Online Science Learning: Best Practices and Technologies Kevn F. Downng DePaul Unversty, USA Jennfer K. Holtz DePaul Unversty, USA
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Dedication
To my wonderful wife Lisa whose passion and dedication to enriching her students’ knowledge is a constant inspiration. To my beloved sons, Alexander and Sean, with whom every second shared is the greatest joy, may your lives always be bountiful in the quest for knowledge. To Mom, Dad, Ray, and Bri, bonis avibus always. –KFD In memory of my father, Arthur F. Peters, Jr., who dreamed of this first for himself, then for me. I miss you, Dad. –JKH
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Online Science Learning: Best Practices and Technologies
Table of Contents Foreword........................................................................................................................ix Preface............................................................................................................................xi
Section.I:. Science.Education.and.Online.Science.Learning Chapter.I Online Science: Its Role in Fostering Global Scientific Capital................................ 1 Building Global Science and Technology Capital ............................................... 2 Valuing Science Education Globally .................................................................... 3 Global Implications for Online Science Education ........................................... 10 Conclusion.......................................................................................................... 10 References .......................................................................................................... 11 Chapter.II Controversies.and.Concurrence.in.Science.Education............................................. 14 The U.S. Failure in Science ................................................................................ 16 Additional Factors Influencing Science Education ............................................ 19 Other Considerations that Influence Online Learning Pedagogy...................... 21 Issues in Learning Science ................................................................................. 22 Learning Theories and Concepts ....................................................................... 22 Conclusion.......................................................................................................... 27 References .......................................................................................................... 27 Chapter.III Virtual.School.Science................................................................................................. 30 U.S. Virtual Schools ........................................................................................... 31 Obstacles to Seamless K-16 Science Instruction in the U.S............................... 34
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Enrichment at the Interface: Coordinated K-16 Online Science Learning ....... 36 Online Professional Development for Science Teachers ................................... 37 Selecting, Employing, and Designing Online Science Learning Objects for Schools..................................................................................................... 38 Contemporary Approaches to Online Science Learning at Schools .................. 40 Conclusion.......................................................................................................... 43 References .......................................................................................................... 44 Chapter.IV Taking.University.Science.Education.Online............................................................ 49 Survey of Undergraduate Distance Science Education (SUDSE©) .................. 50 Revisiting Current Practice ................................................................................ 54 Conclusion.......................................................................................................... 55 References .......................................................................................................... 56 Appendix: SUDSE Online Survey ...................................................................... 58 Chapter.V The.Role.of.Practical.Work.in.Online.Science.......................................................... 73 What is Practical Work? .................................................................................... 74 Where Does Practical Work Take Place?........................................................... 76 A Brief History of Practical Work in the UK and U.S........................................ 76 Purpose and Value of Practical Work ................................................................ 79 Value of Practical Field Work ............................................................................ 80 Additional Purposes for Practical Work ............................................................ 80 Practical Work Controversies ............................................................................ 82 Designing Practical Work Tasks ........................................................................ 85 Epistemological and Procedural Introduction to Practical Work...................... 86 Example: Employing Situated Cognition and Scaffolding in Practical Work ... 87 Conclusion.......................................................................................................... 88 References .......................................................................................................... 89 Appendix: Compilation of Learning Outcomes for Practical Work ................... 93 Chapter.VI Knowledge.Transfer.and.Collaboration.Structures.for.Online.Science................. 98 Collaborative vs. Cooperative Online Learning ................................................ 99 Online Collaboration ....................................................................................... 100 Collaborative Learning and Online Science .................................................... 101 Stages and Models of Online Collaboration .................................................... 101 Effective Approaches to Collaboration and Group Structures ........................ 104 Social Software for Online Science .................................................................. 106 Role of the Instructor in Online Collaboration ................................................ 107 The Sage on the Stage Lives? ........................................................................... 107 E-Moderating ................................................................................................... 108 Gesture and Silence in the Online Science Classroom .................................... 108 Using Collaboratories to Enrich and Sustain Science Knowledge.................. 109 Collaboration in Virtual Worlds to Support Science Learning ........................ 110 Live Online Classrooms ....................................................................................111 Laboratory E-Notebooks .................................................................................. 112
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Science Collaboration Miscellany ................................................................... 113 Evaluating Online Science Collaboration ....................................................... 113 Conclusion........................................................................................................ 114 References ........................................................................................................ 115
Section.II:. Online.Science.Instructional.Strategies.and.Technologies Chapter.VII Online.Science:.Contemporary.Approaches.to.Practical.Work............................ 121 Learning Objects .............................................................................................. 122 Learning Objects Classification ....................................................................... 123 Learning Object Repositories .......................................................................... 124 Multimedia ....................................................................................................... 124 Streaming Digital Video in Online Science ...................................................... 125 Typologies for Web-Enabled Science Laboratories ......................................... 126 Benefits of Simulations in Online Science Learning ........................................ 129 3-D Learning Objects as Simulated Specimens ............................................... 129 Additional 3-D Learning Objects for Online Science Learning ...................... 132 3-D Virtual Worlds ........................................................................................... 133 Caveats of Using Virtual Worlds ...................................................................... 135 Affordances of Virtual Science Environments .................................................. 135 Examples of Online Virtual Science Learning Environments .......................... 136 Educational Science Games ............................................................................. 137 Models for Online Learning Game Development ............................................ 137 Remote Experimentation .................................................................................. 139 Remote Experimentation: Design Approaches and Considerations ................ 139 Examples of Remote Experiments .................................................................... 141 Remote Experiment Affordances ...................................................................... 142 Hands-on Laboratory Approaches for Online Students ................................... 143 Virtual Field Trips ............................................................................................ 145 Actual Field Study to Support Distance Education.......................................... 147 Virtual Puzzles for Learning Science ............................................................... 149 Hybrid or Blended Science Courses ................................................................ 150 Digital Libraries and Repositories for Science Education .............................. 151 Conclusion........................................................................................................ 152 References ........................................................................................................ 153 Chapter.VIII The.Cutting.Edge:.Promising.Technologies.and.Strategies.for.Online. Science.Education...................................................................................................... 159 Emerging Learning Systems for Online Science Education............................. 161 Remote and Virtual Experimentation to Support Student Research................. 163 Mobile Technologies for Online Science Education ........................................ 164 Using PDAs and iPods® in Online Science .................................................... 166 Mobile Learning Objects.................................................................................. 167 Advances in Visualization ................................................................................ 168
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Emerging 3-D Learning Environments............................................................. 171 Haptic Design................................................................................................... 178 Virtual Instructors, Classmates, and Tutors...................................................... 180 Virtual Science Museums and Science Centers................................................. 184 Trends................................................................................................................ 186 Impact of Online Technological Innovation to the Science Professoriate........ 186 Conclusions....................................................................................................... 187 References......................................................................................................... 187
Section III: Assessing Online Science Learning Chapter IX Assessing Science Competence Achieved at a Distance.......................................... 196 Assessment Standards and Science Assessment................................................ 197 Novice-to-Expert Knowledge............................................................................ 198 Aligning Content, Instruction, and Assessment................................................. 199 Interpretive Assessment Online......................................................................... 201 Online Science Assessment Cases..................................................................... 204 Conclusions....................................................................................................... 211 References......................................................................................................... 212
Section IV: Disciplinary Examples in Online Science Courses Chapter X Online Mathematics and Physical Science (Mathematics, Astronomy, Chemistry, and Physics).............................................. 216 Designing Online Math Learning Activities..................................................... 217 On the Design of Physical Science Learning Activities.................................... 218 Courses.............................................................................................................. 219 Simulations and Virtual Labs............................................................................ 224 Collaborations, Virtual Science Museums, and Digital Libraries.................... 234 Trends and Conclusion...................................................................................... 238 References......................................................................................................... 239 Chapter XI Online Geoscience...................................................................................................... 242 Courses.............................................................................................................. 243 Virtual Field Trips............................................................................................. 246 Virtual Laboratories.......................................................................................... 250 Collaboration, Virtual Science Museums, and the Cyberinfrastructure........... 256 Collaboration.................................................................................................... 257 Virtual Science Museums.................................................................................. 258 Cyberinfrastructure........................................................................................... 260 Trends and Conclusion...................................................................................... 260 References......................................................................................................... 262
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Chapter.XII Online.Life.Sciences................................................................................................... 265 Courses............................................................................................................. 266 Virtual Field Work and Laboratories ............................................................... 273 Online Resources.............................................................................................. 285 Trends and Conclusion ..................................................................................... 287 References ........................................................................................................ 287
Section.V:. Best.Practice.Model.for.Online.Science.Learning Chapter.XIII A.Didactic.Model.for.the.Development.of.Effective.Online.Science.Courses...... 291 Considerations ................................................................................................. 293 Phase 1: Course Planning ............................................................................... 293 Phase 2: Design ............................................................................................... 298 Phase 3: Implementation.................................................................................. 304 Phase 4: Course Assessment and Redesign ..................................................... 309 Conclusion........................................................................................................ 310 Epilogue ........................................................................................................... 312 References ........................................................................................................ 312 Appendix: Model Planning Documents ........................................................... 317
About.the.Authors...................................................................................................... 338 Index............................................................................................................................ 340
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Foreword
In November 2007, the Inaugural Conference of the International Mind, Brain and Education Society (IMBES) was held in Fort Worth, Texas. Its purpose was to foster collaboration between practitioners and researchers in the neurosciences, cognitive sciences, and similar fields. Interestingly, and unlike past practice, educators were also included in this group. Each of us knows “default educators,” members of any given profession who believe, because they completed their own professional program, that they can teach in their field. There is no doubt that some are able to do so—a few are remarkably talented—yet many are not. In Online Science Learning: Best Practices and Technologies, we hear from two scientists who made the deliberate decision, years ago, to embrace professional education praxis. The praxis of Downing and Holtz is matter-of-fact, yet thorough, much like the authors themselves. These are researchers and educators who read widely, think globally, and act locally. They use the tenets presented here in each of their courses, whether online or onsite in format, and whether learners are adult or traditional-aged. In fact, Kevin Downing’s emphasis on experiential learning in science made him instrumental in establishing their current online program, and Jennifer Holtz’s previous work with resident physicians informed her current practice philosophy. Their lack of credence with more ephemeral aspects of education and learning theory is palpable, yet they clearly identify valuable features from behaviorism, cognitivism, and constructivism, typically those based on reproducible research. These they merge with neurological advances in learning to posit neuro-cognitive instrumentalism, a learning theory that emphasizes hypothetico-predictive behaviors that current evidence supports as naturally occurring. Their work is well grounded in both contemporary and classic education and learning literature, yet it requires us to think differently, more inventively, about ideas that we believe we understand. Although the titular focus is online science learning, the model presented is also applicable to on-site courses that incorporate—or could incorporate—computer-based learning activities. Furthermore, as Downing and Holtz address in Chapter III, the tenets developed here likely
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have application to secondary, as well as tertiary, education applications that could improve the state of science education for all learners, regardless of the method of instruction. W. Franklin Spikes, Ed.D. Professor & Director Doctoral Program in Adult Education Kansas State University USA
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Preface
Purpose There is an enormous and swiftly growing literature for online learning practices, but relatively little attention has been paid to the special attributes and pedagogy of online science at the community college and university level. As regular authors of natural science courses and instructional materials for the online program for adults at DePaul University, we have long wondered why there was no up-to-date and expansive examination of the best practices in online science learning for university faculty, no general survey of current and emerging technologies for teaching science online, little consideration of the role of online science education as a burgeoning force for building American and global science capital, and no pragmatic models to inform the comprehensive development of online science programs, courses, and constituent learning activities. This book concentrates on this void by providing a general treatment of online science learning in the sciences—a subject area we affirm is an emergent and vital area of science education. While we review and incorporate selected examples from vast literature in computer science and engineering, we have purposefully constrained the chief focus of our treatment to online science learning in the natural sciences. The other fields within the science, technology, engineering, and mathematics (STEM) knowledge areas are certainly deserving of comprehensive treatments of their own online learning practices, but are beyond the scope of this book. Likewise, while our approach is largely U.S. in focus, we have tried, whenever possible, to incorporate non-U.S. considerations and concerns, and hope that this effort is apparent.
Educational.Context The current fervor over distance learning in schools and universities inspires the impression that it is an educational construct borne recently of the computer age, but this is certainly not the case. For almost two centuries, learning separated spatially from teaching has been an approach to acquiring knowledge (Bell & Tight, 1993). In contrast, online learning is a relatively young format for distance teaching and is fostered by and parallels the contemporary
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Figure 1. The domain of online science learning positioned within lifelong learning framework
revolution in communication and information technologies (CIT). The rapid proliferation and tacit acceptance of online instruction in higher education and school instruction has effectively made the terms “distance education” or “distance learning,” in practical usage, synonymous with “online learning”. Likewise, the term “distance education” is often used interchangeably but unsuitably with “e-learning”, which is actually learning that relies on CIT technologies in a variety of contexts; thus it significantly overlaps, but not necessarily involves, distance education (Guri-Rosenblit, 2005). In the hierarchy of learning forms in the “lifelong learning” framework (Figure 1), online science learning is nested within distance learning, e-learning, and online learning, respectively. Other important learning types such as blended learning (also called hybrid and mixed) and mobile learning (also called m-learning) can also be used in conjunction with online science learning.
Organization.and.Character.of.the.Book Online Science Learning: Best Practices and Technologies is organized into five sections (Figure 2) spanning: (1) fundamental issues and concepts in online science learning, (2) emerging online science practices and technologies, (3) assessment of online science activities, (4) current online practices in mathematics and natural science disciplines, and (5) a detailed instructional design model to develop online science activities. Section I reviews the value of science education in terms of scientific capital. It also evaluates global and national
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trends in both science and online science education. In addition, this section examines the epistemological and pedagogical foundations of online science and introduces the character of online science in schools. In the final chapters of this section, contemporary online science practices in higher education are investigated and the essential topics of practical work and collaboration are reviewed from the online science perspective. In Section II of this book, we appraise and provide examples of contemporary approaches in online science instruction and review emerging technologies that may soon significantly affect the character of how science is taught online. Our book’s Section III provides a review of best practices in online assessment of learning, including specific applications to online science learning. In Section IV, we compile and review a substantial number of best practice cases of online science from recent publications in the physical and chemical sciences, earth and environmental sciences, and the life sciences. Our book’s final section is devoted to presenting an instructional best practices model for developing online science exercises, courses, and programs. Our model is didactic and derived from a hypotheticopredictive philosophy consistent with the neurological basis of human learning. This section also provides course authors and designers developmental worksheets to aid in the various designs or redesign phases of an online science course or learning activity.
Figure 2. Organization of Online Science Learning: Best Practices and Technologies
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Online science learning can be a remarkably visual-rich experience and we have attempted to bring some of its vitality through to the reader with the graphics used. We note that our publisher’s cost considerations prohibit a printed color version of this book. However, the reader is encouraged to access the digital rendering of this book, which is principally in color. A brief description of each of the chapters follows: Chapter I provides an overview of the state of global science capacity and online science education initiatives designed to increase that capital, with emphasis on developing countries. We briefly describe the valuation of science education, and establish a base from which advances in online science education is explored in the remaining chapters. Chapter II evaluates trends in online science education within the context of the biggest issues in contemporary science education, the ongoing debate about the definition of science, the proper role of science education and the steps necessary to correct the science gap in the United States. Almost by definition, this controversy falls along theoretical camps—the variety of constructivists versus the movement toward a hypothetical-predictive learning theory more tightly bound to the neurological (i.e., biological) source of learning. Chapter III provides the reader a foundational look at the contemporary character and role of online science learning in virtual schools. With an emphasis on secondary schools, we examine the interdependence and existing obstacles to seamless K-16 science instruction. The affordances of the online science environment to generate a more connected science education strategy for students from K-12 through their university studies are investigated, including the crucial area of professional development for science teachers. To illuminate the salient similarities in the character and efforts between online science learning at schools and universities, we conclude this section with a comparison of practices and technologies applied commonly to each. We offer general guidance on areas of online science learning that can be capitalized on to improve student learning in science within our schools. Chapter IV presents an investigation of the current use of cutting-edge science technologies and explores the pedagogical foundations of online science education that effect how use choices are made. We examine strategies consistent with the neurological basis of learning linked to hypothetical-predictive processes and where those strategies are currently utilized. Chapter V reviews and defends the concept of practical work and its use to support online science instruction. We review practical work’s historical foundation, purpose, and value, as well as controversies concerning practical work’s utility in science instruction. This chapter builds a rationale for practical work’s intentional implementation in online science learning environments and supports subsequent chapters that review current and emerging approaches and technologies to support online practical work. Chapter VI provides a general overview of online collaboration but emphasizes the role and types of collaboration useful to teaching science online. This chapter reviews models and effective approaches to online collaboration including establishing greater lifelong learning ties to scientific information through lasting forms of collaboration facilitated online. Chapter VII presents an analysis of the key forms of contemporary online instructional design concepts and practical work approaches to online science learning such as learning objects, simulations, remote laboratories, and virtual field trips. Our discussion incorporates best practice examples, which form the groundwork of an extensive review of disciplinary science examples in Chapters X-XIII.
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Chapter VIII reviews and encourages the use of innovative technologies to promote effective online science learning. This chapter considers the outlook for the character of online science learning in the near future synthesizing recent research in the CIT and online technology areas. Chapter IX reviews current and emergent best practices in online learning assessment, notes similarities in on-site and online methods, and explores the differences and how those differences are or can be addressed. Particular attention is paid to the assessment of typical online science activities (e.g., practical work) and troublesome theory incongruities (e.g., discrete knowledge). Chapter X provides a review of best practice cases in online science from mathematics and the physical sciences. Examples are grouped into the chief areas: courses, simulations, virtual laboratories, collaborations, virtual science museums, and digital libraries. This chapter provides a foundation of resources to consider in the development or redesign of math and physical science learning activities and courses. Chapter XI’s focus is to present a more discipline-centered review of representative published examples from the geosciences. Our review takes account of courses, virtual field trips, virtual laboratories, collaboration, virtual science museums, and the relationship of the cyberinfrastructure to the geosciences. This chapter provides a variety of resources to consider in the development or redesign of online earth and environmental science learning activities and courses. Chapter XII reviews representative published examples from the life sciences. Our review takes account of courses, virtual field trips, virtual laboratories, collaboration, and virtual science museums. Our goal is to provide the reader with an appreciation of the best practices, innovations, and initiatives in online science in the life science area. Chapter XIII presents our didactic model for online science instruction based upon best practices in both science education and online education coupled with insights from the diverse and substantial literature reviewed in previous chapters. We blend concepts of distance education and science into a practical model that addresses the learning needs of major and non-major students, and the instructional design constraints of their instructors and institutions. We approach the instructional design topic with the assumption that the published online modalities included herein are generally effective as presented, but have noted evidence of ambiguity, where found. The summation of this treatment is an integrated model that takes into account emerging ideas about the neurological basis of human learning and consideration of the different philosophies of science education, although we make no apologies for holding a particular perspective. Our chief goal is to present the reader a process flow and supporting development tools through key course design steps bringing together original learning design structures with sensible best practices from the literature.
Who.is.This.Book.For? We have written this book with the intent of serving several audiences within the science education community of practice. Foremost, our book is intended to serve as a practical resource for science programs and community college and university-level science instructors building new and/or transitioning existing aspects of their science curriculum or courses,
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whether fully online or blended. Accordingly, we provide both a theoretical and practical background on online science learning as well as a model for course development. Moreover, we have deliberately presented many of the best practice cases organized by key scientific areas so that science educators can get a quick view and be inspired by contemporary best practice examples in their own mathematics or natural science disciplines. Although our perspective is through the window of science, our hope is that practitioners of online learning from other disciplines will also find the topics, review of technologies, and strategies informative. In addition, this book should be useful for instructional designers involved with the development of online scientific materials. We anticipate that this book will enhance the dialogue between instructional design staff and science faculty. Utilizing this book’s analysis of practical work and collaboration as well as its review of socio-economic (i.e., valuation) aspects of science, trends in online science, and online science pedagogy; this tome can be employed as an effective resource or text for education department courses on science at the upper division and/or graduate level. Similarly, with the rapidly growing interest in augmenting K-12 education with online activities and resources, this book is also intended as a reference for secondary school educators and administrators. Lastly, we share deeply in the concern regarding America’s “failure” in science education over the last few decades and its long-term consequences for America’s prosperity. Consequently, this book is intended to inform and motivate policy makers to explore and make the most of this important and emerging area of science instruction to increase scientific capital, both here and abroad. Kevin F. Downing Jennifer K. Holtz DePaul University, Chicago, USA October 2007
References Bell, R., & Tight, M. (1993). Open universities: A British tradition? Buckingham: The Society for Research into Higher Education and Open University Press. Guri-Rosenblit, S. (2005). “Distance education” and “e-learning”: Not the same thing. Higher Education, 49(4), 467-493.
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Acknowledgments
The authors extend a special thanks to the three anonymous reviewers whose thoughtful comments improved this book and whose enthusiasm for the project was very welcome. We thank IGI Global for their forms of assistance. Our sincere appreciation to the authors and organizations whose permission to reproduce original figures and Web graphics was integral to communicating the richness of current online science efforts. We thank our colleagues at DePaul University for their many types of support, with special thanks to Dr. Ruth Gannon-Cook and Dr. Beth Rubin for the distance learning expertise they routinely share with us and for their enthusiasm for this project. Special thanks also go to Dr. Michelle Navarre-Cleary and Dr. Gabriele Strohschen. Our thanks to DePaul’s University Research Council and Quality of Instruction Council, for their financial support.
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Section I Science Education and Online Science Learning
Onlne Scence
Chapter.I
Online.Science: Its.Role.in.Fostering.Global. Scientific Capital
Concern for man and his fate must always form the chief interest of all technical endeavors…Never forget this in the midst of your diagrams and equations. – Albert Einstein (1879-1955) Health and illness, flood and drought, want and plenty: each of these dichotomies rests squarely within the province of science education, for science education enables one to think critically and creatively, to collaborate, to investigate, to solve real-world problems, and to apply a body of knowledge that is dynamic and that rewards the lifelong learner with its challenges. Moreover, science is arguably the single most important force behind world economies, for good or ill, the potential for which has been recognized since World War II (Bush, 1945). Of the market categories identified by UNESCO World Development Indicators, five—defense, transportation, power and communication, information technologies, and science and technology—rely on advances in science knowledge. Science education is valued for its immediacy and its investment, as can be seen by remarkable advances across the globe in science capacity. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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However, advances in myriad science and technology fields are not uniform, just as science education is not uniform (Schulman, 2002; UNESCO, 2004). Where Southeast Asia advances, for example, much of the Middle East lags. Science capacity is, truly, global capital, yet capacity must be meaningfully applied in order to be sustainable and carry worth. It has been said, “A Nobel Prize for science will do little by itself to alleviate poverty or generate new business in developing countries,” (Watkins, Osifo-Dawodu, Ehst, & Cisse, 2007, para. 4), emphasizing that science without actionable purpose accomplishes little. In fact, developing countries often lose their most highly skilled scientists to institutions that offer better salaries and the potential for revolutionary work. In this chapter, we provide an overview of the state of global science capacity and online science education initiatives designed to increase that capital, with emphasis on developing countries. We see the online environment as a connective tool to bridge very large gaps in wealth and capacity, an educational bootstrapping mechanism that has not yet been fully tapped. We briefly describe the valuation of science education, and establish a base from which advances in science education will be explored in the remaining chapters.
Building.Global.Science.and.Technology.Capital Whether eliminating hunger or developing global partnerships, the concerted effort to meet the needs of the world’s population requires that those who serve and are served have the ability to take advantage of opportunities developed. That ability is capacity and capacity evolves from education. With increasing frequency, officials in low and middle income countries are coming to the conclusion that they must build up their science, technology and innovation (STI) capacity in order to make demonstrable progress in achieving the Millennium Development Goals (MDGs); raise productivity, wealth, and standards of living by developing new, competitive economic activities to serve local, regional, and global markets; and address social, economic, and ecological problems specific to each country (Watkins, Osifo-Dawodu, Ehst & Cisse, 2007, para. 4). Agricultural and environmental husbandry, access to energy and access to health care are the most visible needs of those in developing countries, yet foundational to these are infrastructure—both regulatory and physical, and collaboration—both as internal, public support and external partnerships (Watson, Crawford, & Farley, 2003). The World Bank identifies four essential factors for successful development of human capital, environments, and support systems that facilitate innovation: •
Education for the knowledge economy refers to foundational secondary and tertiary education and lifelong learning, as well as specialized education in technology, science, and communications;
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
•
Research and development (R&D): Producing and acquiring economically relevant knowledge mandates activities that lead to applied, rather than theoretical knowledge;
•
Technology acquisition and diffusion: Using existing knowledge to improve industrial competitiveness “focus(es) on helping the private sector absorb and utilize technology that is already in use elsewhere in the world” (Science, Technology, and Innovation, 2007, para. 5); and
•
Science and technology policy making capacity refers to the ability of policy makers to “understand the challenges and opportunities flowing from the global economy and to devise appropriate policies” (Science, Technology, and Innovation, 2007, para. 6).
