS that Supports Fast Composition of Educational Services” investigate the domain of open-source educational systems. Their chapter describes Diplek, a platform developed using service oriented architecture to enable easy access to educational content and activities for novice learners and instructors with limited IT skills. The authors describe the design and development of the software, which is intended to be used in small educational settings, where computer experts are hard to reach and the need for an easy to use LMS is prominent. Diplek offers a set of special tools and can be operated even without an Internet connection or a Web browser, e.g. in situations where computing equipment is old and the connection between workstations is limited to a LAN. In “Social, Personalized Lifelong Learning”, Cristea, Ghali and Joy look at how the social Web is changing the way in which instructors and students communicate with each other, as well as the methods of creating and sharing knowledge. The authors’ aim is not only to create personalized learning content but to use content creation and delivery as a means to provide new ways of learning and better learning experiences, customized to a specific learner or group of learners. The authors discuss some well-known models and frameworks for designing personalized e-learning, and show how an existing framework can be extended in order to cover the more complex relationships that emerge when social aspects are considered in lifelong learning and e-learning systems. Their approach allows students to contribute to the authoring phase with different sets of privileges, and differentiates between collaborative authoring (e.g. editing other users’ content, tagging, rating), and authoring for collaboration. The chapter presents the theoretical fundamentals for the framework extension, as well as an implementation and a prototype evaluation. Their findings support the need for an approach that would blend individualized/personalized learning with social learning experiences in future learning environments. “Mash-Up Personal Learning Environments” by Wild, Mödritscher and Sigurdarson exploits previous research in personal learning environments and end-user development to propose a new concept in educational systems design, named mash-up personal learning environment. Mash-up personal learning environments enable learners to utilize an open, heterogeneous set of tools to communicate with each other, access content and tools into a learning network, and collaborate with others on different learning activities. The chapter examines the new concept in relation to existing models for personalized adaptive learning and explains the adaptation mechanisms for learning environment’s construction and maintenance. It demonstrates this approach with a prototypical implementation and describes its use in a lifelong learning scenario. Kurilovas and Dagiene in “Technological Evaluation and Optimization of E-Learning Systems Components” examine the technological quality of e-learning systems components, i.e. learning objects, learning object repositories, and virtual learning environments. The authors analyze existing technological quality evaluation models and methods, and propose a form of evaluation that is organized along two dimensions: learning software “internal quality” and “quality in use.” The first one, “internal quality”, is a descriptive characteristic that describes the quality of software irrespective of the context of use, whilst the second one, “quality in use”, is an evaluative software characteristic that is obtained by making a judgment based on criteria that determine the value of a software for a particular project or user/
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group. The proposed approach is further investigated in terms of optimal parameters, and an additive utility function based on multiple criteria, ratings, and weights, is formulated to optimize the learning software characteristics with respect to learners’ needs.
Section 2: Managing Digital Educational Content and Resources Barbosa and Maldonado in “Collaborative Development of Educational Modules: A Need for Lifelong Learning” acknowledge that lifelong learning has to accommodate needs of a diverse student population. Their work emphasizes the development of “open and collaborative learning materials” through systematic and innovative mechanisms for creating educational content. The authors focus on issues of content modeling aiming at helping developers to determine the relevant parts of a knowledge domain and to structure concepts and related information of a module. Their aim is to create educational modules that can be easily transferred to different institutions and learning environments and can effectively support non-traditional environments, motivating the transition from lecture-based to self-directed and lifelong learning. The produced modules promote better the development of lifelong competences and expertise in several related knowledge domains, engaging learners and teachers in an empowering way. The authors illustrate the applicability of their approach with an example of collaborative development for an educational module in the “software testing” domain. In “Collaborative Learning Design within Open Source E-Learning Systems: Lessons Learned from an Empirical Study”, Kordaki, Siempos, and Daradoumis investigate collaborative learning design from the instructors’ perspective. They conduct a study where a group of prospective computer technology professionals are asked to design collaborative learning courses on Moodle-a well known open source Learning Management System-for students studying computing. The authors’ study reveals a number of serious problems faced by the participants, especially those with limited experience in learning design, such as lack of aligning collaborative tasks with appropriate assessment methods. The authors propose the design and development of a set of computer-based collaborative learning patterns that reflect a variety of collaboration methods in order to support instructors. These learning design patterns are content free and could be used as scaffolding elements for the design of collaborative learning activities for online and blended courses. Lastly, the chapter provides some examples of designs using patterns within LAMS. Birnholtz, Baecker, Laughton, Mak, Causey, and Rankin in “Building Bridges: Combining Webcasting and Videoconferencing in a Multi-Campus University Course” investigate lifelong learning scenarios where participants are geographically distributed. This creates the need for designers to support meaningful learning and instruction using flexible system configurations for content delivery. The authors present a novel system that bridges videoconferencing and webcasting technologies and a study of its use for the delivery of a multi-campus university course. Their system uses webcasting to deliver content and allows webcast viewers to periodically participate more actively via on-demand, temporary two-way videoconferencing links that become a part of the streamed webcast that is visible and audible to all. The authors present an analysis of interaction and awareness in distance learning contexts. Their results suggest that designers should consider issues of awareness and interaction when making educational materials available to lifelong learners via distance learning technologies. Lastly, the chapter presents a list of design guidelines for developing these technologies further. In “Video-Lectures over Internet: The Impact on Education,” Ronchetti looks at how multimedia content in the form of video is used for delivering online lectures. The chapter reviews the various directions that research has taken over the last 10 years to support and enhance this technology for educational
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purposes, and discusses the pedagogical soundness of the idea of using videos over Internet for teaching and learning. It presents overwhelming evidence from the literature in favor of this technology as a creative way to change the didactic paradigm of teaching and create resources that can be eminently suitable for informal lifelong learning or self-study for students, who cannot physically attend classroom lectures. The author also discusses ways to increase the value of video-lectures for e-learning and lifelong learning through the use of advanced functionalities/features. Gutierrez-Santos in “Adaptive Sequencing of Information for Lifelong Learners” investigates issues of reusing learning materials from different sources, and of adapting educational resources to different learners instead of using a one-size-fits-all strategy. The author focuses on the problem of combining and sequencing resources, exploring how to adapt sequences, and how to share them among systems by using the semantics of the well-known IMS Learning Design (LD) standard. The chapter suggests an approach, based on graphs, that is designed for scalability and flexible enough to allow defining cycles in the sequencing process. It relies on hierarchy and compartmentalization to facilitate reuse. The author explores the possibility of using IMS LD as a medium to interchange adaptive sequencings and explains the main challenges involved in this task, providing lessons learnt in the process about the limits of the IMS LD.
Section 3: Developing and Accrediting Skills and Competences Dunlap and Lowenthal in “Learning, Unlearning, and Relearning: Using Web 2.0 Technologies to Support the Development of Lifelong Learning Skills” analyse Web 2.0 technologies, such as blogging, social networking, document co-creation, and resource sharing tools, with respect to their potential for helping educators to achieve teaching objectives, and students to develop lifelong learning skills in changing the social and the professional contexts. The authors explain how educators can use Web 2.0 technologies to create learning experiences that have the potential to help students develop skills and dispositions, e.g. autonomy, reflection, and collaboration, which are needed to become effective lifelong learners. They share several ideas for using Web 2.0 technologies and make recommendations for instructional designers and decision makers in educational institutions. In “Technological Aids to the Efficient Assessment of Prior Learning”, Joosten–ten Brinke, Van der Klink, Kicken, and Sloep examine lifelong learning Assessment of Prior Learning (APL). The authors emphasize the importance of taking into account prior learning experiences and competences when designing lifelong learning courses in order to enhance employees’ further professional development and learning. The chapter discusses the major components of the APL procedure, including the current possibilities for exchange and operability (e.g. the IMS QTI and the IMS ePortfolio). It describes the workflow of APL and the instruments needed in this procedure. The authors explain how re-use of instruments, such as competence profiles, e-portfolios, and rubrics specifications, can reduce costs of conducting APL, and support assessors in their tasks. However, they identify several challenges involved, such as the need for more advanced metadata specifications, and for tools for indexing and browsing structures together with techniques for visualization of competence information objects. Despite the availability of some technical solutions, there is still absence of generally accepted competence profiles, which leads to serious interoperability problems. To alleviate this situation, the authors elaborate a new model of assessment for APL and validate its effectiveness. In “Game Based Lifelong Learning,” Kelle, Sigurðarson, Westera, and Specht explore how game design patterns could be used for developing skills in lifelong learning scenarios. The authors start by examining
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digital games as a means of learning and provide an overview of the current state of the art in this area. They argue that transforming learning activities into games can benefit both learners and course developers, particularly in lifelong learning courses that incorporate elements of distance learning or are run in mixed-mode. Learners’ self-motivation and a sense of “ownership of learning” have been acknowledged as critical factors for learning throughout life, and the authors demonstrate with some simple examples how game design patterns could be used to design learning activities. Chang in “Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context” focuses on the importance of constructive and beneficial feedback through assessment in an etext-based context for the purpose of lifelong learning. Lifelong learners not only need individualized support, but also have high expectations from instructors in terms of timely and quality feedback. The author investigates issues of interaction between instructors and students and discusses teacher immediacy cues. The chapter also suggests practical strategies to exploit online communication tools and encourage students to engage in revisions of their work. Lastly, the author discusses how instructor’s technological skills can be underpinned by theoretical knowledge of the importance of feedback in delivering effective learning and lifelong learning experiences and an understanding of instructors’ role and behavior in this context.
HOW TO USE THIS BOOK The book could be used as an advanced upper-level course supplement and resource for instructors. It also targets educational technologists and staff at educational institutions who research lifelong learning technologies or are involved in decision-making about relevant technologies and infrastructure. Educational technologists and software developers could use this book as an informative introduction to the area. The book can play the role of a reference text as it captures the state-of-the-art and illustrates technical solutions for the development of e-learning and lifelong learning systems. Managerial and research staff working in the areas of technology-enhanced learning, Web-based learning environments, and personalized learning will find in this book material that could help them to assess the benefits of the various technologies for e-learning and lifelong learning and make informed decisions about future research, development, and implementation. For instructors and e-learning systems developers, this book offers chapters that go beyond the level of Learning Management Systems and Virtual Learning Environments. New technologies have been developed, and complex infrastructures are being increasingly implemented in educational organizations worldwide, and gaining understanding of how to embrace them effectively is critical for educational organizations. Lastly, for high-level undergraduate and postgraduate students, this book offers examples of technologies’ design and implementation, covering key stages in their lifecycle and analyses of how they are perceived by users. It could be used as a source of reference and a guide for students taking modules or working on projects related to large scale Information Systems, especially in the context of educational organizations. George Magoulas University of London, UK
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Acknowledgment
I am grateful to the authors of this book who contributed their excellent work, and to the members of the Editorial Advisory Board and the reviewers for providing high quality feedback and insightful suggestions. I would also like to thank the staff at IGI Global for their assistance and understanding throughout this project. Lastly, this book would have been impossible without constant support, patience, and love from Olga and Myrto. George Magoulas University of London, UK
Section 1
Systems, Models and Architectures
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Chapter 1
Techniques, Technologies and Patents Related to Intelligent Educational Systems Jim Prentzas Democritus University of Thrace, Greece Ioannis Hatzilygeroudis University of Patras, Greece
ABSTRACT E-learning systems play an increasingly important role in lifelong learning. Tailoring the learning process to individual needs is a key issue in such systems. Intelligent Educational Systems (IESs) are e-learning systems employing Artificial Intelligence methods to effectively adapt to learner characteristics. Main types of IESs are Intelligent Tutoring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent methods. In this chapter, the authors present technologies and techniques used in the primary modules of IESs and survey corresponding patents. They present issues and problems involving specific IES modules as well as the overall IES. The authors discuss solutions offered for such issues by Artificial Intelligence methods and patents. They also discuss categorization aspects of patents related to IESs and briefly present the work described in some representative patents. Lastly, the authors outline future research directions regarding IESs.
INTRODUCTION The need for lifelong learning is becoming increasingly evident involving education institutes, public/private sector organizations, companies, DOI: 10.4018/978-1-61520-983-5.ch001
research centers, tutors, employees and learners. As knowledge constantly evolves, lifelong learning becomes necessary for almost everybody. Learning throughout life becomes a necessity for personal, professional and social reasons (Jarvis, 2008). Lifelong learning plays an important role in enhancing personal knowledge, social inclu-
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Techniques, Technologies and Patents Related to Intelligent Educational Systems
sion and employability. Furthermore, in modern society, people and organizations undergo processes of transition. Each one must be prepared for transitions, engaging in lifelong learning as a fundamental strategy for handling change (Field, Gallacher, & Ingram, 2009). A main driving force of lifelong learning is e-learning. Numerous computer-based systems have been developed for education during the last decades. Such systems are usually addressed to school students, pre-graduate and post-graduate university students, employees of organizations/companies, unemployed and generally members of online communities. An important requirement when e-learning systems are used for lifelong learning is the capability to personalize instruction to the needs and skills of learners. This requirement becomes increasingly vital as the number of learners accessing an e-learning system increases. Learners have different preferences and learning styles, set diverse educational goals and usually have unequal knowledge levels regarding a specific teaching subject. Lack of sufficient spare time due to pressing (family/professional) obligations is a factor frequently resulting in loss of interest when interacting with ineffective e-learning systems. An e-learning system tailored to learner needs/ skills saves learners time/effort and motivates participation in learning process. Traditional Computer-Assisted Instruction (CAI) systems are based on shallow representation of teaching domain, learner data and pedagogical methods. It is difficult for them to adjust effectively the learning process as they provide limited ways of adaptation and learner evaluation. These drawbacks gave rise to a new generation of educational systems encompassing intelligence called Intelligent Educational Systems (IESs) (Aroyo, Graesser & Johnson, 2007; Brusilovsky & Peylo, 2003). IESs incorporate Artificial Intelligence (AI) techniques/mechanisms to model learners as well as knowledge regarding the teaching subject and tailor learning experience to learner needs. Main IES types are Intelligent Tu-
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toring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) using intelligent methods. IESs support lifelong learning as they provide personalized instruction. IESs place lifelong learners at centre stage by making them more responsible for the results of the learning process (Drachsler, Hummel, & Koper, 2008). Due to the fact that IESs can be used effectively in different contexts, they satisfy lifelong learning requirements. IES functionality can be used in education institutes but can be also integrated into workplace learning and personal development. An interesting aspect of IESs is their constant evolution by exploiting advances in Web-based technologies, AI techniques and Computer Science in general. Advances in these fields provide the impetus to develop advanced IES applications that satisfy learning necessities not covered by previous systems. Various AI methods have been applied to IESs, enabling implementation of several online/ offline intelligent functions to accommodate different IES user types. Use of several AI methods in IESs has been vastly explored. However, use of certain other AI methods has not been adequately explored. Figure 1 depicts an IES’s basic architecture. It mainly consists of the following components: (a) domain knowledge, which contains learning content and information about the learning subject, (b) user (or student) modeling unit, which records learner information, (c) pedagogical module, which encompasses knowledge regarding various pedagogical decisions, (d) user interface, which communicates with users. Sometimes, an extra component is considered, namely expert module. Expert module typically deals with interactive problem solving, e.g. with providing intelligent help. It acts as an expert (tutor) who supervises learners as they solve problems and gives advice, hints, examples etc. This module can be considered as part of pedagogical module in Figure 1. In this chapter, we outline technologies and techniques used in ITSs and AEHSs, in all their
Techniques, Technologies and Patents Related to Intelligent Educational Systems
Figure 1. Basic architecture of an intelligent educational system
basic components. We discuss issues and problems arising in IESs and present relevant solutions. Solutions come from AI methods and patents related to IESs. We outline functions that can be implemented by AI methods in different phases of an IES lifecycle. We survey several recent patents related to IESs categorizing them according to various aspects. At the end, we specify future trends regarding IESs. This chapter may be used as a guide by researchers, students and professionals working in the field of e-learning and lifelong learning as it presents state-of-the-art technologies/techniques in IESs, relevant work that has been patented and future directions. To the authors’ best knowledge, no survey related to AI methods/technologies suitable for IESs and categorization of patents related to IESs has been presented till now. (Hatzilygeroudis & Prentzas, 2009) presents an initial survey on recent patents related to IESs. The structure of the chapter is as follows. The following section describes the necessary background knowledge. The third section presents functions and issues regarding IES modules as well as corresponding solutions from AI methods and patents related to IESs. Then the fourth section discusses issues regarding future research directions for IESs. Finally, conclusions are drawn.
BACKGROUND The first computer-based educational systems were called Computer-Assisted Instruction (CAI) systems. CAI systems started in the 1950s as simple ‘linear programs’, which evolved afterwards. In the 1960s, ‘branching programs’ offered corrective feedback adapting teaching to learner responses. In the 1970s, ‘generative’ systems appeared that in certain domains (e.g. arithmetic) could generate learning content themselves (Yazdani, 1988). A major disadvantage of those systems is their inability to adapt instruction to learners’ diversity and individual needs. Evaluation of learner knowledge is not performed intelligently, but based on final responses to ‘yesno’ and multiple-choice questions. In addition, sequencing strategies of learning items in CAIs follow traditional linear and branching approaches (Brusilovsky & Peylo, 2003). So, CAI systems cannot answer learner questions concerning ‘why’ and ‘how’ the task is performed (Yazdani, 1988). IESs surpass such drawbacks. Intelligent Tutoring Systems (ITSs) constitute a popular type of IESs. ITSs take into account learner knowledge level and skills and adapt learning content presentation to needs and abilities of him/ her (Polson & Richardson, 1988; Yazdani, 1988; Woolf, 1992). ITSs traditionally lay emphasis on AI techniques to achieve their tasks. An ITS should be able to perform tutoring tasks such as
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selecting/sequencing of presented learning items, responding to learner questions concerning instructional goals and content, in-depth analysis of learner responses to presented problems/questions and determining when assistance is needed and how to provide it (Polson & Richardson, 1988). ITSs are suitable for lifelong learners as they can provide the most suitable learning activities to meet learner goals. Another type of educational systems providing personalization is Adaptive Educational Hypermedia Systems (AEHSs) (Brusilovsky, Kobsa & Vassileva, 1998). AEHSs are specifically developed for hypertext environments, such as the Web. They use technologies/techniques from Adaptive Hypermedia (AH). Main services offered are adaptive presentation of learning content and adaptive navigation by adapting page hyperlinks. Compared to ITSs, they offer greater sense of freedom to learners, since they enable a guided navigation to user-adapted educational pages. Furthermore, they dynamically construct or adapt educational pages whereas in ITSs educational pages contents are typically static (Papanikolaou et al., 2003). A number of AHESs use efficient but simple techniques that can hardly be considered as ‘intelligent’ (Brusilovsky & Peylo, 2003). Enhancing AEHSs with ITS techniques creates another type of IESs. AEHSs enhance self-directed lifelong learning as they provide advice/guidance to identify the most suitable learning activities matching their needs. The fields of ITSs and AEHSs were well established before the Internet age (Brusilovsky & Peylo, 2003). During that period, ITSs and AEHSs were usually developed as stand-alone systems. However, emergence of the Web gave rise to various Web-based ITSs and AEHSs, called Web-Based Intelligent Educational Systems (WBIESs) (Hatzilygeroudis, 2004). Several of them combine ITS and AEHS technologies. WBIESs are accessible by a large number of learners giving the opportunity for effective lifelong learning experiences as well as thorough testing and subsequent refinement of their mechanisms/
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services. Large quantities of learner data are stored in WBIESs that can be exploited to extract knowledge regarding the pros and cons of IES learning process. Therefore, the Web offers IESs the chance to become more acceptable by those seeking lifelong learning. As the Web imposes constraints on learning process, the role of AI methods is becoming increasingly important.
CHALLENGES IN DEVELOPING INTELLIGENT EDUCATIONAL SYSTEMS In this section, we discuss issues/problems regarding IESs and present certain solutions/recommendations. Issues involve all three primary IES modules (domain knowledge, user modeling unit, pedagogical module), all stages of IES life cycle (construction, operation and maintenance) and all user types (authors, tutors, learners, knowledge engineers). We also present brief background information concerning IES module functionality.
Domain Knowledge Issues Domain knowledge contains knowledge regarding the learning subject and actual learning content. It usually consists of two parts: knowledge concepts and learning units. Knowledge concepts refer to basic domain knowledge entities. Relations can be defined among knowledge concepts, among learning units as well as among concepts and learning units. Typical relations among concepts are the following: •
• •
Prerequisite: Some concepts are prerequisite of others. A learner should know some or all prerequisite concepts of a concept to proceed to it. Part-of: Many simpler concepts are part of a more complex concept. Is-a: This relation connects a concept with others that are its typical instances.
Techniques, Technologies and Patents Related to Intelligent Educational Systems
Knowledge concepts and learning units form the backbone for more general domain knowledge items such as subsections, sections, chapters and so on. Learning units constitute the learning content presented to learners. Learning units may be static educational pages or fragments from which educational pages are dynamically generated. Each learning unit is associated with one or more knowledge concepts either prerequisite or outcome. Knowledge of learning unit’s prerequisite concepts is required for its comprehension. By studying learning units, learners gain knowledge of outcome concepts. The distinct representation of domain’s pedagogical structure (knowledge concepts) and actual learning content (learning units) greatly facilitates domain knowledge updates. Research has focused on learning objects that is, reusable/sharable learning fragments that can be dynamically selected/assembled to create learning content presented to users (Northrup, 2007). To facilitate management/selection/ordering of learning units, domain knowledge frequently includes a meta-description (metadata) of learning units based on their general attributes. There exist standards for learning unit meta-description such as ARIADNE, IEEE LTSC Learning Object Metadata (LOM), Dublin Core and SCORM. Such standards enable small, reusable and sharable learning content items, existence of interoperable learning content repositories and assembly of learning content on-the-fly. Such goals are significant in Web-based environments in which metadata is exploited by users/systems searching for relevant educational content. Several issues/problems concerning domain knowledge should be dealt with. Such issues are the following: •
Choosing a representation scheme for domain knowledge. The representation scheme should be able to naturally represent structural and relational knowl-
•
•
•
•
edge (Hatzilygeroudis & Prentzas, 2006). Structural knowledge is concerned with types of entities (i.e., concepts, chapters, etc.) and their interrelations. Often, those relationships are hierarchical concerning generalization/specialization relation. Relational knowledge concerns relations among domain entities. Those relations may be causal or dependency relations. Domain knowledge structure creation. Tools/techniques assisting in creating domain knowledge structure would be helpful (Baia & Chen, 2008; Hatzilygeroudis & Prentzas, 2008). Learning unit creation. Creation of learning content (e.g. exercises, examples) is usually done manually requiring time and effort. Tools facilitating learning unit creation are useful (Murray, 1999; Brusilovsky, 2003). Also methods for (semi-)automatic creation decrease IES development time especially if such methods are domainindependent (Fischer & Steinmetz, 2000). Due to the fact that large sets of exercises are necessary for learner assessment, formalization of the concept of parametric exercises (i.e. exercises whose generation is based on a set of parameters) is also required (Gutiérrez, Losa, & Kloos, 2008). Provision of learning unit meta-description (metadata). Learning unit meta-description is often provided manually. This process is time-consuming, expensive and errorprone. Methods to (semi-) automatically provide such data are required (Jovanovic, Gasevic & Devedzic, 2006). Maintenance of domain knowledge items. During IES operation, domain knowledge may undergo changes/refinements to provide improved learning experiences. Such changes may involve domain knowledge structure and/or learning units. Due to domain knowledge size, tools/methods facilitating domain knowledge management are
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Techniques, Technologies and Patents Related to Intelligent Educational Systems
•
necessary (Murray, 1999). Tools/methods recommending necessary changes are also required. Data from learner-IES interaction could be exploited for this purpose (Castro et al., 2007). Support for domain knowledge construction/maintenance by multiple collaborating authors. Web-based technologies enable creation of author communities and therefore multiple authors may collaborate for construction/maintenance of domain knowledge (Nesic, Gasevic & Jazayeri, 2008). Tools/methods facilitating collaborative domain knowledge construction/ maintenance are required.
User Modeling Issues The user (or student) model records learner data required to provide adaptation. The user model should be neither incomplete nor too complex, and so choosing the most appropriate learner characteristics to record is a challenge. Primary learner characteristics recorded in user model (Brusilovsky & Millan, 2007; Polson & Richardson, 1988) are the following: •
•
•
•
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History of learner’s interaction with the system: It may contain information such as domain knowledge parts accessed by the learner, answers given to questions/exercises, requests for hints/assistance, browsing activity, time consumed in studying presented learning content, etc. Goals: Goals can be discerned to high-level goals, e.g. a concept, or low-level goals, e.g. problem solving goals. Preferences: They mainly refer to educational content presentation parameters such as multimedia type preferences (e.g. text, images, or animations) regarding presented learning units or their level of detail. Background and experience: Learner’s background refers to experiences beyond
•
•
the scope of the learning subject such as experience in relevant fields, experience in using computers, familiarity with the system, etc. Knowledge level: Learner’s knowledge about the learning subject constitutes the most important learner characteristic. Estimation of knowledge level is an important problem in user modeling. Learning style: It involves learner’s preferred way of learning. Most learners favor particular approaches of acquiring and processing information. An IES should be able to assess learning styles of learners and tailor instruction accordingly.
Representation of learner knowledge is closely related with learner evaluation, i.e. a way to estimate how well a learner has learnt specific knowledge concepts. Although learner evaluation is often used as part of the user modeling unit, we consider more natural to include it in the pedagogical module. There are a number of ways for representing knowledge level in a user model. The overlay model (Polson & Richardson, 1988) is a popular and simple way of representing learner knowledge based on the pedagogical structure of knowledge domain. Learner knowledge is considered to be a subset of expert knowledge. For each domain knowledge item, the model retains a Boolean/scalar value representing learner knowledge level. For this purpose, fuzzy values may be used. Learning process carries on until learner knowledge is identical to expert knowledge. A disadvantage of overlay model is inability to represent possible learner misconceptions. For this purpose, the buggy model (Polson & Richardson, 1988) is used which represents learner knowledge as the union of a subset of domain knowledge and a set of misconceptions. The buggy model provides great assistance in correcting learner mistakes. A general way of modeling learners is to use stereotypes. Stereotypes denote predefined classes/categories/groups of learners. Stereotypes
Techniques, Technologies and Patents Related to Intelligent Educational Systems
are simple models and can be easily initialized and maintained. However, there may be difficulties in defining stereotype classes and in setting stereotype boundaries. Several issues concerning user modeling should be tackled. Such issues are the following: • •
•
•
Choosing which learner characteristics to record. Choosing the representation scheme for learner characteristics. Often boundaries of various levels (values) of characteristics are vague. For instance, the boundaries of values ‘medium’ and ‘high’ used to characterize learner knowledge level are not clear. Also information regarding learners usually involves uncertainty especially in Web-based environments (Jameson, 1995). Implementation of mechanisms to assess learner characteristics. Such mechanisms should frequently handle vague and uncertain data (Hatzilygeroudis & Prentzas, 2006). Exploitation of user model data to provide useful information to tutors and learning content authors. Such information may enlighten aspects of learner-IES interaction. Abundant learner data become available in WBIESs and its processing for knowledge extraction requires (semi-) automated and user-friendly tools/methods. Extracted information may be used to enhance IES modules (Romero & Ventura, 2007; Castro et al., 2007).
Pedagogical Module Issues The pedagogical module represents aspects of learning process. It provides a knowledge infrastructure to tailor learning content presentation based on user model. Main pedagogical tasks it is called to represent and handle are: course/lesson plan construction, selection of instructional/teaching strategy, selection of learning content, learner support and learner evaluation (see Figure 2).
Pedagogical Tasks A primary task of pedagogical module is to construct a plan of the course/lesson related to relevant learning goal(s) (Brusilovsky & Vassileva, 2003). A course/lesson plan actually consists of an ordered list of knowledge concepts. To construct a plan, the concept structure and user model are used. Due to existence of various relations among knowledge concepts, alternate plans, teaching the same learning goal(s), can be derived for different learners. For flexibility and effectiveness purposes, an IES usually offers more than one instructional/ teaching strategy. The pedagogical module is required to select the most appropriate instructional strategy for each learner. Due to circumstances, it may be necessary to change the instructional strategy at a given point. Selection of the instructional strategy is based on factors such as learner advancement, learner preferences and learning content characteristics.
Figure 2. Pedagogical tasks control flow
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Learning units are selected, sequenced and presented to the learner based on the constructed plan, the selected strategy, relations among knowledge concepts and learning units, user model information and learning unit metadata. To increase teaching effectiveness, plan construction and selection/ sequencing of learning units should not be static, but updated according to learner performance. Based on learner interaction with the IES, learner performance is evaluated, updating the user model accordingly and altering learning content presentation. The Pedagogical module should be able to identify what is wrong/incomplete in students’ responses and any missing knowledge or misconception causing error(s). Learner-adapted assistance is offered based on the ability to analyze final problem solutions and/or individual solution steps. It is important to dynamically track all types of learner errors and provide relevant and meaningful assistance/feedback. In certain cases, a learner may be able to pose dynamic questions to the IES, enabling more natural interaction and support. In case of learner errors, remedial tutoring may be necessary, causing global or local changes to course/lesson plan (Brusilovsky & Vassileva, 2003).
Adaptive Hypermedia Techniques Adaptive presentation and adaptive navigation are techniques coming from AH and used to accomplish pedagogical tasks. Adaptive presentation adapts an educational page’s contents to learner characteristics. A popular technique of adaptive presentation is known as additional explanations (Brusilovsky, Kobsa & Vassileva, 1998). In this method, various pieces of information constituting learning units are associated with conditions. When conditions are satisfied, the corresponding information is presented. This means that some learners will obtain additional information compared to others. Another popular method of adaptive presentation concerns the explanation variants (Brusilovsky, Kobsa & Vassileva, 1998)
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that stores variations of educational content and selects the most suitable one according to the user model. Ways of implementing it use page variants, fragment variants or their combination. Adaptive navigation assists in navigating educational system’s hyperspace by adapting hyperlinks to user model characteristics. Its goal is to provide ‘optimal paths’ through the learning material (Brusilovsky, 2007), i.e. course/lesson planning. Usual adaptive navigation techniques are the following: • • •
•
•
Direct guidance. It shows the next best learning unit to access. Link sorting. The links of a specific page are sorted according to their relevance. Link annotation. According to appropriateness of corresponding pages, links are annotated using link colors, icons, etc. Link hiding, removal, disabling. Links presumed to be of low interest are hidden and presented as simple text, totally removed or disabled. Link generation. It involves creation of new, non-authored links on a page.
Main Issues Several issues involving the pedagogical module should be tackled. Some of these are the following: •
•
•
Deciding which tasks will be implemented. A decision needs to be taken regarding which pedagogical tasks will be implemented. This depends on corresponding teaching subject and available/obtainable knowledge regarding such tasks. Pedagogical knowledge acquisition. Knowledge regarding various pedagogical tasks needs to be acquired from sources such as experts (e.g. tutors), literature and data. Maintenance of pedagogical knowledge. Tools/techniques are needed to manage
Techniques, Technologies and Patents Related to Intelligent Educational Systems
•
and refine the knowledge base(s) of the pedagogical module. Choosing intelligent techniques for implementing pedagogical tasks. Such techniques should be able to reach conclusions based on logical combinations of values of the user model characteristics. So, such techniques should represent heuristic knowledge that is, practical knowledge about how to solve problems based on experience (Hatzilygeroudis & Prentzas, 2006). Often such techniques should manage vagueness and uncertainty.
•
•
General Issues for IESs Apart from the above component-specific issues, there are a number issues related to IESs in general: •
•
•
Provision of authoring tools/facilities for IESs. Construction of an IES may be a time-consuming and expensive process requiring sophisticated personnel. Authoring tools provide facilities for learning content creation/management and/or construction/ management of knowledge bases concerning user modeling unit and pedagogical module. Authoring tools/facilities intend to decrease the effort for constructing IESs and enable quick design/evaluation cycles of prototype IES software (Murray, 1999; Brusilovsky, 2003). The domain type to which the IES can be applied. It is important to have advances in IESs that can be applied to different domains and not to specific ones. Collaborative learning (CL) support. IESs focus mainly on personalization to individual learners. However, CL plays an important role in instruction as quite often learners may learn in groups (Vygotsky, 1978; Antonis, Lampsas & Prentzas, 2008). Incorporation of such functionalities in
•
•
IESs may prove to be useful (Devedzic, 2005). Support of natural language processing or dynamic natural language responses. Such characteristics make learner interaction with the IES more effective by enhancing learning activities/processes and motivating learners to focus on learning goals (Johnson & Holder, 2008). Combination of IES and LMS functionalities. Web-based Learning Management Systems (LMSs) have become popular by supporting conventional classroom instruction (Woods, Baker & Hopper, 2004) and conducting pure distance learning courses (Antonis, Lampsas & Prentzas, 2008). Combination of LMS and WBIES technologies may surpass disadvantages of both LMSs and WBIESs (Kazanidis & Satratzemi, 2008; Simic, Gasevic & Devedzic, 2006). LMSs generally do not incorporate intelligence, whereas IESs do not provide the whole suite of LMS functionalities. Such functionalities not provided by IESs usually concern, among others, learning content management/sharing, synchronous/asynchronous collaboration/ communication, convenient and flexible control/administration to tutors. Tutoring with simulations. Tutoring simulations are considered interesting and motivating lifelong learning methodologies as they mimic learning in a real-world environment providing real-world like experiences. Simulations provide the opportunity for exploring, practicing and performing trials effectively and safely (Alessi & Trollip, 2000). Distributed learning content. Web-based learning content may be distributed to different repositories and not residing in a central repository. Effectively managing/ exploiting such learning resources be-
9
Techniques, Technologies and Patents Related to Intelligent Educational Systems
•
comes an issue (Nesic, Gasevic & Jazayeri, 2008). Communication/collaboration with other IESs. In a Web-based context, communication/collaboration among IESs may offer advantages to learners (e.g. finding additional learning resources (Avouris & Solomos, 2001), data exchange for user model initialization/updating and application of appropriate instructional strategies (Rishi & Govil, 2008)).
Table 1 outlines issues regarding IESs that should be tackled.
SOLUTIONS AND RECOMMENDATIONS In this section, we provide solutions/recommendations to several of the aforementioned issues regarding IESs.
Solutions from Artificial Intelligence Research Various AI technologies/techniques are used in IESs to achieve tasks concerning all stages of an IES’s life cycle (construction, operation, and maintenance), all types of users (authors, tutors, learners) and all its modules (domain knowledge, user modeling, pedagogical module). Knowledge representation and reasoning (KR&R) is of great importance, since what is mainly needed is representation of human reasoning (Russell & Norvig, 2009). Therefore, we briefly discuss issues related to the following methods/techniques: structured and relational schemes, rule-based reasoning, case-based reasoning, neural networks, Bayesian networks, fuzzy logic, constraint-based modeling, genetic algorithms, reinforcement learning, hybrid KR&R techniques, data mining and intelligent agents. Before proceeding, we give brief definitions for the terms ‘classification’, ‘clustering’ and ‘generalization’, which refer to corresponding types of tasks to be carried out. The term ‘clas-
Table 1. Summary of issues regarding IESs that should be tackled
Domain knowledge issues
Choosing a representation scheme for domain knowledge Domain knowledge structure creation Learning unit creation Provision of learning unit metadata Maintenance of domain knowledge items Support for domain knowledge construction/maintenance by multiple collaborating authors
User modeling issues
Choosing which learner characteristics to record Choosing the representation scheme for learner characteristics Implementation of mechanisms to assess learner characteristics Exploitation of user model data to provide useful information to tutors and learning content authors
Pedagogical module issues
Deciding which tasks will be implemented Pedagogical knowledge acquisition Maintenance of pedagogical knowledge Choosing intelligent techniques for implementing pedagogical tasks
General issues for IESs
Provision of authoring tools/facilities for IESs The domain type to which the IES can be applied Collaborative learning support Support of natural language processing or dynamic natural language responses Combination of IES and LMS functionality Tutoring with simulations Distributed learning content Communication/collaboration with other IESs
10
Techniques, Technologies and Patents Related to Intelligent Educational Systems
sification’ refers to building a model that correctly classifies data items into a number of predefined classes (Frias-Martinez et al., 2005). The task of ‘clustering’ is to structure a given set of unclassified instances by creating concepts/classes, based on similarities found on the training data (FriasMartinez et al., 2005). ‘Generalization’ refers to the ability of classifying correctly instances not presented during training or building of the model. Certain AI techniques involve representation of structural and relational knowledge. Such schemes represent knowledge in the form of a graph (or a hierarchy) and can be used for domain knowledge representation. They involve, among others, semantic networks, frames, conceptual graphs and ontologies. Nodes in a semantic network graph represent concepts and edges represent relations among nodes. Nodes in a frame hierarchy have internal structure describing corresponding concepts via a set of attributes. Conceptual graphs are based on semantic networks. An ontology provides a shared vocabulary, which can be used to model a domain (Staab & Studer, 2004). Typical ontology components include individuals (e.g. instances/ objects), classes, individual and class attributes, relationships concerning classes and individuals, events, restrictions, rules and axioms. Ontology languages (e.g. OIL, DAML, DAML+OIL and OWL) are used to encode ontologies. Most of these languages have been developed for the Semantic Web. Ontologies can be exploited to provide different levels of metadata for learning material: they can be exploited to describe the content (semantics), to define learning context and to define relational and structural knowledge involving learning material (Jovanovic, Gasevic & Devedzic, 2006). Ontologies can play an important role in several advanced tasks. They can be exploited for automatic creation of metadata for learning material (Jovanovic, Gasevic & Devedzic, 2006), management of (collaborative) learning material authoring describing interactions between author(s) and learning material as well as relationships among various learning material
versions (Nesic, Gasevic & Jazayeri, 2008), description of heterogeneous, distributed Web-based learning resources. Rule-based reasoning (RBR), is a popular KR&R method (Ligeza, 2006). Rules represent general domain knowledge in the form of ifthen rules: if then . A conclusion is derived when the logical function connecting its conditions results to true. Conclusions are drawn from rules and known facts about the problem at hand. Explanations about drawn conclusions can be provided. Rules can be useful in the following situations: (a) when they are available or easily obtainable from experts or data, (b) when naturalness and provision of explanations is a requirement, (c) when classification tasks need to be performed and all input values are known, (d) when general knowledge is necessary. Rules are often used in most pedagogical tasks (Brusilovsky & Vassileva, 2003; Lanzilotti & Roselli, 2007). Certainty factor rules can be also employed to handle uncertainty. Case-based reasoning (CBR) stores a large set of past cases in the case base (Kolodner, 1993). When handling a new case, CBR retrieves the most relevant stored case and adapts it appropriately. Useful knowledge gained when handling the new case is retained enhancing reasoning capabilities of CBR system and enabling IES self-improvement. CBR can be useful in the following circumstances: (a) an adequate amount of past cases involving learning activities/experiences is available/obtainable and their adaptation is useful for handling similar new learning activities, (b) it is difficult to acquire formal instructional knowledge, (c) empirical (i.e. practical) knowledge is required to teach the specific domain. CBR has been used for instructional tasks such as modeling of teaching strategies (Soh & Blank, 2008), adapting learning contents according to learning styles, emotions and individual needs (Sarrafzadeh et al., 2008), teaching how to solve problems based on previous similar ones, instruction involving constructivism. CBR may also be useful for modeling
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Techniques, Technologies and Patents Related to Intelligent Educational Systems
learner characteristics and for performing user model management either in an environment of collaborating IESs that exchange information to initialize/update the user model or in a distributed learning environment (Rishi & Govil, 2008). Neural networks consist of many simple interconnected processing units called neurons (Haykin, 2008). Each connection from neuron uj to neuron ui is associated with a numerical weight wij corresponding to the influence of uj to ui. The output of a neuron is based on its inputs and corresponding weights. Training data are used to train a neural network to perform its desired function. Different types of neural networks have been developed such as Back-Propagation Neural Networks, Radial Basis Function networks, SelfOrganizing Maps (SOMs), Hopfield networks, Boltzmann machines, ART networks, etc. Neural networks can be useful in the following situations: (a) when classification and clustering tasks need to be performed for which no explanation is required, (b) when generalization is required, (c) when training data is available. Neural networks can be used in various online/offline IES tasks (e.g. online pedagogical tasks, offline analysis of collected data). For instance, in (Villaverde, Godoy & Amandi, 2006) a feed-forward neural network evaluates a user’s learning style based on his/her actions. Also clustering capabilities of neural networks (such as those of SOMs) can be used to cluster learners supporting adaptation (Legaspi et al., 2008). Bayesian networks are graphs, where nodes represent statistical concepts and links represent mainly causal relations among them (Darwiche, 2009). Each link is assigned a probability, which represents how certain is that the concept where the link departs from causes (leads to) the concept where the link arrives at. Bayesian networks are either defined by experts or learned from available data. Bayesian networks enable uncertainty model development in user modeling and evaluation. For instance, in (Butz, Hua & Maguire, 2008) Bayesian networks are used to track learner knowl-
12
edge regarding each domain concept. Liu (2008) uses Bayesian networks to represent composite concepts learning. Besides learner knowledge, Bayesian networks can be used to evaluate other user model characteristics such as learning style (Botsios, Georgiou & Safouris, 2008). Fuzzy methods are used to incorporate vagueness and uncertainty handling into IESs (Jameson, 1995). Fuzzy methods are based on fuzzy sets to express membership degrees of elements into these sets. In fuzzy logic, the degree of truth of a statement can range between [0, 1] and is not constrained to the two truth values {true, false} as in classic binary logic (Klir & Yuan, 1995). Fuzzy expert systems constitute a popular application of fuzzy logic, which use fuzzy rules to infer conclusions. Fuzzy logic inference process includes three phases: fuzzification of inputs (via membership functions), application of fuzzy rules and defuzzification (to produce the output). Fuzzy concepts can be effectively incorporated into other methods/ techniques (e.g. fuzzy clustering, fuzzy decision trees, fuzzy rough sets, fuzzy cognitive maps and fuzzy Bayesian networks). Fuzzy methods can be used when corresponding data (e.g. membership functions, membership degrees, fuzzy rules) can be obtained and when representation of vagueness and uncertainty is required. Fuzzy methods can be useful in various online/offline IES aspects (i.e. domain knowledge construction, user modeling and evaluation, pedagogical tasks and analysis of data collected during IES operation). The approaches concerning use of RBR in IES tasks (mentioned above) could potentially be improved with the use of fuzzy rules. In (Chen & Duh, 2008) fuzzy item response theory is used to evaluate learner ability. In (Jing et al., 2006) fuzzy logic is used for instructional strategy selection. In (Hwang & Yang, 2009) fuzzy integrals were used to assess affective states of the students. Fuzzy cognitive maps have been used for user modeling (LaureanoCruces et al., 2009). In (Baia & Chen, 2008) fuzzy rules and fuzzy reasoning techniques are used to automatically construct concept maps and evaluate
Techniques, Technologies and Patents Related to Intelligent Educational Systems
the relevance degrees among concepts. Learner testing records are also exploited. Fuzziness can be used in domain knowledge representation. For instance in (Cardinaels, Duval & Olivie, 2006) confidence values for metadata facet values are defined, in (Sicilia et al. 2005) the LOM and SCORM standards are extended by defining imprecise links (relationships) among learning items and in (Goodkovsky, 2004) fuzzy structures are used for domain knowledge representation. Fuzzy clustering can be used to analyze accumulated data involving learning activities and experiences. For instance in (Dogan, Camurcu, 2009) learner records are clustered to obtain pedagogically reliable information as feedback to tutors. Constraint-based modeling (Mitrovic & Martin, 2007) is a representation scheme suited for learner knowledge evaluation. A constraint indirectly represents the solutions violating the knowledge domain. The user’s knowledge is represented as a set of constraints that he/she violates or not. Genetic algorithms employ evolution techniques to find adequate solutions to problems (Michalewicz, 1999). Candidate solutions to a problem are represented as strings called chromosomes. Crossover and mutation operators are applied to existing candidate solutions in order to produce new candidate solutions. A function (i.e. fitness function) producing a numerical value is used to evaluate a candidate solution’s ability to solve a problem. If this numerical value is below a threshold, the corresponding candidate solution is not retained in the pool of candidate solutions. Genetic algorithms can be applied to perform pedagogical tasks such as course planning and learning content selection. For instance, in (Chen, 2008) a genetic algorithm approach is employed to provide learning path guidance to learners based on evaluated learner knowledge regarding domain knowledge concepts. In addition, genetic algorithms can be used in tasks such as optimization of IES modules and contents (Koutsojannis et al., 2007) and learner record analysis.
Reinforcement learning is a machine learning approach in which the task is to learn a policy for choosing optimal actions to achieve certain goals (Mitchell, 1997). Delayed reward is provided for performed actions. So, the system is trained to choose sequences of actions maximizing cumulative reward. Not many IESs have employed reinforcement learning. Reinforcement learning can be applied to IES pedagogical tasks since it resembles tutoring based on trial-and-error. The pedagogical module applies instructional strategies tailored to learner needs based on previous interaction of the specific learner or learners with similar characteristics. It assists in avoiding the effort/cost in acquiring extensive pedagogical knowledge. However, data involving simulated learners are necessary in order to provide the IES with an accurate initial pedagogical policy (Iglesias et al. 2009). Recently, hybrid KR&R techniques have started to be used in IESs. Hybrid KR&R techniques combine more than one KR&R technique (Medsker, 1995) and offer advantages in developing IESs (Hatzilygeroudis & Prentzas, 2006). The main goal of hybrid approaches is to surpass disadvantages/limitations of each combined method and simultaneously benefit from advantages of each method. Various types of hybrid KR&R techniques have been developed. According to the employed combination model, the combined components in the hybrid KR&R scheme may be distinct or indistinguishable. Popular such hybrid KR&R approaches involve combinations of RBR with neural networks, combinations of neural networks with fuzzy logic (i.e. neuro-fuzzy approaches), combinations of genetic algorithms with other approaches and combinations of CBR with other approaches (e.g. RBR). For example, formalisms integrating rules and neural networks are used in representing human reasoning in the pedagogical module (Hatzilygeroudis & Prentzas, 2004). In (Stathacopoulou et al., 2007) a neuro-fuzzy approach is employed to evaluate learners’ learning style through classification. In (Papanikolaou et
13
Techniques, Technologies and Patents Related to Intelligent Educational Systems
al., 2003) an AEHS uses neural and fuzzy modules in domain knowledge, learner evaluation, and pedagogical module. In (Koutsojannis et al., 2007) a combination of RBR with a genetic algorithm approach to determine the difficulty levels of the provided exercises is described. In (Bittencourt, Tadeu & Costa, 2006) an agent-based IES combining RBR and CBR is presented. In (Huang, Huang & Chen, 2007) an approach combining a genetic algorithm with CBR is presented. Application of data mining to IESs is a recent trend. Useful knowledge may be extracted from large volumes of data stored in IESs (especially Web-based ones). Data mining is a multidisciplinary area exploiting methods from fields such as AI, Machine Learning, Statistics and Data Bases. There are several data mining techniques that can be employed in IESs such as statistics and visualization, prediction, classification, clustering and outlier detection, association rule mining and text mining (Romero & Ventura, 2007; Castro et al., 2007). Such techniques can be addressed to learners, tutors, authors and academics responsible. They can be used in different perspectives: to enlighten learning process aspects, to perform a specific task of an IES module (e.g. pedagogical module tasks) and to assist in designing/developing/refining IES modules. In the first perspective for instance, association rules could be used to discover associations between learners and learning material. In the second perspective, improved learning experiences could be provided (e.g. using clustering to group similar learners and promote CL). In the third perspective, tools for evaluating learning content could be provided (Castro et al., 2007). Agent-based technology is sometimes used in IESs. Intelligent agents are AI programs with the ability to perceive and act upon their environment (Russell & Norvig, 2009). Characteristics of intelligent agents involve autonomy and ability to learn from perceived information. Various types of intelligent agents can be defined reflecting the kind of information made explicit and used in deci-
14
sion process. Agents can offer great flexibility and make dynamic adaptation feasible. Furthermore, pedagogical agents (i.e. human-like artificial characters) would play an important role in this direction (Chou, Chan & Lin, 2003; Alepis, Virvou & Kabassi, 2007). Multi-agent environments can be also defined. In such cases, the multiple agents may either compete or cooperate. Multi-agent approaches in IESs can prove useful in different ways. IES components can be implemented by employing a multi-agent approach. Employing multi-agent approaches in IESs, we can model situations that occur often in conventional classroom instruction (e.g. CL). For instance, agents may implement artificial learner companions helping human learners learn collaboratively if they want so, even when no other peer human learners are around (Devedzic, 2005). Another such case may involve learning from multiple tutors (Avouris & Solomos, 2001). In Table 2, a summary of possible uses of AI techniques/technologies in IESs is provided.
Solutions from Recent Patents on IESs A number of patents related to IESs have been approved. Patents on IESs can be categorized according to various views such as the following: •
•
•
•
Whether the IES is an ITS or an AEHS. Most of patents on IESs involve ITSs. The patent in (Chakraborty, 2006) involves an AEHS approach. The employed AI techniques/technologies. Such information is shown in Table 3. It can be seen that most patents involve RBR. The domain type to which the IES can be applied.Table 4 presents types of domains to which various patents are applicable. Whether the IES provides authoring tools/ facilities.Table 5 presents authoring facilities provided by patents.
Techniques, Technologies and Patents Related to Intelligent Educational Systems
Table 2. Indicative uses of AI techniques and technologies in IESs AI Technique(s)
Indicative use in IESs
Structured and relational schemes
Representation of structural and relational knowledge Description of learning content semantics Definition of learning context Exploitation for automatic creation of metadata for learning material, management of (collaborative) learning material authoring Description of heterogeneous, distributed Web-based learning resources.
Rule-based reasoning
Representation of general domain knowledge in the form of rules Used in most pedagogical tasks when classification tasks need to be performed
Case-based reasoning
Representation of empirical (i.e. practical) knowledge Instructional tasks (e.g. modeling of teaching strategies, learning contents adaptation according to learner characteristics, teaching how to solve problems based on previous similar ones, constructivism). Modeling of learner characteristics, user model management either in an environment of collaborating IESs or in a distributed learning environment.
Neural networks
Implicit representation of empirical knowledge with high levels of generalization Classification and clustering facilities for online pedagogical tasks (e.g. learner evaluation) Classification and clustering facilities for offline analysis of data
Bayesian networks
Representation of uncertainty Uncertainty model development in user modeling and evaluation
Fuzzy methods
Representation of uncertainty, vagueness Domain knowledge representation (e.g. relevance degrees, fuzzy metadata, imprecise links) Domain knowledge construction (e.g. construction of concept maps) User modeling and evaluation Online pedagogical tasks Analysis of data collected during IES operation (e.g. fuzzy clustering of learner data)
Constraint-based modeling
Representation scheme for learner knowledge and evaluation
Genetic algorithms
Evolution techniques to find adequate solutions to problems Online pedagogical tasks (e.g. course planning, learning content selection) Offline tasks (e.g. analysis of learner data, optimization of IES modules and contents)
Reinforcement learning
The system is trained to choose sequences of actions maximizing cumulative reward Instructional strategies tailored to learners avoiding acquisition of extensive pedagogical knowledge
Hybrid KR&R techniques
Combination of more than one KR&R technique Various functionality depending on combined techniques
Data Mining
Extraction of knowledge from large volumes of stored data Enlightenment of learning process aspects, Performing of IES module tasks (e.g. pedagogical module tasks) Assistance in designing/developing/refining IES modules.
Agent-based technology
Autonomy Ability to perceive information Ability to learn from perceive information, flexibility, dynamic adaptation Pedagogical agents Multi-agent approaches (e.g. implementation of IES modules, modeling situations occurring in conventional classroom instruction)
•
Whether the approach is an IES shell/generator. Shells/generators have been employed in ITS context inspired from the software design drive to write general/reusable software. Shells/generators consist of software architectures, code libraries
•
and/or conceptual frameworks to make ITS construction more efficient (Murray, 1999). Patents (Goodkovsky, 2004; Goodkovsky, 2006) involve an IES shell/generator. Whether the IES involves natural language processing or dynamic natural language
15
Techniques, Technologies and Patents Related to Intelligent Educational Systems
•
responses. Patent (Rajaram, 2006) involves natural language processing as it enables a two-way conversation mode between learner and system. Certain natural language processing is involved in (Johnson & Holder, 2003) since IES reads and interprets solutions typed in by learner. The patent in (Johnson & Holder, 2008) involves provision of dynamic natural language responses through dynamic generation of question-and-answer dialogue. Whether there is combination with an LMS. The patent in (Chakraborty, 2006) involves combination of AEHS and LMS technologies.
•
Whether the IES enables/supports/enhances CL. The patent in (Chakraborty, 2006) supports CL through combination of AEHS and LMS technologies. Also the patents in (Beams & Harris, 2006; Beams & Harris, 2007) explicitly mention support of CL.
In the following, we briefly present representative patents on IES in corresponding sections. Table 6 summarizes the representative patents on IESs grouped by type. We group patents on IESs in six types. This grouping is made in order to focus the ensuing discussion on specific types of patents. The following sections briefly present
Table 3. AI techniques and technologies employed in patents on IESs Approach
AI Technique(s)
(Johnson & Holder, 2003; Johnson & Holder, 2008; Beams & Harris, 2006; Beams & Harris, 2007; Babbitt et al., 2000; Ho & Tong, 1998; Heffernan & Koedinger, 2003; Heffernan & Koedinger, 2007; Bertrand, Zorba & Conant, 2002; Bertrand, O’Connor & Rosenfeld, 2004; Bertrand & Nichols, 2006; Bertrand & Wills, 2008; Nichols, 2004; Lannert et al. 2006; Bertrand, Zorba & Conant, 2006; Nichols, Gilchrist & Poon, 2006; Bloom, 1997)
Rules
(Rajaram, 2006)
Natural Language Processing
(Chakraborty, 2006)
Bayesian classification
(Frasson & Gouarderes, 2002)
Intelligent agents
(Goodkovsky, 2004; Goodkovsky, 2006)
Fuzzy graph, Fuzzy logic
(Mitry, 2001)
Neural networks
Table 4. Types of domains to which patents on IESs are applicable Approach
Type of Domain
(Goodkovsky, 2004; Chakraborty, 2006; Goodkovsky, 2006; Rajaram, 2006; Ho & Tong, 1998; Frasson & Gouarderes, 2002)
Any type of domain
(Johnson & Holder, 2003; Johnson & Holder, 2008; Heffernan & Koedinger, 2003; Heffernan & Koedinger, 2007)
Domains involving problems solved in steps
(Babbitt et al., 2000)
Domains involving simulations or tutoring in a simulator
(Beams & Harris, 2006; Beams & Harris, 2007; Bertrand, Zorba & Conant, 2002; Bertrand, O’Connor & Rosenfeld, 2004; Bertrand & Nichols, 2006; Bertrand & Wills, 2008; Nichols, 2004; Lannert et al. 2006; Bertrand, Zorba & Conant, 2006; Nichols, Gilchrist & Poon, 2006)
Domains involving simulations and specifically tutoring in a real business simulated environment
(Mitry, 2001)
Domains involving life-like simulations depicted within a natural attraction
(Bloom et al., 1997)
Training of Customer Service Representatives in a simulated environment
16
Techniques, Technologies and Patents Related to Intelligent Educational Systems
representative patents on IES for each one of the six types.
Patents Involving General Purpose Functionalities for IESs Certain patents involve functions that can be applied to different types of IESs such as handling learner mistakes in problem-solving by enhancing the typical buggy model (Johnson & Holder, 2003) and generation of customized tests (Ho & Tong, 1998).
In (Johnson & Holder, 2003) the functionality of buggy rules is augmented improving an IES’s handling of learner mistakes in a problemsolving process. For this purpose, consistency rules are employed targeting mistakes that cannot be explained through application of buggy rules. Buggy rules diagnose anticipated mistakes. Consistency rules evaluate if a learner solution violates domain principles. The approach enables diagnosis of multiple errors in a solution. A full diagnosis involves identification of all principles violated (based on the list of violated consistency
Table 5. Authoring tools/facilities provided by patents on IESs Approach
Provision of Authoring Tools/Facilities
(Rajaram, 2006; Johnson & Holder, 2003; Johnson & Holder, 2008; Babbitt et al., 2000; Ho & Tong, 1998; Heffernan & Koedinger, 2003; Frasson & Gouarderes, 2002)
Not mentioned
(Chakraborty, 2006)
Conversion of existing content to SCORM-compliant learning content
(Mitry, 2001)
Learning content creation
(Beams & Harris, 2006; Beams & Harris, 2007; Heffernan & Koedinger, 2007, Bertrand, Zorba & Conant, 2002; Bertrand, O’Connor & Rosenfeld, 2004; Bertrand & Nichols, 2006; Bertrand & Wills, 2008; Nichols, 2004; Lannert et al., 2006; Bertrand, Zorba & Conant, 2006; Nichols, Gilchrist & Poon, 2006; Bloom et al., 1997)
Learning content creation and management of knowledge bases
(Goodkovsky, 2004; Goodkovsky, 2006)
Authoring tools for learning content creation and management of knowledge bases within a shell/generator
Table 6. Summary of representative patents on IES grouped by type Patents
Type
Johnson & Holder, 2003; Ho & Tong, 1998)
Patents involving general purpose functionalities for IESs
(Heffernan & Koedinger, 2003; Heffernan & Koedinger, 2007; Rajaram, 2006; Johnson & Holder, 2008)
Patents Emphasizing Learning Based on Question-And-Answer Dialogs
(Beams & Harris, 2006; Beams & Harris, 2007; Babbitt et al. 2000; Bertrand, Zorba & Conant, 2002; Bertrand, O’Connor & Rosenfeld, 2004; Bertrand & Nichols, 2006; Bertrand & Wills, 2008; Nichols, 2004; Lannert, 2006; Bertrand, Zorba & Conant, 2006; Nichols, Gilchrist & Poon, 2006; Bloom et al. 1997; Mitry, 2001)
Patents involving IESs that teach with simulations
(Chakraborty, 2006)
Patents involving combinations of IES with LMS technologies
(Frasson & Gouarderes, 2002)
Patents concerning agent-based IESs
(Goodkovsky, 2004; Goodkovsky, 2006)
Patents regarding IES shells/generators
17
Techniques, Technologies and Patents Related to Intelligent Educational Systems
rules) as well as identification of any buggy rules matched to additionally determine if the error was furthermore anticipated. In (Ho & Tong, 1998) a rule-based approach involving generation of individually-tailored tests according to learner knowledge level is presented. The approach can be incorporated as a module within any IES. Test questions correspond to specific knowledge concepts. It uses rules to define relationship among different subject areas as well as prerequisite hierarchy. Rule-based evaluation of learner knowledge level incorporates forgetfulness as a function of time. The approach incorporates modules for the following: (a) evaluation of learner knowledge level, (b) generation of recommendations regarding subject areas the learner needs to work on and (c) generation of reports based on recommendations and generation of test questions categorized into at least two concepts.
Patents Emphasizing Learning Based on QuestionAnd-Answer Dialogs Learner comprehension can be facilitated when the learning process is based on interactive questionand-answer dialogs. Questions can be posed by the IES and/or the learner. IES questions guide learners step-by-step in knowledge acquisition and problem solving enhancing learner thinking skills. Learner answers guide the learning process to focus on learner difficulties/misconceptions. An important aspect involves the ability of an IES to provide natural language processing or dynamic natural language responses increasing learner motivation. In (Johnson & Holder, 2008) an approach for dynamic generation of question-and-answer dialogue for IESs is presented. More specifically, a question menu having dynamic questions is being displayed after each problem step. If the learner selects a question from the menu to ask, the corresponding answer is displayed. Questions and answers are generated based on tutorial ses-
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sion state and according to context-specific and pedagogical factors. Rules are used to generate questions, to generate a next step in problemsolving and to impose solution constraints based on fundamental principles. The approach can be integrated as a tool within an IES. The approach in (Rajaram, 2006) enables communication in a two-way conversation mode between learner and an interactive Web-based IES. Tutoring may involve any specific subject matter from any specific curriculum. After logging into the IES, the learner selects a specific subject matter for which the IES provides teaching content which may include problems and/or instructions. The learner may ask any number of questions in natural language at any point. The IES interprets learner messages providing natural language responses. It provides various types of feedback such as step-by-step instructions, hints, graphical images and intelligent questions. The main purpose of this process is to guide the learner in arriving by himself/herself at the final answer sought for the specific subject matter. Not only English but various other languages may be supported as well. The IES may also support automatic text-to-voice conversion.
Patents Involving IESs Tutoring with Simulations IESs tutoring with simulations are very useful in lifelong learning as they provide support for realistic activities. They enable learners to experience real-world consequences for their actions/decisions. Tutoring entails real-time decision making. Babbitt et al. (2000) present an RBR method for tutoring a trainee in a simulator. The system incorporates training scenarios and automated performance measures comprising standards of performance programmed into each scenario using rules. Training scenarios comprise decision points corresponding to cognitive activities. The system provides instructional feedback to a trainee by monitoring trainee’s simulated actions
Techniques, Technologies and Patents Related to Intelligent Educational Systems
(especially when encountering decision points) and comparing them to those of experts. This approach has been applied to train aircraft pilots to a higher level of skill compared to other pilot training approaches reducing training cost (i.e. no presence of a human flight instructor is required). The approach can also be utilized in other domains involving training in a simulator such as training of automobile driving students, medical students, air traffic controllers, etc. A series of patents involve a goal-based IES utilizing RBR to tutor in a real business simulated environment (Beams & Harris, 2006; Beams & Harris, 2007; Bertrand, Zorba, Conant, 2002; Bertrand, O’Connor & Rosenfeld, 2004; Bertrand & Nichols, 2006; Bertrand & Wills, 2008; Nichols, 2004; Lannert, 2006; Bertrand, Zorba & Conant, 2006; Nichols, Gilchrist & Poon, 2006). Previous systems did not provide a creative learning environment by encompassing real business simulations in rules. A training session involves steps such as the following (Beams & Harris, 2006): receiving information indicative of a goal, presenting information indicative of a goal, receiving/analyzing/evaluating learner responses, providing feedback to assist learners in achieving the goal, presenting remedial information. RBR is used to dynamically generate learner-tailored feedback simulating real-world environment and interactions. Such feedback may involve audio, video, graphics, animations, email/telephony/chat information, etc. User input may correspond to collective actions and the system includes logic simulating a business outcome from such collective actions (Bertrand, Zorba & Conant, 2006). Collaborative sessions can be established in which a learner is provided with feedback from at least one other user in order to assist in goal achievement. Multiple ‘roles’ are also available for a learner to learn from each simulated environment from multiple viewpoints (Beams & Harris, 2007). The approach can be executed on a plurality of servers connected through a network (Beams & Harris, 2006; Beams & Harris, 2007).
Bloom et al. (1997) present an approach to tutoring Customer Service Representatives (CSRs). CSRs handle all types of customer calls regarding products/services and need thorough training and on-the-job experience to become proficient. The approach simulates CSR working environment enabling trainees to learn job procedures and exercise conversing with customers. Domain knowledge is rich and includes, among others, discourse grammar describing all possible conversations supported and rules describing actions to be taken in response to specific situations. Conversations are syntactically correct sequences through discourse grammar made up of rule sequences. Mitry (2001) presents an approach offering lessons within simulated environments involving natural attractions and providing lifelike and real-time consequences of learner actions. The user solves problems/puzzles and is rewarded for his/her performance. Neural networks evaluate user performance and produce system feedback. System feedback is synchronized to typographical/ graphical/pictorial illustrations simulating reality and providing dynamic interactions.
Patents Involving Combination of IES with LMS Technologies Chakraborty (2006) presents an integration of LMS and AEHS technologies. Basic components are: learning content storage unit, user modeling unit, a personalization unit for personalizing learning content and a user interface. The learning content can be broken into modules within the SCORM framework called Assignment Units (AUs). The composition of AUs in learning pages varies according to user model. The content storage unit stores various data fragments that constitute the AUs organized as XML fragments. The user modeling unit continuously keeps track of characteristics such as sophistication level, interaction with the system, interest level and others. The system observes user behavior/interaction col-
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Techniques, Technologies and Patents Related to Intelligent Educational Systems
lecting data such as amount of time spent, links on pages visited, scrolling activity, test results and page navigation history. Navigation history is defined by a Markov model implemented using a finite order Markov chain. A finite number of profiles describing the user sophistication level are assumed. Using the aforementioned data and Bayesian classification, probability that the user belongs to any of the profiles is determined. The system provides adaptive navigation and presentation. Presented learning pages are created dynamically by selecting appropriate XML fragments. Furthermore, the same content can be presented in different styles based on user needs. The system includes authoring tools to convert existing educational content to XML fragments.
Patents Involving Agent-Based IESs In (Frasson & Gouarderes, 2002) an approach to a Web-based IES consisting of multiple collaborating agents is presented. The approach provides learner curriculum data and assistance in the form of asynchronous/synchronous explanations. It comprises pedagogical, dialog, service, moderator and negotiating agents. The pedagogical and dialog agents are close to the learner, whereas the other agents are remotely positioned. The pedagogical agent selects the curriculum to be taught and sends a request to the service agent for retrieving it from a curriculum database and transferring it to the pedagogical agent. The pedagogical agent also selects a learning strategy. The pedagogical agent provides the learner explanations when needed/ requested for specific knowledge concepts or curriculum items. When required, the pedagogical agent requests explanations from the dialog agent which handles the process of acquiring the best explanation. Explanations can initially be acquired from a local explanation database or from a remote explanation database stored at the service agent. Explanations in the databases are indexed and ranked according to usefulness in helping learners. This task is performed in the
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remote explanation database by the moderator agent. Explanation usefulness data is acquired from learners. If no suitable explanation can be retrieved from either database, the dialog agent searches for an explanation by communicating to a number of negotiating agents. Negotiating agents include concept vector translation tables to associate different curriculum systems. Each negotiating agent accesses its associated explanation databases to retrieve an explanation. If no suitable explanations can be retrieved from associated databases, the dialog agent requests the negotiating agents to find a user who has overcome a similar learning difficulty in the past. Each negotiating agent has access to a database of users indicating their skill level (i.e., curriculum and knowledge concepts mastered). The retrieved user communicates in synchronous mode with the learner to assist in surpassing the learning difficulty. At the end of an synchronous session, the pedagogical agent queries the learner to author the given explanation and its usefulness. Corresponding explanation data is sent to a moderator agent for insertion into an explanation database.
Patents Involving IES Shells/Generators Goodkovsky (2004) presents an approach to a generic and reusable ITS shell. The approach provides a fuzzy domain/learner/tutor theoretical model-based shell with dynamic planning capability, independent of training paradigms, domains and learners. A fuzzy graph represents domain knowledge structure. Data and metadata can be easily authored. Different instruction modes are provided. The pedagogical module provides, among others, user behavior recognition, cognitive interpretation of learner actions, decisions on sufficiency of different instruction modes and best suitable mode to be applied next, planning and generation of training actions. Fuzzy logic is used to dynamically adapt the available sequence of training actions.
Techniques, Technologies and Patents Related to Intelligent Educational Systems
Goodkovsky (2006) extends Goodkovsky (2004) in several aspects. It separates the logic and media of tutoring completely in order to generalize and reuse logic with any media. The suite of tutoring tasks is extended/simplified. Learning resources are represented uniformly enabling unification/simplification of their processing.
FUTURE RESEARCH DIRECTIONS A key trend for IESs involves efficient creation/ management/searching of reusable learning objects residing in learning content repositories. Such a trend will aid development of WBIESs, combinations of WBIESs with conventional e-learning systems and communication among WBIESs. Moreover, advanced features and generations of learning objects specifically addressed to IESs may appear such as learning knowledge objects, which are knowledge-based, theory-aware, dynamically generated and provide a tutoring service interface (Zouaq, Nkambou & Frasson, 2008). Combinations of LMS and IES technologies have recently started to be developed. Results seem to be promising. It is very likely that such combinations will become a key trend due to increasing popularity of LMSs and WBIESs as well as existence of numerous learning content repositories. Such a development will be beneficial to lifelong learners as they will have the chance to pursue learning using systems offering various ways of communication/collaboration, content delivery and learning activity management (Tomei, 2009; Inoue 2009). Agents constitute a key technology for IESs. It is generally admitted that agent-based technology is important in the context of the Web (e.g. for implementing Web services), but, although it is widely used in other application domains, like e-commerce, its use in web-based education has not become yet a popular trend. Agent-based technology can play a significant role in combination with most of the trends mentioned in this section.
Another trend involves CL. IESs generally do not focus on CL although there is progress in the field of Intelligent Collaborative Supported Learning (Devedzic, 2005). Combinations of LMS and IES technologies will advance towards this direction. Another key technology involves mobile learning (m-learning), an e-learning paradigm exploiting developments in wireless infrastructure to provide ubiquitous access to knowledge with mobile devices (Glavinic, Rosic & Zelic, 2008). Mobile learning will play an important role in lifelong learning (Holzinger, Nischelwitzer, & Meisenberger, 2005) due to the fact that most of the people possess and very frequently use mobile devices. Furthermore, it enables learning in almost every location and in different contexts. Most of existing IESs have not been designed for m-learning. Certain issues need to be addressed when developing mobile IESs (Glavinic, Rosic & Zelic, 2008). It is generally believed that complex problems are easier to solve with hybrid approaches. However, use of hybrid KR&R techniques in IESs has not become yet a popular trend. It is expected that in the following years various IESs employing hybrid KR&R techniques will be developed. It should be also mentioned that AI methods can be used in recommender systems for lifelong learning. Recommender systems search for potential learning activities and recommend the most suitable ones to lifelong learners. CBR has been employed in personal recommender systems (Drachsler, Hummel, & Koper, 2008). Furthermore, agent-based technology can also be used. For instance, in (Santos, 2008) a multi-agent approach to a recommender system for lifelong learning is presented. It is possible that a hybrid KR&R technique could offer benefits to recommender systems for lifelong learning. Finally, Semantic Web-based intelligent educational systems (SWBIESs) is a new category of educational systems (Simic, Gasevic & Devedzic, 2006). Information (e.g. learning resources) in the
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Techniques, Technologies and Patents Related to Intelligent Educational Systems
Semantic Web is given well-defined meaning. The Semantic Web offers advantages regarding representation, search, retrieval and management of resources as well as semantically richer modeling of resources, applications and their users. Semantic Web facilitates semantic interoperability and reusability among different applications. Therefore, an infrastructure for collaborating IESs is provided enabling sharing and reuse of domain and pedagogical knowledge as well as user models. A key technology for Semantic Web towards that direction is ontologies. Given that OWL is based on a description logic (DL), DL-based reasoning will play an important role in SWBIESs (Krdzavac, Gasevic & Devedzic, 2004). The Semantic Web offers technologies supporting more meaningful representations of learners, learning goals, learning content and contexts of its use, as well as better access and navigation through learning resources (Aroyo & Dicheva, 2005). Such technologies enable provision of more efficient/ flexible/personalized services to different IES user types such as instructors, learners and authors.
CONCLUSION In this chapter, we briefly presented main technologies/techniques used in IESs and surveyed various patents related to IESs. We focused on the two main IES types: ITSs and AEHSs. We categorized patents on IESs according to various aspects and present relevant work. Finally, we specified future developments concerning IESs. E-learning and lifelong learning are becoming increasingly popular everyday. Adaptation of elearning tasks to learner needs provides effective lifelong learning experiences. IESs provide the basis for enhancing lifelong learning by exploiting AI methods that give solutions to various online/offline tasks. Advances in (Semantic) Web technologies are expected to intensify the benefits of IESs. IESs may become a major vehicle for getting knowledge throughout life as they will
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increase importance of e-learning to lifelong learners (Inoue, 2009).
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Russell, S., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. Santos, O. C. (2008). Recommending in inclusive lifelong learning scenarios: Identifying and managing runtime situations. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (vol. 3, pp. 651-654). Sarrafzadeh, A., Alexander, S., Dadgostar, F., Fan, C., & Bigdeli, A. (2008). How do you know that I don’t understand? A look at the future of intelligent tutoring systems. Computers in Human Behavior, 24(4), 1342–1363. doi:10.1016/j.chb.2007.07.008 Sicilia, M. A., García-Barriocanal, E., Aedo, I., & Díaz, P. (2005). Using links to describe imprecise relationships in educational contents. International Journal of Continuing Engineering Education and Lifelong Learning, 14(3), 260–275. doi:10.1504/IJCEELL.2004.004973 Simic, G., Gasevic, D., & Devedzic, V. (2006). Classroom for the Semantic Web. In Lytras, M. D., & Naeve, A. (Eds.), Intelligent learning infrastructure for knowledge intensive organizations (pp. 251–283). Hershey, PA: Information Science Publishing. doi:10.4018/9781591405030.ch009 Soh, L.-K., & Blank, T. (2008). Integrating case-based reasoning and meta-learning for a self-improving intelligent tutoring system. International Journal of Artificial Intelligence in Education, 18(1), 27–58. Staab, S., & Studer, R. (Eds.). (2004). Handbook on ontologies. Springer-Verlag. Stathacopoulou, R., Grigoriadou, M., Samarakou, M., & Mitropoulos, D. (2007). Monitoring students’ actions and using teachers’ expertise in implementing and evaluating the neural networkbased fuzzy diagnostic model. Expert Systems with Applications, 32(4), 955–975. doi:10.1016/j. eswa.2006.02.023
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Tomei, L. A. (2009). A theoretical model for designing online education in support of lifelong learning. In Kidd, T. (Ed.), Online education and adult learning: New frontiers for teaching practices (pp. 29–45). Hershey, PA: Information Science Reference. Villaverde, J. E., Godoy, D., & Amandi, A. (2006). Learning styles’ recognition in e-learning environments with feed-forward neural networks. Journal of Computer Assisted Learning, 22(3), 197–206. doi:10.1111/j.1365-2729.2006.00169.x Vygotsky, L. S. (1978). Mind in society: Development of higher psychological processes. Cambridge, MA: Harvard University Press. Woods, R., Baker, J. D., & Hopper, D. (2004). Hybrid structures: Faculty use and perception of Webbased courseware as a supplement to face-to-face instruction. The Internet and Higher Education, 7(4), 281–297. doi:10.1016/j.iheduc.2004.09.002
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Woolf, B. (1992). AI in education. In Shapiro, S. (Ed.), Encyclopedia of artificial intelligence (pp. 434–444). New York, NY: John Wiley & Sons. Yazdani, M. (1988). Intelligent tutoring systems survey. Artificial Intelligence Review, 1(1), 43–52. doi:10.1007/BF01988527 Zouaq, A., Nkambou, R., & Frasson, C. (2008). Bridging the gap between ITS and e-learning: Towards learning knowledge objects. In Woolf, B. P., Aïmeur, E., Nkambou, R., & Lajoie, S. P. (Eds.), Lecture Notes in Computer Science, 5091 (pp. 448–458). Berlin, Heidelberg, Germany/ New York, NY: Springer-Verlag.
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Chapter 2
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions with Adaptive Web-Based Services that Support Standards Olga C. Santos aDeNu Research Group, Computer Science School, UNED, Spain Jesus G. Boticario aDeNu Research Group, Computer Science School, UNED, Spain
ABSTRACT The chapter introduces some key issues of a general framework to support the full participation of students with functional diversity issues (i.e. disabilities) in the learning process by covering the full life cycle of service adaptation at Higher Education institutions. This support is achieved in terms of combining universal design approaches and personalization techniques. Firstly, standards and specifications that try to cover the wide range of possible user needs are considered. Secondly, dynamic contextual recommendations are applied during the course execution to provide the inclusive personalization support. The approach is designed for Higher Education institutions, which are required to integrate this inclusive support into their existing services infrastructure. This framework is analyzed in the context of the EU4ALL project. In particular, the authors of this chapter describe the key components where the research has focused; specifically service based recommendations in order to support some adaptive and inclusive end-user services at UNED. DOI: 10.4018/978-1-61520-983-5.ch002
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
INTRODUCTION Technology should support the needs of Higher Education (HE) institutions and individuals for autonomous and dynamic creation of Lifelong Learning (LLL) communities and of new distributed e-learning services. The chapter introduces some key issues of a general framework to support the full participation of students with functional diversity issues (i.e. disabilities) in the learning process by covering the full life cycle of service adaptation at HE. This support is achieved combining a twofold approach. On the one hand, universal design approaches that are based on standards and specifications that try to cover the wide range of possible needs are used. On the other hand, personalization techniques draw on dynamic contextual recommendations, which are applied during the course execution. The approach is designed for HE institutions, which are required to integrate this inclusive support into their existing services infrastructure. The objective of this chapter is not to present how much accessible a Learning Management System (LMS) can be or how to build yet another LMS that is more accessible than the existing ones. The objective of this chapter goes far beyond LMS with a focus on describing a general framework that can be applied into HE to facilitate the learning autonomy of their students, including those with disabilities. This framework should accommodate existing and future services available to the institution members (i.e. students, faculty, administrative staff) in an intelligent web-based environment; it guarantees support and extendibility making a pervasive use of educational standards. The framework is required to develop the full life cycle of service adaptation, which is, by nature, a step-wise process where different roles and needs (i.e. course designers, tutors and learners) should be supported. The approach draws on combining universal design, modelling techniques following standards and specifications that try to cover the wide variety of user needs,
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and personalization features that offer dynamic contextual recommendations during the course execution. In this chapter, we present this framework and illustrate its application in the EU4ALL project, where we concentrate on a scenario at UNED and comment on components that are on the focus of our research. The chapter will be structured as follows. The next section introduces the background and the motivation to support functional diversity issues in the context of the LLL paradigm and presents how technologies can turn into both a barrier and/ or an opportunity, depending on the way they are applied. Universities cannot be left aside to these needs, but they are much involved in the process. The approach proposed to accommodate these needs in their technological infrastructure will be outlined in the background section. The third section focuses on the personalized features to be provided in LLL scenarios in order to cope with the teaching and learning process in an inclusive way. This is achieved by taking into account that all the services to be delivered are strongly dependent on the management of individual and group profiles and their relationships with contents and context of use. Besides, all of them entail modelling activities focused on adaptive and personalized processes. Instructional design issues are also relevant at setting the educational objectives of the system, and designing the interaction in a way that facilitates achieving those objectives. This implies learner’s requirements being met, which means incorporating scaffolding into the context, tasks, tools, and interface of software learning environments. The fourth section presents a general framework to leverage learning autonomy for disabled students at HE. It introduces the general framework that allows the full participation of disabled students in the learning process, and describes mainly those components where our research group has been working on. The technology focuses on attending the learning needs of the students in a
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
personalized and inclusive way. In particular, the approach relies on combining design and runtime adaptations with a pervasive use of standards and specifications. It follows, as stated above, the full life cycle of service adaptation and is designed to be applied at HE members into an intelligent web-based environment that guarantees their support and extendibility making a pervasive use of educational standards. The fifth section addresses future trends for the next generation of learning environments and comments on the technological and management problems that currently present significant barriers at distance learning universities when trying to meet individual learners’ needs. Finally, a concluding section discusses the technical and even organizational open issues related to the framework described in the chapter.
BACKGROUND Around 10 percent of the world’s population, or 650 million people, live with functional diversity issues, so called disabilities, in the world. They are supposed to have the same rights and obligations as any other member of the society, but unfortunately, due to the lack of support to specific needs, they usually face obstacles that prevent them for enjoying their rights. Access to education is one of the activities where more work is needed to eliminate discrimination at all levels of the educational system. According to the 26th article of the Universal Declaration of Human Rights “Everyone has the right to education” (United Nation, 1948). More recently, in December 2006 the “Convention on the Rights of Persons with Disabilities” was approved by the General Assembly of the United Nations (United Nations, 2006). Article 4 includes all major legal and political instruments for the safeguarding of the rights of disabled people including those relevant for their participation in education. Article 9 is dedicated to accessibility, while article 21 is devoted to access to informa-
tion. Finally article 24 is addressed to education. An “inclusive education system at all levels and lifelong learning” (paragraph 1) are the explicit aims of this article. In Europe, the Ministerial Conference “ICT for an inclusive society” in Riga resulted in a common Ministerial Declaration in 2006 (EU, 2006), where the prominent role of Information and Communication Technologies (ICT) in education is mentioned in the preamble as well as the need for accessible services available to a range of devices. Many barriers to access are mentioned together with service design and personal capacity. The needs of older workers and elderly people are addressed as well as the need to enhance e-accessibility and usability. However, there have always been time and space barriers that made it difficult to physically attend lectures in the classroom. Nowadays with the application of ICT to the learning process time and space barriers can be removed. In relation to this, making public web sites accessible to all European citizens is an important goal of the European i2010 strategy. According to recent studies still “28% of European governmental web sites present barriers which causes significant problems for people with special needs” (Olsen, 2008), hindering access to web content and services for people with disabilities. That affects more than 45 million people in Europe – one in six – aged between 16 and 64, who have a long-standing health problem or a disability, i.e., 16% of the overall EU working age population (EU Disability Strategy, 2007). Currently, e-Inclusion has become a widely used term to refer to accessibility features provided by ICT based products and services. These services are to be used by people with disabilities as well as older people with age-related changes in functional capacities. It is crucial to ensure that ICT products and services are not a further impediment for access or usage by the disabled. The concept of ALL (accessible lifelong learning) focuses on access to education and Lifelong Learning (LLL), which is both more and more mediated through ICT. Further, e-Inclusion is one of the pillars of
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the European i2010 initiative on the Information Society (e-Inclusion, 2008). With the advent of Technology Enhanced Learning (TEL) scenarios users can take part in a course at any time and from any place. TEL is a long term issue supported by most European reports and initiatives ever since the Lisbon strategy (Kok, 2004). TEL insists on presenting students as the central players of the learning process and, consequently, the real drivers of teaching-andlearning tasks. That orientation, focused on “the learning and the learner”, is supported by the eLearning initiative in 2000, and their subsequent actions, such as the ‘eLearning Action Plan 20012004’ (European Union, 2001) and the eLearning Programme (European Union, 2004). One of the key activities supported in the “Lifelong Learning Programme” (European Union, 2007) is the “development of innovative ICT-based content, services, pedagogies and practice for lifelong learning”. TEL aims at “improving the efficiency and cost-effectiveness of learning, for individuals and organizations, independent of time, place and pace” (ECTEL, 2006). Innovative infrastructures, methods and approaches are needed to reach this goal, and will facilitate transfer and sharing of knowledge between individuals and in organizations. However, quite often the technologies used have brought new barriers for people with specific needs, who, for instance, may not perceive the multimedia contents or control the functionalities of the learning environment with a different device than a keyboard. Moreover, it is undeniable that each individual has particular needs and requirements in general, and especially in learning scenarios, since their background and learning goals differ and evolve over time. And when the technology is put into play, there are also different access preferences with respect to the device used. These preferences may be related not only to user disabilities, but also to the user’s particular context. For instance, the user may be interested in taking advantage of a traffic jam to work on a
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lesson from the course at the university. And in that situation, she is not able to use a keyboard, but have to control the device by voice. Universities worldwide are to support usercentered scenarios mediated by TEL and have to respond appropriately to the specific needs of students, including providing support to disable students in higher and further education as well as in the service provision. However, several studies report many pending issues to meet the needs of those presenting functional diversity issues (Kelly, 2007; Seale, 2006). These needs refer to accessible buildings, courses and employment as well as technology mediated services. In particular, distance learning universities are supporting an increasing number of adult learners and students with disabilities. Half the population (over 6000 this year-course 2009-10) of HE students with disabilities in Spain are enrolled at UNED (the Spanish National University for Distance Education), and the Open University in the United Kingdom has over 10.000 students with disabilities. These universities have policy statements and departments with specialized personnel committed to promoting and embedding disability equality and preventing discrimination in all areas (Open University, 2006; UNED 2006). Furthermore, they have experimented from the very beginning with the new technologies even before they have been available in the market as they hold research groups within them, which try to apply their research achievements to improve the quality of their teaching and better support their students in their learning. Having these needs in mind, the framework introduced in this chapter goes beyond supporting the accessibility needs of any particular LMS or more up-to-date Web 2.0 services focused on learning and social user’s needs. The approach addresses the fundamentals requirements from users’ and technological viewpoints when building a general framework to support the development of accessible and personalized learning services in HE mediated by ICT. The former –users’ view-
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
point- relates to the accessibility and adaptiveness that pervades every service which is to meet users’ evolving needs, and the latter –technological viewpoint- considers the latest related standards and specifications as well as semantic web technologies to make the provided solutions sustainable and scalable. Actually, the problem we are facing is the lack of previous research on having a general solution based on standards which is applicable at a large scale in HE to accommodate the existing software at each institution. There are several related projects that address specific issues considered in the general framework we are developing. The GRAPPLE (Grapple, 2009) and FLEXO (Flexo, 2009) projects focus on adaptive learning. There are other service oriented architectures, such as the e-Framework for Education and Research (e-Framework, 2009), the Open Knowledge Initiative (Oki, 2009), and the Fluid Project (Fluid, 2009). A wide range of projects have focused on accessibility and disability issues, to name but a few The WAB Cluster (WAB, 2009), Through Assistive Technology to Employment (TATE, 2008), Benchmarking tools for the Web (BenToWeb, 2007), Multimodal Collaboration Environment for Inclusion of Visually Impaired Children (MICOLE, 2007), European Network of Excellence in Information Society Technologies for Special Educational Needs (SEN-IST-NET, 2003), etc. In addition there are several specifications and standards (e.g. the W3C Web Accessibility Initiative and the IMS family) that impinge on the different components of the framework, as it will be discussed below. In this respect we are coping with the existing gaps, overlapping and open issues related to the actual use of those standards. As for universal access and disability requirements, having the focus on user involvement in technology developments, there have even been a number of projects which have produced tools to support the work in this area. For example the USERfit methodology and manual (Poulson et al., 1996; Poulson and Richardson, 1998) developed
within the USER Project and further elaborated within the IRIS Project (IST-2000-26211) (Abascal et al., 2003). The FORTUNE Project focused as well on providing support for user involvement in R&D projects (Bühler, 1998). Iterative user design (Carroll, 1991; Norman and Draper 1986) where users are interviewed and participate evaluating prototypes along the project life cycle is the advocated approach by the EU4ALL project. More specifically, for service validation, a Constructive Technology Assessment strategy is being followed, which includes technology users and other relevant social actors throughout the entire process of design and redesign (Schot, 2001).
PERSONALIZATION FOR INCLUSIVE LIFELONG LEARNING Although personalized e-learning is no longer a research issue in small-scale settings (Brusilovsky and Vassileva, 2003), its applicability in LMS is still limited, and there are concrete challenges related to the usage of standards to provide adaptations throughout the entire life cycle of e-learning. People with disabilities face numerous problems to access HE. Although figures about disability and HE across different countries differ because of different legal frameworks or social degree of development and different official recognition of some types of disabilities (HEAG, 2009), a common situation in all countries is that the rate of students with disabilities in HE is notably lower than the disability rate among the overall population (e.g. in Spain 3.3% of people with disabilities have access to HE against 9% in the general population). Therefore there is a real need to provide accessible user-centered services that adapt to the needs and demands of the users. To address this issue, several European initiatives have been developed. For instance, the eLearning Programme states that “it is important to ensure that e-learning products and methods are able to take into account individual needs and
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learning-styles, and that they are not based on a ‘one size fits all’ philosophy, in which learners are seen as standardized ‘units’” (European Union, 2004). The purpose here is to put users at the center of the learning process. It will improve participation, open up opportunities for everyone and enhance skills. Moreover, on 25 October 2006, the European Parliament adopted the Commission’s ambitious proposals for a new action program in the field of education and training (European Union, 2006). For the first time, a single program covers learning opportunities from childhood to old age under the umbrella of the “Lifelong Learning Programme” (European Union, 2007). In the same way, personalized learning provides facilities to upgrade the skills of people with disabilities. However, this “student-centered approach” that is being promoted poses too many challenges to both traditional HE institutions and distance learning universities. In particular, the infrastructure to support the required user-centered scenario is still under development and the different pieces are not properly integrated. Firstly, most courses on current LMS hardly offer any information about which didactical methods and models are used. As far as adaptation is concerned, they just offer predefined settings for a particular course that turn out to be the outcome of extensive customizations. Secondly, in HE institutions there exist a wide variety of ICT services, where the adaptation and accessibility requirements are not properly addressed, such as the management of users (faculty staff, students, tutors, and administrative people), contents of varied nature (exams, study guides, calendars, bibliographic resources, videos, audios, etc.) and communication channels and means (e-mail, forums, news, radio, educational TV, IP telephone, etc.). Furthermore, e-learning processes cannot be isolated within a particular system; they should be integrated with the rest of the services provided, especially within the LLL paradigm, where personal conditions and students records are of major importance and where ubiquitous scenarios and informal learn-
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ing are to be considered. This objective is quite relevant considering both, the increasing number and variety of services that could cause problems of usability and accessibility, and the interoperability required to provide many functionalities. For instance, depending on the profile of a particular student (i.e. background knowledge, learning goals, preferences, etc.) alternative sources of information and consultancy services can be provided. Likewise, for instance, a lecturer can develop contents, syllabus, and course calendars in a non-proprietary standard format that could be eventually delivered via alternative means (web, printed study guides, books, etc.). But the real challenge is to cope with the teaching and learning processes themselves. When they become fully exploited, for instance, a particular student, without having to wait for tutors’ feedback, will be able to go through a personalized learning path full of learning activities that will be adjusted readily to cope with the current learner situation. Furthermore, students will receive automatic feedback when reaching un-predicted learning impasses that could come up while following a particular learning path. Faculty staff as well will have to update their current tasks to face the authoring of ICT-based learning tasks that will be eventually performed by many different learners in various technological settings. Accordingly, to support the web-based authoring, qualitative and quantitative reports will be provided to course authors, remarking on predefined assessment features. These reports will include, for instance, what type of students (i.e. their learning style, background, preferences, etc.) have had problems while coping with a particular learning task, or the percentage of learners’ goals that have been successfully achieved in a specific learning situation, in a course, or even in a study program. All these services strongly depend on the management of individual and group profiles and their relationships with contents and context
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
of use, and all of them entail modelling activities focused on adaptive and personalized processes. In line with the functional diversity paradigm (Ondeck, 2003) that we fully subscribe, some relevant stakeholders (e.g. ISO/IEC JTC1 SC36) consider that learners experience a disability when there is a mismatch between the learner’s needs (or preferences) and the education or learning experience delivered. Disability is thus not viewed as a personal trait, but as a consequence of the relationship between a learner and a learning environment or resource delivery system. While pursuing accessibility, providers of e-learning services should adapt learning objects to both personal circumstances and context. Moreover, human factors should be addressed in every stage of any ICT service life-cycle. According to the user centered design methodologies (see Background section for references), targeted users and contexts of use should be considered at the research stage together with ethnographic, social and cultural issues. This would lead to the definition of a set of user requirements, and then to learning systems with built-in capabilities in order to provide a good user experience. Evaluation should be conducted within each individual service component at the design, development and delivery stages, with the evaluation results feeding back into these stages. In addition to evaluating each component, the overall service should also be evaluated in order to ensure it meets the applicable user requirements. The adoption of specific design and evaluation processes, where human factors are addressed, will enable to detect and solve deficiencies in time, increasing the quality of services. The ISO standard 13407:1999 ‘Human-centered design processes for interactive systems’ provides guidance on human centered design activities throughout the life cycle of computer-based interactive systems. The standard is targeted to people who manage design processes. According to the standard, human centered design consists of four different types of design activities:
• • • •
To understand and specify the context of use. To specify the user and organizational requirements. To produce draft design solutions. To evaluate design against requirements.
In the learning domain, learner centered design means designing for the specific needs of a learner. Along this line, the design of an educational system should focus on how people learn (learner’s characteristics) and how learning can be facilitated (according to system functionalities and resources). Instructional design aims at setting educational objectives for the system, and designing the interaction with the system in order to achieve those objectives. This implies learner’s requirements being met, which means incorporating scaffolding into the context, tasks, tools, and interface of software learning environments. Adaptation is essential in any e-learning environment since learning is, by nature, an evolving process that strongly depends on users’ characteristics and their evolution over time. In particular, e-learning users have a wide variety of abilities, backgrounds, interests, level of experience on the use of resources, etc. Therefore, there is a need for a general framework to provide learning autonomy for disabled students in HE based on adaptive environments that support standards that cover the full life cycle of service adaptation in LLL, and accommodate the existing and future services available in HE institutions.