Underlying each of these factors is education and the use of knowledge at multiple levels in science, technology, and innovation, each of them problem-based and solution-oriented. An example of operationalizing the four factors outlined by the World Bank is Juma’s (2007) description of infrastructure initiatives that incorporate funding for engineering education, one way of addressing the World Bank’s emphasis on sustainability at the local level. Juma identifies key elements as road and rail construction and upgrades, improvements to ports and harbors, and enhanced telecommunication systems. Lindholm (2007) describes a combination public-private economic collaboration that revitalized an area of the former East Germany, relying heavily on existing, underemployed scientists and entrepreneurial-minded intermediaries who could facilitate collaboration. Both Lindholm (2007) and Juma (2007) stress the importance of having in place people who can identify strategic opportunities and people to forecast human capacity needs. In fact, the importance of growing the numbers of variously skilled workers is a common point in development, one also stressed by the World Bank. They characterized it as, “Producing knowledge intensive, technologically sophisticated, higher value goods and services is not possible without a trained management cadre and labor force with the appropriate mix of technical and vocational skills” (Science, Technology, and Innovation, 2007, para. 2). Such a group would, by necessity, include scientists experienced in research and development, and engineers and technicians to adapt and use the resultant technologies for specific pursuits. To that end, “vocational, secondary and tertiary education must all contribute to turning out graduates with the necessary skills. Moreover, since the skills required by today’s labor market may not be the same as those that will be required in the future, a process of lifelong learning must be built into the education system. And at all levels and life-cycle stages, the education system must work with the private sector to understand and respond to its needs” (Science, Technology, and Innovation, 2007, para. 2-3).
Valuing.Science.Education.Globally Bateman and Willis’s (1999) work on environment valuation is readily extrapolated to science education. Its primary considerations are those of use value and conservation value, each of which are of significant importance in building science capacity and sustainable Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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modernization among the world’s nations. While their framework is explored in more detail in Chapter IV, a brief introduction here to the valuation model helps put the importance of global examples into perspective. The examples described in preceding sections represent use value. Science education is of direct use value to those who utilize science education in professional science or a career that integrates science knowledge, those who use science knowledge as one comprising an informed citizenry, or for personal enjoyment or avocation. Career applications are evident in both the Juma (2007) and Lindholm (2007) examples. Indirect use value is manifest in infrastructure, commodities, and the general application of science knowledge to societal functions for the common good, again evident in both Juma (2007) and Lindholm (2007). Option use value refers to untapped potential, as Lindholm (2007) described. The conservation value of science education, despite its connotation of a lack of immediacy, is just as crucial to developing countries and areas in need of revitalization. Bequest conservation value refers to the benefit gleaned by future generations from what is done today, a key motivating factor in the development efforts described. Existence conservation value recognizes the worth of science education as a force for reason or progress, what Watkins, Osifo-Dawodu, Ehst, and Cisse (2007) describe as the motivating force for countries on the verge of development. Finally, intrinsic conservation value holds perhaps the weakest position in science education immediacy, referring as it does to, as we state in Chapter XIII, “the natural quest of understanding of a thinking organism.” Science education is of intrinsic value because it enables the educated person to conceptualize issues larger than the immediate, larger than self or of any economic consequence. These valuations are also clearly seen in the science education initiatives occurring throughout the world and, sadly, in those areas without such initiatives, where the lack of use value efforts affects the current populace, but the lack of conservation value efforts bodes ill for subsequent generations. Much of our treatment of online science addresses and incorporates examples from the U.S., UK, and Europe. To provide a broader view of the efforts and the informational technology infrastructure that will assist in building global scientific capital, we present next brief summaries from other regions, particularly in developing countries. These initiatives are usually part of a larger, more comprehensive aid package that addresses health, infrastructure, and education needs, often—especially in the cases of World Bank and the United States Agency for International Development (USAID)—also addressing a variety of needs related to capacity building.
Middle East Scientific Capital Perhaps nowhere is the valuation structure described by Bateman and Willis (1999), and applied here to science capacity, more necessary than in the Middle East and North Africa. The most recent figures available, which are a regional composite, indicate that unemployment approaches 20% in some areas. Despite gains in school enrollment, rates remain low and “there is little evidence that education has contributed to economic growth” (Sarbib, 2002, p. 1). Currently, the majority of students who leave Middle East countries for higher education—most to the U.S.—do not return, and those that do face the same unemployment challenges as the less-educated (Taqrir Washington, June 13, 2007). Yet, as Sarbib (2002) points out, the region faces a larger challenge. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Harnessing the region’s human and social capital so that it can take its place among today’s leading knowledge economies will take less financial investment than policy reform: transforming education systems to meet the demands of a global economy driven by advances in knowledge and technology; encouraging private businesses to invest in research and development; creating business and research set-ups that foster innovation (Sarbib, 2002, p. 1). There are exceptions; sophisticated information and communications technology (ICT) is found in the oil-rich Gulf States, where options for students are more numerous (Taqrir Washington, June 13, 2007). Dubai Internet City (DIC) offers what it calls a “one-stop-shop environment” for technology oriented international businesses, while incorporating a national development plan (The Career Centre, 2007). Away from the Gulf States, and among those disenfranchised by cultural mores, options are fewer. It is, indeed, ironic that Arab countries find themselves with such wide disparity both among themselves and their economic classes, and between themselves and the industrialized world, considering the role of Arabic culture in fostering scientific and mathematical advances prior to the Renaissance.
Jordan While not specifically science education-oriented, Jordan’s Education Reform for Knowledge Economy (ERfKE) program is designed to increase the overall capacity of Jordanian primary and secondary students, better positioning them for higher education. Now in its fourth year, ERfKE is comprised of components that address Sarbib’s (2002) concern for policy reform, including provision of ICT for student use (Education Reform for Knowledge Economy I Program, 2003). The first component is significant in that it addresses overall policy and the refinement of systems responsible for policy implementation, including an effective decision support system and “comprehensive and coordinated educational research, policy analysis, and monitoring and evaluation activities” (Education Reform for Knowledge Economy I Program, 2003, para. 3). The second ERfKE component encompasses revised curriculum and assessment, the provision of professional development and learning resource development and acquisition including, with the third component, the need for both computer and science labs. Component 3 also provides for a sufficient number of safe, uncrowded schools, which, with the fourth component, “promotes readiness for learning through early childhood education. It is designed to enhance equity in low-income areas by providing kindergarten for children of age 5” (Education Reform for Knowledge Economy I Program, 2003, para. 3).
Lebanon Michigan State University, through its College of Communication Arts & Sciences, is working with Lebanese American University to develop an ICT education program “to strengthen the capacity for ICT training and to help Lebanese educators, particularly women, as they develop new strategies for teaching” (U.S. Department of State, 2006, para. 2). The goal is to build regional capacity in ICT, integrating what is known about how women approach Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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technology (Research Around the World: Lebanon, 2007). The project is an initiative of the U.S. State Department’s Middle East Partnership Initiative (MEPI), with equal funding from each university and additional assistance from the U.S. Agency for International Development (USAID) (U.S. Department of State, 2006).
Tunisia and Egypt Tunisia’s Education Quality Improvement Program (EQIP) is dual-phased, with Phase 1 having closed in 2006. Project evaluation documents classified the initial phase as satisfactory in meeting the three project goals: •
Achieve near-universal completion of basic education (Grade 1 through 9).
•
Provide a greater number of students with opportunities for post-basic education.
•
Modernize the sector in ways that improve the quality of outputs and the efficiency with which they are produced (Education Quality Improvement Program, 2000).
Both EQIP Phase 2 (2004) and Egypt’s Higher Education Enhancement Project (2002) are similar to Jordan’s ERfKE program in scope, particularly in improvement of infrastructure for subsequent development initiatives.
Russian Federation Scientific Capital While the core centers of Russia itself and the former Soviet Union (e.g., Moscow, St. Petersburg, Volgograd) have a rich history of science investigation and science education, the areas currently comprised of republics and regions that are more isolated have long suffered from a dearth of options. Two collaborative initiatives seek remedies. The Education Reform Project, in collaboration with the Russian Federation’s Ministry of Education (now the Ministry of Education and Science), seeks to reform existing infrastructure and policy, while initiating what the West knows as vocational education. Specifically, the goals of the project are to “improve quality and standards; promote the efficient and equitable use of scarce public resources for education; modernize the education system (structure of network and institutions); improve the flexibility and market-relevance of initial vocational education” (Education Reform Project, 2001, para.5). The E-Learning Support Project (2004), working with the Ministry of Education and Science, “seeks to improve the accessibility, quality and efficiency of general and initial vocational education,” through ICT (para. 8). •
Building sustainable Russian capacity to produce high quality, affordable and flexible learning materials…
•
Supporting both pre-service and in-service teacher training in the introduction of ICT into classrooms and its embedding in teaching and learning practices…
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•
Establishing in project regions a network of resource centers which would improve regional access to ICT enhanced education opportunities and dissemination of new teaching practices (E-Learning Support Project, 2004, para. 9).
The British Council’s Faulkes Telescopes Project established a collaborative with urban Russia that has the potential for outreach to more remote areas as ICT infrastructure development permits. Users access the remote-controlled telescopes, located in Hawaii and Australia, through a control center in the UK, transmit commands and then receive the requested images through Microsoft® (Redmond, WA) or Apple® (Cupertino, CA) operating systems. Professional astronomers collaborate with individual schools, working with teachers and students to identify a suitable research project. Designated projects require hands-on activity by teachers and students, including data collection “which will contribute to finding answers to research questions which are of interest to professional astronomers. Through a new website, ‘Hands-On Universe, Russia’, schools and astronomers will share and discuss their findings with each other and with schools and astronomers in the UK and across Europe” (Faulkes Telescope in Russia, 2006, para. 3).
Southeast.Asia.Capacity As in the Middle East, development across Southeast Asia is neither uniform nor universal, although the past decade has witnessed tremendous growth and a comprehensive study of the region was recently completed (UNESCO, 2004). Australia, South Korea, and Singapore are vigorously engaged in science education, with special emphasis on distance science education. Typically, “almost all classrooms are equipped with computers and other ICT tools; the student/computer ratio is high; Internet access is available in all schools; curriculum revision ensures nationwide ICT integration; delivery of education is increasingly online” (UNESCO, 2004, p. 9). Later chapters include work from some of these countries, as we discuss standards for best practices (Clark & James, 2005; Rich, Pitman, Gosper, & Jacobson, 1999). Surprisingly, the same UNESCO study found that Japan, typically considered an academic powerhouse, was joined by China, Thailand, Malaysia, the Philippines, and India in a second, less vigorously engaged group. These countries each had developed national ICT education policies, with goals and objectives, but had not yet fully integrated ICT. Other countries (e.g., the Vietnamese peninsula) are in the early stages of ICT development or “have no relevant policies but are running pilot ICT projects. In both instances, however, there is insufficient budget to implement policies and work plans and ICT infrastructure and penetration are poor” (UNESCO, 2004, p. 9).
African.Capacity Juma’s (2007) examination of an initiative that integrates engineering education is only one of many initiatives in place throughout Africa, although most are limited to foundational
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development or infrastructure, rather than science education or promotion of science fieldspecific promotion. An important initiative that was approved as recently as July 2007 for a six-year period is the Niger Basin Water Resources Development and Sustainable Ecosystems Management Project (2007). As with many of the initiatives worldwide, the first component of the initiative is devoted to “institutional strengthening and capacity building,” specifically in and between the Niger Basin Authority and existing water management entities within the existing national and regional governments (2007, para. 4). The second component then focuses on “rehabilitating and upgrading the existing large water infrastructure” (para. 5), specifically the Kainji and Jebba dams and power plants. An integral aspect continues the focus on capacity by assessing optimization and management options for those structures. Finally, the third component focuses on “sustainable management of selected degraded ecosystems and rehabilitation of small water infrastructure” (2007, para. 7). Similar Global Environmental Projects are underway throughout Africa; while the initiatives do not specifically incorporate science education, they develop the infrastructure to accommodate learning environments. An anticipated project is Tanzania’s Science, Technology and Higher Education Reform Program (2007), with the broad outcome of “education for the knowledge economy” (para.1), focusing exclusively on tertiary education. A similar development initiative already under way in Uganda is the Millennium Science Initiative (2006), the objective of which “is for Ugandan universities and research institutes to produce more and better qualified science and engineering graduates, and higher quality and more relevant research, and for firms to utilize these outputs to improve productivity for the sake of enhancing Science and Technology-led (S&T) growth” (para. 2). What neither project does is combine distance technologies with science education, although their efforts are foundational for potential distance education and complements the distance learning initiatives in Mauritania and Burkina Faso. These two projects are similar in that each tests the viability of distance learning in situ, with clear implication that its acceptance is an uncertainty. Description of Burkina Faso’s Development Learning Center Project (2002) pointedly addresses foundational goals, to determine “its ability in approaching international knowledge, to improve implementation of the Poverty Reduction Strategy Paper (PRSP) Bank financed projects, and in the coordination of local training institutions as regards national capacity building policy” (para. 4). Every element of the infrastructure was supplied in the first component, including the laying of electrical and telecommunication lines, and construction of the actual building. In the second component, operating costs for three years were provided on a decreasing basis, to include staff training, development of operational structures and initial program development; the third component included evaluation and measurement. The center opened in June 2006, with its initial programming scheduled for the subsequent fiscal year, pending a request for at least 24 months of additional funding (Status of Projects in Execution FY06, 2006). Mauritania’s Global Learning Center (2001) has experienced substantially greater success, although goals and objectives were essentially the same. The World Bank’s report, Status of Projects in Execution FY2006, documents success in addressing the needs of multiple educational, government, private and non-government organizations (NGOs). Moreover, “the Center is on good track in view of improving its financial sustainability: by the end
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of the reporting period, the Center recovered 55% of its operating costs after 30 months of operations and its revenues are increasing” (Status of Projects in Execution FY06, 2006, p. 370). Demand for use of the system is increasing and has led to development of a portal for distance access (Status of Projects in Execution FY06, 2006).
Central.and.South.American.Capacity As in Africa, most initiatives in Central and South America address not science capacity or access to distance education in the sciences, but basic health, education, and welfare needs. Peru’s Rural Education Project (2003) is not specifically science oriented, but in the first component addresses foundational issues of education equity and quality, followed by improved accessibility through distance delivery of secondary education. Teaching quality in rural areas comprises the second component, followed by a third component focusing on reform of education management and policy, “motivating linkages towards educational standards, by supporting school development, within the national assessment system, through strategic analysis and policy research” (Rural Education Project, 2003, para. 3). Mid-term review found mixed results, with rural initiatives showing lesser progress (Status of Projects in Execution FY06, 2006). Similarly, Brazil’s Ceara Basic Education Quality Improvement Project (2000) demonstrated mixed results by 2006, with indication that there had been recent, rather than consistent, improvements (Status of Projects in Execution FY06, 2006). The Ceara project consists of four components, initially focusing on educator development and early childhood preparation, then moving into televised access and accelerated programs for adult learners, with substantial investment in both educator in-service and development of teaching tools and processes. The revised priorities reported are not detailed, but the report’s omission of accelerated program development may be telling. Yet, Chile’s Life-long Learning and Training Project (2002) is successfully providing young adults with basic and secondary education opportunities, with certification options, as well as “...vertical articulation of technical secondary, with tertiary technical-professional education, through the establishment of technological curricular disciplines. As well, the horizontal articulation of technical secondary, and tertiary education with the labor market, will be constituted through regional networks of educational institutions at the technical, secondary, and tertiary levels” (Life-long Learning and Training Project, 2002, para. 4). Mid-term review documented that these programs are, in fact, realized (Status of Projects in Execution FY06, 2006). Other components of the initiative included enhanced teacher training, “a national system of competency framework, and professional-vocational pathways on selected sectors of the economy” (Life-long Learning and Training Project, 2002, para. 5) and funding for infrastructure. Again, each was documented as functioning well at the mid-term review, with full cooperative of Chile’s Ministry of Education (Status of Projects in Execution FY06, 2006).
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Global.Implications.for.Online.Science.Education It is clear that a global cyber-infrastructure does not exist and that developing nations are in many cases far from capitalizing on the connectivity to online distributed scientific knowledge and education resources that could spur innovation, thus improving economies and the quality of life. In some cases, as evidenced by the reports provided and many others not included, social and geopolitical forces impede such development, yet an increasing number of initiatives ranging from foundational to implementation stage are found around the world. In part, disparity in infrastructure is what prevents full implementation of the innovations explored in the remaining chapters of this book. Such useful learning objects as animations, 2-D and 3-D visualizations and streaming audio and video are the principal alternatives to traditional experiential learning and practical work that make distance science education possible at levels beyond the conceptual. Simple, blended, and Web-facilitated courses are possible with basic Internet access, which would also support elementary, email-based collaboratories. The lack of flexibility inherent to simple ICT infrastructures is invisible—indeed, empowering—to those whose pedagogical options are limited, but such inflexibility restricts them from emerging best practices. In addition to the absence of technological infrastructures, much of the world is restricted from emerging best practices simply because of a lack of collaboration between educators and ICT developers, which is typically unintentional. As we found in our own institution, ICT developers often know what is possible, but lack an application, while educators know what students need, but are unaware of the extent to which ICT can meet those needs. Furthermore, neither has fully explored the pedagogical foundations, including assessment issues, of hybrid and fully online courses, although isolated attempts are underway (Holliman & Scanlon, 2004). These disconnects might very well change as a result of emerging inquiry into collaboratory design of learning interfaces. By no means is the developed countries’ lack of pedagogical and technical integration meant to minimize the challenges faced by developing countries. On the contrary, the comparatively slow pace of implementation bodes well for those countries currently securing infrastructure, as it enables them to enter the field of distance learning on a par with much of the world, having learned from the mistakes of others and with a clear path to bootstrap the way to practices that are pedagogically and financially sound. Many of the innovations explored in later chapters fall short of the sophisticated multimedia currently considered state of the art, but sparsely employed.
Conclusion As the opening quotation of this chapter from Einstein maintains, concern for humans and their fate is the penultimate concern in technological innovation. In the broadest sense, we frame the application of online learning environments to science in this book as tied to that central objective. Building science capacity through the use of ICT to augment education options for and to increase the capacity of people throughout the world, particularly those in
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isolated areas or circumstances, is a worthy goal, one to be embraced by both governments and agencies with the resources to help those governments. The agencies and, increasingly, the governments, consider science education a key to capacity building and development, and those discussed—as well as others not mentioned, but at a similar developmental level—are well positioned to take advantage of emerging technologies that impact best practices in distance science education. As will be discussed in later chapters, there are those who question the verity of this position, and who consider science capacity a neutral element, at best. Moreover, the very definition of science and its importance to a large segment of the world’s populace is considered by critics to be debatable. However, we maintain that only those countries with the most highly sophisticated science capacity have both the time and freedom to concern themselves with innovations in science education, and the results, as later chapters explain, are of varying quality. To those in need, the philosophical debate on the purpose of science education is, rightly, merely specious.
References Bateman, I., & Willis, K. (Eds.). (1999). Valuing environmental preferences: Theory and practice of the contingent valuation method in the US, EU, and developing countries. Oxford: Oxford University Press. Bush, V. (1945). Science, the endless frontier. Washington, DC: U.S. Government Printing Office. Retrieved July 28, 2007, from http://www.nsf.gov/about/history/vbush1945. htm#summary Clark, I. & James, P. (2005). Blended learning: An approach to delivering science courses online. Paper presented at Breaking Down Boundaries: A Conference on the International Experience in Open, Distance and Flexible Learning, Adelaide, South Australia. Development Learning Center Project. (2002). The World Bank Project P076159. Retrieved July 21, 2007, from http://web.worldbank.org/external/projects/main?pagePK=64283 627&piPK=73230&theSitePK=40941&menuPK=228424&Projectid=P076159 Education Reform for Knowledge Economy I Program. (2003). The World Bank Project P075829. Retrieved May 27, 2007, from http://web.worldbank.org/external/projects/ main?Projectid=P075829&Type=Overview&theSitePK=40941&pagePK=64283627 &menuPK=64282134&piPK=64290415 Education Reform Project. (2001). The World Bank Project P050474. Retrieved May 27, 2007, from http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/ RUSSIANFEDERATIONEXTN/ 0,,contentMDK:20440936~menuPK:952979~page PK:141137~piPK:217854~theSitePK:305600,00.html?1=1&l=e&id=43 Education Quality Improvement Program. (2000). The World Bank Project P050945. Retrieved May 27, 2007, from http://web.worldbank.org/external/projects/main?pageP K=64312881&piPK=64302848&theSitePK=40941&Projectid=P050945 Education Quality Improvement Program Phase 2. (2004). The World Bank Project P082999. Retrieved May 27, 2007, from http://web.worldbank.org/external/projects/main?page PK=64312881&piPK=64302848&theSitePK=40941&Projectid=P082999 Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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E-Learning Support Project. (2004). The World Bank Project P075387. Retrieved May 27, 2007, from http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/ RUSSIANFEDERATIONEXTN/ 0,,contentMDK:20440936~menuPK:952979~page PK:141137~piPK:217854~theSitePK:305600,00.html?1=1&l=e&id=34 Faulkes Telescope in Russia. (2006). British Council Russia. Retrieved May 27, 2007, from http://www.britishcouncil.org/russia-science-faulkes-telescope.htm Global Distance Learning Center. (2002). The World Bank Project P071881. Retrieved July 21, 2007, from http://web.worldbank.org/external/projects/main?pagePK=64283627 &piPK=73230&theSitePK=40941&menuPK=228424&Projectid=P071881 Holliman, R., & Scanlon, E. (Eds.). (2004). Mediating science learning through information and communications technology. New York: RoutledgeFalmer. Juma, C. (2007). Engineering in international development: Linking with infrastructure investments in Africa. Science & Technology for Development, January. Retrieved May 1, 2007, from http://www1.worldbank.org/devoutreach/textonly.asp?id=394 Life-long Learning and Training Project. (2002). The World Bank Project P068271. Retrieved July 21, 2007, from http://web.worldbank.org/external/projects/main?pagePK=6431 2881&piPK=64302848&theSitePK=40941&Projectid=P068271 Lifelong Learning Project. (2007). The World Bank Project P095514. Retrieved May 27, 2007, from http://web.worldbank.org/external/projects/main?pagePK=64283627&p iPK=73230&theSitePK=40941&menuPK=228424&Projectid=P095514 Lindholm, P. (2007). Commercialization of science: A key landmark for an efficient national innovation system. Science & Technology for Development, January. Retrieved May 1, 2007, from http://www1.worldbank.org/devoutreach/textonly.asp?id=398 Tarqiq Washington. (2007, June 13). Middle Eastern students face new obstacles in U.S. higher education. Retrieved May 27, 2007, from http://www.taqrir.org/eng/showarticle.cfm?id=69 Millennium Science Initiative. (2006). The World Bank Project P08513. Retrieved July 15, 2007, from http://web.worldbank.org/external/projects/main?pagePK=64312881&pi PK=64302848&theSitePK=40941&Projectid=P086513 Niger Basin Water Resources Development and Sustainable Ecosystems Management Project. (2007). The World Bank Project P093806. Retrieved July 15, 2007, from http://web. worldbank.org/external/projects/main?pagePK=64283627&piPK=73230&theSiteP K=40941&menuPK=228424&Projectid=P093806 Research Around the World: Lebanon. (2007, Spring). Communicator, Spring, 10. Rich, D. C., Pitman, A. J., Gosper, M., & Jacobson, C. (1999). Restructuring of Australian higher education: Information technology in geography teaching and. Australian Geographer, 28(2), 135. Rural Education Project. (2003). The World Bank Project P055232. Retrieved July 15, 2007, from http://web.worldbank.org/external/projects/main?pagePK=64283627&piPK=7 3230&theSitePK=40941&menuPK=228424&Projectid=P055232 Sarbib, J. L. (2002). Special policy forum report: The Middle East and the World Bank, post September 11. Washington, DC: The Washington Institute for Near East Policy. Re-
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trieved July 17, 2002, from http://www.ciaonet.org/pbei/winep/policy_2002/2002_607. html Science, Technology, and Higher Education Reform Program. (2007). The World Bank Project P098496. Retrieved July 21, 2007, from http://web.worldbank.org/external/ projects/main?pagePK=64283627&piPK=73230&theSitePK=40941&menuPK=22 8424&Projectid=P098496 Science, Technology, and Innovation. (2007). Retrieved May 1, 2007, from http://web. worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTEDUCATION/ 0,,contentMDK: 20457068~menuPK:2458448~pagePK:210058~piPK:210062~theSitePK:282386,00. html Schulman, S. (2002). Trouble “the endless frontier”: Science, invention and the erosion of the technological commons. Washington, DC: New America Foundation. Retrieved July 21, 2007, from http://www.publicknowledge.org/pdf/trouble_on_the_endless_frontier.pdf Status of Projects in Execution FY06. (2006). World Bank. Retrieved July 21, from http:// www1.worldbank.org/operations/disclosure/SOPE/FY06/SOPEreportFY06-rev1. pdf The Career Centre. (2007). DIC. Retrieved May 27, 2007, from http://www.careercentre. ae/website/UAENationalDev.aspx UN Millennium Development Goals. Retrieved May 1, 2007, from http://www.un.org/millenniumgoals/ UNESCO. (2004). Integrating ICT into education: Lessons learned. Bangkok: UNESCO Asia and Pacific Regional Bureau for Education. U.S. Department of State. (2006, November 24). Middle East Partnership Initiative announces four new awards. Retrieved May 27, 2007, from http://usinfo.state.gov/ xarchives/display.html?p=washfile-english&y=2006&m=November&x=200611241 603401CJsamohT0.2505304 Watkins, A., Osifo-Dawodu, E., Ehst, M., & Cisse, B. (2007). Building science, technology, and innovation capacity: Turning ideas into actions. Science & Technology for Development, January. Retrieved May 1, 2007, from http://www1.worldbank.org/devoutreach/textonly.asp?id=393 Watson, R., Crawford, M., & Farley, S. (2003). Strategic approaches to science and technology in development. World Bank Policy Research Working Paper 3026. Retrieved May 1, 2007, from http://siteresources.worldbank.org/EDUCATION/Resources/2782001089743700155/StrategicApproachesS&T.pdf
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Chapter.II
Controversies.and. Concurrence.in. Science.Education
Science is facts; just as houses are made of stone, so is science made of facts; but a pile of stones is not a house, and a collection of facts is not necessarily science – Jules Henri Poincaré (1854-1912) The practical application of theory, or praxis, in science education is arguably less straightforward today than it has been in preceding generations. While formal education and learning theories have been promulgated for close to 100 years, the changing disposition and balance of academia, and the consequent dissemination of questionable and unverifiable social theories, have led to a more ambiguous discussion and application of au courant learning theories to science education. Much of what the authors consider the detrimental entanglement in academia of definitions and educational theories about science occurs at the confluence of different professional attitudes and motivation. Scientists are generally complacent in terms of championing and defending their own core philosophy and epistemology, and a scientist’s professional rewards and efforts rarely consist of debunking critics in the so-called other “ways of knowing” (see the Science Wars Web site and the Sokal Affair for a droll exception at http://members.tripod.com/ScienceWars/). The defense of scientific
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Controverses and Concurrence n Scence Educaton
reasoning is not what scientists focus on by training; thus, this is an area that almost certainly needs more systematic attention and treatment in science curricula. By contrast, science’s detractors in the humanities, social sciences, and even education find professional incentive and marketable topic in assailing the science colossus. Most notably, postmodernism with its socially relativistic and radical constructivist theories, replete with the denial of objective truth, have attempted to undermine science, or as Fishman (1996) noted, are attempting to put science on an “indefinite furlough” (p. 95). Like it or not, the science community is at war with nihilistic ideologies and one of the battlegrounds is pedagogy, a deliberation that extends to online science learning environments. While such debates about science may seem to be pedantic and simply the posturing of academics, there are concrete consequences for how science education is carried out in schools and universities. As colleges of education—the fount of pedagogy—accept and convey postmodernist theory (“fashionable nonsense” of Sokal & Bricmont, 1998) and its derivative pedagogies to budding science teachers, the epistemology and stature of science and science reasoning is diminished in the classroom and, thus, the science learning outcomes for students and their subsequent students. It is difficult to develop a science literate society when the foundation of science and reason is bantered about as suspect. As Gross (2000) stated, “Educational constructivism is in whole or in part a postmodernist view of things, and postmodernism questions the objectivity of observation and the truth of scientific knowledge….” (p. 14). As a result of this conflict, not only is how science should be taught debated—a reasonable and proper discussion—but whether and to what extent science belongs in the curriculum has been reduced in academia to a matter of dispute. In our own experience at an interdisciplinary faculty meeting not long ago, a humanities colleague openly stated opposition to restructuring a research methods course, maintaining within the argument that the sciences are no more than inquiry. This revelation effectively negated the value of science subject matter, but even more stunning were the multiple heads nodding in agreement from other humanities and social scientists in the meeting. Osborne (2006) states that this sort of perspective—science as inquiry, rather than content—was documented as early as 1851, when Great Britain was concerned about the quality of its science education; worth noting is that the perspective was rejected as an influencing factor on curriculum. Furthermore, beyond the definition and validity of science as “the world’s most comprehensive, consistent, and successful knowledge acquisition system for nearly 400 years” (Gross, 2000, p. 12) contemporary society’s role for science has also caused considerable misunderstanding among the public, educators, and practicing scientists, as seemingly competing views and needs come to the fore. Society looks to science and the science community to help explain and deal with complex problems that affect the quality of life, such as those involving the environment, health, and natural resources. However, as introduced in Chapter I, there is a prominent, if not growing, gap in science literacy between the public and the scientific community. The rapid expansion of human knowledge in science and technology makes it problematic even for scientists to keep abreast of developments in their own areas, not to mention developments in closely allied disciplines. It might be said that there is a growing literacy gap even between scientists of different disciplines. In this chapter, we summarize competing educational and learning theories, those that apply to science as well as those that apply to online science learning. In this way, our discussion provides the epistemological
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and pedagogical background and rationale behind discussions in later chapters, including support of the model introduced in Chapter XIII, to meet the seemingly disparate needs in online science education.
The.U.S..Failure.in.Science There have been alarming reports about the decreasing scientific competitiveness and so-called failure in scientific education over the last five decades (Rutherford, 2005; Symonds, 2004;
Figure 2.1. Science and engineering degree attainment by country. From college learning for the new global century. The National Leadership Council for Liberal Education & America’s Promise (LEAP). Washington, DC: AAC&U. Government open access document.
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Valverde & Schmidt, 2006). Many researchers contend that the United States, most noticeably, is losing its science and technology edge and, hence, its long-term economic vitality. This line of argument is substantiated by many measures, such as drain of researchers to other countries, trade deficits in advanced technology and the number of patents granted in the U.S. (Lemonick, 2006). Moreover, this trend is suggested also in the comparative percentages of young adults who have attained a science or engineering degree, where the U.S. is twentieth out of 26 nations examined (Figure 2.1). It is also supported by the comparative numbers of engineering graduates in India, China, and the United States. It is estimated that China is graduating as many as five times the number of undergraduates in engineering as is the U.S. Equally troubling is that approximately 60% of the engineering PhD degrees at U.S. institutions are awarded to non-native students, predominantly from India and China (Wadhwa, Gereffi, Rissing, & Ong, 2007). Related to this pattern is the relative decrease in articles published by U.S. scientists to Asia and Western European scientists (Figure 2.2). College and university science faculty maintain that the current strategies for science and mathematics in schools have led to the trend of under-prepared students. That is, the majority of matriculating students are not ready for tertiary level coursework, resulting in prolonged remedial work and unnecessary attrition (Altschud, 2003). To our knowledge, what impact the lack of science preparation or science emphasis plays on driving students away from science careers has not been determined. However, we observe science and math phobia
Figure 2.2. Comparison of scholarly articles published by the U.S., Western Europe, and Asia. From the Association of American Universities 2006 National Defense Education and Innovation Initiative. Used by permission.
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in the vast majority of our adult learners, who report not feeling as if they have adequate background to approach the work; many would avoid these areas completely if possible, to avoid failure anxiety. Meanwhile, others decry the privileging of science knowledge that supposedly keeps many learners from developing science literacy (Roth, 2005; Roth & Barton, 2004). National statistics on science education, such as those provided by the National Assessment of Educational Progress (NAEP), indicate a small increase in scores for students in Grade 4 assessment in the U.S. since the standards-based No Child Left Behind Act (NCLB) (Public Law 107-110) was implemented, but show little change for later grades, indicating that there are still systemic problems in the preparatory lead-up to college science (The Nation’s Report Card: http://nationsreportcard.gov/science_2005/). These results are evidence for those who criticize NCLB’s emphasis on math and reading skills, its reliance on supposed scientifically based research standards that vary widely in validity and generalizability, and its lack of flexibility in working with students whose capabilities are not average (Beghetto, 2003; Ryan, 2004). In addition to the problematic issues of K-12 science instruction, it can be further construed that the U.S. failure in science also involves the rewards system for scientists and the restrained public discourse about science’s pivotal importance to the well-being of U.S. society. These function separately. Scientists are not rewarded for speaking out in public; on the contrary, such efforts reduce the time spent in investigation, where funded work occurs. Bench scientists who choose to embrace education, for example, find very quickly that time for laboratory and field work diminishes dramatically, and, thus, their funding. Yet, it is reported that in China, science researchers who publish in international journals are accorded national acclaim (Wadhwa et al., 2007). Complicating science education reform are hotly debated epistemological beliefs on the value of science knowledge for society at large and what science literacy should mean. Thus, the field of science education finds itself in substantial flux, with reformers debating the nature of science and science standards, particularly the comparative merits and limitations of science literacy, defined by some as citizen science versus power science, as well as the means for achieving each. Citizen science has been characterized as “…a form of science that relates in reflexive ways to the concerns, interests and activities of citizens as they go about their everyday business” (Roth & Barton, 2004, p.9). In this way, its purpose is to empower learners by emphasizing application of science concepts, from the internal workings of a lawn mower motor to the multi-faceted, community investigation of polluted groundwater, while de-emphasizing tenets that would be of interest only to those, not the majority, interested in the cooperative realm of academic and industrial science (Roth & Barton, 2004). Conversely, those in support of teaching power science maintain that de-emphasizing traditional science fails to bring that majority to scientific literacy. In fact, such citizen science programs relegate learners to a level of understanding that effectively removes them from power science and its associated careers, further aggravating the existing perception of professional scientists that college graduates are not adequately prepared to enter the science work environment (Holliman & Scanlon, 2004; Scanlon, Murphy, Thomas, & Whitelegg, 2004). Moreover, an emphasis on citizen science may well diminish the option value of science held within the individual, which can be utilized by the individual or society at a later date.
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Parallel to this argument for citizen science run decidedly career-oriented program initiatives to increase the number of women choosing science as a career, including those to engage girls in authentic science activities as early as middle school (Girls Re-designing and Excelling in Advanced Technology [GREAT!] Judy Brown, NSF Grant 0114669; Women on the Prairie: Bringing Girls into Science through Environmental Stewardship, Beth Montelone, NSF Grant 0114723). Similar programs seek to engage racial minorities (NIH’s Minority K-12 Initiatives for Teachers and Students [MKITS]; Louis Stokes Louisiana Alliance for Minority Participation [LS-LAMP], NSF Grant 0503362). Such initiatives focus on situated, context-sensitive and non-threatening interfaces designed to emphasize personal meaning and personal potential within science, but still convey the substance of a science career, including those elements that do not have direct applicability to students’ personal lives. Where the citizen science and power science camps converge is on application of experiential, inquiry-based learning experiences, although they come from different angles. While those in support of citizen science consider laboratory work—practical work, as described in Chapter V—unnecessary, constructivist learning theory, on which citizen science bases its tenets, is heavily experiential. However, Edelson (1999), whose research is on the implementation of scientific visualization to learning school science, concludes that convergence of content and process is the crux for any student success. Process alone does not lead to science literacy in either citizen science or power science. The most significant criticisms of citizen science relate to its heavy reliance on radical constructivism, education’s primary canon—perhaps dogma—for the last decade or more. Specifically, critics maintain that under the constructivist foundation, which will be discussed in detail later in this chapter, (1) content takes a back seat to socially driven experience, (2) emphasis is placed on derivative and repackaged theory, (3) anti-reason, anti-science, and political philosophy holds sway; and (4) emerging insights on the neurological basis of learning are not taken into account (Gross, 2000; Matthews, 2002; Rezaei & Katz, 2002; this volume). However, as noted, performance in the sciences by U.S. students has been declining compared to other nations for decades. Thus, effective pedagogical approaches to learning content knowledge are yet another area of flux within science education.
Additional Factors Influencing Science.Education Government.Guidelines National standards are developed for primary and secondary school. However, they are developed in collaboration with the National Science Foundation, should be congruent with upper levels of science education and are therefore adequate for extrapolation for tertiary level science learning. This is elaborated on in the context of seamless K-16 online science learning in the following chapter. In addition, quasi-governmental organizations have also weighed in on what is needed in the area of science education. For example, the Association of American Universities’ National Defense Education and Innovation Initiative identifies three general objectives that support U.S. competitiveness and security at the college level:
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Enhance the U.S.’s research capacity in order to sustain scientific and technical innovation.
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Cultivate U.S. talent to enhance the nation’s math, science, engineering, and foreign language expertise.
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Continue to attract and retain the best and brightest international students, scientists, engineers and scholars.
Within the details of these general goals, a key strategy is the training and retraining of K-12 teachers. Online science learning may readily contribute to this area.
Personal.Objectives At the tertiary level, a student’s learning goals generally fall within three specific areas, identified by Houle (1961) in his examination of adult learners. First is the social learner, who, in the case of formal learning, takes a course because of the social aspect. The second type, who learns for the sake of learning, takes a course because it simply sounds interesting. The third is goal-oriented, as in major bound, and takes a course because it is required. Depending on what category a learner falls into, the level of science understanding may be that of citizen science or of power science. Hence, it is inherently difficult to partition citizen science or power science into exclusive sets of learning objectives that can be applied to a diverse classroom.
Figure 2.3. Differences between material taught, depending on student emphasis in science education. Students intending science or science-oriented careers would learn Science Explanations, while other students would learn Ideas-about-Science (from Osbourne & Hennessy, 2006). Used by permission.
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There can be a disconnect between those types in the classroom. A career or vocational science learner may be expected to understand citizen science level materials, while a citizen science level learner may not understand the nuances required for science knowledge. Considering this, there are two issues to address. The first is what Osborne and Hennessy (2006) addressed in their Delphi study of science curriculum, in which respondents were asked to identify what science should be taught to the different types of learners. Respondents to the Delphi study were science educators at the tertiary level and professional scientists. What the study concluded was that a tracking system was necessary, in the opinion of the respondents. Students who foresaw a career in professional science or in a vocation that used the science knowledge would by necessity need to be taught different content than those students who did not intend to enter science as a career (see Figure 2.3). In the case that students should later decide on science careers, they would need to complete whatever remedial work was deemed necessary by their intended programs, just as students do now. While tracking in the classroom environment is known to be complex in operational terms and on psycho-social impact, distance learning lends itself readily to track systems, as students are able to engage in multiple types and multiple levels of learning activities, whether individually or collaboratively. A detailed study of what science and engineering graduates actually do with their degree (Reget, 2006, in Lowell, 2006) is relevant to how personal objectives and employment opportunities influence the use of a student’s scientific capital. This study revealed that more than half of the students receiving science and engineering degrees did not seek an additional degree. Of those who sought advanced degrees, 62% pursued them in professional and non-science areas (e.g., business, medicine). The vast majority of individuals who earned science and engineering degrees considered their learning useful and relevant to their job responsibilities well into their careers, including those that moved into management. This supports the idea that power science plays an important role even for science and engineering students who reposition their careers away from the science and engineering degree they first accomplished.
Other Considerations that Influence Online.Learning.Pedagogy As we will argue throughout this book, the structure of online learning should be pedagogically driven; however, one must not lose sight that there are pragmatic considerations important to the learner that should also inform instructional design and implementation. The adult online learner tends to be goal-oriented in the sense of Houle (1961), as described earlier. For example, in a recent survey of a group of adult student learners, mostly teachers, O’Lawrence (2007) determined that two key factors influence them to undertake online study: incentives from employers and “suitable” offerings from programs. In addition, this study indicated that only 9% of students selected the distance format because of the self-directed learning approach (i.e., pedagogy), but that 89% indicated flexibility was the foremost factor (that is, accommodating transportation, child care, and career demands).
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For this reason, the instructional design of online science learning should also take account of the flexibility needs of students.
Issues.in.Learning.Science Regardless of the goals of the science learner, there are key issues of concern to stakeholders in professional science. Undoubtedly, how to best address the learning needs of students is of primary importance, followed closely by responsibility to the field of science. These must balance each other. The state of science achievement in the U.S. has made some wonder whether too much emphasis has been placed on learner needs with insufficient attention to the needs of the field or even the need to build a society’s scientific capital, as we discuss more fully in Chapter XIII. It is important to address students’ learning needs and styles; however, science educators and scientists have a responsibility not to weaken essential content by adherence to a given learning theory or mandate. In this next section, we provide a background to the common learning theories applied to science and online science.
Learning.Theories.and.Concepts Three core movements comprise the bulk of contemporary learning theory: behaviorism, cognitivism, and constructionism (Mergel, 1998). These theories overlap considerably, although their relationships to each other are not as direct as one might assume. Several emergent learning models and theories (e.g., inventive model, hypothetico-predictive theory) seek to reintegrate constructivism and cognitivism, at least partly as a response to the more extreme, and demonstrably less effective, forms of constructivism (Kirschner et al., 2006; Martens et al., 2007; Matthews, 2002; Rezaei & Katz, 2002). Reexamination and repositioning of learning theory and practice are further warranted in light of a rapidly increasing knowledge of how the brain responds to stimuli and actually learns (i.e., internally and biologically as opposed to coarser external psychological observation) made possible from advances in brain imaging and from neurological theories of learning. These advances seem to justify beliefs from behaviorism and cognitivism that have been denounced by constructivism. The genesis of contemporary learning theory lies in behavioral psychology, particularly the work of Edward Thorndike, John B. Watson, and B. F. Skinner, although extrapolation of classic conditioning (stimulus-response) animal studies by Pavlov was foundational to their work (Skinner, 1976). Behaviorism, then as now, classified learning and thinking as precursors to changed behavior, with environmental experience causing the mental change that leads to changed behavior (Ormrod, 1999). Thorndike’s connectionism theory maintains that experiences affect the strength of the stimulus-response; positive results strengthen connections, while negative results weaken them. Similarly, Watson’s work demonstrated that repeated connection of a particular stimulus with a particular response strengthened the learned behavior change (Mergel, 1998). Skinner’s work on operant conditioning and the
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Figure 2.4. Comparison of psychology-based learning theory characteristics and relationships to each other. The left arrow indicates the comparative balance that newer learning theories, which are based on neurological advances, place on existing learning theories. The right arrow represents the degree of objective and subjective emphasis inherent in the traditional learning theories (adapted from Mergel, 1998).
effectiveness of partial reinforcement was rooted firmly in behaviorism, but gradually he began, however slightly, to incorporate the evolving theory of cognitivism. Jean Piaget and Jerome Bruner developed the core of cognitivism as a learning theory. As Figure 2.4 illustrates, cognitivism shares many characteristics with behaviorism, although the equivalents to audience and behavior components of behaviorism are explored in much greater depth in cognitivism. In fact, the key concepts attributed to cognitivism (e.g., schema, meaningful effects) address learner analysis and task analysis exclusively, while adopting behaviorism’s condition and degree components. Cognitive theorists recognize that much learning involves associations established through contiguity and repetition. They also acknowledge the importance of reinforcement, although they stress its role in providing feedback about the correctness of responses over its role as a motivator. However, even while accepting such behavioristic concepts, cognitive theorists view learning as involving the acquisition or reorganization of the cognitive structures through which humans process and store information (Good & Brophy, 1990, p. 187, in Mergel, 1998). This type of cognitive view is also supported in the recent and seminal discussion of the neurological basis of learning by Lawson (2003). Yet even before behaviorism tackled learning, Vygotsky was developing the concept of zone of proximal development, which became foundational to both his theory of social constructivism and subsequent concepts of scaffolding and cognitive load (Rieber & Robinson, 2004; Sweller, 1988; Wood & Wood, 1996). The related work of both Piaget and Gagne supported development of constructivism, as conceptualized by Vygotsky. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Constructivism, as originally conceived, formed a theory of learning based in constructivist developmental theory in psychology. As a learning theory, it offered an alternative manner in which to involve the learner in constructing meaning as opposed to transmitting knowledge. Because of its developmental stage emphasis, constructivism acknowledges a spectrum of approaches to teaching/learning, depending on the task and the stage of intellectual development of the learner. It was not originally intended to be a replacement for any one particular learning theory’s tenet or principle; rather, it hoped to provide a context in the absence of specific understanding of the neurological bases of learning. Constructivism is not now simply a learning theory. It has become a behemoth that consumes competing beliefs, criticism, and well-intentioned debate under the umbrella of student-centeredness. Critiquing constructivism has essentially become equivalent to being anti-student, especially in science education where educators have to contend with radical constructivism’s key tenet that there is no one right answer. In a quest to address student learning needs and honor their experience, constructivists have largely ignored the values and lessons learned from behaviorism and cognitivism. Yet, constructivism as originally conceived by Vygotsky and Piaget did not go so far as to negate the importance of other perspectives. In fact, techniques that have shown themselves to be completely appropriate, useful, and indeed necessary (e.g., scaffolding) have arisen from work in constructivist learning (Figure 2.5). It has been the more extreme practices of constructivism that dismissed other learning theories or their main tenets and it is these that have recently been shown to be insupportable in light of research into neurological aspects of learning (Kirschner et al., 2007; Martens et al., 2007). Such research demonstrates key principles and tenets of behaviorism and cognitivism emerging as foundational to effective instructional techniques (Rezaei & Katz, 2002). Several emergent learning theories are based on the hypothetical-predictive model, which acknowledges that students have an existing knowledge base prior to instruction (e.g., Lawson, 2003). Students incorporate new knowledge by continuously making informal hypotheses based on their existing knowledge, presumptions about truths and experiences. This type
Figure 2.5. Comparison of supportive learning concept integration into traditional learning theories. Current understanding of developing neurological learning theories seems to indicate that scaffolded material, practical work and collaboration/networking are supported concepts.
Learning Concepts Scaffolded Material Practical Work Situated Learning Anchored Instruction Collaboration/ Networking Metacognitive Tasks
Behaviorism Objective ● ●
Learning Theories Cognitivism Objective ● ● ● ●
Constructivism Subjective ● ● ● ● ● ●
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of model subsumes the elemental ideas of the three learning theories introduced previously. In behaviorist learning theory, reinforcement is part of learning. Cognivistic task analysis is a natural occurrence, according to the neurological basis of learning. The constructivist tenets of need for authentic tasks and experiential learning are equally important. These learning elements are not mutually exclusive to a learning situation, but rather complement and support a learning theory that is focused on the students’ natural learning proclivity; that is, hypothetico-predictive. This hypothetico-predictive model recognizes the importance of instructional techniques that have proven to be of value because these techniques support the neurological basis of learning.