A GENERAL FRAMEWORK TO PROVIDE LEARNING AUTONOMY FOR DISABLED STUDENTS IN HIGHER EDUCATION The chapter presents a general framework to allow the full participation of disabled students in the learning process. This approach follows the life
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cycle of service adaptation and is designed to be applied in HE institutions. First, we introduce the life cycle of service provision and what services are demanded in HE institutions. Moreover, we comment on the availability of existing support mechanisms that follow the design for all approach, and the current insufficient support provided by the specifications that can be complemented with adaptation tasks. Next, we present the foundations of the framework proposed, including a description of the service-oriented architecture to support it. Then, we comment how this generic framework is applied in the EU4ALL project focusing on those components where our research is being carried out. Lastly, we discuss how it is instantiated in the two biggest distance universities in Europe. We focus on the UNED scenario and how the dynamic support at runtime can be provided with recommendations
2004), four phases can be considered to provide accessible and inclusive services. Briefly, these phases, which have been redefined by our research group to accommodate the adaptive and inclusive service provision, deal with the following issues: •
The Life Cycle of Service Provision As introduced in the third section, e-learning processes cannot be isolated within a particular system, but they should be integrated with the rest of the services provided by HE institutions. This is critical, especially within the LLL paradigm, where considerations for personal circumstances and students profiles are of major importance, and there is a need to serve a variety of ubiquitous and informal learning scenarios. In this context learners have specialized needs and requirements, since their background and learning goals differ and evolve over time, and various access preferences with respect to the device used. This is quite relevant when considering both the increasing number and variety of ICT services, which could cause usability and accessibility problems, and the interoperability required to provide many of the functionalities. To properly manage these services, HE institutions are to take over the life cycle of adaptive and inclusive service provision. In the same way as the e-learning life cycle (van Rosmalen et al.,
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•
•
Design: this phase aims at designing the interaction with the services in order to achieve the required objectives. This implies users’ requirements being met, which means incorporating scaffolding into the context, tasks, tools, and interface of the system. In particular, adaptation requirements should be considered from the outset and related ways to evaluate their progress (i.e., improvements in a number of features, such as user satisfaction and involvement, access to learning resources, participation in a wider range of learning activities, etc.) Available standards and specifications can be applied to follow the universal design principles. When needed, the logic for predesign adaptations is provided (i.e. hooks and information required by the runtime adaptation to base its reasoning). Publication: this phase deals with the storage and management of data to be retrieved by the different services. It covers the management of users’ roles and access rights, which are of special interest considering the learning and privacy issues involved (Kobsa, 2007). The usage of standards guarantees the required interoperability. Use:this focuses on the environment while running the service. It deals with the delivery of services taking into account the design specifications, the user profile and the accessibility requirements by properly adapting the user interface and by producing dynamic and contextual recommendations. Users’ behaviour and interaction are to be tracked to facilitate the auditing phase (see below). Empirical testing or userbased methods also take place, including
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
•
direct observation, using the thinking aloud and contextual inquiry techniques, with task-based scenarios. All these techniques made up the user evaluation strategy. Auditing: this phase closes the cycle and provides reports on the actual use of services. During this phase, results from experts, users’ evaluations and automatic processing of interactions are analyzed to feed back the life cycle. For the latter, data mining techniques can be used to extract the knowledge from the interactions and analyze and categorize the sequence of actions followed by learners.
As commented below, standards and specifications can be used in the design phase to try to cover the wide variety of needs. Since it is not possible to cover everything in advance, dynamic contextual recommendations can be applied during the course execution (use phase).
Approaches for Service Provision Universal design or design for all promotes thinking in advance the possible needs of the users to produce a design that copes with the functional diversity. It follows existing guidelines (Sloan et al., 2006), such as the W3C WAI guidelines to provide accessible contents for all (WAI, 2009) or workflow-based specifications, such as the IMS Learning Design (IMS-LD), to describe different activity sequences considering the different needs for the learners. Other specifications such as IMS Access For All (IMS-A4A, 2003) or ISO/ Individualized adaptability and accessibility in e-learning, education and training (ISO/IEC 24751, 2009) provides a common language for describing the primary resource for a content and equivalent alternatives for that resource in a way that matches a user’s stated preferences for the presentations and controlling of resources. These preferences include diverse accessibility needs, such as mobile computing, noisy environments,
etc. They provide the two sides of the match needed to address the needs and preferences of learners. The first one specifies what the learner prefers and concentrates on the display, control and selection of learning content, so that learners with alternative content or interface requirements can be supported (ISO/IEC 24751-2:2008: Part 2: “Access for all” personal needs and preferences for digital delivery). The second labels resources with metadata using the same terms to facilitate searches for content with accessibility support (ISO/IEC 24751-3:2008: Part 3: “Access for all” digital resource description). Application in pilot scenarios shows that implementing in real situations preconceived design approaches, where adaptation is a required feature, is perceived as a complex task (Boticario and Santos, 2007). There exist also some limitations that make not possible to simply follow the universal design approach. On the one hand, the information about the user preferences and needs is not static, neither their learning needs (as considered by the specifications), but evolve over time. On the other hand, there are indicators used to define user preferences that cannot be known in advance, even by the user herself (such as the collaboration level) (Anaya & Boticario, 2009), and these preferences are useful when adapting content to learner’s preferred behaviour. An approach to cope with the life cycle of service provision is to extend the IMS-LD guidelines to articulate the service description and operation along the cycle, including the personalization support. This support can be achieved by synchronizing the properties used to compute the IMS-LD conditions with the attributes that model the users. In this way, adaptive and inclusive services can be delivered to users in HE institutions by extending the activities workflow defined by the IMS-LD specification. Work on this line has already been carried out (Rodriguez-Ascaso et al, 2008a), which differentiates the services in HE institutions into two groups: (i) traditional learning services (those that can be described by
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IMS-LD) and (ii) learning management services (which require an extension of the IMS-LD to describe them). Due to the operational nature of the services, they can be defined as adaptive workflows described in terms of IMS–LD guidelines that implement psycho-educational strategies to support learners who encounter barriers in their interaction with HE institutional services. The goal here is to adapt these services to the users’ needs in an inclusive way through the full life cycle of service adaptation. However, IMS-LD presents limitations, especially for the accommodation of the learning management services. Even for the traditional learning services (despite IMS-LD claims to support all pedagogical scenarios) practice has proven that there are restrictions. First, the learning resources and services, which are needed to complete the design, are not modelled in detail within the specification but are pointed to external spaces (Boyle, 2009). Second, the condition model of IMS-LD presents scalability issues with implication on maintainability of units of learning (Gutierrez et al., 2008). Third, there exist other practical considerations regarding the excessive size of the resulting files when a rich sequencing strategy is authored (Gutierrez et al., 2008). Fourth, it does not properly support cycling workflows (i.e. reflexive cycles), where the course author would like to make the learners go back to some activity already performed (Gutierrez et al., 2008). Fifth, there is not full support for collaborative activities, since there is no way to model groups of users doing the same activity. Some approaches to cope with some of these limitations are described in de la Fuente et al. (2007). Moreover, there are limitations related to the accessibility of the players and the specification itself which do not consider in advance accessibility issues. To cope with the latter our research group is participating in both the accessibility improvement of an IMS-LD player and the combined management of the specification with other user-centered and
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accessibility focused specifications and standards, such as those that manage user profile information (see below). In this area, the work of our research group is proving that specifications such as IMS Learning Design can contribute to the derivation of guidelines for the delivery of some HE services for people with disability taking into consideration psycho-educational requirements (e.g. in the enrolment process). Moreover, it is making contributions for the implementation of appropriate tools to support these users (e.g. for the accessibility of the player interface and the integration with the information stored in the user profile). However, universal design approaches do not suffice (Martinez-Normand, 2007) as in practical situations it is not possible (in terms of time, effort and knowledge) to specify the whole design in advance. Moreover, it is not feasible either to collect directly from the users the values of the required attributes in order to build the model that the adaptation tasks will consider. There is a need for intelligent support that would analyze the learners’ interactions at run time and process them to learn their usage preferences in order to provide runtime adaptation. In particular, artificial intelligence techniques, such as data mining to extract the knowledge from the interactions, and machine learning to classify users according to their behaviour, can be used when dynamic support is required. In turn, collaborative filtering techniques are useful to offer recommendations to the users based on other users’ experience and the similarity of user models (and thus, needs and preferences) among the users. Evaluations with users justify the need of the dynamic support at runtime (Santos and Boticario, 2008a) when trying to support inclusive scenarios. Our research group has proposed the integration of recommender technology for providing dynamic adaptations that overcomes the limitations of the universal design approach. To offer these recommendations in inclusive adaptive standards-based
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
e-learning scenarios, high level adaptation tasks can be proposed that consider the user context and the interactions gathered when interacting with the services provided by the institution. They may provide content personalization and navigation support though the system by taking into account the user features (i.e. user modelling) and contextual features (i.e. device capabilities and user context within the services) (Santos and Boticario, 2008b). To cope with domain independent adaptations in e-learning settings, educational standards and specifications should be used. These adaptations are based on two processes: • •
Modelling users features from the interaction data (offline process). Generating dynamic recommendations, taking into account the user model attributes and the context (online process).
Examples of generic adaptations can address both the traditional learning service and the not so often considered learning management services. The first group includes adaptations such as: (i) dynamically identifying blocking points that appear when learners are following the initial learning design described in terms of existing specifications (e.g. IMS family), (ii) offering contents not considered in the design (maybe because designers were not aware of them and/ or the contents were produced afterwards) which deal with finding useful learning resources from a pool of external resources provided by others members of the institution, and (iii) coping with limitations of the learning paths specified in IMS-LD as commented above. The second group deals with non-learning design based tasks for those matters which do not explicitly deal with IMS-LD, but which also may support members in HE institutions, such as some of the learning management services identified in the next section, and can also include adaptations in the service workflow and the delivery of alternative content.
Services in HE Institutions Existing ICT services in HE institutions have already been categorized in the previous section and relate to the management of (i) users, (ii) contents of varied nature, and (iii) communication channels and means. If personalization is taken into account, alternative sources of information and consultancy services should be provided (see below), depending on the profile of a particular student (i.e. background knowledge, learning goals, preferences, etc.). The real challenge is to cope with the teaching and learning processes themselves and offer services that strongly depend on the management of individual and group profiles and their relationships with contents and context of use. This challenge entails modelling activities focused on adaptive and personalized processes. For instance, the following services for students are identified: •
•
•
•
Contents delivery supported by nonproprietary standards so contents can be eventually delivered via alternative means (web, printed study guides, books, etc.) Personalized learning paths for students full of learning activities that are adjusted readily to cope with the current learner situation without having to wait for tutors’ feedback Automatic feedback when reaching unpredicted learning impasses that could come up while going forward in a specific learning path Personalization support for interactions with the HE ICT services, such as the enrolment process
To provide these services to students, another set of services are to be provided to the faculty staff as well:
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
•
•
•
•
Support for the production of accessible educational material and compilation of best practices Support the authoring of learning tasks that will be eventually performed by many different learners in various settings Reporting of qualitative and quantitative reports remarking on predefined assessment features from the course execution (e.g. reports will include, for instance, what type of students have had problems in a particular learning task, or the percentage of learners goals that have been successfully achieved in a specific learning situation, in a course, or even in a study program) Definition of services workflow for management activities
Finally, the institution itself has to provide organizational services, such as enrolment, personal counselling and exam adaptation. As mentioned above, the required support for these services has impact for the learner and the staff services. In this context, we propose a framework built on a standards-based adaptive and accessible open source web service oriented architecture (SOA) that considers standard-based designs and recommendation strategies. This architecture follows the full life cycle of service provision and accommodates to existing and futures services available in HE.
The Framework for HE Institutions Generally speaking, a framework is a basic conceptual structure used to solve or address complex issues. Following OASIS (2006), abstract frameworks are defined by a reference model that is used for understanding significant relationships among the entities of some environment. It enables the development of specific reference or concrete architectures using consistent standards or specifications supporting that environment. A reference model consists of a minimal set of uni-
40
fying concepts, axioms and relationships within a particular problem domain, and is independent of specific standards, technologies, implementations, or other concrete details. In this case, we are defining a general open and standard-based framework to support the development of accessible and personalized services for HE institutions that accommodate existing and future ICT services. In order to respond to accessibility needs, it has been pointed out the possibility of using frameworks (understood as reference models) to support the development of accessible e-learning. This approach facilitates the development of a common understanding of the components of the domain and their interfaces, and provides a map for service development (Seale, 2006). In a more technical way, a relevant initiative is the E-Framework for Education and Research, which attempts to scope what services could be useful for educational and research institutions to make use of information systems and digital technologies. As the core of this initiative, a service-oriented architecture (SOA) is proposed in order to create systems that are independent of a particular software implementation and can theoretically be reconfigured to take account of the complexity of different organizational demands. Combining both ideas (i.e. understanding the requirements for accessible e-learning and the technological support provided by SOA) our framework for HE institutions aims to provide inclusive and personalized services. The framework is based on standards and facilitates the integration of the institutional services that deal with the management of the learning process with e-learning systems (i.e. LMS) that are focused on the learning process itself. This conceptual framework is also an implementable software architecture (see next section), which can be used to increase the accessibility and personalization of existing LMS and the availability of end-user services for HE institution members. The framework is based on SOA for supporting the personalization approach to accessibility.
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
The general framework described here is in line with the aforementioned SOA initiatives for e-learning (i.e. e-Framework, OKI, FLUID). In particular, it considers existing educational institutions that offer services for their users such as those identified in the previous section (Open University and UNED). Each institution has some specific needs, which means that not only the services offered but also the way these are offered, differs in each of them. Moreover, HE institutions have been trying to offer these services through the Internet to facilitate the access to all their members. However, there are no guidelines describing how to translate those enduser services into ICT services, a.k.a. electronic services (or eServices) that are delivered to the user via web-based environments in an inclusive and personalized way nor appropriate technological support required for it. Figure 1 depicts the general framework as perceived through our research activities and experience in the management of HE services.
The framework is proposed to be applied by HE institutions to provide support for interoperability of services, combining existing services - already provided by the institution - and new functionalities that can enrich current services with personalization and inclusive support. On the left hand side of the figure, two institutions are represented, which have different needs and have already some software running that partially solves their individual needs by offering appropriate ICT services. The general framework (in the middle of the figure) can be seen as a compact structure that aggregates the full list of services (Sa to Se) to be required by institutions. This is called the Open and Accessible Services Architecture (O-ASA), which is built as a standards-based adaptive and accessible open source web service oriented architecture (SOA). As the framework is based on web services1, it shares the benefits of SOA, such as interoperability, re-usability, composability, distributivity and discoverability (Josuttis, 2007; Natis, 2003).
Figure 1. Graphical representation of the application of the general framework in HE to support ICT services
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
The integration defined in the framework is done considering the particularities of each of the HE ICT services (Sa to Se) and is redefined in terms of the framework (S1 to S9). Moreover, different components (C1 to C9) that provide some technical support are required. Using both the O-ASA and the components, each of the institutions can now apply the general framework to their particular needs (right hand side of Figure 1). As it can be seen, the result of applying the framework is different in each of the institutions, since they both had different needs and start from different ad-hoc software infrastructures- they usually do not require covering the whole list of eServices offered in the framework. However, as institutions have applied the framework, the resulting software infrastructure can easily be extended in the future to accommodate new services. They will only require adding the needed components for the new services. The framework provides the guidelines on how to integrate all the pieces so their needs are solved following the reference model for SOA proposed by OASIS. The framework is based on an architecture intended to provide a smooth integration of HE ICT services (e.g. course enrolment), acquiring specific competences for the given subject domain (e.g. lessons) and accessibility (e.g. automatic subtitling of multimedia resources) services, which in turn are built in terms of multiple applications (e.g. content repository), components (e.g. group management, units of learning delivery), network infrastructure (e.g. SOAP protocol) and dynamic guidance (i.e. recommender system). In order to clarify the applicability of the framework, next we comment on how it is being applied in the context of the EU4ALL project. We focus mainly on components and services where our research group is involved.
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Application of the Framework into the EU4ALL Project In the context of the EU4ALL Project (IST-2006034478), the framework aims to provide a set of theoretical and practical tools that can be adopted by educational institutions in order to increase the quality and coverage of the personalization and accessibility services that are offered to all students. The framework is supported through a set of adaptive and accessible components that provide the support for the end-user services through ICT. These components correspond to the C1-C9 elements identified in Figure 1 and provide the technological support required for the service delivery. Users interact with the S1 to S9 services from the O-ASA thanks to the adaptive and accessible components available in the EU4ALL framework (Raffenne et al., 2009). The key element here is the LMS, which acts as the bridge among users and adaptive and inclusive functionalities. On the one side, the users interact –as they are used to- through the LMS to receive traditional e-learning services (such as SCORM or IMS-LD compliant courses and collaboration tools, e.g. forums). These services can provide adaptive and inclusive support as they can access the user model information, for instance, within the properties of the IMS-LD unit of learning. On the other side, other types of user services –that consider pedagogical and psycho-educational support- can also be provided though the same interface. Both kinds of services are supported by the adaptive components that manage information about the user profile, needs and interactions (user model), capabilities of the device used to access the LMS (device model repository), characteristics of the resources (metadata repository), provide personalization of contents by providing pointers to alternative resources that better suit the user needs and preferences, and guide the navigation
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
of users in the system through recommendations (recommender system). This framework is being applied in the context of the EU4ALL project by the two main open distance universities in Europe, which are the ones traditionally more focused on providing LLL support for citizens. First, we comment on what end-user services are required by each institution. Next, we focus on a service based on recommendations to support some adaptive and inclusive end-user services at UNED.
count towards 200 different qualifications. The university promotes distance learning and is open to people, places, methods and ideas. Students do not need to have any formal qualifications to study. Over 10,000 students with disabilities are studying courses with the Open University. In this context, the needs of the OU deal with the following services (Douce, 2009): •
Instantiation of the Framework at the EU4ALL Open Distance Universities Going back to Figure 1, there are two institutions represented. For instance, institution A can be the Open University and institution B can be the UNED. Each of them has its own needs and provides their own services to their members (Sa, Sb and Sc for the ICT services at institution A or Open University, and Sd and Se for the ICT services at institution B or UNED). By applying the framework, the services are articulated though the O-ASA and supported by a set of technological components, which also differ among universities. As a case study, we present how the framework is used to deliver a service at UNED, including the components required. Both institutions have different technical and user requirements that are translated into various services to be offered to their members. These are the services to be managed within the framework following the lifecycle of service provision and supported by the O-ASA. The resulting end user services are open, interoperable, and can be provided to the users thanks to the flexibility of the architecture.
The Open University The Open University has 150,000 undergraduates, 30,000 people taking postgraduate courses. The university offers over 600 courses which
•
• • •
• •
Studying at the Open University: A student contacts the university to register, receives a set of resources and is allocated a personal tutor, who will often run a series of face to face tutorials. Students submit assignments to tutors, which are then returned to them. At the end of the course, students take an exam. Asking questions about courses to make decisions about how best to organize accommodations when the student has additional requirements (e.g. to carry out field trips or require assistants to complete a course). Using courses and resources which provide alternative formats. Ordering and delivering of kits of assistive technologies to follow the courses. Assess the effectiveness of the assistive technology provided to a student to permit the access and use of the system, since in some cases, the choice of assistive technology may not be appropriate, making it difficult for learners to study. Creating learning materials that consider accessibility from the outset. Ordering alternatives for non-accessible materials to create alternative learning experiences or resources.
More details on the services under discussion for implementation are provided elsewhere (Cooper, 2009).
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
The UNED The UNED is the largest university in Spain and one of the greatest universities in the world with an average of 180.000 students enrolled per year. It has over 1400 full time teaching staff, more than 6000 tutors and 2000 administrative staff. It is a global university with 60 study centers and 15 study centers abroad. UNED courses are taught through distance learning for students all over the world (some centers abroad are located in Argentina, Belgium, Brazil, Equatorial Guinea, Morocco, and United States). Nowadays, UNED offers graduate studies in 23 different fields, including Humanities, Social Sciences, Physics, Mathematics, Chemistry, Engineering and Computer Science. The graduate studies are complemented by post-graduate and doctoral studies. UNED also offers post-graduate courses like Masters and specialization courses for those people who have finished their graduate studies and want to continue studying (ongoing education). UNED works intensively to update and improve its distance learning methodology through a pervasive use of ICTs, which is reflected on its management and educational services, including Campus UNED-e, educational TV and Radio, UNED-mobile, UNED-Wi-Fi, etc. Students with disabilities have no-fee enrolment in courses at UNED and are provided with a support department to offer the required services, the so-called UNED Disability Office (UDO). UDO staff is trained to support the accessibility preferences of a wide range of disabilities, including perceptual, motor, and psychic disabilities. Demands on necessities for the study also vary widely, including architectural accessibility, text-book adaptations, web and multimedia adaptations, sign language or Braille translations and, mainly, examination adaptations. UDO, in collaboration with aDeNu research group, have created an online community for UNED’s students with disabilities, as a tool to contribute to the effective integration of people with functional
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diversity as full members of the university community (Rodriguez-Ascaso et al., 2008b; Santos et al. 2006). UDO services are grouped according to the following categories: • •
•
•
Studies and examination processes and related accommodations. Counselling, support and mediation activities to guide teaching staff in the provision of services to attend specific disability needs. Supporting students with disabilities along the whole life cycle of their studies and beyond to help their integration in the labour market. Dissemination and disability related awareness activities.
According to the user requirements and the subsequent specification of services, a set of end user services have been defined in EU4ALL for the UNED pilot site. •
•
•
•
•
Allowing users to complete and/or check the information stored in the system about their accessibility needs and preferences, as well as their psycho-educational style and needs. Inviting students to take a personalized course that will introduce them to the university’s learning environment, as well as to support e-services available at the university. Checking whether a learning media item would be accessible to a student based on the given user profile. Mediating the manual adaptation of an inaccessible resource through a management workflow tailored to conditions found at the institution. Synchronising the user’s accessibility needs and preferences with her portfolio,
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
•
as well as the portability of the user’s preferences to external systems. Supporting resources to accommodate inaccessible course components
The UNED Use Case UNED -funded in 1972- has made an intensive use from its beginning of the communication channels available to allow students communicate by using their preferred medium: telephone, videoconference, postal mail, fax, broadcasting radio and TV programs, and the Internet. For this reason, it cannot surprise that in 1999 a group of professors and students from a Laboratory at the UNED’s Artificial Intelligence Department started to pay attention to a software produced at the Massachussets Institute of Technology (MIT), called ArsDigita Community System (ACS). As the software was released under an open source license, this group downloaded and tested it to work out how it could be used to better support the learning of their subjects. This software evolved along time and turned into an open source software called OpenACS, which offers an educational application called dotLRN. On the one hand, its robustness, and on the other hand, the large community of developers and educational institutions that support it has made it a success for 10 years running, struggling against commercial systems, such as WebCT or Blackboard, and popular open source ones, such as Moodle or Sakai (Santos et al., 2007). One of the key features of dotLRN is the commitment of its developer community to produce an accessible LMS. Fully accessible LMS are still not available due to several open issues yet to be solved (Santos and Boticario, 2008c); for example, controlling the accessibility of the contributions by course designers, or the accessibility of the existing players that deploy courses built following specifications such as SCORM or IMS-LD. However, due to the architectural foundations and the release under open source license of the OpenACS framework (upon which
dotLRN is developed) it is feasible to comply with the accessibility requirements and modify the code accordingly to support them. For this reason, the instantiation of the framework at UNED is based on dotLRN LMS. Technical details on the integration can be read in (Raffenne et al., 2009; Santos et al., 2010). To illustrate the application at UNED, we have selected the adaptive psycho-educational support service from the list of services identified in Santos et al. (2010). In this service a student is invited to take a personalized course that will introduce her to the university’s learning environment, as well as to support eServices available at the university. The course is based on an IMS-LD unit of learning, personalized to student’s identified learning style and needs (managed by the User Model in terms of IMS Learner Information Profile and ISO Personal Needs and Preferences, which can correspond to the component C1- cf with Figure 1). The contents are characterized with ISO Digital Resources Description and IMS Metadata (Metadata Repository which can correspond to the component C2). In addition to this, the student receives dynamic guidance through recommendations (a Recommender System which can correspond to the component C3), based both on user model information and interactions with the system (Tracking and Audit Module which can correspond to the component C4). To provide the inclusive and personalized interaction approach for this eService, a recommender system is used to support personalization/adaptation and interoperability in this inclusive learning scenario. The underlying research goal is to deliver dynamic contextual recommendations to the learner when coping with psycho-educational scenarios and contribute to enrich the existing LMS with adaptive navigation support. This innovative feature considers usercentred design methods and involves the educator in the modelling at design time in order to produce a wide variety of recommendations that take into account the learners’ needs and their evolv-
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
ing context (Santos and Boticario, 2010a). This modelling complements traditional approaches of recommender systems that apply blindly the algorithms to data gathered from interactions. These recommendations can be managed through a recommendations model that is built up following the appropriate standards. Details on the model are described in (Santos & Boticario, 2010b). This formal model allows creating recommendations at design time in terms of applicability conditions and restrictions, which are later used at runtime for selecting the appropriate recommendations for the user in the context at hand. At this point, algorithms can be used to adjust existing recommendations or produce new ones. The model covers the following needs of information (see Tables 1 and 2): what is recommended, when is recommended, how is recommended and why is recommended. This process covers the lifecycle of service provision since the different phases are properly managed. First, a methodology is used (Santos et al., 2009) to support the course designer in describing recommendations in learning inclusive scenarios (design phase). Second, a management tool is required to allow at publication time the
readjustment of the designed recommendations as well as the validation of the automatically produced ones (Santos et al., 2011). Third, the recommendations that match the user needs and context are offered to the user and present additional information to the user to explain why the recommendation has been offered (Santos and Boticario, 2008b). Finally, the system requests explicit feedback from the learner when she has shown interest in the recommendation process to improve the recommender and provides reports on the recommendations tracking. Following the methodology proposed above, several recommendations were identified to offer the dynamic guidance embedded in the adaptive and inclusive psycho-educational support service. The scenario requires users to go through questions that reveal their psycho-educational background in order to build on skills and abilities, such as learning style, competencies already had, educational background, experience of using computers and so on. After fulfilling the questionnaire, learners are invited to take a course that will introduce them to the university’s virtual learning environment, as well as to support eservices available at the institution. The course
Table 1. Example of a recommendation with additional material Recommendation 1: Depending on the marks obtained in questionnaire, additional material is provided to the learner What is recommended
A learning object of the course
When is recommended
When the learner has submitted the responses to a questionnaire
How is recommended
A link to the learning object selected is provided
Why is recommended
The recommendation is provided by taking into account the previous knowledge of the learner
Table 2. Example of a recommendation to foster collaboration Recommendation 2: Foster collaboration by using the forums What is recommended
The forums service in the platform
When is recommended
When the learner has not communicated with her peers
How is recommended
A link to the forum tool
Why is recommended
The recommendation is provided by taking into account the collaboration level of the users in order to promote collaboration among peers in the course
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
has some in-built learning design, personalized to student’s identified learning style and needs. The psycho-educational support embedded in the learning design consists of planning support, easy reading style, additional summaries, conceptual maps, and study registries at the end of each unit. In addition to this, the student receives dynamic guidance through recommendations based both on her preferences and performance while using the system (e.g. marks obtained in questionnaire based assessments, learning objects she has accessed, use of the forums to communicate with her peers, etc.). As an example, the table below presents a couple of the recommendations identified with the methodology and their mapping into the model:
◦⊦
◦⊦
OPEN ISSUES Teaching and learning processes are bound to undergo dramatic changes in the coming years. In this key working area the purpose is to introduce the possibility of managing the teaching and learning processes themselves in an accessible, personalized and standards-based way, not just the contents to be learned. This approach entails solving technological and management problems that currently present significant barriers in HE institutions, especially in distance learning universities when trying to meet individual learners’ needs. In particular, the following issues are to be solved: •
To guarantee a personalized task based learning approach: online courses are to be based on modelling units of learning from different pedagogical perspectives that can be adapted to different types of learners and learning situations. In this respect, it is essential to provide learners with specific, timely feedback on their performance. Crucial issues to be managed through that learner-centered approach in those scenarios are as follows:
◦⊦
Responsiveness of the environment: ability to react quickly to the requirements of the learners (motivate, engage, inspire). To this, a recommender system such as the one discussed in this chapter should be extended to consider further areas of motivation, engagement and inspiration. Covering different approaches: from individual to collaborative learning, from desktop management to ubiquitous activities, and from formal settings (modules with their syllabus) to informal learning (learning units focused on specific skills or knowledge). Although developments have been made in the area of open and flexible CSCL (Computer Supported Collaborative Learning) environments (Bayon et al., 2009), a more flexible and design-based approach in those collaborative scenarios that accommodate IMS-LD is needed (CSCL Challenge, 2009). The idea is to let the designer control the arrangement of phases in the collaboration and consider personalized learning paths which takes advantage of related design issues. Adaptive and intuitive response: adapting to learning behaviours that reflect relevant learners’ features such as learning styles, learners’ competences and skills, preferences and specific needs, diverse functional issues, etc. The objective of the system is to guide the students from their initial intuitions to the final learning path. So far short-term issues have been covered, such as those related to the problems that may encounter while learning in a particular course. There is a wide range of problems, more related to long-term issues that are to be
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A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
◦⊦
◦⊦
48
addressing in the context of the LLL paradigm. For instance, how to support intuitiveness for a particular type of student based on learning personal records. Solving these type of problems may produce a major impact on learners with functional diversity issues, such as prelocutive deafness, since they have difficulties achieving functional levels of the oral and written language, so emotional communication online is deficient (Garay et al., 2006), and some vocabulary adaptations could be of interest. Intelligent monitoring process to adapt to learners expectations and leverage the exploitation of the learning process. Current user tracking approaches are to be extended to monitor the progress of users, with the aim of providing sensible reports to learning designers and course authors, who can then make improvements to the initial design. Auditing facilities will include support for identifying, defining, and evaluating any type of object or learning situation that needs to be audited. Evidences of learning better: one of the most critical issues to guide the learning process via adaptive features is the management of the effectiveness of the learning process and its evolving features. In this regard objectives are twofold: first, the approach will support the identification, labeling and managing of learning measurements so that teachers, designers, tutors and administrators will benefit from specific features that impact on learning effectiveness. Second, learners will be provided with evidences of their learning process to encourage their engagement
•
•
and motivation. The latter can be addressed trough scrutability features so that users are provided with features to see and appreciate the meaning of personal information a user model. It is expected that helping people to become more self-aware and avoid self-deception, because their user model mirrors their real actions, will encourage meta-cognition and deeper learning (Kay, 2006). Flexibility and ubiquitous scenarios: the LLL paradigm, where students are supposed to follow formal and informal learning from any context, demonstrates the need for developing standard user and device profiles that can respond not only to current everyday scenarios, but also can be flexible enough to cope with future scenarios coming from ubiquitous and wearable computing. In addition to these requirements, context/location-awareness, seamless roaming, and portability have also been identified by some authors (Velasco et al., 2004). In this context, there is a need to extend these previous efforts into a standardized framework that will facilitate interoperability and merging of device and user profiles. Privacy and trust problems shall be considered as well (Wiedenback et al., 2003). Long-term modelling and profiling: A user profile/model is an explicit representation of the properties of an individual user and can be used to reason about the needs, preferences or future behaviour of that user. These data includes information for the user about cognitive models, pedagogicalstyle models, educational goals and motivation model, domain expert knowledge model, background model, bugs and misunderstandings model and collaborative learning model. Moreover, other types of user data can be obtained, such as instruc-
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
tional material presentation preferences, preferred tutoring strategy, and curriculum planning and assessment history. Available international standards and specifications allow the description of learners related data and information, such as the IMS Learner Information Profile (IMS-LIP), the IMS Accessibility for Learner Information Profile (IMS-AccLIP), the ISO Personal Needs and Preferences (ISO PNP), and the IMS Reusable Definition of Competency or Educational Objective (IMS-RDCEO). In relation to that, one outstanding issue to further research is how to combine that type of modelling features included in the user model with those that are being included in the ePortfolios, which are considered as potentially pervasive means of learners and other education users recording for themselves and representing to others learning experiences and achievements. There is a growing interest in ePortfolios across all educational levels, and especially in LLL, but still several open issues remain, e.g. how to design user interaction with their ePortfolio, interoperability between ePortfolio and user models in the framework of standards, roaming user profiles across different platforms, etc. From the standards point of view, there is ongoing work in the ISO (International Organization for Standardization). In particular, IMS AccLIP and AccMD have been internationalized in the ISO/IEC JTC1 Individualized Adaptability and Accessibility for Learning, Education and Training (24751). Moreover, the IMS are currently developing version 2.0 of AccMD and AccLIP and these are expected to harmonize with the ISO version. The works in ISO have redefined part of the IMS specification to make it closer to the real needs of the users. Moreover, there are additional open issues related to the combination of the framework with
the new emerging so called Personal Learning Environments (PLE) and Mashups (Attwell et al., 2008; Wilson et al. 2006). Here the focus goes beyond the institutional-driven goals and supplements it with a new model that takes into account the opportunities provided by the Web 2.0 to acquire, assimilate and apply the knowledge constantly being disseminated in the Web. This implies -as it has already been acknowledged in many reports, initiatives and projects from Europe and worldwide- that learning is now a process that follows a person throughout their lifetime and must be embedded at the organizational and personal space. A more active and responsible learner -who is able to select, share and collaborate with other learners without the HE control and guidance- has to be considered. Users will learn in learning networks and share the same particularities with other technology enhanced learning scenarios. Their personal needs, preferences and interests could be supported by flexible frameworks, such as the one presented here, by means of personalization techniques and inclusive approaches. Nevertheless there is still a problem on the institutional side about how to consider and integrate those PLEs within their institutional set of services. There are interesting project and initiatives that are addressing the challenges of this new user-created learning environments. iCAMP (iCAMP, 2008), aims at creating an infrastructure for collaboration and networking across systems, countries, and disciplines in HE. Pedagogically, it is based on constructivist learning theories that put emphasis on self-organized learning, social networking, and the changing roles of educators. On a technological level, iCAMP builds upon a social software approach for HE, whereby the learning tools (building blocks) adopted or developed are applied for different educational purposes. iCamp concentrated much on creating an infrastructure for collaboration and networking and less on providing services which integrate management issues from the institutional viewpoint.
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An interesting project is ROLE (ROLE, 2009), which aims at developing and testing prototypes of high responsive, open learning environments, offering breakthrough levels of effectiveness, flexibility, user-control and mass-individualization. Such learning environments are supposed to advance the state-of-the-art in human resource management, self-regulated and social learning, adaptive education and educational psychology, service composition and orchestration, and the use of ICT in lifelong learning. Learning environment elements can be combined in ROLE to generate i.e. to mash-up) new components and functionalities, which can be adapted by lone learners or collaborating learners to meet their own needs and to enhance the effectiveness of their learning. However ROLE does not consider any semantic relation between users, contents and tools when supports the construction of usercentered services. In addition, the framework that has been introduced can be applied in different settings such as curriculum based and vocational education and not just in those presented at HE institutions. In particular, it is well known that learners with special needs in those contexts encounter difficulties with basic academic tasks such as reading and writing. These learners share some learning difficulties when they are accessing both learning contents and services. In general they have information processing problems and due to some limitations in various cognitive processes (memory, reasoning, attention, language understanding) they perform poorly on the target tasks. They usually miss relevant information for problem solving in the academic and real world context. Thus they may benefit from having extra time for assessment and other activities, supporting in text comprehension and writing and scheduling/organizer functions. To this end we have already developed a psycho-educational support prototype that combines different framework modules (i.e. learning design, user modelling, recommender system and the LMS) to provide personalized learning flows
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according to the student learning style, psychoeducational needs, knowledge and competences (Rodriguez-Ascaso et al, 2008a). The basic idea behind that is to support the student with additional information and guidance when dealing with learning tasks. The student accesses additional content (in audio /video format, etc), which is presented according to their context and interaction needs (thanks to the Content Personalization, Metadata Repository and Device Models Repository), in order to help them to complete the scheduled tasks. When the expectations are not met the Recommender System provides the learner with new recommendations and if they do not suffice, either the tracker informs the tutor/advisor/counsellor who takes over. The open issue here is not the applicability of the solutions included in the framework but the technological infrastructure required to do so. The challenge in these settings (i.e. curriculum based and vocational education) is mainly an innovation issue. Hopefully the shifts of learning paradigms, the new delivery modes (mainly ICT mediated) and the increasing number of driving forces (e.g. the labour market) will promote the application of this type of solutions in these settings (Carneiro, 2007).
FUTURE RESEARCH DIRECTIONS Future research directions should focus on facilitating the management of the teaching and learning processes themselves in an accessible, personalized and standards-based way, not just the contents to be learned. Several technological and management problems should be addressed in order to guarantee a personalized task based learning approach, manage flexible and ubiquitous scenarios and support long-term modelling and profiling. Several issues are involved to guarantee the personalized and inclusive approach: (i) responsiveness of the environment, (ii) addressing different approaches from individual to collaborative learning, from desktop management to
A General Framework for Inclusive Lifelong Learning in Higher Education Institutions
ubiquitous activities, and from formal settings to informal learning, (iii) adaptive and intuitive response, (iv) intelligent monitoring process to adapt to learners expectations and leverage the exploitation of the learning process, and (v) evidence of better learning. Details have been provided in the previous section. This management should be supported by a proper application of standards, which requires an active involvement of the research community in the standardization bodies. Moreover, an emerging research direction is related to the PLE, which go beyond the institutional-driven goals and supplement it with a new model that take into account the opportunities provided by the Web 2.0 context and promote user-created learning environments. There is a need for flexible frameworks that can be applied to solve the technological and management problems in different settings, which not only include distance learning universities but also curriculum based and vocational education. Learners in any of these contexts require a personalized support that meet individual learners’ learning needs.
CONCLUSION The current student-centered approach is inappropriate for an increasing number of students, who are supposed to benefit from the personal training but in practice have to face social, physical and cognitive barriers because they have disabilities. Accessible e-learning cannot be achieved adopting a universal design approach alone. Personalization techniques have to be considered as well in order to meet this goal. In this way, the usability of e-learning systems can be increased for all learners, since their interaction preferences and needs are considered. A general framework based on and adaptive and accessible standards-based open source service oriented architecture has been described. The approach is designed for HE but can be extended
to other types of educational organizations. In any case, the core of the approach is to integrate inclusive support into the educational institution’s existing ICT services infrastructure. The framework supports the full cycle of services inclusive adaptation by combining universal design approaches and personalization techniques. On the one hand, standards and specifications that try to cover the wide variety of needs are used. On the other hand, dynamic contextual recommendations are applied during the course execution. There are many research, technical and even organizational open issues related to the framework that has been described in this chapter. Firstly, till very recently there have not been general approaches that considered learners and their evolving circumstances, i.e. prior knowledge, preferences, learning style, learning activities, learning goals, learning context (Drachsler et al., 2007). Moreover, most of the adaptive systems do not address the accessibility requirements, which are of major importance to provide an inclusive support. Secondly, TEL is a long-term issue which lacks of general and standard-based approaches. In this chapter we have described an integrated framework of learning services which is able to include third-party developments. To this, a Service-Oriented Architecture that allows for interoperability, re-usability, composability, distributivity and discoverability has been designed and implemented. The required interoperability of applications and services in the framework goes beyond current approaches in a twofold way: (i) providing the services-based infrastructure to manage the whole range of standards needed to meet accessibility and adaptations needs of LLL services through the design and implementation of the corresponding components, and (ii) covering the current limitations of adaptive and accessible scenarios by enabling the integration of components that partially solve these needs when they operate in isolation but which provide a holistic approach if integrated together. Developments
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are being validated by different types of users on the demand side and different existing roles on the supply side by the Open University and the UNED- the two universities with the largest number of students with disabilities. Thirdly, putting the learner at the center of the teaching/learning process is still an open issue. User centered methodologies and ontologies that can be mapped to existing standards in learner characterization to model user, content and device (user interface and assistive technology) profiles. The adaptive dynamism can be obtained by mining, analyzing and categorizing the sequence of actions followed by learners to provide the dynamic guidance accordingly. The purpose here is not just to build the desired services but to provide the general framework required for their development addressing key open and fundamental research issues in various interrelated fields. To this end we have to consider the current limitations of available standards to deal with accessibility and functional diversity issues. The applicability of the approach was illustrated in the context of the EU4ALL project and a use case based on psycho-educational support at UNED was discussed in more detail.
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United Nations. (2006). Convention on the rights of persons with disabilities. General Assembly. Retrieved April 20, 2008, from http://www.un.org/ esa/ socdev/ enable/ rights/ convtexte.htm Van Rosmalen, P., Boticario, J. G., & Santos, O. C. (2004). The full life cycle of adaptation in aLFanet e-learning environment. Learning Technology, 6(4), 59–61. Velasco, C. A., Mohamad, Y., Gilman, A. S., Viorres, N., Vlachogiannis, E., Arnellos, A., & Darsenitas, J. S. (2004). Universal access to information services—the need for user information and its relationship to device profiles. Universal Access in the Information Society, 3(1), 88–95. doi:10.1007/s10209-003-0075-5 Wiedenbeck, S., Kracher, B., & Corritore, C. L. (Eds.). (2003). Trust and technology [special issue]. International Journal of Human-Computer Studies, 58(6), 633–812. Wilson, S., Liber, O., Beauvoir, P., Milligan, C., Johnson, M., & Sharples, P. (2006). Personal learning environments: Challenging the dominant design of educational systems. Proceedings of the first Joint International Workshop on Professional Learning, Competence Development and Knowledge Management (LOKMOL and L3NCD), (pp. 67-76). World Wide Web Consortium. (2009). Web accessibility initiative. Retrieved October 20, 2009, from http://www.w3.org/WAI/
ADDITIONAL READING Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6). doi:10.1109/TKDE.2005.99
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Baldiris, S., Fabregat, R., & Santos, O. (2007). Modelling Competency upon dotLRN. In proceedings of the OpenACS international conference. Vienna. Beshears. F.M. (2003). Open Standards and Open Source Development Strategies for e-Learning. Presentation for IS224 Strategic Computing and Communications Technology. Berkeley: Educational Technology Services. Brooks, C., & Kettel, L. Hansen. C. (2005). Building a Learning Object Content Management System World Conference on E-Learning in Corxºporate, Healthcare, & Higher Education (E-Learn 2005). Brusilovsky, P., & Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. [Springer-Verlag Berlin Heidelberg.]. The Adaptive Web, LNCS, 4321, 3–53. doi:10.1007/978-3-540-72079-9_1 Burke, R. (2002). Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 12, 331–370. doi:10.1023/A:1021240730564 Cristea, A. I. (2004). What can the Semantic Web do for Adaptive Educational Hypermedia? Journal of Educational Technology & Society, 7(4). De Bra, P., Pechenizkiy, M., van der Sluijs, K., & Smits, D. (2008). GRAPPLE: Integrating Adaptive Learning into Learning Management Systems. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008, pp. 5183-5188. Dolog, P., & Nejdl, W. (2003). 2003. Challenges and Benefits of the Semantic Web for User Modelling. In Adaptive Hypermedia.
Ghali, F., & Cristea, A. (2008). Interoperability between MOT and Learning Management Systems: Converting CAF to IMS QTI and IMS CP, AH 2008: 5th Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems pp. 296–299. Berlin Heidelberg, Springer Harrigan, M., Kravcik, M., Steiner, C., & Wade, V. (2009). What Do Academic Users Really Want from an Adaptive Learning System? The 17th International Conference on User Modeling, Adaptation, and Personalization (Trento, Italy), 2009, pp. 455-460. Hauger, D. (2009). 09: Adaptation and Personalization for Web 2.0. Using Asynchronous ClientSide User Monitoring to Enhance User Modeling in Adaptive E-Learning Systems. In UMAP. Heckmann, D. (2006). Ubiquitous User Modeling. Berlin, Germany: Akademische. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., & von Wilamowitz-Moellendorff, M. (2005). Gumo – The General User Model Ontology. In: L. Ardissono, P. Brna & A. Mitrovic (eds.), Proceedings of the 10th International Conference on User Modeling (pp. 428-432), Edinburgh, UK: Springer Verlag. Henze, N., Dolog, P., & Nejdl, W. (2004). Reasoning and Ontologies for Personalized E-Learning. Journal of Educational Technology & Society, 7(4). Jameson, A. (2006). Adaptive user interfaces and agents. In Jacko, J. A., & Sears, A. (Eds.), Human-Computer Interaction handbook (2nd ed., pp. 433–458). Erlbaum. Kareal, F., & Klema, J. (2006). Adaptivity in e-learning. In A. Méndez-Vilas, A. Solano, J. Mesa and J. A. Mesa: Current Developments in Technology-Assisted Education, 1, pp. 260-264.
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Leshin, C. B., Pollock, J., & Reigeluth, C. M. (1992). Instructional Design Strategies and Tactics. Englewood Cliffs, NJ: Educational Technology Publications. Longworth, N. (2003). Lifelong learning in action - Transforming education in the 21st century. Kogan page. Martin, L., Roldán, D., Revilla, O., Aguilar, M.J., Santos, O. C., Boticario, J.G. (2008). Usability in e-Learning Platforms: heuristics comparison between Moodle, Sakai and dotLRN. OpenACS Conferences. Mazzola, L., & Mazza, R. (2009) Supporting Learners in Adaptive Learning Environments through the Enhancement of the Student Model. Julie A. Jacko (Ed.): Human-Computer Interaction. Interacting in Various Application Domains, 13th International Conference, HCI International 2009. Mejía, C., Baldiris, S., Gómez, S., & Fabregat, R. (2008). Adaptation process to deliver content based on user learning styles, International Conference of Education, Research and Innovation (IBSN: 978-84-612-5091-2), 2008, International Association of Technology, Education and Development, Madrid (Spain). Morales, L., Castillo, L., Fernandez-Olivares, J., & Gonzalez-Ferrer, A. (2008). Automatic Generation of User Adapted Learning Designs: An AI-Planning Proposal. 5th Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems. Hannover.
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Niederée, C., Stewart, A., Mehta, B., & Hemmje, M. (2004). A Multi-Dimensional, Unified User Model for Cross-System Personalization. In: Proceedings of the Workshop on Environments for Personalized Information Access at AVI’2004 (pp. 34-54), Gallipoli, Italy, from http://www.di. uniba. it/ avi2004/ e4pia/ EPIA2004_ proceedings.pdf Recio-García, J., Díaz-Agudo, B., & GonzálezCalero, P. (2008). Prototyping recommender systems in jcolibri, Proceedings of the 2008 ACM conference on Recommender systems, pp.243-250.
ENDNOTE 1
The reader should clearly differentiate services, end-user services and e-services (which relate to intangible facilities provided to address the users’ needs and which can be provided with or without the support of ICT) from web services, which is a standardbased technology (defined by the W3C) to support interoperable machine-to-machine interaction over a network and which can be used to implement an architecture according to service-oriented architecture concepts, where the basic unit of communication is a message, rather than an operation.
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Chapter 3
Diplek:
An Open LMS that Supports Fast Composition of Educational Services C. T. Rodosthenous University of Patras, Greece A. D. Kameas Hellenic Open University, Greece P. E. Pintelas University of Patras, Greece
ABSTRACT Modern ICT has evolved through the years and is now in position of delivering educational content to specific target groups in remote locations. Advanced e-learning techniques are now used not only for delivering content to high school and university students, but can be used in lifelong training programs. Due to the target group most of these programs have (i.e. professionals with little time to spend, people of a certain age with reduced ICT skills, etc.), it is vital that organizations choose wisely among the many Learning Management Systems currently available. The purpose of this chapter is to describe and examine the features of such a platform. DIPLEK is a platform developed using service oriented architecture to enable easy access to educational content and activities for novice learners and instructors.
INTRODUCTION A Virtual Learning Environment or Learning Management System (VLE or LMS) is a software system or integrated platform that contains a series of services and tools to support a number of activities and course management procedures (Ho, DOI: 10.4018/978-1-61520-983-5.ch003
Higson, Dey & Xu, 2009). Nowadays numerous LMS are available in the market; a few more, like Moodle, Sakai, Atutor, Claroline, etc., are under development by the open source community. Another category of Learning Systems is the so called Learning Design systems (Britain, 2004). This category includes systems like LAMS (Dalziel, 2003) and Coppercore (Vogten et al., 2007). Despite the many tools and services offered by the
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LMS, a number of limitations and disadvantages were reported from users and researchers: 1. The platform complexity and difficulty of use require the continuous IT support and hence require a high investment for instructors and training of supported learners (Mendling, Neumann, Pinterits & Simon, 2005).Due to this complexity these systems cannot be easily used by people involved in lifelong learning and training programs. 2. Low level of reusability and portability of learning content due to the non-standardized way that the educational material is stored (O’Droma, Ganchev & McDonnell, 2003; Avgeriou, Koutoumanos, Retalis, & Papaspyrou, 2000) 3. Limited number of available tools and services for proper monitoring of the learners’ activities throughout the course duration. (Mazza & Dimitrova, 2007) 4. The dependence on web technologies most of the platforms have, obstruct the deployment of distance learning services to internet-less communities and institutions. 5. Most currently available platforms emphasize on technology that facilitates interaction among learners and instructors and neglect personalization of the learning environment (Cheung, Hui, Zhang &Yiu, 2003) The purpose of this chapter is to describe the design and development of Diplek an open-source educational platform that uses to support the needs of instructors with reduced IT competence throughout the main phases of course management lifecycle. There is a plethora of LMS out in the market that can be used. Diplek is not trying to compete with existing LMSs. More likely Diplek is intended to be used in smaller educational domains where computer experts are hard to find and the need for an easy to use LMS is prominent. Most LMS simplify only the services that relate to the content management process; nevertheless,
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instructors have additional needs, such as, to monitor a learners’ progress with means that can be easily handled by a typical non IT specialist instructor and to communicate with learners in real time. Diplek offers a special tool for monitoring purposes, which records a learner’s activities through a session in video format; this recording can be interpreted at a later time by the instructor to extract conclusions about the learners’ progress and the course’s contribution in achieving its purpose. Moreover, the system can be operated without an internet connection or a web browser. This flexibility comes in handy in situations where the equipment is old and the connection between workstations is limited to a LAN. The remainder of this chapter is organized as follows: In the following sections user requirements for LMS are presented, together with currently available solutions. Then, a detailed presentation of Diplek educational platform and its services, tools and features is provided. Finally, the chapter presents future research directions and conclusions.
USER REQUIREMENTS AND EXPECTATIONS Over the past fifteen years LMSs have been embraced by as a technology of significance for creating new revenue streams, reaching new markets, connecting with students in new ways, and/or teaching more efficiently. At a recent research study done in UK, 90% of schools use LMS or even have more than one LMS in the institution (Baziukaitė, Vaira & Idzelytė, 2008).The reason for this wide acceptance and usage of LMSs is the many advantages that they bring: • • •
Users can manage and track their own learning Personalization of learning Access to worldwide learning material
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A LMS addresses two specific target groups: instructors and learners; each one needs to be supported for different roles and actions. Learners are offered educational material and guidance through the LMS, which provides them with tools and services adapted to their needs. Each learner is assigned to an instructor or a group of instructors that are responsible for: creating, delivering and assigning educational material to the learner, monitoring learners’ activities, grading learners’ performance and providing personal guidance. The majority of users (learners and instructors) focus on the ease of usage and the way information is presented to them. In short, users expect (Keenoy et al., 2003): •
• •
An easy to walk round environment with a minimum of steps to perform a certain action. A consistent user interface providing help and guidance at every step Personalized content available for each learner and at each stage of the learning process.
The above features are offered by LMSs in the form of services. Modern LMSs are built using a service oriented architecture which is a new paradigm that extends the object-oriented paradigm to web-based systems. It uses services as base elements for developing multi system platforms. Services are autonomous platformindependent computational elements that can be described, published, discovered, orchestrated and programmed for the purpose of developing massively distributed interoperable applications (Shen, Wang, Li & Ghenniwa, 2006). One can derive seven categories of services offered by LMS (Colace, De Santo & Vento, 2003): • • • •
Educational Content Integrating-Wrapping Content Evaluation & Assessment Communication and Collaboration
• • •
Adaptation & Profile building Learner Monitoring Documentation & Help
Educational Content The principal function of a LMS is to deliver learning material to learners. Most LMS require educational content to be described in a certain form, using metadata (Anido et al., 2002) to facilitate search, definition, and finding the content in which learners are mostly interested. Consequently, in a context where instructional material consists of Learning Objects, LMS could implement new features to take advantage of the benefits of qualitative metadata, e.g. recommending appropriate activities, or allowing learners and instructors to perform activities such as content analysis, information resource location, or enhanced searches, to name a few. While this is a helpful service, novice instructors find it difficult and time consuming. Instructors are also assigned with the task of providing help to their learners which involves: support related to the learning content, support related to the learning process and support related to the learning product (Reid & Newhouse, 2004). Learning content support refers to all instructor activities that concern the subject matter (de Vries et al. 2005). Process related support refers to all tutor activities related to the learning process of individual learners or group collaboration. Product related support refers to all tutor activities that pertain to the summative assessment of learner products, such as checking the authenticity of the product or correcting tests.
Integrating: Wrapping Content One must take into account that a lot of educational material has already been developed in a form that is not presentable by LMS (i.e. standalone applications or material created with a certain authoring tool) and a lot of effort and expertise is required
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in order to transform it to a suitable form. Besides, transforming a Learning Object from its original format to a compatible LMS format entails the risk of affecting negatively its educational value. A LMS should provide the instructor with tools to include/present educational material in any possible format, as long as it can be rendered in the learners’ computer.
Evaluation and Assessment Gronlund (2006) has written that formative assessment is intended “to monitor student progress during instruction...to identify the students’ learning successes and failures so that adjustments in instruction and learning can be made”. Tests and assignments constitute a large part of a learner’s everyday educational practice. Instructors are responsible for checking a learner’s progress through the course duration and one of the tools at hand is test assignment. When it comes to a LMS, this is offered as a built-in service that makes use of a large variety of tools available to assist both the instructors and learners. These tools range from supporting authoring of multiple choice questions up to project (assignments) management.
Communication and Collaboration Communication in a computer learning environment can be analyzed in the broader context of computer-supported collaborative learning (CSCL) (Weinberger & Fischer, 2006). This field deals with issues regarding collaboration during the learning processes, and the use of computermediated communication (CMC) to support collaboration between learners, in order to enhance learners’ learning processes (Kreijns, Kirschner & Jochems, 2003). This group of services is regarded as a fundamental part of the learning process. Communication in a LMS can be implemented by using emails, built in messaging-chat services, forums, wikis and blogs. These services are the
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basic elements of the communication service group offered by most LMS.
Adaptation and Profile Building Adaptation is used to refer to the personalization of learning based on user preferences and user performance along specific criteria. The two main aspects usually involved are adaptivity and adaptability. Adaptivity is defined as the ability to change a lesson using different parameters and a set of pre-defined rules. On the other hand, adaptability is the possibility for learners to personalize a lesson by themselves. (Burgos, Tattersall & Koper, 2007) A user profile can be built based on the user’s behaviour, the educational content viewed, or both. A human behaviour based user model can be learned by observing the user’s actions such as log files, recording service, etc (Kim & Chan, 2008). Building a user profile can be done both manually by the instructor and automatically (e.g. using autonomous agents).
Learner Monitoring LMS gather large logs of data of learners’ activities during courses and usually have built-in monitoring features that enable the instructors to view some statistical data, such as a learner’s frequency of login, time taken on a course or a test, the number of messages the learner has read or sent, marks achieved in tests, etc. Instructors may use this information to monitor the learner’s progress and to identify potential problems. However, tracking data is usually provided in a tabular format, is often incomprehensible, with a poor logical organization, and is difficult to follow. As a result, Web log data is used by only few skilled and technically advanced distance learning instructors. (Mazza & Dimitrova, 2007)
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Documentation and Help
•
As with any other software, users are satisfied by it when an adequate and well written documentation is available (Sacha, 2006). Moreover, in a LMS that can be used by non computer literate users, help must be available at any stage of the learning process. This help is translated to (Sommerville, 2002):
• •
• • •
Printed manual for the whole software Included Help files that are linked and accessible by the current user On line Help
CURRENT STATE A variety of LMSs have already been adopted by educational institutes worldwide; some prefer free and open source solutions while others rely on proprietary software solutions that come with guaranteed support and helpdesk. The need for standardization led a lot of community developers to pursue a way to standardize most of the LMS services. The results of these efforts are reflected in: (a) the available standards of e-Learning architecture (e.g. the IEEE LTSA, Learning Technologies Standard Architecture), (b) the description of learning objects meta-data based on shareable XML-based data structures (e.g. through the IEEE LOM specification) and (c) the assessment and evaluation of user performance (e.g. through the IMS QTI, Question and Testing Interoperability Schemas). The above specifications enable the common description of learning units, questions and tests, learner profiles, etc, so that they can be easily interchanged between different applications (Sampson, Karagiannidis & Cardinali, 2002). A further study of the above specifications led to four main categories of services a LMS should offer to its users. These categories include services for:
•
Communication between learners and instructors Adaptation - Personalization - Extensibility People Grouping and General course Coordination Monitoring learners’ achievements and progress during a course
In the next sections, further explanation and analysis is made of each group of services along with state of the art of LMSs available in the educational community.
Communication Between Learners and Instructors Most LMS contain tools for conducting conversations. Naturally these rely to a great extent on e-mail and message exchange. It is important to consider how well the learning environment leverages messaging technology to support the conversation as an integral part of learning (Britain & Liber, 1999). For example, a good conversation tool should be accessible directly from the learning topic within the course structure and the user should not have to move out of the course work in order to continue the conversation. One should take into consideration whether the communication tool allows attachments to be included within messages, and if so, whether the attachments can be extracted and embedded into the user’s personal folder or portfolio. Another key point is whether the service allows learning goals to be specified and recorded during a conversation. Ideally the agreed learning goal should be in a prominent location with respect to the topic of learning. Some of the most popular communication tools are integrated email clients, forums and live chat rooms. These exist in many LMS platforms. A good example is the COSE LMS (http:// www.staffs.ac.uk/COSE/) that was developed during a research project at Staffordshire University. COSE supports email, forums and chat tools. All
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email interactions are automatically grouped for easy search and indexing. Email is outgoing only since COSE is designed to work with each user’s default email system. Another distinguished example is Moodle (http://moodle.org). Moodle is an open-source platform that is widely used by educational institutions worldwide both for its open architecture and the many components available free on the web. The philosophy behind Moodle has its roots in Social Constructionism (Dougiamas & Taylor, 2003). Moodle offers its users a series of communication services; like instant messaging, forums and chat rooms that are easily configurable and adjustable to course planning. Communication tools can be used while in a course or as an independent part of the platform. Moodle uses a combined method of email and instant messaging. A conversation between two users in Moodle is conducted in the messenger window and is send as an email so that both users have a copy of the conversation. Email is also used to inform forum members about new posts. This is very useful for large installations of the platform where instructors need to keep track of posts in a forum.
Platform Adaptation and Extensibility Adaptation is a term that addresses to both the educational and the technical level of the platform. An e-learning course should be designed to match learners’ needs and desires as closely as possible, and adapt during course progression (Graf & List, 2005). Adaptability is a term used to address the easiness of customizing the platform to learners and institutions needs. Each learner or group should have access to learning content designed or modified for their special needs. The adaptability of the learning environment user interface is also desirable. Not all learners need to have access to all LMS services, as this would increase the
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level of difficulty for performing a specific task. (Britain & Liber, 1999) Extensibility is a term that refers to the technical level of the platform. New and updated components can be incorporated into the platform and provide learners with new functions and services. These components can be obtained from third party developers or can be created by the instructor itself. This kind of adaptation is mostly wanted since the learning environment can be transformed to fit the learner’s needs. A good example of adaptation offered by an LMS is E-class. This LMS was adopted by the Greek Universities’ Network (GUnet) for the support of asynchronous blended learning in Greek Higher Education. It was constructed based on the open source software Claroline (http://www.claroline.net/) with the addition of new features, such as adaptation into Greek (Papastergiou, 2007). Moodle introduces users with a state-of-the-art role management system. Each user is assigned a role in the system or in a course. Each role has access to specific services and modules. Instructors can edit or override learners’ role so that learners can only access and interact with certain portions of a course. Besides the role system, another option is given to course designers, to hide and publish modules in a course. A hidden object can only be seen by instructors or course creators. This is useful for hiding unwanted modules from learners with a click of the mouse.
People Grouping and General Course Coordination Teaching is best organized when learners are organized in small flexible structures. That is the definition of a class or group. Instructors have fewer learners to manage so more attention is given to each member of the group. Most LMSs support class or group formation. Coordination in a LMS is interpreted as scheduling for good resource allocation in the platform. Resources include classes, courses, us-
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ers, assignments and other entities in the system. Coordination in a LMS is necessary since instructors have to be assigned with a certain number of groups-classes, learners must be enrolled in the right group or class and assignments must be given to the right groups. Like classrooms, many LMSs provide limited opportunity for flexibility here. Just as a 1 hour lesson in a lecture theatre encourages coordination by sitting still and being quiet so that the class can all learn together, many LMSs encourage a method that can be caricatured as “read this material, check the forum and do the test”. It takes some effort on the part of the instructor to overcome these, but they do – and are supported to some extent by the design of the system. If you can move the chairs, you have more choice in a classroom; if you can adapt the workflow of a LMS, you can provide more flexibility in your learning opportunities (Britain & Liber, 1999). Almost all of the LMSs available in the education community give the ability to their users to be divided into classes and groups. Some of them allow the customization of what a group of learners has access to do and view. Moodle is one of them. Moodle offers its users the potential to assign more than one instructor to a course for better coordination Blackboard is a LMS which incorporates a wide range of teaching and learning tools into a web-based interface (Taha, 2007). The Blackboard LMS offers a suite of coordination features to facilitate some key administrative processes as group discussion, chat room for promoting exchange of ideas between classmates, virtual classroom, and course calendar.
Monitoring Learners’ Achievements and Progress During a Course The effective use of LMS requires that instructors are provided with appropriate means to diagnose problems so that they can take immediate actions to prevent or overcome that problem (Mazza &
Milani, 2004). Monitoring is usually a core service of most LMSs and is implemented both as a user actions tracking service and as an assessment and evaluation service, each of which has its own benefits. To be able to track down the actions of learners is a major advantage in asynchronous e-learning systems. Instructors can cope with their learners by checking what resources have been viewed during a course session. Furthermore instructors can follow up with conversations between learners by tracking the logs of each chat. Moodle keeps track of user’s actions by using the logs reporting service. Administrators have access to detailed logs which can be filtered per user, date, action and course. An enhancement to this service is the statistics module which enables administrators to have a supervisory view of what is going on the platform by visualizing recorded data and presenting it as graphs. Another way to monitor learners’ progress is via the assessment and evaluation service. Learners are subjected to test and quizzes and according to how well they perform an overall picture can be drawn about their learning progress. Assessment methods should be used to measure what learners can do with what they know, rather than what they know (Struyven, Dochy, Janssens, Schelfhou & Gielen, 2006, pp. 203). The standard method of assessment and evaluation in almost all LMSs are quizzes or multiple choice questions. Another method available is assignments. Instructors assign small projects to their learners and grade them according to what they submitted back in the platform. An instructor using the Moodle platform can add a quiz in a course by selecting questions from the question bank and publish it. Instructors can choose how many questions show up in each page, as well as timing, penalties and grade options. Moodle also provides tools for project assignments. Learners can submit their projects to the platform both as draft and final. Instructors
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can then process them, make comments and grade them using the platform. Like Moodle, Atutor (http://www.atutor.ca/) is another LMS that offers the above services. It is an Open Source Web-based Learning Content Management System (LCMS) which includes tools for Tests, Surveys and Assignments. An instructor can easily create a test by selecting/ create question from the question database. A nice and interesting feature is the certificate of completion. Learners who complete the test are given a certificate of completion.
LMS Services Comparison Tables At this point, a comparison is made in Table 1 among three popular LMS and Diplek (whose platform features and services will be presented in the following section). The LMSs are categorized according to the services and components offered. The comparison involves the following LMS: A-tutor 1.6.1 (open-source), Blackboard Academic Suite (Release 8.0) (commercial licence), Diplek 1.0, and Moodle 1.9.4 (open source). All components taken under consideration are built in the system and are not offered as third party components.
Table 1. LMS services comparison LMS
A-tutor
Components
Blackboard
DIPLEK
Moodle
Communication and Collaboration Services Forum
X
X
X
Chat
X
X
X
X
Email
X
X
X
X
Blogs
X
X
Documents
X
X
X
X
Metadata
X
X
X
X
X
X
X
X
Content management Services
Calendar Teaching Assistant
X
Evaluation & Assessment service Assignments
X
X
X
X
Multiple Choice Questions
X
X
X
X
Grading System
X
X
X
X
X
X
X
X
Monitoring Service Action Logs/ Reports Video Recording
X
Adaptation & Profile Building service Role System
X
X
X
X
Group Management
X
X
X
X
X
X
Dynamic Profile Course Themes
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X
Diplek
DIPLEK PLATFORM Diplek is an open educational LMS that allows learners and instructors to easily manage learning resources in an integrated system. The platform design follows all modern educational software design guidelines and learning standards. Diplek is distributed under a creative commons AttributionShare Alike Unported 3.0 license, where users can copy, distribute, transmit and adapt the platform as long as they distribute the resulting work only under the same, similar or a compatible license. Both the installation and the source code files are freely available to users. The Diplek platform adopts a 3-tier architecture. The platform operation is composed of group of services offered by several autonomous components that cooperate with each other and give the user the look and feel of a modern classroom. Users type their credentials and authentication service processes them. Each user role has a different access level; users are classified into learners, instructors and administrators.
Technical Overview The development of Diplek was based on Microsoft Visual Studio platform and Microsoft Visual Basic 2005 programming language (Balena, 2006). On the backend a MySQL Relational Database Management System (RDBMS) (DuBois, 2008) is responsible for holding all the platform data. The connection between the application and the backend is possible by using the ADO. NET MySQL connector (McClure, 2006) and the TCP/IP protocol. Some of Diplek’s services where developed by reusing and adapting code that came from the Moodle project but due to the different programming languages used (Moodle is developed with the PHP programming language) some refactoring of classes and functions were required. As with any other platform based on Microsoft. Net technologies, Diplek requires the Microsoft.
Net framework (Richter, 2002) to be installed for the platform to run smoothly. To support centralized access to Diplek’s repository, a database server is needed to host the MySQL database. The hardware requirements needed to run the platform are set to a minimum. A recommended configuration for the database server would be an Intel Xeon processor with 2 GB RAM, at least 1 GB of free disk space and a network adapter that supports the TCP/IP protocol. For the workstations, the minimum requirements are: a Pentium III processor with 512 MB of RAM, a VGA compatible Graphics card, at least 100 MB of free disk space and a network adapter. Before setting up the database server it is crucial to point out how many concurrent users will be connected to the platform and the amount of LOs the platform will hold. In order to maximize the number of concurrent connected users on the platform, more processing power is required for the database server. The amount of LOs stored in Diplek’s database is only limited by the free disk space on the database server. All LOs are stored in database tables in binary form. Diplek is tested to work under Microsoft Windows 2000, XP and Vista. A new version will be soon available to support Microsoft Windows 7.
Platform Initialization The platform initialization and loading phase consists of three stages. The first stage concerns user entrance. When a learner user enters the system, the course selection service is activated and the learner has to select a course to attend to. Afterwards, the user has the option to retrieve the last saved state of the system or to start a new session. Either way, the virtual assistant and session recorder services are loaded. The session recorder service is responsible for monitoring the user’s learning path in the platform. The virtual assistant service pops up a kind of “Microsoft agent” component that is programmed to assist the user in his very early steps with the platform, so that
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novice users will know what to do without any guidance from the instructor. The second stage of initialization aims at setting up the user’s learning workspace. This involves retrieving the user’s access level to menus, tools, options and learning material. The service assigned for that purpose is called “Profile Activator” and it is responsible for presenting the user with only what he needs to see or what the instructor has permitted him to see. The final stage of initialization sets up the learning material assigned to the learner, which may include course presentations, project assignments and multiple choice tests. The course outline is always visible to the learner and all other windows appear on top of that. This three stage procedure is repeated each time a user enters the platform. A very similar procedure is followed with the instructors. When a user identified as an instructor enters the platform, the instructor “Profile Activator” service retrieves all the available information needed and forwards it to the corresponding services. One of these is the “Course Configuration” service which provides the instructor with options like course description, course outline, learners and groups admitted to the course, etc. The “User and Groups Configuration” service provides the learner with information about a user, like place of living, email address, full name, mother language, groups involved and courses taken. All these can change with the click of a mouse button. This service is also responsible for setting up the user – group access to tools and resources for a specified course. The “Assignments” service allows the instructor to assign to learners or groups projects and exercises. All the delivered assignments are presented to the instructor for manual correction and evaluation. Finally, there is the “Test Creation” service for creating multiple choice tests and assigning them to learners. This service communicates with the “Grade” and “Statistics” service. An administrator is a user with the same privileges as the instructor. This special user category
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can do exactly what an instructor can; in addition he is eligible for two extra functions. When an administrator user enters the platform, both the instructor and the administrator “Profile Activator” services are loaded. Together with these services, two other services are loaded, the “Database Configurator” and “Session Recording” services. The former is responsible for connecting the platform with a compatible learning material repository. The latter is monitoring system configuration. The instructor/administrator can choose what users or groups are going to be monitored and for how long.
Platform Installation and Deployment Diplek is intended to be installed in both organizational-educational environments and home environments. In the first case, a server is needed to host the platforms database where the educational material and user data are stored. This database can be made available through the Internet so that the client workstations can connect to it from any point in the world. This kind of installation is suitable for schools and universities, where learners are divided into classes and each class is assigned to a group of instructors. Each user (learner & instructor) can connect to the platform through a local area network or through the Internet. Each client workstation needs to have a Diplek client installed in order for the platform to function properly. In the second case, where Diplek is installed in a home environment, the users’ workstation acts both as a client and a server. That means that the platform is installed locally and can serve users connected to that workstation only. This kind of installation is suitable for users who cannot gain access to permanent network connectivity. Diplek comes with an automated configuration utility that helps the system administrator to an easy platform installation. The configuration utility is responsible for setting up the database
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connection and tests whether the platform is installed correctly.
Diplek Layers As mentioned before, Diplek is using service oriented architecture (Erl, 2005; Papazoglou & van den Heuvel, 2007). This design enables the transparent addition of extra functions after the platform is deployed thus offering the users new tools and services. Figure 1 shows the three layers. The database layer is responsible for storing user and course data. These data are stored by using a logical organization like tables. This layer is also responsible for offering the necessary bridge for connecting the stored data with the system services. The services layer is the connecting link between the database and the presentation layer. Before content is displayed to the user, it is filtered and rearranged by the different group of services that intervene. As with any other service oriented system, services interact with each other. For instance, the “Live Chat” service interacts with the “Course Management” service in order to enable members of a course to chat either in private or in a live chat classroom. This service is disabled in some cases, like when the “Test & Evaluation” service is active.
The layer responsible for presenting information to the user is the presentation layer. The entire graphical user interface is controlled through this layer as well as all user-system interactions. Platform services are made available to users according to the user level they have. The authentication service is responsible for distinguishing which user runs what service and sets the level of functionality of the service. For instance, the logging service can be used only by administrators and instructors, but when a learner is authenticated, logging starts only for session recording, not for viewing. Platform services are grouped by their functionality and their target group, e.g. live chat, email and instant messaging are grouped under Communication & Collaboration Services. Some of these services are only offered to instructors whereas others are offered both to instructors and learners. Figure 2 shows the grouping of services along with the services each group has.
EDUCATIONAL MATERIAL AND LEARNING OBJECTS There are many definitions of a Learning Object (LO). Polsani (2003) describes it as “an independent and self-standing unit of learning content that
Figure 1. Layer schema
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Figure 2. Diplek services scheme
is predisposed to be reused in multiple instructional contexts”. According to the Polsani’s definition, a Learning Object can be anything from a simple text document to an interactive multimedia game or even a complete webpage. But more significant is the fact that a Learning Object can be reused in many educational contexts. McDonald (2006) describes a LO as ‘the result of applying a finite set of rules to a simpler learning object, in order to construct some meaning, activity or purpose which is used for learning’. The reason for this being so important is that an instructor can use an already developed, used and tested LO to teach a
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course without the need to create from scratch a new one. Non computer expert instructors consider this as a handy tool. One of Diplek’s key functions is the storage and cataloguing of educational material. Each instructor is responsible for finding and inserting the appropriate resource to Diplek’s main repository. This repository is held into the platforms storage database and access to it is granted to all users. Each user has different access rights, e.g. instructors can store their LO and view what others have stored, whereas administrators can view, change and store LO. Instructors have the
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option to grant others with privileges for copying and changing learning units owned by them. The next level is the presentation of this material. In order to support reuse of LO, Diplek permits access to all material in the system database, even the one that has been created by other instructors (as long as the creator permits it). As said before, this material can be of any type and any size as long as it keeps up with the database storage requirements (amount of data each user can store). These requirements or limitations are defined by the system administrator. As the available educational resources grow larger each day, the need for extra information becomes obvious. The lack of information about the location, properties, educational context or availability of a resource could make it difficult to use. Metadata contributes to solve this problem by providing a standard and resourceful way to conveniently characterize resource properties. In this way, instructors with not much experience in using learning management systems can find the Learning Object they are looking for by describing some of its properties.
Data to Describe Learning Objects (Metadata) The capability to associate metadata with LO makes Diplek a powerful tool for easy search and indexing. Metadata (Jing, Li & Fang, 2005) are conventionally defined as data which is used to describe data and it provides a means to describe the information of learning objects. Metadata are used to describe document contents and structure, and to provide information about accessibility, organization of data, relations among data items, and the properties of the corresponding data domains. However, metadata can also be used to provide descriptions for non-textual objects, like images, videos and sounds. Nowadays, hundreds of collections worldwide already adopted metadata as the basic tool for information representation and cataloguing.
The radical development of LMSs and the use of metadata to describe Learning Objects brought up the need for metadata standardization so as to enable reuse and interoperation among heterogeneous platforms. To accomplish this, an agreement is needed on architectures, services, protocols and open interfaces. Several initiatives took place aiming to deliver a standard set of metadata that would describe a Learning Object, such as the IEEE Learning Object Metadata (LOM) (Edvardsen & Sølvberg, 2007) and the Dublin Core Metadata Element set for education (DC) (Baker, 2005). LOM specifies the syntax and semantics of learning object metadata, defined as the attributes required to fully and adequately describing a Learning Object. This includes element names, definitions, data types, vocabularies, and field lengths. LOM is focused on the minimal set of attributes needed to allow these Learning Objects to be managed, located and evaluated. Related attributes of Learning Objects to be described include author, type of object, terms of distribution, owner and format. Where appropriate, learning object metadata may also include pedagogical attributes, such as teaching or interaction style, grade level, level of difficulty, and prerequisites. The Dublin Core Metadata Element Set (Baker, 2005) is a general-purpose and widely adopted metadata scheme targeted to resource location developed within the Dublin Core Metadata Initiative. It is compact and its elements are the result of a wide consensus. The DC-Education is a Working Group that was formed to develop and make a proposal for the use of Dublin Core metadata for the description of educational resources. Essentially, its task is to propose extensions to the DC metadata set to describe these kinds of resources, taking LOM and the IMS Global Learning Consortium proposal as a basis. Diplek uses the IEEE LOM standard for its learning object repository. The reason for choosing LOM among many other well defined and documented standards is that it fully describes both
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the educational and technical aspect of a Learning Object. Although LOM contains a large number of attributes, Diplek requires only a small set of basic attributes to be inserted in order to allow a Learning Object to be catalogued. In this way, instructors are not obliged to fill in the complete form and hence can save time. The use of IEEE LOM makes Diplek Learning Object repository easy transferable, interoperable and searchable. The set of metadata used to describe a Learning Object includes general data (title, description, language, keywords), life cycle data (creator, status, version, people contributed), educational data (level of education, type of learning object, interaction level, difficulty) and technical data (type of object, installation instructions, platform requirements).The more attributes are filled, the more accurate the result of a search for a Learning Object will be.
available educational material in order to present it to the learners. So importing the educational material is just the first step of creating a course for learners to use it.
Importing Educational Objects into the Platform
The second step is finding the appropriate material that will be presented to learners. To do so, a search tool is available that queries the platforms learning object repository using criteria based on learning object metadata (type, difficulty, level of education, language, keywords, topic, etc.). All Learning Objects that match the required criteria can be selected from the list. When a Learning Object is selected, it is copied in a new list. This list contains all the candidate educational material to be used for the course.
Before creating a course, instructors need to insert educational material to the learning object repository. All Learning Objects are catalogued using the IEEE LOM schema. Diplek provides an easy to complete wizard for novice users, which is divided into four steps according to the categories of metadata schema; general, life cycle, educational and technical attributes. After completing the wizard the final step is the insertion of the Learning Object. Instructors can insert any type of educational material, like multimedia presentations, sounds, WebPages, applications, archives, games, etc. Diplek accepts all file formats. As mentioned before, one of the main advantages of the platform is the ability to import any kind of material, even software, as long as the appropriate viewer is installed on the client’s machine. In this way, all educational material can be used without any modifications or limitations. Diplek is not educational material creation software; it is a platform that utilizes all the
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Content Versioning By using learning object metadata Diplek can store different versions of a Learning Object. This is done by entering the version number in the life-cycle tab. Each version of a learning unit is stored in Diplek’s main repository and can be accessed at any time. Apart from version information, contributors’ names and roles are also provided, along with content status information (final, draft, revised, approved).
Searching for the Appropriate Educational Object
Educational Material Statistical Data Even though instructors have access to all educational material, there are times that an instructor just wants to have an overall picture of what is going on in the whole system. Diplek provides instructors with an easy to use statistics tool. Learning Objects are grouped according to the user-selected metadata information. For instance, one can see the amount of Learning Objects available for each level of education. All the educational material stored in the database can be
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used to provide statistical data to the instructor. These data are presented in a pie chart for better understanding. By examining the appropriate statistical data, an instructor can get a general picture of the LO stored in the platform database and take the necessary measures to increase the volume of educational material in learning areas mostly needed.
Creating Powerful Presentations from Learning Objects Course presentation is the final step of preparing a course. These presentations must not be confused with the usual slide show presentations. A course presentation is an organized structure of learning units categorized in such a way so as to serve the group’s educational needs. All educational units used to form the presentation can be accessed by double clicking with the mouse on their icon. These presentations are assigned to groups or specific learners. The ability to distinguish what each user is viewing-using is a vast advantage since the
instructor has the ability to alter a presentation/ course according to each user’s special needs. Diplek supports the creation of course presentations with an easy to complete wizard. Figure 3 shows the screenshots of the wizard. First, instructors fill up the general course details like title, description, etc. These details are shown to the learner as an introduction text when the presentation is selected. Next, follows the creation of presentation units. Each presentation unit is a set of LOs. So, a presentation unit can be a book chapter, a lab experiment, etc. It is up to the instructor to choose how to present a course. In this form, instructors simply define the presentation units by entering the title of their choice. Presentation units can be used for indexing a presentation and help learners find what they are looking for. The final stage is the distribution of LOs to the corresponding presentation units. A LO can be used in one or more presentation units or presentations. Each educational object assigned in a presentation is represented by an icon. Depending on the
Figure 3. Screenshot: creating a presentation
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type of object, each icon has a different image (a generic icon is used to represent object types not recognized by the file system). A presentation can be deleted or reused. Reusing a presentation can take many forms. One can reuse a presentation to teach the same course or alter the presentation to fit a different educational domain. The presentation created by an instructor can be used by other instructors as is or altered. LO that are copyrighted are protected since copyright information is stored in the LOM set.
SERVICES OFFERED TO USERS User services relate to platform services available to all course users except those not related specifically to course learning material (e.g., messaging between learners/ instructors, calendar, live chat, and document exchange services etc) (O’Droma et al. 2003).
Diplek includes a number of tools to enable learners with organizing time, organizing personal workspace, checking course grades, etc. These services run as components and can be configured by the instructor. All tools use a common User Interface and use simple command buttons to perform an action. The design of these tools was made taking into account the difficulties a novice user faces when using them. The following tools are meant to be used both by learners and instructors. • • • • • • •
Profile Manager My Calendar (Figure 4. A) Sketch book (Figure 4. C) My Grades (Grade Book) My documents (e-portfolio) (Figure 4. B) My mail (Figure 4. E) Live chat (Figure 4. D)
Figure 4. Screenshots of tools and services available to users
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Profile Manager
Sketch Book
Profile Manager is a tool used to allow a user to change personal information (email, name, address, etc.) and account password. This is a general usage tool that helps users to maintain the correct contact and personal information. In order for a learner to change the educational material presentation language, only a click is needed at the relevant field.
Another tool that follows the learning personalization logic is the ‘sketch book’. The use of this tool provides user with a place to enter information coming from different sources (internet, educational objects, chat, etc.) and store them in a container that is always available during a session. Learners can search information on the web and insert it to the sketch book. This information can be saved as formatted text combined with images, and other multimedia features like sound, video, animation, etc. All information stored in sketch book can be formatted, searched and extracted to all known document formats (.doc,.ods,.rtf, etc.). Sketch book can be easily used by novice users since all functions can be accessed from the main menu and by clicking the right mouse button. The layout of the window resembles that of an exercise book and all actions can be performed either from the top menu or by right clicking with the mouse on a word or other text. Sketch book cannot be closed until it is saved or the user confirms closing without save. This is a precaution taken because most users often forget to save the work done before exiting. Diplek provides several other useful tools that can be used to assist learners like a calculator, a drawing tool and a web browser for internet access. Another category of tools provided by the platform are the collaborative-communication tools.
My Calendar Calendar is a powerful feature that allows learners and faculty to manage both academic and personal events. Managing time is one of the most important things a learner and an instructor need to do. A learner must be given the ability to schedule lectures, tests, assignments and other learning processes during a week/month/year. Instructors need to mark important dates and schedule learning activities. This tool is useful for learners who need to schedule their lectures, tests and assignment submissions. All the days are shown on a weekly basis and the dates that have events schedule are shown in bold. Users can schedule an event by just clicking on the date and type the desired text. The calendar service is directly connected to the virtual assistants’ services to inform user with upcoming events.
My Documents Diplek supports personalization of learning and encourages users to have their own personal workspace where they can store files. These files can be anything from a single text file to an interactive multimedia game. All documents (files) can be extracted with a single click of the mouse and can be transferred to another user of the platform or even a learner or instructor of another system.
Collaborative-Communication Tools This category includes tools and services directly related to the learning process that are responsible for distance communication and collaboration of learners and instructors. These tools aim to assist users (mostly learners’) to keep in touch with their peers and instructors (Kear, 2007). Even though, the most frequently used communication tool is e-mail and it is mainly used for personal corre-
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spondence among the learners (Vorides, SanchezAlonso, Mitropoulou & Nickmans, 2007), there are other communication tools like instant messaging, live chat and discussion forums. Communication tools are better used in conjunction with the course instead of as a standalone service. Diplek offers three kinds of communication tools: integrated messaging tool, live chat and email. The email tool is offered as a web service whereas the other two are integrated inside the platform and can be used without the existence of an internet connection.
Live Chat Live Chat (or instantaneous mail) allows a real time discussion between all members of the platform. This is an exciting way for learners to communicate directly with each other in real time, and a unique way for instructors to answer learners’ questions during office hours. Live chat discussions can be archived for later review. Diplek does not require complex setup of chat space and the tool is ready for use when a user selects the chat option. All conversations in the room are public so that everyone can watch and participate. An instructor can use this tool to give guidance to learners from distance while they are watching a presentation or working on a project. This way, all learners get the same information and can ask questions that everyone can see. The use of this tool is needed in order to build a collaborative community that each user learns from the other. This tool is not recommended to be used for personal messages since every one that is in the room can see the message. For that purpose another tool can be used called ‘My Mail’. This tool is a simple messaging system for all the users that have access to the platform.
My Mail A less exciting but still as useful as the live chat tool, is the ‘My mail’ service. This tool is needed for users to communicate with each other in pri-
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vate. The way this tool works is similar to that of the email service, but instead of email addresses, users only have to know the username of the user that they want to communicate with. Also, this tool does not require the existence of an internet connection. All communication logs are available to the user through a centralized system. This system gives the ability to the user to delete, create and check for new messages. An instructor has the ability to send a message to the whole classroom or to a specific user whereas a learner has only the ability to send a message to a specific user (learner or instructor).
A Usage Scenario The local water board of a Greek town decided to apply new and more efficient techniques in water pipe connections. In order to apply those techniques on a right manner, the organization contacted a British company that specializes in pipe mending techniques. The British company decided to offer a six hour distance training session for the Greek organization staff. Due to the short time available, the British company agreed to use Diplek LMS for course delivery and support. Mr Smith is the professional expert responsible for the training course. He gathers some video of field work done on pipe mending and some documents including specs, technical requirements and best practice. After reading Diplek’s usage manual and taking a little practice on his own, he starts by creating a user account on the platform. Then he creates a course and a class by using the wizards available. To speed up the process he imports all learning material (video and documents) and creates a single presentation with three units. Each presentation unit represents a step on the pipe mending procedure and includes both video and documents. To be prepared for the course he creates six learner accounts for the Greek organization personnel and enrolls them in the course and class previously created.
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In order for the session to start, each learner uses the credentials previously created by the instructor. Mr Smith uses the Live Chat service to welcome all users to the training session and presents himself. Then, each learner clicks on the presentation and watches the videos. Some learners have questions and ask Mr. Smith. During the course, Mr. Smith needs to send some extra documents to the class which are not included in the presentation. He opens the My Documents service and shares the documents with the whole class. At the end of the training session Mr Smith logs out of the system but some of the learners stay connected to watch some of the videos again and save the educational material on their usb drives since the instructor gave them permission to do so.
LEARNERS’ EVALUATION AND ASSESSMENT Byers (2001) describes interactive assessment in learning environments as promoting dynamic feedback and course adjustments on the fly. This part of the platform measures learner performance against specified goals, using a variety of services ranging from multiple choice questions to complex assignment handling. In order for instructors to monitor the progress of learners, it is necessary to provide services and tools for evaluation. Evaluation is the procedure by which learners are tested for their understanding of a certain subject. The results of evaluation can show what difficulties the learner encountered when studying this subject. By having these data, the instructor can customize the learning path (learning material, course presentations, tests, etc.) to the learners needs. There are many ways for evaluating a learner’s progress - tests, projects and assignments are commonly used. In order to cover most of these evaluation methods, Diplek includes tools that automate the above procedures. Due to the low usage reported on these tools (Philips, 2006), an
easy and intuitive wizard-based method is introduced in order for instructors to use them.
Multiple Choice Tests Most LMS provide templates for multiple-choice questions, true/false questions, matching questions, or short answer questions (Govindasamy, 2001). Multiple choice tests are an easy way of assessment and evaluation. Nevertheless, it is also a way for learners to check their learning progress. To manage the creation of multiple choice tests, Diplek incorporates services for question and test creation, test delivery and test result analysis. Instructors can create a number of questions with the desired set of answers. Each question can be given a difficulty level and can be included in one or more tests; if the instructor permits it, it can also be shared by other instructors on the platform. When the test is ready, it can be assigned to a learner or a group. Multiple choice tests can be used both for assessment and self evaluation. When a test is finished, a log file is created. Instructors can view all available information by using the test results analysis tool. Instructors have access to information like the number of correct answers, time needed to complete the test, answers given by the learner, total number of tries for each test and the total score. Instructors have also access to test statistical data. This data is collected for each test and can be used for test quality evaluation. In simple words, an instructor can see if a test is suitable for a group or a learner and make the necessary changes so that it fits the learning goals. Multiple-choice questions, as all assessment methods, have limitations and are suitable to some content more than others. In the long run, multiple choice questions are a quick and easy way for the instructor to automate the evaluation procedure. This automation saves instructors a lot of valuable time which can be spent for helping out the learners. This is not the only way used to evaluate learner’s progress.
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Project: Assignment Management System Another method used for assessing a learner’s performance is the project assignment service. This is a very common method used in many educational institutions. Learners are involved in a search and learn procedure guided by their instructor. At the end of the day they present a document with their findings. Diplek supports this assessment method with the use of the project management service. This service can be used for the creation, delivery and evaluation of an assignment. Instructors can create or import projects through a user friendly wizard, where they are asked to insert the guidelines, instructions to learners, relevant files and deadline. After that, they can assign the selected projects to groups or individuals. Learners are notified for the new assignment by either the teaching or the virtual assistant service. When the project finishes, learners deliver it to the instructor by using the platform delivery tool. Instructors evaluate the project and mark it as checked. All checked projects are available to both the learner and the instructor with added comments and corrections.
Grading System Even though, grading learner’s work is not an evaluation method, it is a part of the whole evaluation subsystem of Diplek. Instructors should be able to manage, update and view their learners’ grades. This is a process supported by Diplek with the use of a grading service. Instructors can choose to share all grades or specific grades with learners in the Grade book. Learner evaluation is facilitated by easy access to the thread work, journal work, exams and online activity of each learner. ‘My Grades’ tool provides the instructor with a list of all learners attending a course where he or she can assign a grade to each one of them. Learners have access to their grades and can see them as a list. Sometimes it is more convenient
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to just have a printed list of all learners and their grades to be published in the announcement board or delivered as an email. ‘My grades’ tool gives instructors the option to do that with an export tool that saves a list of grades as a spreadsheet. Instructors can get statistics for each group or course regarding their grades assigned to learners enrolled in the specific class or course. Statistical data is separated to grades per course and grades per group. This way, an instructor can have a general picture of how a class is doing and the suitability of a certain course.
ADJUSTING WORKSPACE TO LEARNERS’ NEEDS Diplek has a dynamically formed workspace. This means that the workspace can be adjusted to fit learners and courses needs. The learners’ workspace includes menus, toolbars and the main screen. The main screen is covered by the active presentation which is course dependant. This means that user’s active workspace is formed according to the instructor’s specifications and course planning. Menus and toolbars can be adjusted by the instructor so that they match the course context. Instructors can adjust what their learners can access during their session. These adjustments include restricting the services offered by user tools, educational tools, communication tools and quick access toolbars icons. Access to these services, when restricted by the instructor, is not permitted in any way including mouse, keyboard or shortcut keys. By restricting access to certain services, we make sure that the structure of a course is maintained. For example it would not make any sense in a theoretical course to use a calculator or when testing learners to have the instant communication service activated. By customizing a learner’s workspace we make sure that simplicity is maintained. All these settings can be made by
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the instructor or the administrator by using the user access service provided. An instructor can customize the workspace of an entire class or a specific user by toggling the lock and unlock button next to each tool (Figure 5).
User Access and Personal Data User access is controlled by Diplek authentication service. This service is responsible for maintaining users’ login credentials and personal data, e.g. home address, telephone number and other sensitive information. For security reasons this data is stored in an encrypted form inside the platform database and is retrieved when the user tries to enter the platform. Along with private data, user access data is also retrieved, enabling the user to access specific platform components and services.
USER GUIDANCE AND SUPPORT Diplek offers besides the standard online and printed manual documentation, two additional ways of helping its users.
Teacher Assistant The large number of settings and services offered by the platform may prove counterproductive for the instructor who just wants to do some simple tasks. That is why Diplek incorporates a tool called the ‘Teaching Assistant’, which is available to all instructors (Figure 6A). This assistant provides information to the user about new messages, new project assignments and things to do from the calendar. User can enable and disable this feature with the click of a button so that it does not occupy screen space when not needed.
Learner Assistant In addition to the Teacher Assistant, a second assistant that has a friendlier look is available to learners. The Learner Assistant has the form of a wizard and shows up after a successful login (Figure 6B). It provides information about date and time of day and informs the user about new messages, projects or tests assigned to him. This information comes with instructions on how to
Figure 5. Screenshot: Setting up the learner’s workspace
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Figure 6. Screenshot: Teacher and learner assistants
get access to the appropriate item so that user will not have to ask how to do that. The Learner Assistant character can be replaced with another one according to user’s preferences and the educational domain.