Cognitive.Load.and.Scaffolding.to.Support.Science. Instruction Cognitive load is instrumental to neurological cognitivism because it addresses the relationship between long-term and short-term memory. The relationship between cognitive load theory (Sweller, 1988) and expertise—that is, the novice-to-expert changes in cognitive load—depends on the ease with which a person can access knowledge retained in long-term memory. The transmission of that knowledge to working memory for incorporation of new information and the processing of that new information is a default mechanism, according to the hypothetical-predictive model. Miller’s (1956) classic research into working memory maintains that it is limited to between five and nine chunks of information, research that is supported by subsequent work (Bapi, Pammi, Miyapuram, & Ahmed, 2005; Gobet et al., 2001). If any of those chunks are not connected to a knowledge structure in long-term memory, the chunk is forgotten and replaced by new information. Thus, as students become more proficient at making the connection between what is in working memory compared to long-term memory their level of expertise—that is, position on the novice to expert scale—moves more toward expert. The goal in experiential fields is to facilitate progression from novice to increasingly expert skill (Holtz, 2002; Sternberg, 1984). An essential element in such learning environments is scaffolding, which refers to supportive structures, just as it does in industry. In learning science, scaffolding takes the form of learning objects that clearly illustrate or facilitate the illustration of key concepts, processes, and theories (Quintana, Krajcik, & Soloway, 2002). From the perspective of science learning, cognitive load can be reduced by scaffolding that eases the burden on working memory by making available instructional aids. For example, in solving a physics problem, formulae can be provided rather than committed to memory. The importance of scaffolding is to ease cognitive load in the learning moment.
Practical.Work.and.Experiential.Learning Experiential learning in the constructivist paradigm is understood to be active, non-theory oriented knowledge construction. In this definition, experiential learning can occur in any learning environment, at home or at work, and in essentially any level of complexity. Within Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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science education, a more precise term would be that of practical work. While students may learn experientially, the content-driven manifestation of science learning is more closely defined, as Chapter V explains in considerable detail. Practical work, while not one of the national standards, is emergent as a theoretical framework and firmly established as a teaching practice within the sciences. The history of practical work is clearly seen as far back as in the guild apprenticeship models.
Cooperative.and.Collaborative.Learning Collaborative learning implies a mutual reliance, shared contribution, and a combining of knowledge sets into a greater whole that constitutes new knowledge. Too often, classroombased projects fall short of collaborative learning and are comprised of merely cooperative learning. Whereas collaborative learning is mutually reliant and results in new knowledge, cooperative learning is non-authentic, in that the failure of one member to contribute can be overcome by the remaining members. In a true collaboration, the loss of one member’s expertise changes the dynamics of the learning process and the quality of the final product. In cooperative learning, the product itself can be completed without substantial loss of quality as the group adjusts its learning process. In the sciences, true collaboration takes on yet a more sophisticated dimension when students have the option to participate in collaboratories, a formalized structure of longitudinal dimensions. Collaboration and collaboratories will be discussed in more detail in Chapter VI.
Learning.Environments When constructivist learning theorists describe the issue of students’ needs being met, it takes on the role of a theoretical construct. In practice, meeting a student’s need can be as simple as one recalled from a high school science class. In a traditional didactic environment, where K, C, and F were compared, the teacher resorted to drawing three thermometers in one beaker, showing the different measurements to illustrate comparative value. A teacher versed in the hypothetical-predictive model would know that the student simply required more and diverse transmission of facts to bring into congruence the mental hypothesis and the presented content. This indicates that flexibility in a learning environment, regardless of educational philosophy or student learning style and preference is, perhaps, the single most important characteristic of effectively developed pedagogy. Different authors maintain that problem-based learning, case-based learning, inquiry-guided learning, project-centered learning, collaborative and self-directed learning are all essential elements of a learning environment. In fact, the ability of an instructor to use each of those modalities, sequentially or simultaneously, in response to student learning needs and, equally, the structure of the content to be addressed is what makes a learning environment effective.
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Conclusion Professional science is calling for more rigorous preparation of students. The biggest issue in contemporary science education is the ongoing debate about the definition of science, the proper role of science education, and the steps necessary to correct the science gap in the United States. Almost by definition, this controversy falls along theoretical camps—the variety of constructivists versus the movement toward a retro-constructivist, hypotheticalpredictive learning theory. Perhaps the most important clarification is realizing, accepting, and not denigrating the substantially different needs of students who anticipate a science career, compared to those who do not. There is no doubt that the needs of learners who intend a career in science or a career that uses science knowledge differs significantly from the students whose science knowledge will be used as a life tool. Although the concept of tracking has been disliked intensely within education, it is difficult to ignore the recent Delphi study of science professionals, including science educators, that indicates the necessity of tracking based on the goal of science learners. At the same time, whether in citizen or power science, the best aspects of primary learning theories combine in the emergent hypotheticopredicative model. Content is not the focus; optimum attendance to student learning needs, as opposed to the social emphasis of radical constructivism, becomes the focus. In this chapter, we have presented a summary of the competing theories, needs, and perceptions in science education. We also presented an emergent learning theory that incorporates advances in neurological bases of learning and the hypothetical-predictive model, and establishes a position from which the information can be considered within the construct of the model introduced in Chapter XIII.
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Edelson, D. C. (1999). Addressing the challenges of inquiry-based learning through technology and curriciulm design. The Journal of the Learning Sciences, 8(3/4), 391-450. Fishman, L. (1996). Feelings and beliefs. In P. R. Gross, N. Levitt, & M. A. Lewis (Eds.), The flight from science and reason (pp. 87-95). New York: New York Academy of Sciences. Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I., & Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5, 236-243. Good, T. L., & Brophy, J. E. (1990). Educational psychology: A realistic approach. (4th ed.).White Plains, NY: Longman. Gross, P. (2000). Politicizing science education. Washington, DC: The Thomas B. Fordham Foundation. Holliman, R., & Scanlon, E. (Eds.). (2004). Mediating science learning through information and communications technology. New York: RoutledgeFalmer. Holtz, J. K. (2002). Effect of graduate medical education on the problem finding and divergent thinking abilities of resident physicians. (Doctoral dissertation, Kansas State University, 2002). Dissertation Abstracts International, AAT 3052580. Houle, C. O. (1961). The inquiring mind: A study of the adult who continues to learn. Madison, WI: University of Wisconsin Press. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86. Lawson, A. E. (2003). The neurological basis of learning, development and discovery. Boston: Kluwer Academic Publishers. Lemonick, M. D. (2006). Are we losing our edge? Time Online. Retrieved July 17, 2007, from http://www.time.com/time/magazine/article/0,9171,1156575,00.html Lowell, B. L. (2006). Projected numbers of foreign computer and engineering workers under the Senate’s comprehensive Immigration Reform Act (S.2611). Washington, DC: Georgetown University. Martens, R., Bastiaens, T., & Kirschner, P. A. (2007). New learning design in distance education: The impact on student perception and motivation. Distance Education, 28(1), 81-93. Matthews, M. R. (2002). Constructivism and science education: A further appraisal. Journal of Science Education and Technology, 11(2), 121-126. Mergel, B. (1998). Instructional design and learning theory. University of Saskatchewan. Retrieved on July 17, 2007, from http://www.usask.ca/education/coursework/802papers/ mergel/brenda.htm Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. O’Lawrence, H. (2007). An overview of the influences of distance learning on adult learners. Journal of Education and Human Development, 1(1), n.p. Retrieved September 22, 2007, from http://www.scientificjournals.org/journals2007/articles/1041.htm Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Ormrod, J. E. (1999). Human learning (3rd ed.). Upper Saddle River, NJ: Merrill. Osborne, J., & Hennessy, S. (2006). Report 6: Literature review in science education and the role of ICT: Promise, problems and future directions. Bristol, UK: Futurelab. Quintana, C., Krajcik, J., & Soloway, E. (2002). Scaffolding design guidelines for learnercentered software environments. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA. Rezaei, A. R., & Katz, L. (2002). Using computer assisted instruction to compare the inventive model and the radical constructivist approach to teaching physics. Journal of Science Education and Technology, 11(4), 367-380. Rieber, R. W., & Robinson, D. K. (2004). The essential Vygotsky. New York: Kluwer. Roth, W. M. (2005). Ethnomethodology and the r/evolution of science education. Canadian Journal of Science, Mathematics and Technology Education, 5(2), 185-198. Roth, W. M., & Barton, A. C. (2004). Rethinking scientific literacy. New York: RoutledgeFalmer. Rutherford, F. J. (2005). The 2005 Paul F-Brandwein lecture: Is our past our future? Thoughts on the next 50 years of science education reform in the light of judgments on the past 50 years. Journal of Science Education and Technology, 14(4), 367-386. Ryan, J. (2004). The perverse incentives of the No Child Left Behind Act. Retrieved September 10, 2007, from http://www.law.nyu.edu/journals/lawreview/issues/vol79/ no3/NYU303.pdf Scanlon, E., Murphy, P., Thomas, J., & Whitelegg, E. (Eds.). (2004). Reconsidering science learning. New York: RoutledgeFalmer. Skinner, B. F. (1976). About behaviorism. New York: Vintage Books. Sokal, A., & Bricmont, J. (1998). Fashionable nonsense: Postmodern intellectuals’ abuse of science. New York: Picador. Sternberg, R. J. (1984). Beyond IQ. New York: University of Cambridge. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285. Symonds, W. C. (2004). America’s failure in science education. Business Week On-Line. Retrieved May 28, 2007, from http://www.businessweek.com/technology/content/ mar2004/tc20040316_0601_tc166.htm Valverde, G. A., & Schmidt, W. H. (2006). Refocusing U.S. math and science education. Issues Online in Science and Technology, 14(2), n.p. Retrieved July 17, 2007, from http://www.issues.org/14.2/schmid.htm# Wadhwa,V., Gereffi, G., Rissing, B., & Ong, R. (2007). Where the engineers are. Issues in Science and Technology, Spring. Retrieved July 28, 2007, from http://www.issues. org/23.3/wadhwa.html Wood, D., & Wood, H. (1996). Vygotsky, tutoring and learning. Oxford Review of Education, 22(1), 5-16.
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Chapter.III
Virtual.School.Science
The schools ain’t what they used to be and never was. – Will Rogers As universities have rapidly increased their Web-based science activities and course offerings, a comparable pattern of Web-based science instruction is occurring in K-12 learning environments. The emerging school science curriculum is one increasingly integrated with multimedia simulations (e.g., Hennessy et al., 2006) and other Web-based learning objects representative of subject matter from chemistry, physics, biology, and earth science (e.g., Kay & Knaack, 2007; Wang & Reeves, 2006). Beyond the basic efficacy of the use of the Web for teaching science, which is well supported in the literature, online science learning at schools now plays a preparatory role in acquainting students with instructional strategies such as simulations, virtual laboratories, and virtual field trips that will be increasingly common in college science courses. That is to say, the success of university-level Web-based science instruction is interconnected with and to some extent dependent on the preparation of students for this learning format by their K-12 learning experiences. More to the point, drawing a distinction between university-level online science education strategies and those of schools is arbitrary and disadvantageous to the goal of improving scientific capital in Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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the U.S. and elsewhere. National science standards should stipulate strategies for seamless K-16 Web-based science instruction. A key issue of concern with the emergence of Web-based science instruction in schools is how this instructional strategy could affect and possibly broaden the so-called digital divide, where those with less access to and instruction in technology can be differentially excluded from the wealth and jobs emanating from this understanding. It is an established fact that a family’s income and a father’s level of education influence digital access at home and later affect the first year college student’s success (Madigan & Goodfellow, 2005). If science education is not to further deepen the chasm through the increased use of Web-based technologies in instruction, then it will be crucial that Web-based science instruction be strategic and ubiquitous in school systems to respond to potential educational and income differences in families. In this chapter, we examine the character and extent of science learning at virtual schools and explore the current deliberations concerning the interconnections and coordination of efforts of online science learning at the critical school-to-college interface. We discuss selected pedagogical approaches popular in virtual school science instruction and express the pronounced correspondence between online technological efforts of schools and those elaborated in later chapters for colleges and universities.
U.S..Virtual.Schools Virtual Schools in the U.S. (a.k.a. e-schools or cyberschools) are state, district, or privately sponsored education entities that provide individual courses, blended courses, or a fully online curriculum to supplement or replace traditional schools. In 1997, there were modest numbers of students involved with online learning and only five states with designated virtual schools (New students are logging in to virtual schools, 2006). Now, approximately 700,000 K-12 students enroll in one or more online-facilitated courses (Damast, 2007). Moreover, estimates suggest that thousands of virtual school courses are offered in the U.S., chiefly in the 24 or so states that currently have thorough programs, and annual enrollment growth is estimated near 30% (Sturgeon, 2007). The explosion of online learning opportunities for K-12 students is not just a national phenomenon, but also one of international scope (Gosmire & Grady, 2007). Links to exemplary for-profit and non-profit virtual schools in the U.S. and germane online science resources are provided in Table 3.1. A review of these resources will afford the reader with a reasonable impression of the current character of the virtual school paradigm. The ubiquity of online learning has convinced the State of Michigan’s Board of Education to recently establish a requirement that all of its high school students undertake at least one online course to prepare for lifelong learning via the Web and as college preparation (Distance Education Report, 1/1/2006). However, some states are struggling with competing funding needs, credit, and oversight issues in the area of online learning for K-12 (Roblyer, 2006). Consequently there are disparate efforts and levels of success by the so-called “virtual school movement” across the U.S. Berge and Clark (2005) review at length the issues surrounding planning for successful virtual schools in a seminal treatment. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 3.1. Examples of virtual schools and science resources
Many of the affordances of online learning contended for higher education are also asserted to support the case for K-12 online instruction. For students, instructors, and administrators, flexibility (time and space) is a core attribute in both K-12 and college settings. At present, for science education a key service of the virtual school is to provide students the opportunity to take advanced placement courses that might not otherwise be available. It is reasonable to presuppose that as virtual schooling enrollment grows. A wider and more innovative variety Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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of standard high school science course offerings will be generally available incorporating the affordances of the online science learning environment such as virtual field trips, virtual laboratories, remote experiments, educational games, and the like. Other benefits of virtual K-8 schools and virtual high schools noted by Lamb and Callison (2005), Mupinga (2005), and Revenaugh (2005) are synthesized here as: •
Accommodating more students;
•
Customizing learning to a student’s learning style;
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Broadening course offerings to include outside experts and resources;
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Supporting particular student needs (at-risk, rural, special needs, accelerated learners);
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Providing high quality courses consistent with standards (e.g., state);
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Providing flexibility such as asynchronous learning;
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Optimizing facilitation of learning with top qualified teachers;
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Cost effectiveness;
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Pedagogical benefits of adding multimedia;
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Regular self-assessment; and
•
Potentially greater personal attention from teacher.
As is the case for online learning at the college level, this learning delivery format is not a panacea for K-12 education and all students. There are certain student attributes that are more favorable for success in online learning. For example, Roblyer and Marshall (2002) determined that four factors correlate to the success of virtual high school students: achievement and self-esteem beliefs, personal responsibility and initiative, technology skills, and organizational skills. These authors conclude that support mechanisms such as pre-course counseling and introductory modules, personalized support during courses, and some faceto-face (i.e., blended) learning activities should be in place to address these success factors for students whose skills are short in these areas. Of course, such factors and support systems are not unique to virtual high school students but also characterize factors and strategies important to support college student success in online learning activities. An essential part of establishing and maintaining quality in virtual schools is the development of training systems and criteria for K-12 teachers. National efforts and criteria are already emerging to prepare and classify pre-service teachers based on the levels of competence they have achieved in virtual school pedagogy. Davis and Roblyer (2005) delineate the four levels, in ascending order of teaching involvedness as: (1) VS counselor (advises students participating in VS activities), (2) VS assistant (teaching assistant for a VS course), (3) VS Teacher, and (4) VS Designer and Teacher. A parallel professional development effort to assist in-service teachers in acquiring greater competence in virtual school learning pedagogy and technology would be an advantageous complement to the emerging in-service standards. For in-service science teachers who will be involved in online teaching, professional training should involve such areas as virtual reality simulations, virtual laboratories, remote experimentation, virtual field trips, learning object repositories for science, and so on. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Lowes (2005) has examined the impact of teaching online for teachers at the Virtual High School (VHS at http://www.govhs.org/) and how that experience brings about positive modification in face-to-face classrooms. This investigation revealed that over 60% of the responding teachers (N=215) had modified their face-to-face courses to: (1) add peer reviews, (2) eliminate poorly designed lessons, (3) revitalize existing lessons, (4) add new lessons following design principles used for online courses, (5) provide more detailed instructions, and (6) incorporate units developed originally for their online course. Hence, the online course experience of teachers can also beget the quality of regular classroom instruction, which is a synergy we have observed in our own teaching practice at the college level.
Obstacles.to.Seamless.K-16.Science. Instruction.in.the.U.S. To an extent, the historic compartmentalization of U.S. education into elementary, secondary, and collegiate levels promotes predictable discontinuities and inconsistencies in a student’s lifelong learning development in science. How and why learning fragmentation occurs in U.S. science instruction is a complex issue, but is the product of such things as: (1) lack of communication and coordination of learning objectives and learning activities between science educators at different education levels, (2) differences in the character and quality of instructor preparation in the sciences at different educational levels (e.g., K-12 science teachers in relation to science research faculty), (3) different stances on the proper pedagogies and purpose of science (i.e., those that permeate colleges of education versus departments in the science disciplines), (4) ill-advised or incomplete portions of national science standards, and (5) the disconnect between educational research in K-12 science and how or whether it is drawn on by science teachers in the classroom. On this latter point, Barnett and Kitto (2004) describe a research-practice gap which they attribute to science and math teachers having little interest in or use for the jargon-laden product of educational researchers. While on the other hand, educational researchers hold to the belief that their research is the antidote for ailing school systems. To breach this gap, Barnett and Kitto (2004) envision the establishment of an online community of practice to include researchers, teachers and students. Such a community of practice, which they term the Internet for Technology, Science, and Mathematics or ITSAM, would jointly explore teaching practice, scientific knowledge, and teaching practice. Although an estimable suggestion, it is difficult to see how such a collective would achieve meaningful change in the main for K-16 science. Paradoxically, another obstacle to science reform in the U.S. is the stone tablet-like perception of national standards, the belief that they are somehow infallible and non-adjustable, which is itself contrary to science’s hypothetico-predictive core beliefs. In terms of the U.S. national science standards, they should not be looked upon as such. For example, Magnusson, Palincsar, Hapgood, and Lomangino (2004) importantly and astutely point out that despite the national standards of science teaching that call for real phenomena as a part of an investigation, much of science learning is second-hand and derived by text-based learning. Their investigation examines the important role of science learning that takes place away Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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from real phenomena. In the case of online science instruction where there is asynchronous interaction with no multimedia such as a streaming video lecture, there is an elevated emphasis in text-based learning. The seeming mismatch between curriculum reform efforts and actual classroom practice in science is not just a U.S. issue but of international scope and concern. For example, Hipkins, Barker, and Bolstad (2005) describe the science education disjunction within the New Zealand system and conclude the mismatch stems from the following interacting and often conflicting factors: •
Lack of consensus about the Nature of Science (NOS);
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Absence of curriculum guidance;
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Inappropriate classroom materials and pedagogical content knowledge (PCK) for NOS;
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Incongruence of teachers personal theories of learning; and
•
Classroom constraints precluding an authentic representation of contemporary science.
The debate in New Zealand over the NOS and consequently how science should be taught in school parallels the form of debate seen in the U.S. (as reviewed in Chapter II) and serves as an example of how K-16 science education becomes fragmented as a result of differing views on the purpose of science. For instance, the clarion call by education researchers for an epistemological-based approach in K-12 instruction built on the nature of science, with its corresponding de-emphasis in science content, is pervasive in the U.S. (e.g., DonovanWhite, 2006; Kenyon, Kuhn, & Reiser, 2006; Millwood, 2006; Ryder, 2002). Those who support the NOS view for K-12 science education effectively advance a populist model of education whose purpose is to prepare the citizen scientist, despite mixed abilities and interests, rather than focus on cultivating young scientists. To those who do not espouse the NOS paradigm, this approach to K-12 science instruction can appear to rely on an almost utopian belief in the prospect of shaping universal citizen scientists who can capably evaluate the complexities of an ever-increasing body of scientific knowledge. The question arises: how many adults will truly become the so-called consumers of science and base personal decisions on a keen epistemological understanding of science? Not to mention whether a lay person trained in science around a NOS framework will ever be able to make a serious evaluation of the claims of science in astrophysics, genetics, medicine, ecology, and so on, without in-depth content knowledge of these subjects. One of the concerns about the NOS paradigm is whether an enveloping emphasis on science epistemology in K-12 could in fact produce generations of students skeptical of science and who perceive it as another place of intellectual uncertainty in data and methods. Moreover, since the NOS paradigm typically advances the idea that science is subjectively constructed and a cultural product without absolute truths, will this translate to turning-off students from aspiring to be future scientists? Somewhere between these perspectives, McFarlane (2006) supports “a curriculum for all which has an equitable balance between investigative empirical science supported with ICT so that it is more effective and investigative critical science which is supported through access to scientific sources and published analysis shared and Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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discussed with peers” (p. 185). This proposed strategy attempts to avoid differentiation in primary school where the “able” follow an empirical science curriculum and those less able follow a more populist model, as described by Osborne and Hennessy (2006). Ideally, a student’s immersion in science should involve a very coordinated (i.e., seamless) epistemology and curriculum from kindergarten to university. Most effective would be an education structure where high school science teachers work closely with K-8 instructors and where university science faculty interact with high school instructors during their professional development concerning science pedagogy and reasoning, learning objectives, content, and the transition of students to successively higher levels of scientific inquiry and content. Others argue that the chief problem throughout all levels of science education, but particularly for young students, is that pupils do not have the chance to undertake authentic role-play to foster their science potential because of their excessive immersion in learning what scientists already know (Alberts, 2005). Consistent with this suggestion are ongoing online science learning programs for schools that are far along in establishing their efficacy, such as the Web-based integrated science environment (WISE) and knowledge integration environment programs (KIE) that employ a deep-knowledge approach on fewer science subjects (Linn, Davis, & Bell, 2004). The exemplary WISE program (http://wise.berkeley.edu/) emphasizes student investigation of real-world data related to important and controversial science topics. In the next sections, we review subject areas where there is coordination and concurrence between different education levels regarding online science learning.
Enrichment.at.the.Interface:.Coordinated. K-16.Online.Science.Learning. Although unexceptional in extent, collaboration does take place between higher education and K-12 in several face-to face contexts and these areas can be advantageously extended to online learning contexts. For example, Thompson (2006) describes three areas for collaboration in online formats between higher education and K-12 education: (1) dual enrollment and advanced placement college level courses for high-school students that provide access to quality classes and professors, (2) professional development for practicing teachers, and (3) training of career professionals to transition to teaching. In terms of impact, the most important of these in helping to turn around the debacle in science education are those related to science teacher training. As Sanders (2004) notes for science and mathematics education, “…the pivot point for change and improvement is the nation’s teachers and the institutions that train them” (p. 3). Therefore, professional development supporting the continual improvement of a science teacher’s competence should be of highest importance and be a well-coordinated aspect of building scientific capacity in the U.S. and worldwide. Such coordination should be a part of professional development for face-to-face K-16 science teachers as well as their counterparts working in emerging online science learning environments. As we have maintained elsewhere (Chapter II), the scientific community and especially university science faculty need to be generally more proactive in promoting scientific reasoning and knowledge. Similarly, it is also argued strongly (see Sanders, 2004) that college science Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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faculty should take an active role in the training of science teachers. We suggest that there should be greater engagement between university science faculty and science teachers in traditional school settings, blended schools, and emerging virtual schools. Training science teachers is not something that many university science faculties typically carry out as a priority, so this goal would require incentives and leadership. In fact, the poor pre-service training of science teachers and large numbers of unqualified teachers documented for K6-12 (Sanders, 2004) in the U.S., as well as a five-decades-long malfunction in science education, makes an interesting case for shifting the focus of training of U.S. science teachers to colleges of science rather than colleges of education. Direct learning exchanges between university scientists and K-12 learners are another way in which cooperation can occur online between educational levels. For example, Vanderbilt University’s Virtual Scientist Program provides teachers and students the opportunity to connect to expert scientists during 30-45 minute science content presentations, often originating from the scientists’ own labs via online videoconferencing (McCombs, Ufnar, & Shepard, 2007). Universities are also developing customized courses to support high school students’ advanced placement pursuits, as in the case of the introduction to computer science course, CyberTech 1, offered by Kennesaw State University (Woszczynski, 2006). Roseman and Koppal (2006), who see science curriculum reform as a design problem, describe another approach to build continuity between a student’s K-12 and undergraduate science. Their proposal is that university faculty should develop a set of science benchmarks and standards and apply a course design model similar to AAAS benchmarks for K-12. The process steps they describe are akin to well recognized best practices for online course development and involve establishing learning objectives, understanding where students are (situated cognition), sequencing activities, developing meaningful questions, monitoring student progress, and revising courses through iterative assessment.
Online.Professional.Development.for. Science.Teachers In terms of teacher training, the online science learning environment provides an ideal platform to meet the characteristics of high-quality professional development for teachers as summarized by Thompson (2006) and presented in column 1 of Table 3.2. We correlate the respective affordances of online science learning that support the professional development of science teachers in column 2 of Table 3.2. What is apparent is that Web-based training for science teachers aligns well with the best practices of professional development. Sustaining professional development beyond single sessions can be accomplished through sequenced modules, communities of practice (i.e., ITSAM described previously) or even through collaboratories where teachers become a part of active science research projects. In addition, the online environment is flexible to teaching schedules, individualized learning, self-assessment, collaboration, and reusability. Finally, the ability to incorporate sophisticated science learning objects online can provide science teachers with a variety of hands-on, practical work experiences. Naturally, these online science learning attributes are also applicable to those students making a career transition to science teaching. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 3.2. Affordances of online professional development for science teachers Characteristics of Excellent Professional Development (Summarized in Thompson, 2006) Ongoing as opposed to one-time Reflect realities of teacher’s schedules Tied directly to teaching and learning objectives Provide hands on experiences Provide opportunities for application and reflection Provide for collegial sharing
Respective Affordances of Online Science Learning Collaboratories, Community of online science practice, Modules Anytime Anywhere Customizable Virtual laboratories, Remote experiments, Virtual field trips Self-assessment Reusability, Collaboration
Online professional development for teachers is already occurring in a wide variety of course contexts. In Section IV of this book, we review several case examples of graduate courses directed at science teachers. In our investigation of online courses, those related to science teacher training were among the most common types of fully online science courses described in recent publications. For example, the Australian-based DESCANT project (Designing E-learning Systems to Celebrate and Nurture Teaching in Science and Technology) takes a generative learning approach to the design of professional development for elementary level science teachers (Forsyth, Mulholland, & Schaverien, 2004). In DESCANT, the teachers, guided by a consultant, develop personal professional development e-learning environments. An important objective is to have the science teachers become competent e-designers so that professional development is more sustainable in the school system. In a further example, Bitan-Friedlander, Dreyfus, and Milgrom (2004) report on an in-service training course to have Israeli science teachers build an innovation into their course. In this online training, guided through e-mail exchange, science teachers rapidly integrated scientific inquiry into a scientific subject that was new to them. The learning was personally scaffolded and guided by a real-time mentor; it was determined that success in accomplishing this training was related to teacher concerns such as the personal ability to take on the task. This example illustrates how even basic e-mail can foster professional development for science teachers.
Selecting,.Employing,.and.Designing.Online.Science. Learning.Objects.for.Schools Wallace (2000) identifies the three key challenges for the use of the Web in high school science classroom: (1) fitting online activities into the curriculum, (2) managing classroom Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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discourse, and (3) establishing accountability for knowledge. As teachers are well aware, employing technology in their lesson plans can be cumbersome and time-consuming, so Web-based activities need to employ efficiently to be useful. Likewise, how to make students accountable for readings, collaboration, and other learning activities, an issue in common with online college instructors, becomes imperative for school teachers employing Webbased learning environments including learning management systems. There are a significant number of online instructional resources and other technology-based materials (e.g., CD-ROMs) available to support science teachers in accomplishing their lesson plan objectives. How does a science teacher sort through the plentiful possibilities? In order to assist teachers and administrators in deciding the appropriateness of computer based educational materials, the Center for Science Education has developed review criteria (Flynn, 2004). The four key areas scrutinized for technology-based materials in this rubric are: (1) student learning to include alignment with standards, pedagogy of inquiry, ease of use for students, collaboration possibilities, and assessment strategies, (2) teaching issues and professional development, (3) equity of student use, and (4) feasibility (technological and financial). Hence, if technological materials pass muster on the CSE criteria, they are likely to be a strong asset for student learning. However, Ilomaki, Lakkala, and Paavola (2006) have determined that some learning objects may not be as effective in supporting pedagogical aims due to the particular pedagogical practice of the teacher, so teaching style is another factor to consider during resource selection. To support science teachers’ professional development and learning around employing new curriculum materials (number (2) previously-noted), Schneider (2006) suggests that complementary teacher-specific educative materials should be designed that develop pedagogical content knowledge (PCK), engage teachers in planning and teaching strategies, and scaffold learning to match teachers’ prior knowledge and skills. Metcalf (2006) describes an example of such online training for a technology-based curriculum for the Technology Enhanced Elementary Math and Science Project (TEEMSS2). The TEEMSS2 project targets grades 3-8, is inquiry-centered, consists of 15 units broadly representative of the sciences, and uses probeware, models, and simulations. Teacher training concerning the technology of TEEMSS2 and its implementation in the classroom is conducted through an online course that includes a discussion board where teachers share their experiences in using the software in the classroom. It is common that teachers are not provided complementary instruction on the pedagogical foundations of the technology they are to employ and this can be a significant inhibiting factor in the use of multi-faceted online science activities such as remote labs and simulations. Consequently, a best practice of online school science is to have the requisite PCK training for key approaches to online learning activities and technology. Efforts to have teachers develop instructional projects based on digital library resources, such as for the National Science Digital Library (NSDL) are already underway. An example of this undertaking, described by Recker et al. (2005), includes two intermeshed approaches, a professional development program on using digital libraries and practical application to develop classroom activities using a corresponding instructional design authoring service called Instructional Architect. The professional development workshop component of this effort is conducted face-to-face in a computer lab following best practices in training (e.g., hands-on, linked to standards, collaborative and design based) and instructs teachers how to search for and incorporate science learning objects. The product of the workshop is a practical Web-based learning activity for students based on digital library resources the Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 3.1. A Web-based project for students on weather using the Instructional Architect authoring service and digital library resources (from Recker et al., 2005). Used by permission from IEEE.
teacher has located such as on the topic of weather (Figure 3.1). Despite the value of the digital library resources expressed by the teachers and the user-friendly authoring service developed, persistent use of this system was not the result of the learning experience (Recker et al., 2005). Among the reasons suggested for this outcome is that the metadata of the digital library systems used do not permit a direct search for learning objects tied directly to state standards in science, which are a primary consideration and concern for the teachers.
Contemporary.Approaches.to.Online. Science.Learning.at.Schools An exploration of the recently published approaches to online science learning for schools reveals that there is considerable convergence in best practices and technologies to those discussed in subsequent chapters of this book for college/university level science. For example, educational game simulations, virtual field trips and laboratories, authentic experimentation, mobile learning, rich multimedia, diverse online collaboration styles, digital libraries, and online assessment are all being used to support virtual or blended schooling formats Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 3.3. Examples of contemporary approaches to online school science Category Simulations: Educational Games
Example Online Games
Digital simulation games for physics
Simulations: Virtual Labs And Field Trips
Interactive virtual reality field trip
Direct manipulation modeling using Haptic features for Physics
Physics and Chemistry Simulation Authentic Experimentation
Hands-on Chemistry: Kitchen Science Investigators
Remote Experimentation: Particle Physics
M-Learning and Multimedia
Modeling of molecules using handheld devices CCC! Video on Demand
Summary To foster scientific habits of the mind by the way they parallel modelbased reasoning Develops student understanding of abstract physics concepts (electromagnetism) Middle and high school students interact with scientists on topics such as the wildlife and geography of Africa Haptic learning assisted environment to improve mental modeling of Newtonian mechanics by middle school students Simulations of electric circuits and temperature effects on enzymes using software packages Students learn the scientific principles behind successful cooking in hands-on informal environments In cooperation with research labs and facilitated with internet connections, High school students evaluate muon activity. Allows middle school students to make 2-D chemistry models on a mobile device Provides digital video content downloadable to mobile devices on science and other subjects.
Author Steinkuehler & Chmiel, 2006
Squire, Barnett, Grant, & Higginbotham, 2004 Cruz-Niera & Lindahl, 2000
Chan & Black, 2006
Hennessey et al., 2006
Clegg, Gardner, Williams, & Kolodner, 2006
Dias et al., 2006
Scott et al., 2004
PC Magazine, 2007
continued on following page
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Table 3.3. Examples of contemporary approaches to online school science (continued) Category Collaboration
Example Virtual Collaborative Inquiry
Digital Study Groups
E-Homebook System
Animated Agents
Reporting and Reading
Web Reports on Science
Automated reading strategy program Digital Libraries
Digital Library for K-12
IdeaKeeper Notepads
Course Websites
Chemical Bonding
Assessment
Online Assessment
Summary Web-based learning environment employed as coordination tool for collaborative inquiry of middle school students Custom online learning environment permits students to access school software from home for additional learning and parental involvement A teacher-parent-student communication interface for elementary school using intelligent agents Animated agent incorporated into science focused multimedia to improve learning results SciNews Online is a web-reporting environment that assists students generating sound scientific explanations To enhance the comprehension of science text A digital library created for use by K12 students using a metadata standard and the resources from the Exploratorium Scaffolded work environment for scientific inquiry incorporating information from digital libraries Assesses student attitudes about chemistry and tests knowledge of subject matter Online testing facilitates determining high school students understanding of genetics
Author Lakkala, Ilomäki, & Palonen, 2007
Clark, Jamison, & Sprague, 2005
Chen, Yu, & Chang, 2007
Dunsworth & Atkinson, 2007
de la Chica, 2007
Taylor, O’Reilly, Sinclair, & McNamara, 2006 Fait & Hsi, 2005
Quintana & Zhang, 2004
Frailich, Kesner, & Hofstein, 2007
Tsui & Treagust, 2007
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Vrtual School Scence
(Table 3.3). We take this common pattern and interest in the affordances of online science learning to be a potential pedagogical bridge between K-12 and college science educators. A more coordinated effort to build coherence between the learning objects built at the different educational levels, including a continuum of scaffolding, would assist in generating a seamless K-16 learning approach. One possibility to coordinate the continuity of efforts between secondary education and colleges is the development of a national virtual high school science standard, tied to national science goals, but strategically addressing the particular affordances and approaches of online science learning at the university level. Along the lines of the National Science Digital Library, such a national curricular effort would necessarily require the regular collection, development, and strategic dissemination of the best science learning objects for use by teachers and students across the country as a way to improve the development of America’s scientific capital and those of other nations.
Conclusion In this chapter, we have examined key components of the online science education effort, namely the virtual school and Web-based science activities at traditional schools. Each is beneficial and precursory to the successful implementation of Web-based science activities at universities. Along with other aspects of online learning to support science, virtual schooling is undergoing vital growth and it is likely to be an increasing facet of a child’s immersion into science. As the quotation at the beginning of this chapter implies, the character of schooling is changing as it always has. In this case, online science will be increasingly used to supplement the K-12 science curriculum. The strong interdependence between online and traditional formats of K-12 science education, college science education, scientific capital, and prosperity requires synchronization to prosper, but there are many obstacles to seamless K-16 science instruction. Mutual enhancement at the interface between university science and K-12 science is modest for many reasons including philosophical discordance, but a promising area to foster conjointly is online professional development for science teachers. Pedagogical content knowledge is a key area that should be provided routinely as professional development to teachers to facilitate the incorporation of online science activities in their traditional or virtual classroom. We conclude that a one-size fits all nature of science approach applied to online science would act against developing young scientists and that as an alternative, there should be scaffolding to accommodate differential abilities and levels of interest in learning and pursuing science. Our appraisal of contemporary approaches to online science learning at schools indicates they closely parallel those in university settings and should be both considered a part of the same whole as well as harmonized that way. We are in accord with the academic voices calling for an integrated and unified strategy for online science learning from elementary school through college in the U.S. If the U.S. and other nations are to maximize the potential of Web-based science to assist in nurturing young scientists, there should not be an artificial discontinuity between school and university. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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In subsequent chapters, we will place emphasis on the investigation of college-level best practices and technologies, but along the way will describe online activities in high school settings that are examples of best practice strategies (e.g., a virtual chemistry laboratory in Chapter X and a virtual collaborative experimentation on earthquakes in Chapter XI). In addition, models such as the one we present in the final chapter of this book and published rubrics for selecting online science materials should help in the design of effective online science learning objects for schools.
References Alberts, B. (2005). A wakeup call for science faculty. Cell, 123(5), 739-741. Barnett, J., & Kitto, R. (2004). Mind the gap: A proposal for science, mathematics, and technology education. Canadian Journal of Science, Mathematics and Technology Education, 4(4), 529-535. Berge, Z., & Clark, T. (2005). Virtual schools: Planning for success. New York: Teachers College Press. Bitan-Friedlander, N., Dreyfus, A., & Milgrom, Z. (2004). Types of ‘‘teachers in training”: The reactions of primary school science teachers when confronted with the task of implementing an innovation. Teaching and Teacher Education 20(6), 607-619. Chan, M. S., & Black, J. B. (2006). Direct-manipulation animation: Incorporating the haptic channel in the learning process to support middle school students in science learning and mental model acquisition. Proceedings of the International Conference of the Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 64-70). Mahwah, NJ: LEA. Chen, H., Yu, C., & Chang, C. (2007). E-Homebook system: A Web-based interactive education interface. Computer Education, 49(2), 160-175. Clark, K., Jamison, T., & Sprague, D. (2005). Digital study groups: Online learning environments in middle school. Journal of Interactive Online Learning, 3(4), 1-12. Clegg, T., Gardner, C., Williams, O., & Kolodner, J. (2006). Promoting learning in informal learning environments. Proceedings of the 7th international Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 92-98). New York: Springer. Cruz-Neira, C., & Lindahl, G. (2000). A voyage into virtual reality: Networking our VR lab to Iowa middle schools and high schools. IEEE Computer Graphics and Applications, 20(2), 16-19. Damast, A. (2007). Be true to your cyberschool. Business Week Online (April 19). Retrieved July 20, 2007, from http://www.businessweek.com/ Davis, N., & Roblyer, M. (2005). Preparing teachers for the “schools that technology built”: Evaluation of a program to train teachers for virtual schooling. Journal of Research on Technology in Education, 37(4), 399-408.
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de la Chica, S. (2007). SciNews online: Scaffolding the construction of scientific explanations. In Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, December 5-7 (pp. 2183-2188). New York: ACM Press. Dias, B., Lourenço, N., Maio, A., Marques, C., Peralta, L., Popovici, G., Popovici, N., Santos, J., & Sil, J. (2006). Introduction to particle physics at high school with a remote experiment. Retrieved from July 17, 2007, from www.formatex.org/micte2006/ pdf/1279-1283.pdf Distance Education Report. (2006). State of Michigan adopts online course requirement for high schoolers. Retrieved May 28, 2008, from http://www.magnapubs.com/newsletters/distanceeducation.html Donovan-White, C. (2006). Teaching the nature of science. ACASEJAEESA, 1(7). Retrieved May 28, 2007, from http://www.unb.ca/fredericton/science/physics/acase/Journal/ Vol1_pdf/ACASEJAEESA_1_7_Donovan-White.pdf Dunsworth, Q., & Atkinson, R. K. (2007). Fostering multimedia learning of science: Exploring the role of an animated agent’s image. Computer Education, 49(3), 677-690. Fait, H., & Hsi, S. (2005). From playful exhibits to LOM: Lessons from building an exploratorium digital library. Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, Denver, CO, June 7-11 (pp. 207-212). New York: ACM Press. Flynn, J. (2004). Selecting computer-based high school science curricula: A guide for teachers. Newton, MA: Education Development Center, Inc. Forsyth, L., Mulholland, G., & Schaverien, L. (2004). Towards systemic professional development: teachers as e-designers. Proceedings of the 6th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 599). New York: ACM Press. Frailich, M., Kesner, M., & Hofstein, A. (2007). The influence of Web-based chemistry learning on students’ perceptions, attitudes, and achievements. Research in Science & Technological Education, 25(2). Retrieved October 15, 2007, from http://www. informaworld.com/10.1080/02635140701250659 Gosmire, D., & Grady, M. (2007). 10 questions to answer for technology to succeed in your school. The Education Digest, 72(8), 12-18. Hennessy, S., Wishart, J., Whitelock, D., Deaney, R., Brawn, R., la Velle, L., McFarlane, A., Ruthven, K., & Winterbottom, M. (2006). Pedagogical approaches for technologyintegrated science teaching. Computers and Education, 48(1), 137-152. Hipkins, R., Barker, M., & Bolstad, R. (2005). Teaching the “nature of science”: Modest adaptations or radical reconceptions? International Journal of Science Education, 27(4), 243-254. Ilomaki, L., Lakkala, M., & Paavola, S. (2006). Case studies of learning objects used in school settings. Learning, Media & Technology, 31(3), 249-267. Kay, R., & Knaack, L. (2007). Evaluating the use of learning objects for secondary school science. Journal of Computers in Mathematics and Science Teaching, 26(4), 261-289. Kenyon, L., Kuhn, L., & Reiser, B. J. (2006). Using students’ epistemologies of science to guide the practice of argumentation. Proceedings of the 7th International Conference
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on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 321-327). New York: ACM Press. Lakkala, M., Ilomäki, L., & Palonen, T. (2007). Implementing virtual, collaborative inquiry practices in a middle school context. Behaviour & Information Technology, 26(1), 37-53. Lamb, A., & Callison, D. (2005). Key words in instruction. Online Learning and Virtual Schools, 21(9), 29-35. Linn, M. C., Davis, E. A., & Bell, P. (Eds.). (2004). Internet environments for science education. Hillsdale, NJ: Lawrence Erlbaum Associates. Lowes, S. (2005). Online teaching and classroom change: The impact of virtual high school on its teachers and their schools. In R. Smith, T. Clark, & B. Blomeyer, (Eds.), A synthesis of new research in K-12 online learning (pp. 24-26). Naperville, IL: Learning Point Associates. Madigan, E., & Goodfellow, M. (2005).The influence of family income and parent education on digital access: Implications for first-year college students. Sociological Viewpoints, 21, 53-62. Magnusson, S. J., Palincsar, A. S., Hapgood, S., & Lomangino, A. (2004). How should learning be structured in inquiry-based science instruction?: Investigating the interplay of 1st- and 2nd-hand investigations. Proceedings of the 6th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 318-325). New York: ACM Press. McCombs, G., Ufnar, J., & Shepard, V. (2007). The virtual scientist: connecting university scientists to the K-12 classroom through videoconferencing. Advances in Physiological Education, 31, 62-66. McFarlane, A. (2006). ICT and primary science: Where are we going? In P. Warwick, E. Wilson, & M. Winterbottom (Eds.), Teaching and learning primary science with ICT (pp. 175-186). Berkshire, UK: Open University Press. Metcalf, S. J. (2006). TEEMSS2: Technology enhanced elementary math and science—year 1 report. Proceedings of the 7th International Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 474-480). New York: ACM Press. Millwood, K. A. (2006). A comparison of students’ conceptions about the nature of argumentation in school and professional science. Proceedings of the 7th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 962-963). New York: ACM Press. Mupinga, D. M. (2005). Distance education in high schools. The Clearing House, 78(3), 105-108. New students are logging in to virtual schools. (2006). T H E Journal, 33(17), 8. Osborne, J., & Hennessy, S. (2006). Report 6: Literature review in science education and the role of ICT: Promise, problems and future directions. Bristol, UK: Futurelab. PC Magazine. (2007). Digital video with class. Retrieved July 20, 2007, from http://www. pcmag.com/article2/0,1895,2146880,00.asp
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Quintana, C., & Zhang, M. (2004). IdeaKeeper notepads: Scaffolding digital library information analysis in online inquiry. In Extended Abstracts on Human Factors in Computing Systems, Vienna, Austria, April 24-29 (pp. 1329-1332). New York: ACM Press. Recker, M., Dorward, J., Dawson, D., Halioris, S., Liu, Y., Mao, X., Palmer, B., & Park, J. (2005). You can lead a horse to water: teacher development and use of digital library resources. Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, Denver, CO, June 7-11 (pp. 1-8). New York: ACM Press. Revenaugh, M. (2005). K-8 virtual schools: A glimpse into the future. Educational Leadership, 63(4), 60-64. Roblyer, M. (2006). Virtually successful: Defeating the dropout problem through online school programs. Phi Delta Kappan, 88(1), 231. Roblyer, M., & Marshall, J. C. (2002). Predicting success of virtual high school students: Preliminary results from an Educational Success Prediction Instrument. Journal of Research and Technology in Education, 35(2), 241-255. Roseman, J., & Koppal, M. (2006). Ensuring that college graduates are science literate: Implications of K-12 benchmarks and standards. In J. J. Mintzes (Ed.), Handbook of college science teaching (pp. 325-349). Arlington, VA: National Science Teachers Association. Ryder, J. (2002). School science education for citizenship: strategies for teaching about the epistemology of science. Journal of Curriculum Studies, 34(6), 637-658. Sanders, T. (2004, October). No time to waste: The vital role of college and university leaders in improving science and mathematics education. Paper presented at the Invitational Conference on Teacher Preparation and Institutions of Higher Education: Mathematics and Science Content Knowledge, Washington, DC. Schneider, R. (2006). Supporting science teacher thinking through curriculum materials. Proceedings of the 7th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 674-680). New York: ACM Press. Scott, L. A., Zimmerman, R., Chang, H., Heitzman, M., Krajcik, J., McNeill, K. L., Quintana, C., & Soloway, E. (2004). Chemation: A handheld chemistry modeling and animation tool. Proceeding of the 2004 Conference on Interaction Design and Children: Building A Community, College Park, MD, June 1-3 (pp. 145-146). New York: ACM Press. Squire, K., Barnett, M., Grant, J. M., & Higginbotham, T. (2004). Electromagnetism supercharged!: Learning physics with digital simulation games. Proceedings of the 6th International Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 513-520). New York: ACM Press. Steinkuehler, C., & Chmiel, M. (2006). Fostering scientific habits of mind in the context of online play. Proceedings of the 7th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 723-729). New York: ACM Press. Sturgeon, J. (2007). Creating an effective virtual school program. District Administration Magazine. Retrieved July 23, 2007, from http://www2.districtadministration.com/ viewarticle.aspx?articleid=1094
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Taylor, R. S., O’Reilly, T., Sinclair, G. P., & McNamara, D. S. (2006). Enhancing learning of expository science texts in a remedial reading classroom via iSTART. Proceedings of the 7th International Conference on Learning Sciences, Los Angeles, CA, June 22-26 (pp. 765-770). New York: ACM Press. Thompson, M. M. (2006). Online K-12 education: Opportunities for collaboration with higher-education. Journal of Asynchronous Learning Networks, 10(3). Retrieved May 28, 2007, from http://www.sloan-c.org/publications/jaln/v10n3/index.asp Tsui, C., & Treagust, D. (2007). Understanding genetics: Analysis of secondary students’ conceptual status. Journal of Research in Science Teaching, 44(2), 205-235. Wallace, R. M. (2000). The Web in high school science teaching: Constructing a technology in practice. In Extended Abstracts on Human Factors in Computing Systems, Vienna, Austria, April 24-29 (pp. 85-86). New York: ACM Press. Wang, S. K., & Reeves, T. C. (2006). The effects of a Web-based learning environment on student motivation in a high school earth science course. Educational Technology Research and Development, 54(6), 597-621. Woszczynski, A. B. (2006). CyberTech I: Online introduction to computer science course for high school students. SIGCSE Bulletin, 38(1), 153-157.
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Chapter.IV
Taking.University.Science. Education.Online
We search for ways to see ourselves. The computer is a new mirror, the first psychological machine. Beyond its nature as an analytical engine lies its second nature as an evocative object – Sherry Turkle (1948) While distance education in various forms has existed for many years, the exponential growth of computer-based, especially Web-based, education has presented a challenge for instructors who learned to operate in a traditional classroom environment. Not only must they design engaging, effective learning modules with authentic assessments, promote interaction and gauge understanding throughout the term, they must now do so without being able to see their students and without the students’ ability to participate in typical classroom activities (Edelson, 2001). This is especially problematic for subject areas such as science and mathematics, where demonstration and experiential activities are essential. Despite these complexities, there is tremendous potential for effective, engaging science instruction through distance learning, but also a pronounced, crippling disconnect between the technology being used in other disciplines—computer modeling, for example—and
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0 Downng & Holtz
instructional design. Online faculty and instructional designers typically do not collaborate with computer science colleagues who have the equipment and cutting edge expertise required to design sophisticated learning modules. Such sophisticated modules would move beyond the context and resource focused modules that are common—and acceptable, while not ideal, for citizen science-level learning—to those that provide tools and scaffolds that meet the needs of all science learners. A wide range of relevant technologies exist, including games, simulations, modeling, virtual instrumentation, and animation (Elgamagl, Fraser, & McMartin, 2005; Kin, 2004; Young-Suk, 2004). This chapter presents original research that investigates current uses of these Web-based instructional technologies.