Two Educational Scenarios Even though education is indented for the masses, the educational procedure does not work that way. The different learning style and ability of each individual have to be supported.
Training People for Using Web 2.0 Tools In order to fully understand the personalization level offered by the platform we shall discuss the following scenario. Mr John is an instructor working for an educational organization involved in adult education and training. He is assigned the task to instruct a group of fifteen school teachers in incorporating web 2.0 tools in the educational practice. He decides to use Diplek to provide modern educational facilities to the learners of this group. He creates a class and a course called ‘Web 2.0’. He organizes the educational material into a presentation with ten units. Then he assigns that presentation to the whole group. Georgia is a novice instructor who enrols in Mr. John’s ‘Web 2.0’ training course. Georgia has difficulty in un-
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derstanding what web 2.0 tools are, and moreover on how they can be used in teaching since she has little experience with modern technologies and computers in education. This is where Diplek platform unties the instructor’s hands. Mr. John creates a new presentation with more introductory units that include examples on how computers are used in education. He then assigns this presentation only to Georgia. This way the rest of the group is progressing normally and Georgia has the chance to learn and keep up with the rest without slowing down the rest of the group. At the same time Georgia’s weakness becomes known only to the instructor, thus allowing Georgia to express her misunderstandings more openly.
Using Diplek in Lifelong Learning Programs The “Greek Ministry of Agriculture” is running a European Union funded program for lifelong learning courses in the area of Organic Farming. They currently offer a course called “Advances in organic farming and Biological products” which deals with modern techniques in farming without fertilizers and best practices for avoiding popular crop diseases. The course will be delivered asynchronously through Diplek LMS with the exception of two one hour sessions for a live presentation and question answering. Learners are farmers from European countries like Spain,
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France, Italy and Poland. The course instructors are two agronomists from Greece, Mr Giorgos and Mr Leonidas, who have the necessary training and expertise in organic farming techniques. Additionally, a guest speaker from Cyprus will present an epidemiology study with the use of a Geographical Information System (GIS). In order for the course to be ready on time, the two instructors decided to split it in two parts and use Diplek’s presentation service to prepare each part. Fortunately there is some work already done by Mr Leonidas, but it is stored on a Moodle LMS that was previously used on a similar course. Mr Leonidas extracts that content from the Moodle LMS and imports it to the Diplek database adding metadata so that in future implementations of this course, the material will be ready to be transferred to a different learning platform if needed. The prepared material consists of technical reports, presentations of new agricultural machinery, pictures and precaution measures taken in other countries to avoid crop diseases. Most of the material is in video and presentation format. Before the course is deployed, the two instructors take some time to collaborate with their Cypriot colleague and give him access to the course presentations in case he needs to add extra material. The collaboration is done with Diplek’s communication services: live chat and messaging. In order to share some files needed for the course preparation, Diplek’s Document Management Service is used. For the last day of the course, the three instructors have created a small project assignment for learners to write down their opinion on how organic farming can be helpful for their countries. Diplek’s project management service is used to distribute that assignment to all learners. During the course days, learners log into the platform and retrieve the necessary material. Most of the time, instructors are bombarded with questions. For answering these adequately, instructors have shared a document with frequently asked questions (FAQ) and when needed they add extra content to the course presentation.
Session Recording Service The session recording service is an innovative approach to logging learners’ progress and learning achievements through a session. The use of this service provides the instructor with video feedback of a learner’s session which consists of all learner actions, conversations, mouse movements, services used, educational material used, etc. The output is not the typical text entry in the database, but a video file which can be played in any video player. Instructors can search for a learner’s recording by selecting his/her class/group and username. They can then select the session recording they are interested in from a list of recordings ordered by date and time. Each learner’s recording, starts from the moment of logging into the platform and ends when he/she logs out or closes the application. Instructors can view these recordings on a daily, weekly or monthly basis. By analyzing each learner’s file, a general conclusion can be drawn about the learner’s progress. Viewing each learner’s video recording is a time consuming job, but in some cases it is necessary for the instructor to be able to track all learner’s actions. Figure 7 shows a screenshot of the Session recorder settings window. The use of this service is directly proportional to the processing power of the learner’s workstation. Due to the large amount of data captured, a performance downgrade might occur on the learner’s workstation. This can be avoided by using a more powerful processor. The session recording service is by default disabled so that instructors can enable it on certain users and on specific time, when it is actually needed. In addition to the session recording service, Diplek keeps text logs of all user actions. These logs are stored into Diplek’s database and can be retrieved by instructors and administrators. Each entry in the log consists of the following information: username, location of user (IP address), date and time, service that originated it, action and any
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Figure 7. Screenshot: Session recorder settings
extra information. Instructors can filter these data by date, user or service and then export it to a.csv file, for further processing outside the platform. Good practice suggests analyzing these data with the use of a graphical tool. A further analysis of log data could lead to results like LOs usage, service usage and user’s preferences in LOs and activities.
FUTURE RESEARCH DIRECTIONS LMSs have risen above the early expectations of being just a trend in education to become a major part of the so-called educational revolution. The deployment of a LMS in an organization should be done in respect to the existing infrastructure. It is more important to have a learning platform that smoothly integrates with all other organization’s supporting tools (Customer Relation Management tools, Enterprise and Resource Management tools, etc.) rather than having an isolated LMS on the organization’s network. This could be achieved by using a Service Oriented Architecture for developing LMSs where only the necessary services are
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exposed to the network and are made available to the rest of the organization’s infrastructure. In order for people to be able to choose the right course for them, a global search engine could be used for all supported LMSs. This search engine could be used not only for searching with keywords for courses but for searching with terms like cognitive level, difficulty level, discipline area, etc. This would be a great improvement for people seeking for lifelong learning opportunities since all this information will not be scattered around, but gathered and presented by a search engine where it is easily accessed. This of course requires the use of compatible metadata from all LMSs taking part and the exposure of the necessary information. As with any other software, Diplek needs to keep up with the rapid developments in the field of modern ICT. The current version of Diplek enables instructors to maintain an easy to use learning environment for their learners’ by customizing the course presentations and platform services according to each users needs. This customization is currently done by hand. In a future version of
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Diplek, a smart agent could be used to customize the presentation based on rules and conditions set by the instructor. For maintaining the simplicity of the platform, another agent will inform and consult the instructor about which learners need more attention and propose changes for the current course and presentation. In order for the agent to be efficient, data mining techniques (Cobos et al., 2007) could be used for extracting user’s data from log files. The use of smart agents (Alonso, Kudenko & Kazakov, 2003) should not be limited in just informing the instructor but the learner as well. The currently available virtual assistant can be reprogrammed to offer advice to learners about what courses and presentations they should attend for better understanding of the course. This advice of course should be based on each user’s profile and personal goals stated by the learner or the instructor at the beginning of the course. On the current version of Diplek platform, the profile management service is based on data entered by the learner at the beginning of the course. These data should be updated during the course when a learner finishes a learning unit, completes a test or an assignment. That is the basis for developing a new dynamic profile service. Instructors can use this service to keep track of their learner’s achievements and progress during the course. Also feedback can be sent to smart agents for proposing new units and presentations to the learner user. Diplek’s GUI is based on a standard theme. The fact that the platform might be used by people with disabilities requires that Diplek supports different themes for each type of user. So, a proposed expansion for the platform would be the support of custom themes. Currently, there is an ongoing evaluation of the Diplek platform in the context of EGIS+ project which is a pilot program based on the Leonardo funded program “Transfer of Innovation”. The
objectives of the project are to further develop results from the previous Leonardo Pilot Project E-GIS (Sponberg, Ossiannilsson, Johansen & Pilesjö, 2003). The results of this evaluation will be available in spring of 2010 when the project ends. Diplek is also used in a postgraduate thesis project involving teaching Geography in elementary school students with the use of ICT. The platform is deployed experimentally in an elementary school and a group of students is using it, under the supervision of the teacher, to learn about phenomena like day-night, the 4 seasons, the tides, etc. The results and feedback of this evaluation are also expected by spring 2010.
CONCLUSION Diplek is a service oriented LMS that was designed to help novice instructors to get involved in e-learning technologies. Using Diplek unties the hands of instructors seeking new innovative ways of keeping control of the class and allows them to manage efficiently the educational process. Most of Diplek services are focused on learner’s needs and requirements. Diplek user interface is consistent with common software engineering and usability rules for developing educational software. Nowadays, educators have access to many modern tools and services that can be used to enhance teaching and learning experience. Diplek is one of them, and it can be used along with other platforms available. The platform was designed to be used in cases where a simple and cost effective solution would be desirable and most preferable. Diplek as mentioned earlier is currently under evaluation. The expected feedback from this process will be taken under consideration in order to release the next version of the platform with changes and features requested by evaluators and users.
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Burgos, D., Tattersall, C., & Koper, R. (2007). How to represent adaptation in e-learning with IMS learning design. Interactive Learning Environments, 15(2), 161–170. doi:10.1080/10494820701343736 Byers, C. (2001). Interactive assessment: An approach to enhance teaching and learning. Journal of Interactive Learning Research, 12(4), 359–374. Cheung, B., Hui, L., Zhang, J., & Yiu, S. M. (2003). SmartTutor: An intelligent tutoring system in Web-based adult education. Journal of Systems and Software, 68(1), 11–22. doi:10.1016/S01641212(02)00133-4 Cobos, C., Niño, M., Mendoza, M., Fabregat, R., & Gomez, L. (2007). Learning management system based on SCORM, agents and mining. In B. Benatallah, et al. (Eds.), 8th International Conference on Web Information Systems Engineering Proceedings (pp. 298-309). Berlin/Heidelberg, Germany: Springer-Verlag. Colace, F., De Santo, M., & Vento, M. (2003). Evaluating online learning platforms: A case study. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS’03). Hawaii: IEEE press. Dalziel, J. (2003). Implementing learning design: The learning activity management system (LAMS). In G. Crisp, D. Thiele, I. Scholten, S. Barker & J. Baron (Eds.), Interact, integrate, impact: Proceedings of the 20th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (pp. 593-596). Adelaide, Australia: ASCILITE. de Vries, F. J., Kester, L., Sloep, P., van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time consuming student support activities in e-learning. ALT-J Research in Learning Technology, 13(3), 219–229.
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Kear, K. (2007). Communication aspects of virtual learning environments: Perspectives of early adopters. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007 (pp. 1505-1514). Chesapeake, VA: AACE.
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Keenoy, K., Papamarkos, G., Poulovassilis, A., Levene, M., Peterson, D., Wood, P. T., & Loizou, G. (2003). Self e-learning networks functionality user requirements and exploitation scenarios. A report for the SELENE Project.
Edvardsen, L. F. H., & Sølvberg, I. T. (2007). Metadata challenges in introducing the global IEEE learning object metadata (LOM) standard in a local environment. Paper presented at the Webist 2007 conference, Barcelona, Spain. Erl, T. (2005). Service-Oriented Architecture (SOA): Concepts, technology, and design. Englewood Cliffs, NJ: Prentice Hall PTR. Govindasamy, T. (2001). Successful implementation of e-learning pedagogical considerations. The Internet and Higher Education, 4(3-4), 287–299. doi:10.1016/S1096-7516(01)00071-9 Graf, S., & List, B. (2005). An evaluation of open source e-learning platforms stressing adaptation issues. In Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05), IEEE Gronlund, N. (2006). Assessment of student achievement (8th ed.). Boston, MA: Pearson. Ho, W., Higson, E. H., Dey, K. P., & Xu, X. (2009). Measuring performance of virtual learning environment system in higher education. Quality Assurance in Education, 17(1), 6–29. doi:10.1108/09684880910929908 Jing, L., Li, Z., & Fang, Y. (2005). Information management in e-learning system. In Fan, W., Wu, Z., & Yang, J. (Eds.), Advances in Webage information management (pp. 275–283). Berlin/Heidelberg, Germany: Springer-Verlag. doi:10.1007/11563952_25
Kim, H. R., & Chan, P. K. (2008). Learning implicit user interest hierarchy for context in personalization. Applied Intelligence, 28(2), 153–166. doi:10.1007/s10489-007-0056-0 Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353. doi:10.1016/S0747-5632(02)00057-2 Mazza, R., & Dimitrova, V. (2007). CourseVis: A graphical student monitoring tool for supporting instructors in Web-based distance courses. International Journal of Human-Computer Studies, 65(2), 125–139. doi:10.1016/j.ijhcs.2006.08.008 Mazza, R., & Milani, C. (2004). GISMO: A Graphical Interactive Student Monitoring tool for course management systems. Paper presented at T.E.L.’04 Technology Enhanced Learning ‘04 International Conference, Milan, Italy. McClure, W. B., Beamer, G. A., Croft, J. J., IV, Little, J. A., Ryan, B., Winstanley, P., et al. Zongker, J. (2005). Professional ADO.NET 2 programming with SQL server 2005, Oracle®, and MySQL®. Indianapolis, IN: Wiley Publishing, Inc.
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McDonald, J. (2006). Learning object: A new definition, a case study and an argument for change. In Proceedings of the 23rd annual ascilite conference: Who’s learning? Whose technology? (pp. 535-544). Sydney, Australia: ASCILITE. Mendling, J., Neumann, G., Pinterits, A., & Simon, B. (2005). Revenue models for e-learning at universities. In Ferstl, O. K., Sinz, E. J., Eckert, S., & Isselhorst, T. (Eds.), Wirtschaftsinformatik 2005 (pp. 827–846). Heidelberg, Germany: PhysicaVerlag HD. doi:10.1007/3-7908-1624-8_43 O’Droma, M. S., Ganchev, I., & McDonnell, F. (2003). Architectural and functional design and evaluation of e-learning VUIS based on the proposed IEEE LTS: A reference model. The Internet and Higher Education, 6, 263–276. doi:10.1016/ S1096-7516(03)00045-9 Papastergiou, M. (2007). Use of a course management system based on Claroline to support a social constructivist inspired course: A Greek case study. Educational Media International, 44(1), 43–59. doi:10.1080/09523980600922787 Papazoglou, M. P., & van den Heuvel, W. J. (2007). Service oriented architectures: Approaches, technologies and research issues. The VLDB Journal, 16(3), 389–415. doi:10.1007/s00778-007-0044-3 Philips, R. (2006). Tools used in learning management systems: Analysis of WebCT usage logs. In Proceedings of the 23rd annual ascilite conference: Who’s learning? Whose technology? (pp. 663-673). Sydney, Australia: ASCILITE. Polsani, P. R. (2003). Use and abuse of reusable learning objects. Journal of Digital Information, 3(4). Retrieved October 15, 2009 from http:// journals.tdl.org/jodi/article/view/89/88
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Reid, D., & Newhouse, P. C. (2004). But that didn’t happen last semester: Explanations of the mediated environmental factors that affect online tutor capabilities. In R. Atkinson, C. McBeath, D. Jonas-Wyer & R. Phillips (Eds.), Beyond the comfort zone: Proceedings of the 21st ASCILITE Conference, Perth. Retrieved from http://www.ascilite.org/au/conferences/perth04/procs/reid.html Richter, J. (2002). Applied Microsoft. NET framework programming. Redmond, WA: Microsoft Press. Sacha, K. (2006). Evaluation of expected software quality: A customer’s viewpoint. In Baresi, L., & Heckel, R. (Eds.), FASE 2006, LNCS 3922 (pp. 170–183). Berlin, Germany: Springer-Verlag. Sampson, D., Karagiannidis, C., & Cardinali, F. (2002). An architecture for Web-based e-learning promoting re-usable adaptive educational econtent. Journal of Educational Technology & Society, 5(4), 27–37. Shen, W., Hao, Q., Wang, S., Li, Y., & Ghenniwa, H. (2006). An agent-based service-oriented integration architecture for collaborative intelligent manufacturing. Robotics and Computer-integrated Manufacturing, 23, 315–325. doi:10.1016/j. rcim.2006.02.009 Sommerville, I. (2002). Software documentation. In R. H. Thayer & M. I. Christensen (Eds.), Software engineering, vol 2: The supporting processes. Wiley-IEEE Computer Society Press. Sponberg, H., Ossiannilsson, E., Johansen, F., & Pilesjö, P. (2003). E-GIS - European level developments of flexible learning models within Geographical Information Science (GIS) for vocational training. Paper presented at the EDEN 2003 Annual Conference on the Quality Dialogue - Integrating Quality Cultures in Flexible, Distance and eLearning, Rhodes, Greece.
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Struyven, K., Dochy, F., Janssens, S., Schelfhout, W., & Gielen, S. (2006). The overall effects of end-of-course assessment on student performance: A comparison between multiple choice testing, peer assessment, case-based assessment and portfolio assessment. Studies in Educational Evaluation, 32(3), 202–222. doi:10.1016/j.stueduc.2006.08.002
Costabile, F. M., Roselli, T., Lanzilotti, R., Ardito, C., & Rossano, V. (2007). A Holistic Approach to the Evaluation of E-Learning Systems. In Stephanidis, C. (Ed.), Universal Access in HCI, Part III, HCII 2007, LNCS 4556 (pp. 530–538). Berlin, Heidelberg: Springer-Verlag.
Taha, A. (2007). Networked e-information services to support the e-learning process at UAE University. The Electronic Library, 25(3), 349–362. doi:10.1108/02640470710754850
Drewes, H., Atterer, R., & Schmidt, A. (2007). Detailed Monitoring of User’s Gaze and Interaction to Improve Future E-Learning. In Stephanidis, C. (Ed.), Universal Access in HCI, Part II, HCII 2007, LNCS 4555 (pp. 802–811). Berlin, Heidelberg: Springer-Verlag.
Vogten, H., Martens, H., Nadolski, R., Tattersall, C., van Rosmalen, P., & Koper, R. (2007). CopperCore service integration. Interactive Learning Environments, 15(2), 171–180. doi:10.1080/10494820701343827
Edmonds, R. (2006). Best practices for e-learning. In Ehlers, U., & Pawlowski, J. M. (Eds.), Handbook on Quality and Standardisation in E-Learning (pp. 485–500). Berlin, Heidelberg: Springer. doi:10.1007/3-540-32788-6_32
Vovides, Y., Sanchez-Alonso, S., Mitropoulou, V., & Nickmans, G. (2007). The use of e-learning course management systems to support learning strategies and to improve self-regulated learning. Educational Research Review, 2(1), 64–74. doi:10.1016/j.edurev.2007.02.004
Efthimiou, E., Sapountzaki, G., Karpouzis, K., & Fotinea, E. S. (2004). Developing an e-Learning Platform for the Greek Sign Language. In Miesenberger, K. (Eds.), ICCHP 2004, LNCS 3118 (pp. 1107–1113). Berlin, Heidelberg: Springer-Verlag.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95. doi:10.1016/j.compedu.2005.04.003
ADDITIONAL READING Antonis, K., Lampsas, P., & Prentzas, J. (2008). Adult Distance Learning Using a Web-Based Learning Management System. In Leung, H. (Eds.), ICWL 2007, LNCS 4823 (pp. 508–519). Berlin, Heidelberg: Springer-Verlag.
Gorissen, P., & Tattersall, C. (2005). A Learning Design Worked Example. In Koper, R., & Tattersall, C. (Eds.), Learning Design: A Handbook on Modelling and Delivering Networked Education and Training (pp. 341–365). Berlin, Heidelberg: Springer. Guri-Rosenblit, S. (2005). ‘Distance education’ and ‘e-learning’: Not the same thing. Higher Education, 49(4), 467–493. doi:10.1007/s10734004-0040-0 Iskander, M. (Ed.). (2008). Innovative Techniques in Instruction Technology, E-learning, E-assessment, and Education. Netherlands: Springer. doi:10.1007/978-1-4020-8739-4 Jin, Q. (2002). Design of a virtual community based interactive learning environment. Information Sciences, 140(1-2), 171–191. doi:10.1016/ S0020-0255(01)00186-4
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Kew, C. (2006). TENCompetence: Lifelong Learning and Competence Development. In Nejdl, W., & Tochtermann, K. (Eds.), EC-TEL 2006, LNCS 4227 (pp. 621–627). Berlin, Heidelberg: Springer-Verlag.
Liaw, S., Chen, G., & Huang, H. (2008). Users’ attitudes toward Web-based collaborative learning systems for knowledge management. Computers & Education, 50(3), 950–961. doi:10.1016/j. compedu.2006.09.007
Klebl, M. (2006). Educational interoperability standards: IMS learning design and DIN didactical object model. In Ehlers, U., & Pawlowski, J. M. (Eds.), Handbook on Quality and Standardisation in E-Learning (pp. 225–250). Berlin, Heidelberg: Springer. doi:10.1007/3-540-32788-6_16
Mazza, R. (2009). Introduction to Information Visualization. London: Springer.
Koper, R. (2005). Designing Learning Networks for Lifelong Learners. In Koper, R., & Tattersall, C. (Eds.), Learning Design: A Handbook on Modelling and Delivering Networked Education and Training (pp. 239–252). Berlin, Heidelberg: Springer. Kotsiantis, B. S., Pierrakeas, J. C., & Pintelas, E. P. (2003). Preventing Student Dropout in Distance Learning Using Machine Learning Techniques. In Palade, V., Howlett, R. J., & Jain, L. C. (Eds.), KES 2003, LNAI 2774 (pp. 267–274). Berlin, Heidelberg: Springer-Verlag. Kritikou, Y., Demestichas, P., Adamopoulou, E., Demestichas, K., Theologou, M., & Paradia, M. (2008). User Profile Modeling in the context of web-based learning management systems. Journal of Network and Computer Applications, 31(4), 603–627. doi:10.1016/j.jnca.2007.11.006 Lee, K., Pillay, H., & Chandra, V. (2008) Linking e-assessment to student’s use of online learning content. In: A. Cartelli, and M. Palma (eds.). Encyclopedia of Information Communication Technology (pp. 532-541). Hershey, PA: Information Science Reference (IGI Global). Liang, W., Zhao, J., & Zhu, X. (2008). Multi-agent Framework Support for Adaptive e-Learning. In Li, F. (Eds.), ICWL 2008, LNCS 5145 (pp. 296–303). Berlin, Heidelberg: Springer-Verlag. doi:10.1007/978-3-540-85033-5_29
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Muñoz-Merino, J. P., Kloos, D. C., & Naranjo, F. J. (2009). Enabling interoperability for LMS educational services. Computer Standards & Interfaces, 31(2), 484–498. doi:10.1016/j.csi.2008.06.009 Nijhuis, G. G., & Collis, B. (2003). Using a webbased course-management system. Evaluation and Program Planning, 26(2), 193–201. doi:10.1016/ S0149-7189(03)00005-3 Prodromou, G. E., & Avouris, N. (2006). e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System. In I. Maglogiannis, K. Karpouzis,and M. Bramer (eds.), IFIP Intemational Federation for Information Processing, Volume 204, Artificial Intelligence Applications and Innovations (pp. 409-416). Boston: Springer. Przybyszewski, K. (2006). A New Evaluation Method for E-Learning Systems. In Rutkowski, L. (Eds.), ICAISC 2006, LNAI 4029 (pp. 1209–1216). Berlin, Heidelberg: Springer-Verlag. Romero, C., Ventura, S., & Garcia, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368–384. doi:10.1016/j. compedu.2007.05.016 Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to elearning students. Computers & Education, 51(4), 1744–1754. doi:10.1016/j.compedu.2008.05.008
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Silveira, A. R., Gomes, R. E., & Vicari, R. (2006). Intelligent Learning Objects: An Agent Approach to Create Interoperable Learning Objects. In D. Kumar, and Turner J. (eds.), International Federation for Information Processing, Volume 210, Education for the 21” Century-Impact of ICT and Digital Resources (pp. 411-415). Boston: Springer.
Teo, H., Chana, H., Weib, K., & Zhangc, Z. (2003). Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of Human-Computer Studies, 59(5), 671–697. doi:10.1016/S1071-5819(03)00087-9
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Chapter 4
Social, Personalized Lifelong Learning Alexandra Cristea University of Warwick, UK Fawaz Ghali University of Warwick, UK Mike Joy University of Warwick, UK
ABSTRACT This chapter discusses a challenging hot topic in the area of Web 2.0 technologies for Lifelong Learning: how to merge such technologies with research on personalization and adaptive e-learning, in order to provide the best learning experience, customized for a specific learner or group of learners, in the context of communities of learning and authoring. The authors of this chapter discuss the most well-known frameworks and then show how an existing framework for personalized e-learning can be extended, in order to allow the specification of the complex new relationships that social aspects bring to e-learning platforms. This is not just about creating learning content, but also about developing new ways of learning. For instance, adaptation does not refer to an individual only, but also to groups, which can be groups of learners, designers or course authors. Their interests, objectives, capabilities, and backgrounds need to be catered to, as well as their group interaction. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. This chapter presents the theoretical basis for this framework extension, as well as its implementation and evaluation, and concludes by discussing the results and drawing conclusions and interesting pointers for further research.
INTRODUCTION Lifelong learning (Aspin & Chapman, 2000) is a key element of our information society (and
recently knowledge society) through which the potential exists for those who want to learn (Fischer, 2001). Lifelong learning is not restricted just to formal learning in schools and universities, but also throughout our life, at work and at home, and
DOI: 10.4018/978-1-61520-983-5.ch004
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more importantly – for the purpose of the current chapter – on the web. The term “Web 2.0” is attributed both to DiNucci (1999) and O’Reilly (2005), and became more widely known when it was proposed by O’Reilly during the Web 2.0 conference (O’Reilly, 2005). Currently it broadly refers to a web development stage which harnesses the power of the users, in which (for example) web-based communities and social networking sites, wikis, blogs, mashups and folksonomies, are integral parts. The infrastructure of Web 2.0 (or the “Social Web”) arguably also permits new means of lifelong learning, where the learners have not only reading but also writing access (rating, commenting, contributing with items, etc.) to communities, which collaborate in order to achieve specific goals (generally these goals are for the learners to learn and expand their knowledge level). These communities provide not only significant (sometimes also supplementary) learning material but also facilitate information sharing and collaboration between experts and(or) peers (Klamma et al., 2007). The shift towards the Web 2.0 (read/write) concept is changing the way in which content and services are being produced (Tapscott & Williams, 2006), and in lifelong learning this change can be seen as a type of communication in which learners can exchange with their teachers the role of being active and leading the processes of learning and knowledge construction (Roberts, 2005). According to Klamma et al. (2007), some of the key factors of Web 2.0 which make it a good opportunity for lifelong learning are as follows. 1. User generated content. Web 2.0 is based on the users and the content created by them. Thus, learners can add to the knowledge collection using a constructivist learning approach (Duffy and Jonassen, 1992). A typical Web 2.0 problem is, however, that a lot of content may be produced but quality may be an issue. A constructivist learning approach will only be useful if the construc-
2.
3.
4.
5.
tion achieves both understanding and a clear expression of the understanding. This problem can be ameliorated via dynamic, changeable privilege settings, depending on the contribution quality, as we shall show later on. Various user types and roles. Users in Web 2.0 can be learners (also referred to in this chapter as students), teachers, authors, administrators, etc. The Web 2.0 context allows for all of these roles to interact with each other, in an ad-hoc, synchronous or asynchronous manner, appropriate for lifelong learning. These roles all contribute to the content and knowledge, in various ways and personalization can be applied to any of these roles, as will be shown later on. Facilitating collaborative creation, sharing, and commenting on the content. This moves peer discussion and learning from the synchronous, curriculum-led classroom environments, to the more informal and socially discursive, asynchronous web environments, where learning can take place outside of scheduled times, and thus becomes more amenable for lifelong learning. Augmenting the content in bottom-up and/or top-down fashion (Carcillo & Rosati, 2007). In the top-down annotation, the system uses predefined metadata (generally ontologies) to index and tag the created content. In the bottom-up annotation approach, the system allows the users (individually or in groups) to annotate the content with freely chosen tags (keywords). This approach allows for both teacher recommendations (usually topdown), as well as peer and student recommendations (bottom up). Emerging groups/communities. This concept identifies a set of individuals who have similar interests, goals, etc. In the context of lifelong learning, where collaborative settings are more frequent than competitive settings, students may recognize that
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they can reach their learning goals if (and if working on a common group goal, only if) the other students in the learning group also reach their goals (Deutsch, 1962; Johnson & Johnson, 1989). Whilst there is no guarantee in general that students would recognize this fact, by visualizing the common goal this recognition could be brought forward by the system. Groups also can be adapted to, as will be shown later. The successor to Web 2.0 is Web 3.0 (Metz, 2007), where the semantic search and browsing are made possible by natural language processing and Semantic Web technologies (Social Semantic Web), and Web 4.0 and beyond (Metz, 2007) are already being discussed. Clearly, these new technologies attract both developers and users alike, and, as lifelong learners are to be found in both categories, lifelong learning providers have to expect a discerning public that expects teaching to use the latest technologies. Thus, lifelong learning and Web 2.0 complement each other: where lifelong learning is about learning anywhere, anytime, Web 2.0 allows for collaboration during learning, as well as during the creation of the learning content. Additionally, both lifelong learning and Web 2.0 rely on the users (learners) more than the content itself, where the users (learners) determine their own learning pace (in lifelong learning), or create, evaluate (rate) and edit the content (in Web 2.0). E-learning 2.0 thus emerges from the combination of ‘regular’ e-learning and Web 2.0 technologies, and in the case of lifelong learning this leads to Lifelong Learning 2.0. However, with the massive amount of (general) information available through Web 2.0, it is becoming harder for learners to learn, or even to find, related communities, peers, and content, and this makes the process of lifelong learning using Web 2.0 less efficient. To overcome this challenge, we perceive adaptive and personalized techniques as
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the key elements for extending learning activities and making the learning process more effective. Personalization, customization and adaptation to the user, are terms frequently used in the areas of user modeling (Rich, 1979) and adaptive hypermedia (Brusilovsky, 1996), and refer to showing each user the exact information they need, when they need it, and where they need it. Adaptivity and personalization can be applied to content, in the sense of delivering appropriate information to the user. More importantly for Web 2.0 applications, unlike adaptation in regular personalized e-learning systems where adaptation is focused on the individual, adaptation can take into account the different interacting users of a system. This means that adaptation can be delivered based on user groups. This can take the form of showing similar content to users with similar interests. Also unlike classical personalization, adaptation can also take the form of bringing users with similar interests together, and allowing them to communicate directly with each other. In educational applications, these users are the learners. Finally, adaptation can also be applied to recommend experts or teachers to learners, or point out to teachers which students are in need of help. In this chapter, we therefore approach the lifelong learning paradigm from the point of view of merging research on personalization and adaptive e-learning with Web 2.0 technologies. As the whole book is dedicated to lifelong learning, we will not attempt to define this paradigm, leaving this to chapters elsewhere. Instead, we tackle the two other topics – adaptation and Web 2.0 – and finally, using a concrete case study, we illustrate how the merge can be achieved. To better understand the theoretical framework underlying such a merge, we begin by making a comparative analysis of previous models and frameworks for adaptive, personalized systems. This analysis allows us later to explain how a social reference framework for adaptive e-learning can be built, both from a theoretical as well as from
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a practical point of view. We illustrate this with a sample implementation, and discuss the results based on an evaluation with real users (learners and teachers). The remainder of this chapter is organized as follows. The following section discusses related research from the areas of Web 2.0 and personalization, and includes a comparative analysis of models and frameworks for adaptive personalized systems. The third section presents a reference framework for social adaptive e-learning, as an illustrative example of the merger of the new technologies and older concepts. The fourth section instantiates this framework based on a prototype implementation, and presents and discusses a sample system. The fifth section presents a case study evaluation of the new paradigm, e-learning 2.0, in which an implemented system is used to support experiments with both students and teachers. The sixth section discusses the findings of the study, and the seventh section addresses future research directions. The final section draws conclusions.
WEB 2.0, PERSONALIZATION AND ADAPTATION Web 2.0 The individual technologies which collectively make Web 2.0 have for several years attracted the interest of educators, and of these, Blogs (Downes, 2004) and Wikis (Lamb, 2004; Guth, 2007) have high profiles. More recently, the availability of such technologies on mobile devices has contributed to an interest in mobile delivery of Web 2.0 based educational services (Yau & Joy, 2008). Web 2.0 is still a controversial term which encompasses a large number of concepts and technologies, each of which has to some extent been applied in an educational context. Whilst a detailed discussion of all of these is beyond the scope of this chapter, the reader should view our
research into personalization as one aspect of educational Web 2.0 which inevitably overlaps with other pedagogic research. Personalization in Web 2.0 brings together a whole new set of requirements and contexts, and to differentiate it from single-user based personalization, we can call it “Adaptation 2.0”. Web 2.0 is principally defined by the content and the users. Each user has a profile (such as preferences and interests), which can be represented by a set of attributes, and similarly the content also has a set of attributes (type, size, etc.). Therefore, Adaptation 2.0 inherits from previous single user personalization approaches matching between the user and content attributes (De Bra, 1999). On the other hand, another important feature of Adaptation 2.0 is that it can be applied to a group of users who share similar profiles, and thus, adaptation is no longer only about the individual, but about the group. From the point of view of social networks and Web 2.0 applications, their increasing rise in popularity means that ever more users must be accommodated, and for some applications millions of users may need to be supported – for example, Facebook (2009) announced that it reached a user base of 200 million people in May 2009, out of which 70% are outside the US. For such massive applications, introducing personalization and adaptation is a useful way of reducing the overall search space. Of course, introducing personalization always raises issues of privacy (Kobsa, 2007), which are out of the scope of the current chapter, but it is sufficient to note here that a balance between personalization and privacy must be struck, as they both affect each other.
Personalization: Models of Adaptive (Educational) Hypermedia Past research into personalization for the web belongs to the larger category of adaptive hypermedia research – the web being an instance of hypermedia, where nodes are pages and links are
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hyperlinks, and personalization is a user-based adaptation. In this section we examine the most important frameworks for personalization on the web, in order to consider the different aspects of adaptation and personalization on the one hand, and on the other to select a platform on which to base social extensions. Many adaptive (educational) hypermedia systems have been launched since the early 1990s; however, until the late 1990s, there was no structural design or standard model for learning adaptive hypermedia systems. One of the first models designed was the Adaptive Hypermedia Application Model (AHAM) (De Bra et al., 1999), followed by the Web Modeling Language (WebML) (Ceri et al., 2000), the Goldsmiths Adaptive Hypermedia Model (GAHM) (Ohene-Djan, 2000), the Munich reference model (Koch, 2001), the XML Adaptive Hypermedia Model (XAHM) (Cannataro et al., 2002), the LAOS framework (Cristea & De Mooij, 2003), and the Generic Adaptivity Model (GAM) (De Vrieze, 2004). The goal of each of these models is to record important concepts in current adaptive (educational) hypermedia systems, such as the node/link structure, user model, adaptation patterns and presentation settings. In this section, we analyze the similarities and differences between these models.
The Adaptive Hypermedia Application Model (AHAM) AHAM (De Bra et al., 1999) is based on the Dexter model (Halasz, 1994), a reference model for hypertext systems. AHAM divides adaptive (educational) hypermedia systems into three layers: the run-time layer, the storage layer and the within-component layer, connected by the interfaces presentation specifications and anchoring. The focus of AHAM is the storage layer with its three sub-models: 1. the domain model, consisting of a set of concepts and concept relationships;
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2. the user model, containing concepts with attributes, used to store user preferences or other information (such as knowledge-of or interest-in domain model concepts); and 3. the adaptation model, which consists of adaptation rules that use the attribute values of concepts in the user model in order to determine if and how to present concepts and links from the domain model. The main advantages of AHAM are that it is a relatively simple model which allows for separations of concerns. The separation into layers helps to define the main components that need to be created by an author. However, AHAM does not make full use of other potential advantages of the separation into layers: for instance, reusability is not supported. In principle, having separate layers would allow for one domain model to be used in different adaptation or user models. However, this is not possible in AHAM, due to the fact that the adaptation rules apply to concrete domain model concepts, and cannot be re-applied to others. Moreover, reusability would mean that authors could be assigned different roles on each layer, and this would speed up the development process by enabling developers to work in parallel on the different layers – which is not possible in AHAM due to the interdependencies between the layers. An example system based on AHAM is AHA! (De Bra & Ruiter, 2001), proposed by Eindhoven University of Technology.
The Munich Reference Model The Munich Reference Model (Koch, 2001), developed at the Ludwig-Maximilians University of Munich, also extends the Dexter storage layer with user and adaptation models, and has a run-time layer, a storage layer and a component layer. It is very similar to AHAM, but its main differences are (Koch, 2001) that it:
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1. uses an object-oriented software engineering approach, whereas AHAM uses a database approach; 2. uses the Unified Modeling Language (UML) specification (AHAM uses an adaptation rule language); and 3. includes the AHAM adaptive engine in the adaptation model, as data and functionality are integrated in the object-oriented method. This integration is less useful for an authoring perspective, as it mixes delivery and authoring, not allowing for authoring for different systems, for instance. The main advantage of the Munich Reference Model is that both (1) and (2) ensure a more widespread approach, in the sense that software engineering and UML are well understood outside the personalization and adaptation communities. On the other hand, the Munich Reference model shares both the other advantages and disadvantages of the AHAM model. For example, just like the AHAM model, the Munich model represents prerequisites in the domain model, and bases its domain structure on pages, adding information about how the content will be presented to the final user directly in the domain model. This makes reuse of any of the layers almost impossible, as they are heavily interconnected.
WebML WebML (Ceri et al., 2000) is also a visual language like UML, but is specifically designed for describing the content structure of web applications. The specification of a website in WebML has four orthogonal perspectives. 1. The structural model describes the content in terms of the relevant entities and relationships. 2. The hypertext model describes how the contents are published on the application hypertext (Ceri et al., 2000).
3. The presentation model describes the layout and graphic appearance of pages, independently of the output device and of the rendition language, via an abstract XML syntax. 4. The personalization model describes users and their organization in groups in the form of entities called user and group, and defines personalization based on the data stored in these entities. The main advantages of WebML as reported by Wright and Dietrich (2008) are platform independence, the inclusion of a CASE tool, and messaging capabilities (allowing the WebML model to access query parameters directly). However WebML lacks browser control, lifecycles, UI modeling, standards and meta-models. From the point of view of this chapter, another advantage of WebML is the only one that allows the concept of group adaptation, in addition to enabling separation of concerns, thus allowing for different authoring roles. However, a disadvantages is the fact that group interaction is not representable (recommendation of one user to another, for instance). Also, the high-level definition of content and structure is closely related both to a given XML DTD1 syntax, which makes it less flexible, and to low-level, presentation-driven aspects (such as scroll), despite the fact that WebML includes a separate presentation model. An example of a WebML model-based system is WebRatio (Roberto, et al., 2004), which allows modelling and automatic generation of Java web applications.
The XML Adaptive Hypermedia Model (XAHM) The XML Adaptive Hypermedia Model (XAHM) (Cannataro et al., 2002) is an XML-based model for adaptive hypermedia systems with an application domain, a user and an adaptation model. Here however the similarity with previous models ends. XAHM not only describes the different (sub-) models from a theoretical point of view, but it also
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dictates the composition of the instances of these models, e.g. the fact that presentation descriptions need to be in XML, fixed by a DTD. Moreover, XAHM is highly reliant on mathematical models, graph theory and probability computations. The user model contains, in addition to data on the current profile, probability distribution functions that map a user over a number of profiles. Moreover, adaptation is represented as a function defined on a three-dimensional input-output space: the user’s behaviour, the technology and the external environment. Finally, the application domain is composed of a graph-based layered model for describing the logical structure of the hypermedia and XML-based models for describing the metadata for basic information fragments, as well as elementary abstract concepts connected via weighted, dynamically computed links for navigation between elements (that transform into probabilities of users actually choosing those paths). The main advantage of XAHM is that it is the first attempt to create elegant mathematical modelling of the adaptation process; another advantages is that of allowing the adaptation in three dimensions (Cannataro, et al., 2001): the behaviour of the user (i.e., preferences and activity history); the technology dimension (operating system, internet connection, access device, etc.); and the external environment (weather, time-zone, geographical location, etc.), which are not sufficiently treated and separated in previous models. However, the main disadvantage is that it hides adaptation and personalization, partially in the user model (via probability density computations), partially in the application domain model (where weights are probabilistically computed between navigational elements), and finally, in the adaptation model. This distribution of adaptation is hard to follow, and tools based on it can be difficult to handle by teachers, for instance. An example of a tool based on XAHM is the Java Adaptive Hypermedia Suite (JAHS) (Cannataro & Pugliese, 2002)
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LAOS The LAOS framework (Cristea & De Mooij, 2003) is a general framework for authoring adaptive hypermedia, based mainly on the AHAM model, presenting however some of the features of the WebML language with which it shares the presentation model. It consists of a Domain Model (DM), a Goal and Constraints Model (GM), a User Model (UM), an Adaptation Model (AM) and a Presentation Model (PM). LAOS differs from other models by introducing the goal and constraints model. This layer supports the original aim of adaptive hypermedia from the perspective of the designer (or teacher, in educational environments, hence pedagogic information, or business logics for commercial sites), something that was missing in previous models (Cristea & De Mooij, 2003). Furthermore, the LAOS AM model is different from that of AHAM. The adaptation model is based on the three layer LAG model (Cristea & Verschoor, 2004) for authoring adaptation, which allows different entry and reuse levels for adaptation specification, depending on whether the author has programming skills or not. Thus, the initial threshold for creating adaptation is lowered. The major difference between LAOS and AHAM (and other models) is a higher level of reuse, due to the clear separation of primitive information (content) and presentation-goal related information, such as pedagogical information in educational systems and prerequisites. For instance, since prerequisites are not hard-wired in the domain model, elements of the domain can be used in different settings and sequences to those initially intended. In this way LAOS facilitates a high degree of information reuse by separating information from its specific context. This separation is expressed by having two different models, instead of one: a domain model (DM) and a goal and constraints model (GM). The separation can be understood easily if we use the following
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metaphor: DM represents the book(s) on which the presentation (such as a PowerPoint presentation represented by the GM) is built. From one book (or DM) one can construct several presentations (here, GMs), depending on the goal. This goal, in a learning environment, can be a set of learning objectives, which are either implicit, or would need to be expressed separately. A presentation does not contain a whole book, just some (constrained) part of it. Furthermore, a presentation can contain information from several books. The separation therefore gives a high degree of flexibility, based on the DM–GM multi-multi dependency. Another important difference is given by the notion of ‘concept’ used in the domain model. In LAOS, concepts have different representations defined via attributes, and are restricted to representing a semantic unity (unlike in AHAM). This is further enforced by allowing only self-contained attributes (without direct or indirect dependencies). This setting allows attributes to be flexibly re-ordered, and links are therefore external and can be dynamic.
Unlike some of the other models, such as XAHM or WebML, LAOS does not prescribe a unique representation for each layer, but just specifies its contents. Thus, each layer could be represented by databases, XML, state machines, etc. Moreover, the adaptation model, LAG, only specifies the different entry levels for reuse (whole strategy, high level adaptation language patterns, or low level adaptation ‘assembly’ language patterns such as if-then rules) but does not enforce a specific language. An example authoring system built on LAOS is MOT (Cristea & De Mooij, 2003). To summarize the main features examined in the previous models and how they compare with LAOS in short, we provide Table 1 with a comparison between these models. For the reasons above, and due to the fact that it provided most of the desired features, as shown in Table 1, we have selected the LAOS framework for further development in our research.
Table 1. Comparison between models of adaptive (educational) hypermedia AHAM
Munich
WebML
XAHM
LAOS
Separation of concerns
Yes
Yes
Yes
Yes
Yes
Reusability
No
No
To some extent
To some extent
Yes
Different user roles
No
No
Yes
No
Yes
Flexibility (different formats, etc.)
Yes
No
No
No
Yes
Pedagogic layer
No
No
No
No
Yes (via Goal and Constraints model)
Group representation
No
No
Yes
No
No
Social interaction
No
No
No
No
No
Approaches
Database /XML
Object-oriented
UML
XML
Database /XML
Target
A(E)HS
A(E)HS
Web App
AHS
A(E)HS
Notes: A(E)HS: Adaptive (Educational) Hypermedia Systems AHS: Adaptive Hypermedia Systems Web App: Web Applications
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A SOCIAL REFERENCE FRAMEWORK FOR ADAPTIVE E-LEARNING The Social Personalized Adaptive Lifelong Learning Scenarios To illustrate the type of adaptation that can be expected in the new framework, we present five social, personalized, adaptive lifelong learning scenarios using SLAOS (Social LAOS). The first scenario, “Help! I’m lost”, explains the situation of a student helping another student. The second scenario, “A group project”, represents the case of the system balancing workload between students. The third scenario, “I am done. What now?”, explains how the system might recommend reading material or another project for an individual student. In the fourth scenario the system recommends a better group for the current student, and in the fifth it recommends content to an author. These scenarios are by no means intended to be exhaustive, and they can be extended with other typical lifelong learning situations. The scenarios below are used as running examples, to introduce later the Social LAOS framework and its definitions, and are also related to the screenshots presented in the implementation section.
Scenario 1: Help! I’m Lost Mary is a hairdresser and a part-time student of Economics. She is following lessons on an online system with social support, adaptation and personalization, based on SLAOS. She is stuck on the topic of ‘Banking crises’ (see a snapshot in Figure 4, left hand menu). The system could recommend her to contact a specific teacher, or some customized reading material (modules or items, such as in Figure 4, where ‘Strategic complementarities in financial markets’ is recommended for a student reading about ‘Speculative bubbles and crashes’). She is however a very social student, and would prefer to chat with another student
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about her progress, instead of going through the official channels. She ‘asks’ the system to recommend someone, and the system finds student Jane for her, who has just finished the item related to ‘Banking crises’. Mary then contacts Jane, who is willing to move on to a chat tool to give her some direct guidance, and maybe to gain a new friend. Requirements: the system should allow personalization of material (items in a module) to a learner, and recommendations of ‘expert students’2
Scenario 2: A Group Project Students Mary and Jane (a previously full-time mother who is planning to return to work and is upgrading her CV) later participate in a group project ‘writing an essay on theories of Financial crises’ (thus they need to author a module with topics such as those illustrated in Figure 5). It’s a three-person project, so after the two register for it, the system recommends another student, Bob (a company worker aiming at climbing up the management ladder), as a third person, who had registered earlier looking for partners for the same project. The activities associated with the work are: Internet search for ‘Marxist theories’ (15% of workload), Internet search for ‘Minsky’s theory’ (15%), Internet search for ‘Models and Games’ (10%), Essay Writing (50%), and Essay Revision (10%). Jane loves writing, so she decides that she will take Essay Writing. Mary then decides that her strength is in browsing, so she takes over all browsing activities. Bob is new in the partnership, so he accepts the remaining revision activity. However, after they log in their initial preferences, the system notices the big discrepancies in their workloads, and thus advises the students to share the load in a more equal manner. Consistent with the initial preferences, the system encourages Bob to take over some part of the writing and searching activities together with the essay revision. Similarly, Jane is advised to keep up to an equivalent workload of 33-34% of writing activity. Finally, Mary is advised to reduce one or more
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of her browsing activities. Although the system makes some suggestions, it is up to the students to decide on the final workload distribution. In our case, Mary takes only two browsing activities, on topics ‘Marxist theories’ and ‘Models and Games’, deciding to do some of the writing (up to the 33% workload) about these topics. Bob takes over the search on the topic ‘Minsky’s theory’, as well as some of the writing on this topic (up to a 33% workload). Jane remains with a slightly higher, but acceptable, workload of 34% in writing only. Requirements: the system should group work, offer recommendationsof peers (students), workload allocation, individual and group feedback
Scenario 3: I’m Done. What Now? John, a company worker, is studying a selected subset of modules that have been recommended by his company. He has finished the whole module on ‘Financial crisis’ (see Figure 5) that Mary was studying earlier. He is wondering what to do next. The system recommends to him related modules to have a look at. In addition to ‘Advanced concepts on Economic crises’, the course also suggests ‘Famous financial crises in history’, as well as some other topics. As John is not yet sure about following the higher level module, he reads a little, for his own amusement and interest, about the famous financial crises in history. Requirements: the system should offer recommendations of similar topics (modules)
Scenario 4: Group Mismatch Student Mario, another company worker studying from his workplace in a different company, has joined students Sara and Jessica from his own company in the group project on ‘writing an essay on theories of financial crises’. However, Sara and Jessica have only just finished the prerequisite study for this group project, whereas Mario has studied much further, and only now has decided to join this group. The system recommends him
to join students John and Lisa who are more advanced, and who also wish to do the same project. The system furthermore recommends the trio to attempt a more complex project, about ‘Economic crises in general’, as this can give them credit towards the easier group project as well. Requirements: the system should offer recommendations for matching tasks and recommendations of peers (students)
Scenario 5: Has this Been Done Before? Helen is a teacher of Economics and is authoring some of the material for this course. She has just started creating an item on ‘Financial crisis’ (see Figure 6). She is wondering whether it has been done before. The system finds for her a publicly available item on the ‘Strategic complementarities in financial markets’. Helen decides (by skimming through the information provided by the system) that she will be able to use this in her module, and adds it to her module by linking to it. Requirements: the system should allow personalization of material for authors
The Properties of a Social Personalized Adaptive Lifelong Learning System None of the previously visited personalization and adaptation frameworks and models has modelled or included the social activities from the Social Web which focus on the relations between the users on the web and their collaborative activities, as sketched in the scenarios above. In addition to the information stored in previous models, the information collected from social annotation can be used to recommend adaptive materials for the delivery/authoring process. The aim behind including collaborative authoring and social annotation modelling is to create a comprehensive framework that allows for the definition of improved adaptive materials based on communities
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of practice (Wenger, 1998), where the learners collaborate actively in the form of groups (communities), rather than being passive in the learning process. The benefit of such a framework is that it is system independent, and thus can be applied to any system wishing to integrate adaptation and Web 2.0 technology. It makes sense, however, not to start from scratch, but to add the social model on top of an existing model for adaptation. Thus, based on reasons highlighted in the previous section, we have built our social model on top of the LAOS framework for authoring adaptive hypermedia. This is how the Social LAOS framework (SLAOS) came into existence, and why it has arguably been kept generic enough to be used by any adaptive Web 2.0 system. In SLAOS (Social LAOS), authors who share the same interests can collaborate to provide more valuable adaptive content within their communities, based on their different backgrounds and knowledge. The collaborative facilities in SLAOS rely on Web 2.0techniques, such as group-based authoring, cooperation in creating the courses,
tagging (labelling) the content, and rating and providing feedback on the content. The collective content works as a state-based system, as each particular instance of it can be used to improve the authoring process by recommending related content to authors, who then can decide on the next state of the collective content based on these recommendations. Additionally, related authors (authors with the same interests) can be recommended, who can help in the authoring process. Furthermore, in SLAOS, teachers are no longer the only authors of the content — students are also considered authors, as they too can add their contributions, controlled by a set of privileges set by the teachers. Thus, similar recommendations can be provided for students. Figure 1 illustrates the smooth transition, in a sliding-scale fashion, between learners (students), teachers, authors and administrators. The X-axis represents the various users of a social e-learning system, whilst the Y-axis represents the rights these users have in the system.
Figure 1. Smooth transition from student to author and beyond in social e-learning
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Figure 1 shows that the different categories of users are not represented by a single point in the users-rights space, but that they could be defined anywhere within a segment of the graph. For instance, a student could have only reading rights and nothing more, being at the beginning of the segment of students. However, a student could have tagging rights, or even rights of editing their own or group items – thus being placed at the end of the segment. Similarly, a teacher could just have rating rights, basically marking students, or could have complex authoring rights, being able to edit their own modules or even modules outside their own group. Authors, by definition, should have at least some authoring rights, e.g. rights for editing their own items. At the end of the scale, authors could author, in groups or by themselves, any given items or modules. Finally, the role with maximum rights is that of the administrator, who can do any of the tasks done by students, teachers and authors, and any other tasks which are present in the system. Note that this graph is for orientation only, and it does not represent all possible users or all possible rights. Whilst we attempted to order the rights for the figure, other orders are possible,
depending on the system they are applied to. Also note that no monotonic increase is assumed. Figure 1 already displays an extended idea of rights for student, teacher, and author, where teachers and authors are just students with more rights. However, in the context of lifelong learning, it is important to note that these segments can be extended even further, and that the fuzzy difference between the roles could disappear altogether, leaving only one type of user with a set of rights. The progression to a higher level of rights has to be established outside this figure, depending on the goal of the system. For example, if the goal of the system is to teach writers, and ultimately to allow them all to collaborate in a wiki-like manner, a user could progress from initial reading rights all the way to editing other modules, depending, for example, on peer evaluations, trust, etc. Figure 2 illustrates the addition of a new layer, the social layer, to LAOS, which expresses all social activities within Adaptive Hypermedia Systems. These social activities include, but are not limited to: 1. collaborative authoring (editing content of other users, describing content using tags, rating, commenting on the content, etc.);
Figure 2. Social LAOS framework
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2. authoring for collaboration (adding author activities, such as defining groups of authors, subscribing to other authors, etc.); 3. group-based adaptive authoring via groupbased privileges; and 4. social annotation (tagging, rating, and providing feedback on the content via groupbased privileges). The Social Reference Model, SLAOS, follows the multi-layered approach of its predecessors, for similar reasons: extracting the semantically different layers (or models) of a generic system allows for mapping of different system components onto the different layers, and thus for a high degree of reuse of these components, in their interaction with others. For example, a domain model can be reused with different adaptation models. These models represent the normalization axes or principal components of, in our case, a generic social adaptation system. SLAOS has taken over the composing models from LAOS, but refined and extended them, according to the social collaborative goals. Beside the social model, the SLAOS framework encompasses two ‘new’ models3: the Resource model and the Environment model. We have used a resource model, inspired by the Dexter model (Halasz, 1994), to separate resources from their domains, and thus allow for a higher degree of reuse. Similarly, we have separated the environment model from the presentation model, to more clearly separate external factors from what is shown on the screen. The environment model is subsequently refined into a physical device model, a network model and an external environment model, emulating dimensions from the XAHM model. The overall structure inherits the conceptual model based structure from LAOS. The figure shows also which models are overlaid, such as the resource, domain, goal, user and environment models, thus concepts from the resource level are used in the domain or goal model to add ad-
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ditional information to them, as will be detailed in the definitions below. Also concepts from the domain model (for instance) can be used by the environment model, if different content can be labelled according to the device it is able to work on, network conditions, etc. The social component acts vertically, and not horizontally, as it affects most of the other layers directly. For example, the resource model layer includes new entities to describe tags, feedback, comments, ratings of the actual concepts, and the relations between these concepts. The domain model overlays the resource model, and thus inherits and manipulates the social activity descriptors. The goal model includes new entities to describe the new constraints on the social activities, i.e. determining who can do what. Moreover, the user model contains new entities to describe the groups and the roles (privileges) for these groups, which will be added to the user model. Additionally, the adaptation layer holds new entities to handle the collaborative adaptive strategies. The presentation layer also contains new entities to describe how to present information to groups of users. The adaptation and presentation models use these elements via data exchange with the package of socially enhanced models. Figure 2 also shows the interaction between the individual models: the social resource, domain and goal model provide a content-based, metadataenriched package to the adaptation model, together with a social user model, and an environment model. The adaptation model specifies how the input from these models is processed, and then how it is output into the presentation model (what the learner gets to see) and the update of the user model (how the information known about a user is updated).
SLAOS Components: General Definitions In the following section, we describe in more formal terms the composition of the SLAOS model,
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based on, and using similar naming conventions to, the LAOS model (Cristea & De Mooij, 2003). We use the extended set of requirements, including social activities, for the smooth transition from student to author, as reflected in Figure 1 and Figure 2, as well as the five scenarios introduced above. These definitions are useful for building adaptive systems for the social web, especially in the field of education and lifelong learning. The overall modelling structure follows the conceptual modelling structure. For reading ease, we use Bold for marking sets of sets, Italics for marking sets, and no marker for single elements. Definition 1: The concept model CM is the set of all concept maps that are used by all possible applications of social personalized adaptive educational systems, C is the set of all concepts, A is the set of all attributes, and L is the set of all links. In the following, the composing models of the SLAOS framework are formally defined in turn: the Social Resource Model, the Social Domain Model, the Social Goal and Constraints Model, the Social User Model, the Presentation Model (in general, as well as represented only by one of its sub-layers, the Physical Device Model), and the Adaptation Model
Social Resource Model Definition 2: The item model IM (also called social resource model) is formed by the set of all items, their relations and their properties (also called resource model; IM ⊆ CM), containing all content of the social adaptive system (SAS) relevant to the application: the set of all items IC ⊆ C, the set of all item links between content items IL ⊆ L, and the set of all attributes describing items IA ⊆ A. Example: Any section in a module can be linked to one item, such as “Banking crises” (see Scenario 1 and Figure 4). This item can have a set of features described as attributes. This is expanded
in the following definition. Links are currently not in use between items in the example system, but are kept in the definition for conformance purposes with the domain model, and for further developments. Definition 3: An item i ∈ IC (or resource) is defined by the tuple where id is the item identifier which can be used to link to the item’s content, T ⊆ IA is a set of tags related to the item i, F ⊆ IA is a set of feedback related to the item i, R ⊆ IA is a set of ratings of item i, given by various users, and Rall is the overall rating for the item i. Example: The “Leverage” item can have a set of tags (keywords) describing the content of this item, such as “crisis”, “leverage” or “Wall Street” (see Figure 5). Also this item can have feedback (comments), from the authors and/or from the learners. The comments are generally related to the content of the item. Moreover, this item can have a set of ratings to value its content. The total rating of the item is defined in the following definition. Definition 4: An overall rating for item i, Rall is defined as follows: Rall = ∑j=1..n Rj,i / n, where Rj,i∈ IA is an arbitrary rating given by person j for item i, and n is the total number of the ratings for item i. Example: The content of the “Leverage” item (Figure 5) has a rating value (typed or not typed; types can include relatedness, interest, correctness, etc.). The rating has a range from 1 to 5, and therefore any user (author or learner) can rate this item according to their point of view. If this item was rated by three users with values of 4, 3, 5, then the total rate will be (4+3+5)/3 = 4 out of 5, or “Very good” (see Figure 5). This could then render this item recommendable to Mary (in scenario 1). Definition 5: An item type t ∈ IA is a tuple with id an item identifier, typeall an overall type name, and TA a set of item type attributes, TA ⊆ IA. 103
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Example: The item can be an image (so typeall = “image”), thus can have attributes such as resolution, width, height, type (JPEG, TIFF). The content of the item type attributes is defined in the following definition. Definition 6: An item type attribute ta ∈ IA is a tuple where id is the item identifier, typeall the overall type name, type is the name of a particular type attribute, and val is the value (contents) of the item type attribute. Example: An item of an overall type ‘image’ can have a subset of attributes, such as width (type = width and value = 400px), resolution (type = resolution and value = 300dp), image file extension (type = file extension, and value = JPG), etc.
Social Domain Model Definition 7: The social domain model DM is formed by the set of all domain maps (also called modules, DM ⊆ CM), containing all information on the social adaptive system (SAS) relevant to the domain of the application: the set of all domain concepts (anchors) DC ⊆ C, the set of all domain links between domain anchors DL ⊆ L, and the set of all attributes describing anchors DA ⊆ A. Example: The collection of all modules is an abstract term, including collections of all modules taught in a social personalized adaptive environment: for example, in a university economics department, these might include “Financial crisis” (Figure 5, also the topic of scenarios 1-5), “The Industrial Revolution: Growth and Living Standards” and “Development Economics (Macroeconomics)”. The composing terms are defined below. Definition 8: A module M ∈ DM (also called domain concept map) of the social adaptive system (SAS) is determined by the tuple , where DC ⊆DC is a set of domain concepts (DC ≠∅, there should be at least one domain concept – an anchor – in the module), DL ⊆DL is a set of domain links between the concepts and 104
DA ⊆DA, where DA is a set of optional domain attributes, which describe the module in general. Example: A lesson on the topic of the financial crisis can be represented as one module, which can have a set of sections (anchors to items) such as “Types of financial crisis” or “Banking crisis” (Figure 5). These sections can be interlinked hierarchically, or in other ways. Definition 9: A domain concept (or anchor) dc ∈ DC is defined by the tuple < Mdc, idc, DAdc, DLdc> where Mdc ∈ DM is the module the domain concept belongs to, idc is an item identifier, DAdc ∈ DA is a set of optional DM concept attributes; and DLdc ⊆ DL is a set of domain links the domain concept is participating in. Example: A domain concept (or anchor) links to a resource. For instance, it could point to a content item called “World system theory” (see Figure 5). Keeping domain concepts and content items separately ensures that a different domain concept could also point to the same item, thus effectively reusing the material within a different module. Constraint 1: Each module M ∈ DM is required to have a minimal set of concepts DCmin (DC ⊇ DCmin≠∅) which corresponds, via the anchoring system, to a minimum set of items Imin (IC ⊇ Imin≠∅). Here, M is an instance of DM. Example: As the module may represent a lesson, it should not be empty and should contain at least one item. Definition 10: A domain link dl ∈ DL is a tuple with S, E ⊆ DC, (S, E≠∅), respectively start and end sets of domain model concepts. Example: A simple example of domain links consists of hierarchical relations. Items can have hierarchical relations (links) between themselves, such as between “Theories of financial crisis” and “Minsky’s theory” (see Figure 5). This relation (link) could be used for adaptation purpose, for instance to show the resources related to the item “Theories of financial crisis” before “Minsky’s theory”. This would fit a depth-first approach, used, for example, for sequential learners. Dif-
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ferent adaptation strategies could, however, use this link in different ways. This can be used for instance in scenario 2 to recommend these items as parts of the same larger group project work. The following constraint completes the definition of a module, as a linked set of concepts. Constraint 2: For any concept in a module there is another concept in that module with which this concept has a relation. Explanation: The expectation is that at least one hierarchical relation exists between the items/ concepts, as is usual in educational environments, where chapters are grouped into hierarchically linked sections and sub-sections. Also, ‘free’, non-linked concepts will not be able to be visited by users when they navigate through the domain maps. Thus it is essential to have at least one type of link linking each concept in the map to at least one other concept. Definition 11. A domain attribute a ∈ DA of a domain dc ∈ DC is a tuple where Mdc is the module the domain attribute belongs to, type is the name of the domain map attribute; and val is the value (contents) of the domain model attribute. Example: An attribute for the “Financial crisis” module (Figure 5) could be the details on the author of this module. Another attribute could be the description of the domain contents gathered in the module. This type of description can help for instance Helen, the teacher in Scenario 5, to be automatically presented with a list of domain concepts and domains that are related and thus relevant to her authored new course. She can then choose from the list the ones that are most relevant (as in Figure 6). Definition 12. A domain concept attribute a2 ∈ DA is a tuple where iddc is an identifier for concept dc, type is the name of the DM attribute; and val is the value (contents) of the DM attribute. Example: An example attribute for the “Leverage” concept (Figure 4) is the very title, “Leverage”. In general, a domain concept attribute helps
in making the link between the concepts (anchors) and the resource items, which are previously defined in the Social Resource Model.
Social Goal and Constraints Model Definition 13: The social goal and constraints model GM is formed by the set of all goal and constraints maps (GM ⊆ CM), containing all information (resources and links between them) about the social adaptive system (SAS) relevant to the overall goal of the application: the set of all goal model concepts GC ⊆ C, the set of all goal model links between goal concepts GL ⊆ L, and the set of all attributes describing goal concepts GA ⊆ A. Example: The previous lesson (module) of “Financial crisis” can have a set of adaptive modules. Furthermore, each of these adaptive modules (e.g. Figure 4) can have different pedagogic goals (adapt to user knowledge, personalize for preferences, etc.) which can be expressed as a set of constraints (conditions) in order to deliver adaptive course materials. These conditions can be defined as attributes as in the following definition. Definition 14: A goal and constraints map GM ∈ CM of the social adaptive system (SAS) is an enriched module, which consists of a tuple , where GC ⊆GC represents a set of goal model concepts, GL ⊆GL is a set of goal model links and GA ⊆GA is a set of goal model attributes. Example: To the “Speculative bubbles and crashes” item in the lesson on “Financial crisis” (Figure 4) can additionally be added, via this model, a label attribute, which defines the knowledge level required for this item (e.g. beginner, intermediate, or advanced). Thus, based on this label, the item can be part of different views (different delivery) based on the learner’s knowledge level. For instance, in scenario 3, if ‘Financial crisis’ is all marked as beginner level, then John can be recommended ‘Advanced concepts on
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Economic crises’ and ‘Famous financial crises in history’, both marked as intermediate. Definition 15: A goal and constraints concept gc ∈ GC is defined by the tuple where Mgc ∈ GM is the goal map the goal model concept belongs to, igc is a domain concept identifier, GAgc ∈ GA is a set of goal model concept attributes; and GLgc ⊆ GL is a set of goal model links the goal model concept is participating in. Example: The goal and constraints concept of the item “Strategic complementarities in financial markets” (Figure 4) adds to this item a set of personalization and adaptation attributes, such as weight and label (e.g., weight = 70% and label = “beginner”), corresponding to the adaptive strategy. Definition 16: A goal and constraints link gl ∈ GL is a tuple with S, E ⊆ GC, (S, E≠∅), respectively, start and end sets of goal model concepts, N a set of labels of the link and W a set of weights of the link. Example: The goal and constraints item “Types of financial crises” can be linked to the goal and constraints item “Banking crises” via a prerequisite link (See Figure 5). This now specifies that the item “Types of financial crises” should be shown before “Banking crises”, as this is now part of the adaptation description. Unlike the use of the domain link between these items, the goal model link has one interpretation only. The purpose is also different, as goal model links can be of a pedagogic nature, whereas the domain links can only be of a domain-related nature - they are descriptive links, and not procedural links. Definition 17: A goal and constraints attribute ga ∈ GA of a goal model gc ∈ GC is a tuple where GMgc. is the goal map the goal module attribute belongs to, type is the name of the goal map attribute; and val is the value (contents) of the goal model attribute. Example: An attribute for the “Financial crisis” module (Figure 4) could be the details its author. Another attribute could be the description of the educational contents gathered in this module, etc.
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Definition 18: A goal and constraints concept attribute ga2 ∈ GA is a tuple where idgc is an identifier for concept gc, type is the name of the GM attribute; and val is the value (contents) of the GM attribute. Example: The goal and constraints item “Banking crises”, can have, for example, an attribute of type ‘label’ with values of ‘visual’ or ‘verbal’, which can be used in adapting this item in this case to a visual or to a verbal learning strategy respectively. Constraint 3: Each goal and constraints item is required to have a minimal set of (standard) attributes,GAmin (GA ⊇ GAmin≠∅). Example: In order to adapt any item, it should have at least one metadata attribute (such as ‘visual’ as per the previous example) which can be used with the adaptive strategy; without these attributes, the strategy cannot adapt the item. Constraint 4: Each goal and constraints item g must be involved in at least one special link gl, called the prerequisite link (link to ancestor item). Example: See the Example for Definition 16. Constraint 5: Each goal and constraints concept g must have at least one special, numerical goal and constraints attribute ga, called an order attribute. This attribute reflects the order of the concept among siblings with the same prerequisite goal and constraints parent concept. Example: If “World systems theory” and “Minsky’s theory” have the same parent prerequisite, “Theories of financial crises” (see Figure 4), than there must be an order between them, for example, “World systems theory” has order = 1 and “Minsky’s theory” has order = 2. This is a weak prerequisite structure, where elements with lower order should be shown before elements with higher order, or could, in principle, appear on the same page.
Social User Model Definition 19: The social user model UM is formed by the set of all user maps (UM ⊆ CM),
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containing all information (resources and links between them) about the users: the set of all user concepts UC ⊆ C, the set of all links between users UL ⊆ L, and the set of all attributes describing users UA ⊆ A. Definition 20: A user map UM ∈ UM of the social adaptive system (SAS) is determined by the tuple , where UC ⊆UC is a set of user concepts, UL ⊆UL is a set of links between users and UA ⊆UA is a set of optional user attributes, which describe the user model in general. Example: The set of attributes can include knowledge level, interest, display preferences, age, etc. Links within user models could appear if, for example, an attribute such as interest can be related, via a formula, to the knowledge level of the user (this is not currently implemented in the example system). Definition 21: A user concept (or, simply, generic user) uc ∈ UC is defined by the tuple where UMuc ∈ UM is the module the generic user is supposed to study, UAuc ∈ UA is a set of UM concept attributes, and ULuc ⊆ UL is a set of user links the user is participating in. Example: A user of the social adaptive system could be represented by their set of preferences, such as knowledge, interest, etc., and could also be related to other users via various relations, such as friendship or class membership. The generic user (or user model) stores the type of attributes that are used for a given module. E.g., it stores the fact that a user’s knowledge and age is important, that the knowledge default value is 0, and that the age default value is 30. Please note that this does not represent user Jonny, who has a knowledge of 79 and age of 44, which would be an instance of this generic user. Definition 22: A user model link ul ∈ UL is a tuple with S, E ⊆ UC, (S, E≠∅), respectively, start and end sets of user model concepts, W a set of weights describing the link, and L a set of labels describing the link.
Example: User links can be subscriptions to other users, or grouping of users, etc. Adding a subscription link between two generic user models, one containing a user’s knowledge and interest, and another one containing a user’s knowledge and availability, for instance, means that in the target design system, a specific user belonging to the first user category can be linked to the second. For instance, a user with low knowledge and high interest can subscribe to a user with high knowledge and availability set to true. The thresholds for what represents high and low knowledge, etc., can be set as user model concept attributes, as defined in Definition 24. Definition 23: A user model attribute ua ∈ UA of a user model map is a tuple where UM. is the user map the user model attribute belongs to, type is the name of the user map attribute, and val is the value (contents) of the user model attribute. Example: A user map would map all generic user types that can appear in a social adaptive system (learner, teacher, etc.). At this level, attributes could represent the number of teachers or of learners the application overall allows for, or the type of groups allowed. Definition 24. A user model concept attribute ua2 ∈ UA is a tuple where iduc is an identifier for user uc ∈ UC, type is the name of the user model attribute; and val is the value (contents) of the user model concept attribute; val can also take the form , to allow for more complex, nested attributes. Example: In the previous example, each attribute can be represented as having the following default values: knowledge level = beginner, interest = 1, display preference = text and images, age = 40. Note that at this modelling level, only generic users are modelled, not actual users (e.g. any user with knowledge level beginner, but not the user Johnny, who also happens to be a beginner). Instances of these attributes can be used to represent the fact that Sara and Jessica are
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intermediate learners, but Mario, Lisa and John are advanced, as in Scenario 4. Definition 25: A user group ug ∈ UL of a user uc ∈ UC is a special kind of user model link, where S = {uc}, W = ∅, L = {group-name}, where group-name is the name of the user group, and E = {ui | ui∈UC, ui is a user concept in the group labelled group-name}. For simplification from the implementation point of view, however, the following definition is used. Definition 26: The groups of a user gu ∈ UA is a special kind of user model concept attribute, where type = “group” and val a set of groups that the user belongs to. Example: A learner can join different groups, such as ALS group, Warwick group (see Figure 4), and in each of these groups, the learner can have different roles, as defined next. The definition above also allows for a user to create new groups directly, or have an administrator – or teacher – create the groups for them. Mary, Jane and Bob form a group in Scenario 2, and in Scenario 4, first Sara, Jessica and Mario form a group, which is disbanded based on the recommendation of the system, in order to form the group of John, Lisa and Mario. Definition 27: A user role attributeur ∈ UA is special kind of user model concept attribute, where iduc is an identifier for user uc, “role” is the name of the main user model attribute, type is the name of the role attribute, and val is the value (contents) of the role attribute. Example: The role can be defined as keyvalue pairs, such as, read = 1, edit = 0, tag = 1, etc. Please note this user model concept attribute uses nested attributes. Definition 28: A user subscriber us ∈ UL of a user uc ∈ UC is a special kind of user model link, where S = {uc}, W = ∅, L = {subscribers} and E = { ui | ui∈UC, ui is a subscriber to uc}. Example: A learner can subscribe to different users, who share same interests, same topics, etc.
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This can be used to recommend other related users during adaptation. Constraint 6: Each user concept is required to have a minimal set of (standard) attributes,UAmin (UA ⊇ UAmin ≠ ∅). Example: Read and write roles should be defined with default values.
Physical Device Model The Environment Model in Figure 2 can be demonstrated by one of its most important submodels, the Physical Device Model. The other environment models (network model, external environment model), and even the presentation model (which has the role to decide what, where and how something is being shown to the user) can be defined in similar way. Definition 29: The physical device model PDM is formed by the set of all physical device maps (PDM ⊆ CM), containing all information (resources and links between them) of machine types on which the presentation is performed: the set of all physical device concepts PDC ⊆ C, the set of all physical device links PDL ⊆ L, and the set of all attributes describing machines PDA ⊆ A. Example: Types of physical device media can be PDA, Desktop Computers, Laptops, etc. There is a need to adapt to the nature of this media, even if the user (learner, author, teacher) is the same, as different screen sizes can affect the information transmitted. Definition 30: A physical device concept pc ∈ PDC is defined by the tuple , where pm is the presentation media, and PA ≠ ∅ is a set of PDM attributes. Example: A PDA is a physical presentation media device. The attributes are defined below. Definition 31: A physical device attribute pa ∈ PDA of a physical device item is a tuple , where type is the name of a particular type attribute, and val is the value (contents) of the type attribute.
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Example: The type of a physical device attribute can be “resolution”. The PDA resolution is 240×160, and the computer screen resolution is 1280×1024. Please note that when more users are collaborating, the common denominator of the different devices used by the different users is the one that is selected, e.g., the minimum resolution at which all collaborating partners can view the item. Definition 32: A user device link ul ∈ PDL is a tuple where iduc is an identifier for user uc, and pm is the presentation media. Example: A user, say “Jonny”, can use different devices, say “PDA”, “Desktop”, “Group device”, etc. The latter is used in connection with the last example in definition 33.
Adaptation Model Definition 33: The social adaptation model AM is formed by the set of all adaptation maps (AM ⊆ CM), containing all information (resources and links between them) of the adaptation (dynamic changes) performed in a social adaptive system, based on all other static models in the framework (Social Resource Model, Social Domain Model, Social Goal and Constraints Model, Social User Model, Environment Model - here: Physical Device Model, Presentation Model). Example: The adaptation model is thus the only dynamic model in the framework, and it uses the other models as ‘ingredients’, to form the overall ‘recipe’ for social adaptivity. This model in itself can have many components. An example is the LAG model (Cristea & Verschoor, 2004), which can be extended towards collaborative adaptation. This is not further pursued in the current chapter, due to lack of space. Definition 34: An adaptation map AM ∈ AM (or an adaptation strategy) of the social adaptive system (SAS) is a collection of mapping functions f:{i(IM)*, i(DM)*, i(GM), i(UM)*, i(EM)*, i(PM)*} -> {i(PM)*, i(UM)*}, where i(X) is an instance of X, and EM represents the
generic environment model (as illustrated by the physical device model, PDM, above). Example: This type of definition is very generic, and allows for the actual implementation to be done either by traditional rule-based systems, or by actual mathematical formulas, or by Bayesian networks, etc. An example is a group-based adaptation support via recommendations techniques, such as recommended learning content (which is rated high using Definition 4) based on the learner’s profile (which is represented in Social User Model). This can be implemented with a function based on the content (from the i(IM)) and the rating (from the same model), and a personal threshold for a given student for accepted rating, stored in the i(UM). This will influence which items will be shown: i(PM). This represents content adaptation as in scenario 1 (item or module), scenario 3,5 (module) and Figures 4 (adaptive reading) and 6 (adaptive authoring). Another example is that of recommended expert learners based on the learner’s profile, as in the second part of scenario 1, as well as in the recommendationof a group member in scenarios 2 and 4. This maps i(UM) to i(UM), and it actually means adding a temporary link between the current user and the recommended person in the user’s user model. In the context of MOT 2.0, the learner’s knowledge (experience level) is the main factor in the recommendation process, as it reflects who is expert in the selected domain. We recognize two steps — the recommendation step and the communication step. MOT 2.0 as presented in this chapter focuses on recommendation, but not on the communication between learners (which is left to further research and implementation). Further work on the communication step has already started. Also various recommendations based on cosine similarity between elements have been added.
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Presentation Model The presentation model in Figure 2 has the role to decide what, where and how something is being shown to the user. Definition 35: The presentation model PM is formed by the set of all presentation maps (PM ⊆ CM), containing all information (resources and links between them) of the content, type, place, etc. about the presentation performed: the set of all presentation concepts PC ⊆ C, the set of all presentation links PL ⊆ L, and the set of all attributes describing presentation PA ⊆ A. Example: Types of presentation can be as simple as deciding if a specific content is to be shown or not, or if the name of a peer student is to be shown or not. Alternatively, it can be complex, such as in deciding how the screen is to be used for the specific presentation, what is to appear where on the screen, etc. Definition 36: A presentation concept p ∈ PC is defined defined by the tuple where Pgc ∈ PM is the presentation map the presentation concept belongs to, ipc is a overlay concept identifier (item, domain map concept goal model concept, etc., PApc ∈ PA is a set of presentation model concept attributes; and PLpc ⊆ PL is a set of presentation model links the presentation model concept is participating in. Example: An overlay over a goal model concept is for instance the Boolean ‘show’ set to True for the goal model concept ‘Financial crisis’ (see Figure 4). This would mean that a student can have reading rights to this concept, such as John in Scenario 3. Further definitions of link, attribute etc. for this model follow the Social Goal and Constraints model example and are not further detailed here.
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IMPLEMENTING ADAPTIVE E-LEARNING 2.0 In the following, we illustrate the definitions of the Social Layer for a specific new system developed at the University of Warwick - the MOT 2.0 system, an adaptive Web 2.0 authoring and delivery system for adaptive hypermedia, first mentioned in (Ghali et al., 2008a). MOT 2.0 is loosely based on the MOT 1.0 authoring system for adaptive hypermedia (Cristea & De Mooij, 2003), but it goes beyond it, as it not only incorporates social aspects, but it also, by removing the boundary between authoring and learning, becomes both an authoring as well as a delivery system. Figure 3 illustrates the fact that the social user model (as defined in definitions 19-28) captures the results of all actions the users made using MOT 2.0; these action results including which groups the user has already subscribed to, what modules the user has created/edited, and what tags the user has already used and for which module. In a future version, MOT 2.0 will capture more information, such as a user’s subscribers, a user’s own subscriptions, user’s own ratings, etc. Group affiliation as shown in Figure 3 is used in scenario 2, where Mary, Jane and Bob eventually belong to the same group working on a common project, and in scenario 4, where Mario moves between two groups, finally reaching one that is better matched to him. Figure 4 expresses the adaptive view of the lesson, which shows other related recommended materials for further reading based on the similarity of the tags (keywords that label the content). This is also in view, to some extent, of scenario 1, which requires that adaptation of items and modules should be supported, and scenario 3, which requires only recommendation of other modules. The content is based on an overall social goal and constraints model, with a hierarchical structure, directly reflecting, in this simplified version, a similarly social structured domain model and social item model underneath. In the
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Figure 3. MOT 2.0: User model 2.0
Figure 4. MOT 2.0: Adaptive reading
following implementation round, we plan to both extend the implementation to more fully reflect the flexibility allowed by these content-based layers, as well as apply other adaptive strategies, as the specification of the strategy will be external and exchangeable, according to the LAG model (Cristea & Verschoor, 2004). Figure 5 describes the social annotations for the actual lesson based on the user’s privileges for the selected group/course. These social activities include rating the content of the item, feedback, and tagging items with a set of keywords (such as defined by definition 3). They are captured and added to the user model in order to provide more adaptive features, and thus more flexibility in the adaptation process. In such a way, the recommended content is based not only on the
background and trace of the user, as in classical adaptive hypermedia, but also on social activities, e.g., on how popular the item is with other readers, or on who recommends it (trusted users versus unknown users). This also is used in scenario 1 for recommended content, as well as in scenario 2 on grouping the work for students. Figure 6 shows how the adaptive authoring works, by displaying other related recommended courses which can be used in creating the course. Scenario 5, with Helen putting together a course based on system recommendations, is directly reflected here. Whether or not something is presented or is based on the pedagogical adaptation strategy, which influences the presentation model (e.g., a Boolean value overlaid over an item that is to be shown is commuted to True). In
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Figure 5. MOT 2.0: Social and Web 2.0 annotation
Figure 6. MOT 2.0: Adaptive authoring
a future version, the authoring process can use different adaptive strategies as defined by the LAG model. Figure 7 is about merging the authoring and the delivering view and processes, as the users may still change the content of the course during or after the delivery, or they may annotate it during its creation. This explains why adaptive strategies can be applied not only for the delivery process, but also for the authoring process. The view shows both goal and constraints maps in viewable (‘view’) and editable (‘edit’) form, as well as the result of overlaying two different adaptation strategies over each of these maps (in
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this case, the editing adaptation strategy, for authors, and the viewing adaptation strategy, for the student role). Figure 8 shows the group-based authoring concept, where users can create groups and have different privileges for different groups (as in scenarios 2 and 4). This setup allows the definition of advanced levels of the relation between tutors and learners based on the latter’s user model. In future versions, MOT 2.0 will update the privileges automatically and semi-automatically, based on the user model. For example, when the learner is a beginner, they can have fewer privileges within a specific group, but during the learning
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Figure 7. MOT 2.0: Authoring versus delivering
Figure 8. MOT 2.0: Group based authoring
process, their knowledge might be increased, as well as their ‘good behaviour’ in the system (contributions, tagging, etc.). This can result in increased privileges. The screenshot only shows the functionality of joining/leaving the group, but the system can allow creating groups as well, and defining different types of privileges on different groups.
CASE STUDY EVALUATION OF E-LEARNING 2.0 The new social layer and MOT 2.0 as presented above have been evaluated with the help of (i) a group of eight course designers from Softwin, an e-learning company in Bucharest, Romania, in addition to (ii) a group of seven students studying ‘Dynamic Web-based systems’, a 4th year undergraduate module at the Department of Computer Science at the University of Warwick, UK. The two evaluations happened at different intervals in time
(January – March 2009 and October – December 2008 respectively) and took place in two different countries, Romania and the UK. The common features of the two evaluations are as follows. The course designers and the students were separately introduced to the system after they had had a few lectures on adaptive hypermedia, user modelling, the semantic web and the social web. The aim was to find out what added value the instantiation of the Social Layer in LAOS could bring to an authoring system. Thus they analysed MOT 1.0, the prior authoring-only environment for adaptive hypermedia engineering based on LAOS, versus MOT 2.0 which is based on the Social LAOS and includes the Social Layer. For evaluating authoring environments, the ideal is to use course designers, who are experts in e-content-based courses. This group of users was represented by the Softwin users. However, as MOT 2.0 blurs the borders between authoring and learning, it was necessary to get feedback from the other end of the spectrum
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as well, thus from students, as represented by the Warwick group of students. The evaluations reported here are based on the comparative analysis of two stages of experiments. The first stage involved two experiments, one carried out by the course designers and one by the students, separately, and each consisting of participants following five scenarios within the authoring system for adaptive hypermedia MOT 1.0 (Cristea & De Mooij, 2003). Similarly, the second stage involved two experiments, also carried out by the course designers and the students respectively, this time using MOT 2.0. The results of stage one were collected before the start of the second stage in both cases. In the second stage, the course designers and the students were asked to perform some standard authoring tasks as in MOT 1.0, as well as specific new tasks with the MOT 2.0 system, which highlighted the new social layer. These tasks involved also reusing the adaptive lectures that they had created previously, as well as creating material from scratch, and, of course, using the social tools (rating, tagging, feedback, etc.). After performing each experiment, participants in all experiments were asked to respond to specially neutralized questions (i.e., questions starting with ‘what do you think of …?’ instead of ‘Do you like..?’) as shown in Figures 9-12. The bulk of the questions were kept identical in the
two stages of the experiments, in order to compare the two systems, representing the initial LAOSframework and the extended social one. A few extra questions were added in the second stage in order to extract feedback on some specific issues related to the social aspects. However, here we concentrate only on the identical set of questions and its comparative results. Figures 9-12 also show the mean values of their responses on a scale of 1-5 (1: not at all useful, 5: very useful), as well as the variance of the results. The scale was kept numerical for further interval processing. These questions are: What do you think about…: Visualisation: Q1. browsing other authors’ domain maps / modules? Q2. browsing other authors’ lessons? Manual collaborative authoring: Q3. keyword-based access for other authors’ content? Q4. copying a domain concept / items across domain map(s) / modules? Q5. linking to concepts from someone else’s domain map(s)? Semi-automatic collaborative authoring: Q6. creating a lesson based on someone else’s domain map(s)?
Figure 9. The means before (MOT 1.0) and after (MOT 2.0) by the authors
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Q7. creating a lesson based on lessons created by other authors? New issues: Q8. adding collaborative authoring (i.e. tagging, rating, commenting on content)? Q9. adding authoring for collaboration (i.e. defining groups of authors, subscribing to other authors)? Note that questions 1-7 are general functionality questions. This functionality was present in both systems, but there were changes (we were trying to find out if they were improvements) in MOT 2.0. Questions 8 and 9 address collaboration functionality – MOT 2.0 had this functionality implemented, whereas MOT 1.0 did not have it. Thus, the question was kept generic, in order to refer to future extensions, in the case of MOT 1.0, and to actual implemented features, in MOT 2.0. Figure 9 shows the mean response of the authors (the Softwin designers) for the two systems, whilst Figure 10 shows the variance. Due to the small number of designers used in this study, we cannot speak directly about statistical significance. Instead, we can observe the general preferences. Overall, both systems scored above the expected average of 2.5 (in fact, they scored above 3). There is a slight preference for the functionality of the new system in all aspects (lesson brows-
ing, keyword access, copying, linking of concepts, lesson creation and reuse, and collaborative authoring). Moreover, the variance for most of these questions is less for the new system, showing a higher level of agreement between testers. The mean is very slightly higher for the first question for the first system. Looking at the qualitative comments, the only criticism is about the domain maps not being in alphabetical order. In the followup implementations, we have already introduced various ways of ordering the domain maps beside the default ones (which are based on the order of creation). More worrisome for the MOT 2.0 implementation is the fact that Q9 on authoring for collaboration was scored lower, suggesting that at least some of the expectations of the designers had not been fulfilled. One designer who had given it a score of 3 was complaining about the rights related to these groups and the exact procedure for forming them. In the version we had given them to test, groups were pre-formed and joining and leaving groups was open to all. An administrator role is necessary for allowing for group formation, since people could otherwise be inviting others into their own groups, as well as restricting unwanted persons from joining their groups. Such functionality is clearly desirable and has been taken into consideration in the follow-up developments.
Figure 10. The variance before (MOT 1.0) and after (MOT 2.0) by the authors
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Our other set of testers were the students (Figures 11 and 12) who, according to the overall philosophy presented here, are treated like authors but possess a subset of normal authors’ rights. In fact, the students who performed the evaluation had identical rights to the designers – they just used another version of the system at a different time. In the initial experiment all users (learners and course designers) had same rights (i.e., full rights), for the purpose of the evaluation only, for ease of experimentation. What can be seen from Figure 11, which shows the mean estimation of student satisfaction with the system, is that they also score both systems above 3. For Q9 on authoring for collaboration, the score is slightly lower for MOT 2.0 in comparison with MOT 1.0. However, as both scores
are very high (> 4.5), the students do not seem to share the designers’ concern about the group formation issue. The students also do not seem to share the concerns about browsing (Q1), however, they seem to slightly prefer copying a domain concept (or item) (Q4), as well as creating a lesson based on someone else’s domain maps (Q6) in MOT 1.0. For Q4, on copying domain concepts within one’s own domain, one student was worried about “what … you do if their item doesn’t fit exactly in to your module … Can I edit their work …?” He further realizes editing is possible, but then raises the issue of copyright. This is a fair point and is something that was not addressed, on purpose, in the first version of the system: group members had full rights over the content, in order to allow
Figure 11. The means before (MOT 1.0) and after (MOT 2.0) by the students
Figure 12. The variance before (MOT 1.0) and after (MOT 2.0) by the students
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them to test the various functionalities. Another issue is that the question referred in MOT 1.0 to an author’s own items, whilst in MOT 2.0, items belonged to a group. The next version will set various rights within a group, having members only allowed to edit, or to read (and this implies link without change), or copy and change the item. Also individual rights on items and modules will be enabled. Another student commented on the possible redundancy of information – “repetitive lessons”. This student however did not understand that the two concepts pointed to the same information item, in order to avoid exactly this type of redundancy. The issue here is however to differentiate between linking to content and copying and re-editing content. This will be taken into consideration in the next version of the system. For Q6 on creating a lesson based on someone else’s domain map(s), students who gave lesser marks were commenting on the speed being “a bit lazy”. However, another student commented that the speed was “quick”. Disregarding the discrepancies in opinions, the speed issue needs to be checked in the next version of the system.
DISCUSSION Moving personalization for lifelong learning into the realm of Web 2.0 raises interesting issues. Personalized environments have in the past been centred on a single user. In the new type of environment, users interact and collaborate, and this can lead to the adaptation to one user influencing the adaptation process for another. For instance, the same user, Mary (in scenario 1) could be recommended Jane, if she finishes her reading on the “Banking crisis” on a Monday, or another student, Mark, if she finishes on Wednesday. Similarly, a student studying about “Speculative bubbles and crashes” on a Tuesday could be recommended “Strategic complementarities in finance”, because they are both tagged “Financial crisis” (see Figure 4), but that student might be recommended
“Leverage” on Thursday, just because a colleague has added a tag to that item, or because the rating of the item has increased. Other issues that have been picked up by our experiments and evaluations are the issues regarding copyright and rights of use in general — when we refer to one user only, there is no problem in allowing that user to edit, change, move or link their own material. However, when there is a cooperative effort, the issues of ownership appear. Editing rights have to be carefully granted, in order not to allow destruction (removal, or permanent change and damage) of content created by others. Even in an ideal, cooperative world, there needs to be a clear differentiation between linking to an item created by others, and editing it. As items are reused in different contexts, changing an item for one context may render it unusable for another context. This is in contradiction to Web 2.0 techniques, such as in Wikipedia, where the content is permanently changing, stopping only when it represents a common denominator. In adaptive, personalized systems, the permanent change is useful, but the representation cannot be a common denominator. Personalization also means addressing the outliers, creating versions of content for various types of users, usage and context. Thus, in such a case, if changes are desired for a particular type of context, a user would have to make a copy of the original item, and edit this copy, instead of the original. Only in the case in which no changes are necessary could a user link to the original item. This however brings with itself the same issue as linking to Internet pages: the owner might change the content, thus changing the relevance to the source of the link, or even remove the concept, in which case empty links could appear. Another issue that is inherent in Web 2.0 applications, and which personalized, adaptive lifelong learning enhancing Web 2.0 applications share, is that of quality of content. In this chapter, we have shown how this issue can be solved by a progressive increase in contribution rights (be they tagging, rating, commenting, or even editing
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of new items), dependant on the overall quality of an individual’s (e.g., student’s) contribution. Thus, poor contributors would lose their contributing rights, and may at some point only be allowed to read content, whereas high quality contributors would be possibly achieving similar rights to authors, or even teachers. We have focused on personalization and adaptation as a key strategy to support lifelong learning, but we should not lose sight of the other technologies and pedagogic developments which will be important in the future. For example, the use of Learning Management Systems in institutions and beyond is pervasive, and effective delivery of educational tools typically takes place through such systems. However, the effective incorporation of educationally rich tools and frameworks (such as those presented in this chapter) within such systems is mainly unresolved (Rößling et al., 2008). The integration between “mainstream learning platforms” and “advanced- (often AIbased) solutions” is beyond the scope of this chapter, but is the main scope of research such as targeted by us and the partnership in the EU projects such as ALS and GRAPPLE. Web 2.0, as a representative of the information society, can provide more information and knowledge to a broader audience and the audience does not have to be in a classroom. This makes Web 2.0 an optimal candidate for lifelong learning, where we do not have to depend only on schools, libraries and experts to gain deeper understanding. However, e-learning is not a means to an end, and schools, libraries and experts are still very important. The two approaches will work together more in the future. For instance, in the context of Web 2.0, experts play an important role as part of the Web 2.0 e-learning system as well – they can help students, interact with them, etc. The added benefit is that of bypassing distance issues, on one hand, and allowing software systems to more easily (automatically, adaptively) pair needs with offers (between learners and experts), to perform scheduling functions, etc.