Survey.of.Undergraduate.Distance. Science.Education.(SUDSE©) Teaching science online can be frustrating, especially when a student mislabels the coracoid process as the trapezius muscle because of two-dimensionality, when a long thread of conversation develops around a mistaken idea posted between instructor logins, or when a colleague steeped in radical constructivism insists that all of science can be taught inductively. In response to these and similar frustrations, and dissatisfaction with extant models that require undue extrapolation to meet the needs of science education, we developed a dual-stage investigation with two goals: to benchmark current practices in Web-based science education at degree-granting institutions of higher education and to develop a best practices, didactic model for Web-based science courses integrating the results of the benchmark study.
Background The strategies by which instructional design can be effectively applied to the distinctive pedagogical issues of teaching science online are a nascent area of educational research. In the past decade, a handful of books have reviewed pedagogical and technological aspects of distance science education such as the classic Open Science: Distance Teaching and Open Learning of Science Subjects by Ross and Scanlon (1995) and the edited volumes Internet Environments for Science Education by Linn, Davis, and Bell (2004) and Mediating Science Learning through Information and Communications Technology by Holliman and Scanlon (2004). Likewise, over the last decade there have been various articles published in education journals and disciplinary science journals describing efforts, usually course-specific initiatives, to develop Web-based science activities or courses. There has been no nation-wide benchmark study to determine the conventional and best practices for Web-based science instruction in the U.S. community colleges and universities. Hence, knowledge of emerging Web-based science practices in the U.S. has been patchy at best. For example, little is known about what is actually going on in Web-based science courses at the level of the disciplines within Liberal Arts and Science (e.g., biology, chemistry, physics, geology) or the corresponding course design features (e.g., course materials,
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learning activities, technological innovations, communication structures, and learning assessment). Organizations that regularly survey the institutional trends in distance education (e.g., the Sloan Consortium) have not focused on the details of instruction within the disciplines. Moreover, detailed information on course materials is often positioned behind secure portals, so while course descriptions and to a lesser extent syllabi may be available for an institution (e.g., through the Illinois virtual campus database http://www.ivc.illinois. edu/), other relevant instructional design details are obscured.
Purpose In order to probe into the fog of emerging practices for online science learning at the undergraduate level, we conducted a pilot study, Survey of Undergraduate Distance Science Education or SUDSE©. Our actual objective was to determine the extent of innovative technologies being employed in online science learning.
Methods The first stage of this investigation was an extensive literature review and content analysis, the results of which stimulated the first four sections of this book. As described, no directly relevant, comprehensive studies existed, so literature from the broad areas of online science learning, computer-mediated learning, and science education were critically examined in this first stage. Parameters noted included types of course activities and characteristics of the online science environment. In response to the variety of language used to describe learning activities and their goals, we revisited the literature to document learning theories and instruction and design approaches, either mentioned directly or alluded to indirectly, as well as assessment methods employed. In the second stage of the investigation, a sample of science educators in nine Midwestern states was identified from the 2004 College Blue Book: Distance Learning Programs and surveyed (Survey of Undergraduate Distance Science Education, SUDSE©, see appendix) to determine their use of methods and techniques identified in the content analysis. The survey was constructed using QuikData2, software developed by DePaul University. Each subject received an e-mail from the investigators, describing the purpose of the survey, explaining how the subject was identified and asking for another contact if the designated subject did not believe that he or she had the requisite experience, and providing the URL of the survey instrument. A follow-up e-mail was sent two weeks later and a final e-mail after another two weeks. Initial contact was limited to Illinois colleges and universities, in case procedural errors were found. At the time of the final e-mail to Illinois institutions, initial contact was made with identified institutions in the remaining eight-state grouping. The first five questions of Section 1 of the survey were designed to identify those programs that were inaccurately or incompletely described in the College Blue Book (2004). Those responding that none of their courses were offered either fully online or in blended/hybrid format (n=3) were asked to not complete the remainder of the survey, as programs that offer only Web-facilitated courses were not our focus. The survey was comprised of 59 questions,
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divided into four unequal sections: Course Offerings and Format (20 questions), Course Communication and Collaboration (seven questions), Forms of Inquiry and Course Activities (24 questions), and Assessment of Student Learning (eight questions). Independent variables were the five initial questions: •
Level of undergraduate course (upper or lower);
•
Percentage of online science courses offered fully online;
•
Percentage of online science courses offered in blended/hybrid format;
•
Percentage of online science courses offered in Web-facilitated format; and
•
Percentage of online science courses delivered asynchronously.
Data were analyzed using SPSS® Exact Module® (Chicago) for small sample size (N=23; 35.9 response rate). There were no significant differences between the independent variables and the categories explored, although interesting trends emerged.
Results. Overall, online courses tend to be lower level (e.g., freshman, sophomore) (78.3 %), asynchronous (83.4%), and in the physical sciences (33.8%) (see Table 4.1 for detail) In addition, courses are more likely in blended/hybrid formats (83.6%), although the fully online format is also common (79.3%), as was Web-facilitated (78.3%). That is, colleges and universities are using each of the three modes of delivery. The overwhelming majority of courses are conceptual in format, requiring no laboratory or field work (87.3%) and use an automated feedback function (e.g., quizzes or tests) (91.9%) for assessment. Communication uniformly occurs through discussion boards or e-mail (100%). Only 66.5% use any sort of synchronous communication and even fewer use course casting technology (17.4%). As documented in Table 4.2, the literature indicates ready implementation of several learning modalities for online science learning, although the extent of their use cannot be determined from the literature. The readiness of editors to publish case reports about some of these techniques (e.g., remote laboratories, interactive virtual reality) presaged the low percentages found with SUDSE©. Other assessment techniques were far more common and largely traditional. All respondents reported using recall-based quizzes and exams, term papers (as opposed to original research) and discussion, and 90.5% reported using portfolios. Concept mapping (61.9%), peer assessment (60.0%), and self assessment (75.0%) were less commonly used than traditional methods, although usage was greater than anticipated by the investigators, as was the opportunity for students to recapture lost points through corrective achievement (70.0%).
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Table 4.1. Distribution of course offerings U.S. National Science Foundation Course Category Physical Sciences Astronomy Chemistry Physics Other Environmental Sciences Atmospheric Sciences & Meteorology Earth Sciences Oceanography Other Life Sciences Agricultural Biological Medical Other Engineering Mathematics Miscellaneous Science
1
Number of Courses 2 3
4+
9 10 5 10
2 1 2 2
0 0 2 0
0 2 0 1
9
0
0
0
10 0 6
2 0 2
1 0 0
1 0 4
1 6 2 2 0 1 2
1 2 0 3 0 4 1
0 1 0 0 0 1 4
0 9 2 3 4 8 2 Total Courses
Total (% of Total Courses) 48 (33.8) 11 13 9 13 35 (24.6) 9 14 0 12 32 (22.6) 2 18 4 8 4 (2.8) 14 (9.9) 9 (6.3) 142 (100)
Table 4.2. Respondent use of literature-cited learning modalities Modality
Collaboration, group projects Laboratories via supplied specimen or apparatus Online modeling simulations, games, or puzzles Remote laboratories, haptic design Interactive virtual reality Internet repositories On-campus laboratories Virtual field trips Self-directed field trips Exploration and analysis of Web sites Field work or laboratory reports
Use for Learning Activity (%) 73.7 59.1 61.9 0 33.8 73.2 75.0 65.0 61.9 100.0 90.5
Of those, Use as Assessment Criteria (%) 54.7 61.9 59.8 0 32.7 50.0 90.0 57.9 75.3 90.0 100.0
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Discussion Although limited in scope, the SUDSE© pilot survey documents minimal use of sophisticated, innovative technologies in online science learning, as well as continued reliance on traditional assessment methods. It also appears to indicate educator discomfort with assessment of those technologies used, an urgent area for research. However, the initial content analysis was effective in highlighting current practices, from which the resultant model, delineated in Chapter XIII, developed. Interestingly, verbiage indicated a heavily constructivist bent, even as assessment measures were described as traditional, which is in keeping with cases profiled in Section IV of this book. Collaboration is not only an emergent practice (Bermejo, 2005), but one considered a science education best practice by the National Research Council (2005). Laboratories, whether virtual, selfdirected, or on-campus, are essential for experiential learning in the sciences, representing the essentially active nature of science knowledge creation (Osborne & Hennessy, 2006). Simulations, virtual reality, and other forms of Web-based interactivity augment the effectiveness of online learning activities, and are believed to counteract the perceived negative effects of student isolation, although the early research on this is inconclusive (Couture, 2004; Dalgarno, Bishop, & Bedgood, 2003; Huang, 2004). Finally, self-directed field work and field trips enhance students control of their own learning, enhancing autonomy (Boyle et al., 2003; Donert, 2003; Ramasundaram, Grunwald, Mangeot, Comerford, & Bliss, 2005; Wolfson, Magnuson, & Marsom, 2005). We expect to conduct a comprehensive SUDSE© study, expanding coverage to the entire United States, in subsequent years. We hope to note an expansion of technology usage in both learning activities and assessment methods, as well as broader coverage of field-specific coursework.
Revisiting.Current.Practice Current distance education efforts in science are negligible relative to other disciplines such as business and education, as evidenced by the comparative quantity of directed related literature. Most documented efforts are remarkably unimaginative and insufficient in meeting the goal of application, whether in citizen science or power science, as evidenced by the comparative quality of directly related literature. Effective science “learning environments comprise four basic components: enabling contexts, resources, tools, and scaffolds” (Kim & Hannafin, 2004, p.4). Contexts and resources are readily available via online delivery systems. In fact, the Web is one of the most valuable resources for any learning, and establishing context comprises the bulk of current online science courses (Evans, Gibbons, & Shah, 2004). However, tools and scaffolds are harder to furnish to students, as few currently exist online. Many online science courses rely on modules provided online by third parties, what the literature describes as virtual field trips. In one of our courses at DePaul University, two modules incorporate Cold Spring Harbor Laboratory’s virtual laboratory and learning site
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(http://www.dnai.org/index.htm), one of the richest Web sites in terms of content and variety of applications. The learning site uses case studies to provide enabling context and resources, while the virtual laboratory—a tool, as defined by Kim and Hannafin (2004)—takes students through generations, enabling them to understand the real science of genetics as compared to eugenics. However, the scaffolding— the step-wise support—provided to students is limited, which is typical with both free-use and commercially sponsored online modules (Sormunen & Pennanen, 2004). Each is developed for a particular audience, and scaffolding, which is expensive and time-consuming to develop, typically meets the audience’s minimum needs (Moehr, Protti, Lau, & Grimm, 2004). Programs designed for medical or nursing students, for example, assume a more sophisticated baseline than one designed for the general public (Fernandez, 2003). However, the vast majority of the population does not anticipate science careers, nor do they enter the course with substantial science background (Scanlon, Murphy, Thomas, & Whitelegg, 2004). Therefore, the level of support required for them must be considerably more detailed, yet less technical, than what is provided on the typical site, including Cold Spring Harbor Laboratory’s Web modules. At this point, incorporating more sophisticated programs, programs that currently exist in computer modeling and fields outside of education, would be ideal, yet it is not happening. The issue becomes even more urgent when students who do plan a career in science require distance courses and practical, hands-on experimentation; indeed, according to best practices in science education, such learning opportunities are desirable for all learners, but essential for those entering science professions. The United Kingdom’s outstanding PEARL (Practical Experimentation by Accessible Remote Learning) project, incorporating real-time distance research, appears to be the most sophisticated application to date (Scanlon, Colwell, Cooper, & Di Paolo, 2004), although similar projects are either in development or operating on a small scale in various countries, including Canada (Stevens, 1999), Italy (Sala, 2003), and the United States (Christian, 2003).
Conclusion While we recognize the limitations of this initial exploration, the SUDSE© results support both our experiences and our suppositions about the state of online science education. It is our hope that both respondents to that first survey and readers of this book use the survey template provided in the chapter appendix to imagine the possibilities open to them. Existing programs and those under development can benefit from comparison to this literature-based benchmark. As we clarify in later chapters, including the model presented in Chapter XIII, each of the options identified in SUDSE© may not be appropriate and possible for a given program. Those that focus on non-majors and lifelong learners, for example, may conclude that investing in production-quality equipment is not feasible, and that their courses must be limited to freely available options (e.g., NASA modules) or shared resources with another institution. Yet, every online science program can benefit from integration of some of the tools and modalities described, including innovative variation in assessment methods. We encourage college and university planners to think broadly along those lines. It is our position that Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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even non-majors and lifelong learners want the richness inherent in experiential support of learning. In fact, the richness of the online science learning experience may foster an expansion of interest and subsequent pursuit of science. We further encourage programs to use the SUDSE© survey as a self-assessment, to determine the status of their implementation in online science. Subsequent chapters build on the findings presented in Chapter IV, exploring important learning and assessment modalities and techniques in depth, and presenting multiple cases that employ both these modalities and alternatives reliant on the technological sophistication extant on site.
References Bermejo, S. (2005). Cooperative electronic learning in virtual laboratories through forums. IEEE Transactions on Education, 48(1), 140-149. Boyle, A., Conchie, S., Maguire, S., Martin, A., Milsom, C., Nash, R., et al. (2003). Fieldwork is good? The student experience of field courses. Planet, (Special Edition 5), 48-51. Christian, C. A. (2003). Scientists’ role in educational content development. Journal of Science Education and Technology, 12(1), 31-37. The College Blue Book: Distance Learning Programs. (2004). New York: Thomson-Gale. Couture, M. (2004). Realism in the design process and credibility of a simulation-based virtual laboratory. Journal of Computer Assisted Learning, 20, 40-49. Dalgarno, B., Bishop, A. G., & Bedgood, D. R. (2003). The potential of virtual laboratories for distance education science teaching: reflections from the development and evaluation of a virtual chemistry laboratory. UniServe Science Improving Learning Outcomes Conference, Sydney, Australia, October 2-3 (pp. 90-95). Sydney, Australia: University of Sydney. Donert, K. (2003). The Virtual Montana Project: Using open and distance learning to support fieldwork-based activities. International Journal of Fieldwork Studies, 1(1). Edelson, D. C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching, 38(3), 355-385. Elgamagl, A., Fraser, M., & McMartin, F. (2005). On-line educational shake table experiments. Journal of Professional Issues in Engineering Education & Practice, 131(1), p. 41-49. Evans, C., Gibbons, N. J., & Shah, K. (2004). Virtual learning in the biological sciences: Pitfalls of simply “putting notes on the Web.” Computers & Education, 43(1/2), 49-63. Fernandez, G. (2003). Lecture notes in computer science. New York: Springer-Verlag. Holliman, R., & Scanlon, E. (2004). Mediating science learning through information and communications technology. London: RoutledgeFalmer. Huang, C. (2004). Virtual labs: E-learning for tomorrow. PLoS Biology, 2(6), 2.
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Kim, M., & Hannafin, M. (2004). Designing online learning environments to support scientific inquiry. The Quarterly Review of Distance Education, 5(1), 1-10. Kin, C. C. (2004). Using virtual instruments to develop a real-time, Web-based laboratory. International Journal of Distance Education Technologies, 2(1), 18-30. Linn, M., Davis, E., & Bell, P. (2004). Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Moehr, J. R., Protti, D. J., Lau, F. Y., & Grimm, N. A. (2004). Project based experiential distance education: An oxymoron? International Journal of Medical Informatics, 73(2), 157-165. National Research Council. (2005). How students learn: History, mathematics and science in the classroom. Washington, D.C.: The National Academies Press. Osborne, J., & Hennessy, S. (2006). Report 6: Literature review in science education and the role of ICT: Promise, problems and future directions (No. 6). Bristol, UK: Futurelab. Ramasundaram, V., Grunwald, S., Mangeot, A., Comerford, N. B., & Bliss, C. M. (2005). Development of an environmental virtual field laboratory. Computers and Education, 45, 21-34. Ross, S., & Scanlon, E. (1995). Open science: Distance teaching and open learning of science subjects. London: Paul Chapman Publishing LTD. Sala, N. (2003). Hypermedia modules for distance education and virtual university: Some examples. International Journal of Distance Learning Technologies, 1(1), 78-97. Scanlon, E., Colwell, C., Cooper, M., & Di Paolo, T. (2004). Remote experiments, re-versioning and re-thinking science learning. Computers & Education, 43(3/4), 153-163. Scanlon, E., Murphy, P., Thomas, J., & Whitelegg, E. (Eds.). (2004). Reconsidering science learning. New York: RoutledgeFalmer. Sormunen, E., & Pennanen, S. (2004). The challenge of automated tutoring in Web-based learning environments for IR instruction. Information Research, 9(2). Retrieved May 28, 2007, from http://informationr.net/ir/9-2/paper169.html Stevens, K. (1999). Two Canadian approaches to teaching biology, chemistry, mathematics and physics to senior high school students in virtual classes. Australasian Science Education Research Association, New Zealand, EDRS. Wolfson, G. K., Magnuson, C. W., & Marsom, G. (2005). Changing the nature of the discourse: Teaching field seminars online. Journal of Social Work Education, 41(2), 355. Young,-Suk, S. (2004). Virtual experiment environments design for science education. International Journal of Distance Learning Technologies, 2(4), 62-76.
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Downng & Holtz
Appendix:.SUDSE.Online.Survey Survey.of.Undergraduate.Distance.Science. Education.(SUDSE)© Instructions Background The continued growth in general studies and liberal arts and science courses and programs online has lead to an escalation in the number of students whose science learning experiences will be, to varying extents, Web-based. Despite the growth in popularity of Web-based learning formats to teach undergraduate science, very little is known about national trends or current practices in this area.
Goals of the Study The goal of the Survey of Undergraduate Distance Science Education (SUDSE) is to provide the first far-reaching analysis of current practices in undergraduate Web-based science education at degree-granting institutions. This survey concentrates on trends in distance education for the physical, environmental, life, engineering, and mathematical sciences. It is intended to address primary questions concerning emerging Web-based science practices in the U.S.: •
What specific instructional design approaches are undergraduate programs employing to provide science instruction online?
•
What online technological approaches and innovations are institutions utilizing to provide science to undergraduates?
•
How are the science disciplines approaching teaching science online through their course materials and learning activities?
•
What communication structures are being used to facilitate learning in online science?
•
How is student learning being assessed in online science courses?
Participation Respondents to this survey will be assisting in the development of a benchmark study on current practices for undergraduate science instruction online. All respondents will receive a summary report of the findings after they have been compiled. All individual responses to SUDSE will be considered confidential and will not be released. Validity testing of the survey showed that the average completion time was just under 13 minutes. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Investigation This study is being conducted by Dr. Kevin F. Downing and Dr. Jennifer K. Holtz, science faculty members of DePaul University’s college for adult learners, the School for New Learning. This project is supported by a grant from the Quality Instruction Council of DePaul University.
Section.1:.Online.Science.Course.Offerings.and.Format Please select only one answer, unless otherwise indicated. *1. What is the level of online undergraduate science courses offered at your institution? (Check all that apply) Lower division (freshman/sophomore)
Upper division (junior/senior)
*2. What percentage of your online science courses are offered Fully Online (require no onsite activities, content is delivered entirely online)? 0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
*3. What percentage of your online science courses are Blended/Hybrid (include some face to-face onsite meetings, but have a significant amount of content delivered online)? 0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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0 Downng & Holtz
*4. What percentage of your online science courses are Web-Facilitated (use course management software for basic information such as the syllabus, but content is delivered face-to-face)? 0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
*5. What percentage of your online science courses are delivered asynchronously (i.e., no requirement for students and instructor to be online at same time)? 0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
If you answered “0%” to both Questions 2 AND 3, you do not need to continue. Thank you! What online science courses, by Scientific Field, are offered at your institution? Please include fully online and hybrid courses, and exclude those courses that are Web-facilitated in your response. For each field, indicate the approximate number of individual courses offered fully online or as a hybrid. 6. Physical Sciences: Astronomy 0
1
2
3
4 or more
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7. Physical Sciences: Chemistry 0
1
2
3
4 or more
8. Physical Sciences: Physics
0
1
2
3
4 or more
9. Physical Sciences: Other (such as General Physical Science and multi-disciplinary courses)
0
1
2
3
4 or more
10. Environmental Sciences: Atmospheric Sciences and Meteorology
0
1
2
3
4 or more
11. Environmental Sciences: Earth Sciences (Geology and related courses)
0
1
2
3
4 or more
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12. Environmental Sciences: Oceanography Sciences
0
1
2
3
4 or more
13. Environmental Sciences: Other Environmental Science Courses (such as multi-disciplinary courses)
0
1
2
3
4 or more
14. Life Sciences: Agricultural (includes such areas as Agricultural Sciences, Animal Science, Conservation, Fish and Wildlife, Forestry, and Horticulture)
0
1
2
3
4 or more
15. Life Sciences: Biological
0
1
2
3
4 or more
16. Life Sciences: Medical (including Nursing and allied medical fields such as Radiology, etc.)
0
1
2
3
4 or more
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17. Life Sciences: Other (includes multi+disciplinary projects within life sciences.)
0
1
2
3
4 or more
18. Engineering
0
1
2
3
4 or more
19. Mathematics
0
1
2
3
4 or more
20. Miscellaneous Science Courses (includes Archaeology, History of Science, and Science Education)
0
1
2
3
4 or more
( * = required field )
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Section.2:.Online.Science.Course.Communication.and. Collaboration 21. What percentage of your online science courses uses discussion boards or e-mail as the main form of communication?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
22. What percentage of your online science courses uses a form of synchronous communication (e.g., chat rooms or real-time audio and video?)
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
23. What percentage of your online science courses uses course casting to support course learning (e.g., iPods and podcasting) ?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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24. What percentage of your online science courses uses team projects and group collaboration facilitated at a distance (e.g., chat rooms or real-time audio and video)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
25. How often are the activities described in Question 24 used in student assessment? Always
Sometimes
Never
Not Applicable
26. What percentage of your online science courses uses a form of online synchronous tutorials (e.g., between the instructor and student)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
27. What percentage of your online science courses uses a software platform that incorporates automatic feedback?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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Section.3:.Online.Science-Forms.of.Inquiry.and.Course. Activities 28. What percentage of your online science courses are conceptual, having no laboratory work requirements?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
29. What percentage of your online science courses makes use of supplied specimen kits or other apparatus (e.g., laboratory conducted by student at home)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
30. How often are the activities in Question 29 used for student assessment? Always
Sometimes
Never
Not Applicable
31. What percentage of your online science courses employs online modeling, simulations, puzzles, and/or games?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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32. How often are the activities in Question 31 used for student assessment? Always
Sometimes
Never
Not Applicable
33. What percentage of your online science courses employs remote labs, where laboratory apparatus is actually controlled at a distance (including haptic design)
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
34. How often are the activities in Question 33 used for student assessment? Always
Sometimes
Never
Not Applicable
35. What percentage of your online science courses uses virtual reality (i.e., 3-D interactivity) as a part of the learning experience (e.g., virtual specimens)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
36. How often are the activities in Question 35 used for student assessment? Always
Sometimes
Never
Not Applicable
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37. What percentage of your online science courses uses Internet repositories (e.g., digital museum resources) as a part of the learning experience?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
38. How often are the activities in Question 37 used for student assessment? Always
Sometimes
Never
Not Applicable
39. What percentage of your online science courses requires complementary onsite laboratory activities (i.e., are hybrid/blended only for their laboratory activities)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
40. How often are the activities in Question 39 used for student assessment? Always
Sometimes
Never
Not Applicable
41. What percentage of your online science courses uses “virtual field trips” (where students use thematically organized/linked Web sites)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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42. How often are the activities in Question 41 used for student assessment? Always
Sometimes
Never
Not Applicable
43. What percentage of your online science courses uses practical field trips where students undertake self-directed experiential activities away from the computer (i.e., are hybrid/blended only for their field trip activities)?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
44. How often are the activities in Question 43 used for student assessment? Always
Sometimes
Never
Not Applicable
45. What percentage of your online science courses requires exploration and analysis of websites as a part of the learning experience?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
46. How often are the activities in Question 45 used for student assessment? Always
Sometimes
Never
Not Applicable
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0 Downng & Holtz
47. What percentage of your online science courses requires field trip and laboratory reports summarizing online learning activities?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
48. How often are the activities in Question 47 used for student assessment? Always
Sometimes
Never
Not Applicable
Section.4:.Online.Science-Assessment.of.Student.Learning Again, referring only to your online science courses that are either fully online or hybrid, and excluding those courses that are Web-facilitated, please indicate which student assessment methods are used in your distance program. 49. In what percentage of your online science courses is student learning assessed through recall-based quizzes or exams?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
50. In what percentage of your online science courses is student learning assessed through development of term papers, as opposed to original research?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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51. In what percentage of your online science courses is student learning assessed through portfolio development?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
52. In what percentage of your online science courses is student learning assessed through discussion with other learners or the instructor?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
53. In what percentage of your online science courses is student learning assessed through concept mapping?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
54. In what percentage of your online science courses do students engage in self-assessment?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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55. In what percentage of your online science courses do students engage in peer assessment?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
56. In what percentage of your online science courses can students recapture lost points through corrective achievement?
0%
1 - 20%
21 - 40%
41 - 60%
61 - 80%
81 - 100%
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The Role of Practcal Work n Onlne Scence
Chapter.V
The.Role.of.Practical.Work. in.Online.Science
I hear and I forget. I see and I remember. I do and I understand. – Confucius (551-479 BC) There are many educational strategies to achieve learning objectives that prepare students to adapt and survive more effectively in life. Many of these approaches involve, to some degree, practical learning experiences structured to emulate meaningful situations, tasks, and the problem solving required of the real world. In science, educators have long held and place particular importance in the idea that hands-on experiential activities are a fundamental tenet of learning. The portion of scientific instruction devoted to learn by doing is called practical work. In this chapter, we explore the concept of practical work in science instruction, including categories of practical work, the historical basis and development of practical work, its purpose and value, controversies concerning practical work’s utility in science instruction, the importance of practical work in online science instruction, and the design of practical work learning environments. This chapter builds a rationale for the broad value and integral importance of practical work in science education at both the K-12 and university level and, as such, the necessity for its intentional implementation in online science learning environments. In this way, the practical work discussion of Chapter V provides the Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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underpinnings to later chapters that review current and emerging forms and technologies to support online practical work.