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Web 2.0 could be said to be a means of creating equal opportunities for different learners from different backgrounds and conditions. Also, specifically in the context of lifelong learning, it creates opportunities for people who have no time to participate in the formal learning settings. Finally, the specific features of lifelong learning — such as allowing people to communicate via various information channels — support a broader, information and people-richer access to such classic learning paradigms as the Socratic dialogue. From a broader social web perspective, the user model as built in MOT 2.0 can be extended towards a distributed user model, able to track users’ activities not only within one system (MOT 2.0), but also on the broader Social Web (e.g., the groups a user is member of on LinkedIn, the tags they used on del.icio.us, the (educational) videos they watched on YouTube, etc.). These types of mash-ups would harness the power of not only one social web system, but several. From a modelling point of view, the Social LAOS framework is perfectly compatible with such an extension — it would only mean that user model variables may be set by calls to external sites, instead of locally — which are implementation details and do not interfere with the framework.
RELATED RESEARCH Related research into supporting adaptation and personalization in collaborative learning environments is relatively limited. Adaptive collaborative tasks support is addressed, for instance, in WebDL (Boticario et al., 2000). The system allows annotations and tagging, and then selects information based on these tags for personal student needs. No specific rules that guide the collaboration process in an adaptive way are envisioned. Work by Tsovaltzi et al. (2008) promotes collaborative adaptation based on scripts of interactions of pairs of students. Prompts about contacting the peers and explaining, talking about consensus,
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etc., are used. Interestingly, the paper reports that, whilst the students might have perceived the adaptive comments as intrusions, the overall result (in terms of learning) was positive. Our approach is closer to this study, as the collaborative adaptation process aims at guiding students towards useful interactions with each other, and with their teachers (recommended learners), as well as guiding students towards useful recommended learning content based on their profiles. However, our research also blends not only the learning process and the collaboration process, but also the learning and authoring processes. Awerbuch et al. (2005) have taken an AI-driven approach, and describe processes of adaptive collaboration in peer-to-peer systems in terms of players (or agents) with shared or exclusive goals, thus cooperating or competing against each other. Their system is not directly aimed at learning, and its focus in on how to minimize the cost for an agent in a world of threats (e.g., from dishonest ‘players’). Whilst this work may be useful for collaborative and competitive systems in general, it is less applicable in the context of learning, where learners might try to ‘beat the system’, but would usually gain little from being dishonest to each other. Our aim is to define a new social personalized adaptation model that can currently be applied in extant learning management systems (LMS), in which learners and teachers can engage in a multi-role, personalized, adaptive learning environment to enhance the learning and authoring processes. In the context of lifelong learning, the APOSDLE (Advanced Process-Oriented SelfDirected Learning environment) project (Lindstaedt & Mayer, 2006) introduced new ways to support informal learning activities (work, learn, collaborate) for the workers in their working environments, which gives learners support, by providing the learners with support for selfdirected searching and learning within the working environment; experts support, by allowing social interaction between learners, and making
the results of this interaction available to other learners in their own learning environments; and worker support, in which the learning process happens within the working context, and the learners access the learning content without the need to change the working environment. Our approach is slightly similar as it supports recommendations techniques, such as recommended learning content based on the learner’s profile and recommended expert learners also based on the learner’s profile. The differences appear in the target — we target not just workers, but lifelong learners, as well as students in formal education. The Ensemble (Semantic Technologies for the Enhancement of Case Based Learning) project (Carmichael et al., 2009) is a relatively new project, which explores the benefits of the Semantic Web to support learners and teachers in a case-based learning approach. The goal of this work is to explore both the nature and role of the learning cases between learners and teachers, using the emerging semantic technologies. This work is very interesting, but is still in progress. Our approach does not rely heavily on semantic web techniques currently, although import from RDF, for instance, is possible. The result of the Ensemble project could be extended in the near future, based on the framework we are proposing. In the ALS project4, the adaptation to collaboration approach is similar to our approach; however our approach extends the collaboration by using the Social Web techniques (such as rating, tagging, etc.). Telme (Sumi & Nishida, 2001) is a communication tool that acts as a moderator between people with different levels of knowledge. The personalizationin Telme occurs by presenting information from a knowledge base customized according to the user’s profile. The system is effective when the user cannot question others directly by concluding the context of the conversation from predefined conceptual spaces. The personalization in the work of Pinheiro et al. (2008) is based on the mobile user’s profile,
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where the context-aware profiles permit mobile users to state their personal preferences for particular situations when using web-based systems. The preferences vary based on the current context. Then a filtering process is applied to the user’s current context and the user’s preferences for this context. The process selects the context-aware profiles that match the user’s current context, and then it filters the available informational content based on the selected profiles. Barkhuus and Dey (2003) argue that contextaware applications are preferred over personalized ones, where personalization in the sense of adaptability is used. Thus, the application allows the user to specify their settings for how the application should behave in a given situation. The learning environment described by Yang (2006) consists of three systems: (1) peer-to-peer content access and adaptation system; (2) personalized annotation management system; and (3) multimedia real-time group discussion system. It uses the ubiquitous learning paradigm, with features such as identifying the right collaborators, contents and services in the right place at the right time, based on a learner’s surrounding context such as where and when the learners are (time and space), what the learning resources and services available for the learners are, and who the learning collaborators are that match the learners’ needs (Yang, 2006). Our approach does not rely on the context of the learner, but it uses user profiles to provide recommended learning contents and recommended users. On the other hand, the context aware ubiquitous learning environment has neither recommended learning materials nor recommended collaborators. The system described by Perscha et al. (2004) provides context information in a presentationindependent format that can be used for mobile learning teams for synchronous and asynchronous communication means. In our approach, we are currently adding a communication facility which can be used by the recommended (expert) learners to help other learners by answering their questions.
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LearnWeb 2.0 (Marenzi et al., 2008) is a platform for sharing and discussing as well as creating knowledge resources, which allows for integration of social networks, such as Facebook and Flickr. The integrated infrastructure in LearnWeb 2.0 relies on external Web 2.0 applications. Therefore, one of the platform’s main challenges is determining which Web 2.0 tool should be used, as not all Web 2.0 applications are open source, and not all of them actually provide APIs to connect to LearnWeb 2.0. In MOT 2.0, we use the concepts of Web 2.0 (rating, tagging, feedback) applied within the system, and not by integrating external ones. Calvani et al. (2008) argue that each model of lifelong learning should take into account the following main factors. 1. The complexity and the variety of the types of knowledge involved. In MOT 2.0, we have covered the variety of the types of knowledge, as the privileges in each group are based on the knowledge level (i.e., the higher the knowledge level the more privileges the user has). 2. The dimension of self-directed learning. This dimension is also covered in MOT 2.0, as the system can track all actions of the user, and uses these actions in the recommendation process (i.e., recommend learning content rated 4/5 or higher, recommend users who are experts). 3. The dimension of informal learning. In MOT 2.0, both formal and informal learning are supported. In the case of informal learning, the Web 2.0 features in MOT 2.0 facilitate the learning process, during the work, the study, or any other activities. 4. Multiple dimensions of the technological solutions. This factor is a challenging one — currently MOT 2.0 is a Web 2.0 application, which can be integrated with any LMS, or any other web applications that support Java and Tomcat.
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StudyNet (Glover & Oliver, 2008) moves away from lecturers to harness the power of connections of the social networks, as it provides the learning materials in a social network environment. StudyNet allows connections not only between staff and students, but also with university alumni. However, due to licence restrictions, StudyNet is only available to enrolled students and academic staff at University of Hertfordshire. In contrast to this, MOT 2.0, can be used by anybody, as it is open to public with no restrictions. Moreover, StudyNet provides neither recommended learning content nor recommended experts. In other words, StudyNet does not support personalizationor adaptation. Bilge et al. (2009) investigated the possibility of attacking social networks to gain access to personal information. While the work proved that it is easy to forge user profiles and create a crosssite cloning profile, it did not provide a solution to this issue. The paper advises us to raise the awareness among users of social networks about privacy and security risks. In MOT 2.0, the privacy and security risks are minimised, as the platform does not support sharing of personal information. The work of Mislove et al. (2008) describes the detailed growth of data in Flickr, by crawling the Flickr sites to find out how the links are constructed, in order to predict how new links will be created. The study concludes that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close within the network. Such work shows the popularity of Web 2.0 applications, and the fact that it is timely to invest in researching the potential such applications bring, including for the important area of lifelong learning.
CONCLUSION The emergence of the Social Web is changing the way in which people communicate with each other, as well as the methods of creating
and sharing knowledge. In particular, learners in higher education institutions are using social tools in their everyday life to support their learning needs. Moreover, mature people engaged in lifelong learning are gradually beginning to use social networks and applications in their work and daily activities. Therefore, the Social Web has a potential to support both learners in higher education as well as in lifelong learners. However, research on personalizing and adapting social lifelong learning has not yet been extensively researched. In this chapter we aim to close this gap with this ongoing study on personalized adaptive social lifelong learning. We have extended the LAOS adaptive hypermedia framework by integrating a social layer, and by blending the authoring and delivering phases (i.e., removing the barrier between tutors, learners and authors, all of whom become authors with different sets of privileges). Our approach allows students to contribute to the authoring phase with different sets of privileges, and distinguishes between collaborative authoring (editing the content of other users, describing the content using tags, rating the content, commenting on the content, etc.), and authoring for collaboration (e.g., adding authors activities, such as defining groups of authors, subscribing to other authors, communicating with other authors). In the future, we expect many systems to take over such a blended approach to adaptive, personalized and customized education in social environments, both as a research topic, as well for commercial systems. Encouraged by the first set of experiments, we have already started adding more adaptation functionality into MOT 2.0 via recommended learning content and recommended experts based on the user profile. Another new feature is that the users within same group can have different sets of privileges. Moreover, a new communication tool has been added to the system in order to facilitate the collaboration among learners via discussion support. The chat tool recommends expert users
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who can help in answering questions and giving feedback. Moreover, the system can now track the reading activity of the learners, which can be used to update the user profile. Finally, the feature of goal visualization was introduced in order for students to be able to recognize easier (a) the fact that members of a group have a common goal, and (b) that they need to work together in order to achieve it. Other types of adaptation for recommending users, apart from their knowledge level, can be applied in the future, borrowed from social recommender systems, such as social proximity or presence in online learning environments, as has previously been covered elsewhere in the ALS project.
Boticario, J., Gaudioso, E., & Hernandez, F. (2000). Adaptive navigation support and adaptive collaboration support in WebDL. Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Trento, Italy. ISBN:3-540-67910-3
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Document Type Definition ‘Experts’ here is used to denote persons with a higher degree of knowledge than the current student on a given item. This definition can be changed depending on the roles existing in a system, and the overall goal of the system. The elements of these models were also integrated in LAOS, but in SLAOS we specify them more clearly. www.als-project.org
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Chapter 5
Mash-Up Personal Learning Environments Fridolin Wild Open University, UK Felix Mödritscher Vienna University of Economics and Business, Austria Steinn Sigurdarson Open University, The Netherlands
ABSTRACT In this chapter, the authors formulate a critique on the contemporary models and theories of learning environment design, while at the same time proposing a new approach that puts the learner centre stage. It will be argued that this approach is more apt to explain technology-enhanced learning and is more helpful in guiding (even end-user driven) engineering and maintenance of personalized learning environments. The authors call this new approach a mash-up personal learning environment (MUPPLE) and it is a vision (and prototype) of the future of personalized, networked, and collaborative learning.
INTRODUCTION Learning environments have probably been designed to facilitate human change ever since the ‘homo habilis’ started using more sophisticated stone tools at the beginning of the Pleistocene some two million years ago – most probably even earlier than that. Since then, however, increasingly larger parts of these learning environments have been transmogrified to be digital and the design of these environments has been subjected to growDOI: 10.4018/978-1-61520-983-5.ch005
ingly more conscious decisions. Today not only institutions for formal education such as schools and universities but also most work places and vocational training providers are equipped with at least some kind of tools that bring together people and content artifacts in learning activities to support them in constructing and processing information and knowledge. And, with a serious history of almost half a century, science and practice have been discussing models on how to bring personalization through digital means to these environments.
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Mash-Up Personal Learning Environments
Within this chapter, we are looking back at this history of personalized, adaptive learning to formulate a critique on the contemporary models and theories, while at the same time proposing a new approach that puts learners centre stage again. We will argue that this approach is more apt to explain adaptive personalization in technologyenhanced learning and is more helpful in guiding (even end-user driven) engineering and maintenance of personalized learning environments. The approach we propose has been developed within the scope of the European IST project ‘iCamp’ (Kieslinger et al., 2006) and is currently extended in the European IST project ‘ROLE’. The rest of this chapter is organized as follows. First, we characterize background assumptions and two important research movements that influenced our own proposal, namely personal learning environments and end-user development. Then we elaborate our critique on the contemporary models for personalized adaptive learning. Subsequently, we are going to show that learning environment design is the missing link, able to avoid the flaws of prior adaptation theories in technology-enhanced learning. Therefore, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up this chapter with possible extensions of this new model and (still) unresolved problems.
BACKGROUND The mash-up personal learning environment approach is strongly based on three assumptions on which the subsequent approach builds. First, we assume that learning to learn while at the same time learning content is a better approach than just (re-)constructing domain-specific knowledge. In other words, we believe that the acquisition
of social, self, and methodological competence (i.e. transcompetences, also known as rich professional competences) prior to or in addition to content competence is superior to only acquiring content competence (i.e. domain-specific skills, facts, rules, and the like). This is not only justified through the added value of transcompetences, but additionally by the decreasing half-life of domainspecific knowledge and through the challenges imposed by lifelong learning (see also Wild et al., 2009). The competence to adapt both flexibly and quickly to changing context becomes vital especially at the transition between education, training, and work – and in between different work places or job roles. Monitoring ones own competence portfolio, identifying knowledge gaps, and remediating shortcomings planfully with learning are key competences in our modern society. We deliberately say ‘constructing’ as in constructivist theory a ‘transfer’ of knowledge does not exist: knowledge can only be created from within the minds of the learners, though of course influenced on sensory experiences provided by their environment. Second and consequently, we presuppose that establishing a learning environment, not in the usual sense of a technology-based environment but a network of people, artefacts, and tools (consciously or unconsciously) involved in learning activities, is part of the learning outcomes, not an instructional condition. This is even more important in lifelong learning, where technology constantly innovates and where changes in location, career, or even profession can easily disrupt an existing environment and cause a need for learning and adapting to a new environment. Adaptation strategies go beyond navigational adaptation through content artefacts along a predefined path: for example, some learners may prefer to email an expert instead of reading an online paper; and managing a professional social network may hence become equally important the skills of using a digital library. Adaptation has to take place along individualized activities
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performed in these environments. Inherently, these learning environments are always networks: they encompass actors, artefacts, and tools in various locations with heterogeneous affiliations, purposes, styles, objectives, and the like. Network effects may make the network exponentially more valuable with its growing size. Third and finally, we consider emergence of behaviour as an unavoidable and natural phenomenon of complex networks. By emergent behaviour we mean that the observable dynamics show unanticipated activity, surprising in so far as the participating systems have not been instructed to do so specifically (they may even not have intended it). Designing for emergence is in our view more powerful than ‘programming’ by rules as the models involved are simpler while achieving the same effect. The solution proposed in this chapter – mashup personal learning environments – has been elaborated from two research strands: ‘personal learning environments’ combined with ‘end-user development’ (of which mash-ups are one possible realization form). In the beginning strongly motivated by the opposition to form a counterpart to learning management systems, personal learning environments (PLEs) today form a research strand in itself. It should be noted, that often in the readings on contemporary PLEs, the term ‘personal learning environment’ is related more narrowly to the piece(s) of software supporting a learning network (the ‘computer-based parts of a learning ecosystem’, as they are defined by van Harmelen (2008). Furthermore, van Harmelen assumes that different PLE approaches build upon the common vision that an empowered learner is capable of selfdirection for whom tightly- and loosely-coupled tools facilitate the process of defining outcomes, planning their achievement, conducting knowledge construction, and regulating plus assessing. The means and approaches chosen, however, vary greatly across those different projects.
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Whereas early work (e.g. Liber, 2000; Kearney et al., 2005) focuses mainly on the introduction of a user- and conversation-centred perspective in the form of a personal space used for developmental planning and aggregating navigational as well as conversational traces (in opposition to course-centred group spaces of learning management systems), the next phase is characterized by an emphasis on interoperability issues (Downes, 2005; Wilson et al., 2007) and a stronger emphasis of linking personal learning environments to social software: data interoperability, most notably RSS/ ATOM-based aggregation, and service integration of web-services such as the storage/retrieval services offered by the Flickr API. Today, PLE projects select from a heterogeneous set of implementation strategies (Wilson et al., 2007), ranging from coordinated use, for example, with the help of browser bookmarks to the involved web applications over simple connectors for data exchange and service interoperability to abstracted, generalized connectors that form the so-called conduits like they are supported, for example, by the social browser Flock or by the service-oriented personal learning environment Plex. State-of-the-art on personal learning environments has been elaborated by Liber & Johnson (2008) or by Wild et al. (2008), dealing with topics such as standards for mash-up functionality, widgets and widget containers, cross-application communication, data and metadata management, knowledge maturing processes, etc. The most recent technological development, also shared across a wider range of technologyenhanced learning projects such as ROLE, Palette, TENcompetence, LTfLL, and Luisa, can be characterized with the dedication of attention towards user interface integration with the help of widgets, portlets, and cross-widget communication. What all previous personal learning environment approaches, however, lack is an explicit support of end-user development, which shall be outlined below and which will result in our MUPPLE prototype.
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The paradigm shift that can be sensed in the recent emancipatory movement on the web turned the original read-write web envisioned by Berners-Lee (1989) into reality (again) – for a wider set of people. This programmable ‘web 2.0’ (O’Reilly, 2005) also has an impact on software development methodologies and usability of learning technology. Going beyond traditional software development methodologies and even beyond the ideas behind user-centred design, new approaches try to shift development tasks from experts and expert programmers to the end-user. According to Lieberman et al. (2006), end-user development deals with the idea that end-users design their environments for the intended usage, evolving systems from being ‘easy to use’ to being ‘easy to develop’ and shifting the focus of control from expert designers to end-users. In the scope of technology-enhanced learning, such an approach builds upon a flexible environment consisting of an open set of interoperable learning tools (‘building blocks’) and an open corpus of artefacts authored by the end-users, instead of utilizing a monolithic learning management system that delivers content from educational providers to their learners. This idea of end-user development on the web touches very much upon the idea of re-use: similar to Excel scripting, end-users can go out, find, and eventually even customize an existing script or complex formula written by another user – as soon as the formula is explicit (and thus accessible for exchange). End-user development on the web is like Excel scripting for web applications: most users have heard of it, a small share of power users is able to do it, but all users benefit from it. Additionally, it enables for a smooth transition from simple to power use. For deeper insights into interoperability standards facilitating personal learning environment mash-ups, see Palmer et al. (2009). These approaches ground in user-centred design (cf. Preece et al., 1998) and agile software project management and development methods (such as extreme programming).
Slightly different and more focused on the recombination of code is a relative new stream called opportunistic design (Hartmann et al., 2008; Ncube et al., 2008). This strand investigates different strategies for mash-ups (Gamble & Gamble, 2008). Furthermore, new creativity stimulating learning methodologies for learning how to programme are already available (Obrenovic et al., 2008). The biggest challenge for opportunistic design is the problem of turning monolithic legacy software into opportunistic assets (Gamble & Gamble, 2008). Looking at the market for technology, both movements open up new niches for software development, as a ‘long tail’ (Anderson, 2006) can also be identified in software development. There is a long tail of small-sized user groups with precise requirements, or at least a strong sense thereof that form a greater number of niche markets (the long tail of software development: Kraus, 2005). The challenge lies for sure in their activation and in the provision of easy-to-develop, flexible recombination of opportunistic assets by end-users.
ISSUES, CONTROVERSIES, PROBLEMS Classically, the field of personalized adaptive learning is based on instructional design theories and utilizes adaptive and intelligent technologies for personalization. Instructional design theories aim at offering explicit guidance to help people learn better and, consequently, they treat learning environments (in the sense of the tools alone!) as an instructional condition and separate from the desired learning outcomes (cf. Reigeluth, 1999). Even in more constructivist instructional theories, the learning environment is assumed to be created by an instructional designer (Mayer, 1999; Jonassen, 1999). In applied research, these design theories
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appear in two different flavours: with and with-out a strong artificial intelligence component. Theories incorporating a strong AI viewpoint are inherently ill-defined as they need to take into account all context variables that may influence the learning process of the learners. Invested in this approach, however, is a naive objectivist assumption that it is possible to create an omniscient artificial system that knows everything (or in a weaker form ‘everything important’) about the current context variables influencing a learner in his information processing and learning work. This is not possible. Learners are not sitting in a glass-box where a teacher can monitor which Wikipedia pages they are reading, who they are talking to in the hallways, and whether their childhood experiences influence them towards reading or watching television. Even if a learner could have lived his whole life in a glass-box, still it would not be possible to distinguish the relevant environmental influences from the irrelevant ones and the resulting representational model(s) would have the same complexity as the original learner. By itself it already would require an infinite amount of adaptation work, which might even grow exponentially with the number of people participating in a learning network. And still – like in Searle’s famous Chinese room – even if a system could number-crunch a problem of this complexity, it would never truly understand what the learner is thinking. Contemporary instructional design theories, however, have abandoned this goal of a strong artificial intelligence monitoring and guiding automatically a long time ago. Usually, they foresee a mixture of minor automatic system adaptations along a coarse-grain instructional design master plan engineered by a teacher or instructional designer. The so-called ‘learning paths’ are fine-tuned along learner characteristics and user profiles to conform to trails envisioned, not necessarily proven by teachers. There are two good reasons why these weak AI theories have to be rejected for personalization.
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First (and less important), there is no ‘perfect’ instructional designer: an environment can only be planned for the average learner, not the individual. Even good instructional designers had to gain their experience, had to make errors in the past to built up effective and efficient strategies. Moreover, in practice instructional designers are most often ‘only’ domain experts for a particular field of knowledge, no didactic experts. Second (and more important), planned adaptation takes experiences away from the learners: external planning keeps them from becoming competent, as it takes chances to self-organize away and personal discovery is prevented. Learners, however, are not only sense-makers instructed by teachers along a predefined path. Learners need to actively adapt their learning environment to their needs so that they can construct the competences necessary for successful learning. And facilitators can coach them along this way. The emergence of a learning environment, in the rich sense of interacting people-artifacts-tools, is one (if not ‘the’) important outcome of a learning process, not just a stage to perform a ‘learning play’. For these good reasons, we therefore consider the better-known instructional design theories to be flawed. Learners are not patients that need an aptitude treatment. They proactively have to (and of course already do) take account of their learning environment. Adaptation technologies can vary in their degree of control: how much end-users are involved in decisions about adaptation. Oppermann, Rashev, and Kinshuk (1997) therefore differentiate between adaptive and adaptable systems with a fluent segue from the one to the other. Systems are considered to be adaptable if the users initiate the adaptation (and vice versa). Similarly, Dolog identifies two perspectives through which adaptation can be seen: adaptations can be performed by humans to cope with changed requirements of the participating stakeholders. Alternatively, adaptation can be a dynamic system adaptation to
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changed parameters in the environment or context (Dolog, 2008). Three important streams of research can be identified as relevant for personalized adaptive learning: (1) technologies from adaptive (educational) hypermedia, (2) learning design technologies, and (3) adaptive hypermedia generators. On a finer level, adaptive and intelligent technologies can be distinguished into curriculum sequencing and problem-solving support technologies (Brusilovsky, 1999). Whereas sequencing deals with adapting the navigational path through pre-existing learning material, problem-solving support technologies deal with evaluating student-created content representations either summatively or – in interactive support technologies – formatively, even during the learning process itself, through the provision of feedback or by presentation of related examples. Furthermore, in the more generic adaptive hypermedia area, adaptive navigation support and adaptive presentation support can be distinguished (Brusilovsky, 1999). Both deal with adapting preexisting content: adaptive navigation deals with path and link adaptation (though in a more open setting – the web), while adaptive presentation is concerned with the presentation of a subset of the content in new arrangements to accommodate user’s needs. Additionally, a third class of approaches is mentioned by Brusilovsky (1999), which deals with student model matching: they try to make use of collaborative filtering aspects (either by identifying matching peers or by identifying differences to avoid problems). Brusilovsky & Henze (2007) identify the lack of reusability and interoperability as a major problem in personalized adaptive learning. When applying adaptation in the web, this results in the ‘open corpus problem’ which can (at least partially) be compensated by gaining more interoperability. For adaptation interoperability, however, standards are still missing (Brusilovsky & Henze, 2007; Kravcik, 2008).
Holden & Kay (1999) postulate that scrutability has to become a key characteristic in personalization strategies: evidence accreted (i.e. collected) from various sources is resolved (i.e. assessed) at request time, while providing control over the input as well as output streams and inspection capabilities for the processing mechanisms. Though this offers triggers for reflective activities, these are not part of the modelled user activities. They merely are performed outside the system, thereby neither supported nor hindered by the system. Although these adaptive (educational) hypermedia technologies all differ, they share one characteristic: they deal primarily with the navigation through content, i.e. the represented domain specific knowledge. Information processing and knowledge construction activities are not in the focus of these approaches. Consequently, they do not treat environments as learning outcomes and they cannot support learning environment design. Koper & Tattersell (2005) state that in their learning design (LD) introduction they will be using ‘learning design’ synonymously with ‘instructional design’, though there may be a slightly different accent in the meaning of both. Specht & Burgos (2007) elaborate on the adaptation possibilities in general and particularly within IMSLD. However, among the generic components of educational systems that can be adapted, they list only pacing, content, sequencing, and navigational aspects. Neither does the environment (not even in the sense of tools) appear in this list, nor is it a driving factor for information gathering, nor method of adaptation (Specht & Burgos, 2007). Towle & Halm (2005) discuss how adaptive strategies can be embedded with units of learning by filtering or reordering resources, changing methods, slotting learners into roles (and scaffolding role transitions), or by changing activities. Van Rosmalen & Boticario (2005) investigate how – besides design time – also run-time adaptation can be realized with LD, thereby interfacing LD with distributed multi-agent systems. They tweak LD to incorporate agents (by adding them as ‘staff’,
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i.e. a human actor being a tutor, teaching assistant, mentor, etc). Though, adaptation of the environment only takes place to a very limited degree: the aLFanet project does not foresee to help in managing a complex set of tools and services out of an even more complex, not determined portfolio at run time. Olivier & Tattersall (2005) explain the possibilities of integrating learning services in the environment section of LD. Besides the already mentioned restriction that these components both in practice and in the guidelines are only touched by instructional designers, the services are postulated to be known at design time: they are approved in the specification (LD 1.0 has four services!), and additionally they have to be instantiated through formal automated procedures. From the perspective of standardization, Olivier and Tattersall predict application profiles that enhance LD with the services provided by particular communities, though interoperability to other LD players then no longer will be given. Within the TENcompetence project, extensions have been proposed that allow for more bottom-up oriented authoring of the units of learning (Vogten et al., 2008): formalization, reproducibility, and reusability of learning designs can also be catalyzed through the use of a personal competence manager that facilitates the development of learning materials through learners themselves. Though in principle people, activities, artefacts, and services (not tools!) are components of LD, the standard does not offer support for communication and reflection on technology use on a higher granular level, nor facilitates environment building and maintenance. Moreover, LD is based on the assumption that the services that can be deployed in an environment have to be shared by all executing software players. Hereby, ‘services’ differ from ‘tools’, as tools relate additionally to the perceivable surface of a learning network. Both interface human activity with machine communication (i.e. digital thus manipulable information). However, tools also incorporate their user interfaces and their design influences the processes pursued with
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them, as has been shown for example by Pituch & Lee (2006). Additionally, agreement on the standardization of services can always only be a second step after innovating new services. We have to state that current LD (together with the available authoring tools and players) fails to support competence achievement in learning environment design and does not consider environment set-up and maintenance within the learning activities. A third block of research can be identified in the group of adaptive hypermedia generators (Ceri et al., 2005). Cristea, Smits, and De Bra (2007) report on LAG, a language used to express information on assembly, adaptation, and strategies plus procedures of intelligent adaptation applications. It was developed specifically for adaptive educational hypermedia. LAG follows the structure of hypertexts and expresses rulebased path adaptation (the ‘adaptation dynamics’) for automatically adapting course contents. WebML in combination with UML-Guide has been deployed to realize client-sided adaptation of e-learning web-applications (Ceri et al., 2005). WebML follows a generic hypertext model and contains the structural elements of site views, areas, pages, and content units. A site view is a hypertext consisting out of areas which again can integrate sub-areas and pages. Pages are the actual containers for information to be given to the user. They consist out of elementary content units that extract data with queries from data sources. By combining it with slightly extended UML state diagrams (UML-Guide), user navigation through a system can be modelled, and – through both – personalized applications can be generated (Ceri et al., 2005). Though not restricted in principle, the environmental aspects of a typical design process are recommended to be executed by a designer rather than a learner. The environment design itself, however and again, is restricted to content and path design. Although generator technologies could take account of more recent advancements in end-user development (the long tail of software development,
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cf. Lieberman et al., 2006), all of the approaches have been focused on the classical instructional design paradigm: learners are executing along minor adaptations what instructional designers (mostly teachers) have foreseen. Consequently, emergence does not play a significant role in these approaches. The prevailing paradigm is ‘rule’, not ‘environment’!
SOLUTION AND RECOMMENDATION Subsequently, we are going to sketch a new model for personalized adaptive learning which strongly focuses on learning environment design in the rich sense outlined above. We discuss representational aspects of this model by proposing a learning interaction scripting language with which environment design can be formalized. Furthermore, we demonstrate the feasibility and illustrate our approach with an example performed with our research prototype. Basically, a mash-up personal learning environment (MUPPLE) describes the idea of learners utilizing an open, heterogeneous set of tools to connect with each other, content, and tools into a learning network and to collaborate with other
actors on shared artefacts along different activities (Wild et al., 2008). Figure 1 visualizes a typical situation in which one learner is involved into two activities, a collaborative task for which four tools are utilized and an individual tutoring situation in which one tool is used. From a technological point of view, MUPPLE requires a flexible, web-based architecture, i.e. a framework for distributed systems as an enabler for end-user development of personal learning environments and networked collaboration. As sketched in Figure 2, the bottom layer primarily deals with data-interoperability, for instance, in the form of RSS feeds or Simple Query Interfaces (SQI). The middleware layer includes typical services between the different systems, e.g. in form of mediation, retrieval, or feed management services (Wild & Sigurdarson, 2008), while the presentation layer on top deals with the user interfaces of the involved learning tools and their web application mash-up (Mödritscher et al., 2008). We have realized a first prototype in the form of a web application which allows learners to manage their learning activities and the personal environments belonging to them. An online demonstrator is available under http://mupple.org. The
Figure 1. Exemplary mnash-up personal learning environments for two activities
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Figure 2. Layered architecture of a mash-up PLE
proposed approach for mash-up personal learning environments enables the design of learning environments and supports building and sustaining networked learning communities. As shown in
Figure 3 the prototype therefore displays a learning activity in the form of a page that forms the central user interface to the digitally accessible personal learning environment parts. At the top, the type (activity or activity pattern) and the title of the page are presented. To the left, all activities of a user are listed. Clicking on an activity folds out the list of its actions and loads it into the content area. Below there are various functions for creating new activities (blank or from a predefined pattern), deriving new patterns from the current activity, or starting a new action. For designing the activity, MUPPLE supports the learner through recommending action-object-tool triples. Below the surface, we have equipped MUPPLE with a domain-specific scripting language specifically developed for learning environment design – the so-called learner interaction scripting language (LISL) introduced in Mödritscher et al. (2008b). Within the tab ‘log’ (see Figure 3), the content area displays the materialization of the
Figure 3. MUPPLE page for an activity ‘Getting to Know Each Other’
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learner interactions: the LISL code of the current activity page and the output of the LISL commandline interpreter. The tab ‘code’ shows an inline editor for editing the LISL code (not shown in the figure). By switching the view to the tab ‘preview’ (open by default and highlighted with a red rectangle in Figure 3), learners see the web application mash-up space generated from the executed LISL code. The code itself is created either by logging traces of the interactions with the web-based widgets shown on the MUPPLE page or by manual scripting which is supposed to be done by technically experienced learners only. On executing a LISL script, the activity model is built up, and the web-based applications are launched within designated areas of screen. Now, learners can work with the web-based control widgets, whereby their interactions are materialized by appending new LISL statements to the page. On returning to a page, its last state is restored. Furthermore, any part of the learning activity is subject to adaptation by the user (facilitators and learners) to ensure scrutability. Technically, LISL is similar to AppleScript (http://www.apple.com/ applescript), but it does not limit the user to automate interactions with one application. Instead, it enables learners to reflect their learning processes and to collaborate in learning networks.
As stated in Mödritscher et al. (2008b), LISL has been developed according to relevant language design principles given by traditional software development, user-related principles for modelling, and young streams like end-user development. Due to the possibility that (experienced) learners might prefer scripting their environment instead of constructing it through the web-based widgets, the focus has been laid upon usability issues like learnability, efficiency, simplicity, readability, uniqueness, seamlessness, reversibility, and supportability. More technical issues, e.g. security, fast translation, or efficient object code, are considered to be of subordinate importance. With respect to these principles, we decided to engineer a close-to-natural language for learning environment design. Subsequently, our learner interaction scripting language (LISL) is described in detail. The lexical and syntactical structure of LISL is kept simple, as shown with a code example in Figure 4. The tokenizer splits each line of code into tokens separated by white spaces. Single or double-quoted constructs are considered to be one token. Moreover, tokens are case-sensitive, and identifiers are restricted to alphanumeric strings (beginning with a letter) and may be framed by quotation marks.
Figure 4. Example LISL script
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Syntactically, the following key constructs within this domain-specific language have been realized. According to the example LISL script in Figure 4, we differentiate between the following statement types: •
•
•
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‘Define’ statements (cf. lines 1 to 8) are used to initialize the mash-up personal learning environment, i.e., to declare actions, outcomes, and tools available in a mash-up page. The actor is considered to always be the current user. Each defined entity stands for a variable in the ‘programme’, whereby an action can have an optional URL and a tool always must have one. Outcomes (here ‘objects’) can refer to an abstract goal or a concrete artefact and can be used as placeholders in a URL (line 2), whereby a value must be assigned by user input if such a URL is executed later on (line 11). Defining one of these three constructs always start with the command ‘define’ followed by the type of entity (‘action’, ‘outcome’, ‘tool’) and a unique identifier. If a URL can be specified, the keyword ‘url’ followed by the URL itself is expected, for assigning a value to an outcome the keyword ‘value’ has to be used. Additional tokens are ignored, so that constructs like ‘with the url http://[...]’ or ‘with value peers’ are valid. ‘Connect’ statements (line 9) are necessary for interoperability reasons, thus tool interactions can be made up by the user. Syntactically, such a statement allows combining two tools with each other, so that data is sent from one to the other. What this exactly means and how we are realizing these tool interactions, will be clarified when explaining the semantics of this statement. ‘Action’ statements (lines 10 to 12) comprise the actions to be performed by a learner and consist of three parts: the ac-
•
tion, the outcome, and the tool. Generally, the first token, plainly or quoted, stands for the (user-defined) action, while the second one represents the outcome. The tool has to be specified right after the keyword ‘using’, other tokens are ignored. If one of the elements of such a statement can not be resolved, an error with an explanation is displayed. ‘Learner interaction’ statements (line 13) materialize the user interactions with the learning environment, i.e. they describe if the learner navigates between two different learning tools or she rearranges the application on her screen.
The semantics expressed with LISL follow a simplified learning activity model borrowed from activity theory. Activity theory states that an activity is shaped by its surroundings (Leont’ev, 1947; 1981; Engeström, 1987). For example, tools do have certain affordances: a door knob lends itself to opening. On the contrary, activities do also shape their surroundings: they can result in the construction of a tool. For our application, a learning activity has been modelled to consist of a set of (learner) actions bound to a particular outcome and involving a particular tool (see Figure 5). These actions specify typical learner interactions envisioned for the activity. They are defined directly by the learners. Templates (patterns) provide scaffolds that are typically modified by learners when instantiated into practice and during the carrying out of the specified activity. Action statements in LISL can have a specific URL going with them that, then, is typically used to perform a functional operation within a learning tool, e.g., creating a new Wiki page. An action without a URL launches the first tool named in the ‘using’ section of the statement. If a URL contains a placeholder an adequate outcome with a value is required. If this outcome is not specified (while executing earlier statements
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Figure 5. Semantic model of learning activities and a concrete example
of the script), the system initiates a user dialogue to specify its contents. In analogy to variable declarations, an outcome can optionally be defined with a specific value. An initialized outcome means that it has a predefined value. An outcome without a value, however, is indicating that it is either an abstract goal or artefact, or the ‘value’ is assigned dynamically if it is used somewhere, e.g., if a URL placeholder has to be resolved. In the example described in the last three figures, for instance, the very first action requests the learner to create a VideoWiki recording that is supposed to be the outcome ‘self-description’. In addition, the bookmarking-action is intended to trigger data exchange between the videoWiki and scuttle – a tool combination that requires a certain degree of interoperability of the learning tools. As a precondition for these connect statements, we built upon an API for distributed feed networks (Wild & Sigurdarson, 2008). If this kind of interoperability is not supported, an error is given. Technologically, this prototype is realized as part of the OpenACS system, an open source development framework for building scalable, community-oriented web applications (see http://
openacs.org). The LISL interpreter is written in the programming language Tcl, precisely its object-oriented extension named XoTcl (cf. http://media.wu-wien.ac.at/whatIsXOTcl.html). It builds upon the free Wiki generator XoWiki (see http://openacs.org/xowiki) and is part of the MUPPLE package. The package, including the source files, can be retrieved from the iCamp code repository at Sourceforge (http://sourceforge.net/ projects/icamp). Figure 6 visualizes how the MUPPLE prototype works beneath the surface. While interacting with the user interface, the learner interactions are materialized in the form of LISL commands which are appended to the script of the current activity. This new LISL code is persisted into a Wiki page, using the persistence and version control functionality of the XoWiki component. Furthermore, each new line of code is parsed and interpreted by the LISL interpreter, which creates and updates the activity structure and the web application mash-up. Additionally, the MUPPLE prototype includes also learner support functionality, like recommendation services and manual practice sharing facilities, as will be explained in the upcoming subsection. Interpreting the whole LISL script, the interpreter calculates
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the latest version of the model and the last state of the mash-up space, before this information is transferred via a JS-based API to the browser. Updates are made incrementally, so that learners (facilitators and peers) can execute single lines of code at any time by either typing them in manually or by using the web-based widgets. Overall, we have realized the MUPPLE approach according to the 14 design guidelines for end-user development given by Repenning & Ioannidou (2006). As a result, learners are supported in designing their learning environment in the form of tool mash-ups for each activity they are involved at. The syntactic guidelines ensure that errors are prohibited whenever possible and explained to the end-user if necessary. The semantic guidelines justify the simplicity of our activity model as well as web-based control facilities for designing and using the tool mash-up in a comfortable way. Finally, the pragmatical guidelines deal with the incremental development of the learning environment, with reusing and testing these prototypes designed by learners, with the possibility to modify the script manually, and with scaffolding designs and community-based activities.
Figure 6. Architecture of MUPPLE
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Concerning privacy, a MUPPLE page is considered to be accessible only by the owner (and system-wide administrators). Parts of the pages are subject to semantic analysis, e.g. for actionoutcome-tool recommendations), or can be shared by the learners with other users, e.g. through the generation of activity patterns; both approaches will be explained in the following.
Example: Collaborative Writing The educational approach of MUPPLE aims at supporting lifelong learners and stresses methods to build up and sustain a learning network of actors, artefacts, and activities, which increase the motivation to learn and aims at developing more complex competencies (cf. Koper & Olivier, 2004). Similar to web 2.0-driven platforms like Facebook, learners require facilities to get involved into collaborative activities and to regulate collaboration and social interactions with peers. To illustrate the MUPPLE approach and the utilization of the design language we introduced in the previous section, the following part describes an in-depth scenario and an outline of how the personal learning environment constructed in this
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scenario can be reflected with a LISL script. For this example we will assume that some of our authors are using MUPPLE as their PLE platform. Reaching beyond the before-mentioned activity ‘Getting to Know Each Other’, we focus on a typical activity in the field of higher education: collaborative writing. In this concrete lifelong learning scenario, a knowledge worker wants to collaborate with experts from other organizational contexts in writing the concept for a new project. With a few clicks, she creates a personal learning environment for this activity that consists out of seven steps encompassing actions on identifying and sharing background literature, subsequently summarizing the state of the art with the help of these documents, distributing section assignments to the collaborators, and finally elaborating the text of the assigned parts and reviewing plus proof reading it for quality assurance. She benefits from earlier users of the system who already configured several of the tools she is going to use, most notably Scuttle, a social
bookmarking tool: MUPPLE already knows how particular actions can be executed in specific tools and which specific URLs address these actions. Without really noticing, as she is using the graphical user interface, MUPPLE in the background added a couple of additional lines to her new activity script (see Figure 7). Most of the lines 1-5 and 6-13 have been added by the system automatically according to her intended use of the actions that are reflected in lines 14-21 of the depicted script. Actions and tools have to be defined, while outcomes will be defined implicitly on starting an action if not given. The definition of an outcome is only necessary if its value is used somewhere (not shown in this example). While working on the activity, user interface interactions are appended to the script (beginning with line 22) in order to materialize what the learner did so far. All the information of the script is used to rebuild the semantic model behind and the web application mash-up of the corresponding MUPPLE page.
Figure 7. LISL script for activity ‘Collaborative Paper Writing’
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After setting up the LISL script (either by scripting or by using the graphical user interface to compile it with a few clicks), the knowledge worker can start working with her personal learning environment. For the ‘Collaborative Paper Writing’ activity the LISL interpreter creates the web application mash-up shown in Figure 8. With this MUPPLE page the knowledge worker can arrange the tools according to her preferences, minimize the ones that are not relevant at the time, and maximize those that require more space or are of central importance for completing an action. If the knowledge worker considers one action to be finished, she can declare this state by simply clicking on the checkbox next to the action in the section ‘activity space’. If all of the actions are declared finished the whole activity is marked as completed.
So far, the scenario primarily showed how one learner can design her personal learning environment for a certain activity and work with it. In order to collaborate with others, the learner has two possibilities (see Figure 9): on the one hand, she can simply invite other users to participate in this activity by using a single learning tool, e.g., the Wiki. Thus, the collaborators would be involved into the paper writing activity by editing the Wiki pages (paper, review) with a standalone application, but they could not use the other features of the activity. Alternatively, other users might also want to create a mash-up personal learning environment for elaborating the paper collaboratively and interface their environments with each other. Therefore, they can build up their own activity and receive the most important information. As this typically involves a lot of articulation work to
Figure 8. MUPPLE page for activity ‘Collaborative Paper Writing’
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Figure 9. Conceptual diagram of an action flow within a collaborative activity
exchange on which tool to use for what, MUPPLE also allows for creating an activity pattern that contains the relevant actions (e.g. browse literature, elaborate paper, review paper, and so forth). This pattern can then be shared with the peers to instantiate an activity automatically without requiring them to design their own collaborative actions, set up all relevant URLs to shared artefacts, etc. The conceptual diagram of MUPPLE applied in a collaborative activity (see Figure 9) also indicates that each actor has a certain role within the learning flow. These roles are characterized by the necessary actions (or even within the flow of these actions) and can be determined for our example by the main author (the knowledge worker), coauthors, and reviewers. While the main author is responsible for setting up the activity and taking care of other management issues, a co-author might contribute a single chapter and a reviewer might improve the paper in form and content or only give qualitative feedback. Particularly for attracting new users, the success of MUPPLE highly depends on immediately recognizable benefits for learners. To enable this sought transparency, we foster good practice sharing through activity patterns which can be
provided by both facilitators and learners: The flexible and tested solution of a problem is turned into an abstraction and put into the form of a design pattern made available to the community. Similarly to the idea of scripting collaborative activities (Dillenbourg & Jermann, 2007), we use LISL to materialize these activity patterns. As learning activities are encoded in the form of LISL scripts, they can be exported into activity patterns and shared with other learners; vice versa, peers can use available good practice patterns and instantiate their own activities from them. Referring to our scenario, an experienced writer might prepare patterns for the different roles within the collaborative activity. Therefore, the knowledge worker creates a pattern for initiating and managing the collaborative writing (her own role in this learning flow). Then, she makes up one pattern with typical actions relevant for co-authors and one for reviewing and quality assurance. Figure 10 gives an overview of the possible action-object-tool triples for these three patterns. The main author has to complete several administrative tasks, like assigning the chapters to co-authors, requesting the (internal) reviews, and submitting the paper. Furthermore, she has the same duties like the co-authors, i.e., finding
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and collecting relevant literature, summarizing related work, and elaborating the paper. On the other hand, reviewers only need to check the collected literature and comment on the state-ofthe-art section and the paper. In our example, they use the commenting features of a Wiki in order to give feedback on the paper. Activity patterns can already include specific URLs to the paper or the state-of-the-art section, if a generic action should be executed within a tool (e.g. create a new Wiki page) or a learner intends to share concrete artefacts. Patterns, however, can also consist of URLs with placeholders in order to allow for URL-based instantiation linking to personal artefacts. For instance, the URL to the paper (‘http://teldev.wu-wien.ac.at/ xowiki/paper’) might be changed to ‘http://teldev. wu-wien.ac.at/xowiki/%%path_to_paper%%’ for hiding details of personal learning experiences on sharing them. As typical for Wiki systems, XoWiki would request the user to create any new page when calling its URL for the first time(s). Utilizing the activity patterns is easy. A user simply can derive an activity by switching to the tab ‘activity patterns’ in the navigational area (on the left-hand side right below the user’s ‘activity space’). Here, the patterns are ranked according to the usage frequency (left-hand side of Figure 11); the most frequently used are listed on the start page of MUPPLE (right-hand side of Figure 11).
From there, they can be instantiated with a single click. In our scenario, the co-authors and reviewers who have been invited by the main author can derive an activity from the pre-defined patterns (if they do prefer the mash-up personal learning environment to using the individual tools directly). Next to this explicit way of sharing learning experiences with other users, MUPPLE provides facilities for implicit good practice sharing which is based on automated analysis of MUPPLE pages and recommendation services, which will be explained in the following. Concluding this first part about good practice sharing, it has to be noted that users control what they really share with others and, on the other hand, that they also have full control over what they reuse from others. In any case, good practice sharing is an important concept for initializing meaningful learning scenarios and, therefore, for attracting new users to MUPPLE. Besides collaboration in learning networks and learner-driven good practice sharing, the bottomup approach of MUPPLE also supports personalized learning beyond traditional adaptation concepts. Basically, a mash-up personal learning environment serves as the interface to a network of actors, artefacts, and activities. Learner-controlled and system-driven personalization of learning takes place not only in a specific system and according to a pre-defined, accurate user model but
Figure 10. Role-based patterns for the activity ‘Collaborative Paper Writing’
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Figure 11. Instantiation of a MUPPLE page from an activity pattern
primarily within a complex socio-technical system of actors and tools in which traditional models (learner model, domain model, and adaptation model) are distributed. So far, MUPPLE supports the following different kinds of personalization. First, MUPPLE pages can be simply individualized by the corresponding user, as the owner of such a page has full control over it. Learners can modify every aspect of their MUPPLE activities and, among others, exchange tools, rename actions or remove outcomes. Second, personalization can be achieved through the activity patterns and the placeholder mechanism mentioned above. By substituting parts of a URL, it is possible to anonymize confident or private parts of each activity and, at the same time, make it instantiable by other users. On starting such a URL within the web application mash-up,
MUPPLE asks the user to enter a value for this placeholder (see Figure 12). Further, the value entered to this dialog is materialized through a LISL command within the page. Consequently, this value is set as the default for the placeholder and used every time the page is opened again, until the corresponding LISL code is removed or the value of the outcome is overwritten with a new one. However, this mechanism allows for the adaptation of activity pages to accommodate the requirements of single users or groups of users. Third, MUPPLE also supports the beforementioned analysis of existing learning scripts in order to personalize different aspects of learning by recommending action-outcome-tool triples to inexperienced learners. This also strongly supports learnability and efficiency when using MUPPLE in practice. Therefore, a scheduled mechanism
Figure 12. Resolving a placeholder on URL execution
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analyses the MUPPLE pages automatically and extracts typical action-outcome-tool triples into an own database. This information is used for inexperienced users if they want to start a new action using the corresponding facilities in the navigation area (below the widgets for creating new activities). As shown in Figure 13, MUPPLE suggests a list of tools that were used by other learners within their activities. So far, the list of recommended tools for the statement ‘publish call-for-paper’ is unordered, but additional information (usage in activities derived from the same pattern, closeness to the action and/or the outcome, etc.) could be used to rank and filter these recommendations. Overall, these aspects of personalization in MUPPLE outline the differences and similarities to traditional streams like adaptive educational hypermedia or intelligent tutoring systems. On the one hand, MUPPLE is based upon a simple pedagogical model of learning activities that consist of a set of action-outcome-tool triples. This semantic model is easy to understand and a solid base for planning and realizing further adaptation strategies. On the other hand, adaptation mechanisms are not realized according to designer-driven, top-down, ex-ante design. They are either based on services, like the action-object-tool recommendations, or come from propagation effects within the learning network, e.g., when pages are derived from patterns or during col-
laboration on shared artefacts. In the end, the personalization effects on the learning environment are reflected through the visible surface of the mash-up PLE.
FUTURE RESEARCH DIRECTIONS Despite the possibilities of our MUPPLE approach, a few disadvantages have to be outlined here. Primarily, these problems concern technological issues. First of all, it could be even nicer to have a high degree of interoperability between web applications, which now is not always the case. This specifically relates to single-sign-on procedures and communication channels to transfer both data and events from one application to the other. For example, a the wiki and the webmail client require authentication, so learners right now have to login separately in each application (not that they were not used to it). The approach would benefit from authentication mechanism such as OpenID (http://openid.net), to avoid these repetitive logins in each participating application. Regarding communication channels, we have proposed a specification how to realize distributed feed networks with buffered-push capabilities (cf. Wild & Sigurdarson, 2008). We intend to further investigate these means (beyond the ‘connect’ statements available today). We can think of other
Figure 13. Recommendation of tools within an activity
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approaches, though, and we do not have a solution for the efficient communication of events. Secondly, the utilization of iframes causes problems in cross-domain scripting (cf. Jackson & Wang, 2007) and the look & feel as well as stability of the resulting web pages. Redirection services, for example, can easily overload the surrounding MUPPLE runtime container. Widgetbased approaches such as proposed by Wilson et al. (2008) or Sire & Vagner (2008) may a possible solution for this.
CONCLUSION Hannafin et al. (1999) have stressed the fact that with the expected growth of both information and technology, new models for instructional design have to be sought: an information-age paradigm of instruction. Given our analysis of today’s instructional design theories and adaptation technologies, we go even further: learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account; instruction itself as the predominant paradigm has to step down. Classical instructional design theories assume that the environment should more or less automatically adapt to the user. But it should be the other way around: the user should easily adapt the environment to her needs. It is not about learning design it is all about learning environment design. Managing distributed cognition (Poirier & Chicoisne, 2006) through manipulating the learning environment is a key competence for successful learning in the web 2.0. Within this contribution we have proposed our alternative: Mash-up personal learning environments (MUPPLE) including our learner interaction scripting language (LISL) as a design language model for creating, managing, maintaining, and learning about learning environment design. It is complemented by a proof of concept, the MUPPLE
platform prototype. In our new approach, we particularly tried to avoid the problematic aspects of expert-driven, content-model based, instructional adaptation strategies. Learning is designed from the perspective of the learners by analysing their (digital) interactions along a simple activity model. Personalization of the learning process takes place through customization of the learning environment, network effects on collaborating with peers, and recommendations and support given by MUPPLE. It may comprise a new generation of personalized learning environments – the future will show.
ACKNOWLEDGMENT This research was supported by the European Commission within the ROLE project (Grant agreement no. 231396). The ICAMP project has been funded by the European Commission under the ICT programme of the 6th Framework Programme (Contract number: 027168).
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Chapter 6
Technological Evaluation and Optimization of E-Learning Systems Components Eugenijus Kurilovas Institute of Mathematics and Informatics, Lithuania Valentina Dagiene Institute of Mathematics and Informatics, Lithuania
ABSTRACT The main research objective of the chapter is to provide an analysis of the technological quality evaluation models and make a proposal for a method suitable for the multiple criteria evaluation (decision making) and optimization of the components of e-learning systems (i.e. learning software), including Learning Objects, Learning Object Repositories, and Virtual Learning Environments. Both the learning software ‘internal quality’ and ‘quality in use’ technological evaluation criteria are analyzed in the chapter and are incorporated into comprehensive quality evaluation models. The learning software quality evaluation criteria are further investigated in terms of their optimal parameters, and an additive utility function based on experts’ judgements, including multicriteria evaluation, numerical ratings, and weights, is applied to optimize the learning software according to particular learners’ needs. DOI: 10.4018/978-1-61520-983-5.ch006
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Technological Evaluation and Optimization of E-Learning Systems Components
1. INTRODUCTION: THE PROBLEM OF MULTIPLE CRITERIA EVALUATION AND OPTIMIZATION OF LEARNING SOFTWARE The problem of evaluation and optimization of the technological quality of e-learning systems components, i.e. Learning Objects (LOs), Learning Object Repositories (LORs) and Virtual Learning Environments (VLEs), is high on the agenda of the European research and education system. Evaluation can be characterized as the process by which people make judgements about value and worth. However, in the context of learning technologies, this judgement process is complex and often controversial. Although the notion of evaluation is rooted in a relatively simple concept, the process of judging the value of learning technology is complex and challenging (Oliver, 2000). Different scientific methods are used for evaluating the quality of e-learning systems components (i.e. learning software). The chapter is aimed to consider the problems of expert evaluation of the technological quality of LOs, LORs and VLEs. The basic notions, principles and methods applied in the Chapter are as follows: LO is referred to any digital resource that can be reused to support learning (Wiley, 2000). LORs are considered here as properly constituted systems (i.e. organized LOs collections) consisting of LOs, their metadata and tools / services to manage them (Kurilovas, 2007). Metadata is referred to structured data about data (Duval et al., 2002). VLEs are considered as specific information systems which provide a possibility to create and use different learning scenarios and methods. Quality evaluation is defined as “the systematic examination of the extent to which an entity (part, product, service or organization) is capable of meeting specified requirements” (ISO/IEC 14598-1:1999). Multiple criteria evaluation method is referred here as the experts’ additive utility function (e.g. Equation 3 in section “Experts’ Additive Utility
Function”), including the learning software evaluation criteria, their ratings (values) and weights. Expert evaluation is referred to a multiple criteria evaluation of the learning software that is aimed at the selection of the best alternative based on score-ranking results. According to Dzemyda & Saltenis (1994), if a set of decision alternatives is assumed to be predefined, fixed and finite, then the decision problem can be formulated as a task of finding the optimal alternative or ranking the various alternatives. In practice, usually experts (decision makers) have to deal with the problem of making the optimal decision in the multiple criteria situation where the objectives are often conflicting. In this case, according to Dzemyda and Saltenis (1994), “an optimal decision is the one that maximizes the decision maker’s utility”. The authors of the Chapter apply here one the software engineering principles which claims that one should evaluate the software using two different groups/types of evaluation criteria: ‘internal quality’ and ‘quality in use’. According to Gasperovic and Caplinskas (2006), ‘internal quality’ is a descriptive characteristic that describes the quality of software irrespective of any particular context of use, and ‘quality in use’ is an evaluative characteristic of software obtained by making a judgment based on criteria that determine the worthiness of software for a particular project or user/group. According to Gasperovic and Caplinskas (2006), it is impossible to evaluate the quality in use without knowing the characteristics of internal quality. The rest of the chapter is organized as follows. The next section presents the literature review and a short analysis of the existing technological evaluation models (i.e., sets of evaluation criteria) and methods for evaluation of LOs, LORs and VLEs. Then, multiple criteria evaluation and optimization of learning software for the particular learner needs are described. The fourth section offers further research trends whist conclusions are provided in the fifth section.
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2. LITERATURE REVIEW: EXISTING WELL-KNOWN TECHNOLOGICAL EVALUATION MODELS AND METHODS FOR LEARNING SOFTWARE First of all, let us review and briefly analyze the literature on existing well known evaluation models/tools (i.e. sets of evaluation criteria) and evaluation methods of LOs, LORs and VLEs. Although the main attention is paid to the sets of evaluation criteria, several evaluation methods concerning the application of ratings (values) and weights of the evaluation criteria are also provided.
2.1. Existing Technical Evaluation Tools for Learning Objects 2.1.1. The LORI Quality Criteria and Values The need to evaluate LOs requires the development of criteria for judging them. Vargo, Nesbit, Belfer and Archambault (2003) developed the Learning Object Review Instrument or LORI to evaluate LOs. The LORI approach uses the following ten criteria when examining LOs: 1. 2. 3. 4. 5. 6. 7. 8. 9.
Presentation: Aesthetics. Presentation: Design for learning. Accuracy of the content. Support for learning goals. Motivation. Interaction: Usability. Interaction: Feedback and adaptation. Reusability (technical criterion). Metadata and interoperability compliance (technical criterion). 10. Accessibility (technical criterion). The criteria were drawn from the review of pertinent literature on instructional design, computer science, multimedia development and educational psychology. Each measure was weighted equally
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and was rated on a four point scale from “weak” to “moderate” to “strong” to “perfect”. The LORI process involved both individual and group rating of LOs (Vargo et al., 2003).
2.1.2. Paulsson and Naeve’s Quality Criteria of Learning Objects Six areas for establishing LO technological quality criteria are proposed by Paulsson and Naeve (2006): 1. 2. 3. 4. 5. 6.
A narrow definition. A mapping taxonomy. More extensive standards. Best practice for use of existing standards. Architecture models. The separation of pedagogy from the supporting technology of LOs.
According to Paulsson and Naeve (2006), most LO implementations do not nearly meet this vision. For those reasons it is essential to establish common criteria of quality for LOs. Technical quality criteria are specific characteristics and properties that LOs must (or, in some cases, ought to) adhere to, including the best practice, guidelines and standard specifications, in order to be regarded as LOs. Paulsson and Naeve (2006) have focused on the technological quality criteria for LOs. Other quality criteria, such as the pedagogical quality, usability or functional quality were beyond the scope of their study. Such aspects of quality are addressed by Van Assche and Vuorikari (2006), where the authors suggest a quality framework for the whole life cycle of LOs. The evaluation conducted by Paulsson and Naeve (2006) focuses on: •
•
Architecture – in terms of separation of data, logics, presentation, and implementation of interaction interfaces. Pedagogical contextualization.
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•
The use of standards and the extent to which they are decomposable or composable.
According to Paulsson and Naeve (2006), “many of those issues are directly or indirectly related to the lack of explicit definitions and clear architectural models, together with technical (as well as other) quality criteria that are directly related to technical architecture. Many of the pedagogical dependencies and shortcomings seem to be caused by technical bindings of content to presentation and application logics as well as built in instructional design elements”. The study of Paulsson and Naeve (2006) has shown that there is a huge discrepancy between different definitions of the LO concept. This makes it difficult (if not impossible) to develop LOs bearing the qualities that LOs are often ascribed to in terms of reusability, interoperability, and context independence. Paulsson and Naeve (2006) suggest the technical and pedagogical definitions of LOs to be separated – within a common definition of LOs. To address the identified problems Paulsson and Naeve (2006) offer six areas for action in order to establish technological quality criteria for LOs: •
•
•
•
There is a need for a common (more narrow) definition of what is, and what is not a LO. In connection to narrowing down the definition, there is a need for a taxonomy that is reflected in the definition where granularities as well as special properties are regarded. Standards used for LOs should be extended to go beyond descriptive information, such as metadata, sequencing, and packaging to also embrace standards for interfaces, ‘machine readable’ descriptions of technical properties and interaction interfaces. There is a need to establish standards and recommendations that address the internal use of data formats and data structure. General technology standards of this kind
•
•
exist, but seem to be rarely used in the LO community. It should be prescribed that the architecture of LOs be layered as a part of the best practice, in order to separate data, presentation and application logics. This would enhance the level of decomposability and context independence. Layering (or multi-tier architectures) is frequently used in many other areas of application/system development for the very same reasons. Pedagogy should preferably be kept outside the LO in order to facilitate pedagogical context independence. It is suggested the pedagogical model to be added as LOs are assembled to form learning modules. Using such a methodology, it becomes possible to perform pedagogical contextualization at a later stage in the authoring process, and enhance reusability of different components as well as mutual pedagogical context independence of components.
According to Paulsson & Naeve (2006), in some cases there might be a need to add such ‘instructional properties’ inside LOs, but, in such cases, this should be handled in a separate layer, using standard specifications for that purpose, and not by hard coded implementations.
2.1.3. The MELT Quality Criteria of Learning Objects MELT (2008) set of the LOs quality criteria has been divided into five categories: 1. 2. 3. 4. 5.
Pedagogical. Usability. Reusability. Accessibility. Production.
The list is by no means prescriptive and not all of the criteria can always be applied to all
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LOs. For example, some LOs may score high in terms of reusability, because they include open source code that facilitates adaptation to learning scenarios other than the one originally intended. However, the same LOs might actually score poorly in terms of interactivity. The set of quality criteria, therefore, needs to be seen more as a minimum framework that should be used in a flexible way. However, it is also important to appreciate that some very high-quality LOs may meet the specific needs of the national curriculum of a particular country, but may not always have a chance to be used as effectively (or, maybe, at all) by schools in other countries. For example, a text-heavy lesson plan in a minority European language may work splendidly in a national context, but may simply be unusable by teachers in other countries. With this in mind MELT has begun to develop quality criteria that are defined in terms of the extent to which the learning content has the potential to ‘travel well’; i.e. the extent to which LOs/assets can be easily used across the national borders and in different curricula. An initial assumption in MELT was that content is more likely to ‘travel well’ if it is: • •
Modular: the parts of a content item are quite functional on their own. Adaptable: the LO can be modified, for instance from a configuration file, from a plain text file, or because it is provided along with its source code or an authoring tool.
•
• •
explanatory, or have just a few text labels or icons/buttons for start, stop, etc. Have been designed to be language customizable and are already offered in more than one language. Address curriculum topics that could be considered trans-national. Are adaptable from a technical (e.g., LOs are supplied along with an authoring environment or tools) or IPR perspective.
2.1.4. ‘Quality for Reuse’ Quality Criteria of Learning Objects A quality assurance strategy was implemented in the “Quality for Reuse” (Q4R, 2008) scientific project initiated by Tele-University of Quebec to improve effectiveness, efficiency and flexibility of LOs as well as proper storing and retrieval strategies. Q4R quality assurance strategies are classified into four main groups, namely organizational strategies, and then three strategies inspired by the life-cycle of a LO, that is from its conception to its use∕reuse (adaptations) – before, during and after LO inclusion in the LOR. Strategies before LO inclusion in the LOR are based on the following principles: • •
Only build or integrate Los that can be certified for quality. Interactive LOs are software and as such they should satisfy software quality criteria.
The cross-border reuse of content will be more likely if LOs:
Strategies during LO inclusion in the LOR are based on:
•
• •
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Have a strong visual element and users can broadly understand what the intended learning objective or topic is. For example, LOs may have little or no text; include animations and simulations that are self-
The principle of reducing form-filling. The use of guiding wizards, smart automatic and semi-automatic computer agents to assist in assuring technical interoperability.
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Strategies after LO inclusion in the LOR are based on the following principles: •
•
Provide interesting and easily understood user statistics, such as stars, percentages, voting systems. Include recommendations for reuse by the user, both to the next user and to the designer.
2.2. Existing Technological Evaluation Tools for Learning Objects Repositories 2.2.1. The SWITCH Learning Object Repository Quality Evaluation Grid The first LOR quality evaluation tool presented here is the SWITCH project tool (SWITCH collection, 2008) developed while evaluating DSpace (2009) and Fedora (2009) LORs in 2008. An overview of the tool in Table 1 shows that there is no clear division of the criteria into ‘internal quality’ and ‘quality in use’. According to the principle (see the Introductory Section), ‘internal quality’ criteria should mainly be the area of interest of the software engineers, and ‘quality in use’ criteria should mostly be analysed by the programmers taking into account the users’ feedback on the usability of software. Nevertheless, we can notice that ‘Architecture’ group’s sub-criteria are mainly engineering criteria, and therefore they could be analyzed as ‘internal quality’ criteria, while all other criteria are mainly user-related and could, therefore, be treated as ‘quality in use’ criteria.
2.2.2. The Catalyst IT Technical Evaluation Tool for Open Source Repositories The second LOR quality evaluation tool presented here is the tool developed by Catalyst IT while evaluating DSpace (2009), EPrints (2009) and Fedora (2009) LORs in the “Technical Evaluation
of Selected Open Source Repository Solutions” (2006). As the overview in Table 2 shows there is no division of the criteria into ‘internal quality’ and ‘quality in use’ criteria in this tool. We can notice that ‘Scalability’, ‘Security’, ‘Interoperability’, and ‘Ease of deployment’ criteria are mainly engineering criteria, and could therefore be considered as ‘internal quality’ criteria. All other criteria in Table 2 are mainly users-related and could be interpreted as ‘quality in use’ criteria.
2.2.3. The OMII Evaluation Criteria of Software Repository The next tool presented here is the “Software Repository – Evaluation Criteria and Dissemination” prepared by S. Newhouse of the Open Middleware Infrastructure Institute (OMII). Newhouse (2005) has specified three critical phases of the software repository process: 1. Information that must be captured when a product is created within the repository and a specific release submitted to the repository. 2. Assessment criteria that should be used to review the software contribution. 3. How product and release information, coupled with the evaluation results, is presented within LOR. The tool combines three types of criteria: documentation, technical and management (see Table 3). Although, as in other tools described above, there is no clear division of the criteria into ‘internal quality’ and ‘quality in use’ criteria, we can notice that the ‘Technical’ group’s sub-criteria are mainly engineering criteria, and could therefore be analyzed as ‘internal quality’ criteria. The ‘Documentation’ and ‘Management’ criteria are mainly users-related, and could be considered as ‘quality in use’ criteria.
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Table 1. SWITCH LOR quality evaluation criteria Criteria
Sub-criteria
Description
Flexible and modular system
Flexible and modular architecture that allows us to include various extensions in the future
Possibility to use a LOR system as part of a federation
Architecture
Metadata
API for storage engine
Basic operations accessible through well-defined, documented and designed API. API: SOAP, REST or comparable. Basic operations: create new, edit existing, search, query (needed for LMS-LOR integration)
API for user access rights
User access rights can be assigned in program form
API for federation functions
APIs for metadata harvesting and federated search
Metadata search with heterogeneous schemas
Metadata search works with heterogeneous schemas across the LOR federation and within a LOR. Applies metadata mapping or another best-effort approach
Full-text search
Full text search: in federation, in objects of the most common formats, i.e. HTML, XML, TXT, PDF, PPT, DOC, IMS-CP (2009), SCORM (2009)
Performance
A common single-server installation is able to reasonably deal with at least 50000 objects
Scalability
Possibility to support up to 1Mio objects, if necessary with clustering etc
Security
No intrinsic vulnerabilities. Bullet-proof access rights system
Interoperability
OAI-PMH (2009), federated search
Persistent links
Possibility to add a persistent link system like OAI, handle, URN
Internationalization
Possibility to support at least EN, IT, DE, FR
Minimal metadata schema
Possibility to define a minimal metadata schema with overall mandatory items
Predefined sets of metadata
Possibility to pre-define common metadata schemas (IMS, MPEG7, DC etc.) that can be used when necessary
Customizable metadata schema
Institutions, groups of interest or individuals can extend the minimal metadata schema according to their needs
Metadata mapping for metadata search
Possibility to map metadata items between schemas. For example, “contributor” of mandatory schema to “author” of the IMS schema
Unicode support
Graphical user Interface
Social tagging
Possibility to support dynamically defined social tags
Complete standard UI
Complete standard UI available that covers all important functions for administrators and end-users
Extensible standard UI
The standard UI can be customized and extended
Multiple standard UIs
Multiple standard UIs can be configured to run on the same repository system (needed to support multiple institutions)
Custom UIs
Possibility to add special purpose UIs as needed, like stripped-down query interface, spec. video portal, etc.
AAI authentication
Possibility to add the AAI authentication system
Associate copyright license
Pre-defined copyright licenses (like Creative Commons) can be easily associated
Direct distribution
Objects can be directly accessed through a URL
Direct streaming
Video/audio objects can be directly streamed
Alternative protocols for data upload
Access trough https, WebDAV, (s)ftp.
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Table 1. continued Criteria
Sub-criteria
Description
Object can be of any format
Storage
Multi-part objects
One LO may consist of multiple elements: a CD consists of audio tracks and scanned booklet images
Access rights
Possibility to define read and write access rights on four levels: world, institution, self-defined group, private
Hierarchical organization
LOs can be stored in a self-defined hierarchical structure like Institution – Domain – Department – Teacher
Property and metadata inheritance
Object properties (access rights) and metadata items can be inherited within the hierarchy. Support for predefined properties and possibility to override it
Versioning system
Re-uploading an object with the same ID does not overwrite the original, but creates a new version.
Large objects
Support for large objects like videos of several GBytes
Strength of development community Strength of users’ community Other
Code quality Documentation quality Ease of installation
2.3. Existing Technical Evaluation Methods for Virtual Learning Environments 2.3.1. The Methodology of Technical Evaluation of Learning Management Systems The “Methodology of Technical Evaluation of Learning Management Systems” – LMSs (or VLEs) is part of the Evaluation of Learning Management Software activity undertaken as part of the New Zealand’s Open Source LMS project (2004). The evaluation criteria expand on a subset of criteria, focusing on technical aspects of VLEs (Kurilovas, 2005): •
Overall architecture and implementation (suitable for technical evaluation): Scalability of the system; System modularity and extensibility; Possibility of multiple installations on a single platform; Reasonable performance optimizations;
•
•
•
‘Look and feel’ is configurable; Security; Modular authentication; Robustness and stability; Installation, dependencies and portability. Interoperability (suitable for technical evaluation): Integration is straightforward; LMS/VLE standards support. Internationalization and localization (suitable for technical evaluation): Localizable user interface; Localization to relevant languages; Unicode text editing and storage; Time zones and date localization; Alternative language support. Accessibility (suitable for technical evaluation): Text-only navigation support; Scalable fonts and graphics.
2.3.2. Adaptation Evaluation Instrument for Open Source Platforms Graf & List (2005) present an evaluation of open source e-learning platforms/LMSs where the main focus is on adaptation issues – adaptabil-
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Table 2. Catalyst IT LOR quality evaluation criteria Criteria
Scalability
Ease of working on the code base
Security
Sub-criteria
Description
Scale Up
Ability for the Repository to scale higher by adding more resources (CPU, ram, etc.)
Scale out
The repository supports caching, adding more instances, and other mechanisms to scale higher
Architecture
The repository can be divided into different local parts and put into different machines (e.g. separate the database, data directory, components from the repository to distribute to different machines)
Add/change digital object type
The work involved in adding or changing a digital object type such as adding or changing metadata
Documentation of the code and code consistency & style Data encryption
Supports encryption of data while transmitting the content, such has using SSL/ https
Server security
What does the repository require for installation? Does it follow good security practices, e. g. proper file permissions, secure database connection?
Authentication
Authentication mechanism used by the repository to authenticate the user
Authorization/ access rights
Support for different roles to properly manage the content and administer the system
Ability to restrict access at the repository item level
E.g. view metadata but not content
OAI-PMH Compliant (essential) SOAP, UDDI SRU / SRW Interoperability
Bulk import and export
Support for batch/bulk import and export of digital objects
Institution exit mechanism to withdraw their content from the repository farm (essential)
Ease of deployment
Authentication
Use external authentication mechanism, such as LDAP (2009)
Standard metadata
Dublin Core (2009), METS (2009), etc.
Software and hardware requirements
The repository only requires common/basic software and hardware
Packaging and installation steps Separate repository and branding for each institution (essential)
System administration Internationalization Open source Workflow tools
Ability to customize look and feel
Change the header, theme, footer
Ease of publishing
Inexperienced users of the repository can easily publish content
Localizable UI Unicode text editing and storage Open source license (required) Defined roadmap for the future Workflow integration
Support to use different workflow tools
Support for different workflows
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Table 2. continued Criteria
Sub-criteria
Description
Quality and completeness of information on the product’s web site Size and level of activity in the developer community Size and level of activity in the user community Community knowledge base
Availability and use of a range of communication channels (email, forums, IRC, wiki, etc.) Software release history for evidence of sustainability and vitality Documentation on how to set up and manage a repository farm
One code base, many independent repositories
ity, personalization, extensibility, and adaptivity capabilities of the platforms. Adaptation received very little coverage in elearning platforms. An e-learning course should not be designed in a vacuum; rather, it should match students’ needs and desires as closely as possible, and adapt during course progression. The extended platform will be utilized in an operational teaching environment. Therefore, the overall functionality of the platform is as important as the adaptation capabilities, and the evaluation treats both issues. There are only a few LMSs evaluations available in the current literature. Their main focus is on commercial products. In contrast, the work in (Graf & List, 2005) is focused on open source products only, and on customizable adaptation that can be performed without programming skills. The LMSs adaptation evaluation criteria proposed in (Graf & List, 2005) are as follows: 1. Adaptability: it includes all facilities to customize the platform/LMS to suit the educational institution needs (e.g. the language or design); 2. Personalization: this indicate the facilities of each individual user to customize his/her own view of the platform; 3. Extensibility: in principle, this is possible for all open source products. Nevertheless, there can be great differences; for example, in programming style or the availability of
a documented Application Programming Interface (API); 4. Adaptivity: it indicates all kinds of automatic adaptation to the individual user’s needs (e.g. personal annotations of LOs or automatically adapted content). The evaluation (Graf & List, 2005) is based on the Qualitative Weight and Sum approach (QWS). QWS establishes and weights a list of criteria and is based on the use of symbols. There are six qualitative levels of importance for the weights, most frequently, symbols are used: 1. 2. 3. 4. 5. 6.
E = Essential; * = Extremely valuable; # = Very valuable; + = Valuable; | = Marginally valuable; and 0 = Not valuable.
The weight of a criterion determines the range of values that can be used to measure a product’s performance. With the criterion weighted #, for example, the product can only be judged #, +, |, or 0, but not *. It means that lower-weighted criteria cannot overpower higher-weighted criteria. To evaluate the results, the different symbols, given to each product, are counted. As an example, the results can be 2*, 3#, 3| or 1*, 6#, 1+. The product can now be ranked according to these numbers.
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Table 3. OMII LOR quality evaluation criteria Criteria
Sub-criteria
Description/Ideal
A high overall score here indicates that the user can be reasonably confident that the supporting documentation will answer the majority of the queries. How comprehensive and useful is the provided documentation? Ideal: a high score indicates that the documentation provides sufficient depth and coverage to be useful for those trying to utilize the product for its main purpose/function
Documentation
Introductory docs.
Concise summary of the software. Ideal: Information as to what the software does and how one can quickly get started with it
Pre-requisite docs.
Information relating to the environment required for running this software. Ideal: is the environment required for this software well described?
Installation docs.
Information on how to install the software. Ideal: clear instructions on installation procedures of the software
User docs.
Information on the API. Ideal: clear simple user manual with usage scenarios and the sample code
Admin docs.
Information on how to administer the software. Ideal: clear instructions on how to configure the software and maintain it in operation
Tutorials
Details how to use the software. Ideal: clear, simple step-by-step description on how to use the software with code samples, if appropriate
Functional specification
Functional specification of the software. Ideal: clear, simple description of product’s functionality
Implementation specification
Implementation details of functional specification. Ideal: this document should contain not only the implementation details but also justifications for any choices
Test documents
Details of product testing. Ideal: details of the test plans, test code and results from running on various platforms and scenarios. Also describe how the user can repeat the same tests
The evaluator will use the provided documentation to try and use the software. Their success (or failure) in using the software will demonstrate whether the contributed software provides useful functionality. Examination of the technical components of the software. Ideal: can the product be deployed and does it run successfully using the provided documentation? Pre-requisites
Software and environment changes necessary to support the installation of the software. Ideal: are the pre-requisites accurately described and in sufficient depth to install and run the software?
Deployment
Deployment of the product into a server or client environment. Ideal: how easy is the deployment of this software into the required environment?
Verification
Evaluating the correct operation of the product. Ideal: is it clear how one can verify that the software has been successfully deployed and is operating correctly, e.g. postinstallation tests?
Stability
Determination as to the stability of the production. Ideal: does the software run reliably under reasonable usage and are there any tests to support this?
Scalability
Assessment of the scalability of the software. Ideal: how well does the software respond to high levels of utilization and concurrent client activity and are there any tests to support this?
Coding
Inspection of the code within the software
Technical
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Table 3. continued Criteria
Sub-criteria
Description/Ideal
Decision to use software within the project is driven by many non-functional issues such as support, adoption, etc. – non-functional issues relating to the product. Ideal: does the product appear to have a supported, sustainable future? Management
Support
The support model provided by the contributors. Ideal: is the software supported through community or dedicated resources?
Sustainability
Information about the products future. Ideal: Is there a roadmap with support of funding that defines the product’s technical future?
Standards
Ideal: Does the software support mainstream specifications that are standards or becoming standards?
But the results are sometimes not clear. There is no doubt that 3*, 4#, 2| is better than 2*, 4#, 2|, but it is not clear whether it is better than 2*, 6#, 1+. In the latter case, a further analysis has to be conducted. In (Graf & List, 2005), the authors have adapted the QWS approach in a way that the essential criteria are assessed in a pre-evaluation phase. These minimum criteria cover three general usage requirements: an active community, a stable development status, and a good documentation of the platform. The fourth criterion incorporates the didactical objective and means that the platform’s focus is on the presentation of content instead of communication functionalities. At the beginning of the evaluation, Graf & List (2005) have chosen 36 platforms and evaluated them according to the minimum selected criteria. Nine platforms (ATutor 1.4.11, Dokeos 1.5.5, dotLRN 2.0.3, based on OpenACS 5.1.0, Ilias 3.2.4, LON–CAPA 1.1.3, Moodle 1.4.1, OpenUSS 1.4 extended with Freestyle Learning 3.2, Sakai 1.0, and Spaghettilearning 1.1) meet the criteria. Next, these nine platforms were tested in detail. A questionnaire and an example of a real life teaching situation, covering the instructions for creating courses, managing users, and simulating course activities, were designed and applied to each platform. Finally, Graf & List (2005) established eight categories: communication tools, LOs, management of user
data, usability, adaptation, technical aspects, administration, and course management. While examining the results from a vertical perspective, it can be seen that the adaptability and personalization subcategories yield a broad range of results. The majority of the platforms were estimated as very good with regard to extensibility. In contrast, adaptivity features are underdeveloped. As a result, Moodle can be seen as the best LMS concerning adaptation issues. Moodle provides an adaptive feature called “lesson” where learners can be routed automatically through pages depending on the answer to a question after each page. Furthermore, the extensibility is supported very well by a documented API, detailed guidelines, and templates for programming. Also, adaptability and personalization aspects are included in Moodle. The templates for themes are available and can be selected by the administrator. Students can choose out of more than 40 languages (Graf & List, 2005).
3. MULTIPLE CRITERIA EVALUATION AND OPTIMIZATION OF LEARNING SOFTWARE FOR THE PARTICULAR LEARNER NEEDS A complex decision problem often requires to explicitly consider several points of view. The
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classical approaches in the field of operations research consider only a single objective function to be optimized; such an approach models one aspect of the decision problem, or aggregates relevant aspects into a single criterion (the aggregation being usually rather simplistic). Many multidimensional approaches have been proposed as extensions of the classical ones. A first one was the so-called Multicriteria Decision Making (MCDM), developed by the so-called American School. More recently, the European School has created a new type of approach to these problems, called Multicriteria Decision Aid (MCDA). Many real life applications have successfully validated the feasibility of this approach. MCDM/MCDA deal with different classes of decision problems (choice, classification, sorting, ranking), explicitly taking into consideration several points of view (multiple attributes or criteria, i.e. attributes with an ordered domain), in order to support decision makers in finding a consistent solution of the problem at hand (MCDM, 2009). Despite recent advances in electronic technologies for e-learning, a consolidated evaluation methodology for e-learning applications is not available. The evaluation of educational software must consider its usability and more in general its accessibility, as well as its didactic effectiveness (Ardito et. al, 2006). Despite the widespread use of e-learning systems and the considerable investment in purchasing or developing them, there is no consensus on a standard framework for evaluating the system quality (Chua & Dyson, 2004). The authors approach is to use a multiple criteria evaluation method expressed by an experts’ utility function, which is presented below in the section“Experts’ Additive Utility Function”, including evaluation criteria of alternatives, their ratings (values) and weights for evaluating the technological quality of learning software. According to this method, in order to evaluate the e-learning system components, we should
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identify their evaluation criteria, ratings (values) and weights. First, the authors’ comprehensive sets (tools) of criteria for evaluation of e-learning system components, created according to the principle, are described in the next section. Then, the ratings, weights and an example of experimental evaluation of VLEs according to the method are presented.
3.1. Comprehensive Technological Evaluation Models for Learning Software 3.1.1. Comprehensive Technological Evaluation Model for Learning Objects While analysing the aforementioned LOs evaluation criteria, it was necessary to exclude all the evaluation criteria that do not deal directly with LOs technological quality problems, on the one hand, and to estimate interconnected/overlapping criteria, on the other hand. This analysis has shown that all the analysed sets of LOs evaluation criteria have a number of limitations from technological point of view: •
•
•
LORI (Vargo et al., 2003), Paulsson and Naeve (2006) and MELT (2008) criteria do not examine the different LO life cycle stages. Q4R (2008) set of criteria insufficiently examines technological evaluation criteria before the LO inclusion into the LO repository. All these criteria insufficiently examine LO reusability, including interoperability.
It is obvious that a more comprehensive set of criteria for LOs technological evaluation is needed that should comprise both LO quality evaluation criteria suitable for the different LO life cycle stages, including criteria for before, during and after LO inclusion into the repository, and LO
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reusability criteria (Kurilovas, 2007; Kurilovas & Dagiene, 2009), which should have the same weight as the other criteria. Therefore a comprehensive set of criteria is proposed for LOs technological evaluation that is based on a flexible e-learning system approach as well as on the analysis of LO quality evaluation criteria, presented in the previous section. It combines LORI (Vargo et al., 2003), Paulsson and Naeve (2006), MELT (2008), Q4R (2008) and other research results, published in (Kurilovas, 2007; Kurilovas & Dagiene, 2009).
The comprehensive set of criteria includes LOs technological evaluation criteria suitable for different LOs life cycle stages (before, during, and after LO inclusion in the LOR), as well as LO reusability criteria. It combines both ‘internal quality’ criteria suitable for all LOs (such as ‘Interoperability’, ‘Architecture’, ‘Working stability’) and ‘quality in use’ criteria suitable for particular projects or learners groups. Therefore, this set of criteria is suitable for the expert evaluation of LOs ‘quality in use’ as well as the ‘internal quality’ (see the Introductory Section). Teachers and learners are reputed as the main user groups
Table 4. Technological quality evaluation criteria of LOs (Kurilovas & Dagiene, 2009) Narrow definition compliance Reusability level: interoperability
Reusability level: decontextualization
Criteria before LO inclusion in LOR
Reusability level: cultural and learning diversity principles Reusability level: accessibility LO architecture
Metadata accuracy Compliance with the main import/export standards (IMS CC (2009), SCORM (2009)) Is the LO indivisible (atomic)?
LO aggregation (granularity) level Is the LO modular?
Does the LO have a strong visual element? Is the LO flexible (can be modified)? LO suitability for localization LO internationalization level Is the LO designed for all? Compliance with accessibility standards (W3C, 2009) Is the LO architecture layered in order to separate data, presentation and application logics?
Working stability Aesthetics Navigation Design and usability
User-friendly interface Information structure Personalization
Criteria during LO inclusion in LOR
Criteria after LO inclusion in LOR
Membership of contribution control strategies Technical interoperability Retrieval quality Information quality
Using LO harvesting Obligatory membership Automatic verification of capability with known protocols Automatic metadata generation or simplified metadata tagging User should be able to retrieve LO in different ways Display information strategies Feedback techniques
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here. The original set of LOs evaluation criteria is presented in Table 4. Additional LO evaluation criteria interconnected with technological criteria could be (1) licensing (clear rules, e.g. compliance with Creative Commons (2009)), and (2) economic efficiency which is taking into account the probable LO reusability level (Kurilovas, 2007).
3.1.2. Comprehensive Technological Evaluation Model for Learning Object Repositories The principle presented in the Introductory Section claims that there exist both ‘internal quality’ and ‘quality in use’ evaluation criteria of the software packages (such as LORs). The analysis shows that none of the tools, presented in the previous section, has clearly divided the LORs quality evaluation criteria into two separate groups: LORs ‘internal quality’ evaluation criteria and ‘quality in use’ criteria. Therefore it is difficult to understand which criteria reflect the basic LORs quality aspects suitable for all software package alternatives, and which are suitable only for a particular project or user, and therefore need the users’ feedback. While analysing the LOR quality evaluation criteria, presented previously, we noticed that several tools pay more attention to the general software ‘internal quality’ evaluation criteria (such as the ‘Architecture’ group criteria) and some of them to the ‘customizable’ ‘quality in use’ evaluation criteria groups suitable for a particular project or user: ‘Metadata’, ‘Storage’, ‘Graphical user interface’ and ‘Other’. According to the principle, the comprehensive LOR quality evaluation tool should include both the general software ‘internal quality’ evaluation criteria and the ‘quality in use’ evaluation criteria suitable for a particular project or user. The LOR quality evaluation tool proposed by the authors is presented in Table 5. This tool is mostly similar to the SWITCH tool (c.f. with
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Table 1) but it also includes criteria from other presented tools as well as authors’ own research. The main ideas for the constitution of this tool are to clearly divide LORs quality evaluation criteria in conformity with the principle as well as to ensure the comprehensiveness of the tool and to avoid the overlap of the criteria. The advantage of the tool proposed is its comprehensiveness and the clear division of the criteria: ‘internal quality’ criteria are mainly the area of interest of software engineers, and ‘quality in use’ criteria are mostly to be analysed by programmers, taking into account the users’ feedback on the usability of software. Two of the criteria in Table 5 could be interpreted from different perspectives: ‘Accessibility: access for all’ could be included into the ‘Architecture’ group, but as it requires users’ evaluation, it has been included in the ‘Quality in use’ criteria group. Also, ‘Property and metadata inheritance’ could also be included in the ‘Metadata’ group, although it deals with ‘Storage’ issues as well. In any case, we have 34 different evaluation criteria in this model (set of criteria), from which 11 criteria deal with ‘Internal quality’ (or ‘Architecture’), and 23 criteria deal with ‘Quality in use’. The twenty three ‘Quality in use’ criteria are further divided into four groups to increase the precision and convenience in practical evaluation. Different experts (programmers and users) could be used for different groups of the ‘Quality in use’ criteria. Indeed, ‘Metadata’, ‘Storage’ and ‘Graphical user interface’ criteria need different kinds of evaluators’ expertise.
3.1.3. Comprehensive Technological Evaluation Model for Virtual Learning Environments While analyzing VLEs evaluation methods in the previous section, it was necessary to exclude all the evaluation criteria that do not deal directly with VLEs technological quality problems, on the one
Technological Evaluation and Optimization of E-Learning Systems Components
Table 5. LORs technological quality evaluation criteria (Kurilovas, 2009) 1. Flexibility and modularity of the LOR system 2. Possibility to use LOR system as part of a federation 3. Performance and scalability 4. Security Internal quality evaluation criteria
5. Interoperability Architecture
6. Stability 7. Ease of deployment 8. API for storage engine, user access rights and federation functions 9. Coding: an inspection of the code within the software 10. Full-text search 11. Internationalization 12. Minimal metadata schema 13. Predefined sets of metadata
Metadata
14. Customizable metadata schema 15. Metadata mapping for metadata search 16. Unicode support 17. Social tagging 18. Object can be of any format 19. Access rights
Storage
20. Hierarchical organization 21. Property and metadata inheritance
Quality in use evaluation criteria
22. Large objects 23. Complete standard UI Graphical user interface
24. Customizable and extensible standard UI 25. Multiple standard UIs 26. Direct distribution 27. Strength of development community 28. Strength of users community 29. LOs retrieval quality: user able to retrieve LOs in different ways
Other
30. Ease of installation 31. Accessibility: design for all 32. Sustainability 33. System administration: ability to customize look and feel 34. Documentation quality
hand, and to estimate interconnected/overlapping criteria, on the other hand. This analysis has shown that both VLE technological evaluation methods analyzed have a number of limitations: (1) the method developed
in New Zealand’s Open Source LMS project (2004) practically does not examine the adaptation capability criteria, and (2) the method proposed by Graf & List (2005) insufficiently explores the general technological quality criteria.
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Therefore, in the authors’ opinion, a more comprehensive tool/set of criteria for VLE technological evaluation is needed. It should include the general technological evaluation criteria, based on modular approach and interoperability, as well as the adaptation capability criteria (Kurilovas & Dagiene, 2009). The VLE adaptation capability criteria should have the same weight as the other criteria. According to the principle, the comprehensive VLEs quality evaluation tool should include both the general software ‘internal quality’ evaluation criteria and the ‘quality in use’ evaluation criteria suitable for a particular project or user.
The authors’ comprehensive set of criteria (tool) for VLEs technological evaluation is presented in Table 6. This approach is suitable for expert evaluation of both the VLEs ‘internal quality’ criteria (see criteria 1–4) and the ‘quality in use’ criteria (see criteria 5–8). This tool provides a clear instrumentality identifying what dimensions experts are necessary to analyse and what kind of VLEs quality criteria should be used in order to select the best VLE software to accommodate their needs. The main ideas for the constitution of this tool are to clearly divide VLEs quality evaluation criteria in conformity with the principle as well
Table 6. VLEs technological quality evaluation criteria (Kurilovas & Dagiene, 2009) Scalability Modularity (of the architecture) Possibility of multiple installations on a single platform 1. Overall architecture and implementation
Reasonable performance optimizations Look and feel is configurable Security Modular authentication
Internal quality (General) evaluation criteria
Robustness and stability Installation, dependencies and portability 2. Interoperability
Integration is straightforward VLE standard support Localizable user interface Localization to relevant languages
3. Internationalization and localization
Unicode text editing and storage Time zones and date localization Alternative language support
4. Accessibility 5. Adaptability (facilities to customize to suit the educational institution’s needs) Quality in use (Adaptation) evaluation criteria
Scalable fonts and graphics Language Design
6. Personalization aspects (facilities for each individual user to customize the platform) 7. Extensibility 8. Adaptivity (all kinds of automatic adaptation to individual user’s needs)
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Text only navigation support
Good programming style Availability of a documented API Personal annotations of LOs Automatically adapted content
Technological Evaluation and Optimization of E-Learning Systems Components
as to ensure the comprehensiveness of the tool avoiding the overlap of the criteria.