What.is.Practical.Work? Practical work is suitably characterized as the linking of the domain of ideas with the domain of real objects and observable things, and more specifically, “any teaching or learning activity that involves at some point the student in observing or manipulating real objects and materials” (Millar, 2004, p. 2; Millar, Le Maréchal, & Tiberghien, 1999). Within this definition of practical work, direct representations of objects or materials also constitute practical work (Millar, 1998). Similarly, Hofstein and Lunetta (2003) define a laboratory as, “learning experiences in which students interact with materials and/or with models to observe and understand the natural world” (p. 31). This definition too is inclusive of virtual practical work by its acceptance of models as legitimate lab activities. Finally, in a general review of America’s high school labs, Singer, Hilton, and Schwiengruber (2005) describe laboratory experiences as “opportunities for students to interact directly with the material world (or with data drawn from the material world), using the tools, data collection techniques, models, and theories of science” (p. 4). Thus, under these inclusive definitions of practical work, audio-visual representations, virtual objects, simulations, and other representations in the vein of online laboratories and field trips are legitimate forms and/or instruments of practical work. There are various explanations of what practical work comprises. For example, Woolnough and Allsop (1985) classified practical tasks into four categories that included: (1) exercises-to corroborate theory or teach procedure, (2) experiences—to improve the perception of science phenomena, (3) investigations—that allow the direct application of scientific reasoning to a theoretical problem, and (4) illustrations—to demonstrate scientific laws and theories. Barton (1998a) further categorized computer-assisted practical work as either illustrative having stepwise instructions or investigative involving observations and predictions. As regularly construed, practical work in science comprises the laboratory and field work exercises in a course that characterize the style of scientific inquiry itself as well as the modeled activities of that discipline’s science professionals (e.g., chemical experiments, biological sample collection, astronomical observations, geological mapping, etc.). However, practical work is more expansive than just laboratory work and field work. For example, Woolnough and Allsop (1985) categorization also entails aspects of lectures and homework exercises that illustrate theory and procedure. Moreover, the meaning of practical work when applied to science education is, in the main, expansive and not only incorporates scientific techniques of the laboratory or field (what scientists do), but also scientific behaviors (who scientists are). What is more, practical work in science is not just the dominion of nascent scientists and has other levels of purpose. Practical work can support learning in a variety of science dimensions such as those demarcated by Chin, Munby, Hutchinson, Taylor, and Clark (2004) as: (1) bench science—whose purpose is to generate new science, (2) school science—whose purpose is to promote science literacy, and (3) workplace science—whose purpose is to support the goals of the workplace. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
The Role of Practcal Work n Onlne Scence
Authentic practical work is a subdivision of practical work that specifically entails building practical learning tasks associated with workplace science contexts. To distinguish practical work that is not authentic, that is, not related to the professional workplace, we assign the idiom model practical work. Authentic practical work is a sequence of realistic experiences to prepare the budding professional scientist or the student pursuing a technical vocation in the principles, techniques, and professional behaviors of science. Alternatively, for learners who are not pursuing a science profession, such as non-scientists engaged in school science, practical work learned as a school or college requirement is an applied excursion through science as a way of knowing. The rationale of immersing the non-scientist in practical work is, among other things, to help demystify the body of knowledge that greatly shapes a person’s world. Consequently, practical work promotes scientific literacy and the development of an informed citizenry. In a more affective learning capacity and throughout all dimensions of science, practical work can also provide a tangible opportunity for students to attain the gratifying benefit of knowledge, where the active learning experience is itself the reward. Practical work is often the vehicle by which an instructor transforms scientific theory into action for students, thus functioning as praxis. However, under certain circumstances practical work may itself be the foundation and vehicle by which a learner deduces the scientific principles, such as during a well-designed experiment that “reveals” the relationships and parameters of a scientific principle or law. Yet this latter inquiry approach can be difficult to manage in practice and has been criticized (e.g., Millar, 2004). Practical experiences often work at the interface of the educational realm and the real world, permitting students to interact and collaborate with professional scientists. It should be clear that practical work is the essence of science. We summarize various designated types of practical work as well as common forms in Table 5.1.
Table 5.1. Types of practical work in science education Practical Tasks Woolnough and Allsop (1985)
Exercises Promoting Skill Development Experiences Investigations Illustrations of Theory
ComputerAssisted Practical Work Types Barton (1998a) Illustrative
Investigative
Types of Practical Work
Learning Outcomes Millar et al. (2004)
Examples of Practical Work Forms for Distance Education
Authentic
Identification
Demonstrations
Learning Facts
Collaboration Experiences Animation/Modeling Simulations
Model
Learning Concepts Learning Relationships Learning Models/ Theories
“Home” Laboratories Virtual Laboratories Remote Experiments Field Work Virtual Field Trips
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Where.Does.Practical.Work.Take.Place? Practical work is central to instruction in many disciplines and is achieved within a classroom, outside of a classroom, and in physical and/or virtual spaces. For example, law students taught the dynamics of the courtroom by way of a moot court or business students learning decision-making skills in a mock boardroom are learning by means of in-class practical work in a physical space. Practical work is also manifest in a variety of outside-of-class formats and physical spaces such as internships, externships, stage performances, travel courses, and field study. In an online science course, a student’s practical work may occur in their home as in the case of a science kit, may be in a virtual space during a simulated experiment, or happen in a hybrid of space formats as in the case of a remote experiment operated using a virtual interface.
A.Brief.History.of.Practical.Work.in.the. UK.and.U.S. United.Kingdom The value of learning by doing is not a modern-day happening but one ensconced in ancient wisdom. The prevailing era of scientific inquiry, which elevated and codified practical work coalesced in the experimental methods advanced by England’s William Gilbert of Colchester, physician to Elizabeth I. Gilbert’s seminal and widely read De Magnete published in 1600, rekindled the experimental approach of earlier practitioners of scientific inquiry such as the Islamic mathematician Ibn al-Haitham (965-1040) and the English philosopher and Franciscan monk, Roger Bacon (1213-1294). Gilbert’s treatise on magnetism, with its corresponding emphasis on scientific procedure, was a direct influence on the more renowned experimental work of Galileo (Gribben, 2002). In consequence, De Magnete helped liberate science from the qualitative Aristotelian philosophy of logic and reason that characterized the late middle ages. Despite the popularization of the “scientific method” and experimentation in the Renaissance, the implementation of practical work as a mainstay of science education would come about centuries later in the Victorian schools and colleges in England. The argument for and incorporation of practical work as the hallmark of science instruction in schools in mid-18th century England arose as the science community promoted science education to foster the scientific habit of mind. In the last half of the 19th century, Victorian England was in the midst of an intensely technological “second industrial revolution” with a burgeoning number of scientists and engineers. Professional scientists and their organizations were actively popularizing science and validating their professional status through a variety of journals that arose at this time (Barton, 1998b). The notion of practical work’s value in educating students is captured in the British Association for the Advancement of Science report of 1868 (BAAS, 1868). This report argued the rationale for teaching science by means of practical work consisted of five key learning outcomes. The outcomes were: (1) mental training, (2) appealing to certain students who were not motivated by other disciplines, (3) Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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to diminish the disadvantages to those not versed in scientific philosophy, (4) to improve the quality of the after life, and (5) to promote science, as it was materially useful and impacted future civilization (Jenkins, 1998). Therefore, the British scientific community’s position early on was that practical scientific work in schools and colleges could support societal values such as equity and prosperity, as well as the more individualized learning objectives of intellectual challenge and religious reassurance. Following the assimilation of practical work into the school curricula in England in the Victorian era, the rationale for practical work has repositioned owing to several waves of educational reform. Wellington (1998) and Jenkins (1998) identify three major phases of curricular emphasis in practical work since the 1960’s. The first phase, discovery, involved an inductive approach to learning and was a consequence of the Nuffield Science Teaching Project of the 1960’s. This phase stressed science abilities and skills rather than theory. Hodson (1996) has labeled this phase naïve inductionism because of its characteristic presumption of the ability of students to reveal the principles of science on their own without prior mastery of supporting ideas. The discovery phase of practical work was followed by the process science movement in the 1980’s (Fullick, 2004) which operated under the assumption that the laws and theories of science could be divorced from a student’s engagement with the process of science. In the process approach students focused on aspects of the procedures of the scientific method such as observing phenomena, making interpretations, and reaching conclusions. Subsequently, in the 1980’s-90’s and set off by the emergence of the National Curriculum, SC1, the experimental and investigative phase brought about a re-emphasis of teaching concepts (Banks & McCormick, 2005) as well as learning science through the control of variables and evaluation of evidence (Wellington, 1998). The contemporary UK National Curriculum in science (www.nc.uk.net) has a separate program of study for each of a student’s key stages centering on the four chief science learning objectives or sections, SC1-SC4 (Figure Figure 5.1. Aspects of the UK National Curriculum Structure for science. Kevin Downing, personal image.
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5.1). The practical work anchored SC1 emphasizes both the process of science (ideas and evidence) as well as investigative skills.
United.States In the United States, state-maintained public schools were an outgrowth of its early industrial evolution from 1820-1850 (Carlton, 1906). The great expansion of the U.S. public college system followed the public school expansion after passage of the Morrill Act in 1862 establishing 37 public land-grant institutions. Although industrialization was a key impetus for the development of the public school system, the curriculum did not concentrate on science and technology; rather it placed an emphasis on teaching the classical languages for mental development. By the second half of the 18th century, there was a growing advocacy by scientists to replace the emphasis on classical languages with an emphasis on science in both the school and college curriculum (DeBoer, 1991). This was most notably marked by the 1867 publication of The Culture Demanded by Modern Life edited by Edward Livingston Youmans in which scientists from both America and England made the case for teaching science to develop mental discipline. Despite the mounting recognition in the United States of the importance of science and the need for its greater incorporation into both the school and college curriculum, it was not until the 1893 publication of the influential National Education Association’s Committee “of Ten” Report on Secondary Schools, that secondary schools nationwide more comprehensively incorporated practical work into their curricula. The Committee of Ten Report placed significant emphasis on practical work as DeBoer (1991) notes: “[the report] strongly supported the use of the laboratory in teaching science courses, advocating double periods for lab instruction… and one afternoon per week to be set aside for out-of-door instruction in geography, botany, zoology and geology” (p. 49). After this interval of great emphasis in placing practical work in the core of the science curriculum, science education in the United States became enmeshed in the progressive era, from 1917-1957, and its emphasis on student-centeredness and social engagement. As for the progressive era’s impact on nationwide science education goals, DeBoer (1991) concludes that this was largely a period of confusion including the inability of educators to come to concurrence about the appropriate use of the laboratory to meet learning objectives. The next major realignment of scientific education strategies occurred in the late 1950s, referred to as the Sputnik era of science education. During this phase of educational reform, science education flourished in the United States predominantly as the government passed the National Defense Education Act (NDEA) of 1958 to assure its competitive edge in science during the Cold War. Subsequent to the NDEA, three major phases of practical work are identified: (1) discovery, (2) process approach, and (3) constructivism (Hodson, 1996). The first two paradigm shifts, discovery and process approach, parallel the evolution of the practical work strategies described previously for England. The last and existing phase, the constructivist movement, more strongly emphasizes the idea that the individual student assembles their own knowledge base from their experiences and iterative reflection and that there is no ontological reality. The National Academy of Science’s 1996 National Science Education Standards (NSES) are the current canon of science standards for K-12 in the U.S. The NSES emphasizes and subdivides the study of science into science as inquiry, physical
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science, life science, earth and space science, science and technology, science in personal and social perspectives, and the history and nature of science.
Purpose.and.Value.of.Practical.Work Numerous studies have discussed the purpose and value of practical work as a learning strategy in science education. While there is no generally accepted archetypal set of values or purpose for practical work in science, there is overlap in many areas among studies. For example, in their seminal book on science distance education, Open Science, Ross and Scanlon (1995) clustered the aims of practical work into the following four categories: (1) training in technique (psychomotor, observation, data handling), (2) learning basic ideas of the subject, (3) learning how to do research, and (4) motivating through interest. In another study, Hofstein and Lunetta (2003) summarized the recent claims of the benefits of the science laboratory in schools as: (1) understanding science concepts, (2) stimulating interest and motivation, (3) developing practical science skills (i.e., scientific inquiry) and problem solving (i.e., scientific reasoning), (4) developing scientific habits of the mind, (5) understanding the nature of science, and (6) assisting the student in the use of science for everyday life. In one more case, Ma and Nickerson (2006) developed a four-fold framework for laboratory objectives based on the standards of the Accreditation Board for Engineering and Technology that consists of conceptual understanding, design skills, social skills, and professional skills. Lastly, Singer et al. (2005) in America’s Lab Report summarized the goals of laboratory experiences as: •
enhancing mastery of subject matter;
•
developing scientific reasoning;
•
understanding the complexity and ambiguity of empirical work;
•
developing practical skills;
•
understanding the nature of science;
•
cultivating interest in science and interest in learning science; and
•
developing teamwork abilities.
In these examples, purpose is assigned to practical work predominantly based on its use value (sensu “contingent valuation” of Bateman and Willis, 1999) to the student (i.e., purpose is student-centered). Another manner in which to consider the purpose and value of practical work in science is its relationship to both the learner and society. Namely, practical work can be viewed and contextualized through traditional frameworks of student learning modes, educational objectives as well as its value to society. For instance, scientific practical work can be purposed to support: (1) an individual’s key learning modes such as the cognitive, affective, and psychomotor categories of Bloom’s Taxonomy (1956), (2) a learner’s key personal
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educational objectives, akin to Houle’s (1961) typology, such as to achieve specific goals, partake in social activities, or for pure learning, and (3) learning rationales that are tied to the economic value of scientific learning such as indirect or option value to the individual or a broader portion of society such as a family, employers, a community, a nation, and humanity (i.e., future value of Bateman & Willis, 1999, brought into play in Chapter XIII). Thus, in the enumerated rationales for scientific practical work described above, the first two involve practical work framed about the individual and the third is guided by factors external to the individual linked to societal objectives. As an example, a nation’s general interest in having all its students understand the functions of an ecosystem, so they can make informed decisions about a future environmental issue, would be a rationale for practical work in supporting indirect or option value to that society. A wide-ranging compilation of selected aims for practical work is provided in the Appendix for Chapter V.
Value.of.Practical.Field.Work While practical work is often more closely associated with a K-12 school or university laboratory in the literature, field work is another essential form of practical learning adaptable to online science environments. A summary of the reasons for incorporating field work into the design of instructional tasks in environmental science is provided by van Marion (1999) and serves as a good general rationale for other disciplines that are complimented by fieldwork. Within this exemplar, the key ways in which field work can promote science learning outcomes are: (1) making subject matter tangible, (2) placing subject matter in a realistic context, (3) providing students with practical applications of scientific knowledge, (4) providing practical experience in science methods and experience with scientific instruments, (5) improving observation skills, (6) cultivating student cooperation and team roles, (7) developing positive attitudes about the outdoors, and (8) gaining insight into the connection of nature and society and specifically into the world’s resource use (van Marion, 1999). For many of us who teach science courses that rely on field activities and observations, there is great satisfaction when a student rapidly achieves understanding after directly experiencing the subject matter that has been bewildering them in a lecture or a textbook. In later chapters of this book, we examine ways to undertake practical field work in the online science learning context.
Additional.Purposes.for.Practical.Work Affective.Skills Practical work in science also contributes to developing affective skills in learners. For example, Soloman (1999) reviewed the role of play and exploration in practical work as it promotes an acquaintance with the movements necessary to work with apparatus and in Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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formulating plans of how to proceed with an exploration. In Solomon’s model of play and exploration (“envisionment”) the developmental steps are: (1) play, (2) curiosity, (3) interest in cause and effect, and (4) investigation of an original agenda. The key idea of play in affective learning is that it can be an entry point and motivator for more complex use of science skills and ideas.
Personal.Knowledge Practical work may also support personal knowledge, described by Woolnough (1998) as “the type of knowledge which the individual has absorbed, through experience and practice, and which is useful to that person as he or she seeks to make sense of the world” (p. 110). Woolnough (1998) suggests that practical work in science provides a place to develop and apply personal knowledge, partake in the cultural activity of science, promote personal knowledge useful to employers, and act as bridge to public knowledge of science.
Conveying.the.Language.of.Science There are many challenges to the international goal of building scientific cultures (i.e., UNESCO, 2005) and bridging the knowledge divides within a society. Science and technology are principal generators of new words and communication structures and inherently generate knowledge divides within a society. Practical work can and should play a role in bridging the knowledge gaps in these areas between science and society. Gough (1998) describes a creative perspective on the rationale for practical work to address science language skills in which the school laboratory serves as a “theatre of representation” for science. Gough highlights the value and importance of the laboratory as a place to develop scientific language skills that can act as a bridge from a student’s colloquial language to scientific language. Whether vocabulary is more effectively transferred in a lab situation versus other aspects of a science course (discussion and readings) is uncertain, however the applied nature of scientific vocabulary during practical work is at very least contributory towards closing knowledge gaps.
Using.Online.Practical.Work.to.Make.Science.Visible A great deal of research has been conducted on hands-on investigation tools for K-12 that are facilitated by online learning environments under an inquiry framework. One of the capable systems is the Web-based Inquiry Science Environment (WISE) described by Slotta (2004) for grades 5-12, with over 50 inquiry activities for students. This system is founded on the Scaffolded Knowledge Integration Framework for Instruction (SKI) of Linn, Davis, and Eylon (2004). SKI includes four major tenets: making science accessible, making thinking visible, helping students learn from others, and promoting autonomy and lifelong learning. The key use of the computer with WISE is the area of making things visible (animating, modeling, graphing, etc.) and this is complimented with strategic idea generation, collaboration, and reporting activities. A broad university-level resource or consortium for inquiry Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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analogous the WISE model that faculty could tap into has not been ventured, although improvements in digital libraries such as the National Science Digital Library are a sign of progress in this area.
Authentic.Practical.Work.in.Support.of.Vocational.Training Authentic work, as introduced earlier in this chapter, involves providing learning environments that closely replicate the professional scientist’s working environment and affording the opportunity for students to engage in the everyday behaviors and activities of a science vocation. For example, Fox (2006) reports on a role-play exercise to motivate and provide students with profession-specific problem solving activities in epidemiology. Other medical areas have similar emphases on authentic activities, such as simulated patients in pharmacy and nursing. One of the key rationales for authentic practical work is that it provides training in desired characteristics that employers prefer. Expanding on this idea, Coles (1997) identified the employer-preferred characteristics served by practical work activities as: (1) commitment to interest, (2) core skills of communication, numeracy, and information technology skills, (3) team work, (4) self-reliance and resourcefulness, (5) analysis, (6) creativity, (7) professional integrity, and (8) specific knowledge of work fields. Exclusive of how authentic practical work may positively affect vocational training, it has been shown to produce favorable learning outcomes in K-12 school settings. For instance, Woolnough (1998) found that National award schemes in England, involving authentic work (e.g., CREativity in Science and Technology CREST) produced high quality student work.
Practical.Work.Controversies Diminishing.Practical.Work.in.Science.Education? Despite practical work’s central curricular role in science education for over a century and its bona fide and tangible outcomes (i.e., the technological world we live in), the utility and application of practical work in science education, particularly at the K-12 level, is not universally accepted and there is a significant amount of literature decrying the potential limitations of practical work. Some researchers conclude that in K-12 science there has been an overemphasis on practical work and that students are served best when examining the discourse and language of science, with training in science replaced by an education in science (Osborne, 1998). A key argument is that there is little educational value in practical work because the ideas and principles of science are neither straightforward nor easily reproduced in the lab. We have heard these same sorts of arguments for undergraduate adult learners in university settings; that an education in science centered on conceptual courses without practical work is sufficient emersion in science. As another case in point, Wellington (1998) reviews counter arguments for the cognitive, affective, and skills development arguments that are advanced to buttress the importance of practical work in K-12 science education. The detractors of practical work argue that Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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students are often confused cognitively if concepts are not introduced before practical experiences (i.e., that practical work is not a good venue to introduce theories). They also argue that some students react negatively and are not motivated by practical work, some may be marginalized during collaborative tasks and that there is little evidence that skills (e.g., observation, measurement, prediction, inference, and dexterity skills) are transferable (Wellington, 1998). In contrast, some researchers argue just the contrary, that virtual science laboratories may be beneficial to student learning in areas other than the transfer of science content and principles, particularly in the way they can promote student collaboration (e.g., sharing data), resource collection skills, and reporting skills (Wardle, 1999). Of course, the grounds used to disparage the use of practical work could and do occur in learning activities outside of practical work tasks in the sciences, as well as in the learning activities of all disciplines. In many cases, miseducative experiences and breakdowns in learning are the result of a failure to recognize the diversity of learning characteristics of the students and to customize the practical tasks. For instance, Felder and Brent (2005) recognize three key aspects of student learning diversity that are requisite in instructional design in engineering education: learning styles, approaches to learning, and intellectual development levels. A well-constructed learning activity will incorporate a variety of approaches, activities, and levels to provide a student-centered (i.e., individualized) learning experience that optimizes intellectual growth (Felder & Brent, 2004) as well as features to motivate the student and promote social interaction (Hofstein & Lunetta, 2003). Hence, if practical work activities are to be successful they too need to heed successful pedagogical principles.