3.2. Multiple Criteria Evaluation and Optimization of E-Learning Systems Components 3.2.1. Ratings of the Quality Evaluation Criteria There is a number of methods to explore the level of customization offered in learning software. The authors suggest using the multiple criteria evaluation method of the learning software quality that employs an experts’ utility function (see Equation 3 below) and includes evaluation criteria of alternatives, their ratings (values) and weights. The evaluation criteria used in this method should conform to the software engineering principle based on the evaluation criteria division to ‘internal quality’ and ‘quality in use’ criteria. The scientists who have explored the quality of software consider that there exists no simple way to evaluate the functionality characteristics of the internal quality of software. According to Gasperovic and Caplinskas (2006), it is a hard and complicated task that requires relatively high overhead in terms of both time and labour. According to Zavadskas and Turskis (2008), each alternative in the multicriteria decision making problem can be described by a set of criteria. Criteria can be qualitative and quantitative. Usually they have different units of measurement and a different optimization direction. Also, following the multiple criteria evaluation method, we also need LORs and VLEs evaluation criteria ratings (values). The wide-used measurement criteria of the decision attributes quality are mainly qualitative and subjective. In this context, decisions are often expressed in natural language and evaluators are unable to assign exact numerical values to different criteria. Assessment can be often performed by linguistic variables: ‘bad’, ‘poor’, ‘fair’, ‘good’ and ‘excellent’. These values are imprecise and
Table 7. Conversion of linguistic variables into triangular fuzzy numbers (TFNs) Linguistic variables
TFN
Excellent
(0.700, 0.850, 1.000)
Good
(0.525, 0.675, 0.825)
Fair
(0.350, 0.500, 0.650)
Poor
(0.175, 0.325, 0.475)
Bad
(0.000, 0.150, 0.300)
uncertain; they are commonly called fuzzy values. Integrating these different judgments to obtain a final evaluation is not evident. Therefore, Ounaies, Jamoussi & Ben Ghezala (2009) suggest using the fuzzy group decision making theory to obtain final assessment measures. First, linguistic variable values are mapped into triangular fuzzy numbers (l, m, u) as in Table 7. After the defuzzification procedure which converts the global fuzzy evaluation results, expressed by a TFN (l, m, u) to a non-fuzzy value E, the following equation has been adopted: E = [ (u – 1) + (m – 1) ] / 3 + 1
(1)
The non-fuzzy values E for all the aforementioned linguistic variables calculated according to the Equation (1) are presented in Table 8.
3.2.2 Experts’ Additive Utility Function If we want to evaluate (or optimize) the technological quality of learning software (e.g. VLEs) for particular learner needs (i.e. to personalize his/her learning process in the best way in conformance with the prerequisites, preferred learning speed and methods, etc.), we should use the experts’ additive utility function together with the weights of evaluation criteria. The weight of the evaluation criterion reflects the experts’ opinion on its importance level in comparison with the other criteria for an individual learner/user.
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Table 8. Conversion of linguistic variables and QWS symbols into non-fuzzy values E Linguistic variables and QWS symbols
Non-fuzzy values E
Excellent (E or *)
0.850
Good (#)
0.675
Fair (+)
0.500
Poor (|)
0.325
Bad (0)
0.150
m
f (X ) = ∑ ai fi (X ), i =1
For example, for the most simple case, where all the VLE evaluation criteria are of equal importance, the experts could consider equal ‘normalized’ weights ai = 0.125 according to the normalization requirement m
∑a i =1
i
= 1 , ai>0.
obtained by adding all the criteria together with their weights. It is valid from the point of view of the optimization theory, and a special theorem exists for this case. Therefore, we have here the experts’ additive utility function:
(2)
for the VLEs quality evaluation criteria i = {1, …, 8} (see Table 6). A possible decision could be to transform a multi-criteria task into a one-criterion task
m
∑a i =1
i
= 1 , ai>0.
where fi (Xj) is the rating (non-fuzzy value E) (see Table 8) of the criterion i for each of the examined alternatives (we examine three alternatives here) Xj: X1 – ATutor (2009), X2 – Ilias (2009), and X3 – Moodle (2009). Here i denotes the order numbers of the VLE quality evaluation criteria presented in Table 6. The first four of these criteria are the general ‘internal quality’ VLE quality criteria, and the other four represent the VLE adaptation ‘quality in use’ criteria (see Table 6). The higher the value of the utility function (3) is, the better a VLE meets the particular learner needs.
Table 9. Summary of the VLEs technological evaluation Technological evaluation criteria
ATutor
Ilias
Moodle
General criteria ratings Architecture and implementation
0.500
0.325
0.850
Interoperability
0.675
0.675
0.500
Internationalization and localization
0.325
0.500
0.675
Accessibility
0.850
0.325
0.500
Interim rating
2.350
1.825
2.525
Adaptability
0.325
0.500
0.675
Personalization
0.675
0.675
0.500
Extensibility
0.675
0.850
0.850
Adaptivity
0.325
0.150
0.325
Interim rating
2.000
2.175
2.350
Total evaluation rating
4.350
4.000
4.875
Total rating f(X) (weights = 0.125)
0.5437
0.5000
0.6093
Adaptation criteria ratings
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(3)
Technological Evaluation and Optimization of E-Learning Systems Components
3.2.3 Example of the Evaluation of Virtual Learning Environments In the general case, all the VLE evaluation criteria are of equal importance. The values of utility function (3) for a particular evaluation scenario, where non-fuzzy values E for all variables in Table 8 are calculated according to Equation (1) and all the VLE evaluation criteria are of equal importance, are presented in Table 9. According to the normalization requirement (2), all ai = 0.125. The results of this evaluation scenario demonstrate that the VLE Moodle match the quality up to 60.93% in comparison with the ideal VLE (which is lower than expert’s linguistic judgement ‘good’ in Table 8), ATutor gets a rating of 54.37% (it is higher than ‘fair’), and Ilias 50.00% (i.e. ‘fair’). Thus for the requirements of this scenario, the VLE Moodle is the best alternative (among the three evaluated VLEs) from technological point of view in the general case. This alternative has shown the highest ratings in terms of both ‘internal quality’ (see General criteria ratings) and ‘quality in use’ (see Adaptation criteria ratings). In a more specific case, e.g. when experts (decision makers) would like to select the most
suitable VLE for students with special education needs/disabilities, higher weights for certain criteria should be chosen: Accessibility (e.g. a weight a4 = 0.2) and Personalization (e.g. a weight a6 = 0.2). Then using the ‘normalization’ Formula (2), all other criteria weights are set to ai = 0.1. In this scenario, the experts find that, in contrast to the simple general case (see Table 9), both ATutor and Moodle could be considered as optimal alternatives for learners with special needs (see Table 10). The results in Table 10 show that the VLEs ATutor and Moodle satisfy the quality up to 58.75% in comparison with the ideal VLE for students with special needs (this reflects a linguistic judgement that is between ‘fair’ and ‘good’’), while Ilias gets a rating of 50.00% (which corresponds the linguistic variable ‘fair’). If we want to select, for example, the most suitable LOR for students with special education needs/disabilities, we should assign higher weights for the particular ‘quality in use’ criteria, such as ‘Customizable metadata schema’ (e.g. measuring the weight a14 = 0.05), ‘Customizable and extensible standard UI’ (a24 = 0.05), ‘Accessibility’ (a31 = 0.06), and ‘Ability to customize look and
Table 10. Summary of VLEs technological evaluation for the learners with special needs Technological evaluation criteria
ATutor
Ilias
Moodle
General criteria ratings Architecture and implementation a1 = 0.1
0.0500
0.0325
0.0850
Interoperability a2 = 0.1
0.0675
0.0675
0.0500
Internationalization and localization a3 = 0.1
0.0325
0.0500
0.0675
Accessibility a4 = 0.2
0.1700
0.0650
0.1000
Interim rating
0.3200
0.2150
0.3025
0.0325
0.0500
0.0675
Adaptation criteria ratings Adaptability a5 = 0.1 Personalization a6 = 0.2
0.1350
0.1350
0.1000
Extensibility a7 = 0.1
0.0675
0.0850
0.0850
Adaptivity a8 = 0.1
0.0325
0.0150
0.0325
Interim rating
0.2675
0.2850
0.2850
Total evaluation rating f(X) (different weights)
0.5875
0.5000
0.5875
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Technological Evaluation and Optimization of E-Learning Systems Components
feel’ (a33 = 0.06) (see Table 4). In this case, all the other criteria weights could be considered equally important and according to the normalization Formula (2) get a weight ai = 0.026. The choice of the particular values of the weights usually depends on the experts (decision-makers). In this particular scenario, applying the utility function (3) would give us Fedora (2009) as the optimal LOR for users with special needs, as compared with DSpace (2009) and EPrints (2009) LOR packages. The main reasons for this outcome are the following: (1) modular approach, (2) metadata schema extensible without restrictions, (3) all UI-projects are open source and can be adapted, (4) high ability to customize ‘look and feel’, etc.
where r is the number of experts; m is the number of the parameters under evaluation; S is the square sum of the evaluated deviations of importance rates’ value from the experts’ aggregate average. In its turn,
3.2.4 Minimization of Experts’ Subjectivity
The authors have analyzed several well-known tools and methods for multiple criteria evaluation of learning software, such as LOs, LORS and VLEs. Future research will concentrate on further analysis of more learning software quality evaluation models and tools with a view to create comprehensive sets of learning software evaluation criteria according to the principle, presented in the Introductory Section. Additional research is also needed to avoid the overlap of the learning software technological quality evaluation criteria. Furthermore, all the new models are to be validated. Along this line, validation of the proposed LORs quality evaluation model is scheduled for the autumn 2010 in Lithuania, involving three researchers and software engineering experts to validate ‘Internal quality’ criteria, and 12 (3 for each of the 4 groups) programmers and users to validate ‘Quality in use’ criteria. The authors have analyzed the application of the only scientific method available, represented by Equation (3), for multiple criteria evaluation and optimization of the learning software. Other methods of vector optimization could be used in the future research, and their efficiency should be compared.
Another very complicated problem for such multiple criteria evaluation and optimization tasks is the minimization of the experts’ (decision makers’) subjectivity. Experts’subjectivity can influence the quality criteria ratings (values) and their weights. There are some scientific approaches to alleviate this situation, such as the one formulated in (Kendall, 1979). According to Kendall (1979) the experts’ influence is different in general and therefore this importance should be assessed using the appropriate methodology. It is important to form the expert group purely in line with their competences. Furthermore, according to Kendall (1979), we should eliminate the extreme experts’ assessments of the ratings and weights. In order to pursue the compatibility of the experts’ assessments, we should calculate the so-called concordance rates W and distributions λ2: W =
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12S , r (m 3 − m ) 2
(4)
l 2 = Wr (m − 1) =
12S , rm(m + 1)
(5)
The compatibility of the experts’ assessments is considered satisfactory if the value of concordance rate W is 0.6–0.7 (Kendall, 1979).
4. FUTURE RESEARCH TRENDS
Technological Evaluation and Optimization of E-Learning Systems Components
An additional very complicated problem for the application of such a method is minimization of the experts’ (decision makers’) subjectivity. There are some scientific approaches concerning this issue, but this is out of the scope of this Chapter and will be analyzed separately in future works. The issues of personalization and adaptation of the learning software also deserve further investigation and a more detailed analysis in the scientific literature is needed.
Q4R. (2008). Quality for Reuse project website. Retrieved from http://www.q4r.org
5. CONCLUSION
Chua, B. B., & Dyson, L. E. (2004). Applying the ISO9126 model to the evaluation of an elearning system. In R. Atkinson, C. McBeath, D. JonasDwyer & R. Phillips (Eds.), Beyond the comfort zone: Proceedings of the 21st ASCILITE Conference (pp. 184-190). Perth, 5-8 December.
A scientific principle was proposed for software evaluation that is suitable for various learning software multiple criteria evaluation tasks. The authors have developed comprehensive LOs, LORs and VLEs technical quality evaluation tools (sets of criteria) created according to this principle. The authors proposed to use a learning software quality evaluation method, represented by the experts’ additive utility function, which is based on the transformation of a multiple criteria task into a one-criterion task, obtained by adding all the criteria ratings (values) together with their weights. This method is applicable to LOs, LORs and VLEs practical expert evaluation in order to meet particular learner needs. Such an approach has never been applied before for solving the learning software evaluation and optimization tasks. This could be of importance for public and private sector experts (decision makers), software engineers, programmers, and users.
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Ounaies, H. Z., Jamoussi, Y., & Ben Ghezala, H. H. (2009). Evaluation framework based on fuzzy measured method in adaptive learning system. Themes in Science and Technology Education, 1(1), 49–58.
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Section 2
Managing Digital Educational Content and Resources
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Chapter 7
Collaborative Development of Educational Modules: A Need for Lifelong Learning Ellen Francine Barbosa University of São Paulo – ICMC/USP, Brazil José Carlos Maldonado University of São Paulo – ICMC/USP, Brazil
ABSTRACT Lifelong learning has to accommodate a variety of types of learners who differ in age, learning experiences, media preferences, learning styles, capability for working in teams, among others. To be more effective, lifelong learning scenarios require the establishment and integration of innovative methods, tools, and procedures into well-defined processes, aiming at producing customized and high-quality educational products, capable of better engaging the students (and teachers as well) in an active learning process. Collaborative development plays an important role in this perspective, providing means for developers from different domains, working on multi-disciplinary and heterogeneous teams, geographically dispersed or not, cooperating, sharing data and information regarding the materials being developed. At the very end, the envisioned scenario is to evolve collaborative development in collaborative learning, broadening the learning opportunities to actively involve learners in their own knowledge construction process. In this chapter, the authors explore the collaborative development of educational modules and its implications in lifelong learning scenarios. They discuss the establishment of a systematic process for developing educational modules, providing a set of guidelines and supporting mechanisms to collaboratively create, reuse and evolve them. Also, as part of the process, the authors focus on issues of content modeling aiming at helping the developers to determine the relevant parts of the knowledge domain and to structure the concepts and related information. They illustrate the application of ideas by the collaborative development of an educational module for software testing domain. The module has been preliminarily evaluated; in general, positive attitudes toward the quality and flexibility it provides can be observed. DOI: 10.4018/978-1-61520-983-5.ch007
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Collaborative Development of Educational Modules
INTRODUCTION Education has been through enormous changes in the last decades. The need for a global education, capable of crossing international, cultural and social borders in order to prepare the learners for the global market has been rapidly changing the concept of learning (Barbosa & Maldonado, 2006b). Besides that, the fast evolution of information and communication technologies has leveraged and multiplied the possibilities of learning. Several initiatives have been investigated in order to provide new learning opportunities and facilitate the learning process. In this evolving educational landscape, the idea of lifelong learning has emerged – it is ever more important for college graduates and professionals to be able to take their place in the changing world scene and to be adaptable and creative within the organization that employs them (Peat et al., 2005). Also, in addition to a diversified student population in terms of ethnicity, social status and expectations, the proportion of nontraditional older adult reentry students is increasing significantly. Higher education plays an important role in this context, having a mission to provide older adult learners with re-education or retraining such that they can be able to remain competitive in the workforce of today’s technologically sophisticated society (Inoue, 2007). The growing worldwide demand for more flexible, self-directed, informal and formal lifelong learning opportunities points out the need for more efficient and productive learning development scenarios. For instance, the changes within education have brought about changes to the roles of teachers and students and to the nature of the learning process. As stated by Koper (2005), in lifelong learning students can be (co-) producers of course materials, can perform assessments, and can support other students. Indeed, lifelong learning implies on exploiting the heterogeneity of learners by setting up learning communities in which novices collaborate with more experienced
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people. Similarly, teachers and experts can teach and learn at the same time in a certain field of expertise. The main challenge in building lifelong learning experiences is how to provide ways to establish flexible and high-quality educational products, capable of stimulating the learners (and teachers as well) and effectively contribute to their knowledge construction processes in active learning environments. In this scenario, collaborative issues can be explored under two different but complementary perspectives: collaborative development and collaborative learning. In the first perspective, the idea is to provide means for developers from different domains, working on multi-disciplinary and heterogeneous teams, geographically dispersed or not, cooperate, sharing data and information regarding the product being developed. In the second perspective, the goal is to design personalized content and foster collaborative and cooperative activities for learners working in different places, at different times, and with varying facilities. In the emerging approaches of learning, such perspectives are ever more related, where the term “developer” refers not only to the designer professional and/or to the teacher, but also to the learner. In such cases, collaborative development turns into collaborative learning and vice-versa. In this chapter we explore the collaborative development of educational modules and its implications in lifelong learning scenarios. Educational modules correspond to concise units of study, composed of theoretical and practical content, which can be delivered to learners by using technological and computational resources (Barbosa, 2004; Barbosa & Maldonado, 2006a; Barbosa & Maldonado, 2006b). In a very broad definition, IEEE/ LTSC states that a learning object corresponds to “any entity, digital or non-digital, that can be used, reused or referenced during technology supported learning… Examples of learning objects include multimedia content, instructional content, learning objectives, instructional software and
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software tools, and persons, organizations, or events referenced during technology supported learning” (IEEE/LTSC, 2000). Considering the IEEE/LTSC’s definition of learning objects, both theoretical and practical content can be seen as learning objects; therefore, in our definition an educational module can also be represented as a collection of learning objects. Similar to software products, educational modules require the establishment of systematic development processes to produce customized, reliable and high-quality products. Besides the collaborative issues mentioned above, there is also a need for adaptability, scalability, interoperability and reusability – educational modules should be seen as independent units of study, subject to be adopted in different learning scenarios, according to parameters such as learner’s profiles and skills, instructor’s preferences, learning goals and course length, among others. Openness is another trend to be considered – the idea is to provide a culture for “open educational modules”, so that the use and evolution of them by a broader community would be better motivated and become a reality, particularly for active and lifelong learning environments. Despite its relevance, none of the initiatives to address the problem of creating educational modules considers a collaborative, well-defined process for developing them. Motivated by this scenario, in this chapter we discuss the establishment of a systematic process for developing educational modules, aiming at providing a set of guidelines and supporting mechanisms to collaboratively create, reuse and evolve them (Barbosa, 2004; Barbosa & Maldonado, 2006b). SP-DEM (Standard Process for Developing Educational Modules) is based on ISO/IEC 12207 standard (ISO, 2004), taking also into account practices from instructional design (Gagné et al., 1992; Dick et al., 2001), aspects of “open products” development (McConnell, 1999) and of distributed and cooperative work (Maidantchik, 1999). Besides that, in the same line as the CMMI model
for software (Chrissis et al., 2003), a capability maturity model for educational modules, named CMMI/Educational, is proposed. The main goals are to guarantee that distributed projects can be developed with unlike maturity level teams and to improve each working group capability. As part of the SP-DEM definition, we have also addressed issues of content modeling (Barbosa, 2004; Barbosa & Maldonado, 2006a), working on the establishment of an integrated approach for modeling educational content – IMA-CID (Integrated Modeling Approach – Conceptual, Instructional and Didactic) (Barbosa, 2004; Barbosa & Maldonado, 2006a). By means of a set of models, IMA-CID helps the developer(s) to determine the relevant parts of the knowledge domain, providing a systematic way to structure the concepts and related information. Considering a collaborative development process, the IMACID models can be particularly useful to represent the instructional design rationale, playing a key role to easier evolve and maintain the resulting educational products. This chapter is organized as follows. Firstly, we provide a literature review regarding models of instructional design and content modeling initiatives that have been adopted in the development of educational products. Secondly, we provide a definition for educational modules, describing its main components and characteristics. Thirdly, we present SP-DEM and discuss some issues of process specialization and instantiation; CMMI/ Educational is briefly described as well. Then, we summarize the relevance of the content modeling activity, presenting the IMA-CID approach and describing the set of models it comprises. Following, we illustrate the application of SPDEM and IMA-CID in the development of an educational module for software testing. Results from preliminary evaluations on the learning effectiveness achieved are presented as well. We also summarize our main contributions and perspectives for further work. Besides that, we briefly discuss some opportunities and emerging
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trends in active and lifelong learning, focusing on open issues regarding collaborative development applied to personal learning environments and learning networks. Finally, we provide a discussion on the overall coverage of the chapter, including our concluding remarks.
BACKGROUND In this section we provide an overview of the research and literature associated with the development of educational modules. In the first part of this review, we briefly describe some models of instructional design (ADDIE, CLE and HDM models, Learning Object Design model, and LODAS theory). At the same time that these models provide an overall understanding about the fundamental activities a process for educational modules should consider, they also help on identifying some relevant activities that have not been covered by any model yet. In the second part of our review, we focus on some specific initiatives for modeling educational content (EMLs and LD specifications, PALO language, MISA’s models, and DAPHNE, EHDM and MAPHE modeling approaches). The idea is to illustrate how different are the existing content modeling initiatives and motivate the reader about the need for an integrated modeling approach in order to create well-designed, highly flexible and configurable educational modules.
Models of Instructional Design The most basic model of instructional design is the ADDIE model (Gagné et al., 1992; Dick et al., 2001). ADDIE is the acronym for Analysis, Design, Development, Implementation and Evaluation, which correspond to the five stages of the model. The model begins with an analysis of instructional needs and solutions, followed by the design and development of learning objectives and methodologies, implementation of the educational
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content, and a summative evaluation of the resulting product. Formative evaluation and revision occur through the entire development process in order to guarantee the product is adherent to the instructional goals. ADDIE is considered the starting point to derive specific models for developing educational products and, for this reason, most of its development practices were taken in account in the establishment of SP-DEM standard process. Later in this chapter we will return to ADDIE model, discussing the main activities it comprises. Besides ADDIE, there are several other models of instructional design. The CLE (Constructivist Learning Environment) (Jonassen, 1999), for instance, focuses on authentic learner problemsolving model. The Jonassen’s model conceives of a meaningful problem, question or project as the focus of the environment, surrounded by interpretative and intellectual support systems such as related cases and information resources; cognitive, conversation and collaboration tools; and social context that support learner problemsolving (Jonassen, 1999). The learner’s goal is to interpret and solve the problem or complete the project. Basically, CLE establishes a list of learning activities that students should perform (Exploration, Articulation and Reflection) and a list of instructional activities that the environment should provide in order to support the learners (Modeling, Coaching and Scaffolding). HDM (Hypermedia Design Model) (McManus, 1996) is a constructivist model of design created for the Web and other hypermedia environments. The model comprises six stages. The first two stages define the instructional content, goals and format. HDM then splits into two paths (Dewald, 1999): (1) in the guided path, HDM provides suggestions to the learner as to the design goal and includes multiple paths to follow; (2) in the learner-controlled path, learners are able to specify their own learning objectives and are able to navigate a path of their own creation. The final step in HDM is to encourage learner
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self-reflection in order for learners to determine if their learning objectives have been achieved. Aiming at applying the principles of objectivist and constructivist design, Farrell & Carr (2007) proposed the Learning Object Design Model. In short, the model implements the strengths of the ADDIE, CLE and HDM models by integrating ADDIE’s comprehensive and systematic approach to design, CLE’s focus on relevant and engaging problem solving, and HDM’s provision for learner control and design guidance. LODAS (Learning Object Design and Sequencing Theory) (Wiley, 2001) addresses the issues of granularity (scope and design) and sequencing (combination) for developing learning objects. LODAS was designed to support the instructional use of learning objects and facilitate a significant amount of reusability across objects. By combining a number of existing instructional design theories, including Elaboration Theory (Reigeluth, 1999), Work Model Synthesis (Gibbons et al., 1995), Domain Theory (Wiley, 2001), and the Four-Component Instructional Design model (Van Merriënboer, 1997), LODAS provides taxonomy and design guidance for different types of learning objects. Wiley’s proposal is composed of six steps (Wiley, 2001): 1. Preliminary activities: In this step the designer needs to determine the appropriateness of using the LODAS for achieving the organization or course goals. 2. Content analysis and synthesis: In this step the designer needs to: (a) identify the necessary cognitive skills to achieve the overall goal of instruction; (b) break larger tasks into their associated smaller components, getting simpler as the decomposition continues until no more decomposition is possible; and (c) synthesize work models, i.e., the constituent skills are recombined into activities that people perform in the real world. 3. Design practice and information presentation: In this step the designer needs to identify
the practice and instruction necessary for each task. 4. Learning object selection or design: In this step the designer needs to: (a) review preexisting learning objects available in metadata repositories; and (b) create new learning objects. 5. Learning object sequencing: In this step the designer needs to sequence educational resources based on their cognitive complexity. 6. Loop back for quality improvement: In this step the designer has finished the instructional design and the development of learning situation and then he/she starts a process of quality improvement, which should become an ongoing activity. Formative and summative evaluations can be developed to ensure quality improvement. At this point, the LODAS cycle is completed and it is up to the instructor to initiate design or instructional strategies that either enhance the instruction or strengthen weaknesses in the initial program design. By analyzing the models of instructional design described in this section, we can notice that all of them address the fundamental (primary) activities related to the development of educational products. However, when applying a specific instructional model in the practice, many other relevant activities must be considered as complement to the fundamental ones. Supporting activities (e.g. Configuration Management, Documentation) and organizational activities (e.g. Coordination, Communication, Infrastructure) are examples of activities that should take place when developing an educational product. Besides that, despite of the several models of instructional design, no process for structuring the activities and tasks to be performed during the development of educational products has been defined so far. Indeed, as important as the selection of the appropriate model of instructional design is the definition of the adequate supporting
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technologies and tools to be used, the human resources and their roles in the development, as well as the deliverables (i.e., the precisely described products of this process). In this perspective, there is a lack of “(collaborative) processes for instructional design”, capable of defining a complete and systematic way to produce personalized, reliable and quality educational products. This scenario motivates us to work on the establishment of SPDEM, described later in this chapter.
Content Modeling Initiatives All models of instructional design point out the need for structuring and organizing the educational content. In general, the establishment of models for representing educational content involves several different aspects. For instance, we have to consider the specific characteristics related to the knowledge domain, to define the practical tasks and the evaluation mechanisms that will be applied to learners, and to establish pedagogical sequences for presenting the information. Besides that, different levels of abstraction in the content modeling activity can also be considered. EMLs (Educational Modeling Languages) (EML, 2000) have been proposed to support the description of instruction mechanisms and resources used during learning. According to Villani (2007), they provide a meta-model that enables to capture the resources (e.g. texts, figures and tools) used during the instruction as well as the instructional design information that establish in which manner such resources are intended to be used. An EML focuses on the coordination of the entities (e.g. persons, documents, tools) involved in instruction instead on the pedagogical approaches or instruction elements. Indeed, the main goal of EMLs is to support the modeling of the coordination issues between such entities (e.g. the documents/tools that can be accessed/ used by a learner) together with the establishment of particular goals responsible for driving and controlling the way in which such entities
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are intended to participate and interact. To allow this kind of modeling, an EML meta-model uses an activity scheme involving three main entities: (1) the Goals that have to be achieved in each Activity, which are usually related with an Object to be produced; (2) the Subject(s) that have to carry out each Activity, who participate playing specific Roles; and (3) the Environment where each Activity has to be carried out. Following the basic EML activity scheme, LD (Learning Design) specification (IMS, 2003) provides a notation to support the description of instruction in computational environments. Such notation is expressed using XML tags that must be arranged in accordance with the LD meta-model. As pointed by Paquette et al. (2005), the LD specification leaves open the choice of instructional models and tools that can support designers in the development of educational products, especially for those aiming at distributed, networked or online education. The main problem related to LD specifications is the lack of explicit support of the instructional design rationale. Thus, LD uses low level coordination mechanisms to describe the coordination of the learning elements, but it does not explicitly capture the coordination rationale involved in the instructional design. Villani (2007) argues that this problem can be solved considering a similar solution as in computer programming languages, where high level languages and assembler languages are focused on different concerns. EMLs have already been considered as assembler languages (e.g. LD) and high level educational modeling languages remains to be developed yet. In a related perspective, Rodríguez-Artacho (2002) introduced the PALO language as a cognitive-based approach to EMLs. Basically, the PALO language provides a layer of abstraction for the description of learning material, including the description of learning activities, structure and scheduling. The language is adherent with a reference framework to describe learning materials (Rodríguez-Artacho & Verdejo Maíllo, 2004).
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Such framework makes use of domain and pedagogical ontologies as a reusable and maintainable way to represent and store educational content, and to provide a pedagogical level of abstraction in the authoring process. MISA (French acronym for Méthode d’Ingénierie des Systèmes d’Apprentissage) is a method for instructional engineering based on a problem solving approach (Paquette et al., 2005). The method comprises six phases: (1) identify the educational problem; (2) define preliminary solution; (3) build learning system architecture; (4) design instructional materials; (5) model, produce and validate materials; and (6) prepare delivery of learning system. In each of phases 2 to 6, MISA proposes the development of four axes (Paquette et al., 2005). The Knowledge Model focuses on the graphical representation of the content domain. Facts, concepts, procedures and principles are displayed and interrelated. Target and prerequisites competencies are also identified. The Instructional Model consists of a network of learning events and units, to which knowledge and target competencies are associated. The Learning Material Models are useful to describe materials, their media components, source documents, presentation principles, and so on. The Delivery Models are multi-user workflows, where actors use or produce resources, while assuming different roles. Finally, there are some approaches specifically designed for developing educational hypermedia applications. DAPHNE (Portuguese acronym for Definição de Aplicações Hipermídia na Educação) (Kawasaki & Fernandes, 1996) is based on the Concept Mapping Theory (Novak, 1990) and on the Information Mapping Technique (Horn, 1989). EHDM (Educational Hyperdocuments Design Method) (Pansanato & Nunes, 1999) is based on the Concept Mapping Theory and on the Michener’s work (Michener, 1978). MAPHE (Portuguese acronym for Metodologia de Apoio a Projetos de Hipertextos Educacionais) (Pimentel, 1997) incorporates to the Concept Mapping
Theory some usual relationships of the ObjectOriented Modeling (Rumbaugh et al., 1991). By analyzing the content modeling initiatives described in this section, we can notice that each approach addresses different modeling perspectives, which can be suitable for a given learning scenario but inadequate for others. EMLs, for instance, are in a higher level of abstraction, focusing on coordination issues. On the other hand, the MISA’s models as well as DAPHNE, EHDM and MAPHE focus on the instruction elements. PALO language, particularly, is in an intermediate level, addressing some coordination and some instruction issues as well. Even considering only the modeling initiatives in the same level of abstraction, we can observe that each approach addresses specific issues of content modeling. Conceptual aspects, for instance, are emphasized in DAPHNE, MAPHE and MISA’s Knowledge Model; EHDM just provides mechanisms to support the domain modularization. Instructional issues are addressed by DAPHNE, EHDM and MISA’ Instructional Model, while MAPHE does not provide specific mechanisms for dealing with them. Didactic aspects are considered in all approaches by means of precedence relations. In our work, we are interested in an “integrated” approach for modeling the educational content, capable of providing a complete set of models to address the conceptual, instructional and didactic perspectives. Also, we intend to investigate a way to represent “dynamic” contexts of learning, where the elements of the content can be dynamically determined according to specific parameters defined in terms of the characteristics of the course, learners and instructors. Such characteristic is important to foster aspects of customization, reusability and adaptability of the educational products in order to better engage the students in an active learning process. These research points have not been considered by the existing modeling approaches so far, motivating us to the proposition of IMA-CID.
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EDUCATIONAL MODULES The concept of educational modules provides the foundation for our research line. In a few words, an educational module corresponds to a concise unit of study, composed of theoretical and practical content, which can be delivered to learners by using technological and computational resources (Barbosa, 2004; Barbosa & Maldonado, 2006a; Barbosa & Maldonado, 2006b). For theoretical content, instructors use books, papers, web information, slides, class annotations, audio, video, and so on. Practical content corresponds to the instructional activities and associated evaluations, as well as their resulting artifacts (e.g. executable programs, experimental studies, collaborative discussions, wikis, and so on). Domain-specific tools related to the subject domain to be taught as well as the results obtained from their application can also be seen as practical content. Such tools can be integrated as part of an educational module in order to enable the application of fundamental concepts in realFigure 1. Main components of an educational module
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istic scenarios. This integration fosters training situations and promotes exchange of technology between industry and academia, also providing learners with domain-specific skills. Theoretical and practical content are integrated in terms of learning materials. In order to deliver the learning materials, an adequate infrastructure is needed. Learning environments (e.g. Moodle (Moodle, 2006), Sakai (Sakai, 2006), WebCT (Goldberg et al., 1996), DotLRN (DotLRN, 2009) as well as technological and computational resources (e.g. e-Class (Brotherton & Abowd, 2004), CoWeb (Dieberger & Guzdial, 2003)), illustrate some of the required infrastructure related to the educational modules. Figure 1 shows the main components of an educational module. The development of educational modules should take into account some key characteristics of knowledge. We have to consider, for instance, the knowledge structure and organization, i.e. how the information related to the subject domain can be integrated and how to establish a well-
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defined structure to represent it. Thus, the way the educational module is structured and organized deeply impacts its learning effectiveness. So, it is fundamental that when creating an educational module designers (not necessarily the domain experts) clearly understand the subject domain, being able to identify and organize concepts and relevant information and, also, to specify practical activities and related evaluations. Another characteristic to be considered is the dynamic and evolutionary aspect of knowledge, i.e. new knowledge is continuously produced and referenced in response to previously acquired learning experiences. In each class, either traditional or distance learning mode, new content (e.g. slides, annotations, texts, results and subproducts from practical activities) is created when delivering the module to learners, and should be incorporated into the content previously defined. In fact, the learning material is continuously expanded (active growth) as a consequence of contributions of all course participants (learners and teachers). Furthermore, such material is frequently referenced (intrinsic reference), aiming at both consolidating the previous knowledge acquired as well as motivating the apprenticeship of new concepts and information inter-related. Finally, issues regarding reuse and sharing of knowledge should also be addressed. Reusability allows the content developed in a given learning context to be easier available and transferable to another one, with different educational purposes. However, different needs, backgrounds and skills can represent a barrier to effective learning. Since there can be widely different viewpoints and assumptions regarding the same subject matter, the consequent lack of a shared understanding can lead to poor communication and collaboration, impacting the learning processes in general. Indeed, a shared conceptualization of the knowledge domain represents the basis for constructing and for reusing high-quality educational modules.
SP-DEM: A STANDARD PROCESS FOR DEVELOPING EDUCATIONAL MODULES Similar to software products, educational modules require the establishment and integration of innovative methods, tools and procedures into systematic processes aiming at producing customized, reliable and high-quality products. The development of such modules can involve developers from different domains, working on multi-disciplinary and heterogeneous teams, geographically dispersed or not. They should cooperate, sharing data and information regarding the project. Furthermore, we should consider the adoption of supporting tools, which can be used either as part of the educational module under construction or as a mechanism to automate its development process. Next we discuss the standardization of processes for developing educational modules, describing the main characteristics of SP-DEM – a Standard Process for Developing Educational Modules (Barbosa, 2004; Barbosa & Maldonado, 2006b).
Development Issues In the establishment of SP-DEM some specific issues for the development of education modules were considered – practices for instructional design, content modeling, “open products” development and collaborative and distributed work. Content modeling will be considered in a specific section of this chapter. The other issues will be briefly discussed next.
Instructional Design Instructional design (Gagné et al., 1992; Clark, 2000, Lee & Owens, 2000; Dick et al., 2001) consists of the systematic application of scientific principles about how people learn aiming at developing instruction. The most basic model of instructional design is the ADDIE model (Gagné
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et al., 1992; Dick et al., 2001). The model is based on the Gagné-Briggs’ Theory of Instructional Design, according to which instructional design must consider both external and internal conditions of learning. External conditions include the learning environment and sequencing of instructional content while internal conditions refer to learner mind-set, goals and prior understandings (Farrell & Carr, 2007). Instruction is then “a deliberately arranged set of external events designed to support internal learning processes” (Gagné et al., 1992). The model comprises five stages, iteratively applied. The results from each stage act as entries for the next one: •
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•
•
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Analysis: This stage is primarily characterized by the determination and analysis of the instructional goals. It normally involves the instructional needs and solutions, actual and expected levels of knowledge and/or performance, prerequisites, learners’ profile, etc. The Analysis stage can be divided in Needs for Instruction and Front-End Analysis. Design: The focus of this stage is to make a blueprint of the scenario related to the educational product. Furthermore, it specifies the elements related to the strategies, interactivity, feedback, structure, usability guidelines, etc. Development: This stage addresses the events related to the creation of educational products – elaborating the module interface, producing media elements, generating and/or connecting practical activities and evaluative instruments, integrating domain-specific tools and resources, establishing navigation sequences, accomplishing usability tests, among others. Implementation: This stage focuses on delivering the educational product to learners. Testing the module in different environments (learning platforms, hardware,
•
configurations, etc.) is also considered at the Implementation stage. Evaluation: This stage covers the entire product, aiming at measuring the learning effectiveness achieved. Data from final users (learners and/or instructors) should be collected. The obtained results are compared with the instructional goals previously identified.
One of the strengths of the ADDIE model is that it offers a series of questions to ensure a critical examination of instructional goals, learning objectives and learner needs at each stage of the design process. The model proceeds from one stage to another with revision occurring throughout the design process to ensure that the product of design does not run askew from the instructional goals (Farrell & Carr, 2007). Although several models for instructional design have been developed, most of them are based on the core ideas of ADDIE model.
“Open Products” Development The development of open source software has emerged in industries, universities and research centers. Basically, the software is considered open source when it is accompanied: (1) by permission for use, copy, distribution and redistribution, with modifications or not, with no cost (free) or paying a tax; and (2) by the source code, in order to allow changes. Changes do not need be communicated, but should be identified and distributed “openly” (McConnell, 1999). Among the reasons for the increasing interest in open source software, we can point out: stability, portability to different platforms, support to the users, access to the source code, and low cost. The idea of open source software can also be extended to the context of educational products. An “open educational module” would have a license for use and distribution, being allowed to the users to modify the delivered content in order
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to adapt the module to specific instructional goals and needs. Considering this perspective, the development of educational modules can be conducted in agreement with characteristics and principles of open source software development. Indeed, some common developmental characteristics can be observed. The first one refers to the need for continuous evolution, in which updated versions of the software are released frequently, in response for the users’ needs. The same characteristic is essential to the development of educational modules, especially due to the dynamic and evolutionary aspect of knowledge, from which the modules should be continuously evolved in consequence of previous learning experiences. The geographical distribution of developers, which can participate in the construction process in a collaborative way (several times, as volunteers) is another common characteristic. The main idea is that each developer can contribute for the product (software or educational module) and these contributions will be filtered in a “darwinian” way, i.e. the best code (or the best content) will survive, being incorporated to the product. Obviously, there is a need for strong coordination among developers. The development of “open products” also requires a set of collaborative technologies (e.g. e-mail, discussion forums, web, versioning controller systems, information repositories) to guarantee the communication and interaction among developers, geographically dispersed or not. In the case of educational modules, the adoption of collaborative technologies is crucial not only for its development process, but also for delivering and using the module, in order to conduct the activities and evaluations proposed to the learners. As a final remark, it is important to highlight that Learning Networks and Personal Learning Environments (EDUCAUSE, 2009; Downes, 2007) (emerging trends in lifelong learning – see Section Future Research Directions) can benefit from the idea of openness – continuous evolution, geographical distribution, and collaborative tech-
nologies – in order to better engage the learners in the process of creating, modeling and managing educational content.
Collaborative and Distributed Work The development of educational modules can involve people from different knowledge areas, working on multi-disciplinary and heterogeneous teams, geographically dispersed or not. Such teams should cooperate and interact, sharing data and information related to the project (e.g. specifications, domain models, content, and results from learners’ performance, among others). Besides that, the teams’ skills can vary, not only due to the human resources but also in terms of the technological, computational and economical resources available. Moreover, the activities conducted by a given team can be required for another team, characterizing dependence relations among them. Maidantchik (1999) highlighted that the quality of the products developed by geographically dispersed teams depends of effective communication, coordination of the distributed teams, systematic traceability of activities and artifacts, and availability of information regarding the development process. Similarly, the characteristics and needs identified in the distributed software development can also be observed in the context of educational modules: •
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Teams coordination: A strong and effective coordination among the development teams should be provided as well as the establishment of each team capability and the allocation of activities and responsibilities accordingly. Activities coordination: A work flow regarding the development activities and tasks should be established and managed. Concurrent tasks requiring collaboration among teams should be identified and controlled.
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•
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Artifacts control: Integration problems regarding the module’s components, distributed authoring and modeling, visibility of evaluations and performance results, changes notification, and configuration management should be addressed. Communication support: Development teams should interact, exchanging experiences, problems, solutions and results. Also, all project information should be available in order to guarantee the effective management of the module development process.
In this section we dealt with collaboration under a developmental viewpoint. However, as previously mentioned, the learning process can also benefit from collaborative issues. In this sense, collaborative learning, where several learners learn together and from each other according to well-designed learning methods, has proven to be a successful method in traditional classroom settings. In spite of that, in the context of lifelong learning little attention has been paid to collaboration so far (Wessner et al., 2002).
SP-DEM Definition As stated by Paquette et al. (2005), software engineering brings some interesting solutions to meet demands required by innovative technologies used in learning. By adapting software engineering principles to instructional design, well-defined process and principles can arise, helping developers to produce precisely described and high-quality educational products. Motivated by this idea we have proposed SP-DEM. The ISO/IEC 12207 standard (ISO, 2004) aims at defining and standardizing processes and basic activities of the software development process. Standardization of processes results on a standard process, which is responsible for describing the main elements that should be incorporated in any defined process of the developing organization.
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SP-DEM is based on ISO/IEC 12207, tailored to the context of educational modules by including aspects of content modeling, practices from instructional design, and issues of “open products” development and of distributed and cooperative work. SP-DEM establishes a set of processes that can be employed to acquire, supply, develop, deliver, operate, and maintain educational modules. Three categories of processes are defined: (1) the primary processes deal with the main activities and tasks performed during the life cycle of an educational module; (2) the supporting processes support other processes and contribute to the success and quality of the development project; and (3) the organizational processes are employed by an organization to establish, implement and improve an underlying structure made up of associated life cycle processes and personnel. Figure 2 shows the overall structure of SP-DEM. Dashed rectangles are the processes adapted from ISO/IEC 12207. Dotted rectangles are the processes adapted from the standard process for geographically dispersed working groups (Maidantchik, 1999). White rectangles are the processes specifically developed to the context of learning. The establishment of the primary processes considered principles and practices from instructional design, according to the five stages of ADDIE model. These practices were spread out through the activities and tasks related to the primary processes. Table 1 shows the correspondence among the ADDIE model stages and the SP-DEM primary processes. For instance, Operation and Delivery processes address issues of the implementation stage of the ADDIE model. Acquisition and Supply processes are not included in the table since ADDIE does not consider specific stages for addressing these issues. The Definition Process is responsible for determining the learning problem to be solved. It identifies the users’ needs for instruction and establishes the learning requirements to be satisfied. The following activities are established: (1)
Collaborative Development of Educational Modules
Figure 2. Overall structure of SP-DEM
definition of learning problem and needs for instruction; (2) initial definition of learning requirements; (3) analysis of project viability; (4) determination of the module scope; (5) construction of a terminology repository; (6) documentation of the definition process; and (7) revision and approval. For instance, activity (1) is based on the analysis stage of the ADDIE model; it involves the following tasks: determination of actual learnTable 1. Correspondence among ADDIE model stages and SP-DEM primary processes ADDIE Model Stages
SP-DEM Primary Processes
Needs for Instruction
Definition
Front-end Analysis
Development
Design Development Implementation Evaluation
Planning Development Development Operation Delivery Maintenance
ing conditions; definition of learning goals and related skills; prioritization of learning goals; and identification of learning discrepancies. Activity (5) is especially important since it involves the construction of a terminology repository, i.e. a dictionary of terms about the subject knowledge domain. This mechanism is relevant for supporting distributed development teams to share concepts and related information, and to adopt a uniform and consistent terminology during the entire project. Moreover, the construction of a dictionary of terms is important not only for collaboration and distribution perspectives, but also for the development of “open products”. Such dictionaries can be extended to knowledge bases or even domain ontologies (Uschold & Grüninger, 1996), allowing knowledge sharing among developers. The purpose of the Planning Process is to review the module’s requirements aiming at defining its structure and establishing the plans to be used for managing the project and guaranteeing the quality
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of the module. The related activities are: (1) initial specification; (2) determination of the module’s components; (3) planning; (4) determination of standards; (5) documentation of the planning process; and (6) revision and approval. In the case of distributed development teams, such activities should be organized in two levels – one for each work team, and another considering the project as a whole. In the case of “open modules” development, the planning activities can be conducted in a more flexible way. However, emphasis should be given for team planning, selecting developers and allocating them adequate roles, according to their personal interests and motivations for participating in the project. The Development Process aims at changing a set of learning requirements into an educational module that meets the stated needs. Basically, it addresses the life cycle activities for the development of educational modules: (1) process establishment; (2) analysis; (3) design; (4) implementation and integration; (5) testing; (6) installation; (7) metadata construction and updating; (8) management of the terminology repository; (9) documentation of the development process; and (10) support for acceptance. Particularly, the task of content modeling is established as part of activity (3). Such task will be described later in this chapter. Finally, the Delivery Process establishes the activities and tasks that should be performed by the module’s instructor, being specifically defined for the context of educational modules development. The following activities are established: (1) initiation; (2) delivery; (3) monitoring and instructional support; and (4) identification of problems and improvements. For instance, activity (4) addresses the problems and weaknesses found when using the module. They should be registered and sent to the Maintenance Process. Learning goals and objectives not achieved as well as the knowledge resulting from using the module should be identified, documented and published for future improvements. Cases of success and recommendations for future developments should also be reported.
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In this section we briefly described some of the SP-DEM primary processes, including related activities and tasks. Supporting and organizational processes were established in a similar way as the primary ones. Readers can find further details about the complete set of SP-DEM processes, activities and tasks in (Barbosa, 2004).
SP-DEM Specialization and Instantiation The standard process is responsible for the establishment of a unique development structure to be adopted and followed by the entire organization (Barbosa & Maldonado, 2006b). However, changes in organizational procedures, learning paradigms and principles, learning requirements, development methods and strategies, as well as the size and complexity of the projects, among other aspects, impact the way the educational module is produced. Thus, the processes should be defined on a case by case basis, taking into account the specific features of each particular project. Process specialization and instantiation have been explored in order to apply the SP-DEM into specific learning environments and organizations (Barbosa & Maldonado, 2008b). In short, the definition of a process for developing a specific educational module should consider its adequacy to: (1) the adopted technologies, supporting mechanisms and budget; (2) the domain of the educational application; (3) the characteristics of the module; (4) the maturity level of the development team; and (5) the characteristics of the organization. In this sense, processes into different levels of abstraction can be defined. The main aspects of SP-DEM instantiation and specialization are briefly discussed next.
Specializing SP-DEM: the CMMI/Educational Model In the same line as the CMMI model for software development (Chrissis et al., 2003), a capability
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maturity model for educational modules development has been proposed and used as a supporting mechanism to the SP-DEM specialization (Barbosa, 2004; Barbosa & Maldonado, 2006b, Barbosa & Maldonado, 2008b). The main goals are to guarantee that collaborative and distributed projects can be developed with unlike maturity level teams and, also, to improve each working group capability. CMMI/Educational was adapted from CMMI to the context of learning. The capability maturity model for geographically dispersed software development (Maidantchik, 1999) was also considered to establish the tasks and practices related to the collaborative and distributed creation of the modules. Both continuous and staged representations were addressed. In this section, we describe the staged representation of CMMI/Educational. Details about the continuous representation are available at (Barbosa, 2004). Similar to the staged representation of CMMI for software, CMMI/Educational establishes five maturity levels: 1. Initial: There is no defined process for the entire organization or, if so, it is not adopted and/or followed. The quality of the educational modules depends on competence of individuals, i.e., changing people impacts on the quality of the modules. Most of the problems are managerial, not technical. 2. Managed: Organizational policies guide the projects, establishing management processes. Well-succeed development practices can be repeated in new projects. 3. Defined: There is a well-defined process for developing the modules. Such process is approved, documented and accepted by the entire organization; it is adapted for each project, according to specific needs. Organization is concerned with the collection and dissemination of lessons learned of a given project for the other ones.
4. Quantitatively Managed: The process is quantitatively measured and managed. Process performance can be foreseen. 5. Optimizing: Changes in technologies, learning paradigms and principles, and even in the process itself can be managed to avoid impacting on the quality of the modules. Table 2 presents the overall structure of CMMI/ Educational, considering the process areas (PAs) established in each maturity level. PAs in bold are specifically defined for the educational modules development. For instance, Knowledge Evolution Management (KEM) is responsible for: (1) identifying, choosing and evaluating the new information related to the subject domain; and (2) establishing and maintaining the supporting mechanisms to integrate the new information into the module. Besides that, some specific practices were included as part of existing PAs. For instance, at Level 5 we defined a new practice – Change Management of Educational Paradigms and Principles –, included as part of the PA Organizational Innovation and Deployment (OID). Further information regarding CMMI/Educational can be found at (Barbosa, 2004). By determining the correspondence among the SP-DEM issues and the PAs of CMMI/Educational we can identify the process categories that would require more attention and generate the SP-DEM specializations. The specialization of a given maturity level is generated by excluding the activities of higher levels. So, the specialization of the second level does not contain the activities of the third, fourth and fifth levels. Instead, it contains only the activities related to the PAs of Level 2.
Instantiating SP-DEM An instance of a process should take in account the development and organizational environment; it may address specific features of a particular project. Process instantiation consists of the selection and
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Table 2. CMMI/Educational: Staged representation. Staged Grouping Maturity Level 1 Initial
Maturity Level 2 Managed
Maturity Level 3 Defined
Maturity Level 4 Quantitatively Managed
Maturity Level 5 Optimizing
Acronyms
Process Areas
–
–
REQDM
Requirements Distributed Management
PDP
Project Distributed Planning
PDMC
Project Distributed Monitoring and Control
SAM
Supplier Agreement Management
AAM
Activities Assignment Management
PPQA
Process and Product Quality Assurance
CDM
Configuration Distributed Management
KDM
Knowledge Distributed Management
IDM
Infrastructure Distributed Management
COMM
Communication Management
WGCM
Work Group Capability Management
MA
Measurement and Analysis
OPF
Organizational Process Focus
OPD
Organizational Process Definition
OPSD
Organizational Process Specializations Definition
OT
Organizational Training
KEM
Knowledge Evolution Management
IPM
Integrated Project Management
RISKM
Risk Management
DC
Distributed Coordination
RD
Requirements Development
TS
Technical Solution
PI
Product Integration
VER
Verification
VAL
Validation
DEI
Domain Experts Interaction
UDMM
Utilization Distributed Monitoring and Management
DAR
Decision Analysis and Resolution
OPP
Organization Process Performance
OPSP
Organization Process Specialization Performance
QPM
Quantitative Project Management
QUM
Quantitative Utilization Management
OID
Organizational Innovation and Deployment
CAR
Causal Analysis and Resolution
allocation of development methods and techniques as well as human, technological and computational resources (Barbosa & Maldonado, 2006b).
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In order to illustrate the SP-DEM instantiation we consider its application in a specific kind of educational projects. Basically, these projects
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should be conducted in a given academic institution and the resulting modules can be applied either as part of an one-semester course, at the academic institution itself; as a short-course, a tutorial or an invited talk, in scientific events; or as a training course, at industrial organizations. To instantiate SP-DEM we defined, among others: (1) the human resources and their roles in the process; (2) the produced and consumed artifacts; (3) the life cycle model and development methods and techniques; and (4) the automated tools and supporting mechanisms for the process. Regarding the human resources and their roles in the development process of educational modules, the following actors were selected to compose the development team (Barbosa & Maldonado, 2008b): •
•
•
• •
Domain expert: Provides support and clears doubts related to the establishment of components and relevant parts of the educational module. Plays a fundamental role in content modeling, particularly on the construction of the conceptual model and on the determination of the related knowledge categories. Also, performs the instructional validation of the module. Project manager: Assigns activities, integrates results, specifies the module’s metadata, and defines the validation mechanisms to be adopted. Team coordinator: Coordinates the development team, fostering communication among the team members and the project manager. Version manager: Maintains the different versions of the module. Developer: Develops the educational module. Several different roles can be assigned to him/her: ◦⊦ Analyst: Specifies the module requirements. Also, performs the module validation.
◦⊦
•
•
Instructional designer: Models the educational content and designs the module interface. ◦⊦ Implementer: Implements the module, i.e., edits the content, integrates the media components, verifies and tests the module. ◦⊦ Operator: Provides operational support for the users. ◦⊦ Maintainer: Maintains the module. Technician: Establishes and manages the technological and computational resources used in the project. Provides technical support to the module development and delivery. Instructor: Establishes the instructional needs, delivers the module and monitors its use. Also, can help on verification and validation activities.
Figure 3 illustrates the relationship among the team members and the main roles assigned to them. Produced and consumed artifacts are also illustrated. As the life cycle model to be adopted through the projects we chose the ADDIE model (Gagné, 1992; Dick et al, 2001). It is specifically designed for the development of educational products, establishing mechanisms for the systematic application of practices and principles of instructional design. For modeling the educational content, we chose the IMA-CID approach (Barbosa, 2004; Barbosa & Maldonado, 2006a), described in the next section. In terms of technological and computational resources, tools and mechanisms to automate and support the instantiated process should be selected according to the roles they would play in the context of each specific project. Two basic categories of tools should be analyzed (Barbosa & Maldonado, 2006b, Barbosa & Maldonado, 2008b):
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Figure 3. Development team and assigned roles
•
•
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Authoring tools support the creation of the educational content, taking into account representation, integration and management aspects of the subject knowledge domain. We consider as authoring tools: (1) tools for content modeling; (2) tools for knowledge integration; and (3) tools for content editing. Educational tools consist of the required infrastructure for integrating the learning materials and for delivering/publishing them to the learners. They are also responsible for providing support to perform practical tasks and evaluations. We consider as educational tools: (1) presentation tools, which support delivery of learning materials; (2) collaborative tools, which support collaborative work and augment communication and discussion among in-
structors and learners; (3) evaluative tools, which support the evaluation of learner’s performance; and (4) capture tools, which provide ways to transform the content of a traditional lecture into browsable, searchable and extensible digital media that serves both short- and long-term educational goals. In fact, capture tools can serve as one part of a rich and dynamic educational repository. Additionally to the authoring and educational tools, domain-specific tools should also be considered. The main goal is to enable the application of basic concepts in realistic situations, fostering training situations and promoting exchange of technology between industry and academia. The results of the identification and analysis of supporting tools constitute the alternatives to
Collaborative Development of Educational Modules
Figure 4. The IMA-CID modeling approach
instantiating the process. Indeed, each different instance of SP-DEM can establish a specific set of automated tools and supporting mechanisms to be applied. In our instantiated process, we analyzed the adequacy of different tools; for the complete references on the supporting tools analyzed see (Barbosa, 2004). In short, we adopted specific tools (Moodle and WebCT) and generic tools (web and PowerPoint) as the presentation tools. As the collaborative tool, we adopted CoWeb; as the infrastructure for capturing the classes, we chose the eClass environment. As the support for authoring the educational content, we adopted generic editing tools (Word, PowerPoint, FrontPage, Visio, LaTeX). To support communication among team members we chose electronic mail and CoWeb.
IMA-CID: AN APPROACH FOR MODELING EDUCATIONAL CONTENT Content modeling plays a fundamental role in the development process of educational modules (Barbosa & Maldonado 2006a); for this reason, it was also considered in the establishment of SP-DEM. Basically, it helps the developer to determine the main concepts to be taught, providing a systematic
way to structure the relevant parts of the subject domain. Also, how the content is structured and organized directly impacts the reusability, evolvability and adaptability of the module. Despite its relevance, there are few approaches specifically designed for modeling educational content. Furthermore, each model addresses different perspectives, which can be suitable for a given learning scenario but inadequate for others. Motivated by this scenario, we have proposed IMA-CID (Integrated Modeling Approach – Conceptual, Instructional and Didactic) – an integrated approach for modeling educational content (Barbosa 2004; Barbosa & Maldonado, 2006a). IMA-CID is composed of a set of models, each one addressing specific issues to structure and represent the educational content. Figure 4 summarizes the key points of the approach.
Conceptual Model The Conceptual Model consists of a high-level description of the knowledge domain, representing its main concepts and the relationships among them. The relationships can be divided into two classes. Structural relationships are useful to set up taxonomies among concepts and make inferences about the knowledge, representing a generic
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category of relationships, applicable to any kind of domain. Relations such as type-of and part-of are examples of structural relationships. On the other hand, domain-specific relationships are userdefined and have their meaning associated to a particular subject, carrying their own semantics. The construction of the conceptual model is based on the Conceptual Mapping Technique, proposed by Novak (Novak, 1990). Among the reasons for choosing this technique we point out: (1) it is suitable for representing concepts and for structuring the knowledge domain; (2) it is intuitive and easy to use; (3) it is based on educational principles, having a good acceptance among educational specialists and professionals; and (4) it is adopted by the majority of existing modeling approaches for educational content. In addition to the rules for creating conceptual maps, some specific notations aimed at representing the relationships of concept taxonomy (type-of) and concept composition (part-of) were included.
Instructional Model Besides concepts, information items and instructional elements should also be considered as part of the knowledge domain. In the Instructional Model we are interested in defining such additional information related to the concepts previously identified. We are not interested in how the information will be associated, but in what kind of information we can use to develop more significant and motivating educational content. The construction of the instructional model involves two phases: (1) the refinement of the conceptual model; and (2) the definition of the instructional elements. In the first phase we have to specify what kind of additional information can be incorporated to the concepts already represented in the conceptual model. We call them information items. Several theories and techniques can be referred to support the establishment of information items. Michener (1978), for instance, specified three basic elements for structuring
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mathematical knowledge: concepts, results and examples. Horn (1989) proposed the Information Mapping Technique, which consists of dividing the information related to the nodes of a conceptual map into small portions of information, called information maps (concept, structure, procedure, fact, process, classification, and principle). Similarly, information maps can be divided into smaller parts, called information blocks (definition, example, classification list, rule, synonym, theorem, exercise, and so on). In our work we have adopted the Component Display Theory (CDT), proposed by Merrill (1983). Regarding content, CDT specifies the following elements (Clark, 2000; Robles, 2007): •
•
• •
Concepts: Symbols, events and objects that share characteristics and are identified by the same name. Concepts make up a large portion of language and understanding them is essential to communication. Facts: Logically associated pieces of information. Names, data and events are examples of facts. Procedures: A set of ordered steps to solve a problem or accomplish a goal. Principles: Work through either causeand-effect or relationships. They explain or predict why something happens in a particular way.
In the second phase we have to define the instructional elements, used as a complement to the information items. Three types of elements can be defined (Barbosa, 2004; Barbosa & Maldonado 2006a): •
Explanatory: Deals with the complementary information used for explaining a given topic – examples, hints, suggestions of study, and so on. They can play a number of different roles depending on their purpose. An example, for instance, can be associated according two distinct perspec-
Collaborative Development of Educational Modules
•
•
tives – to motivate the study of the topic, or to illustrate its use. Exploratory: Allows the learner to navigate through the domain, practicing concepts and other relevant information. Guided exercises, simulations and hands-on assignments are representative of this category. Evaluative: Allows assessing the learner’s proficiency on the domain. Diagnostic, formative and summative evaluations, in terms of subjective and/or objective questions, are examples of evaluative elements.
As a support to construct the instructional model, we adopted the HMBS (Hypertext Model Based on Statecharts) model (Turine et al., 1997). In short, HMBS uses the structure and execution semantics of statecharts to specify the structural organization and the browsing semantics of hyperdocuments. We focused on the mechanisms for hierarchical decomposition HMBS provides, complementing the idea of hierarchical organization, already explored in the conceptual model. To make HMBS suitable for modeling the instructional aspects, it was extended for representing the different knowledge categories, i.e., concepts, information items and instructional elements. We highlight that IMA-CID does not prescribe the use of the Merrill’s knowledge categories as mandatory. For instance, a user of AIM-CID could adopt the Michener’s categories (Michener, 1978), structuring the information content into concepts, results and examples. Actually, regardless of the theory or technique adopted, the main goal is to provide adequate mechanisms to specify and differentiate the information, avoiding inconsistencies and/or ambiguities. The flexibility of choosing the knowledge categories to be represented aims to guarantee the modeling approach to be independent of particular learning theories and/ or principles, which can be defined according to the author’s preferences and needs.
Didactic Model The Didactic Model is responsible for the establishment of prerequisites and sequences of presentation among conceptual and instructional elements. In short, it can be used to illustrate the way the didactic space is modified while being navigated by the user, i.e., which information becomes active/inactive when a given path is traversed. Moreover, it is useful to represent dynamic contexts of learning, where the elements of the content are determined according to specific parameters defined in terms of the characteristics of the course, learners and instructors. Since HMBS addresses relevant requirements under the didactic perspective (history mechanisms, event propagation and learning contexts definition), it was adopted in order to construct the didactic model. Additionally, by using HMBS we can validate the educational content through the analysis of the subjacent statechart properties (Turine et al., 1997). As an extension to HMBS at the didactic level of IMA-CID, we introduced the idea of open specifications, providing support for the definition of dynamic contexts of learning. Depending on aspects such as audience, learning goals and course length, distinct ways for presenting and navigating through the same content can be required. An open specification allows representing all sequences of presentation in the same didactic model. So, from a single model, several versions of the same content can be generated according to different pedagogical aspects. Moreover, when an educational module is implemented based on an open specification, its navigation paths can be defined by the user (the instructor, in the case of traditional classes; the learner, in distance and active environments; or both, in the case of blended learning), in “execution time”. During the presentation, the user is able to dynamically decide which topics should be navigated and in which sequence based on the based on the learner’s skills, understanding and feedback, for instance.
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Ultimately, open specifications are an important mechanism to foster aspects of customization, reusability and adaptability of the educational products, contributing to better engage the students in an active learning process. Aiming at representing open specifications, HMBS was extended with the idea of DD (Dynamically Defined) states and hierarchies of DDsuperstates. In short, all OR substates of a DD state (ORDD) are totally connected to each other. From any substate of a DD state X, we can reach all other substates of X. For the sake of legibility, transitions and events are implicitly represented. A hierarchy of DD-superstates establishes that leaving a DD state X can active the ORDD states from the hierarchy of DD-superstates of X. Both mechanisms (DD states and hierarchies of DDsuperstates) help to establish open specifications since they allow representing all sequences of presentation in the same didactic model. More detailed information on DD states and on hierarchies of DD-superstates can be found in (Barbosa, 2004). It is important to highlight that everyone interested in teaching and learning can benefit from ↜IMA-CID: instructors, domain experts, content designers, education and training professionals, and learners as well. The main concern regarding the IMA-CID application is the need to be familiar with the structure and execution semantics of statecharts, what can lead to some additional costs to initially develop IMA-CID -based materials. On the other hand, the quality factors of the produced materials, such as customization, evolvability, maintainability and reusability, would increase the long-term benefits and decrease the overall costs.
SOFTTEST: AN EDUCATIONAL MODULE FOR SOFTWARE TESTING SP-DEM and IMA-CID have been applied into the development of educational modules for different domains: software testing, programming
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foundations, open source methods and technologies, critical embedded systems, and elementary materials on mathematics. Particularly, the training materials produced in the context of two broad projects have been developed according to them: (1) QualiPSo Project (Quality Platform for Open Source Software – www.qualipso.org), funded by the European Community (IST-FP6-IP-034763); and (2) INCT-SEC (National Institute of Science and Technology – Critical Embedded Systems – www.inct-sec.org), financed by the Brazilian funding agencies. Both projects bring together important players from industry and academia. Next we illustrate the application of SP-DEM and IMA-CID in the development of SoftTest – an educational module for software testing. We chose the testing area since it is one of the most relevant activities regarding software development (Myers et al., 2004) but, at the same time, it is a difficult topic to learn or teach without the appropriate supporting mechanisms (Shepard et al., 2001; Edwards, 2003).
Applying the SP-DEM Instantiated Process The SoftTest educational module was developed according to the instance of SP-DEM previously discussed. The main aspects regarding the module development are summarized next. Basically, most of the learning problems identified in the software testing domain can be related to the characterization of: (1) testing goals and limitations; (2) testing steps and phases; (3) testing requirements, testing criteria, and testing tools. So, our learning goal was to foster theoretical, empirical and tool specific knowledge by providing learners with a broad and deep view of the testing activity fundamentals and of their practical application by mastering testing tools. The target audience of SoftTest was graduate and undergraduate students, as well as professionals from industry. Although each group of learners can require a different way for presenting
Collaborative Development of Educational Modules
and navigating through the module, the content should be essentially the same. The module should be as flexible as possible (by applying the idea of open specification) in order to be adequate to different profiles without having to modify its structure significantly. Methodologies and tools used for developing SoftTest were in agreement with the SP-DEM instance. As development methodologies, we adopted ADDIE model and IMA-CID approach. Word, PowerPoint, FrontPage, Visio, LaTeX, Moodle, WebCT, CoWeb, and e-Class were chosen as authoring and educational tools. As a domainspecific tool, we selected Proteum (Delamaro et al., 2001). The development team was composed of three members: (1) a teacher, acting as the domain expert as well as the instructor of the module; (2) a graduate student, performing the roles of project manager, version manager, coordinator and developer; and (3) an undergraduate student, acting
as developer and technician. All members worked collaboratively for developing the module. Their roles were also in agreement with the SP-DEM instantiation (Figure 3). SoftTest was composed of 16 sub-modules; Figure 5 shows its overall structure in terms of a conceptual map. For each sub-module, concepts, facts, principles, procedures, examples and exercises were modeled and implemented as a set of slides, integrated to HTML pages, text documents, learning environments and testing tools. Figure 6 presents an overview of SoftTest, illustrating its main components and their integration. Following SP-DEM process, SoftTest was evaluated according to three perspectives: (1) standards verification, checking the module against the interface standards established; (2) editorial verification, looking for grammar errors; and (3) functional verification, looking for logical errors through the navigation. We have also applied SoftTest in realistic learning scenarios aim-
Figure 5. Overall structure of SoftTest
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Figure 6. The SoftTest educational module
ing at evaluating its practical use. The results obtained will be briefly discussed later in this chapter.
Modeling the SoftTest Content The IMA-CID models were collaboratively constructed for each one of the 16 sub-modules of SoftTest. Following we focus on the construction of the models for a particular subject of the Testing Techniques sub-module – the Mutation Analysis criterion (Demillo et al., 1978). The first step was to construct the conceptual model. Figure 7 shows the conceptual model for mutation analysis. We can infer, for instance, that mutation analysis is one of the testing criteria of the error-based technique, and assumes the principles of competent programmer hypothesis and coupling effect. The other relations among concepts can be inferred from the figure.
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The next step consisted of specifying the other types of information related to the concepts previously identified. Information items and instructional elements were represented in the instructional model. In the context of mutation analysis, information items could be, for instance: facts (when mutation analysis was proposed and by whom); principles (competent programmer hypothesis and coupling effect); and procedures (basic steps for applying mutation analysis). Regarding the instructional elements, examples could be excerpts of mutants generated by specific operators, while exercises and evaluations could be proposed in terms of tasks involving the application of mutation analysis through automated tools. Figure 8 shows the instructional model for mutation analysis. Consider, for instance, the Application state, a procedure was specified considering both textual (ApplicationMA: procedure:text) and graphical (ApplicationMA:procedure:figure)
Collaborative Development of Educational Modules
Figure 7. Conceptual model for mutation analysis
representations. First and second slides of Figure 8 illustrate how the different knowledge categories of the Application state were implemented in the SofTest module. Next, we had to determine the instructional elements. In the case of SoftTest, only explanatory and exploratory elements were considered. Consider, for instance, the exercise represented by the state ApplicationMA:exercise:text. Basically, it consists of the application of mutation analysis to test the factorial program. Explanatory elements, included to provide some help for solving the exercise, were represented by the states FactorialImplementation:complementary:figure and FactorialHintMA:complementary:figure. Some required tools for doing the exercise were modeled too. The Coweb:tool state represents the CoWeb collaborative learning environment (Dieberger & Guzdial, 2003), used as a discussion space among learners and instructors. The Proteum:tool state corresponds to the Proteum testing tool (Delamaro et al., 2001), used for ap-
plying mutation analysis. The third slide of Figure 8 illustrates the proposed exercise regarding the mutation analysis application. The last step consisted of defining the sequences for presenting all the components of the knowledge domain. Figure 9 illustrates part of the didactic model (only the instructional items are represented) for mutation analysis; it corresponds to an open specification, in which all possible sequences of presentation among the modeled objects are represented. Consider, for instance, the MutationAnalysisDetails state. By exploring the notion of DD states, the MutationAnalysisDetails substates (ORDD states) – MutantOperator, MutantGeneral, MutationScore, Application and ApproachesGeneral – are all connected to each other by implicit transitions, responsible for establishing the navigation paths among them. So, from MutantOperator we can get to the states MutantGeneral, MutationScore, Application and ApproachesGeneral (and vice versa). Similarly, consider the Mutant state. From Mutant we are
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Figure 8. Instructional model / slides for mutation analysis
Figure 9. Didactic model / slides for mutation analysis: Open specification
able to get to MutantClassification (and vice versa). Actually, both states are sub-states of MutantGeneral (DD state), connected to each other by means of implicit transitions.
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We can also explore the idea of a hierarchy of DD-superstates. For instance, consider the sequence (MutantGeneral, MutationAnalysisDetails, MutationAnalysisGeneral, ErrorBasedTechnique, TestingTechnique, SoftwareTestingTheoryPractice)
Collaborative Development of Educational Modules
as the hierarchy of DD-superstates of the Mutant state. According to this hierarchy, from Mutant we can reach all ORDD states of MutationAnalysisDetails. To define the full set of states we can reach from Mutant, the same analysis should be carried out for all states of the hierarchy of DD-superstates of MutantGeneral. Notice that we cannot get to the states AlternativeApproaches and ApproachesClassification from the Mutant state. Indeed, ApproachesGeneral does not pertain to the hierarchy of DD-superstates of Mutant. Besides the open specification, either a partially open specification or a close specification could also be considered in order to define the didactic model for mutation analysis. In a partially open specification, while some sequences of presentation are established in “execution time”, others are previously defined by the domain expert and/or the instructor during the development of the module. Indeed, instead of having just implicit transitions, the idea is to make some of them be explicitly represented in the didactic model. On the other hand, in a close specification all sequences are predefined, i.e., only one fixed sequence of presentation is available in the module. In this case, the transitions are explicitly represented. Notice that the sequences of presentation derived from partially open specifications and from close specifications represent subsets of the total set of sequences established by an open specification. As highlighted before, a didactic model defined in terms of an open specification can be seen as the basis from which all sequences of presentation are derived. So, based on the didactic model of Figure 9, several implementations of the same content about mutation analysis can be obtained. Such characteristic is essential to generate differentiated, personalized content, whose topics, depth and sequences of presentation are established according to some particular aspects (e.g. learners’ profiles, instructors’ preferences, course length, pedagogical goals). The decision on which kind of specification to use should be based on: (1) the learning ap-
proach to be considered; and (2) the users of the module. For instance, considering a traditional learning approach, one strength of using open specifications would be the flexibility to navigate through the material according to the feedback and questions of the audience. Nevertheless, the instructor would have to make sure to achieve the objectives of the lessons in order to keep the learners localized. Indeed, while for less experienced instructors a close specification seems to be the better choice, for the most experienced ones an open specification would be an adequate alternative too. On the other hand, from the point of view of a collaborative lifelong learning approach, the adoption of an open specification would make the learners “free” to dynamically decide which topics to navigate, progressing more or less deeply into them according to their own motivation and needs for new knowledge. A close specification, in this case, could narrow the possibilities of exploring the subject of study, frustrating the students regarding their learning expectations.
Evaluating the SoftTest Educational Module To provide a preliminary evaluation on the SoftTest effectiveness, it was applied as part of a three-hour short-course on software testing for a group of about 60 undergraduate students with previous knowledge of software engineering. We focused on theoretical aspects of software testing, providing an introductory perspective on the subject. Practical aspects were illustrated but, due to time constraints, there was no direct participation by the audience on using any of the materials. The effects of applying SP-DEM and IMACID were informally evaluated by applying a voluntary survey to the students after they had finished the course. The survey was composed by four sections, covering the students’ attitude toward: (1) content, regarding to the concepts, additional information, examples and exercises used in the module; (2) usability, in terms of the
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interface of the module; ↜(3) navigation aspects; and (4) overall aspects about the module. Sections 1, 2 and 3 were composed of objective questions while section 4 consisted of subjective questions. By analyzing the results from the survey, we remark the following: •
•
•
•
Content: Students pointed out as positive aspects the way the module was structured and how it addressed the topics discussed. Connections between concepts were highlighted and the examples and additional information was considered appropriate. In terms of the proposed exercises, we noticed some expectation for practical tasks where the students could actively participate. Although practical exercises involving the use of testing tools had already been integrated to the module, the short time available to the course made them trackless. The results pointed to the need of more concise exercises, which can be explored in such particular kind of course. Usability: The schema of colors, the distribution of information through the pages/ slides and the representation of the interface functions (icons, links, and so on) were, in general, well accepted by the students. Specific comments indicated some disappointment with respect to the size of fonts and figures. Navigation: Students pointed out a very positive attitude toward the flexibility on choosing the sequences of presentation. Despite the large amount of information available, the students did not “get lost” in the module. Overall: Aspects such as instructor’s energy, enthusiasm and objectiveness were also noticed by the students.
Besides the students’ evaluation, the instructor’s responses were also observed by his comments after using the module. The possibility
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of having defined the sequences of navigation through the module during the “execution time”, based on the learner’s understanding and feedback, was a significant point highlighted. Although preliminary, one significant result observed by applying SoftTest was the very positive attitude, from the students and from the instructor, toward the flexibility provided by the module. In the case of students, particularly, even without an active participation on using the module, they were able to realize the different possibilities of navigation explored by the instructor. Such flexibility, achieved by modeling the content as an open specification, was considered the key factor for better motivating and engaging instructor and students in the course. From the developers’ viewpoint, one benefit observed from applying SP-DEM in the collaborative development of SoftTest was the resulting available documentation, mainly in terms of the IMA-CID models. Besides helping to structure and organize the concepts and related information, such models were used as the instructional design rationale, playing a key role to easier evolve and maintain the module after its delivery. In a related perspective, IMA-CID was also useful to help detecting faults and omissions during the development process. For instance, when a concept definition was missing, such an omission could be easily detected by constructing the instructional model. Actually, an important characteristic of IMA-CID is the possibility to always return for reviewing and revising the models, contributing for the quality of the materials being produced. SofTest was also applied in two one-semester undergraduate courses at University of São Paulo (Brazil). The main goal of both courses was to explore the fundamentals of V&V (Verification and Validation). The module was delivered in expositive classes, exploring the theoretical aspects of testing activities and related supporting tools. At the end of each class, practical exercises were proposed. Aiming at evaluating the module, we replicated an extended version of the Basili &
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Selby experiment (Basili & Selby, 1987), originally used for comparing V&V techniques, now considering the educational context. The experiments in each course involved 36 (9 teams) and 52 (13 teams) students, respectively. The results were mainly analyzed in terms of the students’ ability and uniformity on: (1) detecting existing faults; (2) generating test cases; and (3) covering the test requirements. In general, students were able to master the involved knowledge and perform the practical activities properly. Such results provided some evidences of the learning effectiveness achieved by the SoftTest module. Details can be found at (Barbosa et al., 2008a). Besides software testing, SP-DEM and IMACID have been applied into several different domains, especially in the context of two broad projects – QualiPSo and INCT-SEC. Such projects involve heterogeneous and geographically dispersed teams; therefore, they are particularly interesting in order to evaluate the collaborative issues addressed by SP-DEM and IMA-CID. In general terms, the same benefits observed during the development of SoftTest have been confirmed in the development of the educational modules for the QualiPSo and INCT-SEC projects. We also highlight that in the evaluations performed until now, the focus was to assess the effects of SP-DEM and IMA-CID, mainly in terms of collaborative development, on the overall quality of the resulting educational products. In this sense, we point out the need for conducting more systematic and controlled experiments, considering not only the perspective of collaborative development but also aspects of collaborative learning. Such experiments have already been planned, involving different courses, regarding software testing and other domains, offered to graduate and undergraduate students as well as to professionals from industries. Different learning approaches should be considered, especially focusing on lifelong learning experiences. Students, instructors and developers’ attitudes toward
SP-DEM, IMA-CID and the resulting modules produced should also be evaluated. The adoption of SP-DEM and IMA-CID in the context of QualiPSo and INCT-SEC projects will contribute in such further evaluations.
FUTURE WORK In this section we summarize our ongoing work as well as our plans for future enhancements of SPDEM standard and IMA-CID modeling approach. Recent research motivates efforts to achieve semantically rich, well-structured, standardized and verified educational content. In this direction, one of the perspectives we are now investigating refers to the use of ontologies (Uschold & Grüninger, 1996) as supporting mechanisms for modeling the educational content. The goal is to evolve IMA-CID to allow that both conceptual mapping and ontologies can be used for structuring and representing the knowledge domain. By using ontologies in the conceptual level of IMA-CID we intend: (1) to provide a better comprehension of the knowledge domain to be taught; (2) to ease collaboration and knowledge sharing among developers; (3) to provide a well-established structure for a knowledge repository; and (4) to provide support for interoperability, considering the relationship among different paradigms and languages. In the didactic level, the adoption of ontologies should also be explored together with the idea of open specifications aiming at providing adaptive personalization in different learning contexts. Besides that, in the instructional level of IMACID we are exploring the use of the ALOCoM (Abstract Learning Object Content Model) ontology (Verbert et al., 2005) – a formal representation for learning objects and their components. The idea is to adopt ALOCoM for establishing the media (continuous and discrete elements) related to information items and instructional elements.
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The establishment of adequate media, specially the continuous ones, is important for developing interactive educational content, capable of motivating the learners and effectively contributing to their knowledge construction processes in selfdirected, lifelong learning environments. ALOCoM also supports the use of XML schemas for importing and exporting the educational content for different models and specifications. The standardization obtained by using ALOCoM can guarantee interoperability, sharing and reuse of the educational content developed according to IMA-CID. In the future, we intend to define an IMA-CID-based language, providing a layer of abstraction for the description of educational content, including the description of concepts, information items and instructional elements, as well as the whole set of presentation sequences among them. Another matter for further investigation is related with automated learning environments and their support for content modeling. The fast evolution of information and communication technologies has significantly increased the number of learning environments available; just to cite some examples: WebCT (Goldberg et al., 1996), Moodle (Moodle, 2006), Sakai (Sakai, 2006), DotLRN (DotLRN, 2009), among others. In summary, the existing learning environments provide: (1) the required infrastructure for integrating the learning materials and for delivering/publishing them to the learners; (2) support to perform practical tasks and evaluations; and (3) support collaborative work and augment communication and discussion among instructors and learners. However, except for some specific efforts (e.g. ADISA and MOT+, for MISA’s models (Paquete et al., 2005)), no mechanism for modeling the related knowledge domain is provided. Indeed, in most of the cases, the activity of content modeling is left in charge of the author, without any systematization. At most, some support for the storage and retrieval of educational content is provided.
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In this perspective, we are working on the specification and development of a supporting tool for content modeling, particularly for constructing the IMA-CID models. Indeed, applying IMA-CID without an automated support can be an errorprone activity. Additionally, the lack of automated tools for content modeling represents a constraint for the effective adoption of SP-DEM instances. IMA-Tool is an online tool for helping the “open” and collaborative construction of the IMA-CID models (Borges & Barbosa, 2009). Mechanisms for automatically generating content and for importing ontologies will also be provided. For the sake of illustration, Figure 10 shows a software testing ontology being imported as an OWL file; it plays the role of the conceptual model of IMACID. IMA-Tool has been developed in Java, as a Web application. It represents an ongoing research; its main features and scenarios of usage will be properly discussed in further papers. In another work, we are investigating the development of educational modules supported by innovative technologies (e.g. digital TV, tablets, and mobile devices) aiming at motivating the transition from lecture-based to active learning. Such emerging technologies are especially interesting for promoting e-learning and lifelong learning opportunities. In short, we intend to investigate the adequacy of SP-DEM and IMA-CID in the context of these new technologies as well as to evaluate the implications of using the resulting products in different learning scenarios. Finally, we are also interested in explore the input of learners in the very early stages of the module development, similarly to the participative approach in software development. Such point is particularly important to address issues of collaborative learning and, also, to foster learning networks experiences. The establishment of “agile methodologies” for collaboratively developing and evolving open educational modules should also be further considered.
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Figure 10. IMATool: Importing ontologies
FUTURE RESEARCH DIRECTIONS In this section we provide some insights about the emerging trends in lifelong learning, focusing on open issues of collaborative development and collaborative learning and their implications to the establishment of innovative learning opportunities. Personal Learning Environments (PLEs) can be pointed out among the promises to the next generation of active and lifelong learning. The term PLE describes the tools, communities, and services that constitute the personal educational platforms used by learners to drive their own learning and achieve their educational goals (EDUCAUSE, 2009). In short, PLEs represent a shift away from the model in which learners consume information through independent channels (e.g. libraries, textbooks, LMSs) moving instead to a model where learners draw connections from a growing matrix of resources that they select and organize. Hence, PLEs put students in charge of their own learning processes, challenging them to actively consider and reflect on the specific tools
and resources that can lead to a deeper engagement with content to facilitate their learning. PLEs are a result of the evolution of Web 2.0 and its impact on the learning process. As stated by Downes (2007), the values that underlie the PLEs and Web 2.0 are the same: (1) the fostering of social networks and learning communities; (2) the emphasis on creation rather than consumption; and (3) the decentralization of content and control. •
Learning in communities: Communities of practice are formed by people who engage in a process of collective learning in a shared domain – people share a concern or a passion for something they do and learn how to do it better as they interact regularly (Downes, 2007). So, the necessary condition to learning occurs is the active participation in the community, what involves, essentially, a conversation between the learner and the other members of the community. Such conversation, in the Web 2.0 era, consists not only of words but of images, video, multimedia and much
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•
•
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more. Furthermore, this conversation should form a rich matrix of resources, dynamic and interconnected, created not only by experts, but by all members of the community, especially the learners. Creation over consumption: PLEs highlight the idea of making learners move beyond content consumption and memorization into stages of critical thinking, collaboration, and content creation. On the other hand, content creation sites have formed the vanguard of Web 2.0, emphasizing the idea that the web is a place where people can create and communicate, i.e., to network. The possibility of making the content creation occur, or be largely supported, online, converts the act of creating content into a social and connected act, broadening the learning opportunities to actively involve learners in their own knowledge construction process. Learning therefore evolves from being a transfer of content and knowledge to the production of content and knowledge. Context over class: According to Downes (2007), when learning becomes the creation of content in the context of a community of practice, it also becomes something that is characterized not by instruction in a classroom, but rather by dialogue and communication within a given context. In an increasingly global world, learning environments are becoming ever more multi-disciplinary, i.e., learning from a large number of disciplines is required. Such environments cut across disciplines. Instead of studying subjects in an isolated way, students will learn the subjects as need, progressing more deeply into them as the need for new knowledge is provoked by the demands of real world applications. Learning opportunities – either in the form of interaction with others, in the form of learning objects, or in the form of interac-
tion with mentors or instructors – will be embedded in the learning environment, sometimes presenting themselves spontaneously, sometimes presenting themselves on request. The main goal of PLEs is to allow a learner (or anyone) to engage in a distributed environment consisting of a network of people, services and resources. Taken together, the ideas that underlie the PLE constitute an instance of a more general approach that may be characterized as Learning Networks. If properly designed, such networks can represent reliable producers of high-quality knowledge and learning. Through the process of interaction and communication, the entities that constitute the network will form a mesh of connections. Knowledge is embedded in this mesh of connections, and therefore, through interaction with the network, the learner can acquire the knowledge (Downes, 2007). In this sense, talk about Learning Networks implies on considering not only the use of networks to support learning but also networks that learn. The core concept of Learning Networks is that these two things are one and the same. Downes (2007) also describes the properties of the network that are known to most reliably lead to network knowledge: •
•
Diversity: Entities in the network should be diverse. Diversity allows us to have multiple perspectives, to see things from a different point of view. As a consequence, the learner can reach beyond him/ her groups and to connect with, and learn from, a wide range of influences. Autonomy: Each entity operates independently of the others. The network operates according to an individual and internal set of principles and values. Autonomy is what allows diverse entities to respond and react in a diverse manner.
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•
•
Interactivity or connectedness: The knowledge produced by a network should be the product of an interaction between its members, rather than a simple aggregation of the members’ perspectives. Openness: Each entity in a network must be able to contribute to the network, and each entity needs to be able to receive from the network. Particularly, openness offers the opportunity to narrow the boundaries between producers and consumers as consumers themselves become producers, through creating and sharing. One implication is the potential for “open” learning materials, through learners themselves becoming producers of open content, books, multimedia, and so on (Attwell, 2007).
According to the ideas discussed above, planning for the future in active and lifelong learning assumes investigating innovative principles, processes, methods and technologies to foster learning opportunities capable of promoting autonomy, encouraging diversity, enabling interaction and supporting openness. In this regard, collaborative development will play a role ever more important, putting the learner as the main responsible for designing customized, adaptable, evolvable, reliable and high-quality educational products. At the very end, the envisioned scenario is to make collaborative development turns into collaborative learning and vice-versa. Particularly, we believe that systematic processes, supported by well-defined content models, will be the basis from which the learner will draw connections to acquire, evolve, disseminate and collaborate in using the knowledge information.
CONCLUSION Lifelong learning has to accommodate a variety of types of learners, who differ in age, learning experiences, media preferences, learning styles,
capability for working in teams, among others. Collaboration plays an ever more important role in order to deal with such diversity. Besides that, there is a need for the establishment and integration of innovative methods, tools and procedures into well-defined processes, aiming at producing customized and high-quality educational products, capable of better engaging the students (and teachers as well) in more active learning processes. Considering this scenario, this chapter provided a discussion of supporting mechanisms for the collaborative development of educational products: (1) a standard process for developing educational modules (SP-DEM); and (2) an integrated approach for modeling the educational content (IMA-CID). The application of SP-DEM and IMA-CID was illustrated by the collaborative development of an educational module for the software testing domain. The main contribution of this chapter is to motivate the use of systematic and innovative mechanisms for collaboratively creating highquality educational modules. By using the resulting modules, the goal is to better promote the development of lifelong competences and expertise in several different but related knowledge domains, engaging learners and teachers in an empowering way. In this sense, the produced modules should provide: (1) transferability to different institutions and learning environments; (2) effective support to traditional learning approaches; and (3) effective support to non-traditional environments, motivating the transition from lecture-based to self-directed and lifelong learning. Many issues regarding collaborative development and lifelong learning remain opened and must be further addressed. At the very end, we intend to establish a culture for “open and collaborative learning materials” so that the use and evolution of them by a broader learning community would be better motivated and become a reality. The existence of a well-defined process to systematize the development of learning materials and, at the same time, flexible enough to be adaptable to
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different knowledge domains and development teams, plays a key role in crossing international, cultural and social borders in order to prepare the learners to be successful in a global market, with diverse groups of people. As a final remark, in spite of the increasing costs to initially develop an educational module based on the ideas discussed herein, we highlight that the quality factors of the produced materials, such as evolvability, maintainability, reusability, among others, would increase the long-term benefits and decrease the overall costs.
ACKNOWLEDGMENT
Barbosa, E. F., & Maldonado, J. C. (2008b). Specialization and instantiation aspects of a standard process for developing educational modules. 3rd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation – Track: Processes, Methods and Tools for Developing Educational Modules to Support Teaching and Technology Transfer (pp. 1-16). Kassandra, Chalkidiki, Greece. Barbosa, E. F., Souza, S. R. S., & Maldonado, J. C. (2008a). An experience on applying learning mechanisms for teaching inspection and software testing. 21st Conference on Software Engineering Education and Training (pp. 189-196). Charleston, SC.
The authors would like to thank the Brazilian funding agencies (FAPESP, CAPES, CNPq) and to the QualiPSo Project (IST-FP6-IP-034763) for their support.
Basili, V., & Selby, R. W. (1987). Comparing the effectiveness of software testing strategies. IEEE Transactions on Software Engineering, 13(12), 1278–1296. doi:10.1109/TSE.1987.232881
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Chapter 8
Collaborative Learning Design within Open Source E-Learning Systems:
Lessons Learned from an Empirical Study Maria Kordaki Patras University, Greece Haris Siempos Patras University, Greece Thanasis Daradoumis Open University of Catalonia, Spain
ABSTRACT This chapter addresses a number of serious ‘collaborative learning design’ problems faced by adults within the context of e-learning systems and outlines some innovative solutions. Specifically, thirty-three Computer Science students at the Hellenic Open University participated in an experiment aimed at designing collaborative learning courses for Computer Science concepts within MOODLE, a well known open source Learning Management System. The systematic study presented in this chapter argues and specifies that these Prospective Computer Science Professionals (PCSPs) have serious difficulties with the formation of both collaborative learning activities and collaboration procedures, and with realizing them within e-learning settings. The proposed solutions emphasize the design and development of a set of computer-based collaborative patterns reflecting diverse collaboration methods. These patterns are content free and could be used as scaffolding elements for the design of collaborative learning activities for online and blended courses. Specific examples of possible implementation of these patterns within well-known Web-based open source environments that support learning design are also presented. DOI: 10.4018/978-1-61520-983-5.ch008
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Collaborative Learning Design within Open Source E-Learning Systems
INTRODUCTION E-learning has provided education with many benefits in terms of flexible opportunities to learn anytime and anywhere as well as to communicate and collaborate virtually throughout the world (Harasim, Hiltz, Teles & Turoff, 1995). Recent studies of e-learning have pointed out that involving learners in collaborative learning activities could positively contribute to extending and deepening their learning experiences, test out new ideas, improve learning outcomes and increase learner satisfaction, at the same time decreasing the isolation that can occur in an e-learning setting (Palloff & Pratt, 2004). Furthermore, collaborative learning situations can provide a natural setting for demanding cognitive activities which can also trigger collaborative learning mechanisms such as knowledge articulation as well as sharing and distributing the cognitive load (Dillenbourg, 1999). Within the context of online collaborative learning, students could also be provided with opportunities to be motivated to actively construct their knowledge (Scardamalia, & Bereiter, 1996) and to enhance their diversity and their understanding of the learning concepts in question as well as to acquire a sense of belonging online (Haythornthwaite, Kazmer, Robins, & Shoemaker, 2000). However, many teachers remain unsure of why, when, and how to integrate collaboration into their teaching practices in general as well as into their online classes (Panitz, 1997; Brufee, 1999). Here, it is also worth mentioning that the abundance of theoretical considerations and models that provide teachers with resources for ‘learning design’ remains largely unused in their real teaching practices (Fosnot, 1966; Brufee, 1999). At this point, we shall use the term ‘learning design’ to indicate all the elements of learning activity design, e.g. a learning task to be posed to the students, a set of questions, the group formation, the learning materials to be used by
the students, learning assessment, etc. (Koper & Tattersall, 2005). The essential role of suitably-designed tools to support teachers in their mindful and appropriate ‘learning design’ has been acknowledged by many researchers (Lloyd & Wilson, 2001; Babiuk, 2005; Kordaki, Papadakis, Hadzilakos, 2007; Kordaki & Daradoumis, 2009). In fact, teachers require more specific support in their learning design practices, such as specific tools and good examples of lesson plans. Thus, teacher encouragement and support for learning design is clearly needed. To this end, the role of learning design patterns has been acknowledged as essential (McAndrew, Goodyear, & Dalziel, 2006). Learning patterns looks to work on Architectural Patterns (Alexander, 1979) as a way to capture knowledge from designers and share them with practitioners. Especially when it comes to Computer Science (CS) Education, educators have adopted a rather deficient approach to ‘learning design’, possibly because CS Education is a recently-developed scientific discipline. Yet, learning design should be an essential part of CS teachers’ education. A number of studies have investigated CS teachers’ opinions on CS curricula and on teaching and learning in CS as well as their real classroom practices (Kalyva, & Kordaki 2006; Kordaki & Kalyva, 2006). In addition, some studies have investigated the role of CS teachers in the formation of collaborative learning activities (Voyatzaki and Avouris, 2005). However, studies investigating Prospective Computer Science Professionals’ (PCSPs) attempts to design learning courses incorporating ‘computer supported collaborative learning design using some essential, specific and context free collaboration methods’ have not yet been reported. Specifically, these methods are referred to: Brainstorming (Osborn, 1963), Student Teams Achievement Divisions (STAD; Slavin, 1978), Jigsaw (Aronson, Blaney, Sikes, Stephan & Snapp, 1978), Group Investigation Method (Sharan & Hertz-Lazarowitz, 1980), Co-op Co-op (Kagan, 1985), Guided Reciprocal
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Peer Questioning (Palincsar, & Brown, 1984; Martin, & Blanc, 1984; King, 1990), Three Step Interview (Kagan, 1994), Paired Annotations (Millis & Cottell, 1998), Double entry journal (Berthoff, 1981). In this chapter, we investigate PCSPs’ attempts to integrate the aforementioned collaboration methods within their approaches to ‘learningdesign’ performed in the context of a specific field study aiming to: (a) address specific problems they face and (b) exploit the results of this study to provide solutions to these problems. These solutions concern the design of appropriate computer tools that can support teachers in their attempts to design and implement online and blended collaborative learning settings. To this end, the design and implementation of specific collaboration patterns within the context of open source Learning Management Systems is proposed. This chapter is organized as follows: In the next section, the rationale for both the previouslymentioned field study and the design patterns proposed is presented. Then the context of the said field study is reported and, subsequently, its results are depicted and lessons learned are drawn. Next, the design of the proposed collaboration design patterns using the tools provided by LAMS is demonstrated. Such design patterns have not yet been reported. Finally, the proposed solutions are discussed while conclusions and future research plans are also drawn.
2. THE RATIONALE The idea that collaboration is a basic form of human activity, essential for cultural development, is intensively stressed by many researchers throughout the history of psychology (Vygotsky, 1978; Engestrom, 1987; Bruner, 1996; Lipponen, 2002). Nowadays, in a rapidly changing society, to prepare learners for participation in socially organized activities is also one of the essential
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requirements (Hakkinen, & Jarvela, 2006). In fact, collaboration is appreciated as a lifelong skill. In collaborative situations, the participants are mutually involved in shared activities; they must coordinate their efforts if they are to solve problems together. Contrariwise, in cooperative settings the task is split into subtasks and each participant is responsible for solving a portion of the problem at hand. In cooperating settings, learners usually produce separate solutions, whereas in collaborative learning, constructing a shared solution is essential (Liponen, 2002). Recent studies have indicated that some amount of structuring may help teams achieve effective collaboration (Lehtinen, 2003; Lipponen, 2002). One way to structure collaborative processes employs the so-called computer supported collaboration scripts (Dillenbourg, 2002). Such scripts are intended to facilitate collaborative learning processes and guide learners’ activities. A script segments the task into phases, defines roles and places various constraints on the interactions. In scripted collaboration, the participants are supposed to follow directions and undertake shared learning tasks. Another way of structuring collaboration is through the use of collaborative patterns which could be well-integrated within ‘learning design’ based e-learning environments. In fact, a ‘learning design’ is defined as the description of the teaching-learning process that takes place in a unit of learning (e.g., a course, a lesson or any other designed learning event such as a specific collaboration structure) (Koper & Tattersall, 2005). An important part of this definition is that pedagogy is conceptually abstracted from context and content, so that excellent pedagogical models can be shared and reused across instructional contexts and subject domains. Specifically, best pedagogical practices can be reflected in the formation of ‘design patterns’ which are context free and could be shared and reused across instructional contexts and essentially assist online learning. A pattern is seen as something that will not be reused directly
Collaborative Learning Design within Open Source E-Learning Systems
but can assist the informed teacher to build up their own range of tasks, tools or materials that can draw on a collected body of experience (McAndrew, Goodyear, & Dalziel, 2006). The key principle in ‘learning design’ is that it represents the learning activities that have to be performed by learners and teachers within the context of a unit of learning. In the context of “learning design’, the role of collaborative design patterns is to indicate clearly the flow of collaboration activities using specific collaboration methods. The IMS Learning Design (LD) specification aims to represent the design of units of learning in a semantic, formal and machine-interpretable way (LD, 2003). Various examples of e-learning environments close to the LD specification are mentioned in the literature. COLLAGE is also a system close to IMS-LD specification that is friendly for teachers to use and which supports collaboration using design patterns (HernándezLeo, Villasclaras-Fernández, Asensio-Pérez, Dimitriadis, Jorrín-Abellán, Ruiz-Requies & Rubia-Avi, 2006). However, despite the fact that the IMS-LD specification offers many pedagogical benefits when compared with earlier open specifications for eLearning, it is not easy for teachers to understand and work with it (Griffiths, & Blat, 2005). To this end, it seems clear that teachers need high level tools to understand learning design and it is likely that tools specialized for a particular pedagogic context will be easier to use (Griffiths, & Blat, 2005). To this end, it is worth noting that the type of editor that classroom teachers usually need should be similar to the authoring environment provided by LAMS. Specifically, LAMS (Dalziel, 2003) is a well known integrated open source e-learning system that effectively supports the idea of ‘learning design’. Open source software is software that has been released under an Open Source Initiative (OSI) certified license. Each of the licenses approved by the OSI meets the conditions of the Open Source Definition (http://www.opensource.org/ docs/definition.html). That definition includes
10 criteria. Perhaps the most important of these are the free redistribution of the software, access to the source code, and the permission to allow modifications to the software and derived works that may be distributed under the same licensing conditions. Open source is a development method for software that harnesses the power of distributed peer review and transparency of process. The promise of open source is better quality, higher reliability, more flexibility, lower cost, and an end to predatory vendor lock-in (http://www. opensource.org/). LAMS (Learning Activity Management System; http://www.lamsfoundation.org/) is an open source tool for designing, managing and delivering online collaborative learning activities. In fact, LAMS offers a set of predefined learning activities, shown in a comprehensible way for teachers that can be graphically dragged and dropped in order to establish a flow chart of sequence of activities. When using LAMS, teachers gain access to a highly intuitive visual authoring environment for the creation of sequential learning activities. LAMS is based on the belief that learning does not arise simply from interacting with content but from interacting with teachers and peers. The creation of content-based, self–paced learning objectives for single learners is now well understood in the field of e-learning. However, the creation of sequential learning activities which involve groups of learners interacting within a structured set of collaborative environments - referred to as ‘learning design’ - is less common; LAMS allows teachers to both create and deliver such sequences. In essence, LAMS provides a practical way to describe multi-learner activity sequences and the tools required to support these. Furthermore, LAMS provides tools that support various activities such as communication, presentation of information, writing and sharing resources as well as posing and answering questions. Nevertheless, Dalziel (2003) has commented on the absence of tools supporting broader ranges of collaborative tasks and also on missing support
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for the concepts of group creation and monitoring. In fact, despite the availability of all tools mentioned above, collaborative activity sequences for the performance of the aforementioned specific collaboration methods within LAMS have not yet reported. One of the contributions of this chapter is to propose specific implementations of a number of essential collaboration methods using the previously mentioned tools of LAMS.
3. THE CONTEXT OF THE EMPIRICAL STUDY The Method of the Study This empirical study focuses on the investigation of PCSPs’ attempts to integrate specific collaboration methods into their online ‘learning design’ approaches. Exploiting the results of this study, the design and implementation of a set of web based collaborative patterns reflecting diverse collaboration methods is proposed. These patterns can be used as scaffolding tools to help teachers take into consideration essential collaborative learning methods in their learning design practices. In terms of methodology, this study is a qualitative research educational methodology and can be characterized as a case study (Cohen & Manion, 1989). In terms of the method used, this study is a field study. Qualitative methodologies are usually suggested to illuminate what really happens in under-researched areas such as in PCSPs’ collaborative learning design approaches. This methodology was used in order to investigate the PCSPs’ collaborative learning design approaches and to form conclusions based on the data coming from the field experiment. Below the method used for this investigation is presented as a sequence of steps regarding the following issues: (a) focus of the study, (b) setting the learning experiment, (c) data resources, (d) data analysis, (e) presentation of results, (d) lessons learned from the said empirical
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study. In the following section, the description of the aforementioned steps is reported.
Focus of the Study This study focus on the investigation of PCSPs’ attempts to integrate specific collaboration methods into their online ‘learning design’ approaches. To this end, specific emphasis is put on the investigation of the kind of learning activities –that PCSPs designed during this empirical study- related to all the specific parts an online course consists of, namely; stating the stage, scheduling of the course, integration of learning materials, class organization, learning tasks, communication, collaboration and evaluation.
The Learning Experiment The learning experiment took place during an elective course entitled ‘Informatics and Education’ provided to its CS undergraduate students by the School of Science and Technology of the Hellenic Open University. Specifically, thirty-three PCSPs at the Hellenic Open University participated in a learning design experiment aiming at the design of short online courses using MOODLE (Dougiamas & Taylor, 2002). In this experiment, PCSPs were asked to take into account modern constructivist and social views (Jonassen, 1999; Vygotsky, 1978) of learning and a set of specific collaborative methods to accomplish the following task: ‘design a short online course for the learning of iteration algorithmic structures by secondary level education students’. In particular, in the context of this course, PCSPs were asked to design specific lesson plans, integrating appropriate learning materials, collaborative learning activities and collaborative communication structures, as well as questions and teacher interventions that could encourage students’ critical thinking. To successfully address this task, PCSPs were provided with instructions in the form of text-based learning materials regarding: (a) modern social
Collaborative Learning Design within Open Source E-Learning Systems
and constructivist views of learning, (b) specific collaboration methods, including guidelines for the formation of collaborative learning activities, (c) diverse teacher interventions encouraging student engagement in the tasks at hand, (d) diverse types of questions encouraging the development of student critical thinking, (e) specific structures including guidelines for the encouragement of effective collaborative communication activities, and (f) diverse learning activities to be included in specific parts of a lesson plan. PCSPs were asked to take into account all the guidelines included in the said learning materials in order to design their online courses. As regards the formation of appropriate lesson plans, it was considered critical for them to comprise learning activities related to the following specific parts: (i) student emotional and cognitive preparation for the learning of the subject matter in question, including; motivation of students to be actively and passionately engaged in the tasks proposed, clarification of the aims of the course and of each learning activity proposed for students, investigation of students’ previous and prerequisite knowledge for the understanding of the concepts in question, (ii) introduction of students to the learning of the previously mentioned concepts, (iii) consolidation of the said concepts by the students, (iv) assessment of the knowledge constructed during the lesson, (v) development of student metacognitive skills, and (vi) extension of the lesson by providing learning materials and activities for further study. Regarding the design of collaborative learning activities, PCSPs were provided with learning materials on specific collaboration methods to design collaborative learning tasks and also group/ whole class communication. Specifically, these materials concerned the following context free collaboration methods: Brainstorming (Osborn, 1963), Student Teams Achievement Divisions (STAD; Slavin, 1978), Jigsaw (Aronson, Blaney, Sikes, Stephan & Snapp, 1978), Group Investigation Method (Sharan & Hertz-Lazarowitz, 1980),
Co-op Co-op (Kagan, 1985), Guided Reciprocal Peer Questioning (Palincsar, & Brown, 1984; Martin, and Blanc, 1984; King, 1990), Three Step Interview (Kagan, 1994), Paired Annotations (Millis & Cottell, 1998), Double entry journal (Berthoff, 1981). These methods were selected as being representative of the achievement of diverse learning objectives such as: the generation of a large number of ideas for the solution of a problem (Brainstorming), motivating students to encourage and help each other, while at the same time accelerating their achievement (STAD), emphasizing interpersonal inter-dependence (Jigsaw), cultivating student ability to approach problems with different structures (Group Investigation Method, Co-op, Co-op), encourage critical thinking (Guided Reciprocal Peer Questioning), enhancing team building and engagement of students in conversation (Three Step Interview), developing the ability to concentrate on important terms (Double entry journal) as well as promoting cooperative learning through accountability and positive interdependence (Paired Annotations). To avoid repetitions, the said methods will be analytically presented in combination with their implementation within LAMS in a specific section of this chapter (see Implementation within LAMS later on).
Data Resources and Analysis The data collected consisted of the specific online courses within MOODLE formed by each PCSP as well as their written reports describing/documenting these courses. In the first stage of data analysis, each individual PCSP’s approaches to the said task were identified and reported in terms of design of learning activities related to all the specific parts an online course consists of, namely; stating the stage, scheduling of the course, integration of learning materials, class organization, learning tasks, communication, collaboration and evaluation. In the second stage, data was codified using themes that had emerged. Next, the focus was
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put on tracking down the PCSP’s best practices, as well as drawbacks in their learning designs for short online courses, with an emphasis on the design and implementation of collaborative learning events.
3. RESULTS Based on the analysis described in the previous sections, the results emerged from this study are reported in the following section. The main points of these results are also briefly reflected in Table 1. Setting the stage: Most PCSPs (25 PCSPs) used some brief provocative expressions/examples/jokes/figures to motivate their students and draw their attention to the subject matter in question. A few PCSPs (only 2 PCSPs) also designed discussions - using whole class forums - asking each of their students to give an example of their own life that related to the learning concepts in question, so as to stimulate them to actively and passionately participate in the course at hand. Most PCSPs (27 PCSPs) also defined certain cognitive and technical goals of their courses and presented them explicitly in the main page of their courses. Regarding the investigation of students’ previous and prerequisite knowledge of the said concepts, a considerable number of PCSPs (20 PCSPs) used specifically designed quizzes while others (2 PCSPs) used the brainstorming method utilizing a whole class chat room. However, it should not be ignored that some PCSPs (6 PCSPs) failed to initiate any action to prepare their students emotionally and cognitively to actively and effectively participate in the learning of the subject matter through the said online courses. It was probably due to the fact that, usually, in Computer Science departments emphasis is given in the presentation of the subject itself with less attention on the development of an appropriate emotional climate for its understanding. Scheduling of the online courses: All PCSPs designed their online courses to last two weeks
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at minimum. The first week was usually devoted to the introduction of the learning of the primary aspects of the concepts in question while the second week was usually dedicated to consolidation and extension of these concepts, as well as to evaluation procedures. To this end, PCSPs used most of the blocks provided by MOODLE such as Calendar, Latest News, Upcoming Events, Participants, Grades and Activities. The typical flow of learning events that most PCSPs (30 PCSPs) suggested for their students was as follows: (a) completing quizzes to express their previous knowledge related to the subject matter in question, (b) participation in groups, (c) reading the learning materials provided, (d) fulfilling the learning tasks at hand during the ‘introduction’ part of the course, (e) completing quizzes to assess the knowledge acquired during this part of the course, (f) fulfilling the learning tasks at hand during the ‘consolidation’ part of the course and (g) completing quizzes to assess the knowledge acquired during the said part of the course or the knowledge they acquired during the whole course. Integration of learning materials: Here, as well, all PCSPs integrated various learning materials to help their students acquire some knowledge about the subject matter in question as well as background issues. These learning materials were in the form of text documents, Power Point presentations, links on the Web, Glossaries and online Encyclopedias, appropriate educational software and, finally, online tutorials about MOODLE. Most of these materials provided information and solved examples to help the students grasp the learning concepts in focus. However, it is important to note that some PCSPs integrated so many learning materials – and usually failing to emphasize the most important aspects of the subject matter in question – that they could become boring for the students to navigate and read. Class organization: The majority of PCSPs (26 PCSPs) organized their students in two ways; as a whole group and as small groups, mainly
Collaborative Learning Design within Open Source E-Learning Systems
Table 1. PCSPs’ attempts to form small collaborative online courses within MOODLE PCSPs’ attempts to form small collaborative online courses within MOODLE
Number of PCSPs
Setting the stage Use of specific expressions to engage students in the course
25
Design of whole class discussions to engage students in the course
2
Formation of cognitive and technical goals
27
Investigation of students’ previous and prerequisite knowledge using: €€€€€• Quizzes €€€€€• Whole-class Brainstorming
20 2
Scheduling of the online courses Design of a 2-week course
33
Use most of the blocks provided by MOODLE: Calendar, Latest News, Upcoming Events, Participants, Grades and Activities
33
Integration of learning materials Use of: text documents, Power Point presentations, links on the Web, Glossaries and online Encyclopedias
33
Use of: educational software and online tutorials about MOODLE
6
Class organization Whole class setting
33
Formation of 3-student, heterogeneous groups
33
Group formation by the teacher
31
Design of quizzes to assess student knowledge in order to classify them into heterogeneous groups
17
Learning tasks given During the introductory and consolidation parts of the course
33
During the evaluation part as well as after the end of the course
8
Non collaborative tasks
32
Collaborative tasks
1
Tasks that stem from the students’ world
33
Communication Use of: whole class and group chat rooms and forums for synchronous and asynchronous communication
26
Use of e-mail
17
Establishment of specific communication guidelines for chat rooms/ forums
6
Use of the Guided Reciprocal Peer Questioning method to structure communication in forums
3
Use of specific pre-defined questions to structure communication in forums/chat-rooms
4
Establishment of specific days and hours for the chats integrated in PCSPs courses
23
Design of non ending forums as well as loose and unstructured communication procedures to take place within forums and chat-rooms
24
Collaboration Use of the STAD collaboration method (with non collaborative tasks)
23
Design of rewarding procedures
17
Evaluation Design of the evaluation of students’ achievement using quizzes
33
Design of specific procedures for course evaluation
6
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consisting of three students. The allocation of students into groups was mainly viewed as a teacher task by the majority of PCSPs (31 PCSPs), and group formation was mainly based on students’ heterogeneity in terms of their achievement. At this point, it is worth noting that half the PCSPs used specifically designed quizzes to assess their students’ knowledge in order to classify them into heterogeneous groups. Learning tasks: All PCSPs designed learning tasks to be performed by their students during the introductory and consolidation parts of the course. Some PCSPs (8 PCSPs) also designed tasks to be faced by their students during the evaluation part of the course– as well as after the end of the course - for the extension and further consolidation of their knowledge. It is worth noting that all of these tasks were taken from the students’ world, so that they would be actively and passionately involved in constructing their solution structures. However, the majority of these tasks (all except one) were simple enough not to require collaboration among team members for them to be successfully realized. Some PCSPs assigned such tasks to an entire group and others to each individual student. Communication: The majority of PCSPs (26 PCSPs) used both whole class and group chat rooms for synchronous communication, as well as both whole class and group forums for asynchronous communication. The e-mail facility was also used to inform students about their allocation in groups. Whole class forums were mainly used for welcoming the students onto a specific course, for the assessment of the course by the participants and for the recognition of students’ good work. Whole class chat rooms were mainly used for the investigation of students’ previous knowledge (brainstorming), for meta-cognitive assessment of students’ progress at the end of the course and for the expression of students’ difficulties with the learning of the concepts in question. Group forums and group chat rooms were mainly used to provide students with opportunities to exchange
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ideas about the difficulties they encountered facing the learning tasks given. Due to the fact that these tasks were not mainly designed to be realized by a group, but could be easily performed by an individual student, students’ contributions within group forums and group chat rooms took the form of expression of students’ difficulties in successfully performing these tasks. However, few PCSPs (6 PCSPs) established specific communication guidelines within chat rooms and forums, while some (3 PCSPs) used the Guided Reciprocal Peer Questioning method to structure communication in forums. Few PCSPs (4 PCSPs) also formed specific pre-defined questions to structure the communication in forums and chat-rooms. A considerable number of PCSPs (23 PCSPs), however, established specific days and hours for the chats they integrated in their learning designs, although most PCSPs (24 PCSPs) designed non ending forums as well as loose and unstructured communication procedures to take place within forums and chat-rooms. Collaboration: The favorite collaboration structure used by a considerable number of PCSPs (23 PCSPs) was the STAD structure. This structure emphasizes heterogeneous grouping, individual and group assessment as well as recognition of the students who performed the best work. However, the tasks designed were not appropriate to be realized by teams. In addition, some PCSPs (17 PCSPs), designed rewarding procedures whereas others did not. Evaluation: All PCSPs designed evaluation procedures for the investigation of students’ achievement while few PCSPs (6 PCSPs) designed additional procedures for the investigation of the effectiveness of their courses. As regards the evaluation of students’ achievement, all PCSPs designed quizzes including multiple-choice and true-false questions. These quizzes were assigned to be performed by the students after each part of the course (introductory and consolidation parts) and, in some cases, also at the end of the whole course. In addition, for the evaluation of students’
Collaborative Learning Design within Open Source E-Learning Systems
achievement, their performance in facing the tasks posed during both of the said parts of the course was taken into account. In fact, the total grade of each student in most cases was the sum of his/ her grades gained from the quizzes and the tasks posed during the course, while in a few cases the students’ grade emerging from their participation in the communications realized within forums and chats was also added. Regarding the grading of the learning tasks, it is worth noting that when a task was assigned to each individual student, in some cases, she/he gained a specific grade from her/his performance, while in other cases the median of individual grades gained by a group was viewed as the grade of each student participating in it. When a task was assigned to a group, the grade gained by this group was assigned as a grade to each individual student belonging to this group.
4. LESSONS LEARNED FROM THE EMPIRICAL STUDY At first glance, the results emerging from this study show that the design of student-centered collaborative online courses was a tricky task for the PCSPs who participated in this experiment. Specifically, PCSPs had emphasized emotional preparation of their students to motivate them to be actively involved in their own learning. However, this motivation was designed according to teacher hypotheses about students’ interests and mainly took the form of an action (usually a statement) performed by the teacher. Only a few PCSPs designed collaborative communication activities around a question so as to enforce student-centered motivation in terms of encouragement to express their personal opinions and experiences of the subject matter in focus. As regards cognitive preparation, most PCSPs used quizzes to diagnose students’ previous and prerequisite knowledge in order to allocate them into groups. Needless to say, quizzes are useful in informing the teacher about students’ knowledge.
However, most important is the structuring of the teaching procedure, so as to allow students to become aware of their knowledge, including misconceptions and difficulties. In addition, if students are allowed to share and negotiate their knowledge with their fellow students, they can enrich and clarify their approaches to the subject matter in focus. Class organization was also mainly left in teachers’ hands. In fact, no attempts were designed by PCSPs to guide their students to form groups according their own preferences. On the other hand, group work was completely left up to the students. Specifically, students were provided with forums and chat rooms to interact as both a whole class and in small groups. However, no structure for this interaction was suggested. In fact, the concept of sharing ideas and negotiation of meanings was not satisfactorily addressed by PCSPs throughout the online courses they designed. In addition, students were asked to face learning tasks by collaborating with their teammates, but these tasks were not designed to support collaboration. To this end, the collaboration structures designed by PCSPs were mainly used in a non collaborative way. In particular, despite the fact that a considerable number of PCSPs used the STAD cooperation structure, its configuration was only partly used. Specifically, the organization of students into groups and the recognition of the best work in front of the students were emphasized, leaving out the organization of their contribution to form solutions to the tasks given. Evaluation procedures were also aimed at each individual student. In particular, the feedback – in terms of grades and suggestions - was designed to be received by each student, rather than from their classmates. As to the learning materials incorporated into the PCSPs’ courses, we can say that, in technical terms, various and diverse materials were used. However, in terms of quality, many of these materials can be characterized as ‘chatty’, and some of them were not necessary.
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Based on the results emerging from this study, we can conclude that the design of collaborative online courses is not an easy task. Knowledge of the subject matter is not enough for the formation of appropriate collaborative online learning courses. The information that can be selected from the Internet to be integrated into these courses may be abundant, but this does not mean it is of acceptable quality. Moreover, also placing individualistic tasks within the frame of a collaborative structure does not mean that collaboration will be encouraged. Furthermore, grouping students into small teams and presenting them with team forums and team chats, in isolation from the design of specific structures that encourage sharing and negotiation of meanings, does not necessarily produce the benefits of collaboration. In our view, it seems that PCSPs rely mostly on their own previous experience of schooling that did not encourage collaboration. In fact, it appears that teachers tend to reproduce this experience, despite the fact that they read a lot about collaboration methods during this undergraduate course. However, one course appears to be not enough to familiarize PCSPs with such big issues as collaboration, learning-design and e-learning in general, especially when these are referred to the framework of modern theories of learning. To this end, it could be claimed that teachers needed more support in the design of collaborative online courses. Some ways of support could be to emphasize: (a) the use of online environments that explicitly and intuitively support learning design, such as LAMS, (b) the provision of essential content-free collaboration patterns, within the frame of the said online environments, (c) the provision of good examples of online courses that incorporate collaborative methods, (d) teachers’ involvement in teams aiming at the design of collaborative online courses and (e) the participation of teachers as learners in teams, within the context of such courses. As LAMS is designed to collaborate fully with MOODLE, the features of both environments can also be exploited by teach-
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ers for the design of online collaborative courses. To this end, in the next section of the chapter, the implementation of the previously mentioned content-free collaboration methods –in the form of design patterns- within the LAMS framework is reported.
5. IMPLEMENTATION OF ESSENTIAL CONTENT-FREE COLLABORATION METHODS WITHIN LAMS The said content-free collaboration methods were implemented within LAMS using some of its essential tools (http://wiki.lamsfoundation.org/ display/lamsdocs/Home). These tools are demonstrated in its interface (Figure 1) and briefly presented below: •
• •
•
•
•
•
The Assessment tool that allows sequence authors to create a series of questions with a high degree of flexibility in total weighting The Chat Activity runs a live (synchronous) discussion for learners The Chat and Scribe Activity combines a Chat Activity with a Scribe Activity for collating the chat group’s views on questions posed by the teacher The Forum Activity provides an asynchronous discussion environment for learners, with discussion threads initially created by the teacher The Forum and Scribe Activity combines a Forum Activity with a Scribe Activity for collating Forum Postings into a written report The Mindmap activity allows teachers and learners to create, edit and view mindmaps in the LAMS environment. Mindmaps allow for the organising of concepts and ideas, and exploring how these interact The Multiple Choice activity allows teachers to create simple automated assessment
Collaborative Learning Design within Open Source E-Learning Systems
•
•
•
•
•
•
questions, including multiple choice and true/false questions The Notebook Activity is a tool for learners to record their thoughts during a sequence of activities The Noticeboard Activity provides a simple way of providing learners with information and content. The activity can display text, images, links and other HTML content. The Question and Answer Activity allows teachers to pose a question or questions to learners individually, and after they have entered their response, to see the responses of all their peers presented on a single answer screen The Share Resources tool allows teachers to add content into a sequence, such as URL hyperlinks, zipped websites, individual files and even complete learning objects The Submit Files Activity allows learners to submit one or more files to the LAMS server for review by a teacher The Survey Tool presents learners with a number of questions and collects their re-
•
sponses. However, unlike Multiple Choice, there are no right or wrong answers The Wiki Tool allows authors to create content pages that can link to each other and, optionally, allow learners to make collaborative edits to the content provided.
In the next section of this chapter, the set of collaborative methods referred to in the previous section are briefly presented in combination with their implementation as collaborative design patterns using the previously mentioned tools of LAMS. Specifically, each method is presented in terms of: (a) a short introduction and general information (b) its’ goals (c) description of its processes in terms of appropriate steps to be performed (d) its diagrammatic implementation as a design pattern within LAMS. The presentation of these patterns is referred to the context of synchronous collaboration. However, these patterns could be used also for asynchronous collaboration by substituting the function of “Chat and Scribe” by the “Forum and Scribe” function.
Figure 1. Tools for learning design presented on the interface of LAMS
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5.1. Brainstorming Brainstorming (Osborn, 1963) is a group management technique designed to promote the generation of a large number of ideas for the solution of a problem. The main goal of the technique is to encourage group members to adopt a more liberal approach in the expression of personal opinions. Goals: 1) to facilitate quick generation of ideas, 2) to encourage creativity and indirect thinking, 3) to get all the team involved, 4) to underline the importance of collaborative study. Process: 1) Generation of ideas and writing up, 2) Commenting on ideas, 3) Asking for criteria for idea categorisation and 4) Presentation of the main ideas. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 2.
5.2. Student – Teams – Achievement - Divisions (STAD) STAD (Slavin, 1978) is considered to be one of the basic approaches to introduce learners to cooperative learning. The use of this method is thought of as an effective and efficient way to teach well defined educational subjects. The teams are heterogeneous, made up of learners of diverse academic achievement, race, and nationality. The reward of the best teams motivates better students
to encourage the other members of team in order to achieve the mutual goal. Goals: 1) to motivate students to encourage and help each other, 2) to accelerate student achievement, 3) to facilitate gains in self esteem, liking of class, 4) to improve behaviour. Process: 1) Personal assessment, 2) Assignment presentations, 3) Team collaboration, 4) Collaborative writing of reports, 5) Team assessment, 6) Praise for best reports. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 3.
5.3. Jigsaw The Jigsaw method (Aronson, E., Blaney, N., Sikes, J., Stephan, G., & Snapp, M. 1978) is a cooperative learning strategy which enhances the process of listening; commitment to the team; interdependence and team work. Each member of the team has to excel in a well defined subpart of the educational material undertaking the role of expert. The experts form a different group discussing the nuances of the subject and later they return to their teams to teach their colleagues. The ideal size of teams is 4 to 6 members. Goals: 1) to build interpersonal and interactive skills, 2) to ensure that learning revolves around interaction with peers, 3) to hold students accountable among their peers, 4) to encourage active student participation in the learning process.
Figure 2. Implementation of Brainstorming as a design pattern within LAMS
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Figure 3. Implementation of STAD as a design pattern within LAMS
Process: 1) Divide the problem into subproblems, 2) Assign roles and material to each student, 3) Form group of experts, 4) Experts study the material and plan how to teach their colleagues, 5) Create heterogeneous groups, 6) Experts teach in their groups, 7) Assess students. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 4.
5.4. Group Investigation Method This method was proposed by Sharan and HertzLazarowitz, (1980). It is based on the four main elements of learning process: 1) Investigation, 2) Interaction, 3) Interpretation, 4) Intrinsic motivation. During the operation of this method, groups work on similar problems using versatile
approaches. The whole process leads to the active construction of knowledge. Goals: 1) to organise the class, 2) to design activities promoting versatile approaches, 3) to promote plural discussion on learning material, 4) to enrich teacher-student interaction. Process: 1) Teacher sets the problems to be studied, 2) Teacher shares educational materials, 3) Groups analyse the given problem in subproblems, 4) Each member of the group studies a specific sub-problem, 5) Teacher provides additional material, 6) Discussion and drawing of conclusions, 7) Collaborative writing of reports, 8) Assessment and enhancement of reports in discussion with teacher, 8) Presentation of the main ideas, 9) Final interaction between students, 10) Assessment.
Figure 4. Implementation of Jigsaw as a design pattern within LAMS
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Figure 5. Implementation of the Group Investigation method as a design pattern within LAMS
A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 5.
5.5. Co-Op, Co-Op Method This method was proposed by Kagan (1985). It belongs to the category of methods focusing on the development of group consciousness inside class (class building techniques). The learner undertakes the responsibility to control what and how he learns. There is a little interaction among the teams. Goals: Similar to the previous structure. The main aim is to cultivate the ability of students to approach problems with different structures. Process: 1) Division of the problem into team sub-problems, and later into student sub-problems,
2) Sharing of the educational material, 3) Each student prepares his subject, discussing it in class in order to collect more info, 4) Creation of groups, 5) Each student presents their report to their group, 6) Discussion of the connection of the sub-subject to the whole, 7) Preparation of the team report, 8) Presentation in class of group reports. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 6.
5.6. Guided Reciprocal Peer Questioning The method of Guided Reciprocal Questioning guides learners how to assess their understanding when studying (Palincsar, and Brown, 1984; Martin & Blanc, 1984. This method is based on
Figure 6. Implementation of Co-op, Co–op as a design pattern within LAMS
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questions influenced by the well known Bloom taxonomy. Specifically, this method allows learners to identify the question patterns of their teacher, and to recognize more easily the important ideas to be learned. Goals: 1) to encourage critical thinking, 2) to make the student understand what information is important, 3) to help in the introduction of previously unknown material, 4) to stimulate discussion on specific subject. Process: 1) Present the problem, 2) Study the material for 10-15 minutes, 3) Teacher shares a set of semi-completed questions, 4) Each student prepares the answers to questions and submits them to the teacher, 5) Discussion on the subject, 6) Assessment based on the given questions. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 7.
5.7. Three Step Interview The Three Step Interview (Kagan, 1994) can be used as a tool to support the better comprehension of ideas through discussion with peers. Each learner listens to others’ opinions, enriching their cognition about the specific topic. Even the weaker
learners with little prior knowledge will gain a better understanding of the subject because of the participation in the interviews. Goals: 1) team building, 2) reinforcement of the comprehension of information based on lectures or textbooks, 3) students engagement in conversation. Process: 1) Sharing of material, 2) Assignment of the roles of the interviewer and interviewee, 3) Formation of a team, 4) Timed discussion and inversion of roles, 5) Formation of groups with 4 members, 6) Discussion between pairs. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 8.
5.8. Paired Annotations Millis and Cottel (1998) suggest this method as capable of improving the ability of learners to comprehend faster. The main idea is the formation of student pairs who try to identify key ideas. The frequent alternation of the pairs may help the further development of metacognitive skills. Goals: 1) to enable students to identify key points, 2) to develop literature review skills, 3) to encourage students to make connections between
Figure 7. Implementation of the method of Guided Reciprocal Questioning as a design pattern within LAMS
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Figure 8. Implementation of the method of Three Step Interview as a design pattern within LAMS
new and existing bodies of knowledge, 4) to promote cooperative learning through accountability and positive interdependence. Process: 1) Sharing of the educational material, 2) Grouping in pairs, 3) Discussion about key points, 4) Grouping in teams of 4 members, 5) Further discussion within the bigger groups about the key points, 6) Collaborative writing of summary of the learning material. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 9.
5.9. Double Entry Journal This method (Berthoff, 1981), belongs to the category of reflective techniques. The learner has to play two different roles: a) The role of researcher, who collects information and builds knowledge, and b) the role of reviewer, who compares his findings with the established wisdom. All these roles are realized in an environment of collaborative learning. Goals: 1) to help students focus on key points, 2) to provide an alternative method of study, 3) to help students becoming more involved with the material they study, 4) to improve students’ comprehension and vocabulary.
Figure 9. Implementation of Paired Annotations as a design pattern within LAMS
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Process: 1) Sharing of educational material, 2) Grouping in teams, 3) Discussion about the given subject and research for additional material, 4) Sharing of more specialised material, 5) Teams compare their findings with the new material, 6) Conclusions are discussed in class, 7) Presentation of the main ideas and conclusions. A diagrammatic representation of this method –as a design pattern within LAMS- is presented in Figure 10.
6. CONCLUSION This chapter addressed some serious problems faced by adult students – Prospective Computer Professionals at the Hellenic Open University in collaborative learning design and indicated a number of innovative solutions. Specifically, thirty-three PCSPs participated in an experiment aiming to design short collaborative online courses - taking advantage of the tools provided by MOODLE - for the learning of iteration structures. PCSPs participated in this experiment in the context of a one-year, specific course, entitled ‘Informatics in Education’, on the design and use of Computer Technology for teaching and learning. The analysis of the data shows that the design of such courses is a prickly task, especially for adults with limited experience of learning design. In fact, the courses designed by
these PCSPs used various facilities provided by MOODLE but these tools were mainly used to support individualistic, non-collaborative learning. Despite the fact, that PCSPs allocated their students within groups and provided each of them with forums and chat-rooms, the interaction within these communication devices was left loose/non structured and subsequently non-productive. The learning tasks designed were also very simple, so they could be performed by individual students. Furthermore, when a collaboration structure was used, some essential parts of it were ignored. These parts related to the lack of design negotiation of meanings and mutual understanding as well as lack of design specific contributions of each individual student to complete the tasks at hand. The evaluation procedures implemented were also oriented towards each individual student, thus not permitting student-learning from their classmates’ learning diversity, including their mistakes. On the whole, the students’ interdependence, through their contribution to the tasks at hand and also in communication procedures, clearly did not emphasize negotiation of meanings and mutual understanding of the concepts in question. PCSPs also integrated various learning materials into their courses, in some cases unnecessarily. Taking into account the results of this study, and in our attempt to help novices and teachers in their approaches towards successful collaborative learning design, we designed and implemented a
Figure 10. Implementation of Double Entry Journal as a design pattern within LAMS
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number of collaborative design patterns within LAMS, reflecting essential collaboration methods, namely: Brainstorming, Student Teams Achievement Divisions, Jigsaw, Group Investigation, Coop Co-op, Guided Reciprocal Peer Questioning, Three Step Interview, Annotations, and Double entry journal. Based on the results of this field study, we also plan to provide novices and teachers with extra support in their attempts to design collaborative online courses, namely: a) good examples of online courses incorporating the previously mentioned collaborative structures, b) engagement of teachers in teams aiming to design collaborative online collaborative courses, c) training teachers for collaborative online learning design by encouraging them to participate as learners in teams within the context of such courses. Finally, the use with real teachers of the previously-mentioned collaboration patterns - as implemented within LAMS - is in our future plans. In this way, the effectiveness of these patterns in the form of this specific implementation could be explored.
7. FUTURE RESEARCH DIRECTIONS We end this chapter with a brief note on the implication of this study towards future research directions in learning design, computer science education and teacher education. Our study clearly suggests that CS teachers need support in their everyday teaching practices. To this end, more research is needed to: (a) investigate the most significant teaching weaknesses –especially for CS teachers- through specific field studies (b) form appropriate design patterns for teachers taking into account their needs (c) form and evaluate sequences of learning activities appropriate for the learning of Computer Science concepts in all levels of education at the same time taking into account the students’ diversity and (d) investigate ways of teacher education to encourage teachers in general and CS teachers in particular, to imple-
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ment essential learning designs and sequences of collaborative learning activities.
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Kordaki, M., & Kalyva, G. (2006). Teacher views on computer science curricula in secondary education: Present and future. In Proceedings (CDROM) of IFIP WG 3.1, 3.3, & 3.5 Joint Conference: Imagining the Future for ICT in Education, 26-30 June, Alesund, Norway. Kordaki, M., Papadakis, S., & Hadzilacos, T. (2007). Providing tools for the development of cognitive skills in the context of learning designbased e-learning environments. In T. Bastiaens & S. Carliner (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare & Higher Education (E-Learn 2007), (pp. 1642-1649). October, 15-19, Quebec, Canada, USA, Chesapeake, VA: AACE. LD. (2003). IMS Learning Design. Information model, best practice and implementation guide, version 1.0 final specification. IMS Global Learning Consortium Inc. Retrieved June 30, 2009, from http://www.imsglobal.org/learningdesign/ Lehtinen, E. (2003). Computer-supported collaborative learning: An approach to powerful learning environments. In de Corte, E., Verschaffel, L., Entwistle, N., & van Merrieboer, J. (Eds.), Powerful learning environments: Unravelling basic components and dimensions (pp. 35–54). Amsterdam, The Netherlands: Pergamon. Lipponen, L. (2002). Exploring foundations for computer-supported collaborative learning. In G. Stahl (Ed.), Computer support for collaborative learning: Foundations for a CSCL community. Proceedings of the Computer-supported Collaborative Learning 2002 Conference (pp. 72–81). Hillsdale, NJ: Erlbaum. Lloyd, G., & Wilson, M. (2001). Offering prospective teachers tools to connect theory and practice: Hypermedia in mathematics teacher education. [Norfolk, VA: AACE.]. Journal of Technology and Teacher Education, 9(4), 497–518.
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Martin, D. C., & Blanc, R. (1984). Improving reading comprehension through reciprocal questioning techniques. Lifelong Learning, 7(4), 29–31. McAndrew, P., Goodyear, P., & Dalziel, J. (2006). Patterns, designs and activities: Unifying descriptions of learning structures. International Journal of Learning Technology, 2(2-3), 216–242. doi:10.1504/IJLT.2006.010632 Millis, B. J., & Cottell, P. G. (1998). Cooperative learning for higher education faculty. American Council on Education, Series on Higher Education, Oryx Press. Osborn, A. F. (1963). Applied imagination: Principles and procedures of creative problem solving (3rd rev. ed.). New York, NY: Charles Scribner’s Sons. Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175. doi:10.1207/ s1532690xci0102_1 Palloff, M. R., & Pratt, K. (2004). Learning together in community: Collaboration online. In 20th Annual Conference on Distance Teaching and Learning, August 4-6, 2004, Madison, Wisconsin, Retrieved on 25, June, 2009, from http://www. uwex.edu/disted/conference/Resource_library/ proceedings/04_1127.pdf Panitz, T. (1997). Faculty and student resistance to cooperative learning. Cooperative Learning and College Teaching, 7(2), 2–4. Scardamalia, M., & Bereiter, C. (1996). Computer support for knowledge-building communities. In Koschmann, T. (Ed.), CSCL: Theory and practice of an emerging paradigm (pp. 249–268). Mahwah, NJ: Erlbaum.
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Sharan, S., & Hertz-Lazarowitz, R. (1980). A group-investigation method of cooperative learning in the classroom. In Sharan, P., Hare, P., Webb, C., & Hertz-Lazarowitz, R. (Eds.), Cooperation in education (pp. 14–46). Provo, UT: Brigham Young University Press. Slavin, R. E. (1978). Student teams and achievement divisions. Journal of Research and Development in Education, 12, 39–49.
Slavin, R. E. (1990). Cooperative learning: Theory, research, and practice. Englewood Cliffs, NJ: Prentice Hall. Voyiatzaki, E., & Avouris, N. (2005). On teachers’ involvement in design and evaluation of collaborative activities: Case studies in secondary and higher education. Proceedings of the Computer Supported Inquiry Learning workshop, 18-20 May 2005, Genoa. Vygotsky, L. (1978). Mind in society. Cambridge, MA: Harvard University Press.
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Chapter 9
Building Bridges:
Combining Webcasting and Videoconferencing in a MultiCampus University Course Jeremy Birnholtz Cornell University, USA & University of Toronto, Canada Ron Baecker University of Toronto, Canada Simone Laughton University of Toronto at Mississauga, Canada Clarissa Mak University of Toronto, Canada Rhys Causey University of Toronto, Canada Kelly Rankin University of Toronto, Canada
ABSTRACT Supporting lifelong learning can be challenging in that participants are often geographically distributed, have significant time constraints, and widely varied skills and preferences with regard to technology. This creates the need for designers to support flexible configurations of systems for delivering content, in ways that still allow for meaningful learning and instruction to take place. In this chapter, the authors present a case study of experience in offering a university course using a novel system that bridges videoconferencing and webcasting technologies. These have historically been separate. Webcasting scales easily to accommodate large audiences, but only supports one-way transmission of audio and video. Videoconferencing allows for two-way interaction in real time, but uses more bandwidth, and does not scale as easily. Our system allowed for increased participation in webcasts, which had benefits for both DOI: 10.4018/978-1-61520-983-5.ch009
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Building Bridges
instructors and students. This chapter presents an analysis of interaction and awareness in distance learning contexts, and concludes with design principles suggesting that designers of future systems focus on: (1) developing novel displays and visualizations for presenting information about students, (2) reducing inequalities between modes of participation by making it clearer when, say, questions are asked by text or who is speaking when there are multiple images displayed, and (3) accommodate a range of student preferences and capabilities by supporting multiple modes of presentation.
INTRODUCTION Lifelong learning presents many challenges to both curriculum and technology developers (de Freitas, et al., 2006). Namely, lifelong learners are different from traditional students in that they often have many career and family responsibilities, so cannot relocate or focus on education full-time. Moreover, they are often at varied life stages and have varying levels of educational background. As such, there is utility in exploring novel ways to deliver educational content to geographically distributed groups of diverse individuals. One way to achieve this is to broaden access to existing educational opportunities to include lifelong learners who might not otherwise benefit from them. Indeed, there is considerable interest in the use of e-learning technologies to increase access to education (Serif, et al., 2009; Shea, Picket, & Li, 2005). While some universities have invested in the reshaping of existing courses and curricula for novel online learning environments (Bourne, 1998; Hazemi, Hailes, & Hailes, 2002), many have also sought to leverage existing resources by broadening access to courses already being offered on campus (Anderson, et al., 2000; Cogburn, Zhang, & Khothule, 2002; Shea, et al., 2005). Lecture-style presentations are common on university campuses (McKeachie, 2002; Bligh, 2000) and it has been said that they “serve good students well and can function as effective learning events for many” (Allert, 2004). Given that they are already being presented to large audiences, lectures are an easy opportunity to make educational content available to lifelong learners
participating from home or other remote locations – the content need only be captured and streamed. Serif et al. (2009) describe a range of strategies for delivering e-learning content to geographically distributed groups, including scenarios where small groups of participants gather in a shared physical space to join a larger remote group, as well as those where participants join in from home. The authors suggest that content can be delivered to these types of participants via webcasting and conferencing technologies, but treat these largely independent of one another. Webcasting uses media streaming technologies to allow for live one-way audio and/or video presentations to large, geographically distributed audiences (Baecker, 2003). One-way streaming means easy scaling to accommodate very large audiences (Weinstein, 2005), and that barriers to access are low – only a PC with a dial-up modem and a web browser is required for basic performance. One drawback, however, is that most current webcasting technologies (e.g., Accordant, Adobe Connect Virtual Classroom, etc.) do not facilitate natural two-way interaction between the presenter and remote audience members during a webcast. Instead, systems treat webcasting as a one-way presentation that is distinct from a more interactive format. In this regard, webcasting stands in contrast to videoconferencing, which allows for real-time interaction via rich media. While this is useful in facilitating interaction, multi-point conferencing requires substantial bandwidth and does not easily scale to accommodate large numbers of simultaneous remote participants using basic hardware
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and software (i.e., without some investment in facilities, connectivity and equipment). This separate treatment of these two modes of delivery is artificial and potentially problematic for several reasons. First, it forces designers and educators to choose in advance whether or not the audience will be able to participate or respond to a presentation, which is a constraint rarely faced in face-to-face educational settings, even where the audience is large. Second, this choice may have important consequences in lifelong learning contexts, where audience members may have very different preferences (e.g., based on age, experience, background) about whether and how they wish to interact with the system (Chrysostomou, Chen, & Liu, 2009) and the utility of educational technology generally (Caruso & Kvavik, 2005). Third, there is substantial evidence that both learners and presenters benefit from opportunities for interaction. We argue that substantial benefits could be derived from combining these approaches to e-learning in ways that afford both interaction opportunities and configuration flexibility for designers, educators and participants. In this chapter, we present a case study of our experience in offering a multi-campus university course using a novel prototype system that builds real-time, dynamic bridges between videoconferencing and webcasting, hereafter called the “modified ePresence” system. Our system uses webcasting to reach a broad audience, but also allows webcast viewers to periodically participate more actively via on-demand, temporary two-way videoconferencing links that immediately become a part of the streamed webcast that is visible and audible to all. We conclude with design principles and implications for designers of future systems.
BACKGROUND As background for our case study, we discuss the principles that led to our system we describe below. We focus first on the value of interaction
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in systems for distributed lectures, and then to past systems intended to support awareness and interaction in lectures.
Interaction in Lectures There has been substantial study of lecture-style presentations, and the role of interaction in these presentations. While the effectiveness of lectures depends, of course, on the individual style of the lecturer (Fardon, 2003; Saroyan & Snell, 1997), the amount of student participation and interaction also have a substantial impact (Steinert & Snell, 1999). Similarly, the amount of instructor-student interaction (Moore, 1989) can impact faculty satisfaction with online instruction (Shea, et al., 2005). These findings motivated our interest in improving interaction in lectures to distributed audiences. In particular, we focused on instructor-student interaction, as contrasted with, say, student-student interaction (Moore, 1989). In exploring instructor-student interaction behavior, Birnholtz (2006) observed several lecturers and found frequent, though varied use of interactive techniques ranging from asking frequent questions of students to allowing students to raise their hands and ask questions. Building on this, Birnholtz et al. (2008) interviewed instructors to better understand how they interact with and respond to their students. In addition to explicit interactions such as questions, participants reported that being able to see at least some of their students’ faces enabled them to gauge whether or not material was being understood, and to adjust the presentation accordingly. All of these results served as the foundation for the system we present below. Motivated by the potential benefits to both students and instructors, we aimed to design a system that would facilitate live interaction and questions, in addition to basic instructor awareness of student presence and comprehension.
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Videoconferencing Approaches to Interaction
Streaming and Webcasting Approaches to Interaction
Video has been used in distance learning for many years (see Mood, 1995, for a review). It is particularly useful in geographically distributed groups because it facilitates both voice interaction and some visual awareness of contextual information (e.g., who else is present) and nonverbal cues (e.g., gaze direction, facial expressions, raised hands) (Clark & Brennan, 1991), which are important in face-to-face classrooms, as described above, in that they allow for more natural interaction, and improve the capacity for mutual understanding (Clark, 1996). With this in mind, Chen (2001, 2002, 2003) sought to enhance basic two-way interaction by providing augmented awareness of student and presenter gaze direction and eye contact (Chen, 2001), and of student participation patterns (Chen, 2003). While this experience highlights the importance of the awareness questions we explore, this system, like other videoconferencing systems, restricted participation by using high-bandwidth technologies and specialized sensors. While current video technologies mean that only an ordinary Internet connection is required to participate in a basic videoconference, there are frequently quality issues with performance and reliability that can impede frequent and natural interaction (Anderson, et al., 2000). Still, there are tools such as Microsoft’s Conference XP (Needham, 2006), the ISABEL project (http://isabel. dit.upm.es/), and the satellite system described by Serif et al. (2009) which can all yield good results when bandwidth and network quality are favorable. Indeed, videoconferencing provides many benefits, but restricts participation to those with available bandwidth and other resources; and also restricts participation to the number of simultaneous participants that can be supported in a conference, which is often lower than is possible in a webcast.
A second approach to distributed lecture-style presentations is to use one-way streaming media. Isaacs et al. (1994) developed an early system to support streaming presentations, and many of the features of their system persist in what are now called webcasting technologies (Weinstein, 2005). These technologies allow for transmission of some combination of audio, video and presentation media (e.g., slides). Webcasting uses streaming technologies, which support many simultaneous users and use buffering to ensure relatively high reliability. At the same time, however, these advantages can make awareness and interaction a difficult problem. ePresence (http://epresence.tv), an open source webcasting infrastructure, improved on basic webcasting by supporting the transmission of presentation media along with streaming video and audio, and offering two-way text interaction between the presenter and audience (Ron Baecker, 2003). We describe the modified ePresence system used in this study below. Others have also sought to improve the speaker’s awareness of remote attendees in webcasting. The TELEP system developed by Jancke et al. (2000), for example, was used to webcast live presentations on the Microsoft campus to those who did not wish to leave their offices to attend. To facilitate awareness, it allowed remote attendees to share webcam video or still images of themselves, which were displayed on the wall of the presentation space within view of the speaker and face-to-face attendees. Remote attendees could ask questions via a text chat interface. In using TELEP, however, many remote attendees did not share video images of themselves, reportedly because many were multitasking and did not want the speaker to see that they were focusing only intermittently on the presentation. As the system was used only on the Microsoft campus, remote attendees were not physically far
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from the live presentation, and those who were very interested could simply walk over to the auditorium. This is critically different from the lifelong learning settings that we focus on here where attendees are more broadly dispersed and do not have this option.
Figure 1. Lecture hall organized for a webcast
STUDY DESCRIPTION AND CONTEXT To better understand how to facilitate instructorstudent interaction in presentations to distributed audiences, and to explore the potential problems inherent in combining webcasting and videoconferencing approaches, we conducted and present data from a case study simultaneously offered on two campuses of our university. Students could attend the course on either campus, or participate from any Internet-accessible location. Such flexible setups can aid in accommodating the varied needs of lifelong learners. We sought to address questions that come from two different perspectives: •
•
Students: ◦⊦ How well did the system work? Were all students able to participate and grasp the content? ◦⊦ What were student perceptions of their experience? Did they feel they were able to participate? What were their reactions to the system? Instructors: ◦⊦ Did the system facilitate instructor awareness of students in all locations? Did the system enable instructors to interact with students at other locations?
In fall 2006, we deployed our experimental system (Figures 1-3) in an advanced Computer Science course about creating new commercial software ventures. The course was offered to stu-
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dents on two campuses of our university, located 25 kilometers apart (and with separate networks, both independently connected to the Internet). Approximately 60 students were enrolled at the “main” campus (where the instructor was based), and 15 at the “satellite” campus. The class met weekly for 3 hours, and consisted of lectures and discussion led by the instructor and guest speakers. Guests were entrepreneurs and professionals from software companies, who described and derived lessons from their experiences. These presentations were, for the most part, delivered in a lecture hall on the main campus, though there were occasional presentations to the entire class by teaching assistants at both campuses, and two of the guest speakers delivered their presentations from remote locations using our system. Students had the option of attending the course at whichever campus was most convenient for them, or attending remotely from any Internet-accessible location. Students who elected to do this will be referred to here as “remote” students or participants, to distinguish them from the students at the “satellite” campus. At the main campus, the lecture room was configured as shown in Figure 1. There were two video cameras, and two staff members who operated the cameras, controlled which online participants had permission to speak (i.e., were
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Figure 2. The in-class awareness display, with identifying information blurred. The top right box represents the satellite campus, the top middle box is a guest observer, and the remainder are remote student participants.
Figure 3. The satellite and remote viewing interface. (a) Webcast Video, (b) User Controls, (c) Active Speaker, (d) Remote Participant List, (e) Presentation Content, (f) Chat.
System Description part of the videoconference) and selected which of the two camera shots would be webcast. Students at the satellite campus sat in a smaller room where the presentation was projected on a large screen at the front of the room (see Figure 2). There was also a single camera, and one staff member who was responsible for setup and camera operation. For VoIP interaction, there was a wireless handheld microphone. Students indicated their desire to speak by raising their hand, and the microphone was brought to them. Students who could not be present at either campus could also log in and participate fully in the lecture from any location. If they had a webcam, their video could optionally be displayed on the awareness display (see Figure 3) in the lecture room. We note that the primary instructor in this course is one of the authors of this chapter. The other authors were conscious of this potential conflict in the design and execution of the study, in that data gathering efforts focused mainly on students, guest speakers and teaching assistants, none of whom were involved with the technology development or its evaluation.
Here we provide a very basic description of our experimental system, as it was seen by our participants. For a much more detailed description, see (Baecker, et al., 2007). This chapter builds on the prior paper by providing empirical evaluation data about experience with the system described in detail in that paper.
The Viewing Interfaces The remote viewing interface is shown in Figure 3. As with traditional webcasts, participants receive the video feed in sync with presentation material, such as slides. Questions and comments can be sent to other remote participants and the in-room display using a persistent chat tool. The chat interface is based on the BackTalk system described by Fono and Baecker (2006), and allows for tagging and formatting of messages to categorize them or attract attention, as well as browsing past conversations. Remote and satellite students could interact with others in real time via multi-point videoconferencing between the instructor and a subset of the webcast viewers. This videoconference conversation was then streamed immediately to the
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remaining webcast viewers as part of the normal webcast audio/video stream. Participation in the videoconference was facilitated using a hand raising metaphor. Participants at the satellite campus would literally raise their hands and the teaching assistant would bring the microphone to them. Remote participants clicked an icon, and were then included in the videoconference by the moderator, and granted permission to speak. Their status bar (see ‘b’ in Figure 3), typically gray, would then turn green and their status changed from “watching” to “on air.”
The In-Room Awareness Display The in-room awareness display (Figure 2) satisfies our goal of providing the speaker with awareness of the remote audience, and lets the speaker and local audience quickly assess the composition of the satellite and remote audiences, and their level of engagement. It consists of visual representations of remote participants, and a persistent chat window that displays text questions, comments, and contributions to discussion. Text is not intended to be the primary means of communication, but rather serves to augment voice conversation as described by McCarthy and Boyd (2005). The awareness display also indicates via color which remote participants have permission to speak via the videoconference.
Research Methods Our study uses the case study research method (Yin, 2009). In seeking to understand our case, we used multiple data sources to gauge student and presenter response. Questionnaires. Four questionnaires were administered to all students at periodic intervals. The first gathered baseline demographic data and student attitudes toward and experience with technology, using an established instrument by Caruso and Kvavik (2005).
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The remainder assessed student experience in the course and with our system. Some questionnaire items were borrowed from course evaluation scales used at our university. Others came from established measures of presence in virtual environments (Witmer & Singer, 1998), and a small number were developed for this study. Response rates varied between 50-60%. Interviews. Semi-structured 20-60 minute interviews were conducted with 7 students, with 4 interviewed multiple times during the term. Similar interviews were conducted with five guest speakers, and the teaching assistant at the satellite campus. Field Observations. Field observations were conducted at both campuses. Three independent observers conducted a total of 11 1-3 hour observation sessions and recorded detailed field notes that were later typed and expanded upon for analysis. Observers paid particular attention to student experience at both of the sites and the smoothness of interaction within and between the sites. Four observation sessions were at the satellite campus, and seven at the main campus. One observer conducted observations on both campuses for comparison.
Data Analysis Interview transcripts and typed field note documents were read and re-read several times and a preliminary coding scheme was developed. This scheme focused on themes that were important to us, but that were also clearly recurring as we read through the data. These included: Interaction: We noted when students and presenters interacted. In particular, we paid attention to how these incidents started and ended, how smoothly they seemed to function, what media were used (e.g., text, video/audio or face-to-face), and who was involved. Breakdowns: In looking at interactions, we were particularly cognizant of breakdowns in social process or the technical system being
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evaluated. For notetaking purposes, we considered breakdowns to be incidents where events did not seem to unfold as expected for at least one of the parties involved in the incident. Attention: In observations and interviews we focused on what participants seemed to be (or said they were) visually attending to during the class. We asked them if they were paying attention to the students (for instructors) or the instructors (for students), and also noted when they appeared to be paying attention to other things. For instructors, we were particularly interested in the extent to which they paid attention to the satellite and remote students versus the local ones. Awareness: We were also interested in the extent to which students and instructors at both sites were aware of each other. While we did look for signs of this in our observations, we relied mostly on the interview data. Here we coded instances where participants mentioned awareness of specific people or groups of people. For the questionnaire data, we used a combination of single scale items and aggregations. Where aggregated constructs were used, they were tested for consistency using Cronbach’s α, and values were above.7, within the range acceptable for social science research (Nunally, 1978).
RESULTS In this section we describe the results from our case study. We describe student performance in the course using the modified ePresence system, student experience with the system and then discuss the experience of presenters.
Student Performance To address whether or not the modified ePresence system had an impact on students, we first explored their performance, using final marks (unadjusted grades) in the course. We first checked to see if there was a performance difference between the
two campuses. Surprisingly, there appeared to be a difference (MMain= 78.37, SDMain = 5.99, MSatellite = 73.72, SDSatellite = 7.09, t = 2.15, p
You have succesfully completed this module.
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The “Continue” Box The only mechanism defined in IMS-LD to indicate that interaction with an activity has finished is to complete it. However, completion of activities cannot be undone, and the influence of this fact on the exporting process is important. The mechanisms explained in the specification do not allow expressing that a user has introduced all data required for doing an exercise, then grade the results, move to another activity and then come back to the same exercise and solve it again. In other words, there is no “Continue” button in IMS-LD. A complicated technique has to be used to implement this behavior. Inside the resource master file, each of the DIV elements is concatenated with a . (This is not showed in the XML excerpt 3 for the sake of simplicity. It would be written in the section marked as “XHTML code of... ”.) This element is invariable and is showed in XML Excerpt 4 When the LD player finds this tag in the XHTML resource, it generates a text box in which the user can introduce directly a value for the property called “export SG2LD-last-unit”. The element is one of the four global elements in IMS-LD. Global elements are the method designed in IMS-LD for allowing users to interact with the learning design through the activities. In particular, permits to introduce data, e.g. the answers to an exercise. When the learning design is created from a SG, this technique is used for receiving the answers from the exercises as well as any other input data needed from the student. The interesting point here is that the “continue” box achieves the same behavior as a “Continue” button. When the user has finished with one activity and wants to advance to the next one he/ she must enter the word continue in the text box, and then press “OK”. This is showed in Figure 5. When the user presses the “OK” button, the value “continue” is introduced in property export SG2LD-last-unit. This change fires the evaluation of all conditions that are related to it, i.e. conditions related to edges. The process evaluation of those conditions finishes the update of several variables (including export SG2LD-last-unit) and a change on the visible state of the DIVs. The complete flow of information involves five different properties and cannot be shown here for the sake of space. The interested reader is referred to (Gutierrez-Santos et al., 2008).
Conditions, Variables and Hierarchy The most delicate phase of the process is translating all the semantics of a sequencing graph into IMS-LD conditions and actions. The main difficulty comes from the fact that we are changing
XML_4: Invariant set-property for export_SG2LD-last_unit
Figure 5. “Continue” box in RELOAD
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the paradigm. SG follow an imperative paradigm: if some condition applies, then follow this edge; after that, if you have followed an exit edge, then look for more conditions, etc; after that, if you arrive to a container node, then go down one level, etc. On the other hand, the conditions in IMS-LD are more related to a functional paradigm (Mauny & Cousineau, 2003). All conditions are evaluated at the same time; if a fires some action that produces some change in a of the system, all conditions are evaluated again. There is no logical or chronological order or precedence between all instances of . Conditions in IMS-LD must capture all the semantics of those in the SG, plus that of the spatial distribution of activities. To achieve this, some special properties had to be defined. These properties are local (refer to the current UoL) and personal (refer to the current student), and carry information regarding the spatial position in the graph, the traversal of edges, etc. An additional property holding the value 0 had to be introduced, because of limitation of IMS-LD when defining arithmetical operations in which both operands are constants. The interested reader is referred to (Gutierrez-Santos et al., 2008) for additional details and relevant XML excerpts. As it has been explained above, the process of exporting SG to IMS-LD involves flattening a hierarchical structure. Thus, two SG variables with the same name (e.g. time, qualification, etc) at different levels of the hierarchy, or even on two siblings of the same level, collide once they are put at the same unique level on the imsmanifest. xml. In order to avoid this problem, a prefix is added to the name of each IMS-LD property. The prefix is the name of the node in which the original value was declared (i.e. the node of its section) as well as the name of every ascendant node up to the root. The root is not included because it is common for all variables in a graph; thus, the variables declared at the root node wear no prefix when exported.
THE LIMITS OF IMS-LD FOR SEQUENCING IN LLL The design of the exporting process described above, its implementation, and the subsequent tests have drawn some interesting conclusions about the limits of the IMS-LD specification. First, the functional paradigm of the condition model in IMS-LD does not scale well. If the number of conditions grows, it quickly becomes very difficult to keep track of all conditions; therefore, the probability of side effects increases dramatically. Every condition must be checked for every possible case, leading to a debugging process that is both tedious and error-prone. Although this may be adequate for small control tasks, the creation of a set of conditions and actions large enough to control a big number of activities becomes infeasible unless automatic tools are used to help. This clearly shows the value of higher level tools that produce an IMS-LD output, reducing this complexity. In (Gutierrez Santos, 2007), it is shown how the introduction of a simple loop in a small graph results in a sequence of conditions that is several pages long. Complex sequencings like those involved in any lifelong learning process are totally impossible to create and/or maintain using IMS-LD conditions directly. Second, IMS-LD does not provide a good support for cycles when accessing the material. This is a substantial limitation, because learning is improved if a system grants the possibility of going over some already covered material for revision and reflection. This is especially true when long periods of time are considered, like it is the case in lifelong learning scenarios. IMS-LD, on the contrary, is oriented towards a sequence of activities that is strongly linear. This comes from the metaphor of the theatre play, and produces consequences as critical as the fact that an activity cannot be “uncompleted” once it has been set as “completed” by the student. This chapter has shown that it is possible to express loops in IMSLD, but this comes at a cost: resulting imsmanifest.
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xml files are immense and a peculiar text box has to be created that introduces noise in the learning process and may distract the student. The restriction that, once an activity is completed, it “stays completed in the run” [IMS, 2003c], has been found to be excessive and limiting. Such a behavior is justified when dealing with synchronization elements (e.g. ), but not in the case of activities, when it could be interesting to come back to them and perform them again (e.g. exercises). This restrictions hampers the use of elements like , as they can be used only once. A possible solution to this problem is to include a distinction between the state “completed and unable to be uncompleted” and the state “completed, but able to be uncompleted and completed again”. In such a scenario, both types of completion events would have the ability of performing actions ) when they occurred. This would simplify the adaptation of graph-based sequencing definitions to IMS-LD, making it possible to use some tool like SG to define a complex sequencing strategy without producing enormous results that take a significant amount of resources to process. As a final conclusion, it has to be said that several problems have arisen when trying to express graphs in terms of IMS Learning Design. It is true that it is possible, and this permits to define a flexible set of adapted sequencings of learning material with a SG and then use IMSLD to use them in many platforms. But the cost of the process is high: it introduces noise in the approach (i.e. the “continue” box) and the files (the manifest and the master resource file) are extremely big, even when the original graph is not very complex.
Why Not Use IMS-SS, IMSQTI, or IMS-CC? There are three other IMS specifications that have some relationship with the adaptive sequencing of learning material: IMS Question and Test
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Interoperability (IMS, 2006), IMS Common Cartridge (IMS, 2008), and especially IMS Simple Sequencing (IMS, 2003d). IMS Question and Test Interoperability (IMS QTI) is another specification that offers a common representation for tests and exercises. IMS QTI defines a data model specifically oriented towards the design and reuse of questions. This kind of questions can be automatically graded to produce feedback for the student. Variables in QTI can allow for a certain level of adaptation, like in IMS LD. However, the adaptation in QTI focuses on adaptive presentation and grading of the exercises, rather than their sequencing. IMS Common Cartridge (IMS CC) is relatively new, compared to the others. It comprises a combination of IMS Content Packaging, IMS QTI, IEEE LOM, and IMS Authorization Web Service. The IMS CC specification is gaining a lot of attention because it simplifies many aspects of the costly design process. However, one of its limitations is the definition of a static sequence of the learning resources. IMS Simple Sequencing (IMS SS) is a specification used to describe paths through a collection of learning activities. IMS SS relies on the concept of learning units that are organized into a hierarchy tree. A parent activity and its children are referred to as a cluster of activities. Clusters may have sequencing rules and limit conditions associated with them. Sequencing rules are used to influence the order in which activities are presented to the learner. Limit conditions, such as attempt limits, duration limits and date limits, are used by the sequencing rules to further influence which activity is sequenced next to a student. Sequencing rules and limit conditions are part of the definition model that describes the vocabulary, semantics and values required to execute IMS SS behaviours. The IMS SS Tracking Model (IMS, 2003e) can only keep track of three aspects: timing and completion progress of each attempt on an activity, timing and completion progress over all attempts
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on an activity, and status of the objectives of an activity. IMS Simple Sequencing lacks a student model in which to store part of the information that the system is able to track about him/her, apart from these three aspects. Thus, it is not possible to fully express all sequencings that can be defined by a SG in terms of this specification. For example, the sequencing cannot be influenced according to a “skill level” of the student, because there is no such concept in the specification and there are no means to include it.
CONCLUSION AND FUTURE WORK Sequencing Graphs have been designed to define a set of adaptive sequencings given some learning activities. Sequencing graphs are a specialization of finite automata that take into account many of the particular aspects of the learning process, and have been used before in several elearning systems (Gutierrez et al., 2004; Prieto-Linillos et al., 2006). Additionally, they try to fulfill four design goals: simplicity, expressiveness, scalability, and reusability. They have been designed to be simpler to create than other alternatives, yet able to define any type of sequencing, in particular those involving cycles. They are hierarchical, which helps to manage big numbers of learning activities and makes the approach scalable. The other main goal they have been designed for is reuse of sequencings, from a double point of view: the upgrade of an activity does not affect the sequencing of the set; and a set of activities with their sequencing defined as a graph is suitable of being integrated in a broader set, that is, to be included as a container node in a graph of a higher level of hierarchy. A sequencing graph can be expressed in terms of IMS LD, thus providing interoperability between IMS LD compliant systems and systems based on sequencing graphs. Although the specification is not exactly designed for sequencing, the mechanisms provided (i.e. properties, conditions and actions) permit to articulate the same strategy
in a UoL; although the mechanism is not trivial. The process of expressing Sequencing Graphs in IMS LD semantics has shown several limitations of the specification when it is used to define adaptive sequencings. High-level authoring tools are thus necessary to implement such complex reorganizations of content in IMS-LD, and Sequencing Graphs can provide that functionality by presenting a simple metaphor that hides part of the complexity of the specification (c.f. RELOAD Learning Design editor). One of the main limitations of IMS LD is the difficulty of introducing adaptation in the UoL during runtime. The method showed in the chapter makes use of global elements, but there are other two interesting proposals. The first one is the integration of active components into the IMD-LD player, as proposed in (de la Fuente et al., 2009). The authors propose the integration of the player with active external components. They prove their point using Google Docs spreadsheets, but claim their architecture is general enough to integrate other plug-ins and services. Another possibility is the design of an architecture that supports access to information on runtime (Zarraonandia et al., 2006). The goal is to enhance reusability of the UoL introducing simple modifications into the original learning process.
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IMS. (2003b). IMS learning design best practice and implementation guide (Vol. 1, p. 0). Final Specification. IMS. (2003c). IMS learning design information model (Vol. 1, p. 0). Final Specification. IMS. (2003d). IMS simple sequencing (Vol. 1, p. 0). Final Specification. IMS. (2003e). IMS simple sequencing information and behavior model. Final specification. IMS. (2006). IMS question and test interoperability (Vol. 2, p. 1). Public Draft. IMS. (2008). IMS common cartridge (Vol. 1, p. 0). Final Specification. Karampiperis, P., & Sampson, D. (2004). Adaptive instructional planning using ontologies. In Proceedings of IEEE International Conference on Advanced Learning Technologies, (pp. 126–130). Khuwaja, R., Desmarais, M., & Cheng, R. (1996). Intelligent guide: Combining user knowledge assesment with pedagogical guidance. In Intelligent tutoring systems, (LNCS 1086). Springer-Verlag. Klamma, R., Chatti, M. A., Duval, E., Hummel, H., Hvannberg, E. H., & Kravcik, M. (2007). Social software for life-long learning. Journal of Educational Technology & Society, 10(3), 72–83. Koper, R. (2005). Designing learning networks for lifelong learners. In Koper, R., & Tattersall, C. (Eds.), Learning design: A handbook on modelling and delivering networked education and training. LTSC, I. (2002). IEEE standard for learning object metadata (LOM). Final Specification v.1.0. Ludwig, J., Ramachandran, S., & Howse, W. (2002). Developing an adaptive intelligent flight trainer. In Proceedings of the Industry/Interservice, Training, Simulation and Education Conference (I/ITSEC 2002).
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Section 3
Developing and Accrediting Skills and Competences
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Chapter 12
Learning, Unlearning, and Relearning:
Using Web 2.0 Technologies to Support the Development of Lifelong Learning Skills Joanna C. Dunlap University of Colorado Denver, USA Patrick R. Lowenthal University of Colorado Denver, USA
ABSTRACT Given ever-changing societal and professional demands, lifelong learning is recognized as a critical educational goal. With postsecondary students’ increased demand for online learning opportunities and programs, postsecondary educators face the challenge of preparing students to be lifelong contributing members of professional communities of practice online and at a distance. The emergence of powerful Web 2.0 technologies and tools has the potential to support educators’ instructional goals and objectives associated with students’ professional preparation and the development of lifelong learning skills and dispositions. In this chapter, the authors explain how postsecondary educators can use the Web 2.0 technologies associated with blogging, social networking, document co-creation, and resource sharing to create intrinsically motivating learning opportunities that have the potential to help students develop the skills and dispositions needed to be effective lifelong learners. DOI: 10.4018/978-1-61520-983-5.ch012
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Learning, Unlearning, and Relearning
INTRODUCTION The world we live in is changing right before our eyes, as well-illustrated by Dr. Michael Wesch’s thought-provoking YouTube video, “A Vision of Students Today” (http://www.youtube.com/ watch?v=dGCJ46vyR9o). One basic point made in this video is that information and communication technologies are drastically changing the world we live in, and institutions of higher education are now scrambling to attend to these changes. Specifically, universities are trying to adequately respond to a trifecta of emerging trends: •
•
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Today’s economy depends increasingly on employees who are quick and efficient lifelong learners (Hinrichs, 2004). Employers are now looking for employees who can think critically and solve a range of problems, move easily from one task to another, work efficiently and effectively in team situations, and constantly adjust and enhance their knowledge and skills to meet ever-changing needs (Casner-Lotto & Barrington, 2006; Dunlap, 2005). Postsecondary education has been involved in a paradigm shift from teacher-centered learning to student-centered learning (Barr & Tagg, 1995; Boggs, 1999; Harden, 2000). This shift substitutes teacher-centered learning’s goal of providing instruction through transfer of knowledge with student-centered learning’s goal of producing learning through student discovery and construction of knowledge (Barr & Tagg, 1995). Universities have been encouraged to focus their strategies and resources on this paradigm shift in an effort to make learning more meaningful and lasting for students. The postsecondary audience is demanding more distance and online learning opportunities (Grabinger & Dunlap, 2004; LudwigHardman & Dunlap, 2003). This demand
is no longer based solely on geographic obstacles and schedule constraints; many students report a preference to the online learning format for a variety of reasons. For example, some students perceive oncampus course experiences as high-pressure, uncomfortable, and even exclusionary because of cultural differences, social class background, lack of facility with the English language, age, and so on (Burbules & Callister, 2000a). Additionally, because these students typically have full lives and busy schedules with which to contend, they want what they want, when they want it: (1) students expect their learning opportunities to be available immediately, and (2) students need learning experiences that are directly applicable to their needs and immediately transferable to their professional settings (Grabinger & Dunlap, 2004). Reflecting and in response to these specific trends, lifelong learning is increasingly recognized as a critical educational goal. Lifelong learning is intentional learning that people engage in throughout their lives for personal and professional fulfillment to improve the quality of their lives (Dunlap & Grabinger, 2003). The emergence of Web 2.0 technologies, and the participatory culture those technologies engender, has great potential to support lifelong learning endeavors, allowing for informal, just-in-time, day-to-day learning. Unfortunately, people are often ill-equipped to engage in lifelong learning (Dunlap, 2005), let alone take full advantage of the abundance of resources available at their fingertips via Web 2.0 technologies. We believe that postsecondary educators preparing students for professions in this day and age are obligated to help students develop into competent lifelong learners. In this chapter, we will describe and present examples of how online technologies are making just-in-time, at-your-fingertips lifelong learning a possibility.
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More specifically, we will focus on how Web 2.0 technologies such as blogging (e.g., using tools like Blogger or WordPress), microblogging/microsharing (e.g., Twitter), social networking (e.g., Facebook, MySpace, Ning), document co-creation (e.g., Google Docs), and resource sharing (e.g., Flickr, Slideshare, Diigo) can be used by postsecondary educators to help students develop as lifelong learners.
CHARACTERISTICS OF LIFELONG LEARNERS Long before there was a Web 2.0, Alvin Toffler, author of Future Shock and The Third Wave, foresaw the importance of lifelong learning and broadening our conception of what makes a person literate. Toffler, by quoting Herbert Gerjuoy, argued that, “The new education must teach the individual how to classify and reclassify information, how to evaluate its veracity, how to change categories when necessary, how to move from the concrete to the abstract and back, how to look at problems from a new direction—how to teach himself. Tomorrow’s illiterate will not be the man who can’t read; he will be the man who has not learned how to learn.” (1973, p. 414) Toffler was basically suggesting, what many of us are coming to find out today, that the illiterate of the 21st century will be those who cannot learn, unlearn, and relearn—in other words, those who lack lifelong learning skills and dispositions. Lifelong learners embody specific characteristics that empower them to learn, unlearn, and relearn. They are able to learn and adapt because they reflect on the quality of their understanding and seek to go beyond what they know (Dunlap, 2005). This requires a love of learning and willingness to engage in learning—in other words, a disposition toward lifelong learning. This disposi-
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tion includes characteristics, such as risk taking (Brookfield, 1985/1991), intellectual curiosity (Dunlap & Grabinger, 2003), persistence (Grow, 1991), taking responsibility for decisions related to learning (Candy, 1991), and viewing learning as an ongoing process (Dunlap & Grabinger, 2003); it also includes a specific skill set: the capacity for self-directed learning supported by metacognitive awareness (Dunlap & Grabinger, 2003; Dunlap, 2005). Unfortunately, many students struggle with online learning because they do not possess the necessary self-directed learning and metacogntive-awareness skill set: self-discipline, the ability to work alone, time management, learning independently, the ability to develop a plan for completing work, and so on (Burak, 1993; Dunlap & Grabinger, 2003; Hancock, 1993; Ludwig-Hardman & Dunlap, 2003). Coincidently, this is the very skill set needed for lifelong learning.
Self-Directed Learning Self-directed learning is crucial for lifelong learning (Dunlap, 2005; Dunlap & Grabinger, 2003; McFarlane & Dunlap, 2001). According to Malcolm Knowles (1975), one of the first scholars to seriously focus on the concept of self-directed learning, self-directed learning is: The process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing learning strategies, and evaluating learning outcomes. (p. 18) Although often described as a hallmark of adulthood, a lot of people are not self-directed learners (Kerka, 1994). Yet, most guidelines and assessment tools that describe successful online learners list self-direction as a primary quality of successful online students (Ludwig-Hardman &
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Dunlap, 2003); in fact, many universities provide criteria to potential students so they can self-assess if online education is for them, such as:
•
•
•
•
•
•
University of Illinois’s website asks, “Do you have self-discipline and motivation?” (http://www.online.uillinois.edu/students/ well_suited.asp) The Minnesota State Colleges and Universities System notes, “Online learning is often accelerated and requires that you are motivated and can work independently” (http://www.mnonline.org/started/ rightforyou.html) The College of Nursing at University of North Carolina Chapel Hill lists self-motivation, self-direction, self-discipline, and initiative as the first four skills of successful online learners (http://nursing.unc.edu/ current/rn-bsn/program/) Colorado State University shares, “If you are an independent learner, self-motivated, and interested in accelerating your course of study, online learning may be appropriate for you.” (http://www.learn.colostate. edu/answers/faq/index.dot?tag=Online+L earning&tagCount=11#online_right)
As these assessment tools illustrate, there is a clear expectation in distance and online learning programs that require students to take on a high level of responsibility and initiative for their own learning (McLoughlin & Marshall, 2000). Therefore, to be successful online students, students need the skills required for effective online learning, and those skills need to be explicitly taught and supported in the online learning environment. Self-directed learning focuses on how students internally and psychologically control their own learning (Candy, 1991; Hancock; 1993; Long, 1989; Overly, McQuigg, Silvermail, &Coppedge, 1980). Some ways that students accomplish this is through (Barrows, 1985; Burak, 1993; Hancock, 1993):
•
•
•
•
Identifying and defining a problem or learning need; Establishing goals and objectives for addressing the problem or learning need; Developing action plans and timelines to guide learning activities; Identifying, finding, using, and critiquing resources for solving the problem or meeting the learning requirement; Capturing and applying information from resources to the problem or learning need; and Critiquing information, skills, and processes used to solve problems or meet learning requirements.
A common misconception though about selfdirected learning is that it happens alone; selfdirected learning, however, does not mean learning in isolation (Brookfield, 1985/1991). Self-directed learners—in addition to using direct instruction, print materials, and technology-delivered materials—take advantage of a variety of human-oriented resources including peers and colleagues, teams, informal and formal social networks, and communities of practice (Kerka, 1994). However, self-directed learning is not enough: to truly be able to learn, unlearn, and relearn, students must be metacognitively aware (Dunlap, 2005; Dunlap & Grabinger, 2003).
Metacogntive Awareness Students must possess metacognitive awareness if they wish to become effective lifelong learners who are able to learn, unlearn, and relearn in the 21st century (Dunlap, 2005; Dunlap & Grabinger, 2003). Metacognition is essentially the learner’s knowledge and regulation of cognitive process. More specifically, metacognition, according to Biggs and Moore (1993), is the “awareness of one’s own cognitive process rather than the content of those processes together with the use of
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that self-awareness in controlling and improving cognitive processes” (p. 527). Metacognitive awareness is important for a number of reasons. Learners who are metacognitively aware perform the following activities (Brockett & Hiemstra, 1985; Brookfield, 1985/1991; Glaser, 1984; Ridley, Schultz, Glanz, & Weinstein, 1992; Von Wright, 1992): • • •
• •
Take conscious control of learning; Plan and select learning strategies; Monitor and evaluate effectiveness of learning strategies through self-assessment and review; Adjust learning behaviors, processes, and strategies; and Reflect on learning.
People with well-developed metacognitive skills engage in effective problem solving and reasoning activities (Bereiter & Scardamalia, 1985; Bransford et al., 1986; Chi, Feltovich, & Glaser, 1981). On the other hand, people with poorly developed metacognitive skills have difficulty recognizing when they have failed to adequately meet learning goals or complete tasks (Bransford, Sherwood, Vye, &Rieser, 1986). Therefore, the capacity for self-directed learning supported by metacognitive awareness is key to effective lifelong learning. This is especially true today, given frequently changing professional needs and demands and the explosion of information and technologies; one cannot effectively use Web 2.0 technologies, let alone engage in lifelong learning, without the capacity for self-directed learning supported by metacognitive awareness.
STRATEGIES FOR USING WEB 2.0 TECHNOLOGIES FOR LIFELONG LEARNING In order to prepare students for lifelong learning, educators must provide students with educational
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opportunities to develop their capacity for selfdirection, metacognitive awareness, and an overall disposition toward lifelong learning (Dunlap, 2005). To determine what teaching strategies help students develop as lifelong learners, Dunlap and Grabinger (2003) investigated well-established instructional approaches, such as problem-based learning, that appear—based on foundational theory and empirical research—to enhance students’ lifelong learning skills and dispositions. They concluded that educators can support students’ lifelong-learning development by attending to five specific instructional objectives when designing courses and other educational opportunities: • • • • •
Develop student autonomy, responsibility, and intentionality; Encourage reflection; Enculturate into a community of practice; Encourage discourse and collaboration; and Provide intrinsically motivating learning activities.
While there are many ways that educators can attend to each of these instructional objectives, a number of new online Web 2.0 technologies—many of which students are already using (Greenhow, Robelia, & Hughes, 2009; Lenhart, Madden, Macgill, & Smith, 2007; Madden & Fox, 2006)—can be used to attend to these specific instructional objectives in new and creative ways and as a result help to develop lifelong learning skills (i.e., self-directed learning and metacognitive awareness skills) in students. The term Web 2.0 was originally coined by DiNucci (1999) and later popularized by Dougherty and O’Reilly (see O’Reilly, 2005a, 2005b) to describe how the Web is changing from a readonly web to a read-and-write web that facilitates participatory, collaborative, and distributed practices (Antonelli, 2009; Downes, 2005; Greenhow, Robelia, & Hughes, 2009). Therefore, Web 2.0 is more than just new technology; according to
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Greenhow et al., Web 2.0 “is both a platform on which innovative technologies have been built and a space where users are as important as the content they upload and share with others” (p. 347). These technologies coupled with the participatory and distributed practices they engender are changing the way people learn (Greenhow et al.) and some even argue are challenging universities to rethink how they do business (Barnes & Tynan, 2007). We have found that Web 2.0 technologies help make just-in-time, at-your-fingertips lifelong learning possible in ways that typical learning management systems (LMS)—with their highly bounded, asynchronous, threaded, and removedfrom-professional-context structure—cannot. As a result, we are continually exploring ways that we can integrate these online technologies into our courses. In the following paragraphs, we describe how a few of these Web 2.0 technologies—namely, blogs, social networks, online document creation, and resource sharing—can be used to help students develop lifelong learning skills and dispositions by attending to the specific instructional objectives presented above.
Blogging to Encourage Student Intentionality and Reflection As educators, we need to strive to help students become intentional and reflective learners if they are to engage in lifelong learning. Intentional students are self-directed and possess metacognitive awareness. Purposeful, effortful, and active (Palincsar & Klenk, 1992), these students are autonomous, responsible learners who focus on understanding and performance rather than the accumulation of decontextualized facts (Bereiter & Scardamalia, 1989). We can promote the development of autonomy and responsibility by encouraging students to (Dunlap & Grabinger, 2003): •
Assess what they know and do not know about a topic;
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• • •
Set specific goals and objectives for their learning; Ask both knowledge and wonderment questions to focus learning on goals and objectives; Create plans for achieving their goals and objectives; Set a time line for achieving their learning goals; and Identify resources that they may use while studying.
Related, reflective students have the ability to think about themselves as intentional subjects of personal actions, and consider the consequences and efficacy of those actions (Von Wright, 1992). Students need to have opportunities to examine their methods and options in order to develop the skills needed for lifelong learning. Blakely and Spence (1990) describe several basic reflective strategies for developing metacognitive awareness and self-directed learning skills: •
• •
•
•
Ask students to consciously identify what they “know” as opposed to “what they don’t know”; Use journals or logs to help students reflect upon their learning processes; Engage students in guided self-evaluation through individual conferences and checklists to help them focus on their thinking processes; Utilize collaborative activities to enable students to test and challenge each other’s knowledge; and Involve students in think-aloud, role-play and structured walkthrough activities that encourage them to describe their thinking, learning, decision-making, and processes.
One Web 2.0 technology that can be used to support the development of student intentionality and reflection is blogging. Blogs are web-based journals in the form of frequent, chronological
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publications of thoughts and ideas, typically within a specific theme or area of interest; they can be set up as public or private and can enable commenting. The most popular blogging applications are WordPress and Blogger. It has been estimated that in early 2008, there were over 110 million blogs (Richardson, 2008). Over the past few years, blogs have received positive attention from educators (e.g., Downes, 2004; Dunlap, 2008; Richardson, 2008; Warlick, 2007) for their ability to promote literacy, collaboration, and participation. At the same time, though, others like Keen (2008) have criticized blogs and the new read-and-write web for encouraging a “Cult of Amateurs”. Keen, while a bit extreme, seems to suggest that blogs are, collectively corrupting and confusing popular opinion about everything from politics, to commerce, to arts and culture. Blogs have become so dizzyingly infinite that they’ve undermined our sense of what is true and what is false, what is real and what is imaginary. These days, kids can’t tell the difference between credible news by objective professional journalists and what they read on joeshmoe.blogspot.com. For these Generation Y utopians, every posting is just another person’s version of the truth; every fiction is just another person’s version of the facts. (p. 3) Thus, while some see the value in creating a “society of authorship” (as cited in Richardson, 2008), others like Keen are quick to point out potential pitfalls of flattening our world and enabling any “amateur” the ability to read/write and publish what he or she thinks. Similarly, Bauerlein (2008) argues that “for most young users, it is clear, the Web hasn’t made them better writers and readers, sharper interpreters and more discerning critics, more knowledgeable citizens and tasteful consumers” (p. 110). However, we, like a growing number of educators, see the potential educational value of
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blogs and blogging, specifically as it relates to encouraging students to be intentional and reflective learners. In our experience, in order to create and maintain a blog, students need to identify and define a focus for their blog; establish goals and objectives for how and when they will contribute to their blog; identify, find, use, and critique content and ideas to include in their blog; appropriately share content and ideas to an audience via their blog; and critique the effectiveness of their blog posts to meet their goals and objectives for their blog and the needs of their audience (Dunlap, 2008; Dunlap & Stevens, 2009). These activities are directly related to self-directed learning and metacognitive skills, serving to help students develop those skills for lifelong learning (Dunlap, 2005; Dunlap & Grabinger, 2003). In addition, having students maintain their own blogs is an effective way of engaging them in intentional, reflective practice, accomplishing several objectives related to students’ development as lifelong learners: •
•
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It requires students to articulate their ideas and perspectives, encouraging them to be brave and bold about their contributions to the greater discourse. It engages students in reflection on the domain, requiring them to critically analyze ideas, perspectives, theories, research, and designs. It makes their thinking visible, and this public context encourages a unique caliber of thoughtfulness that does not typically happen in private journals. It reminds students that they are contributing members of a professional community, using their blogs as (1) vehicles for idea dissemination, (2) avenues for garnering feedback from peers and colleagues, and (3) opportunities for collaboration with peers and colleagues. It helps them establish themselves as knowledgeable practi-
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•
•
tioners, and develop positive professional reputations. It helps students express themselves in professional and articulate ways. It also requires them to make time for writing, organize their writing, and develop a habit of writing. It helps students develop the skills and dispositions needed to use technology in support of self-expression, inquiry, knowledge construction, and collaboration; and, of course, use these technologies to support lifelong learning endeavors.
In our graduate program, students use blogs as academic and professional portfolios. Via their blogs, our students present their work (e.g., presentations, instructional materials, podcasts, videos, design documents, and research reports); write opinion pieces and summaries of readings; build repositories of design ideas and resources; and archive coursework and course materials. Their blogs are public, and therefore are accessed by the local community (e.g., faculty, students, and alumni of the program) and the professional community of practice. The activity of public sharing and professional contribution that occurs with their blogging involves students in reflective activities—such as goal setting, identifying valuable learning resources, self-evaluation, and collaboration—that support the development of their self-directed learning skills and metacognitive awareness for lifelong learning (Dunlap, 2005; Dunlap & Grabinger, 2003). Blogging is one way to promote lifelong learning through the development of self-directed learning skills and metacognitive awareness. Microsharing and social networking are other legitimate—and arguably often overlooked— strategies that can support the development of students’ lifelong learning skills and dispositions.
Enculturating Students through Microsharing and Social Networking Learning—and, therefore, lifelong learning—is a social process that is situated in a context (Brown, Collins, &Duguid, 1989; Lave & Wenger, 1991). When learning activities are contextually situated, students participate in the authentic culture of the discipline they are studying—using the physical and mental tools of the discipline. In order for students to use the “physical and mental tools of the discipline” they must develop their self-directed learning skills and metacognitive awareness. Brown, Collins, and Duguid (1989) explain that “to learn to use tools as practitioners use them, a student, like an apprentice, must enter that community and its culture. Thus, in a significant way, learning is…a process of enculturation” (p. 3). The process of enculturation—and becoming effective self-directed, metacognitively-aware learners that can grow and thrive in, and contribute to, the discipline—becomes especially important in postsecondary programs. For postsecondary programs that aim to prepare students to work in a specific discipline—and for the culture that the discipline is situated in—students need to learn not only “about” a field of study but also how “to be” a full participant in a particular field (Brown & Adler, 2008). Educators can reinforce and encourage enculturation—participation in a legitimate community of practice as self-directed, metacognitively-aware learners—through: •
•
Practice with cultural exemplars including solving authentic problems, putting knowledge they acquire to use, and transferring knowledge and skills to new problems (Dunlap, 2006, 2008; Dunlap & Grabinger, 1996; Tishman, Jay, & Perkins, 1993); Cultural interactions where students assume the roles of members of a community of practice in solving the problem and engaging in the culture’s thinking and processes (Brown, Collins, & Duguid, 1989;
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•
Dunlap, 2006; Lave & Wenger, 1991; Stepien & Gallagher, 1993); and Direct instruction in cultural knowledge and activities where students engage in leading, recording, discussing, facilitating, making decisions, collaborating, confronting misconceptions and ineffective strategies, making presentations, and evaluating the learning activity (Brown, Collins, & Duguid, 1989).
The National Research Council’s prominent study on “How People Learn” (Bransford, Brown, & Cocking, 2000) calls for students to be connected to outside practitioners and professional communities of practice in ways that allow for feedback, reflection, and revision opportunities— in other words, it recommends that educators find opportunities to enculturate students into professional communities of practice. The Web is a great source for opportunities to develop students’ self-directed learning skills and metacognitive awareness within a discipline while connecting them with communities of practice (Dunlap & Lowenthal, 2009b). Two categories of Web 2.0 technologies that are particularly useful when it comes to enculturating students into a community of practice and, therefore, developing self-directed learning skills and metacognitive awareness for lifelong learning are microsharing tools such as Twitter and social networking tools such as Facebook, MySpace, and Ning.
Twitter Twitter (http://www.twitter.com) is a multiplatform Web 2.0, part social networking—part microblogging tool, freely accessibly on the Web (Stevens, 2008) with an estimated 18 million users (Ostrow, 2009). Twitter’s website describes Twitters as, “a service for friends, family, and co–workers to communicate and stay connected through the exchange of quick, frequent messages.” In 140 characters or less, people who participate in the
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Twitter community share ideas and resources, ask and answer questions, and collaborate on problems of practice; in a recent study, researchers found that the main communication intentions of people participating in Twitter could be categorized as daily chatter, conversations, sharing resources/ URLs, and reporting news (Java et al., 2007). Twitter community members post their contributions to Twitter via the Twitter website, mobile phone, email, and/or a Twitter client like Twirl— making it a powerful, convenient, communitycontrolled microsharing environment (Drapeau, 2009). Depending on whom you choose to follow (i.e., communicate with) and who chooses to follow you, Twitter can be effectively used for professional and social networking (Drapeau, 2009; Thompson, 2007) because it can connect people with like interests (Lucky, 2009). This becomes especially important for students because by following other professionals in their field on Twitter, they can begin to see how professionals in their field interact (Dunlap & Lowenthal, 2009a, 2009b) and, therefore, slowly become enculturated in the professional community they are entering. Besides the networking potential, students can also receive immediate feedback on their questions and ideas from practicing professionals on Twitter (Dunlap & Lowenthal, 2009a, 2009b), which serves to enhance their learning and their enculturation into their professional community of practice. In our university courses, we invite students to participate in Twitter with us. Our initial reason for adopting Twitter as an instructional tool was because we wanted to have an informal, just-intime way for our online students to connect with each other and with us throughout the day. We have an overarching interest in enhancing social presence in online learning experiences (Dunlap & Lowenthal, 2009b, 2010a, 2010b; Lowenthal & Dunlap, 2010; Lowenthal, 2009; Lowenthal & Parscal, 2008), and have found that we cannot accomplish all we want to accomplish in terms of
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social presence within the structure of a learning management system (LMS). What has consistently been lacking when we rely on the LMS only is the informal, playful banter and chit-chat that we have with students in our on-campus courses. This banter helps students connect with us and experience our personalities. Also, it helps them connect with each other and us in a more natural, immediate way; when relying on an LMS for all student communication, the delay involved in logging in, accessing the correct course, locating the appropriate forum, posting a comment or question, and then continuing to monitor the forum while waiting for a response leads to a formal, less-than-immediate exchange of information. We determined to use Twitter, therefore, because it had the potential to promote the informal interaction we desired. However, we quickly discovered that Twitter was also able to engage students in a professional community of practice—connecting them to practitioners, experts, and colleagues—that served to enculturate them into the community (Dunlap & Lowenthal, 2009b). For example, our students experienced these types of interactions in the Twitter community: •
•
A student has a question about the chapter on multimodal learning. She immediately tweets her question to the Twitter community, and gets three responses within ten minutes—two responses from classmates, and one from her professor. This leads to several subsequent posts, including comments from two practicing professionals. A student working on an assignment is wondering about embedding music into a presentation. He tweets a question and gets a response from his professor and a practicing professional. Both point the student to different resources that explain how to embed music and provide examples to deconstruct. Within a half hour, the student has embedded music in his presentation.
•
•
•
A student finds a great video about storyboarding on YouTube and posts the URL to Twitter. Her find is retweeted three times because others also think the video is great and worth sharing. A student tweets that he just posted a new entry to his blog on how vision trumps all other senses during instruction and provides the URL. His classmates, as well as other practicing professionals, read his blog post. He receives three tweets thanking him for sharing his ideas. As part of a research project on legacy systems, a student poses a question to the Twitter community regarding the prevalent need for COBOL programmers. She receives responses from several IT professionals, some with links to helpful resources and contacts that can help her with research.
As illustrated by the examples above, students’ participation in Twitter allowed them to practice with cultural exemplars, assume the roles of practicing members of the community of practice, and engage in direct instruction of cultural knowledge and activities—the very activities needed to develop lifelong learning skills and dispositions (Dunlap & Grabinger, 2003).
Facebook / MySpace / Ning As popular as Twitter is, social networking sites like Facebook (http://www.facebook.com/), MySpace (http://www.myspace.com/), and Ning (http://www.ning.com/) have many more users and more visits to their sites each month (Goldman, 2009). [Note: Ning changed their pricing structure in 2010, which is likely to influence their popularity in the future.] For example, Facebook alone has an estimated 300 million users (Facebook, n.d.). These larger networks arguably have even more potential for lifelong learning than smaller networks like Twitter. Educators, though, need to
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recognize the differences between these sites and the different ways that students might use these sites to learn, unlearn, and relearn (see Boyd 2009a, 2009b for an interesting discussion of the differences between MySpace and Facebook). Social media researchers have differentiated between friendship-driven and interest-driven types of participation in social media and social networking sites (Ito et al., 2010). While social networking tools like Twitter and Facebook attract both types of participation, we have found in our experience that Twitter attracts more interest-driven participation than Facebook— which was originally designed for and, to some degree, continues to be used predominantly for friendship-driven types of participation. We see this changing though. Social networking sites like Facebook want to continue to attract and support both types of participation. Evidence of this can be seen in Facebook’s evolution from a site that only college students joined, to their introduction of news feeds (see Boyd, 2008) and unique URLs (Price, 2009), to their public status updates—à la Twitter (Smith, 2009). Additionally, Ning has emerged as a more professionally-oriented social networking forum that allows various levels of moderation and monitoring to support both open and bounded learning communities, and is being adopted by educators as a “space for students to ask questions about common issues, vendor choices, favorite books, and instructional practices within a trusted, monitored community of peers and...faculty” (Summers, 2009, p. 50). Even though online social networking tools like Facebook, MySpace, and Ning began as ways for friends to connect, they have morphed into spaces where students can easily connect with practicing professionals, in much the same way as Twitter. In fact, a growing number of people use 3rd party mash-up tools to post their Twitter updates automatically to their Facebook account and vice versa. One appealing aspect of sites like Facebook in particular—due in a large part to its overall popularity and millions of us-
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ers (Wauters, 2009)—is that a growing number of organizations and professional associations have a presence on Facebook, MySpace, or Ning, and sometimes on all three. For example, in our domain, the Association for Educational Communications and Technology (357 members), the International Society for Technology in Education (3,419 members), the International Society for Performance Improvement (827 members), Sloan-C (1,114 members), the Association for the Advancement of Computing in Education (1,723 members), Educause Learning Initiative (1,373), and the American Society for Training and Development (1,913 members) all have a presence now on Facebook. Also, online social networks, like Facebook in particular, enable people to self-identify their profession and interest; again, in our domain, there is a Facebook group called “Instructional Designers” that has 988 members. Although younger users were historically attracted to these sites, Facebook has seen a 276% growth in users 35-54 years of age (Corbett, 2009), with the other social networking sites reporting similar growth and demographic shifts. With the expansion of the social networking audience to include professional communities of practice, these social networking tools are increasingly becoming forums for professional networking, sharing, collaboration, and lifelong learning and, therefore, offer great potential for student enculturation into the reflective and lifelong learning practices of professionals. While participating in online social networks like Twitter and Facebook, students can develop and practice self-directed learning and metacognitive awareness skills such as making claims, collecting evidence in support of their claims, and evaluating and responding to counterarguments from others in the network. If educators encourage the use of social networking tools for this type of knowledge-building activity, there is great potential for students to reflect on specific aspects of their learning and thinking processes, and consider the impact of opinion, bias, contro-
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versy, debate, and negotiation on their thinking and learning (Glaser, 1991)—again, all skills needed to be effective lifelong learners. Additionally, participating in social networks can help students learn how to ask questions based on personal knowledge deficits and formulate learning goals to address those deficits. If students can learn to ask questions to guide their knowledge building, and are encouraged to do so—thus assuming more control and ownership over their learning activities (Scardamalia & Bereiter, 1991)—students are more likely to take ownership of learning activities, find personal relevance during learning activities, and cultivate a lifelong-learning disposition (Dunlap & Grabinger, 2003). These skills will also help students to be wiser consumers of online information, and—in general—more effective users of Web 2.0 technologies to support their lifelong learning.
Supporting Dialogue and Collaboration with Document Co-Creation and Resource Sharing Tools Another key strategy to use when providing students opportunities to develop an overall disposition toward lifelong learning is to encourage dialogue and collaboration. Through dialogue and collaborative work, students experience and develop an appreciation for multiple perspectives; refine their knowledge through argumentation, structured controversy, and the sharing of ideas and perspectives; learn to use colleagues as resources; and are more willing to take on the risk required to tackle complex, ill-structured problems (Dunlap & Grabinger, 2003). Collaboration elevates thinking, learning, and problem solving to an observable status (Glaser, 1991; Von Wright, 1992), which then enables learners to receive feedback and to reflect on their learning, and cognitive and metacognitive processes; collaboration, therefore, helps students develop their metacognitive awareness so they can better engage in self-directed learning
and, ultimately, lifelong learning. Educators can enable and promote dialogue and collaboration by involving students in: • • • • • •
Problem analysis, hypothesis formulation, and solutions brainstorming; Debate and argumentation to test and challenge each other’s knowledge and learning; Teaching each other; The negotiation of meaning; Small group problem solving and projects; and Peer evaluation and review.
Luckily, there are a number of Web 2.0 technologies that educators can use to support this sort of dialogue and collaboration, such as document co-creation tools (e.g., Google Docs) and resource sharing tools (e.g., Flickr, Slideshare, Diigo).
Document Co-Creation Many of us have to collaborate and co-create products day-to-day in our jobs, often with people in different geographic locations. Collaboration and co-creation is no longer an option but more of an imperative in the world we live in today. Unfortunately, we often assume that students know how to collaborate and co-create, activities that require self-directed learning skills and metacognitive awareness; but in our experience many do not. In the past, to collaborate and cocreate, we might begin with a meeting or two to discuss the project and brainstorm and then once we began to co-create, we would send different versions of a Microsoft Word document back and forth to our colleagues using track changes or the commenting tool. This should sound familiar. In fact, many people still work this way. However, this is often not the most effective or efficient way to collaborate and co-create—especially when more than two people are working on a project at the same time.
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When it comes to co-creating documents in a different way, wikis—and the most popular wiki, Wikipedia—typically come to mind. According to Wikipedia (n.d.), “a wiki is a website that uses wiki software, allowing the easy creation and editing of any number of interlinked Web pages, using a simplified markup language or a WYSIWYG text editor, within the browser” (para 1). Wikis have grown in popularity (e.g, Wikipedia has nearly 11 million users (Wikipedia, n.d.)) because they enable anyone who sets up an account to co-create and add and delete to the document. Further, they typically have the functionality to enable contributors the ability to discuss and cocreate as well as to track revisions. Today there are over 100 different types of wiki software/ solutions to choose from (http://www.wikimatrix. org/). And while the popularity of wikis grow and organizations continue to use them for everything from technical support and project management (Majchrzak, Wagner, & Yates, 2006) to internal documentation (Angeles, 2004; Wallace, 2008), newer document co-creation tools like Google Docs provide learners with much more flexibility, control, and options to create and co-create documents. In fact, this very chapter was co-created using Google Docs. Google Docs (http://docs.google.com) and other similar tools (e.g., Adobe Buzzword http://buzzword.acrobat.com and Zoho - http:// www.zoho.com) enable users to collaborate and co-create in ways that wikis or traditional word documents cannot. The Google Docs suite includes document, presentation, and spreadsheet applications similar to those in Microsoft Office, which enables people to collaborate and co-create on a number of different types of projects. Further, these projects can be kept private, or published on the Web and made public. The Google Docs suite also has a chat tool built into the application, which enables users to chat and co-create—and therefore dialogue and collaborate—in one space. We often use Google Docs in our online courses to support students’ document co-creation
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activities. One example of this use is students’ cocreation of a Top-100 List of Design Guidelines, used to support their instructional-materials design work. Developed in Google Docs over the course of the semester, students contribute new design guidelines with supporting citations based on the coursework and readings. By the end of the semester, students walk away with a robust set of design guidelines summarizing the readings that can be used as they continue their design work outside of the course. Google Docs makes it possible for our online postsecondary students to collectively develop a unique document, each sharing expertise, reviewing each others’ contributions for appropriate modifications and redundancy reductions, summarizing and synthesizing what they have learned from the course readings, and reflecting on the value of their individual contributions and the value of the collection of guidelines in general. When involved in the co-creation of a document (or any content), collaborators must determine the purpose of their work and brainstorm approaches; negotiate shared meaning and teach each other through the sharing of multiple perspectives and divergent ideas; work together to create a coherent end product; and engage in mutual peer evaluation and review. Note that these activities, required for effective document co-creation, reflect the very strategies educators can employ to enable and promote the type of dialogue and collaboration needed to support students’ lifelong learning skill development. Therefore, it is through collaborating and co-creating using Web 2.0 technologies, like a wiki or Google Docs, that students can begin to learn different and more effective ways to collaborate and co-create with others and therefore, learn, unlearn, and relearn as effective lifelong learners.
Resource Sharing Lifelong learners using Web 2.0 technologies to support their learning activities must be meta-
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cognitively aware so they are able to evaluate the information they access; this involves differing levels of critical judgment and requires the ability to be both critical readers and hyper-readers (Burbules & Callister, 2000b). Critical readers have specific questions or goals in mind when accessing information online. Critical readers gather information, and select, evaluate and judge the acquired information in relation to their predetermined needs. Hyper-readers, more often than not, transform their own inquiry as they build links and connections between and among acquired information. Hyper-readers are also able to read across links, and can use links in ways that redefine, enhance or otherwise alter the information presented. Involving students in resource sharing activities using Web 2.0 technologies can help them develop and enhance metacognitive skills associated with the critical reading and hyper-reading needed to use the Web as a source for lifelong learning. Three of the more popular Web 2.0 resource-sharing tools are: • •
•
Flickr (http://www.flickr.com), an online photo management and sharing application; Slideshare (http://www.slideshare.net), an application for sharing PowerPoint presentations, Word documents, videos and zipcasts; and Diigo (http://www.diigo.com), a social bookmarking tool.
These three tools have similar interfaces and functionality. In cooperation and collaboration with others, people locate and/or create content that they then contribute in an organized and crosscategorized way to a common, shared forum. These shared resources are then available for use by the contributor and other participants. It is important to note that two of these applications—Flickr and Slideshare—also enable contributors to specify levels of accessibility and use, making it possible to upload content that is only available to a
subset of the greater contributing community, or available to everyone. Active participation in a resource-sharing community, like those supported by Flickr, Slideshare, and Diigo, requires students to use both critical reading and hyper-reading skills. The “publicness” of students’ contributions encourages them to carefully select, evaluate, and judge the content they upload for community use. Once uploaded, to fully realize the potential usefulness of the new and existing content, students must explore other uploaded resources, looking for and establishing new connections and enriching the knowledge base of resources for the community as a whole; this activity requires hyper-reading skills. In our postsecondary online courses, we use all three of these resource-sharing tools to support the dialogue and collaboration needed to help students develop as lifelong learners. Below, we share a couple of examples of our use: •
Presentation Prowess project. For the Presentation Prowess project, our postsecondary students create a presentation slideshow that contributes something new and of value to the community of practice and is worthy of winning SlideShare’s “World’s Best Presentation” contest. To determine what they can create that contributes something new and of value to the community, students have to use their metacognitive skills to critically read and evaluate existing presentations. Once they have determined they have something unique to contribute, they create their presentations. Once the project is complete, students post their presentations to SlideShare (or other resource-sharing Web 2.0 tools, such as Slideboom, YouTube, Dailymotion, Prezi, or Voicethread). By posting their work publicly, students engage in dialogue with course colleagues and practitioners about their presentation via SlideShare’s commenting feature. In addition, this project
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•
involves students in contributing potential learning materials—their presentations— to the professional community of practice, supporting the larger community’s pursuit of professional development via lifelong learning. Design Lessons Learned project. For this project, based on Stefan Sagmeister’s Things I Have Learned In My Life So Far book and website (see http://thingsihavelearnedinmylife.com/ for more information), our postsecondary students reflect on what they have learned about the creative design of instructional materials during the course, requiring them to be metacognitively aware of their own learning. They then complete the follow steps: ◦⊦ Consider what you have learned about the creative design of instructional materials. What are you sure about? What do you believe now? What advice/words of wisdom do others need to know about? ◦⊦ Pick one of those design lessons learned and write it down. Design it digitally. Photograph it. Draw it. Use paint, sculpture, whatever. I don’t care as long as it’s interesting. ◦⊦ Post a digital photo of your creation to our Flickr group account.
Again, as with the Presentation Prowess project, students post their work to Flickr and then engage in dialogue with course colleagues and professional practitioners about the goal of the work, the value of the work to the community, and the effectiveness and limitations of the work. Engaging in this dialogue enhances students’ metacognitive skills. By involving students in the use of Web 2.0 resource-sharing technologies, educators can help students develop metacognitive awareness— specifically, critical reading and hyper-reading skills—within an authentic context. In addition,
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reflecting on the list of strategies educators can use to enable and promote dialogue and collaboration, participating in and contributing to Web 2.0-driven resource-sharing communities involves students in problem analysis (e.g., determining what content needs to be located and created, and why), teaching others (e.g., through the contribution of relevant content to the community), negotiating meaning (e.g., via the exploration of connections across and between shared content, and creating new content as a result), and peer evaluation and review (e.g., as community members, they select, evaluate, and judge the value of contributed content)—with all of these activities requiring self-directed learning and metacognitive-awareness skills that support lifelong learning. Consequently, students’ use of resource-sharing and document co-creation Web 2.0 technologies for purposes of dialogue and collaboration has the potential to enhance their overall effectiveness of using the Web for lifelong learning.
Involving Students in Web 2.0-Enhanced, Intrinsically Motivating Learning Activities Intrinsically motivated students are more likely to be lifelong learners because they have a desire and passion to learn, are willing to attempt more problems and solutions, and are focused on improving the problem-solving process (Condry & Chambers, 1978; Kinzie, 1990). Intrinsically motivated students expend more effort on tasks and activities they find inherently enjoyable and interesting, even when there are no extrinsic incentives (Keller & Burkman, 1993), making them more self-directed in their learning. As educators, we can promote intrinsically motivated learning and ultimately lifelong learning by: • •
Relating learning to students’ personal needs and goals; Placing students in authentic and decisionmaking roles, or roles to which they aspire;
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• •
•
Having students solve professional problems of practice; Asking students to build products that solve problems and meet real professional needs; and Having learners work on and accomplish real tasks (Dunlap & Grabinger, 2003).
The foundational concept that underlies this list of strategies is relevance; if students perceive the learning activity as relevant, they are more likely to be engaged and motivated to learn (Wlodkowski, 1999). The Web 2.0 technologies described in this chapter are widely used in the workplace and by professional communities of practice, especially by those organizations and communities that are widely distributed. Therefore, an important—and relevant—instructional goal for educators preparing students for their professions is to help students learn to use these technologies for lifelong learning, teamwork, collaboration, document and idea sharing, inquiry, and so on. We have found that our students appreciate our use of Web 2.0 technologies for academic purposes: as tools that help them (a) communicate and collaborate, (b) access and contribute to information-rich resources, and (c) solve problems and build products. Additionally, our students tend to be quite curious about how to effectively and creatively use these tools to support their professional goals and needs; their interest and, therefore, motivation to engage is evoked by the novelty of emerging Web 2.0 technologies. As Downes (2004, p. 30) shares, “The process of reading online, engaging a community, and reflecting it online is a process of bringing life into learning”, and “bringing life into learning” can make learning activities personally relevant and intrinsically motivating for students, encouraging students to be self-directed and lifelong learners. As stated at the start of this section, educators must provide students with educational opportunities to develop their capacity for self-direction, metacognitive awareness, and an overall disposi-
tion toward lifelong learning (Dunlap, 2005). To achieve this goal, educators can use the Web 2.0 technologies associated with blogging, social networking, document co-creation, and resource sharing to create intrinsically motivating learning opportunities that have the potential to help students develop the skills and dispositions needed to be effective lifelong learners. The Web 2.0 technologies described in this section have such strong potential to support lifelong learning skill and disposition development, and lifelong learning activities in general, because they have the individual and collective power to attend to the impulses of communication, construction, inquiry, and expression—the basic student interests that contribute to engagement and make learning possible (Dewey as cited in Bruce & Levin, 1997). In addition, unlike the typical LMS that is a highly bounded community (Wilson, Ludwig-Hardman, Thornam, & Dunlap, 2004), Web 2.0 technologies enable students to participate in and with their discipline’s community of practice; engaging in and with the community of practice supports the development of the self-directed learning and metacognitive-awareness skills needed for lifelong learning in a much more authentic context—the context in which the students will ultimately engage in lifelong learning—than can occur behind the protected walls of a typical LMS environment. In the next section, we share some recommendations for using Web 2.0 technologies in postsecondary settings to support lifelong learning skill and disposition development.
RECOMMENDATIONS FOR USING WEB 2.0 TECHNOLOGIES TO SUPPORT LIFELONG LEARNING SKILL AND DISPOSITION DEVELOPMENT One thing to consider when adopting the Web 2.0 technologies described in the previous section to
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support students’ development of lifelong learning skills and dispositions is that they are only effective if students fully engage in their use, and use them for academic and professional pursuits. Therefore, in this section, we offer specific recommendations for using these technologies for lifelong learning skill and disposition development. •
•
•
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Select Web 2.0 technologies based on learning objectives, not because they are cool. In other words, let pedagogy rather than technology dictate whether or not to use a certain Web 2.0 technology. Every month or two a new (and often very “cool”) Web 2.0 technology is developed. And while it is easy to get wrapped up in the newest and greatest technology, it is important to focus on technologies that support the learning objectives. Further, quality is more important than quantity; it is often more beneficial to meaningfully integrate one Web 2.0 technology into a course than to superficially integrate a number of different technologies. Establish relevance for students. Students need to see the relevance of using these new Web 2.0 technologies both within their studies and beyond. This can often be accomplished by demonstrating how your own personal learning network (PLN) supports your learning, professional development, inquiry, and so on. Often it is helpful to recommend to students a list of professionals they can follow, blogs they can read, and networks they can join. Students also benefit from seeing examples of how people use these tools to establish their expertise—that is, become known—as well as how future employers use these tools. Finally, building Web 2.0-derived results into assessments can also help establish relevance for students. Define clear expectations for participation. Web 2.0 technologies and the par-
•
ticipatory culture that they engender are often at odds with students’ traditional concepts of education and schooling. It is extremely important to clearly define expectations for participation. This includes not only addressing the public and private dimensions of many of these technologies, but also clearly explaining the differences between social/personal vs. academic/professional uses of these technologies. For instance, are you comfortable befriending your students in Facebook? Are they comfortable befriending you? Regardless of the answers to these questions, it is important to clearly address these issues and give students the option of establishing a new avatar (e.g., online identity) instead of allowing access to their own (or your own) strictly social/personal accounts. Model effective Web 2.0 technology use. We often assume that students know how to effectively use these Web 2.0 technologies. And while this might be true to a degree, students often use many of these technologies in different ways in their personal life than they might be expected to use for class. Therefore, if you are going to ask students to use specific tools for a course, it is helpful to have already established your own personal record of use so that you can model best practices for achieving the learning objectives (in this case, lifelong learning skill and disposition development).
Finally, educators must recognize the limitations and possible drawbacks of using these new Web 2.0 technologies. For instance, using Web 2.0 technologies, like the one’s discussed in this chapter, inevitably pushes student learning and the online classroom outside of the LMS. Even though some educators are attracted to a do-ityourself approach to online learning that does not attempt to contain (or constrain) all learning
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within a LMS (see Wikipedia for more information on the Edupunk movement, http://en.wikipedia. org/wiki/Edupunk), the majority of educators (or at least administrators) appear to value the affordances provided (e.g., the ability to keep a record of everything) by the mainstream trend of keeping online learning nice and neat behind the lock and key of the LMS. Using Web 2.0 technologies also typically requires students to set up multiple accounts to be able to use different Web 2.0 tools and applications. In our experience, while the majority of students do not mind (and many already have accounts), there are sometimes a few students who resist setting up another account, with another username and password. Faculty should also keep in mind that Web 2.0 technologies come and go; a technology used one semester might not be available (e.g., because the company went out of business or changed its pricing model) the next semester. Finally, the possible “publicness” and digital foot-print of many of these tools also needs to be considered (for more on publicness, see Lowenthal & Thomas, 2010).
FUTURE DIRECTIONS AND CONCLUSION Web 2.0 technologies and the participatory culture they encourage are relatively new. Educators have only recently begun to experiment with these different tools—specifically, blogs, mirco-sharing, social networking, document co-creation and resource sharing. There are two main things educators and researchers alike must begin to do. First, while many of us have had positive experiences using these new Web 2.0 technologies, it is time to begin researching the efficacy of using these new tools in our courses. Projects such as APT STAIRS are starting to attend to this need; APT STAIRS is a project aimed at helping different audiences (e.g., academics, students, and researchers) use collaborative Web 2.0 tools like Google Docs to enhance collaborative working practices (see
http://sites.google.com/a/jiscapt.net/project-plan/ Home for more information). Further, we must begin experimenting with different ways of using these tools to meet educational goals with different learning audiences, and formally evaluate the effectiveness of bringing these tools into our courses; our use of these technologies has been with postsecondary students, most of who are digital immigrants in graduate-level programs, so inquiry into the use of Web 2.0 technologies to support the lifelong learning skill and disposition development of other audiences—including investigation into the differences between digital natives and digital immigrants—is needed. Secondly, postsecondary educators find themselves in a time where they are expected to do more with less. Many find it difficult enough to teach online and to use the standard LMS. Therefore, educators need targeted faculty development that helps them not only understand how many of these new technologies work but how and why they might use them in their courses to support specific learning objectives and overall student engagement. By taking these steps, postsecondary educators and the university as a whole can more effectively address the challenges mentioned at the start of this chapter. Web 2.0 technologies are making possible “new kinds of open participatory learning ecosystems that will support active, passion-based learning: Learning 2.0” (Brown & Adler, 2008, p. 32). Brown and Adler go on to predict that Web 2.0-enriched learning environments may “encourage students to readily and happily pick up new knowledge and skills as the world shifts beneath them” (p. 32), enabling them—as lifelong learners—to meet the ever-changing needs and demands of their workplace and profession. In this chapter we have shared our ideas for using the Web 2.0 technologies associated with blogging, social networking, document co-creation, and resource sharing to create intrinsically motivating learning opportunities—Learning 2.0 opportunities—that have the potential to help students develop the
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skills and dispositions needed to be effective lifelong learners. Specifically, educators can use these technologies to help students develop autonomy, responsibility, and intentionality; encourage student reflection; enculturate students into a community of practice; and enjoin students to participate in discourse and collaboration. These fundamental skills are needed to engage in the selfdirected learning and metacognitive processing that is at the heart of effective lifelong learning. There is little doubt in our minds that current and yet-to-be-realized Web 2.0 technologies and tools can be used by postsecondary educators to support student learning in powerful and meaningful ways, while at the same time address head-on the emerging trends and challenges facing postsecondary education today. To realize the potential of Learning 2.0, we as postsecondary educators need to continue our exploration of Web 2.0 technologies for teaching and learning, discovering new ways these tools can help us achieve our instructional goals and objectives; and, in the process, help students develop the mandatory skills that will enable them to perpetually learn, unlearn, and relearn as the world shifts beneath them.
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Chapter 13
Technological Aids to the Efficient Assessment of Prior Learning Desirée Joosten-ten Brinke Open University of the Netherlands, The Netherlands Marcel Van der Klink Open University of the Netherlands, The Netherlands Wendy Kicken Open University of the Netherlands, The Netherlands Peter B. Sloep Open University of the Netherlands, The Netherlands
ABSTRACT It is generally acknowledged that learning and education play a prominent part throughout employees’ careers across their entire lifespan. In the era of lifelong learning, Assessment of Prior Learning (APL) is a powerful means for enhancing employees’ further professional development and learning, formally and informally. Though there is a growing attention for APL, the procedures, design, development and maintenance of APL remain a quite costly and time-consuming experience. After a description of the background and features of APL, this chapter examines the possibilities for re-using and interoperability by means of e-technologies. The chapter discusses the major components of the APL procedure, including the current possibilities for exchange and operability (e.g. specifications of QTI, IMS). The chapter concludes with a description and validation of an educational model of assessment for APL.
INTRODUCTION At present, we are witnessing what according to some amounts to a digital revolution. Information DOI: 10.4018/978-1-61520-983-5.ch013
and communication technologies intrude ever deeper in our society to the point that they have become inescapable (Sloep et al., in press). Examples abound, ranging from online banking and shopping to the keeping of records of our medical histories or mobile phoning behaviour. This digital
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revolution can only continue to flourish if people possess the skills and knowledge to design, build, operate, maintain and, indeed, use the technologies that sustain this information society. According to De Haan and Van ‘t Hof (2006, p. 225, translated from the Dutch by the authors), while quoting a European study (EMCC, 2003): ‘Technological innovations have brought new opportunities to communicate and collect data within reach of increasing numbers of people. It is to be expected that this trend will only become more intense and ever stronger influence our lives in a variety of ways’. This trend is often referred to as the arrival of the information society. However, in its connection also such terms as post-industrial society (Toffler, 1980), knowledge society (WRR, 2002), and networked society (Castells, 1996) are used. Setting aside the nuances of the distinctions between them, three different undercurrents to this trend may be discerned. First, there is an increased need for more and deeper knowledge; second, the half-life of existing knowledge decreases; and third, as our society at large changes, we as its participants need to continuously adapt to it. These aspects can be directly translated into an equal number of challenges for society: How can we educate more people better? How can we educate people faster? How can education keep pace with the changing society? Meeting these challenges requires people to be educated not once in their lifetime, but throughout their life; and this applies to almost everyone. This means that educational programmes must be efficiently and effectively developed, tailoring the programme to the competences people already mastered through previous learning experiences. To tailor educational programmes, recognition of such prior experiences, however acquired, is important and the key to successfully meet the three challenges discussed. In this chapter we will elaborate on this line of reasoning. To that end, we provide insight in the effects of the changing society on the needs of lifelong learners. We will also look into ways
to meet these needs, particularly the re-use of educational materials for faster adaptation will be discussed. In this discussion, we include the current possibilities for exchange and operability by means of specifications like IMS ePortfolio and IMS QTI. First, the consequences of the transition towards an information society for learning and training on employees’ competence development and, with that, on the importance of the recognition of prior learning are described. Second, we zoom in on procedures for the Assessment of Prior Learning and on the conditions that have to be met for assessment of prior learning to be a viable solution. Finally, as assessment of prior learning tends to be both expensive and time consuming, means are discussed to overcome this.
TRENDS AND CHALLENGES In this section we describe three trends that seriously impact many aspects of our contemporary society. A first trend is that the information society needs more knowledgeable people, meaning that more people than ever before should receive more education than ever before. All venues of life, all professions, and ever more countries have increasingly come to rely on the technological artefacts to run society. Many examples are available and here we present two of them. Some 40 years ago, cars were still predominantly mechanical appliances, nowadays they are a mixture of mechanics and electronics. Clearly, this requires a different expertise to design, build and maintain them, and even to drive them. The car mechanic now needs to understand the output of the computerised diagnosis system and the owner needs to be able to interpret the various messages shown by the car’s display. Something similar applies to the medical profession. This profession has always been a profession that heavily relied on technology. However, with the advent of computers technology has invaded virtually all walks of a
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medical professional’s life. This again means that multidisciplinary teams will research, devise and implement new systems to provide diagnostic or therapeutic medical care. Consequently, technicians should be educated to operate the new equipment, physicians should know how to use it wisely, and patients should be able to understand how it affects them. The consequences of the information society described for these two professions, apply widely. What is more, and this represents the second trend, is that it seems that the pace at which new knowledge is required increases. The pace at which existing knowledge becomes obsolete quickens. To some extent, the increased number of people who are involved in researching new technologies plays a part in this. However, the increase of obsolete knowledge seems to be mostly driven by the speed with which computer chips become faster: every 18 months their speed has doubled (Barnes, 2005; based on Moore’s law (Moore, 1965). Faster chips are able to perform more calculations and thus take more complex decisions. Innovations that for some time were imaginable but not realisable, all of a sudden become feasible with the advent a new generation of chips. At the same pace at which computer chips increase their speed, the artefacts that use them can become more complex. The computers from the 80s were simply not fast enough to do in real time the calculations that a motor management system needs to adjust the fuel mixture that it injects into the engine to the changing demands that are made on the engine. Similarly, processing images consisting of several millions of pixels each 32 bits deep has been beyond the capacity of computer chips but for the most recent generations. The increased computing speed requires an update of knowledge about existing artefacts, roughly at the same pace. Parenthetically, the problem of knowledge obsolescence is especially grave for those whose job it is to maintain artefacts, those with a lower vocational education. As artefacts change, their
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knowledge may become fully obsolete and in need of complete replacement. Regarding the third trend, society at large changes through the introduction of technology. Not only does set our changing society sets demands for more and deeper knowledge at an ever increasing speed, technology changes the very fabric of our society. It is a mistake to view technology from a functional perspective only, as aids that behave according to our commands and wishes, optional, as something one can choose to use or ignore. Technologies have a tendency to affect society beyond their intended usage. Technology changes our culture, as always it has and always will, where culture is understood to be the complex whole of knowledge, skills, beliefs, laws, values, habits, and preferences of the people that make up some society. The arrival of the steam engine dramatically changed society, as did the introduction of electricity and as now does the advent of the computer (Thurow, 1999; Steyaert & De Haan, 2001). Although some elect to try and avoid sharing the blessings of modern artefacts, for instance the Amish in North America (Kraybill & Olshan, 1994), for most people this is not an option. Indeed, many in so-called developing countries actively strive to attain the level of development that the information society affords. For this majority, the impact of technology transcends the instrumental functionality for which it was designed. For them, technology mediates between the human being and its environment and in doing so reveals latent, unintended uses of technology (Borgmann, 1984; Hickman, 1990). However, this kind of ease in dealing with technology, at the purely instrumental level but certainly at a supra-instrumental level, requires that people are sufficiently educated. And again, as artefacts become more complex and more numerous, education needs to respond to a digital divide between those who are conversant with technology and use it to enrich their lives and those who have no clue about it and are consequently left behind.
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Therefore, the aspects that are brought about by these three trends will seriously influence learning and education in the 21st century. From a labour market perspective, the sketched transition towards an information society indeed increases its dynamics and upsets the content and security of occupations (Schmid, 1998). Lifetime employment within one company is replaced by the notion of employability throughout the career. Some knowledge and skills become obsolete and at the same time, large groups of employees will experience that their work requires continuous learning across the life-span. Whilst in the 20th century learning equalled formal learning offered by educational providers, learning in the 21st century will need to be increasingly characterised as informal and non-formal. Indeed, there is a growing recognition that formal learning represents only a minor fraction of all the human capital gathered. Informal learning in various daily-job situations is at least an equally important source of learning (see for example the work of Tough (1979) and Lave and Wenger (1991)). Studies have shown that informal learning already has become the most important type of learning within organisations. Marsick (2006) estimated that 60-80% of the learning in today’s workplace occurs informally. Canadian national surveys revealed that 82% of the employees considered themselves to be engaged in job-related informal learning with an average of six hours a week (Livingstone & Eichler, 2005). However, such job-related learning does not suffice. Remaining attractive and employable in the 21st century requires that employees themselves must take responsibility for their own learning and career development, which, in turn, presupposes a high level of employee self-directedness. Unfortunately, there are several problems attached to the concept of the self-directed employee. Research shows that not all employees are willing or able to take up the responsibility to consciously steer their own learning and career. Employees differ strongly in their willingness and ability to utilise learning opportunities in their own work
setting (Van der Heijden, Boon, Van der Klink, & Meys, 2009). This also applies to highly educated professionals (Raemdonck, 2006; Van der Klink, Schlusmans, & Boon, 2007). If we really want employees to take charge of their own career and hence learning, we need to develop a (digital) learning infrastructure that supports them in this respect (Van Merriënboer, Kirschner, Paas, Sloep, & Caniëls, 2009). Recognition of prior learning will be a major cornerstone of such an infrastructure. After all, collecting, classifying and judging informal and non-formal learning will support employees in the process of making informed decisions about their future learning endeavours.
ASSESSMENT OF PRIOR LEARNING Assessment of prior learning (henceforth indicated as APL) supports lifelong learning by assessing and recognizing someone’s competences obtained in informal and non-formal learning environments. It is a procedure that assesses competences independently of the attended learning path (Joostenten Brinke, 2008). In APL, it is important that learners make visible what they have learned in the past. To this end, they have to provide authentic evidence of their competences. In general, APL procedures consist of four phases, (1) candidateprofiling phase, (2) evidence gathering phase, (3) assessment phase and (4) recognition phase. These phases will be described in more detail hereafter based on the findings of various research projects,, like the dissertation of Joosten-ten Brinke (2008), and the research scans that were conducted by Van der Klink, De Bie, Evers and Walhout (2007) and Schlusmans, Joosten-ten Brinke and Van der Klink (2006), respectively. This section concludes with a table that provides a condensed summary of the main challenges and obstacles for successful APL procedures.
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The Candidate-Profiling Phase In this phase, the institution gathers information about the candidate’s personal characteristics and needs. The candidate gathers information about the steps, procedure’s expectations, the standards, possible learning sources and possible outcomes of the APL procedure. The standards in APL are crucial. These basic competence profiles act as a mirror to the prior learning experiences. What prior learning matches with which part of the competence profile? Or the other way around, which competence profile fits best the candidate’s prior learning? A basic self-assessment is mostly available for candidates to assess whether APL might be suitable or not. This suitability depends on the level and amount of prior learning, but also on the possible outcomes of an APL procedure. The outcomes may be identification and/or recognition of competences that can be used for entry into one of the stages of a formal educational programme (entrance, positioning or certification) or for further development in the labour market. At the end of the first phase, the following must be clear for APL candidates: (1) the prior learning that is required described in terms of competences, knowledge and skills, (2) the possible outcomes, (3) the form in which evidence should be presented, (4) the assessment method and assessment standard, and (5) the support that is offered to candidates by the institution for self-assessment and portfolio construction. Personalised advice during the entire APL procedure and offering this advice face to face is time consuming (Kalz, Van Bruggen, Rusman, Giesbers, & Koper, 2007; Joosten-ten Brinke, 2008).
The Evidence-Gathering Phase In this phase, candidates collect and classify evidence about previous qualifications and experiences in order to support a claim for credit with respect to a specific competence profile. In this
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phase, a portfolio is used. The portfolio should be structured. Educational programmes within the same subset can use a similar structure. The structure of the portfolio should suit the candidate’s prior informal and non-formal learning and the competences required by the institute (Baume & Yorke, 2002; Bjørnavold, 2001; McMullan et al., 2003). Therefore, an institute must be aware of the possible prior learning experiences a candidate will use and the evidence the candidate will present of his/her prior learning. In line with Livingstone’s (2001) conclusion that the kinds of sources for prior learning are broad, but related to the study a candidate wants to start, Joosten-ten Brinke, Sluijsmans and Jochems (2009) recommend to inform candidates in the portfolio’s template about the relevant sources. Also in this phase a self-assessment on the standard by the candidate is required. Self-assessment involves learners taking responsibility for monitoring and making judgments about aspects of their own learning. It requires learners to think critically about what they are learning, to identify appropriate standards of performance and to apply them to their own work. The results of the self-assessment, evidence and arguments are stored together into the portfolio. A variety of difficulties are present in this phase. Candidates experience tremendous difficulties in their search of relevant and reliable evidence of their prior experiences. Quite often, they misunderstand the criteria about what is regarded as sound evidence. Support of candidates during the collecting evidence activities occurs in practice by mentors. Sometimes some preliminary checks are performed to inform the candidates whether the evidence is sufficient and appropriate. However, this is time consuming. In tracking the development of competences in learning networks, a large amount of competence information can be gathered from diverse sources and diverse types of sources, which is subject to uncertainty and unreliable (Miao, Sloep, Hummel, & Koper, 2008). There is often no one-to-one relation between
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items of evidence and a particular competence. In practice one item may be related to several competences. Storing these kinds of evidence in a paper-based portfolio causes often difficulties for candidates as well as for the assessors who need to judge the evidence. In most cases, candidates are required to indicate to what extend they already posses a particular competence by rating its level. Practice shows that many candidates have trouble in choosing the appropriate competence level, often resulting in overestimating their own mastery of competences. Advice (of mentors) and the presence of clear examples are interventions to support candidates during the process of classifying the evidence. There is also a strong need for some kind of ‘organizer’ that supports candidates in keeping overview of the pile of evidence.
The Assessment Phase In this phase, assessors review the quality of candidate’s evidence using assessment standards and rubrics. The assessment helps to establish whether the candidates have attained the standards and provide prescriptive feedback to assist candidates in reaching their goals. A combination of methods (simulations, knowledge tests, performance assessments, interviews) is used to assess evidence of prior learning (Fjortoft & Zgarrick, 2001). The most common instruments are portfolio assessment and a criterion based interview. Interview protocols, scoring rubrics and scoring forms are used for a valid and reliable assessment. The assessment results should be an answer to the question whether the candidate should gain recognition of prior learning. Difficulties in this phase are abound. Not in all APL procedures there is a check on the candidate’s portfolio before it is handed over to the assessors. This check assures that the portfolio meets all the criteria and prevents that costly assessors’ time is spend on incomplete portfolios. Lack of clear and specified standards and rubrics hamper the assess-
ment of the candidate’s portfolio. In some cases, APL is performed by a single assessor instead of two or more assessors. This seriously harms the reliability of the outcomes of the procedures. Not in all procedures assessors were trained for their task as APL assessor. Time constraints diminish the possibilities for scheduling activities that require two or more assessors performing assessment activities simultaneous. Though criterion-based interviews are very often used, this is not always the most appropriate instrument to assess a candidate’s competency level. Simulations, demonstrations, on-the-job performance assessments are in many cases much more appropriate instruments.
The Validation Phase In this phase, the verification by the department responsible for awarding the assessment outcome takes place and will result in an APL certificate. The worthiness of this APL certificate varies in different contexts. Sometimes, the outcome is an overview of recognized competences, but in other contexts, the outcome is a general learning plan or specific credit points. At the end of this phase, the candidate’s dossier has to be stored. The interpretation of the results is a problem in this phase. Clear instructions on how to report the outcomes of the APL procedures into a certificate reduces its value. Educational providers experience difficulties in describing a candidate’s competence level in a non-educational language, which decreases the use of the certificate for other than educational purposes. If there is no clear format agreed (on branch level or national level) upon how to describe the APL outcomes then the descriptions of candidates’ competency levels will vary significantly. In designing and developing APL procedures, quality criteria, such as authenticity, meaningfulness, fairness and educational consequences are essential (for a full description of assessment quality criteria for competence assessment, see
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Baartman, 2008). The question is how to combine these quality requirements with the limited resources, like persons and time, that are usually available. After all, the development of reliable and valid assessments is time-consuming and expensive (Bélanger & Mount, 1998). Table 1 summarizes the obstacles as they occur in the various stages of the process of APL. The table also describes the different instruments that support a particular phase of the APL procedure. As mentioned previously the research findings that we presented in this section are mainly applicable to APL procedures that are common in educational settings in which employees consider attendance of an educational track preceded by participating in an APL procedure in order to determine size and content of their study program. Reliable research findings related to other applications of APL procedures outside the educational domain are, to our knowledge, until now not available.
CHALLENGES RESULTING FROM THE DESIGN AND IMPLEMENTATION OF APL APL readily becomes a time-consuming and hence costly exercise. To avoid this, one had better re-use APL procedures once they have been developed. However, because of essential differences between procedures, not all aspects are re-usable. To find out which are and which are not, a further elaboration of these aspects is needed. If we require that developers of APL procedures can exchange parts of these procedures in electronic form, using whatever software and hardware systems, Interoperability enters the scene. It comes in two flavours, syntactic and semantic interoperability. Syntactic interoperability is the capability of two or more software systems to exchange information and then act on it. Semantic interoperability builds on syntactic interoperability and guarantees that the information exchanged is actually used the way it is
Table 1. Challenges and supportive instruments for successful APL procedures Stage of the procedure
Clarification of challenge
Supportive instruments
Candidate-profiling phase
• Need of personalised advice is time consuming
• Competence profile • Self-assessment instrument • Personal Development Plan • Website with APL information • Interactive FAQ-lists
Evidence gathering phase
• Difficulties with collecting appropriate evidence. • No check on portfolio • Very diverse evidence • The amount of evidence • Misinterpretation of competences and standards • Need for advice, clear examples and some kind of ‘organizer’
• Support system for composing portfolio • Portfolio template (with good and bad examples) • Electronic seeking and presenting of analogous cases • Competence profile
Assessing the portfolio
• Lack of clear and specified standards and rubrics • Sometimes one in stead of two or more assessors • Lack of training assessors • Time constraints • Appropriateness of assessment instruments
• Rubrics and scoring forms • Interview protocol • Criteria overview • Database with jurisprudence on assessment results
Validation phase
• Lack of clear instructions to report the outcomes • Difficulties in describing competence levels • No national/sectoral agreement on format
• APL certificate • Dynamic overviews of recognisable programme elements
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intended. As a result, different software systems may effectively provide the same service, in whole or in part, to the end-user. This way, parts of assessments, like the rubrics or the competence profiles, can be exchanged between developers. They all can edit, store and re-use them. In a computer-interpretable (machine-readable) form, this assessment information might be delivered to a candidate by a computer. The key issue here is to create and manage information in such a way that opportunities for exchange and re-use, either within or between institutions, are maximized (Miller, 2000). To reach such an ambitious goal a specification for exchangeability and interoperability of assessments is required. Generally speaking, a specification prescribes, in a complete, precise, and verifiable manner, the requirements, design, behaviour, or characteristics of a system (Beshears, 2003). One of the main benefits of a specification is that it offers a shared (controlled) vocabulary in which core concepts and ideas about a specific topic area can be expressed. Using open specifications means that the specification has many more people who look critically to another’s work, resulting in a more stable, and ultimately more satisfactory result. Obviously, APL stands to profit immensely from the use of open specifications. A few specifications are available. Technologies can be used to improve the efficiency of APL at two levels: task level and process level. Software tools can help the user to perform tasks easier and quicker. For example, as described, management of cross-referenced evidences in a paper-based portfolio is difficult. A portfolio editor with a repository will make it easy to manage them. Process level support means that computerized mechanisms coordinate actions and exchange artefacts. Because of the well-structured process of APL support at process level seems easy. The difficulties are at task level and some problems can not be solved by technologies. In the following, we will successively describe the obstacles and hurdles to the task level in which competence profiles, self-assessment instruments,
portfolio templates, interview protocols, rubrics and scoring forms and APL certificates play an important role. A good understanding of these instruments is necessary in order to understand what could and should be re-usable in the development of APL procedures. Hereby, we give the existing developments on interoperability improvement.
Competence Profiles A competence profile provides an overview of competences and skills in relation to a job profile. It describes the most relevant and important competences and skills an employee needs in order to adequately perform job related tasks and activities. In order to assess the candidate’s prior learning, the evidences for prior learning needs to be compared to competence standards represented by competence profiles. The development of these competence profiles is a complex task. However, when fulfilled, the next step is to create an interface in which the competence profile is placed and can be used for the self-assessment. Figure 1 depicts a screenshot of an interface used by a web-tool to identify e-competences. Such an instrument can for example be used as input for development plans, as a communication tool about and clarification of visions and interpretations regarding required competences and the opportunities and threats (Stalmeier, 2006) One of the premises is that APL only can become successful if it is firmly grounded in a competence-based approach to learning. The advantage of a competence-based approach is that it allows describing the learning outcomes gathered through different learning settings (formal education, non-formal and informal learning). Van der Klink and Boon (2003) signalled that the competence-based approach is widely applied in various types of education as well as in companies’ human resource management policies. Though the rise of competence-based approaches is evidently this does not imply that all approaches are based on a similar concept of competences. Van
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Figure 1. Screenshot of a webtool to identify competences (from Stalmeier, 2006, p. 43)
der Klink and Boon point at the confusion surrounding the concept of competence, which is clearly illustrated in Table 2. As this table shows notions on competences differ strongly which harm the exchangeability of competence profiles. For example, profiles applied in companies for performance assessments cannot be easily transferred into profiles for educational purposes. It goes without saying that the
further attuning of the various competence profiles is not going to happen easily but at the same time this is a prerequisite for the ultimate success of APL. Especially for this reason, it is important to define competence profiles in such a way that others understand the meaning of the profiles. Different people may interpret the same competence of the same person at the same time differently. The question is, which interpretation is
Table 2. Various perspectives on the concept of competences Perspective Geographic
Field of application
Underlying learning theory
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Position
Difference in definition
USA
Competency refers to behaviour and personal traits that contribute to excellent performance
UK
Competence refers to collectively agreed occupational standards such as national vocational qualifications
Germany
Kompetenz refers to the capacity of a person to act. It is more holistic than competency or competence, comprising not only content or subject matter expertise but also more generic abilities
Training and education
Competences are defined as clusters of skills, attitudes and knowledge that can be learned
Selection and recruitment
Competences are perceived as partly trainable and as partly rather stable traits that are difficult to change.
Performance assessment
Competences are mainly defined as the output of tasks and jobs
Cognitivism
Emphasis on observable and measurable performance. Stronger focus on top-down development of competence-based systems
Constructivism
Stresses values and beliefs as important components of competences. Stronger focus on employees’ participation in the development of competence-based systems
Technological Aids to the Efficient Assessment of Prior Learning
correct/reliable (Miao, Sloep, Hummel, & Koper, 2008). Metatags for competence profiles may support this understanding. To match the candidate’s prior learning with a competence profile, a bottom-up or top-down approach can be taken. Bottom-up, the prior learning of the candidate is compared to and matched with a competence profile. Top-down, a competence profile is compared to the candidates’ prior learning. Technical specifications of interest in this context are the IMS Learner Information Profile specification (IMS LIP, 2001), the Human Resource-XML (HR-XML consortium, 2007) and IMS Reusable Definition of Competency or Educational Objective (IMS RDCEO, 2002). IMS LIP is of importance to declare the information of the learner in a population. HR-XML supports a variety of business processes related to human resource management. IMS RDCEO is developed to create common understandings of competencies.
Self-Assessment Instrument Assessment of Prior Learning is not always useful or beneficial for all candidates. The level and amount of prior learning might not be relevant or sufficient to start an APL procedure, because the outcomes will not provide any benefits for the candidate’s career choices and career development. To assess whether an APL procedure will be beneficial for a candidate, a webbased self-assessment instrument can be provided to candidates who consider participating in an APL procedure. This self-assessment instrument (a) helps candidate to rapidly identify their competences, (b) compare these competences with competence profiles, and (c) decide on the usefulness of an APL procedure, given the outcomes of the self-assessment. The self-assessment instrument can be part of the PDP, but in APL it is often a separate instrument.
E-Portfolio After a candidate has decided to start APL, the evidence-gathering phase is initiated. In this phase, the candidates visualise their prior learning in an e-portfolio. The e-portfolio is one of the most common instruments in APL and is usually a database of collected evidence for competences. Examples of international initiatives that stress the important role of e-portfolio are EuroPortfolio (Eifel, 2009), which is a European consortium for the digital portfolio and Europass (Europass, 2009), an initiative of the European union to stimulate mobility and learning in Europe. An e-portfolio is a collection of artefacts or evidence (e.g., documents, products) of attainment and achievement in formal, informal or nonformal learning contexts, reflecting the candidates’ competence development. It is a synthesis of the personal, social and occupational experiences to highlight competences (Colardyn & Bjørnavold, 2004). In their portfolio, candidates collect and classify authentic evidence of their competences to support a claim for credit with respect to a specific competence profile. Whether the evidence is sufficient for this claim is decided in the assessment phase by the assessors. They decide whether the standards (i.e., the competence profiles) are achieved. The e-portfolio can contain any evidence the candidate can provide. In this way, the portfolio takes into account the individual differences between candidates and acknowledges informal and non-formal learning. An e-portfolio has many advantages over a paper-based portfolio and is therefore highly recommended to make APL more efficient. The e-portfolio is virtual and can be accessed anytime from any place if it is a web-based portfolio, it is easy to maintain, edit and update (Heath, 2005), and evidence can easily be checked by cross references (Canada, 2002)
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Portfolio Template The diversity of evidences in e-portfolios makes the assessment of portfolios for APL a complex task for assessors. Moreover, the APL is influenced by the ability of candidates to construct a well structured e-portfolio with relevant artefacts and by the assessors’ ability to objectively assess the competence level based on the content of the portfolio. The high competence level of a candidate might not stand out due to an ill-structured portfolio with badly selected artefacts or ill-developed ict-skills, or the assessor can be prone to base his assessment on subjective arguments. It is thus important that both the candidate and the assessor are supported in the use of an e-portfolio for APL (Kicken, 2008; McMullan et al., 2003). A portfolio template can support candidates to prepare their portfolio for the APL procedure. To support candidates in the construction of their e-portfolio and to structure the diversity of evidence in the
portfolios for assessment purposes, candidates should be provided with a portfolio template. This template should guide candidates to (a) gather relevant evidence, (b) present the evidence in a structured self-explanatory manner, and (c) selfassess their competences based on the selected evidence and the competence profiles. By using hyperlinks the candidate is electronically guided through the template (see Figure 2 for an example of an interactive portfolio template. The use of hyperlinks in word-documents is a simple way to guide learners in their portfolio construction. A more sophisticated way to guide learners through a portfolio might be the use of support systems. These systems use an interactive dialogue to compose the important information in the portfolio. To gather relevant evidence, the template could provide a checklist of relevant contexts in which competences could have been developed by the candidate, including informal and non-formal
Figure 2. Example of an interactive portfolio template
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learning contexts. For each context examples of relevant and irrelevant evidences should be provided to help the candidates select evidence in their situations. The template should also help candidates to present their evidence in such a way that an assessor understands directly how the evidence is related to and reflects the candidates’ competence development. The portfolio template should prompt the candidate on what additional information should be provided about the evidence. The template could contain short instructions telling the candidate what to write about the evidence. An existing example of such instruction is STARR, which instructs candidates how to describe their experiences in such a way that it becomes evident that this experience has contributed to the candidates’ competence development. STARR asks candidates to describe the Situation, the Task(s) they performed in this situation, the Activities they undertook and the Result of these activities. Finally, they are asked to Reflect on this experience and explain how it has contributed to the development of their competences. After helping them to present their evidence, the portfolio template should assist candidates to self-assess their competence level. This can be done by providing candidates with a comprehensive overview of the competence profiles and scoring rubrics. The competence profiles inform the candidates what level of competence is expected from them for specific occupations. The scoring rubrics help candidates to judge to what extent they possess the required competences. In this self-assessment functionality of the portfolio, the candidate is provided with the same information the assessors are provided with. This makes the APL procedure more transparent, which has a positive influence on effective use of the portfolio by the candidates (Baume & Yorke, 2002; Black & Wiliam, 1998; Kicken, 2008; Stiggins, 2001). Beside a well developed portfolio template to help the candidates, several examples of both
well structured and ill-structured portfolios are also effective in supporting candidates to develop their skills to prepare their portfolios for an APL procedure. Another important issue regarding the use of an e-portfolio, is its compatibility across systems. Most of the e-portfolios do not reflect accepted open standards, and have no facilities for importing and exporting e-portfolio information conform accepted standards. To move e-portfolios between systems a specification for the re-use of portfolios is needed. There are a few Interoperability standards for e-portfolios available, such as IMS e-portfolio, LEAP2A, IMS-LIP and HR-XML8. These standards support the possibilities of portfolio assessment in a technical way. It enables exchange of portfolios from school to work or from organization to organization. It allows educators and institutions to better track competencies, it enhances the learning experience and improves employees’ development. This will all be related to the portfolio as an artifact and not yet in relation to candidates in an assessment. The simplicity of the standard is key to its success, and to its ability to allow data to move between very different systems (Horner, 2009).
Relevant Technologies In addition to e-portfolios, a personal development planner or personal development portfolio could be used for the process of APL (Brouns & Firssova, 2008). In this type of portfolio the learner does not only reflect on his competences but uses the outcomes of this reflection to plan for personal, educational and career development. This takes the APL even one step further. A Personal Development Plan (PDP) is preferably a software tool by which people determine their progress on their own competence development. The software supports the user by gathering and sorting evidence and by (self-)assessing the level of mastery (Brouns & Firssova, 2008). Most of
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the times a PDP is not an instrument used within the APL procedure. However, the content of the PDP might be the basis for the self-assessment and after the APL procedure, the PDP can be used to record the results of APL and encourage learners to formulate new goals. Webbased PDPs have the possibility to share information with colleagues, tutors, executives or customers. An example of such a PDP is Personal Development Planner Web client V2.0 (Georgiev, 2009).
Scoring Rubrics and Scoring Forms The assessment phase of the APL procedure involves a complex task for assessors. Due to the diversity of evidence and descriptive, qualitative nature of the evidence, the assessment can be influence by the assessor’s subjectivity, which negatively influences the reliability of the APL procedure. To increase reliability, assessors need to use scoring rubrics during the assessment phase. Providing assessors with scoring rubrics and scoring forms can support them in the assessment of the diverse portfolios. Scoring rubrics include one or more criteria on which performance (as presented by the evidence) is rated and a rating scale or levels for each criterion. Descriptors and examples are provided to illustrate the criteria. The levels of each criterion are illustrated by examples. The criteria and levels are derived from the competence profiles. Scoring rubrics and forms not only make the assessment more reliable and easier, but also increases its transparency. A specific specification for rubrics is given by IMS Rubric (2004). The Rubric specification deals with the assessment of a portfolio, no other assessment types are addressed. In case documents in the portfolio have to be assessed, the use of Latent Semantic Analysis techniques may facilitate this process (Van Bruggen et al., 2004)
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The Interview Protocol The interview protocol is the leading scheme for the criterion-based interview with APL candidates that takes place after the portfolio assessment. A criterion-based interview aims at assessing a combination of skills, knowledge, behavior and personal qualities by means of questions related to specific examples of how the candidate behaved in different situations. This information is gathered and weighed against a criterion. The basic information for assessors for this interview is the portfolio. Assessors should be trained because the interview involves several complex aspects. The assessor has to interpret the portfolio in a correct way before the start of the interview. During the interview, the assessor has to manage the time and has to evaluate constantly whether he or she gets all the information necessary for the competence assessment of the candidate. In this phase, the use of standard evaluation questions that steer the interview into the desired direction is desirable. These kinds of protocols can be re-used by colleague assessors.
APL Certificate Finally, the results of the assessment (i.e. the candidate’s competence level), has to be described in a non-educational language, which increases the use of the certificate for other than educational purposes. Agreements upon the format used for APL certificates will enhance its societal acceptance, value and applications. The development of thorough e-instruments for APL is a good step in the direction of reuse. However, it is important to have a digital learning infrastructure in which these instruments are easy to use in combination with each other. In line with the reasoning on steps to be taken after the process of APL, the e-infrastructure is the basis to transfer the outcomes of the APL.
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PROMISING PERSPECTIVES In this section, two promising perspectives will be presented. In the description above, specifications for Interoperability are given for different instruments. All these specifications are specified for a single component of the whole procedure. The leading specification for the exchange and interoperability of entire assessments is the Question & Test Interoperability specification (IMS QTI, 2004). One of the core concepts of this model is the assessment structure model that defines Assessment, Section and Item layers. The QTI specification includes a set of XML bindings to describe questions and tests. It does so by (a) providing a well documented content format for storing items independent of the authoring tool used to create them; (b) supporting the deployment of items and item banks across a wide range of learning and assessment delivery systems, and (c) providing systems with the ability to report results in a consistent manner (Joosten-ten Brinke, Gorissen & Latour, 2004). The primary goal of this specification is to enable the exchange of questions (called ‘Items’) and tests (called ‘assessments’) between Learning Management Systems. QTI supports different types of questions and it is split up in two parts, the content of the evaluation part and the results from the evaluation part. Both parts can be used separately or together. The QTI specification is more or less limited to those assessment types for which an unambiguous definition in technical terms can be specified. Interoperability is limited to classical multiple choice items and their variations (Gorissen, 2003). The structure of multiple choice items proved to be well-suited for storage in item bank systems and delivery in digital format as the structure was not complex. The QTI specification offers good opportunities for exchange of items in standardized assessments. Though IMS QTI can be regarded as the leading specification for the exchange and interoperability of assessments, the
question remains to what extend this specification adequately supports emerging new and complex assessment forms like APL. Miao et al (in press) argue that QTI does not posses sufficient expressiveness, since it only addresses the task aspects of APL, but does ignore process-oriented aspects such as who performs what kinds of assessment activities in what sequence. Thus QTI can not independently support APL. However, as Miao et al (in press) propose, combining QTI and IMS Learning Design (LD) seems to increase significantly the level of expressiveness to represent complex assessment forms, but even then, serious issues need to be addressed. Apart from technical issues there is the issue of the user-friendliness, since average teachers are not able to model their teaching and assessments with QTI and LD; they need a high-level assessment modelling language that can be transformed into an executable model represented in LD and QTI. One way out of this dilemma is to design assessments in such a way that they can be shared amongst assessment developers and re-used in other contexts (Williamson, Bauer, Mislevy & Behrens, 2003). A model for re-use in assessment is the educational model for assessment (Joosten-ten Brinke, Van Bruggen, Hermans, Burgers, Giesbers, Koper, & Latour, 2007). This model gives the opportunity to understand others’ assessments by using the same concepts of assessment and to exchange parts of an assessment. The educational model for assessment is built on several sub models, each fit to the following stages in the assessment process: assessment design, item construction, assessment construction, assessment run, response rating and decision-making. In Figure 3, the model for the assessment design part is given as an example. For the entire educational model for assessment, we refer to Joosten-ten Brinke et al. (2007). The educational model of assessment is cast in terms of UML class diagrams (the UML classes are the squares and the lines indicate the
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Figure 3. UML class diagram for the assessment design
type of relation between the UML classes) and complies with the requirements of a complete conceptual model as defined by Koper and Van Es (2003): •
•
•
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Flexibility: The assessment model must be able to describe assessments that are based on different theories and models. Formalization: The assessment model must be able to describe assessments and their processes in a formal way, in order to be machine-readable and to enable automatic processing. Reusability: The assessment model must make it possible to identify, isolate, decontextualize and exchange useful objects (e.g. items, assessment units, competen-
•
•
•
cies, assessment plans), and to reuse these in other contexts. Interoperability and sustainability: Separation between the description standards and interpretation technique, thus becoming resistant to technical changes and conversion problems. Completeness: The assessment model must cover the whole assessment process, including all the typed objects, the relationship between the objects and the workflow. Reproducibility: The assessment model must describe assessments so that repeated execution is possible.
The question is whether this educational model for assessment is a sufficient solution to alleviate the burden of following the entire workflow
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Table 3. Scenario for APL Actor
APL candidate
Pre condition
Assessment policy is available in the system. Standards for APL are nationally established for formal educational programs in the vocational domain. On the labour market, standards are available for some professions, like assistant professor or management assistant, and for some general competences, like presenting or organizational sensitivity. These standards are the basic assumption for an APL procedure (assessment policy).
Scenario description
1. APL candidate (candidate) assumes having competences in domain of management. 2. APL candidate selects in the system the domain and searches for the standards for management (trait). 3. System delivers standards (trait) with lower level standards (complex trait) and indicators for these lower level standards (elementary trait). 4. APL candidate starts self-assessment for these standards (according to assessment plan) 5. System delivers e-portfolio template after the self-assessment. 6. APL candidate has to provide evidence with argumentation (item; demonstration item) in portfolio template. 7. APL candidate uploads portfolio.
Post condition
Portfolio template and self assessment are stored and send to assessors.
of APL. In case of APL, does the model fit the above-mentioned requirements? Therefore, the educational model for assessment is validated for APL. For this purpose, use cases and scenarios are used. In Table 3 one of the scenarios is given. The corresponding concepts of the assessment model are placed in italics between brackets. Based on this validation, we conclude that the model fits APL.
Support System for Learners in Learning Networks It goes without saying that institutes like to offer personalized learning arrangements to the candidates that finished their APL procedure. However, composing personalized learning arrangements is a time-consuming process for which Kalz (2009) has proposed a technological solution that reduces time and costs and even improves the quality of APL. His technical solution consists of a webservice for lifelong learning that pre-analyses documents and consequently assists in deciding the relevancy of these documents for the further course of the APL procedure. Katz applied Latent Semantic Analyses (LSA), which is a method for extracting and representing the contextual-usage
meaning of words by statistical computations. The technologies Kalz developed appear to be very promising and therefore should be further developed in future research.
CONCLUSION In the contemporary information society people are best seen as lifelong learners. However, if we want people to act as lifelong learners then we need to assure that there is an e- infrastructure to support their learning endeavours. This learning infrastructure does not only offer more, better and faster education, but most importantly should fit people’s learning needs. One of the key components of this emerging learning infrastructure consists of people’s recognition of their prior learning, since it will shorten their education tracks and will motivate them throughout their learning life-histories. Assessment of Prior Learning (APL) however is time-consuming and expensive. In this chapter we have described the workflow of APL and the instruments needed in this procedure. We also explained how re-use of these instruments can contribute to decreasing the expensiveness of the development of APL.
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Re-using seems to be possible for existing instruments such as competence profiles, e-portfolios, and rubrics specifications. The educational model for assessment seems to be a promising approach that will increase the efficiency and effectiveness of APL procedures. However, further elaboration on this model resulting into more advanced metadata for general descriptions of the objects is definitely needed. Considering the question of technical solutions from a more aggregated level result into at least two challenges that need to be considered in the development of effective APL (see for a more comprehensive discussion Miao et al, 2009). First, APL allows the storing of various information from different sources and different types of sources in a candidate’s e-portfolio. It is likely that information fusion technologies may support the (human) assessors in their task of accurately assessing one’s portfolio. Second, if we want to support candidates in the process of matching their own prior learning to one or more competence profiles then the application of spatial index and browsing structures together with visualization of competence information objects need to be seriously considered. These techniques provide accessible information that make explicitly clear how one’s personal competency profile match to a profile applied in an APL procedure, which will definitely support candidates in making informed decisions on enrolment in APL procedures. Unfortunately, although some technical solutions are available, the absence of generally accepted competence profiles inhibits the exchange and re-use of some of the main APL instruments. If educational institutes and associations like federations of employers and unions are not able to adjust competence profiles to one another, the issue of maximizing Interoperability becomes an insurmountable problem. Nevertheless, with the educational model for assessment, we are one step closer to fulfil our life long learners’ needs.
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ACKNOWLEDGMENT We like to thank Yongwu Miao and the anonymous reviewers for their helpful comments on a previous version of this book chapter.
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Livingstone, D. W., & Eichler, M. (2005). Mapping the field of lifelong (formal and informal) learning and (paid and unpaid) work. Joint keynote at the Future of lifelong learning and work international conference, Toronto, June 20th, 2005. Marsick, V. (2006). Informal strategic learning in the workplace. In Streumer, J. N. (Ed.), Workrelated learning (pp. 51–69). Dordrecht, The Netherlands: Springer. McMullan, M., Endacott, R., Gray, M. A., Jasper, M., Miller, C. M. L., & Scholes, J. (2003). Portfolios and assessment of competence: A review of the literature. Journal of Advanced Nursing, 41, 283–294. doi:10.1046/j.1365-2648.2003.02528.x Miao, Y., Boon, J., Van der Klink, M., Sloep, P., & Koper, R. (in press). Support interoperability and reusability of emerging forms of assessment using IMS LD and IMS QTI. Handbook of research on e-learning standards and interoperability. Miao, Y., Sloep, P., Hummel, H. G. K., & Koper, R. (2008). Competence information fusion: Concepts and challenges. Presented in a colloquium at OUNL. November, 18, 2008, Heerlen, the Netherlands. Retrieved on June 25, 2009, from http://hdl.handle.net/1820/1631 Miao, Y., Van der Klink, M., Boon, J., Sloep, P., & Koper, R. (2009). Toward an integrated competence-based system supporting lifelong learning and employability: Concepts, model and challenges. In Spaniol, M. (Eds.), Advances in Web-based learning, ICWL 2009 (pp. 265– 276). Berlin/Heidelberg, Germany: Springer. doi:10.1007/978-3-642-03426-8_33
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Miller, P. (2000, June). Interoperability: What is it and why should I want it? Ariadne, 24. Retrieved March 10, 2004, from http://www.ariadne.ac.uk/ issue24/interoperability/
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Raemdonck, I. (2006). Self-directedness in learning and career processes. A study in lowerqualified employees in Flanders. Unpublished dissertation. Gent, Belgium: Gent University, faculty of Psychological and Pedagogical Sciences.
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Rubric, I. M. S. (2004). IMS Rubric specification. Version 1.0 Public Draft. IMS Global Learning Consortium, Inc. Retrieved September 23, 2004 from http://www.imsglobal.org/ep/epv1p0pd/ imsrubric_specv1p0pd.html Schlusmans, K., Joosten-ten Brinke, D., & Van der Klink, M. (2006). Accreditation of prior learning in higher education. Paper presented at the EARLI SIG Professional Learning and Development Conference. Heerlen, The Netherlands: Open University. Schmid, G. (1998). Transitional labour markets: A new European employment strategy. Berlin, Germany: WZB. Sloep, P., Boon, J., Cornu, B., Klebl, M., Lefrère, P., & Naeve, A. (in press). A European research agenda for lifelong learning. International Journal of Technology Enhanced Learning. Stalmeier, M. (2006) E-competence profiles: An instrument for e-competence management. In I. Mac Labhrainn, C. McDonald Legg, D. Schneckenberg, & J. Wildt (Eds.), The challenge of e-competence in academic staff development. Galway, Ireland: CELT, NUI. Steyaert, J., & De Haan, J. (2001). Geleidelijk digitaal; een nuchtere kijk op de sociale gevolgen van ICT. Den Haag: Sociaal en Cultureel Planbureau. Stiggins, R. J. (2001). Student-involved classroom assessment (3rd ed.). Upper Saddle River, NJ: Prentice-Hall, Inc.
Tough, A. (1979). The adults learning projects. Ontario, Canada: Ontario Institute for Studies in Education. Van Bruggen, J. M., Sloep, P., Van Rosmalen, P., Brouns, F., Vogten, H., Koper, E. J. R., & Tattersall, C. (2004). Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning. British Journal of Educational Technology, 35(6), 729–738. doi:10.1111/j.14678535.2004.00430.x Van der Heijden, B. I. J. M., Boon, J., Van der Klink, M. R., & Meijs, E. (2009). Employability enhancement through formal and informal learning. An empirical study among Dutch nonacademic university staff members. International Journal of Training and Development, 13(1), 19–37. doi:10.1111/j.1468-2419.2008.00313.x Van der Klink, M., De Bie, M., Evers, A., & Walhout, J. (2007). Accreditation of prior learning in teacher education: Some findings. Paper presented at the annual conference of the Association of Teacher Education Europe (ATEE), August 26, 2007, Wolverhampton. Van der Klink, M., Schlusmans, K., & Boon, J. (2007). Designing and implementing views on competencies. In Sicilia, M. (Ed.), Competencies in organizational e-learning. Concepts and tools (pp. 221–233). Hershey, PA: Idea Group Inc. Van der Klink, M. R., & Boon, J. (2003). Competencies: The triumph of a fuzzy concept. International Journal of Human Resources Development and Management, 3(2), 125–137. doi:10.1504/ IJHRDM.2003.002415
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WRR. (2002). Van oude en nieuwe kennis; de gevolgen van ICT voor het kennisbeleid. Den Haag: Sdu. Zeichner, K., & Wray, S. (2001). The teaching portfolio in US teacher education programs: What we know and what we need to know. Teaching and Teacher Education, 17, 613–621. doi:10.1016/ S0742-051X(01)00017-8
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Chapter 14
Game Based Lifelong Learning Sebastian Kelle Open University of the Netherlands, The Netherlands Steinn E. Sigurðarson Open University of the Netherlands, The Netherlands Wim Westera Open University of the Netherlands, The Netherlands Marcus Specht Open University of the Netherlands, The Netherlands
ABSTRACT Digital Games as a means of learning have become more important in recent years. Infrastructural and sociological developments have created fertile grounds for game innovations, by exploiting the latest technologies, and a new generation of learners have welcomed this form of learning. This chapter focuses on an overview of the current state of the art of learning games, explaining different perspectives. As the gamers’ generation has now grown up, the educational contexts for lifelong learning like higher and vocational education are moving into the scope of game based learning, and therefore deserve special attention.
1. INTRODUCTION One of the striking observations when looking at game-based learning is that its definition and background go oddly beyond the human aspect of learning sciences. Animals (in essence, all placental mammals) have the ability and the drive to learn through play (Burghardt, 2005). Zoological research illuminates the importance of play for young animals to learn essential skills (Hawes, DOI: 10.4018/978-1-61520-983-5.ch014
1996). Gaming, hence, is a very natural way of self-directed learning during a phase in life, which is the stage of most rapid cognitive development. Therefore it is safe to assume that game-based learning has existed for a very long time going back to prehistoric times. Being a subspecies of the class of placental mammals, also young humans engage upon their drive to learn by gaming. The natural drive to learn through play, however, is coerced by modern society. Acknowledging the obvious demand for games that is a culturally universal phenomenon, the notion of learning
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through games, though, has a somewhat unserious flavour, especially from the perspective of formal educational systems. In lifelong learning, however, education is usually not formally imposed on the learner. There is a high degree of “ownership of learning” that turns the situation around (Wilson et al., 2006). The learners themselves are now the main motivational instance and equipped with a fair deal of initial self-motivation. Much like subscribing to a gym, this initial motivation can, however, decrease quickly when they discover disadvantages, like getting bored or overtaxed. The main objective of a game-based learning approach for lifelong learning is thus the sustenance of this motivation and helping learners over the hurdle of getting truly comfortable with the overall learning process they have engaged upon. While this may seem like a noble goal, the challenge is far from trivial. For several years experts in the field of education have made thousands of games designed for education. Nevertheless, the advantages of such an approach to learning have remained obscure, and the factors required to successfully create a learning game out of a situation appear seemingly random (O’Neill, Wainess & Baker, 2005). In this chapter we explain the advantages of using a systematic approach that makes use of game design patterns (Bjork & Holopainen, 2004). These can be used for the following purposes: • • •
Identifying hidden game elements in a non-game-based educational scenario Making a game out of a non-game-based scenario If there is nothing to build on, designing a game from scratch.
On a general note, also we can subsume that the latest technological developments (Johnson, Levine & Smith, 2009) have created an enormous potential for learning games to be revisited. The arrival of the Web 2.0, semantic web technologies,
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as well as cinematic computer graphic and mobile technologies have opened the gates to a world of possibilities we do not want to miss out on.
2. CURRENT STATE-OF-THE-ART The state-of-the-art of games in lifelong learning is difficult to pinpoint, as scientifically relevant results on the exact intersection of the two spectra are scarce. However, there exist some approaches that can be placed in the topical proximity of our focus, showing very promising perspectives. We picked a couple of examples coming from different directions in order to provide a certain deal of coverage. One of the more notable approaches, for example, can be found in the field of mobile learning games dealing with lifelong learning of the homosexual minority in India (Roy, Evans & Sharples, 2009). The targeted people have the societal disadvantage of being pushed into obscuring their sexuality from daily life, which makes it difficult for them to access relevant knowledge that could help them avoid related problems such as HIV infection or drug abuse. While the learning game as such makes use of the pattern of role play, hence enabling a good deal of identification with the game character, it also allows the target group to stay anonymous: the game is realized as a text message-based quest game, moderated by anonymized “peer educators”. The users can play the game accessing relevant educational content without revealing their personal attitudes to their social surroundings: operating a mobile handset is nothing that draws a lot of bystanders’ attention. While this example may seem very specialized and unique, it illustrates the enormous potential of a game-based life-long learning approach. Another example with more generic properties is the “UniGame”, described by Pivec & Dziabenko (2004), which is used to learn social and knowledge management skills. In this approach several teams collaborate on a simulated project,
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for example the building of a tunnel. The team that produces the best project plan to offer to an imaginary stakeholder wins. Each team gets a certain amount of “chips” which need to be used to allocate limited resources for emphasizing certain topics over others. The game developed by Sigurðarson (2008) is a web-based personal glossary and quizzer module for a popular virtual learning environment. The glossary offers support to create on- the-fly a quiz from either a single user’s glossary or all users’ existing glossaries, to test their skills at translating words from one language to another. A choice is made on how many words to include in the quiz, and after completing one, the users are able to see how fast they completed it, the percentage of right and wrong answers, as well as which words they translated correctly and which not. Other indicators can for example make use of a “high score list” pattern, visualized as a current placement indicator, akin to those found in racing games, displaying “3rd” or “1st”, depending on a player’s current position. Their words per minute might even be displayed with a mock speedometer. Another series of games with a big potential for use in education are location-based games making use of latest mobile technology. According to Grohé (2009), already a small but fair choice of games exist that make use of location-based services, using the Global Positioning System. The games mentioned are “Fast Foot Challenge”, “Geocaching”, “GPS:Tron”, “REXplorer”, “Gowalla”, “Mobile Dead”, “The Go Game” and “Metal Gear Solid: Portable Orbs”, all of which make use of the possibility to monitor the player’s geographical location in relation to other game elements (other players, objects). The possibility to use these games for learning emerges from their social and geographical dimensions: being able to experience real life situations creates a strong immersion factor, while the virtual mobile indicators augment those situations with important information needed for the game experience. For example Geocaching, the oldest and
most widely used location based game of the lot, uses a quiz pattern for people to figure out the coordinates of a cache (a physical treasure hidden somewhere). Here, we already have to deal with a sub-game which can be used for learning. After that, the exploration and cooperation pattern is being extensively used to put together a team of explorers who head out to find the cache, which might even be hidden in locations that are very difficult to access: For example, there are caches on Antarctica, Mount Everest or under water. Another interesting rather scenario-based approach is the EMERGO methodology (Nadolski et al., 2008), which is essentially an authoring toolkit for serious games that makes use of adaptive storytelling and role play. Here is where adaptivity becomes of interest and the learning game approach finds relation to adaptive hypermedia (Brusilovsky & Maybury, 2002). The story being told can change according to the actions of the player, and depending on the skills of the learner, in terms of problem solving; the game may be lost or won, or won with according delay. The system was specifically developed to facilitate the authoring of learning games, so the actual design of a learning game depends on the game author. Even though the approach technically so far only accounts for single user games a competitive or collaborative pattern could be introduced as socalled “extra-game pattern” for example by two players taking turns trying to reach the goal of a quest more quickly than the other. The approach is designed for higher education training but can also work for a lifelong learning purpose.
3. ADVANTAGES OF GAMES FOR LIFELONG LEARNING Although the above examples show a big potential, it might not be immediately clear what makes learning games specifically attractive for use in a lifelong learning context. The reasons are of practical nature: people who are already working
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in a full-time job ideally combine their lifelong learning activities with recreational activities, which is in line with the notion of informal learning (Coffield, 2000; Foreman 2004). On the other side of the coin, especially in times of world economic crisis people lose jobs and often have to start over, trying to get qualified in different areas, or getting back to school to get the degree they never completed. This is how lifelong learning and formal education encounter and create a potentially painful combination. Especially for people who already have been in a workplace and gathered professional experience, getting “back to school” may be perceived as big throwback. Under these circumstances, but also in a more general context, learning tends to reside down at the lower end of the scale, while games and other frivolous activities are ranked higher. A reason for this could be that learning is usually understood by authorities, and experienced by people as the passive act of “receiving” knowledge from a speaker, text or video in a formal setting (of a school or institution). Enjoyable side-activities happening within this context, such as socializing or even playing games, are usually discouraged or forbidden- a state which increases their scarcity, and thus their value. However, the pleasurable value of learning is greatly decreased by the stark contrast to the individually more valuable, discouraged activities happening in between lessons or classes (Schank & Cleary, 1995). Due to these very reasons, many researchers in the field of pedagogy have struggled for years to show that what is happening within the classroom is not “really” learning (Illich, 2000). It is simply a structured way of delivering information, in the hope that through a process of content delivery, and repeated exercises, with their respective mistakes and successes, the participants will get a proper understanding of the topic at hand. This system, which roots back in the introduction of factories during industrialization (Keegan, 1994), remains in use, although nowadays many educators believe that a more flexible approach, harnessing the
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power of the Internet and computers, for example, clearly offers better alternatives. We acknowledge and build upon the fact that formal education is perceived by most as an unpleasant activity and many learners spend most of their cognitive power “playing the system”. This is a problem we refer to as Naeve’s knowledge emulation problem (Peña-López & Naeve, 2007) and seems to be centred on the testing aspect of formal education. Tests, usually few of which will have an actual impact on a learner’s grading, are spaced out far enough from each other, to give the teacher ample opportunity to design them, as well as to review the student’s scores. The possibility of repeating an exam or a test of the same nature is rarely offered, causing many learners to struggle finding ways of emulating the knowledge at all costs, since failure is not an option. According to Naeve and Peña-López this behaviour unfortunately means that many learners avoid reflective thinking or contemplation on their subjects, as they are more concerned with passing these “rare” tests, thus avoiding any delays in their career, rather than mastering their subjects. This presents one of the major challenges for modern educators, to break free of the factory-mentality of formal education, and restore fun and discovery to the learning process. One way to accomplish that is to add gaming elements to an existing learning activity; how to do so with some of the more effective generic game patterns is explained below. Games by their very nature are often simulations of real world activities, or simply “naïve” problem solving enhanced by instant gratifications in various forms: points or audio/visual “rewards”. A simple method for turning exercises into a game is the introduction of a scoring system. A scoring system allows a learner to gauge their performance from exercise to exercise, as well as enabling comparison to their peers and thus competition. Even the most simplistic scoring system may revitalize a learner’s efforts as they offer easy alternatives to the sparsely taken and heavily weighted tests mentioned earlier. In contrast to
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taking tests, learners are put in charge of personally reviewing their own performance in each task, reflecting on it, and possibly redoing the task to improve their performance. This reflection and the critical thought involved in analysing a past performance, what went wrong, or what may be improved, is a critical part of the learning process. In many formal educational scenarios this is suppressed by knowledge emulation, which suggests that a lifelong learning context is a more fertile environment for our direction.
just with themselves. Competition between people can take many forms, the simplest of which is already realized by the first factor: the ability to compare your performance to others. Things start becoming interesting when actions of a player can influence the performance of others. This paradigm is common and at the core of most competitive games. Examples from a few different styles of games: •
4. IDENTIFYING MOTIVATION DRIVERS: COMPETITION, COLLABORATION AND INDICATORS Psychological research has revealed several factors influencing human performance both positively and negatively in relation to gaming (Becker, 2005). These factors are either in line (positive) or in conflict (negative) with the findings of educational research on motivation theory (Keller, 1983), experiential learning theory (Kolb, 1984) and instructional theory (Gagné, 1965). Considering how these approaches complement each other, we believe that there are only few main drivers responsible for games being as engaging as they are, and people are not equally affected by them. One of these factors is the ability to see progress in an obvious and continually available manner. When users are able to gauge their progress and realize the effects of their actions with regard to any type of measurement (usually realized as “points” in a game) this introduces the first and most basic form of gaming: the ability to compare performances. This first factor holds true whether the person is alone, comparing their performance to their own previous ones, or in a group and comparing with others. The moment we, however, introduce more people into the activity, a myriad of possibilities appears as many are more easily motivated in direct competition with others, than
•
•
In a first person shooter video game a possible influence is to destroy other players’ avatars, and thus causing them to lose their weapons/items they have accumulated, as well as increasing own score, for example by picking up items dropped by another player who got “destroyed”. In a game of competitive Tetris, by eliminating the gaps in a line on the screen, a player causes a new line of blocks with random gaps to appear on the other player’s screen. In a game of soccer, each player is able to intercept the ball whenever it is passed around the field, and even to intercept players of the opposing team and outplay them for the ball.
This would be the second basic factor: the ability to affect the performance of others. Now, this may seem like a factor, which is not too easy to include in simpler settings, but as we will demonstrate there are several patterns or ways to achieve this kind of competition. In scenarios where there are more players than two, this pattern introduces another aspect of gaming, which is the opposite of competition, namely collaboration. While, generally, the element of competition stays the ultimate driving force of a game, collaboration between players can provide so much mutual benefit that players who choose not to play as part of a team are greatly disadvantaged. Upon forming teams in a game, an element of in-team collaboration is introduced that includes a competitive aspect
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as players of the same team compare their stats, forming a sub-game out of the overall game. A third and very crucial factor is the challenging role of being in charge. Game worlds have a high potential to simulate the notion of control and ownership to the player. This is especially notable in role play scenarios, where the identification with the game character is fostered. However, consequences remain in-game and have no effect on the player in terms of real risks, which therefore gives players more freedom to try out different tactics and develop their own ways to solve problems. Finally and most obviously the “game content” itself is an important factor. If a certain innovativeness and aestheticism is warranted, the gaming experience becomes more attractive and the player enjoys exploration (Kiili, 2005). These simple additions to any activity are powerful ways to motivate participants in exciting new ways. However, in an organisational context, these factors may introduce additional challenges for many medium to large enterprises that possess a varied workforce in terms of age, gender, originating cultures and professional abilities. There are definitely those within each workforce who are threatened by the idea of competing with their co-workers, especially in a learning scenario. Maddock, (1999) states that mostly males under the age of 50 enjoy pure competition, while other groups either prefer competing with their own performances, or, as we theorize, working and competing in teams. In a team scenario those who do not necessarily believe they are the best, may still play an important role. Game-based learning has the potential of introducing fun and engaging ways for improving personal performance. Facilitators of game-based learning activities in a lifelong learning scenario should take care that the patterns they introduce to their learning scenarios to embed them with gaming properties, do not cause participants to feel pressured or intimidated, rather mildly stimulated.
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In the text above we briefly mentioned indicators and the important role they play in providing one of the basic elements required for gaming: a scoring system. Scoring systems may however be constructed in a variety of ways, ranging from a simple dichotomy, where a participant is either successful or not, to the more common “points” system, where a linear number denotes performance, to the most complex scoring systems, where a score is given in any number of different areas of game-play. Examples of these more elaborate scoring systems are, for instance, the ones encountered in many strategy games, where players are awarded a number of points in areas such as resources owed, resources exploited appropriately, unit production, unit destruction, building production, and destruction. A very elaborate system of scoring each match is being deployed in many multiplayer action games, such as Halo 3 (Piggyback, 2007). In Halo 3 different scoring systems apply depending on the type of game being played, but as well as each player receiving a general score for performance based on the number of times a player destroyed another player, there are many rewards to be achieved, and after a match is completed players are able to review and reflect upon their “kills/deaths” ratio, their accuracy or favoured weapons, and even which opponents they most often clashed with successfully or unsuccessfully. It is a fact that humans give objects value based on their scarcity, so a reward for an “excellent” performance should be hard enough to achieve for it to be valuable, while not being unattainable and thus demotivating. A prime example of the relationship between scarcity and value is that of a gemstone: its value is directly linked to its scarcity, to the fact that not everyone can obtain it. The same is true for virtually anything, points or rewards in a gaming scenario are only worth the effort involved with acquiring them: the lesson we learn is that even if a reward has no real value, it will be valued simply due to its scarcity. In fact it may be argued that when turning real-world te-
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dious activities into a game, rewards programmed into the game should not be valuable in the real world, and if so only as a token. Increasing the stakes too much will decrease the gaming factor. Now we have established one aspect of indicators, and one of their main functions: to indicate a player’s score. Additionally, the indicators themselves may have reciprocal properties. An important dimension of any indicator in a technology-enhanced learning scenario is the timing of display. When engaged in an interactive activity which may or may not be competitive, the factor of whether or not the performance may be gauged in real time is an important one for the overall experience: seeing a clear indicator of your performance at each given time may encourage or discourage depending on the person. This may be viewed similarly to a speedometer or an odometer, or both. Seeing the current speed and total distance travelled while cycling, for example, is very useful for cyclists who know they need to cycle 20 kilometres in one hour, so as long as their speed is 20km/h or more, they may feel secure and motivated to keep their pace, if they are unable to keep such a pace however, this indicator could affect their performance.
5. GENERIC GAME LEARNING PATTERNS There is already in-depth research done on design patterns for learning purposes, for example as conducted in line with the EU Kaleidoscope project (Mor et al., 2006). However, under the notion of game patterns we understand game design patterns (Björk & Holopainen, 2004) as used in conceptual game design in its broader sense (theoretically also including non-digital games). They are used for matching learning scenarios with implementable game elements to enhance the scenario. To conceive this, we suggest a content based approach that gives us the possibility to come up with an instructional design in connection to the story
line of a respective game. The possibilities for this are context dependent, relating to what is the desired main educational outcome. A choice derived from fundamental pedagogical theory as described in Robinson’s (1998) learning goals can be subsumed as: • • •
The acquisition of information The practice of tangible skills Training of problem solving collaboration
and
Depending on this classification, one can decide what kind of game should be used (Quest type, real world simulation, highly reactive multiplayer game, etc.) and what will be the respective game elements. To do so, game design patterns are chosen that form the actual game design by being combined with each other, for example resource patterns, stating the score of players’ attributes, being able to be traded, lost or gained. The fundamental difference between software design patterns used for building learning environments, such as suggested in (Retalis et al., 2006), is that game design patterns are in fact used for an earlier stage than the actual application development. The patterns are meant to help a conceptual game designer to spawn a combination of game elements that works well to achieve a learning objective. On a whole scale, according to Björk and Holopainen (2004), game design patterns are grouped into the following classes: •
•
Actions and Events Patterns: these patterns describe the most granular level of game elements, such as simple player actions and reactions of the environment. Patterns for Narrative Structures, Predictability and Immersion: these patterns describe the storytelling of a game and the identification factors of the player’s character enabling the users to identify themselves with that role.
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•
•
•
•
•
•
Patterns for social interaction: these patterns describe the specific properties of a multiplayer gaming environment. Patterns for Goals: these patterns are needed for the end of a game or its subdivisions. Once a goal is achieved a certain special event occurs that indicates so. Patterns for Goal structures: goals can be modified during gameplay, which is described by these patterns. Patterns for Game sessions: these patterns describe the overall characteristics of player participation in a game. Patterns for Game Mastery and Balancing: these patterns described the abilities and kills of a player in the game. Patterns for Meta Games, Replayability and Learning Curves: these patterns describe factors outside the game, such as possible contextualizations.
As sketched in Figure 1, game design patterns consist of different structural components. First, the core of the pattern describes its functionality, what element of a game it represents, how it is used and what the consequences are. Also possible conflicts with other game elements are indicated lest they appear in the same game together. Relating to this, a metric of combination rules can be applied: patterns can be combined by modulation (one pattern influences the other), and instantiation (the existence of one pattern leads to the coexistence of another). In theory this construct leads to “game based” compound learning objects, such as described by Boyle, (2003), however in a less concrete form. It is, however, important to stress that a single game pattern does not suffice to create a whole learning game. Therefore it should be argued that the metrics for combination are of paramount importance for a sound game design. The specifics of software design, derived from a conceptual design, can be extended to fulfill the requirements for implementation by using a layered composition of elements as suggested by
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Boyle. Another point is that the reverse engineering of existing game-based learning scenarios may elicit game elements that have already shown to be of educational use and can then be mapped to form the appropriate contextualized taxonomy of game patterns for learning (Becker, 2007). In our approach we focus on the social interaction patterns, such as competitive patterns and collaborative patterns. Example: The “Cooperation pattern”: Players cooperate, i.e., coordinate their actions and share resources, in order to reach goals or sub goals of the game. To form a game out of this starting pattern, it is necessary to instantiate into more specific patterns that state facts about how in detail the cooperation is conceived. “Alliances”, “Team Play” and “Shared Rewards” could be possible sub-patterns. Example: In juxtaposition of aforementioned pattern, the “Competition pattern” is of relevance: Competition is the struggle between players or against the game system to achieve a certain goal where the performance of the players can be measured at least relatively. This pattern for example is instantiated with the “Enemies” pattern, “Player Elimination” or “Incompatible Goals” patterns, only to name a few self-explaining examples. As pointed out above, an important element is the reflection of the player’s progress or status, which can be achieved by making use of resource/ score patterns, raising the awareness of success and failure, especially when it comes to social comparison. Finding the right selection of game design patterns according to the classification mentioned above (acquisition of information, practice of skills, collaboration) can be systematized in conjunction with the pedagogical processes relevant to the learning scenario, and comparing these requirements with the description of the game design patterns in question. For example, the acquisition of information (a process critical for learning) can be reflected in-game by the “gain
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Figure 1. General structure of a game design pattern
information” pattern, a pattern which is described as the goal of performing actions in the game in order to be able to receive information or make deductions. To foster collaboration, the “shared rewards” (The players who were involved in some way in reaching a goal in the game share the reward.) pattern may be of use, while the practice of a tangible skill could be reflected back to the learner in terms of progress and success indicators, such as “score” or more specifically “high-scorelists” as such introducing a competitive element. While this procedure illustrates how one pattern leads to another, it is important to balance the game patterns in such a way that a residence within the bandwidth between engagement and on the other hand content is achieved: Learners should be motivated and “drawn” into the game, but not be overly distracted from the learning
goal. It is a matter of ongoing research to find out which learning functions best profit from what game design patterns calculated over the broad range of suitable domains and contexts as well as target audiences. A way to do this according to our preliminary findings is to match game elements with pedagogical processes, using pedagogical taxonomies as intermediary step. Figure 2 shows how such a mapping is carried out. The pedagogical taxonomies mentioned are largely based on the theories by Gagné (1965), Keller (1983), and Kolb (1984). Additionally, the classification of Heinich et al. (1999) (educational design theory), and Robinson (1998) (pedagogical goals) can be considered. Indeed, comparing these taxonomies with above mentioned classification of game patterns, a striking similarity becomes obvious. Almost all game design patterns can be
Figure 2. The mapping between pedagogical functions, educational taxonomies, game design patterns and finally the corresponding game element after implementation. Note that a game element can be composed of a combination of game design patterns.
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directly mapped to instances or concepts of those pedagogical taxonomies. Example: With respect to dealing with prior knowledge in an educational scenario the pedagogical concepts associated with this entity are tutorial, demonstration, presentation (Gagné, 1965), stimulating recall of prior learning (Robinson, 1998) self-awareness of knowledge construction, ownership of learning (Heinich et al. 1999) observation (Kolb, 1984) and relevance (Keller, 1983). These concepts can be mapped with the game design pattern reconnaissance (known areas in the game and detection of changes). The pedagogical taxonomies mentioned comprise a very large spectrum of all kinds of learning activities fit for various domains and contexts. Limiting the focus on social interaction patterns may be rather ideal for domains where communication and collaboration are of critical importance. In a nutshell the approach is meant to help an educational game designer find game patterns to build a game either from scratch or out of a non-game based learning scenario. In lifelong learning, especially well designed games that follow a logical and structured lead, help to avoid irregularities that can quickly become frustrating. Finding and choosing game patterns on their own is not always required, sometimes a course designer may instantly see options for introducing a game pattern to an existing activity, but browsing through the wealth of existing game patterns is likely to spark some creativity and help with future applications of the technique. Indeed the application of game patterns in innovative ways, and their utilization in contexts beyond their examples, and the documentation of such applications should be considered valuable input to the ongoing research of game patterns and their application in a learning context. Furthermore, a pattern-based approach helps to streamline the design process of a learning game from a software engineering point of
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view: conceptual patterns can be transformed into actual software modules more easily, while heeding important requirements like reusability and interoperability (Mor et al., 2006), (Winters & Mor, 2009).
6. FUTURE RESEARCH DIRECTIONS The current number of projects with relation to game based learning indicates that the approach is taken seriously not only from the scientific community but also the decision makers. At the EU level various projects have been initiated, such as the ELEKTRA and the 80 Days projects (Elektra Project, 2009; 80 Days, 2009) as well as a number of sub-initiatives in various other EU projects on Technology Enhanced Learning. There is potential for making many discoveries, not only due to the many variables to be accounted for research-wise, but also due to the fast-paced innovation development of the gaming sector as such. New technologies continuously emerge, and the state-of-the-art proves there is an exciting field to master and participate in. While our approach of systematic pattern-based learning game design might seem somewhat schoolmasterly, it is noteworthy that the discovery and addition of new patterns or pattern structures is greatly welcomed, and will be of highest relevance to the research field, enabling a sound and up-to-date matching with current educational practices. It is also our own aim to continuously contribute new mappings between education and game design and experiment with those, which will be our focus in upcoming publications following this chapter. New dimensions of gaming are introduced every year with advances in the video gaming industry. Recent interesting additions which greatly enhanced options for educational content development would be the significant advances in human interface technologies that are heralded by the arrival and popularity of the Nintendo Wii. With the advent of Microsoft’s “Project Natal”
Game Based Lifelong Learning
(Project Natal, 2010), all the major gaming consoles offer a level of motion interaction, of which Project Natal is currently the most advanced. Its capabilities of capturing a person’s movements as a wireframe have the potential of enabling a whole host of new learning capabilities, for example learning activities that involve teaching movements and interactions, with minimal material costs or overhead. Another aspect of learning games obviously comes with mobile and location-based technologies, breaking with the prejudice of games being screen-locked. The most powerful 3D graphic engine is in fact the real world which is available without programming effort. By augmenting the real world with mobile indicators, not only do we save time and effort in creation of learning games, but we also enhance the overall experience of the digital world. It was inconceivable only a few years ago that playing computer games actually could ever involve physical exercise, fresh air, sunshine and socializing; factors we find especially attractive for use in lifelong learning scenarios. Even though these new possibilities may be highly stimulating with respect to the spawning of new types of learning games, at the same time it is of interest to explore and keep track of which types of learning can best be supported by which mode of gaming. This is why the game pattern approach is important and needs to be constantly updated and extended, as well as continuously used for validation of “gaming/learning” hypotheses that emerge at the same pace as new game technologies.
7. CONCLUSION Our intention in this chapter was to argue for the use of gaming patterns in lifelong learning and to demonstrate some simple ways in which a normal learning activity can be made into a game. By illustrating the latest research efforts in the field we hope to have laid the cornerstone for
a meaningful approach that will benefit learners and course developers from all different fields. While we have to acknowledge that institutional learning cannot only be “fun”, it is clear that games can help motivate learners and turn tedious or seemingly mundane tasks into enjoyable ones. By introducing the gaming aspect it is possible to tap into that raw human emotion, the competitive (or team collaborative) spirit, where those previously frowned upon or “useless” activities come to life with the urgency and joy with which our survival instincts react to competition. Although this might be in conflict with the element of collaboration, forming competing teams, it bears the same challenges as a purely singular competition while additionally providing the element of social responsibility which elevates the urgency even more. Self-motivation is a critical factor especially in the more informal setting of lifelong learning where “ownership of learning” is of high importance to the learner. Considering that many lifelong learning programs being offered are distance based, or mixed on-site and off-site, self-motivation must never be underestimated as a factor for success of such a program. In such a scenario a learner might be well advised to try out novel ways of learning, such as game-based learning, because many of the classical factors contributing to appreciation and quality such as social factors offered by peers, direct guidance, and motivation and support by a teacher are missing. Indeed it may be argued that adding game patterns, especially competitive ones, to existing learning activities in a distance learning context, could affect the social experience of learners in a positive way as they would cause students to gather within the learning environment at the same time, greatly increasing the chances of them socializing, which the extreme popularity of social network sites in the last few years has demonstrated is a very agreeable online activity. It is our conclusion that facilitators and developers eager to increase the rate of success in
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their lifelong learning programs, would do well to examine options for including a game-based activity, or to redevelop an under-appreciated activity by adding some gaming elements. A possible starting point to do this is the pattern-based design approach we presented here. The key criteria to judge in game-based enhancements are appreciation and learning outcome. By using a systematic approach it can be traced which pattern or which combination of patterns have measurably positive effects – a method which will continuously improve the research society’s understanding of why, how and when learning games work, or not.
8. REFERENCES Becker, K. (2005). How are games educational? Learning theories embodied in games. DiGRA, Vancouver, Canada. Becker, K. (2007). Instructional ethology: Reverse engineering for serious design of educational games. In Proceedings of the 2007 conference on Future Play (pp. 121-128). Toronto, Canada: ACM. doi: 10.1145/1328202.1328224 Bjork, S., & Holopainen, J. (2004). Patterns in game design (1st ed.). Charles River Media. Boyle, T. (2003). Design principles for authoring dynamic, reusable learning objects. Australian Journal of Educational Technology, 19(1), 46–58. Brusilovsky, P., & Maybury, M. T. (2002). From adaptive hypermedia to the adaptive Web. [New York, NY: ACM.]. Communications of the ACM, 45(5), 30–33. doi:10.1145/506218.506239 Burghardt, G. (2005). The genesis of animal play. Integrative and Comparative Biology, 45(5), 953–953. Coffield, F. (2000). The necessity of informal learning. Bristol, UK: The Policy Press.
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80Days. (n.d.). Retrieved July 9, 2009, from http:// www.eightydays.eu/ Elektra Project. (n.d.). Retrieved July 9, 2009, from http://www.elektra-project.org/ Foreman, J. (2004). Game-based learning: How to delight and instruct in the 21st century. EDUCAUSE Review, 11. Gagné, R. M. (1965). The conditions of learning. New York, NY: Holt, Rinehart and Winston. Grohé, M. (2009). Der Weg ist das Spiel. GEE, 4, 58–61. Hawes, A. (1996). Jungle gyms: The evolution of animal play. National Zoo FONZ. Retrieved July 7, 2009, from http://nationalzoo.si.edu/Publications/ZooGoer/1996/1/junglegyms.cfm Heinich, R., Molenda, M., Russell, J. D., & Smaldino, S. E. (1999). Instructional media and technologies for learning. Upper Saddle River, NJ: Merrill Prentice Hall. Illich, I. (2000). Deschooling society. Marion Boyars Publishers Ltd. Johnson, L., Levine, A., & Smith, R. (2009). 2009 horizon report. Austin, TX: The New Media Consortium. Retrieved March 9, 2009, from http:// wp.nmc.org/horizon2009/ Keegan, D. (1994). Otto Peters on distance education: The industrialization of teaching and learning. Routledge Studies in Distance Education. Keller, J. M. (1983). Motivational design of instruction. Instructional design theories and models: An overview of their current status. (pp. 386-434). Kiili, K. (2005). Digital game-based learning: Towards an experiential gaming model. The Internet and Higher Education, 8(1), 13–24..doi:10.1016/j. iheduc.2004.12.001
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Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Maddock, S. (1999). Challenging women: Gender, culture and organization. London, UK: Sage. Mor, Y., Winters, N., Cerulli, M., & Björk, S. (2006). Literature review on the use of games in mathematical learning, part I: Design. Nadolski, R. J., Hummel, H. G. K., van den Brink, H. J., Hoefakker, R. E., Slootmaker, A., Kurvers, H. J., & Storm, J. (2008). EMERGO: A methodology and toolkit for developing serious games in higher education. Simulation & Gaming, 39(3), 338–352..doi:10.1177/1046878108319278 O’Neil, H. F., Wainess, R., & Baker, E. L. (2005). Classification of learning outcomes: Evidence from the computer games literature. Curriculum Journal, 16(4), 455–474. doi:10.1080/09585170500384529 Peña-López, I., & Naeve, A. (2007). Web 2.0 and education seminar (V): Ambjörn Naeve: The human Semantic Web – increasing the global organizational performance of Humanity Inc. ICTology. Retrieved July 6, 2009, from http:// ictlogy.net/20071018-web-20-and-educationseminar-v-ambjorn-naeve-the-human-semanticweb-increasing-the-global-organizational-performance-of-humanity-inc/ Piggyback. (2007). Halo 3: The official strategy guide. Prima Games. Pivec, M., & Dziabenko, O. (2004). Game-based learning in universities and lifelong learning: UniGame: Social skills and knowledge training. A game concept. Journal of Universal Computer Science, 10(1), 14–26.
Project Natal. (n.d.). Retrieved April 9, 2010, from http://xbox.wikia.com/wiki/Project_Natal Retalis, S., Georgiakakis, P., & Dimitriadis, Y. (2006). Eliciting design patterns for e-learning systems. Computer Science Education, 16, 105–118.. doi:10.1080/08993400600773323 Robinson, P. (1998). Strategies for designing instruction in Web-based computer conferencing environments. Roy, A., Evans, C., & Sharples, M. (2009). Mobile game based learning for peer educators of males having sex with males community in India. Paper accepted for publication at the mLearn 2009 conference. Schank, R. C., & Cleary, C. (1995). Engines for education. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Sigurðarson, S. (2008). Moodle.org: Personal glossary & quizzer. Retrieved December 8, 2009, from http://moodle.org/mod/data/view. php?d=13&rid=1185 Wilson, S., Liber, O., Beauvoir, P., Milligan, C., Johnson, M., & Sharples, P. (2006). Personal learning environments: Challenging the dominant design of educational systems. Proceedings of the 2nd International Workshop on Learner-Oriented Knowledge Management and KM-Oriented Learning, in conjunction with EC-TEL 2006 (pp. 67-76), Crete, Greece. Winters, N., & Mor, Y. (2009). Dealing with abstraction: Case study generalisation as a method for eliciting design patterns. Computers in Human Behavior, 25(5), 1079–1088. doi:10.1016/j. chb.2009.01.007
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Chapter 15
Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context Ni Chang Indiana University South Bend, USA
ABSTRACT Most students favor feedback from an instructor to their assignments, as it informs them of whether or not their finished work is on the right track. However, solely awarding a grade or score to students’ assignments, even if with very brief comments, does not enable students to know how to improve their work. This type of assessment tends to gauge students’ work for evaluation, making students become competitive with peers for grades or scores (Harlen & Crick, 2003; Nicol & MacFarlane-Dick, 2006). Deviating from this orientation, this chapter converges on the importance of constructive and beneficial feedback through the assessment process in an e-text-based context, for the purpose of lifelong learning. The chapter also discusses teacher immediacy cues, which are intended to assist the reader in developing a better understanding of how feedback should be provided to students. To aid the reader to walk along this path, this chapter also provides suggested practical strategies. DOI: 10.4018/978-1-61520-983-5.ch015
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context
INTRODUCTION Most students favor feedback from an instructor to their assignments that informs them of whether or not their finished work is on the right track, as a student commented, “I like being made sure that I am on the right direction.” However, sometimes, if students receive such feedback, “This is a good point,” they may not be appreciative of the instructor’s effort. One student explained, “I would like to know why this is a good point.” If a grade or score that a student receives denotes less satisfactory work or if a general comment provided by an instructor reads, “This is not up to the expectation” without any or much justification for it, a certain level of frustration may quickly arise. The student would like to know, “Why did my work fail to meet the expectations.” Some students may even feel resentful when an instructor’s comment is judgmental: “This is incorrect.” These types of poor feedback are not conducive to student learning, as there is a lack of specific information as to why the work is insufficiently accomplished and how to reach the criteria or objectives of an assignment. Likewise, a grade or score does not enable students to know how to improve their work either, as it tends to assess students’ work for evaluation, making students become competitive with peers for grades or scores (Harlen & Crick, 2003; Nicol & MacFarlane-Dick). Assessing student’s work is for the purpose of teaching and learning as written assignments or learning activities afford students avenues to develop a coherent understanding of course content. An instructor’s feedback to students’ submitted assignments is instructional coaching through assessment, assisting students in a thoughtful transfer of knowledge and skills to new situations (Chang & Petersen, 2006). Thus, assessment should be part of the instructional process and be treated as formative assessment, but not as summative assessment all the time. “Formative assessment, effectively implemented, can do as much or more to improve
student achievement than any of the most powerful instructional interventions, intensive reading instruction, one-on-one tutoring, and the like” (Russo & Bensen, 2005, p. 276). The result of formative assessment is usually represented by feedback. In Sadler’s (1989) point of view, effective feedback not only needs to be closely associated to performance standards, but also provides appropriate strategies for students to make improvement. For example, with respect to a misconception expressed by a student, an instructor’s comment that is perceptive while being constructive may be, “Jennie, I understand this point here. However, focusing solely on “fun” activities is inappropriate. Please review Chapter 6 again along with our guidelines for this assignment to enhance your understanding. Then, please write back to me with your renewed understanding of this concept. Please feel free to contact me if an additional explanation is needed. Thank you!” This feedback along with many other comments given to the student’s work is detailed, which conveys teacher immediacy cues, such as calling the student by her first name at the beginning of a comment, acknowledging the understanding of the point made, explaining clearly why rereading is necessary, providing constructive suggestions for subsequent improvement, and showing care about the student learning by letting the student know that the instructor is willing to assist her. This type of formative assessment and feedback not only facilitates this student learning, but also shows the responsibility of the instructor. In addition, with the use of computer-mediated text-based communication system, the instructor’s response can be delivered quickly to the student, which benefits student learning, since timely feedback constructively reinforces acquired concepts (Berge, 1995). With the reflection and rethinking through the process of revision requested by the instructor, the student’s understanding of the concept improves. The appropriate scaffolding may motivate the student to explore and read more in
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an effort to enhance knowledge. The instructor’s provision of formative feedback may also be partially responsible for developing the skills of self-motivation, self-monitoring, self-regulation, and self-management, which are some of the essential components for lifelong learning. Taken all the aforementioned elements into consideration, it is vital to analyzing and discussing the power of the joint forces—human-to-human interaction (a two-way interaction by means of formative feedback with the use of teacher immediacy behaviors) and the computer-mediated communication tool (a computer-mediated text-based communication system) in student learning and in leading to possible lifelong learning. The purpose of this book chapter, therefore, is to help those responsible for text-based instruction to learn how to provide useful formative feedback for learners that will lead to lifelong learning. To reach the end, this chapter places the focus on the following sections: (1) Importance of Formative Assessment and Feedback, (2) Feedback with High Teacher Immediacy Cues to Positive Affect for Learning and Lifelong Learning, (3) Limitations of Computer Technology in the Provision of Feedback with Nonverbal Teacher Immediacy Behaviors (4) Significance of Blended Instruction: Joint Forces of Technology and Human-to-Human Interaction, (5) Practical Strategies and Experiences Concerning Formative Feedback and Lifelong Learning, (6) Conclusion, and (7) Implications.
IMPORTANCE OF FORMATIVE ASSESSMENT AND FEEDBACK Formative assessment refers to opportunities teachers build to assess how students are learning. The information gained from the assessment is then utilized to make beneficial changes in instruction or to adapt teaching and learning to meet students’ needs (Boston, 2002). According to Black and Wiliam (1998), the analysis of student accomplished assignments, such as in-class
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performances, class activities, homework, and tests, is part of formative assessment. As a result of the analysis and in response to an action taken by a student (the student’s completed work), an instructor generates educational feedback benefiting student learning (Mason & Bruning, 1999). Specifically, the purpose of transmitting feedback messages to students is to inform them of where their academic work stands in line with the standards or criteria of an assignment and of why and how improvements are necessary for subsequent successful learning (Chang, 2009a, 2009c; Nicol & MacFarlane-Dick, 2006). Goals/standards/criteria of performance or of an assignment affect how students are going to complete a task and interpret feedback by an instructor. Students’ engagement in completing a given assignment is for the purpose of deepening understanding based on the objectives set in advance. Unfortunately, it is quite common that students may have trouble understanding objectives and goals fully before an assignment is completed. One of the reasons for this phenomenon is that making assessment goals or criteria explicit can be a difficult undertaking (Rust, Price, & O’Donovan, 2003). “Statements of expected standards, curriculum objectives or learning outcomes are generally insufficient to convey the richness of meaning that is wrapped up in them” (Yorke, 2003, p. 408) and “[t]hey are often ‘tacit’ and unarticulated in the mind of the teacher” (Nicol & MacFarlane, 2006, p. 206) owing to their complex and multidimensional nature. To bring about effective performances, an instructor is obligated to aid students in establishing a good understanding of goals, which comes, in part, from the assessment process by offering constructive feedback. Grading students’ work, in this sense, should not equate with simply assigning a grade or a numeral score. Providing feedback should depart from a discretely dominant one-way street of information message delivery by an instructor or that of information interpretation by students, but should
Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context
serve as an avenue of dynamically interactive communication between an instructor and students. Interactive communication between an instructor and students, as a matter of fact, promotes students’ self-assessment and self-reflection, which in turn enables them to participate in deciphering goals/objectives as well as feedback information actively. This intrapersonal process of communication has been interpreted by Nicol and MacFarlane-Dick (2006) as the internal interactive thinking process essential for learning success (see Nicol & MacFarlane-Dick for the related model in detail). The explication of this internal interactive thinking process that follows has been modified to a certain extent in order to underline the discussion underway. The internal interactive thinking process starts with an academic assignment set by an instructor in class. The assignment triggers various levels of self-regulatory processes in students and expects them to draw on their prior knowledge as well as their motivational beliefs to make a decision as to how to tackle the task. In the decision making process, students have to construct a personal interpretation not only of what the assignment means, but also of the expectations of the instructor. If students decide only to pass the task or if they cannot apprehend given expectations, the discrepancy between the goals set by the instructor and that by the students themselves would not be marginal. The motivation for the students to complete the task might be low as well. Since feedback offers information regarding how the students’ current state of learning and performance relates to goals stipulated by an instructor (Nicol & MacFarlaneDick, 2006), students spawn an internal thinking process, after receiving external feedback from an instructor. This thinking process propels them to make another decision, where they need to decide whether they would keep current progress as is or if some kind of change is necessary. Feedback offered by an instructor in this fashion conveys assessment results formatively. The resulting internal thinking process will have an effect on
student learning cognitively, affectively, and motivationally (Nicol & MacFarlane-Dick) and is a by-product of purposeful engagement in a task (Nicol & MacFarlane-Dick, 2006). Evaluative results, such as scores or grades, do not lead students to give attention to purposeful learning, but are high-stakes summative assessment (Nicol & MacFarlane-Dick, 2006), as they only inform students that they need to be competitive with peers for grades (Harlen & Crick, 2003, Nicol & MacFarlane-Dick). They cannot assist students in judging their own performance, making self-corrections, and directing them to know how to proceed next (Freeman & Lewis, 1998). Conversely, feedback that is informational and explanatory is low-stakes assessment (Nicol and MacFarlane-Dick), which helps students see what deficiencies exist in relation to criteria. This form of formative assessment can simultaneously galvanize students’ thinking and provide students with opportunities to learn, relearn, or revisit concepts studied. Gibbs and Simpson (2004) posited that if students received feedback regularly or quite often as formative assessment, they were enthusiastic about using external feedback for improvements. Thus, the power of internal interactive thought process would gradually help students learn how to better monitor and self-regulate their progress. Self-regulated learning refers to the students’ capability to regulate their thinking. Not only can this capability motivate students to learn intrinsically, but also promotes them to assess their own performance against goals (Duffy & Holmboe, 2006; Nicol & Mcfarlane-Dick; Sadler, 1989). Provided that this positive cycle of the quest for learning is repetitive, students would maintain the momentum of a passionate spirit of learning throughout their lives (Boud, 2000). It has also been affirmed by Bolhuis (2002), Duffy and Holmboe, Krathworhl et al., in Mottet et al., and Nicol and Mcfarlane-Dick that self-direction, selfmonitoring, self-regulation, and self-motivation are some of the foundational elements in lifelong learning.
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FEEDBACK WITH HIGH TEACHER IMMEDIACY CUES TO POSITIVE AFFECT FOR LEARNING TO LIFELONG LEARNING There are two dimensions in terms of teacher immediacy behaviors: verbal and nonverbal immediacy behaviors. Verbal immediacy is primarily concerned with ways an instructor talks and lectures in the traditional classroom. In contrast, nonverbal immediacy involves behaviors that are only observable to receivers or communicators. Utilizing these immediacy behaviors allow students to feel close to their instructor (Andersen & Andersen, 1982) and to perceive their instructor to be competent, knowledgeable, and trustworthy (Christophel, 1990). Mottet, Parker-Raley, Beebe, and Cunningham (2007) examined how instructor nonverbal immediacy (e.g., relational messages or relational immediacy) and his or her course-load demands (e.g. task messages) affected student perceptions of their positive affective learning. According to Mottet et al., the purpose of verbal messages is to convey the content of messages relating to tasks or “the task dimension of communication” while nonverbal messages are intended to convey the emotional meanings in messages or “the relational dimension of communication” (p. 149).This study concluded that highly immediate instructors were those who were highly relationally skilled, who were able to cultivate students’ positive affect for learning even though there might be more workload than those who were not highly immediate although the workload demand was low. Apparently, there is a close link between teacher immediacy behaviors and students’ affect for learning (Chesebro & McCroskey, 2001; Christophel, 1990; Christophel & Gorham, 1995; Fredericksen, Pickett, Shea, Pelz, & Swan, 2000; Frymier & Hourser, 2000; Mottet et al., 2007;
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Mehrabian, 1971, 1981; Ni & Aust, 2008). Affective learning is about students’ attitudes, beliefs, and values toward subject matters (Kearney, 1994; McCroskey, Richmond, & McCroskey, 2006). It is of emotional responses toward course content, a course instructor, or a learning environment (Andersen, 1979; Witt, Wheeless, & Allen, 2004). In other words, affect for learning is concerned with a feeling or an emotional state of acceptance or rejection (Krathwohl, Bloom, & Masia, 1964 in Mottet et al., 2007). It impacts a student’s learning (Rodriguez, Plax, & Kearney, 1996; Salovey & Sluyter, 1997) and affects his or her motivation to learn (Christensen & Menzel, 1998; Frymier & Houser, 2000). Students’ affective learning is the result of the communicative interaction between an instructor and students. Rodriguez et al. (1996) interpreted that student affect for learning was the central causal mediator between teacher immediacy behaviors and students’ cognitive learning. When students have positive affect for learning, it is probable that they are able to internalize their learning, recall information easily (Kelley & Gorham, 1988), and transfer what they have learned from the classroom to their personal and work lives. These learners also tend to seek and acquire knowledge continuously outside the formal structure of a course (Krathwohl et al., 1964 in Mottet et al., 2007). They can thus become self-motivated, self-regulated, self-management, and self-monitoring, which are some of the fundamental components to sustain their desire to learn throughout lives (Bolhuis, 2002; Duffy & Holmboe, 2006; Krathworhl et al., in Mottet et al.; Nicol & Mcfarlane-Dick, 2006). The aforementioned analysis has clearly illustrated the intimate association between formative feedback with the use of teacher immediacy cues and students’ affect for learning, which leads to students’ possible sense of lifelong learning.
Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context
LIMITATIONS OF COMPUTER TECHNOLOGY IN THE PROVISION OF FEEDBACK WITH NONVERBAL TEACHER IMMEDIACY BEHAVIORS Teacher immediacy behaviors, in essence, are central to student learning in a conventional classroom. However, they appear to have no perceptible place to live in a text-based learning environment owing to a lack of multisensory capacities. Despite the fact that there are various forms available as a course delivery means, such as videoconferencing, Web 2.0, Web cam, and other multi-media course delivery management systems, visual technological message delivery mechanisms may not be accessible, without difficulty, to some students as well as to faculty members who intend to communicate. These communicational tools may be costly and relatively complex to install and/or to use. Thus, in the technological oriented and contemporary communication world, one of the most convenient and popular means to communicate with one another is through a computer-mediated text-based context, such as email, discussion forums, and other types of communication media that are text-only. These computer-mediated communication tools would allow communicators to deliver messages quickly and effortlessly. However, in an e-textbased communication context, the perceptibility is hardly existent, leaving many nonverbal immediacy behaviors indecipherable due to its lack of capacity for either sight or sound. Written communication thus becomes dominant and nonverbal cues become nonfunctional and invisible to those who are communicating. Hershkowitz-Coore (2003) characterized words appearing on a screen this way, “Words on a... screen are ‘dead.’ With no voice inflection, no eye contact, and no body language to help a reader grasp the words in the way the writer intends, the burden is on the writer to write so the reader cannot misunderstand” (p. 49). To put it differently, readers at different sites must solely rely on words written on screens to
understand messages sent by the writers. Sometimes, without hearing the tone of voice and viewing nonverbal immediacy cues, on-screen reading could easily engender a misunderstanding or misinterpretation of the message read. This led LaRose and Whitten (2000) to argue that only a face-to-face instruction could allow a continual flow of immediacy cues. A text-based learning environment thus would make the passing of such a physiological arousal difficult. Teaching and learning in an e-text-based context have apparently posed quite a few challenges for instructors (Gallien & Oomen-Early, 2008; Picciano, 2002), as the Internet medium seemingly places a ceiling on the utilization of teacher immediacy cues and generates a negative impact on student affective and cognitive learning (LaRose & Whitten, 2000). On account of the aforementioned, it is imperative to discuss how to make up the limitations of the computer-mediated text-based learning context for the benefit of student successful learning.
SIGNIFICANCE OF BLENDED INSTRUCTION: JOINT FORCES OF TECHNOLOGY AND HUMANTO-HUMAN INTERACTION With the use of joint forces of technology and human-to-human interaction, Norin and Wall (2009) found that the student retention rate jumped from 8% to 20.5% after switching from full-scale web-based learning to blended learning. Included in computer-mediated learning are face-to-face meetings that have obviously won the students’ hearts, because the added means of the interactions became opportunities for students and an instructor to communicate with one another. A student happily commented, “This was an excellent learning experience. When I needed any help or information, it was always readily available.” This student’s comment demonstrated that due to the reassurance of the availability of the instructor and other students through the face-to-face ses-
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sions, students’ attitudes and beliefs about learning changed from the negative to the positive. This implies that learning is emotionally oriented and closely associated with the social presence of other people (Rippe, 2009), especially, that of a course instructor, as he or she plays various roles critical to student learning (Chang, 2007, 2009b). Weaver and Albion (2005) endorsed the significance of the instructor’s social presence after working with 60 students over a semester using a sequential exploratory design. They found that the instructor’s social presence positively correlated with the students’ level of motivation. The diminished presence of the instructor during the course might be the major factor for students’ increased feelings of distance rather than closeness to their instructor. This study shows when the level of perceived social presence inclines downward as a semester progresses, the degree of students’ motivation to learn degrades. This means although technology enables the ability of human mind to create an illusion that would manufacture feelings of connectedness, even though an instructor and students are distantly separated from one another, high social presence must be mediated and maintained by the effort exerted by an e-course instructor (Chang, 2007, 2009a, 2009b, 2009c) with teacher immediacy behaviors. Ni and Aust (2008) studied how verbal immediacy behaviors affected students’ online postings through a computer-mediated text-based communication system and found that teacher verbal immediacy was a significant predictor of learners’ posting frequency on the online discussion forums. This led them to extrapolate that it was the inviting messages written by the instructor that motivated the students to take academic actions to post their perceptions on discussion boards frequently. The instructor’s act with the use of teacher immediacy behaviors simultaneously also cultivated students’ self-regulation and self-management; the skills are essential to lead to learning success and to a sense of lifelong learning (Boud, 2000; Bolhuis,
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2002; Duffy & Holmboe, 2006; Krathworhl et al., in Mottet et al., 2007; Nicol & McfarlaneDick, 2006). Even though Ni and Aust (2008) intended to examine the role that teacher verbal immediacy behaviors played in the desires of students’ online postings, the inviting messages sent by the instructor seemed to work as relational messages (relational immediacy) (Mottet et al., 2007). It is apparent that teacher nonverbal immediacy led to the development of the students’ positive affect for learning, which drove their active online postings. Thus, obvious is the power of the joint forces: technology (i.e., computer-mediated text-based context) and human-to-human interaction (i.e., formative feedback with teacher immediacy cues) in student learning. Technology allows expeditious delivery of an instructor’s responses or messages as well as unlimited space for communicators to express and explain ideas. Yet, it is the desirable and effective human-to-human interaction initiated and maintained by an instructor with the use of teacher immediacy behaviors that can motivate students to learn and to consciously regulate, monitor, manage, and control their own learning, leading to lifelong learning (Boud, 2000; Bolhuis, 2002; Duffy & Holmboe, 2006; Krathworhl et al., in Mottet et al., 2007; Nicol & Mcfarlane-Dick, 2006).
PRACTICAL STRATEGIES AND EXPERIENCES CONCERNING FORMATIVE FEEDBACK AND LIFELONG LEARNING Given the merits of the joint forces addressed above, it is the time to focus on the concern of how to deliver and implement formative feedback in a computer-mediated text-based communication context. Yet, before practical strategies are shared, let’s first look at some questions and answers. When students receive returned writing assignments, what are they looking for right away?
Formative Assessment and Feedback with Teacher Immediacy Behaviors in an E-Text-Based Context
Yes. You’ve got it. A grade or score assigned to the paper. What do most students normally do afterwards? Yes. You are right again. They stuff the paper into their backpacks or folders. What do you think students would do if the paper contains comments? About the answer to this question, let’s see what Bart (2009) said, You pore over students’writing assignments, adding what you feel are insightful and encouraging comments throughout each paper. Comments you hope your students will take to heart and use to improve their writing next time around. Then you return the papers and the students quickly look at the grade and stuff the paper into their backpacks.. perhaps mumbling something under their breath as they do... This same scenario plays out with each subsequent writing assignment, and each side gets more frustrated. The instructor can’t understand why he sees the same types of mistakes over and over again, and then students resign themselves to the fact that “I’m just not a good writer. Bart’s (2009) narration seems to indicate that students do not like feedback given by the instructor. However, it is not the case. Part of Gallien and Oomen-Early’s (2008) study focused on what type of feedback was preferred by students. They randomly divided 71 participants into two different treatment groups with one receiving personalized feedback and the other collective feedback. The researchers surveyed the online students to solicit their perceptions of course satisfaction and perceived connectedness to the instructor with two different types of methods of feedback offering: the individualized and collective. It was found that the students receiving individualized feedback from their instructor not only performed better, but also felt more satisfied with the course than those given collective feedback. The finding of this study is consistent with Chang’s (2009c) study, which reported that the students felt very satisfied with the way that the instructor provided them
with individualized feedback. Specifically, 66% of 30 participants selected 5 (strongly preferred) while 36% chose 4 (preferred) when answering the survey question: “From your perspective, do you support the online feedback provided by Dr. Chang to your own submissions of assignments? Please choose a numeral on the following scale with 1 indicating the least support and 5 the most support.” The following present a few justifications for their feelings: “The comments are helpful for learning the materials.” “They are helpful in correcting and improving work, which also increases the amount of learning acquired.” “All of the feedback promotes further learning and thinking. They help me have a better understanding of what I did wrong and right, and how I can improve.” “I like to know when I have a good idea and the teacher notices it. Feedback also prompts me to expand my observations, because sometimes I don’t include enough details.” “I appreciated how timely the feedback was given because of it being online.” Students also acknowledged that the effective formative feedback enabled them to become the owner of their own learning: “It [feedback] all allowed me to be responsible for my own learning.” “Dr. Chang’s online feedback was always prompt and encouraging to do better as a student to further our education.” With the understanding of the instructor’s effort in providing formative feedback to assist students in constructing deep levels of knowledge, now let’s look at practical strategies that are derived from my research, literature review, and experiences and that may help those who are interested in and responsible for developing, delivering, and implementing formative assessment and feedback with the inclusion of teacher immediacy behaviors in a computer-mediated text-based communication system. Please note that, for a better understanding, each suggested strategy will then be given a brief explanation.
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PRACTICAL STRATEGIES An instructor needs to: 1. be committed to student learning success 2. explain explicitly the objectives or criteria of an assignment and make sure that there is not much confusion about them prior to the students’ implementation of the work. 3. consider breaking up the whole into parts in order to lessen an otherwise overwhelming workload. 4. review students’ work with New Comment and Track Changes features. 5. start a comment with a student’s first name. 6. tone down when writing constructive or corrective feedback. 7. remember asking questions as often as possible to provoke students’ thinking. 8. explain a concept, point, or other concerned matter in a cohesive, logical, and well-organized manner with straightforward language succinctly. 9. care about student learning by grabbing teachable moments. 10. be responsible for student learning by pointing out mistakes of grammar and spelling. 11. accept a means or format freely selected by a student among several choices that an instructor has introduced at the beginning of a semester. 12. encourage relearning by allowing students to engage in revisions, but refrain oneself from granting a grade or score during a first review. 13. provide scaffolding by allowing students to revise their work further based on feedback even after a grade or score is awarded in the second review. Yet, remember readjusting the previously assigned grade or score if re-revision is fully or partially accepted. 14. include general feedback at the bottom of a student’s paper, which will also be used as a chief email message sent to the
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student along with the attachment containing the instructor’s complete review notes. Remember writing caring and inviting notes while reviewing the paper or while writing the general note. 15. be thoughtful, sensitive, responsive, and creative when it comes to offer feedback with the use of teacher immediacy cues. The following provide the brief explanation of each of the practical strategies given above. 1. Making a commitment to the success of student learning is the first and foremost thing for an instructor to undertake. Without it, communication with students is unlikely to be effective. 2. Without a clear understanding of standards/ objectives/goals of an assignment, students would evaluate their learning based on the goals set by themselves rather than by an instructor. When feedback makes little or no sense to them, students tend to view it as puzzling, senseless, or “incorrect” (Nicol & MacFarlane-Dick, p. 206). These judgments consequently engender in students a sensation of frustration; they would probably refuse to go along with the instructor’s constructive feedback for improvement. 3. When taking advantage of the unique function of a computer -mediated text-based learning system that operates 24 hours a day seven days a week, an instructor needs to be flexible while being firm. That is, the deadline of an assignment submission should be made clear while an early submission prior to the deadline is permitted. Thus, an instructor does not have to wait until a physical class meeting to collect and/or return students’ completed assignments or until a batch of the work is submitted all at once. Breaking a whole load of work into pieces may help diminish the overwhelmed feelings while the instructor is able to produce quality feedback through thoughtful and reflective formative assessment. 4. New Comment and Track Changes are two of the useful features by Microsoft® Word. An instructor can use New Comment to offer detailed
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comments by highlighting the point targeted (Figures 1 and 2), as it helps students see what needs to be improved. This method is distinctly supported by the students: “Having the remarks on the side left no question to what you were talking about. It prevents questions.” “It was good to have the comments at exactly where the comment was regarding the assignment instead of at just the bottom of the assignment.” In revising their work, students can freely select either or both of the features to respond to the instructor’s questions or comments (Figures 2 and 3). 5. While using New Comment and/or Track Changes to offer feedback, please remember to first type in the student’s first name, at least, for the first few messages as this act represents one of the suggested immediacy cues (see Figures 1, 2, and 3). 6. Be mindful of how to write a good intentioned message. Due to a lack of capacity for either sight or sound, misinterpretation may occur easily. For example, I once write, “.... Read the criterion #4 again please,” because of which I instantly received a furious reaction from the student. My puzzlement led me to solicit the reason behind the students’ unhappy feeling, which helped me obtain the answer: “The way you wrote shows
that you are angry at me. Otherwise, you should have written, “Please read the criterion #4 again.” 7. How to pose questions that may provoke student thinking should be seriously considered and implemented, as asking questions has been recognized and acknowledged by many researchers as one of the teacher immediacy behaviors to narrow the psychological gap between instructor and student. For that some of my students appeared to agree with the researchers: “I really like when I receive feedback that makes me rethink what I wrote and also gets me to analyze something more.” “She gave very thorough feedback that made me think about what I was doing and learning.” 8. According to Titsworth (2001), highly immediate teachers can make lectures clear by their explicit organization, which is conducive to students’ notetaking, leading them to retain more information than without. By the same token, to help students understand the point made by an instructor, writing should be logically and cohesively structured in a succinct manner while it also needs to be easily understood. A student said, “I liked that feedback was straight forward.” 9. Being committed to and responsible for student learning, an instructor should capture teachable moments to extend students’ knowledge
Figure 1. New comment feature used by an instructor to provide a comment and by a student to respond to the instructor’s comment
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Figure 2. An instructor uses new comment and track changes features when providing formative feedback and a student uses new comments and track changes when responding to the instructor’s comments
acquisition and should not let go a misconception that does not fall into the requirement of an assignment. If the misconception is not acted on, an instructor’s silence may become a misguided signal to the student that the misconception is acceptable. However, pointing out and making a comment on a misconception should not affect
the student’s grade negatively, but be treated as a learning opportunity for the student to enhance his or her understanding of the content being studied (Figure 4). 10. It has been a subtle issue of whether or not grammar and spelling mistakes should be pretermitted or allowed to pass unmentioned during the
Figure 3. Student’s use of track changes to correct grammar and mechanical errors and the new comment to respond to the instructor’s comment
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Figure 4. Teachable moment outside the peripheral of criteria
review of a student assignment that is other than English composition writing. Picking out the mistakes easily upset some students. For example, one student wrote, “It overwhelmed me sometimes and shut me down.” Students’ displeased feelings like this are, sometimes, reflected through course evaluation as well. However, without having the student correct those mistakes, but awarding a grade or score that only matches the content, such a practice may be harmful, at least, to my students, who are going to become teachers after graduation. Indeed, noting mechanical mistakes is an added workload on both sides, which requires extra time. And yet, this endeavor represents an instructor’s
care for student learning, one of the immediacy cues recognized by many researchers. For that, such a practice is not absolutely disliked by all the students. Some students stated, “It allowed me to be responsible for my own learning. I can learn from (and correct) my mistakes.” “I continue needing help in spelling and grammar.” I believe this issue still needs to be further discussed. 11. At the beginning of a semester, an instructor explains and/or introduces how students can revise and send back their revised work to an instructor and then post them on the Internet for students to use. Figures 3, 5, 6, 7, and 8 show a few examples.
Figure 5. The instructor’s asking questions with the use of new comment feature and the student’s answers with the use of track changes
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Figure 6. Using bold font to respond to an instructor’s comment without using either track changes or new comment
Figure 7. Using new comment to respond to an instructor’s comment
Figure 8. Using an email dialogue format to respond to an instructor’s comment
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12. When receiving feedback from the instructor for the first time, a student cannot find a final grade or score on the paper. Instead, the student can notice the points to be deducted, if there is no relevant modification or correction to that specific area in accordance with the rubrics (see Figure 3). This practice is intended to encourage the student to control his or her own learning by making a decision of whether or not a revision will be pursued. This same process would also engage students in rethinking and reflecting on what has been done. The student’s engagement in revisions affords the student with learning opportunities as pointed out by a student, “It is nice how you gave us lots of opportunities to revise and to correct our work. You helped us to learn more.” If a student decides not to make any revision and if revisions made do not meet the criteria, the points deducted at various spots across the paper will be added up as a final grade of that paper or project (see #14 below). 13. After receiving the revised work, I re-read the work and award a grade or score. Yet, students can still revise their work after the reception of the graded work from me. Followed by my review of a second revision made by a student’s effort, a newly readjusted grade or score is awarded, which is based on the revision status. If the revision fully meets the criteria, it would receive 60% of the missing points. For example, if an assignment is awarded 80/100 in the first review, the readjusted grade would then be 92 if this second revision is up to the expectations. The formula is 20 (missing points) X.60% (the maximum % of the missing points one can earn provided that the work is in good quality) = 12 (newly gained points as a result of the revision), which is then added to 80 points (the original points earned during the instructor’s second review) = 92. This grading policy works for the students’ ensuing revisions, which may end when there is neither further revision request from an instructor nor any revision response from a student received by the instructor.
14. I write a general summary at the end of each paper, reporting briefly general positive and negative aspects of the paper and making improvement suggestions. Then, I copy and paste this summary onto an email message to the student (Figures 9 and 10). Meanwhile, I make sure that my specific and detailed feedback is enclosed as an attachment as well. Additionally, I always remember concluding the general note with words, such as “Good Night,” or “Have a nice day!” which was recognized by a student, “I like getting the overall comments—the big picture of our assignments. I enjoyed how polite your closing remarks always were—“good night” etc.” 15. Generally, there are no clear-cookie-cutter approaches in communicating with students when it comes to provide formative feedback. My experiences have taught me that an instructor needs to be sensitive and responsive to students’ written message while being creative in composing the messages that are likely to be read by students.
CONCLUSION Currently, there is a growing demand from students for feedback on their performance (Siew, 2002). Today’s students not only need more individualized support from instructors, but also have higher expectations than before (Peat & Franklin, 2002). Students expect both timely and quality feedback (MacDonald &Y Twining, 2002), which a computer-mediated asynchronous text-based system alone cannot make happen. A text-based learning context appears to have more cons than pros when it comes to the provision of feedback with the use of teacher immediacy cues, especially nonverbal teacher immediacy behaviors. However, by no means does it indicate that an instructor must completely abandon the idea. Creatively making the impossible possible should be the goal on that an instructor sets with mind and heart. In fact, there are already several ways to reach the end, such as calling students
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Figure 9. Three rounds of general feedback along with the final grade and the adjusted grade after completion of the second revision from the student
Figure 10. General feedback appearing at the bottom of a student’s work and used as the main text of an email to the student
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by their first name, asking questions, sharing examples and useful ideas, clearly giving explanations to misconceptions, offering perceptive statements, substantively praising for good work done, linking to objectives or criteria, providing constructive suggestions, making feedback that drives students to think deeply and to engage in extensive thoughts, making compassionate commentary, and making comments inviting. All these cues are intended to build a sense of psychological closeness. The instructor’s online scaffolding with the use of teacher immediacy behaviors not only heightens the visibility of the instructor, but also promotes students’ positive learning attitude and their positive affect for learning, which are vital to the establishment of the students’ sense of lifelong learning.
IMPLICATIONS In regard to the use of teacher immediacy cues in a text-based learning environment, an instructor needs to pay attention to ways of balancing between the task-oriented and the relationshiporiented messages. Teaching and learning are not simply about delivering information and completing assigned tasks. Students’ state of emotion in learning should also be heeded equally. Missing either one would make student learning undesirable or unsuccessful. In academic settings, specific targets, criteria, and standards help define learning goals. An instructor’s feedback to students’ assignments helps clarify and explain the goals. Measures to encourage students to take advantage of feedback the instructor offers should be properly and clearly included in course design and implementation (Wolsey, 2008). Reading students’ assignments and responding to their work requires a lot of time. Time commitment is one of the major variables in accomplishing successful e-teaching. Additionally, the instructor should possess certain levels of writing
skills, typing abilities, and computer capabilities (Hara & King, 2000). Gallien and Oomen-Early (2008) reported that 70% of the students’ participants indicated that family and work commitments might be factors outside of the course that would affect their overall performance. In addition, there are a variety of characteristics of students participating in online learning. To effectively expand student’s thresholds for higher expectations of assignments, feedback provided by an instructor should be useful, helpful, constructive, encouraging, inviting, convincing, intriguing, and motivating. Marsh (2001) argued that when students perceived the course assignments to be useful and worth their time even if they were rigorous, the students’ evaluations of teaching effectiveness was higher than those that required obligatory work or that were intended to keep them busy. Therefore, in providing written feedback, it is imperative for an instructor to take into account how to respond to students’ assignments so that students’ attention would be paid to actual comments rather than grades or scores. To this end, higher education institutions should meticulously devise a comprehensive plan that can aptly prepares them before they formally interact with e-students via computer-mediated text-based communication systems. DeBard and Guidera (2000) sharply pointed out that poorly trained and underprepared faculty members communicating with online students had resulted in criticism made by both instructors and students. One of the reasons for this might be that “It’s rare that an instructor can make the leap to the online classroom without a few missteps... it is wise to make sure instructors are familiar with the best practices of online teaching” (Faculty Focus, 2009). Best practices not only solely emphasize strategic approaches to online teaching, but also must touch on how to build a learning environment that supports and promotes students’ positive affect for learning.
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Since this chapter focuses on formative assessment and feedback by means of teacher immediacy behaviors in an e-text-based learning environment, plans for the relevant training are suggested as follows, (1) Effort should be made to increase the awareness of instructors that e-text-only communication tools are essentially omnipresent, such as email and discussion board/forums. These communication tools are relatively cost-effective and comparatively popular among many people, instructors and students alike. Therefore, it is judicious to adopt such a tool to communicate with students and to facilitate their learning. And learning how to use this kind of tool for communication is not at all a hard undertaking. (2) With the technological skills mastered, instructors and others ought to be helped to understand theoretically why it is imperative to engage in formative assessment and feedback, what teacher immediacy behaviors refer to, why teacher immediacy cues are needed in the provision of formative feedback, and how these behaviors are associated with higher-order affective learning as well as with lifelong learning. (3) Specific practical strategies of how to communicate with students in a computer-mediated text-based learning context can be introduced and discussed at this stage. Emphases should be placed on how to deliver formative feedback with New Comment and Track Changes, where and why comments should be posted, how to encourage students to engage in revisions, and other related concerns and matters. (4) To conclude the training session, it should be made explicitly clear that an instructor’s committed mind and heart, flexibility, creativity, sensitivity, and responsivity are crucial in effectively working with students in an e-textbased learning environment.
FUTURE RESEARCH DIRECTION Although technology is becoming ubiquitous in the higher education classroom today, research
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examining the interaction between instructor and students in an online learning environment is still in its infancy (Gallien & Oomen-Early, 2008). Notwithstanding that much has been addressed concerning communication with students via online discussion, little has been done on the subject of formative assessment and feedback with the use of teacher immediacy cues in a text-based classroom environment. Engaging in a research study in this genre would be helpful for instructors since students come to learn with a certain expectation of their instructor’s competent communication behaviors (Witt & Schrodt, 2006). In communicating with students in writing, Myers and Bryant (2004) only mentioned sending and receiving emails and grading assignments. There was no mention as to how and in what way assignments were graded, let alone what types of feedback had been offered to students’ assignments. In the provision of students’ performance feedback, at times, an instructor’s good intention is not always appreciated by students, often leaving an ill-fated effect on both the instructor and student. The instructor views the practice as a waste of time if the student becomes upset about it. The student, on the other hand, is frustrated, as he or she perceives the feedback to be unhelpful. Hence, it might be beneficial to study how to provide formative assessment feedback that not only represents relational messages, but also embodies constructive and useful guidance.
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About the Contributors
George Magoulas is Professor of Computer Science and Director of Postgraduate Studies at the Department of Computer Science and Information Systems, Birkbeck College, University of London. He is also a member of the London Knowledge Lab (www.lkl.ac.uk). The main thrust of his work is in the development of intelligent techniques for learner modelling and personalised learning environments. He was an invited speaker at the Adaptive Hypermedia and Adaptive Web-Based Systems 2003 (AH2003), part of the 9th International Conference on User Modeling (UM’2003), and keynote speaker at the International e-Learning Forum 2007, Tokyo, Japan. He has secured 1.2m+ GBP funding from UK funding agencies as Principal Investigator or co-Investigator of research and development projects that have attracted a total of 3.5m+ GBP in competitive external funding. Within these and other projects he has worked closely with partners active in various areas of information technology, Technology Enhanced Learning (TEL) and lifelong learning, and was involved in collaborations with public bodies, such as the Linking London Lifelong Learning Network, the UCAS and the learndirect. He was co-editor of the books: “Adaptable and Adaptive Hypermedia Systems” (2005) and “Advances in Web-based Education: Personalized Learning Environments” (2006). George is currently working with practising teachers to research, and co-construct, an interactive Learning Design Support Environment (LDSE) to scaffold teachers’ decision-making from basic planning to creative TEL design. He is leading the research on the intelligent components of the LDSE and the design and implementation of the interactive environment. Through an iterative research-design process the LDSE team hopes to address some important challenges in the area of learning design and build the means by which the teaching community can collaborate further on how best to deploy TEL. *** Ioannis Hatzilygeroudis received the Diploma in Electrical Engineering from the National Technical University of Athens (NTUA), Greece, in 1979 and the MSc and PhD degrees from the University of Nottingham, UK, in 1989 and 1992 respectively. He is currently an Assistant Professor at the Department of Computer Engineering & Informatics, University of Patras, Greece. His research interests include intelligent/expert systems, knowledge representation, knowledge engineering and intelligent educational systems. He has published over 70 research papers in international journals, edited volumes and proceedings of conferences and workshops. He is the Associate Editor-in-Chief of the Inetrnational Journal of Artificial Intelligence Tools (IJAIT) and member of the Editorial Boards of the International Journal of Hybrid Intelligent Systems (IJHIS) and the International Journal of Web-Based Communities (IJWBC). He was the PC chair of IEEE ICTAI 2007 and Co-General Chair of IEEE ICTAI 2008. He
About the Contributors
is currently in the Steering Committee of IEEE ICTAI and member of IEEE, ACM, AAAI and EETN (Hellenic AI Society). Jim Prentzas received the Diploma in Computer Engineering & Inofrmatics and the PhD degree from the Department of Computer Engineering & Informatics, University of Patras, Greece, in 1997 and 2002 respectively. He is currently an assistant professor in the Department of Education Sciences in Pre-School Age, Democritus University of Thrace, Greece. His main research interests include artificial intelligence, intelligent tutoring systems, knowledge representation, web applications and geographical information systems. He has published over 35 papers in international journals, edited volumes, and proceedings of conferences and workshops. He has participated in a number of National and European research projects. Jesus G. Boticario is Ph.D. in Physical Sciences at UNED (prize awarded as the most outstanding PhD, 1994). B.Sc. in Computer Science, School of Computer Science at Madrid Polytechnic University. Associate Professor at UNED’s CSS. Invited speaker at National and International conferences. He has published over 200 research articles. Participation in 18 R&D funded projects (Spain, USA, EU). Head of aDeNu research group (https://adenu.ia.uned.es). Current scientific coordinator in a European and National funded projects. Program Committee member of National and International Conferences. He has co-chaired International Workshops in the areas of User Modeling and Accessibility. He is reviewer of research projects and international journals. Hold several positions at UNED in the ICT area (e.g. Innovation and Technological Development Vice-principal). dotLRN Board of Directors member. He is an expert counselor at the UNED’s Center for Supporting Students with Disabilities. He is the coordinator of an educational innovation network “Accessibility and Functional Diversity” (UNED). Olga C. Santos is the R&D Technical Manager of aDeNu group at the UNED (Spain). Her research interests focuses on taking into account adaptation and accessibility requirements, both at design and runtime, to provide open educational user-centred services to ubiquitously and dynamically adapt to the evolving needs in the e-learning context. For this, user-centred design methods are being applied. The focus is put on applying recommendation strategies to provide adaptive navigation support in existing learning management systems. She has participated in 12 international and national research projects, published over 80 research articles in conferences and journals, been member of the program committee of several conferences and journals and co-chaired workshops on user modelling and accessibility. For details, see aDeNu group website. Achilles Kameas is an Assistant Professor with the Hellenic Open University, where he teaches software design and engineering. He is also the Director of Research Unit 3 (http://daisy.cti.gr) at the Research Academic Computer Technology Institute (CTI). Since 2007 he is Deputy Dean of the School of Sciences and Technology (SST) of the Hellenic Open University and Director of the Educational Content, Methodologies and Technologies Lab (http://eeyem.eap.gr). He has participated as researcher in several EU R&D projects, such as e-Gadgets, Astra, Plants, Social and Atraco (he was the scientific coordinator of the first two). He has published over 100 journal articles, conference papers and book chapters and co-edited more than five books. His current research interests include ubiquitous computing systems, eLearning systems and engineering of ontologies and ontology matching. He is a voting
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About the Contributors
member of IEEE, ACM, Technical Chamber of Greece and Hellenic Society for the Application of ICT in Education. Panagiotis Pintelas is a Professor of Computer science at the Dept. of Mathematics, Univ. of Patras. His recent research interests include computer technologies in education concentrating in areas such as CAL/CAI/CBT/ITS, Open and Distance Learning (ODL) and Artificial Intelligence techniques in education. He has participated or directed a large number of National and EU research and development projects in these areas. He is directing the “Computers and Applications Laboratory” of the Mathematics Department as well as the “Educational Software Development Laboratory” while he is in charge of the postgraduate program on “Computers and new technologies in education”. He is a member of the British Computer Society, member of the Association for the Development of Computer-Based Instructional Systems (ADCIS) and ex-president of the Hellenic Association of scientists for Information and Communication Technologies in Education. Christos Rodosthenous is a postgraduate student at the Department of Mathematics of University of Patras. He currently pursues a Phd in Artificial Intelligence and Multiagent systems. He holds a degree in Mathematics, specialized in Computational Mathematics and Informatics. He also holds a MSc. in Computational Mathematics and Educational Software Design. He is an IT Engineer and a software developer for Research Academic Computer Technology Institute (R.A.C.T.I). He is involved in several EU and National funded projects which include developing and customizing web based platforms and e-learning systems. He also has experience in teaching programming languages and software design in vocational training institutes. His current research interests involve artificial intelligence, data mining, e-learning standards and multiagent systems design. Alexandra I. Cristea is associate professor in the Department of Computer Science at the University of Warwick. She is the Director of Graduate Studies in Computer Science, and Coordinator of the Intelligent and Adaptive Systems group. Her research interests include adaptive educational systems, authoring of adaptive hypermedia, user modelling, intelligent tutoring systems, semantic web technologies, concept mapping, and artificial intelligence. She has published more than 150 papers, given invited talks, and has been principal investigator or co-investigator in many international projects on these subjects. She has her PhD from the University of Electro-Communications, Tokyo, Japan, and two MSc degrees, one in Computer Science and one in Economical Engineering, from the ‘Politehnica’ University of Bucharest, Romania. She is executive peer reviewer of the IEEE LTTF Education Technology and Society Journal and she is co-editor of the Advanced Technologies and Learning Journal. She is an IEEE and IEEE CS member. Fawaz Ghali, MSc, is a PhD student in the Department of Computer Science at the University of Warwick. His main research interests are: Authoring of Adaptive Hypermedia, Adaptive Hypermedia, Semantic Web, Ontology Design and Engineering, Social Web, Web 2.0, E-Learning 2.0. Fawaz was awarded his master degree with Distinction in Internet Engineering and Web Management from the University of Greenwich, UK. His work has already leaded to several papers at international conferences, such as Adaptive Hypermedia, EC-TEL 2008, AIED 2009, ICWL 2009, ICALT 2009. Fawaz is also Sun Certified Java Programmer (SCJP) since 2004.
415
About the Contributors
Mike Joy is an associate professor in the Department of Computer Science at the University of Warwick and a member of the Intelligent and Adaptive Systems research group. His research interests include educational technology, computer science education, object-oriented programming, and Internet software, and he is the author or co-author of over 100 papers. Dr Joy has MSc degree in Mathematics from Cambridge University and in Post-Compulsory Education from the University of Warwick, a PhD in Computer Science from the University of East Anglia, and has both CEng and CSci status. He is a Chartered Fellow of the British Computer Society and a Fellow of the Higher Education Academy. Felix Mödritscher received an MSc in Computer Technics (2002) and a PhD in Computer Science (2007) from Graz University of Technology. Since November 2003, he has been participating in the research projects AdeLE (nationally funded), APOSDLE (IST FP6/IP), iCamp (IST FP6/STREP), and ROLE (IST FP7/IP). In the scope of these projects, he has been dealing with personalisation and adaptive behaviour in e-learning systems, infrastructures and services for technology-enhanced learning, and personal learning environments and learning communities. Currently, he is a postdoctoral fellow at the Institute for Information Systems and New Media of the Vienna University of Economics and Business. Steinn Sigurdarson is a Software Developer at the Centre for Learning Sciences and Technologies (CELSTEC) at the Open University of the Netherlands and works on interoperability and social software tools for learning. Steinn has a background in data integration projects in the corporate sector with a focus on open-source developments. Since 2006 he has been concentrating on social software development, among others in the Minerva-funded project Covcell and the EU IST funded project iCamp. Fridolin Wild, M.A., is researching within the EU IST funded project ROLE and LTfLL leading the work package on language technology infrastructures for lifelong learning and dealing with applying social software tools for technology-enhanced learning. Fridolin is the treasurer of the European Association of Technology-Enhanced Learning (EATEL). He works as a research associate at the Knowledge Media Institute of the Open University of the United Kingdom. Eugenijus Kurilovas, PhD is Senior Research Scientist in Institute of Mathematics and Informatics, Associate Professor in Vilnius Gediminas Technical University, the Head of International Networks Department of the Centre for Information Technologies in Education of the Ministry of Education and Science of Lithuania. He is a member of Program Committees, editor, and reviewer of 11 international journals and conferences on technology enhanced learning (incl. 5 ISI Committees and 3 IEEE Committees). He has been a leader of work packages and Lithuanian team in more than 10 EU-funded international R&D projects, as well as the expert evaluator in PHARE, Leonardo da Vinci programs and European Regional Development Fund. He has published over 40 scientific papers (abstracted / referenced in Thomson ISI Web of Science, ISI Web of Knowledge, INSPEC, IEEE, Springer, etc.), as well as the chapters in the books and the monograph “Information Technologies in Education: Experience and Analysis”. Valentina Dagiene is the Head of the Department of Informatics Methodology in Institute of Mathematics and Informatics and Professor in Vilnius University. She is the vice chair of the Technical Committee of IFIP for Education, the member of the Group for Informatics in Secondary Education and for Research of IFIP, vice chair of IFIP Special Group on Digital Literacy, the member of the European
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About the Contributors
Logo Scientific Committee, International Committee of Olympiads in Informatics, an Editor-in-Chief of international journals „Informatics in Education“, and “Olympiads in Informatics”. She is the member of ISSEP Program Committee and the invited speaker in ISSEP 2010: http://issep2010.org/invitedspeakers. html. She has published over 100 scientific papers (incl. ISI Web of Science), the monograph and more than 50 books in the field of Informatics and ICT in secondary education. Ellen Francine Barbosa received her BS in Computer Science in 1995 from the State University of Londrina (UEL/Brazil), her MS in Software Engineering in 1998 from the University of São Paulo (ICMC-USP/Brazil), and her DS in Software Engineering in 2004, also from ICMC-USP. During her DS, she was a visitor scholar at Georgia Institute of Technology (GATECH/USA) and at University of Florida (UFL/USA). Since 2005, she is a professor at ICMC-USP. Her research interests are related to CS and SE education and training, collaborative and distance learning, knowledge management, software engineering, software testing and validation, and software quality. A list of her main publications can be found on Lattes Platform (http://lattes.cnpq.br). José Carlos Maldonado received his BS in Electrical Engineering/Electronics in 1978 from the University of São Paulo (USP/Brazil), his MS in Telecommunications/Digital Systems in 1983 from the National Space Research Institute (INPE/Brazil), his DS degree in Electrical Engineering/Automation and Control in 1991 from the University of Campinas (UNICAMP/Brazil), and his Post-Doctoral at Purdue University (USA) in 1995-1996. He worked at INPE from 1979 up to 1985 as a researcher. In 1985 he joined the University of São Paulo (ICMC-USP) where he is currently Full Professor and Vice-Director. He is also the President of the Brazilian Computer Society. His main research interests are related to software engineering, software testing and validation, experimental software engineering, software quality, SE education and training, web systems, and reactive and embedded systems. A list of his main publications can be found on Lattes Platform (http://lattes.cnpq.br). Maria Kordaki holds a PhD in Educational Technology, a Masters in Education, a diploma in civil engineering and a Bachelor in Mathematics from the University of Patras, Greece. Recently, she has been elected as an assistant professor of Educational Technology in the Department of Cultural Technology and Communication, University of the Aegean, Greece. During the last decade she also served as collaborative professor in the Hellenic Open University as well as in the Dept of Computer Engineering and Informatics and in the department of Mathematics, University of Patras, Greece. Her research focuses on the use of social and constructivist learning theories in the design of educational software and technology supported learning–design, towards critical and creative thinking within various educational settings including: paper and pencil, online, blended, collaborative and technology-based learning. She also serves in the editorial board of various international and national Conferences, and Journals. Finally, she has published over 120 scientific papers and 9 books. Haris Siempos is a computer engineer and post graduate student in Computer Engineering and Informatics Department at University of Patras in Greece. His research interests include educational technology and Web 2.0 technologies. He has published articles in Greek and international conferences in the areas of online collaborative learning, learning design and LAMS. He has designed and conducted workshops on the use of open source technologies in education. He is a member of Technical Chamber of Greece.
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About the Contributors
Thanasis Daradoumis holds a PhD in Computer Science from the Polytechnic University of Catalonia-Spain, a Masters in Computer Science from the University of Illinois, and a Bachelors in Mathematics from the University of Thessaloniki-Greece. Currently he is assistant professor at the Department of Cultural Technology and Communication, University of the Aegean, Greece and he is also collaborating professor at the department of Computer Sciences, Multimedia and Telecommunications at the Open University of Catalonia, Spain, and at the Hellenic Open University. His research focuses on e-learning and network technologies, Web-based instruction and evaluation, distributed and adaptive learning, CSCL, CSCW, interaction analysis, and grid technologies. He is co-director of the DPCS (Distributed Parallel and Collaborative Systems) Research Laboratory [http://dpcs.uoc.es/]. Finally, he has written over 100 papers. Ron Baecker is Professor of Computer Science, Bell Universities Laboratories Chair in HumanComputer Interaction, and founder, founding director, chief scientist, and interim director of the Knowledge Media Design Institute at the University of Toronto. Professor Baecker is an active researcher, lecturer, and consultant on human-computer interaction and user interface design, user support, software visualization, multimedia, computer-supported cooperative work and learning, the Internet, entrepreneurship and strategic planning in the software industry, and the role of information technology in business. He has received his B.Sc., M.Sc., and Ph.D. from the Massachusetts Institute of Technology. Jeremy Birnholtz is an assistant professor in the Department of Communication and the Faculty of Computing and Information Science at Cornell University. He also holds an appointment in the Department of Computer Science at the University of Toronto. Jeremy received his Ph. D. from the School of Information at the University of Michigan in 2005, and is interested in improving the usefulness and usability of collaboration technologies through a focus on human attention, and in the intersections of social science theory and technology design. He uses both laboratory and field methods and has conducted field research in a diverse range of settings. Rhys Causey is a Software Engineering graduate from the University of Toronto, with expertise in Human-Computer Interaction (HCI) and software design. Rhys worked on a number of research projects during his time in University, and had his work published in conference proceedings from CSCW (Computer Supported Collaborative Work) 2006 and HCI International 2007. This work also led to an honorable mention in the CRA (Computing Research Association) 2007 Outstanding Undergraduate Awards, which recognizes undergraduate students in North American universities who show outstanding potential in an area of computing research. In 2009, Rhys joined ePresence, a software start-up that spun out of the University of Toronto, as CTO and Lead Developer. Simone Laughton is an Instructional Technology Liaison Librarian at the University of Toronto Mississauga Library. In her current work, she explores the use of different technologies to assist with online information literacy competency assessments and the effective use of technology to support teaching and learning. Prior to becoming a Librarian, she worked with community-based non-profit organizations, private for-profit businesses, and with federal, provincial, and municipal governments on diverse topics such as economic development (industrial sector), health and social planning, multiculturalism, and long-term care. In September 2005, she began volunteering with the Canadian Advisory Committee
418
About the Contributors
of the ISO/IEC JTC1 SC36. She is project Co-Editor for ISO/IEC 24763 Conceptual Reference Model for Competencies and Related Objects, and participates in several SC36 Working Groups. Some of the SC36 issues she focuses on include: competencies, eAssessment, and quality. Clarissa Mak is completing her Honours Bachelor of Science with a major in Human Biology, and minors in French and Sociology at the University of Toronto. She will be pursuing an interdisciplinary career in nursing and public health research. Kelly Rankin has been a student and staff member at the University of Toronto for the past ten years. Her professional role has been in project management, in particular the ePresence Project - an interactive webcasting and archiving system for eLearning. She was also instrumental in establishing the university’s first Open Source Consortium and helped launch a software start-up company based on ePresence technology. Currently, Kelly is pursuing her interests in communications and communications technology as a member of the University of Toronto’s Strategic Communications team. Marco Ronchetti is associate professor in Computer Science at the Università di Trento, Italy. After some years spent in Physics dealing with computer simulation of liquid, amorphous and quasicrystalline many body systems, his interests drifted to Computer Science. He has been working in the web and software engineering areas, and in the most recent years on e-learning, videos and on extraction of semantic information from text. In these areas he has approximately 70 international publications. He has been coordinator of the EASTWEB EU project. Presently he is director of the Master in technologies for e-Government at the Università di Trento. Sergio Gutierrez-Santos obtained his Engineering degree and his PhD from University Carlos III of Madrid. His PhD thesis combined artificial intelligence and IMS Learning Design to achieve adaptive and auto-organised sequencings of learning material. He has worked in several projects with Spanish, British and European funding. Among other topics, he has published several papers related to sequencing of learning material, adaptive hypermedia, and the use of learning standards to achieve interoperability. His other research interests include artificial intelligence techniques, from Bayesian inference to genetic algorithms and swarm intelligence; and their application to engineering education, exploratory learning environments, user modelling and games. Since 2007 he works as a Research Officer at the London Knowledge Lab (Birkbeck College). Joni Dunlap is an associate professor of instructional design and technology at the University of Colorado Denver. An award-winning educator, her teaching and research interests focus on the use of sociocultural approaches to enhance adult learners’ development and experience in postsecondary settings. For over 12 years, she has directed, designed, delivered and facilitated distance and eLearning educational opportunities for a variety of audiences. Joni is also the university’s Faculty Fellow for Teaching, working through the Center for Faculty Development to help online and on-campus faculty enhance their teaching practice. Patrick Lowenthal is an Academic Technology Coordinator at CU Online at the University of Colorado Denver. He is also a doctoral student studying instructional design and technology in the School of
419
About the Contributors
Education and Human Development at the University of Colorado Denver. His research interests focus on instructional communication, with a specific focus on social and teaching presence, in online and face-to-face environments. In addition, he often writes about issues and problems of practice related to post-secondary education. He has an MA in Instructional Design and Technology as well as an MA in the Academic Study of Religion. Patrick has been teaching and designing instruction since 1998. Desirée Joosten-ten Brinke is an assistant professor at the Centre for Learning Sciences and Technologies of the Open University of the Netherlands. Her research and implementation activities are in the context of lifelong learning and focus on (formative and summative) testing and assessment in competence-based education. The emphasis of her work is on Assessment of prior learning and computer based assessment. In this domain, she is a project manager and trainer. She is member of the editorial staff of a Dutch professional journal on testing, and she represents the Open University in a national committee on APL Wendy Kicken is an assistant professor at the Centre for Learning Sciences and Technologies of the Open University of the Netherlands. Her research focuses on the use of development portfolios and supervision meetings to improve learners’ self-directed learning skills. Furthermore, she is involved in several European projects in which she contributes to the development of workshops, training programs and curricula to enhance self-directed learning, competence development, and communication skills. Peter B. Sloep is a professor of Technology Enhanced Learning at the Centre for Learning Sciences and Technologies of the Open University of the Netherlands. He directs its Research and Technology Development Programme on Learning Networks, online social networks that have been designed with non-formal education in mind. His research focuses on learning and professional development as well as innovation enhancement in networked, online environments. Of particular interest are the social affordances and their technical implementation in services that make learning in such environments effective, efficient, enjoyable and sustainable. Dr. Sloep is a reviewer of several journals on Technology Enhanced Learning. He also chairs the Dutch standards body’s (NEN) committee on learning technologies. Marcel Van der Klink is an associate professor at the Centre for Learning Sciences and Technologies of the Open University of the Netherlands. His consultancy and research projects focus on various aspects of lifelong learning, such as implementation of new forms of assessment, workplace learning, continuous professional development (of teaching staff) and e-learning. He co-edited several books and special issues regarding abovementioned themes and he is a member of the editorial board of Impact, a journal of applied research in workplace e-learning. He is involved in national and international journals in the domain of human resource development. Sebastian Kelle is researcher at the Centre for Learning Sciences and Technologies (CELSTEC) at the Open University of the Netherlands. His research focus is Digital Game-Based Learning, which is also the topic of his PhD thesis. He received the Master’s degree in Computer Science from Freiburg University in 2006 and has worked on an assistant researcher position at Vienna University of Economics at the Institute for Information Systems, before joining CELSTEC in 2008. Internationally he is also
420
About the Contributors
involved in EU-ICOPER and GRAPPLE projects and has a function as advisory board member in the EU-ROLE project. Steinn E. Sigurdarson is a Software Developer at the Centre for Learning Sciences and Technologies (CELSTEC) at the Open University of the Netherlands, working on interoperability and social software tools for learning. Steinn has a background in data integration projects in the corporate sector with a focus on open-source developments. Since 2006 he has been concentrating on social software development, among others in the Minerva-funded project Covcell and the EU IST funded project iCamp. Marcus Specht is professor for Advanced Learning Technologies at Centre for Learning Sciences and Technologies at the Open University of the Netherlands. He is currently involved in several national and international research projects on competence based life long learning, personalized information support and contextualized learning. He received his Diploma in Psychology in 1995 and a Dissertation from the University of Trier in 1998 on adaptive information technology. From 1998 until 2001 he worked as senior researcher at the GMD research center on HCI and mobile information technology. From 2001 he headed the department “Mobile Knowledge” at the Fraunhofer Institute for Applied Information Technology (FIT). From 2005 he was Associated Professor at the Open University of the Netherlands and working on competence based education, learning content engineering and management, and personalization for learning. Currently he is working on Mobile and Contextualized Learning Technologies, Learning Network Services, and Social and Immersive Media for Learning. Wim Westera is a professor of technology-enhanced learning at the Centre for Learning Sciences and Technologies of the Open University of the Netherlands (CELSTEC). He is specialised in media for learning and teaching. He holds a PhD in physics and mathematics, and has worked in educational media development and educational technology since the mid 1980’s. From 2000 he lead a group at CELSTEC of around 60 instructional designers, media developers and IT developers, being responsible for researching and applying new educational methods, models and technologies in distance education and blended settings. In 2008 he initiated the Learning Media Research Programme at CELSTEC, which specialises on social media for learning as well as mobile media and gaming for learning. Ni Chang is Associate Professor of Education at Indiana University South Bend. She received her master’s and doctoral degrees from Peabody College of Vanderbilt University. She has over 14 years of web-based and hybrid teaching experiences. Over the course of these years, she has presented at conferences at regional, national and international levels and published book chapters, and refereed journal articles. The areas of her research interests include online and hybrid/web-enhanced teaching and learning, quality feedback provision online and offline, and curriculum in higher education.
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422
Index
A
B
accessible lifelong learning (ALL) 31 adaptation 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 44, 45, 51, 54, 55, 56 Adaptation 2.0 93 Adaptation Model (AM) 96, 103, 109 adaptive authoring 102, 109, 111 Adaptive Educational Hypermedia Systems (AEHSs) 1, 2, 4, 22, 23 adaptive e-learning 90, 92, 93 Adaptive Hypermedia (AH) 1, 4, 8, 23, 24, 26, 90, 92, 93, 94, 95, 96, 97, 100, 101, 110, 111, 113, 114, 121, 122, 123, 124, 131, 132, 146 Adaptive Hypermedia Application Model (AHAM) 94, 95, 96, 97, 123 adaptive learning 33, 52, 127, 129, 131, 133, 147, 148 adaptive sequencing 271, 272, 286, 288 adaptive sequencing definition 272 adaptive sequencing techniques 272 affective learning 350, 354, 366, 369 Artificial Intelligence (AI) 1, 2, 3, 4, 10, 11, 14, 15, 16, 21, 22, 23, 24, 25, 26, 27, 28 artificial intelligence in education 1 artificial intelligence in e-learning 1 Assessment of Prior Learning (APL) 316, 317, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 331, 332 asynchronous blended learning 64 asynchronous education 253 asynchronous e-learning 65 asynchronous video-lecture 258 Automated Speech Recognition (ASR) 264
background knowledge 34, 39 blended courses 212 blended instruction 350 blended learning 64 bottom-up 325 buggy model 6, 17
C cognitive development 337 collaborative authoring 99, 101, 114, 115, 121 collaborative development 175, 176, 177, 178, 202, 203, 205, 207 collaborative learning 59, 62, 85, 87, 88, 126, 146, 175, 176, 186, 199, 203, 204, 205, 207, 212, 213, 214, 215, 216, 217, 218, 228, 229, 230, 231, 232 collaborative learning design 212, 213, 216, 229, 231 collaborative learning events 218 collaborative patterns 212, 214, 216 communication mechanisms 272 Computer-Assisted Instruction (CAI) 2, 3 computer learning environment 62 computer-mediated communication (CMC) 62 computer-mediated learning 355 computer-mediated text-based communication 351, 352, 356, 357, 365 computer-mediated text-based learning 355, 358, 366 Computer Science (CS) 2, 22, 23, 24, 25, 26, 28, 212, 213, 216, 218, 230, 232 computer-supported collaborative learning (CSCL) 62, 85, 87
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Index
Concept Mapping Theory 181 constructivism 126 constructivist instructional theories 129 Constructivist Learning Environment (CLE) 178, 179 content modeling 175, 177, 178, 180, 181, 183, 186, 188, 191, 192, 204, 208 cooperative work 260, 264 corresponding patents 1 course designers 113, 114, 116 course management 59, 60, 85, 86, 87, 88 cultural differences 293 cultural knowledge 300, 301
D deeper knowledge 317, 318 Definição de Aplicações Hipermídia na Educação (DAPHNE) 178, 181 design for all 29, 36, 37 design patterns 212, 213, 214, 215, 222, 223, 230 digital divide 318 digital library 127 digital revolution 316 digital technologies 272 digital world 272 Diplek 59, 60, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83 Diplek platform 67, 80, 83 distance education 234, 253, 254, 258, 266, 267, 269 distance learning 9, 22, 23, 24, 25, 31, 32, 34, 43, 44, 47, 51, 59, 60, 62, 253, 258, 259, 265, 266, 267, 347 distance technology 260 diversity issues 29, 30, 31, 32, 48, 52 document co-creation 292, 294, 303, 304, 306, 307, 309 domain concept 104, 105, 106, 114, 116 domain knowledge 2, 4, 5, 6, 10, 11, 12, 13, 14, 15, 20, 21 Domain Model (DM) 96, 97, 103, 104, 105, 109
E educational content 5, 6, 8, 20, 59, 61, 62
educational domain 322 educational goal 292, 293 Educational Hyperdocuments Design Method (EHDM) 178, 181 educational landscape 176 Educational Modeling Language (EML) 178, 180, 181, 209 educational module 175, 176, 177, 178, 182, 183, 184, 185, 186, 188, 189, 191, 193, 195, 196, 198, 203, 204, 207, 208 educational multimedia 253 educational opportunities 235 educational settings 236 educational standards 29, 30, 31, 39 educational technology 253 education research 253 e-learning 1, 2, 3, 21, 22, 24, 25, 28-42, 51, 54, 55, 56, 57, 59, 64, 65, 83-93, 100, 113, 118, 122, 150, 151, 157, 159, 162, 163, 171, 172, 212-215, 222, 231, 232, 235, 236, 253-256, 264, 267, 269, 270 e-learning 2.0 92, 93, 122 e-learning environments 214, 215, 232 e-learning patents 1 e-learning system 150, 162, 163 e-learning technologies 235 end-user development 127, 128, 129, 132, 133, 135, 138, 148 e-text-based context 350, 355 evaluation criteria 150, 151, 152, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172 existing knowledge 317, 318 expert module 2 external feedback 353
F Facebook 292, 294, 300, 301, 302, 308, 310, 311, 312, 314, 315 face-to-face classrooms 237 face-to-face instruction 355 face-to-face lecture 258 formal learning 319, 320, 325, 326, 332 formative assessment 351, 352, 353, 357, 358, 366, 367, 369 friendship-driven 302
423
Index
fuzzy values 6
G game based learning 337, 338, 342, 344, 346, 347, 348, 349 game design 337, 338, 343, 344, 345, 346, 348 gaming environment 344 gaming scenario 342 generic adaptive hypermedia 131, 146 Generic Adaptivity Model (GAM) 94 Goal and Constraints Model (GM) 96, 97, 103, 105, 106, 109 Goldsmiths Adaptive Hypermedia Model (GAHM) 94 Greek Higher Education 64 Greek Universities’ Network (GUnet) 64 group-based authoring 100, 112
H heterogeneous teams 175, 176, 183, 185 Higher Education (HE) 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 47, 49, 50, 51, 57, 64, 84, 85, 86, 87, 293, 312 Higher Education institutions 29, 30, 34, 35, 36, 37, 39, 40, 41, 47, 50 homo habilis 126 human capital 319 human-to-human interaction 352, 355, 356 Hypermedia Design Model (HDM) 178, 179
I immediacy behaviors 350, 352, 354, 355, 356, 357, 359, 363, 365, 366, 367 immediacy cues 350, 351, 354, 355, 356, 358, 359, 361, 363, 365, 366 IMS Access For All (IMS-A4A) 37 IMS Common Cartridge (IMS CC) 274, 286 IMS Learning Design (IMS-LD) 37, 38, 39, 42, 45, 47, 271, 272, 274, 280, 281, 282, 284, 285, 286, 287, 288 IMS Question and Test Interoperability (IMS QTI) 286 IMS Simple Sequencing (IMS-SS) 274, 280, 286, 287 individualized coaching 350
424
Information and Communication Technologies (ICT) 29, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 50, 51, 53, 58 in-room awareness 240 in-room awareness display 240 instructional coaching 351 instructional design 129, 130, 131, 133, 145, 175, 177, 178, 179, 180, 183, 184, 186, 191, 202, 208, 209, 211, 279, 337, 343 instructional designer 129, 130 instructional design theories 129, 130, 145 instructional interventions 351 instructional practice 272 instructor-student awareness 246 instructor-student interaction 236, 238, 244 Intelligent Educational Systems (IESs) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25 intelligent tutoring systems (ITS) 1, 2, 3, 4, 14, 15, 20, 22, 23, 24, 26, 27, 28, 273, 274, 280, 288 intelligent web-based environment 30, 31 intentional learning 293 interactive communication 350, 353 interest-driven 302 internal quality 150, 151, 155, 163, 164, 166, 167, 168, 169 internet-based instruction 253 internet-based technology 126, 253 Internet traffic 253 IT competence 60 IT education 59
J Java Adaptive Hypermedia Suite (JAHS) 96 Jigsaw 213, 217, 224, 225, 230
K knowledge articulation 213 knowledge-building 302 knowledge concepts 4, 5, 6, 7, 8, 13, 18, 20 knowledge construction 175, 176, 204, 206 knowledge domain 175, 177, 180, 183, 187, 192, 193, 194, 199, 203, 204, 273 knowledge emulation 340, 341 knowledge level 3, 6, 7, 18
Index
knowledge sharing 126 knowledge society 317
L LAOS framework 94, 96, 97, 98, 100, 101, 118 Latent Semantic Analyses (LSA) 331 learner knowledge 3, 6, 7, 12, 13, 15, 18 learner knowledge level 3, 6, 7, 18 learning activities 4, 9, 11, 13, 21, 34, 36, 39, 51, 126, 127, 132, 133, 137, 141, 144, 340, 342, 346, 347 Learning Activity Management System (LAMS) 212, 214, 215, 216, 217, 222, 223, 224, 225, 226, 227, 228, 229, 230 learning autonomy 30, 35 learning-by-doing 260 learning communities 176, 205 learning content 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 19, 20, 21, 22, 26, 90, 92, 109, 119, 120, 121 Learning Content Management System (LCMS) 66, 88 learning context 337, 339, 341, 346, 347 learning design (LD) 59, 87, 88, 131, 132, 145, 147, 148, 149, 178, 180, 212, 213, 214, 215, 216, 222, 223, 229, 230, 231, 232, 329, 334 learning environment 30, 31, 32, 35, 44, 45, 46, 49-53, 56, 59, 60-64, 82, 84, 85, 87, 126-150, 172, 176, 177, 178, 188, 197, 204-211, 339, 343, 347, 349, 354, 355, 365, 366, 367 learning environment design 126, 127, 131, 132, 133, 134, 135, 145, 147 learning experience 90, 175, 176, 183, 185, 203, 207 learning games 337, 338, 339, 347, 348 learning goals 32, 34, 36, 39, 51 learning independently 294 learning infrastructure 319, 328, 331 learning management system (LMS) 30, 32, 33, 34, 40, 42, 45, 50, 59-67, 71, 76, 77, 80, 81, 82, 83, 88, 129, 212, 297, 301, 307, 308, 309 learning networks 178, 204, 210
Learning Object Design and Sequencing Theory (LODAS) 178, 179 learning objective 176, 178, 179, 184, 343 Learning Object Metadata (LOM) 5, 13, 59, 63, 71, 72, 74, 85 Learning Object Repositories (LORs) 150, 151, 152, 155, 164, 165, 167, 170, 171 Learning Object Review Instrument (LORI) 152, 162, 163 Learning Objects (LOs) 5, 21, 150, 151, 152, 153, 154, 155, 157, 159, 161, 162, 163, 164, 165, 166, 170, 171, 173 learning opportunities 175, 176, 204, 205, 206, 207 learning process 29, 30, 31, 32, 34, 35, 40, 48, 51, 52, 175, 176, 181, 186, 196, 205 learning resources 36, 38, 39 learning scenario 29, 32, 36, 39, 49, 55, 175, 176, 177, 197, 204, 272, 273, 285, 342, 343, 344, 346 learning software 150, 151, 162, 167, 170, 171, 172 learning strategy 258 learning styles 175, 207 learning technology 151, 172 learning units 4, 5, 6, 8, 271, 275, 278, 286 lifelong learning experiences 176, 203 lifelong learning (LLL) 1, 2, 3, 4, 9, 18, 21-36, 43, 48, 49, 51, 52, 60, 80, 82, 84, 90, 91, 92, 98, 99, 101, 103, 117, 118, 119, 120, 121, 124, 127, 139, 147, 175-178, 185, 186, 201, 203, 204, 205, 207, 210, 211, 212, 234, 237, 238, 245, 248, 249, 264, 265, 271-275, 285, 292-316, 330, 331, 334-342, 346-356, 365, 366, 367, 368 lifelong learning scenarios 175, 176, 272, 273, 285 lifelong learning students 176 local audience bias 244
M main campus 238, 240, 243, 244, 248 mash-up personal learning environment (MUPPLE) 126, 127, 128, 133-149 metacognitive awareness 292, 294, 295, 296, 297, 299, 300, 302, 303, 306, 307, 314
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Index
meta-description 5 Méthode d’Ingénierie des Systèmes d’Apprentissage (MISA) 178, 181, 204 Metodologia de Apoio a Projetos de Hipertextos Educacionais (MAPHE) 178, 181, 211 Minsky’s theory 98, 99, 104, 106 mobile communication 272 mobile learning 338 mobile learning games 338 monitoring 59, 60, 61, 62, 67, 68, 85 MOODLE 212, 216, 217, 218, 219, 222, 229 MOT 1.0 110, 113, 114, 115, 116, 117 MOT 2.0 90, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121 multicriteria evaluation 150 multimedia streaming 253 multimedia technology 260 multiple criteria evaluation 150, 151, 162, 167, 170, 171 Munich reference model 94 MySpace 294, 300, 301, 302, 310, 313
N networked learning 126, 134 networked society 317 non-formal learning 319, 320, 325, 326, 332 non-game based learning 346 non-learning design 39
O obsolete knowledge 318 one-on-one tutoring 351 online banking 316 online courses 216, 217, 218, 219, 221, 222, 229, 230 online interactive communication 350 online learning 214, 222, 230, 235, 249, 292, 293, 294, 295, 300, 308, 309, 313 online learning environments 235 open educational modules 177, 204 open-source 212 open-source educational platform 60 open source environments 212 Open Source Initiative (OSI) 215 open source software 29, 45
426
optimization 150, 151, 167, 168, 170, 171 overlay model 6
P paradigm shift 129 patents concerning intelligent educational systems 1 pedagogical module 2, 4, 6, 7, 8, 9, 10, 13, 14, 20, 21 personal development 2 Personal Development Plan (PDP) 322, 325, 327, 328, 333 personalization 29, 30, 37, 39, 40, 41, 42, 45, 49, 51, 54 personalized adaptive learning 127, 129, 131, 133, 147 personalized coaching 350 personalized e-learning 90, 92 personalized learning 126, 127, 142, 145, 331 personal learning environments (PLEs) 126, 127, 128, 129, 133, 134, 136, 138, 139, 140, 142, 144, 145, 147, 148, 149, 178 post-industrial society 317 postsecondary educators 292, 293, 294, 309, 310 postsecondary students 292, 304, 305, 306, 309 Presentation Model (PM) 96, 103, 109, 110 Prospective Computer Science Professionals (PCSPs) 212, 213, 214, 216, 217, 218, 219, 220, 221, 222, 229 psycho-educational 38, 42, 44, 45, 46, 47, 50, 52
Q qualitative data 242, 243 quality evaluation 150, 155, 156, 158, 160, 162, 163, 164, 165, 166, 168, 170, 171 quality evaluation criteria 150, 156, 158, 160, 162, 163, 164, 165, 166, 168, 170 quality in use 150, 151, 155, 163, 164, 166, 167, 168, 169 Question and Test Interoperability (QTI) 286
R recommender systems 29, 46, 55, 57, 58
Index
Relational Database Management System (RDBMS) 67 resource sharing 292, 294, 297, 303, 305, 307, 309 Reusable Learning Objects (RLO) 272, 273
S satellite campus 239, 240, 241, 242, 243, 244, 245, 246, 248, 249 screencast 263 self-assessment 353 self-directed learning 292, 294, 295, 296, 297, 298, 299, 300, 302, 303, 306, 307, 310, 314, 337 self-directed lifelong learning 4, 25 self-management 352, 354, 356 self-monitoring 352, 353, 354 self-motivation 352, 353 self-reflection 353 self-regulation 350, 352, 353, 356 semantic interoperability 322 semantic search 92 semantic unity 97 Semantic Web 92, 119, 124 Semantic Web technologies 92 sequencing graph 271, 272, 275, 276, 277, 278, 279, 283, 284, 287 service oriented architecture (SOA) 29, 40, 41, 42, 51, 54 simple sequencing 271, 277, 289 simple sequencing graph 277 social adaptive e-learning 93 social adaptive system (SAS) 103, 104, 105, 107, 109 social annotation 99, 102, 111, 123 social component 102 social extensions 94 Social LAOS (SLAOS) 90, 98, 100, 101, 102, 103, 113, 118, 125 social media 302, 313 social networking 292, 294, 299, 300, 301, 302, 307, 309 social networks 292, 295, 297, 302, 303 Social Semantic Web 92 Social Web 91, 99, 118, 119, 121 sociological developments 337
socio-technical design 126 software testing 175, 177, 196, 201, 203, 204, 207, 208, 210 split-attention effect 257 standard process 175, 178, 186, 188, 207, 208 stereotypes 6 student awareness 243, 246 student-centered approach 34, 51 student-centered learning 293 student learning 351, 352, 353, 355, 356, 358, 359, 361, 365, 369, 370 Student Teams Achievement Divisions (STAD) 213, 217, 219, 220, 221, 224, 225, 230 syntactic interoperability 322 synthetic lecture 259, 260
T teacher-centered learning 293 teacher immediacy 350, 351, 352, 354, 355, 356, 357, 358, 359, 363, 365, 366, 367, 370 teacher immediacy behaviors 350, 352, 354, 356, 357, 359, 363, 365, 366, 367 teacher immediacy cues 350, 351, 354, 355, 356, 358, 363, 365, 366 technological quality evaluation 150, 165, 166, 170 technology enhanced learning scenarios 29, 49 Technology Enhanced Learning (TEL) 32, 51, 53, 55, 126, 127, 128, 129, 343 text-based learning environment 355, 365, 366 the two-sigma problem 273 time management 294 top-down 324, 325 traditional classroom 258, 260 traditional lecture 258, 260 Twitter 292, 294, 300, 301, 302, 312, 313, 314, 315
U Unified Modeling Language (UML) 95, 97 UniGame 338, 349 Unit of Learning (UoL) 281, 283, 285, 287 universal design 29, 30, 36, 37, 38, 51 user development 126, 127, 128, 129, 132, 133, 135, 138, 147, 148
427
Index
user interface 2, 19 user modeling unit 4, 6, 9, 19 User Model (UM) 96, 103, 106, 107, 109
V video archives 253 video-based education 265 videoconferencing 234, 235, 236, 237, 238, 239, 243, 246, 250 video-lecture 253, 255, 256, 257, 258, 259, 260, 261, 263, 264, 265, 266 video-lecture technology 260 video retrieval 253 video streaming 253, 257, 269 virtual cameraman 262 virtual classroom 65 virtual learning 59, 84, 85, 89, 150, 172, 339 virtual learning environment (VLE) 59, 85, 150, 151, 152, 157, 162, 164, 166, 167, 168, 169, 170, 171, 172, 339 vodcast 263
W web 2.0 129, 138, 145
428
Web 2.0 90-93, 100, 110, 112, 117, 118, 120, 121, 124, 263, 265, 292, 293, 294, 296, 297, 300, 303-314, 355 Web 2.0 context 90, 91 Web 2.0 technologies 90, 92, 292, 293, 294, 296, 297, 300, 303, 304, 305, 306, 307, 308, 309, 310 Web 3.0 92, 124 web-based environment 5, 7, 30, 31 Web-Based Intelligent Educational Systems (WBIESs) 4, 7, 9, 21 Web-based ITSs 4 web-based learning 355 Web-based technologies 2, 6 webcasting 234, 235, 236, 237, 238, 246, 249 webcasting technologies 234, 235, 237 Web Modeling Language (WebML) 94, 95, 96, 97, 123 workplace learning 2 world wide web (WWW) 273, 289
X XML Adaptive Hypermedia Model (XAHM) 94, 95, 96, 97, 102, 122 XML syntax 95