Constructivism.and.Practical.Work The rigid constructivist view of practical work in science advances the idea that scientific facts are not constrained by nature but are socially constructed or made up in the laboratory (see review by Cole, 1996). It is not surprising, therefore, that some of the most skeptical viewpoints regarding practical work and the effectiveness of the school laboratory originate from scholars allied with the field of sociological construction of technology. For example, Jenkins (1999) provides an unsympathetic view of current practical work strategies and aims and outcomes in science education, suggesting that many practices are artifacts of history and that science education has not evolved in its pedagogy to consider the more complex systems of knowledge production (i.e., those that sociological introspection can reveal). In addition, Jenkins (1999) argues that it is difficult to replicate the methods of science in the school setting and that the aims of science in the laboratory are not regularly met, thus diminishing the claims about the importance of school practical work. Likewise, Millar (1998) concludes that the scientific accounts of phenomena are only modestly supported by practical work tasks in the laboratory and that they are better positioned as a communication strategy of how research laboratory investigations take place and the corresponding difficulty in replicating the original discovery. Jenkins (1999) concludes that practical work may best serve to melt disciplinary boundaries, break barriers between the real world and school, motivate students, provide some insights into the nature of science, and promote collaborative working and communication skills (i.e., an emphasis on affective skills and less on science as a way of knowing). Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Supplanting.the.Constructivist.Paradigm Constructivism has been the epistemological panacea for much of curriculum and instructional design in the past few decades. However, there are fundamental weaknesses if not incurable flaws in the application of rigid constructivism to teaching. To begin with, most of what constructivism “is” has actually been derived from earlier scholars, but repackaged as new jargon to largely serve nihilistic ideologies rather than based on fundamental principles of learning (see seminal review by Matthews, 2002). Likewise, radical constructivism, which shuns the assumption of an objective world and requires students to discover scientific principles individually, has also been shown to be baseless. As argued by Rezaei and Katz (2002), “it is preposterous to expect every student to be another Darwin, Newton or Einstein” (p. 4). It is clear that the minimally guided instruction espoused by constructivism fails until students have a foundation of science knowledge commensurate with, and the level for internal guidance necessary for, independent learning (Kirschner, Sweller, & Clark, 2006). What is more, growing evidence from research on the neurological basis of learning has found that knowledge acquisition at the fundamental biological level follows a hypothetico-predictive form in which “self-generated ideas/representations are tested by comparing expected and observed outcomes… In the final analysis, ideas—including scientific hypotheses and theories—stand or fall, not due to social negotiation, but due to their inability to predict future events” (Lawson, 2003, p. xiv). The implication of the neurological basis of learning for science instruction is that the pattern of scientific thinking is congruent with the natural basis of thinking and that instruction must be fundamentally attentive to helping students understand the interconnections between hypotheses, predictions and evidence as it impacts learning (Lawson, 2003). It is our contention that constructivism is overdue to be supplanted in science education by learning models more tightly founded on the neurological basis of learning and the hypothetico-predictive approach. If we are to achieve as Mayer (2005) rightly argues a learner centered approach based on how the human mind works to aid human cognition, rather than a technology-centered approach to multimedia learning environments, then we submit that this approach should be hypothetico-predictive. Moreover, whether there is a causal link between the constructivist movement’s impingement on science education and the U.S.’s recent failure in science is a research question needing serious exploration. The implication of a post-constructivist paradigm for practical work is that its importance will be elevated. As practical work has a fundamental tie to experimentation and direct scientific thinking, it is evident that it can and should play a large and central role in supporting hypothetico-predictive approaches in the classroom as well as their expression in online science instruction.
Critiques.of.Online.Practical.Work Practical work is part of the historical and pedagogical foundation of learning in the knowledge area of science. Consequently, if the online science format is to be a vital and credible practice in science instruction in schools and universities, the approaches to practical work should result in comparable outcomes to onsite practical experiences. As Couture (2004) Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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aptly points out, “…the credibility of an instructional simulation is the most important issue in distance education, where it may replace hands-on activities” (p. 40). Although virtual labs are commonplace in both high school and colleges, they are not universally accepted as a legitimate means of promoting practical work. For example, the College Board, the association of schools and colleges involved in U.S. college admissions, recently decided not to recognize high school advanced placement courses lacking hands-on laboratory activities as a part of the learning (Dillon, 2006). Although the College Board is in the process of revisiting the issue, this event highlights the continuing maturation and slow acceptance of Web-enabled practical work in science. One of the common criticisms of online practical work activities is that they are lesser in the extent and quality of information they afford. For example, Millar (2004) argues that since real events contain more information then representations of them (videos, computer simulations, etc.) representations cannot wholly replace experiences from practical work. However, Millar also acknowledges that practical tasks in the teaching laboratory are also reproductions and thus are not faultless representations of reality. In fact, online practical work learning environments have affordances that can actually make them preferable to a real laboratory or field setting. For instance, the potential benefits of online illustrative practical work are such things as timesaving (e.g., to set up experiments, etc.), supplementation of the practical exercise (e.g., graphs), and narrowing exercises to just the vital components to avoid “information clutter” (Barton, 1998a). These affordances and others are discussed and elaborated throughout the subsequent chapters of this book.
Designing.Practical.Work.Tasks Many approaches have been advanced on the design of practical work as well as the design of online practical work. Building on the definition of practical work as the link between objects and ideas, Millar et al. (1999) developed a useful map of practical work. This study identified four main dimensions of practical work instructional design including: (1) learning outcomes—content and/or process, (2) design features-broken down into what students do with objects, ideas, whether task is object or idea driven, the “openness” of task (e.g., degree of learner vs. student teacher selected activities), and extent of the learners practical involvement in the learning task, (3) the context and execution of the practical task (e.g., materials provided, duration, interactions), and (4) the student’s record of work and its assessment (deliverables, the purpose of activities to be assessed, the audience for deliverables). These four sequential aspects of the task and the effectiveness of each are mediated by such variables as the teacher (views on science and learning), the student (views on science and their learning), and the institution (influence on the practical task through facilities, resources, and educational practices). In terms of learning outcomes, Millar, Tiberghien, and Le Maréchal (2002) emphasize that practical work strategies in science will vary depending on the learning objectives categorized by them as identification, learning facts, learning concepts, learning relationships, and learning models or theories. How the design of practical work is implemented in an institution was addressed and depicted by Hegarty-Hazel (1990). In this sensible model (Figure 5.2 adapted from Hegarty & Hazel, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 5.2. The Hegarty-Hazel model for the development of practical work. The three phases of planning, design, and implementation are mitigated by institutional approaches, policies and constraints. Interactive improvement is facilitated by learner and teacher inputs (adapted from Hegarty-Hazel, 1990, the student laboratory and the science curriculum, figure on page 30). Used by permission from Cengage Learning Services Ltd.
1990), there are three key development phases: (1) curricular and instructional planning, (2) design including establishing learning outcomes and producing learning experiences, and (3) implementation of the course or study module with feedback from teachers and learners. A key point of the Hegarty-Hazel model is the importance of institutional influence (budgeting, availability of resources, infrastructure, etc.) on the character and quality of the resulting course or module and the associated practical work. Faculty who have been involved with an academic initiative from its conception or early evolution have likely experienced the compromises and interplay between the original creative idea and the resulting course activity. For example, for many years at our institution we had to compromise multimedia elements favorable to online practical work because they were unsupported by our learning management system.
Epistemological.and.Procedural.Introduction.to. Practical.Work Another important identified aspect of developing effective practical work tasks is preparing students with an epistemological and ontological background to the science laboratory. Leach (1999) found that students, particularly older students, had difficulty deploying the appropriate forms of reasoning in laboratory contexts, thus arguing for direct and strategic instruction in the epistemological representations foundational to the particular lab task. Furthermore, the way in which individual students conceive science can influence their learning about the world of science. For example, Abd-El-Khalick and Lederman (2000)
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found that views on science are not significantly modified even for students immersed in history of science courses. Leach (1998) summarizes the key areas where student views can affect the effectiveness of practical work as: (1) nature of data, (2) nature of knowledge claims, and (3) status of knowledge claims within the scientific community. Therefore, a necessary step in instruction in practical science is to teach students about the world of science to provide a foundation to more advanced learning activities. Based on interviews with school physics, chemistry, and biology science teachers in the UK, Pekmez, Johnson, and Gott (2005) determined that there has been little emphasis on instructing students in the area of procedural understanding including the collection, interpretation, and validation of evidence. They argue that students need to be able to determine whether data merits an interpretation and that formative instruction on procedural methods is required to accomplish this. Despite the adverse connotation of educational training, Masters and Nott (1998) also conclude that for practical work to be effective, students need training in the skills and context of experiments prior to undertaking them.
Example:.Employing.Situated.Cognition.and. Scaffolding.in.Practical.Work As we have considered in a previous chapter, many pedagogical approaches can be used to develop effective learning environments for science and practical work. Aufschnaiter and Welzel (1997) and Welzel, von Aufschnaiter, and Schoster (1999) provide one additional rigorous example employing situated cognition and scaffolding in practical work. Based on their review of video-taped sequences of students and teachers interacting during physics lab instruction, they developed a heuristic learning strategy comprised of 10 different levels of complexity corresponding to a student’s situated cognition. The cognition sequence in order of increasing complexity has the following designations and qualities: 1.Objects 2 Aspects (links between objects and designation of features) 3 Operations.(variations of objects)4.Properties (classes of objects).5.Events (links between properties of objects) 6.Programmes (systematic variation of a property according to other properties) 7.Principles.(stable co-variations of properties) 8.Connections (links between several principles with variable properties) 9 Networks ( systematic variation of principles with respect to other principles 10.Systems (construction of stable networks of variable principles) A key conclusion of the Welzel et al. (1999) research is that learning interactions are most successful when the teacher and the student are communicating at the same level of complexity, say networks to networks or principles to principles. It follows that their heuristic distinctions and model is useful for instructional design purposes including application to the scaffold design of online practical work.
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Conclusion As we have described in an earlier chapter, a rapidly increasing number of students in schools and colleges are learning a portion of their science, including practical work, through online-facilitated activities. In fact, some researchers have concluded that it is now a necessity to introduce students to online practical work. For example, Braund and Riess (2006) have argued that if students are to be attracted to studying science, laboratory work needs to be authentic and complimented with the actual world (field work), the presented world (museums), and virtual worlds (available online). Likewise, other studies have recognized the utility of using innovations in technology and the Web to enhance the practical work learning environment. For instance, Hofstein and Lunetta (2003) describe the importance of what they term “inquiry empowering technologies” (p. 41) that permit students more time to observe and reflect on the conceptual knowledge of the laboratory through improved data gathering, analysis, and visualization. We are in strong agreement with these authors. A core contention of this book is that practical work is an indispensable part of learning science and that practical work can be designed to effectively facilitate the teaching of science online. In summary, the concept of scientific practical work is multi-faceted with a multitude of stated objectives; however, it has the following core qualities: •
practical work is tightly bound to the ancient traditions of learning by doing and experimentation;
•
for the learner practical work links objects with ideas;
•
practical work in science involves both laboratory and field work activities;
•
there is a longstanding and undeniably successful link between practical work, science education, and prosperous technological societies;
•
despite its detractors practical work is fundamental to teaching science;
•
the aims of practical work are many and varied, but are chiefly to teach scientific techniques, scientific procedures, and to promote scientific literacy (see Chapter V Appendix for details);
•
scientific thinking is congruent with the natural basis of human thinking (hypotheticopredictive) and practical work centrally supports this learning approach.
In order for online science instruction and its constituent form of practical work to have an essential role in meeting the demands to educate learners in a technological society, it is requisite to incorporate sound online science practices at the interface between sound science teaching practices and sound online teaching practices. In the following chapters of this book, we will elaborate on the affordances of online science instruction including how practical work can be accomplished by demonstrations, collaborative experiences, animation and modeling, simulations, home laboratories, virtual laboratories, remote experiments, fieldwork, and virtual field trips.
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References AAAS. (1989). Science for all Americans. Washington, DC: American Association for the Advancement of Science. Abd-El-Khalick, F., & Lederman, N. G. (2000). The influence of history of science courses on students’ views of nature of science. Journal of Research in Science Teaching, 37(10), 1057-1095. Aufshnaiter, S. V., & Welzel, M. (1997, March). Learning processes in the field of electricity: Results of a cross age study. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago, IL. BAAS. (1868). Report of the Dundee Meeting, 1867. London: Murray. Banks, F., & McCormick, B. (2005). A case study of the inter-relationship between science and technology: England 1984-2004. Paper presented to the PATT-15 Conference in Haarlem, Netherlands, April 18-22. Retrieved May 1, 2007, from www.iteaconnect. org/Conference/PATT/PATT15/Banks.pdf Barton, R. (1998a). IT in practical work-assessing and increasing the value-added. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 237-251). Denmark: Roskilde University Press, Denmark. Barton, R. (1998b). Just before nature: The purposes of science and the purposes of popularization in some English popular science journals of the 1860s. Annals of Science, 55, 1-33. Bateman, I., & Willis, K. (Eds.) (1999). Valuing environmental preferences: Theory and practice of the contingent valuation method in the U.S., EU, and developing countries. Oxford, U.K.: Oxford University Press, Oxford. Bloom, B. S. (1956). Taxonomy of educational objectives, handbook I: The cognitive domain. New York: David McKay Co. Inc. Braund, M., & Reiss, M. (2006). Towards a more authentic science curriculum: The contribution of out-of-school learning. International Journal of Science Education, 28(12), 1373-1388. Carlton, F. T. (1906). Economic influences upon educational progress in the U.S., 1820-1850. History of Education Quarterly, 7(2), 260-262. Chin, P., Munby, H., Hutchinson, N. L., Taylor, J., & Clark, F. (2004). Where’s the science?: Understanding the form and function of workplace science. In E. Scanlon, P. Murphy, J. Thomas, & E. Whitlegg (Eds.), Reconsidering science learning (pp. 118-134). London: RoutledgeFalmer. Cole, S. (1996). Voodoo sociology: Recent developments in the sociology of science. In P. R. Gross, N. Levitt, & M. W. Lewis (Eds.), The flight from science and reason (pp. 274-287). Baltimore: John Hopkins University Press. Coles, M. (1997). What does industry want from science education? In K. Calhoun, R. Panwar, & S. Shrum (Eds.), Proceedings of the 8th Symposium of IOSTE. Vol. 1.,
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Edmonton, August 17-22 (pp. 292-300). Edmonton, Canada: Faculty of Education, University of Alberta. Couture, M. (2004). Original article realism in the design process and credibility of a simulation-based virtual laboratory. Journal of Computer Assisted Learning, 20(1), 40-49. DeBoer, G. (1991). A history of ideas in science education: Implications for practice. New York: Teachers College Press. Dillon, S. (2006). No test tubes? Debate on virtual science classes [Electronic Version]. New York Times. Retrieved October 28, 2006, from http://www.nytimes.com/2006/10/20/ education/20online.html?ex=1318996800&en=f4f508ee263b6f87&ei=5088&partn er=rssnyt&emc=rss Felder, R. M., & Brent, R. (2004). The intellectual development of science and engineering students. 2 teaching to promote growth. Journal of Engineering Education, 93(4), 279-291. Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57-72. Fox, K. (2006). Authentic alternatives to practical work. School Science Review, 88(322), 45-51. Fullick, P. (2004). Why school science pupils should discuss practical science work online. In IDATER On-line Conference: E-Learning in Science and Design and Technology. Retrieved July 12, 2007, from http://www.lboro.ac.uk/departments/cd/research/groups/ ed/elearning/Lead%20papers/patrickfullick.pdf Gough, N. (1998). “If this were played upon a stage” school laboratory work as a theatre of representation. In J. Leach & A. Paulsen (Eds), Practical work in science education: Recent research studies (pp. 69-89). Denmark: Roskilde University Press. Gribben, J. (2002). Science, a history: 1543-2001. New York: Penguin/Allen Lane Publishers. Hegarty-Hazel, E. (1990). The student laboratory and the science curriculum: A model. In E. Hegarty-Hazel (Ed.), The student laboratory and the science curriculum (pp. 125189). London: Routledge. Hodson, D. (1993). Rethinking old ways: towards a more critical approach to practical work in school science. Studies in Science Education, 22, 85-142. Hodson, D. (1996). Laboratory work as scientific method: Three decades of confusion and distortion. Journal of Curriculum Studies, 28(2), 11-135. Hofstein, A., & Lunetta, V. (2003). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28-54. Houle, C. O. (1961). The inquiring mind: A study of the adult who continues to learn. Madison, WI: University of Wisconsin Press. Jenkins, E. (1998). The schooling of laboratory science. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 35-51). New York: Routledge Publishing. Jenkins, E. (1999). Practical work in school science—Some questions to be answered. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 19-32). Denmark: Roskilde University Press. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86. Lawson, A. E. (2003). The neurological basis of learning, development and discovery. Boston: Kluwer Academic Publishers. Leach, J. (1998). Teaching about the world of science in the laboratory. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 52-68). New York: Routledge Publishing. Leach, J. (1999). Learning science in the laboratory: The importance of epistemological understanding. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp.143-147). Denmark: Roskilde University Press. Linn, M. C., Davis, E. A., & Eylon, B.-S. (2004). The scaffolded knowledge integration framework for instruction. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 47-72). Mahwah, NJ: Lawrence Erlbaum Associates. Masters, R., & Nott, M. (1998). Implicit knowledge and science practical work in schools. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 206-219). Denmark: Roskilde University Press. Ma, J., & Nickerson, J. V. (2006). Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Computing Surveys, 38(3). Matthews, M. R. (2002). Constructivism and science education: A further appraisal. Journal of Science Education and Technology, 11(2), 121-126. Mayer, R. E. (2003). Learning environments: The case for evidence-based practice and issue-driven research. Educational Psychology Review, 15(4), 359-366. Millar, R. (1998). Rhetoric and reality: What practical work in science education is really for. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 16-31). New York: Routledge Publishing. Millar, R. (2004, June). The role of practical work in the teaching and learning of science. Paper presented at the National Academy of Sciences, Washington, D.C. Millar, R., Le Maréchal, J., & Tiberghien, A. (1999). “Mapping” the domain: Varieties of practical work. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 33-59). Denmark: Roskilde University Press. Millar, R., Tiberghien, A., & Le Maréchal, J. F. (2002). Varieties of labwork: A way of profiling labwork tasks. In D. Psillos & H. Niedderer (Eds.), Teaching and learning in the science laboratory (pp. 9-20). Dordrecht: Kluwer Academic. National Education Association. (1893). Report of the committee of ten of the committee on secondary school studies. Washington, DC: US Government Printing Office. Osborne, J. (1998). Science education without a laboratory? In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 156-173). Denmark: Roskilde University Press. Pekmez, E. S., Johnson, P., & Gott, R. (2005). Teachers’ understanding of the nature and purpose of practical work. Research in Science & Technology Education, 23(1) 3-23. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Rezaei, A. R., & Katz, L. (2002). Using computer assisted instruction to compare the inventive model and the radical constructivist approach to teaching physics. Journal of Science Education and Technology, 11(4), 367-380. Ross, S. & Scanlon, E. (1995). Open science: The distance teaching and open learning of science subjects. London: Paul Chapman Publishing. Singer, S. R., Hilton, M. L., & Schwiengruber, H. A. (Eds.). (2005). America’s lab report: Investigations in school science. Washington, DC: National Academies Press. Slotta, J. D. (2004). “Web-based inquiry science environment”. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp 203-231). Mahwah, NJ: Lawrence Erlbaum Associates. Soloman, J. (1999). Envisionment in practical work: Helping pupils to imagine concepts while carrying out experiments. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 63-74). Denmark: Roskilde University Press. UNESCO. (2005). Towards knowledge societies. Paris: UNESCO Publishing. Van Marion, P. (1999). Changing teachers’ practise: Practical work in environmental education. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 250-264). Denmark: Roskilde University Press. Wardle, J. (1999). Virtual science—A practical alternative? In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 271-281). Denmark: Roskilde University Press. Wellington, J. (1998). Practical work in science: Time for a re-appraisal. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 3-15). New York: Routledge Publishing. Welzel, M., von Aufschnaiter, C., & Schoster, A. (1999). How to interact with students? The role of teachers in the learning situation. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 298-313). Denmark: Roskilde University Press. Woolnough, B. (1998). Authentic science in schools. In J. Leach & A. Paulsen (Eds.), Practical work in science education: Recent research studies (pp. 109-125). Denmark: Roskilde University Press. Woolnough, B., & Allsop, T. (1985). Practical work in science. Cambridge: Cambridge University Press. Youmans, E. L. (1867). The culture demanded by modern life; A series of addresses and arguments on the claims of scientific education. New York: D. Appleton & Co.
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Appendix:.Compilation.of.Learning.Outcomes.for. Practical.Work YEAR 1867 1868
1960’s
1980’s
Learning.Outcomes For mental discipline Mental training To appeal to students whom non-science produced “slight effect” To diminish the disadvantages of those who were not trained in the scientific philosophy For greater pleasure in the afterlife Because it is materially useful “Discovery” Promoting science abilities and skills rather than theory Promoting processes Observing, hypothesizing and deducing patterns To encourage accurate observation and careful recording To promote simple, common-sense, scientific methods of thought To develop manipulative skills To give training in problem-solving To fit the requirements of practical examination regulations To elucidate the theoretical work so as to aid comprehension To verify facts and principles already taught To be an integral part of the process of finding facts by investigation and arriving at principles To arouse and maintain interest in the subject To make biological, physical, and chemical phenomena more real through actual experience “Process” of Science Procedures of the scientific method such as observing phenomena, making interpretations, and reaching conclusions
REFERENCE Youman, 1867 BAAS, 1868
Wellington, 1998; Jenkins, 1998
Kerr, 1962, in Pekmez et al., 2005
Fullick, 2004
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1989
Utility.
AAAS, 1989
Will the proposed content—knowledge or skills—significantly enhance the graduate’s long-term employment prospects? Will it be useful in making personal decisions?
Social Responsibility. Is the proposed content likely to help citizens participate intelligently in making social and political decisions on matters involving science and technology?
The Intrinsic Value of Knowledge. Does the proposed content present aspects of science, mathematics, and technology that are so important in human history or so pervasive in our culture that a general education would be incomplete without them?
Philosophical Value. Does the proposed content contribute to the ability of people to ponder the enduring questions of human meaning such as life and death, perception and reality, the individual good versus the collective welfare, certainty and doubt?
1991
Childhood Enrichment.
Will the proposed content enhance childhood (a time of life that is important in its own right and not solely for what it may lead to in later life)? “Experimental and investigative phase” SC1 Pedagogical tool to promote student’s active involvement with learning Concepts as well as learning science through the control of variables and evaluation of evidence
Banks & McCormick, 2005; Wellington, 1998
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1993
1993
1995
1996
1998
1999
1999
Melting of disciplinary boundaries Breaking Barriars between real world and school For motivation of students For insights into the nature of science For the benefits of collaborative working For the distribution of competencies within a group For communication skills For motivation For acquisition of laboratory skills For expertise in the scientific method For a scientific attitude of openness and objectivity Training in technique (psychomotor, observation, data handling) Learning basic ideas of the subject Learning how to do research Motivating through interest To develop employer-preferred characteristics Commitment to interest Core skills of communication, numeracy, and information technology skills Team work Self-reliance and resourcefulness Analysis Creativity Professional integrity Specific knowledge of work fields. For “private knowledge” A place to develop and apply their personal knowledge Partake in a cultural activity Promote personal knowledge useful to employers As a bridge to public knowledge of science Conveying the Language of Science To work with real objects and materials To develop students understanding of scientific inquiry For students enjoyment Play and Exploration Curiosity Interest in cause and effect Investigation of an original agenda
Jenkins, 1999
Hodson, 1993
Ross & Scanlon, 1995
Coles, 1997
Woolnough, 1998
Gough, 1998 Millar et al., 1999
Soloman, 1999
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1999
1999
2004
To understand specific products of science (laws, etc.) To understand and participate in scientific and technological aspects of society Make subject matter tangible Subject matter placed in realistic context Students see practical applications of scientific knowledge Practical experience in science methods Use of scientific instruments Observation skills improved Cooperation cultivated Develop positive attitudes about the outdoors Gain insight into connection of nature and society Gain insight into the world’s resources including use Understanding science concepts Stimulating interest and motivation Developing practical science skills (i.e., scientific inquiry) and problem solving (i.e., scientific reasoning) Developing scientific habits of the mind Understanding the nature of science To assist the student in the use of science for everyday life Provide students with evidence for a particular theory Help develop an understanding of the methods used by scientists To provide students with phenomena that they are unlikely to have observed in their everyday lives Links the domain of ideas and the domain of real objects and observable things Feel for the uncertainty of measurement Platform for data testing Knowledge of science and about science Identification Learning facts Learning concepts Learning relationships Learning models/theories To make science visible
Leach, 1999
van Marion, 1999
Hofstein & Lunetta, 2003
Fullick, 2004
Millar, 2004
Millar et al., 2004
Linn et al., 2004
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2005
2006
2007
1956
1961
1999
Singer et al., 2005 Enhancing mastery of subject matter; Developing scientific reasoning; Understanding the complexity and ambiguity of empirical work; Developing practical skills Understanding the nature of science Cultivating interest in science and interest in learning science; and Developing teamwork abilities Ma & Nickerson, Conceptual understanding 2006 Design skills Social skills Professional skills This volume Developing hypothetico-predictive learning skills General Aims for Learning Applicable To Practical Work Bloom, 1956 Cognitive (knowledge) Affective (attitudes) Psychomotor (skills) Houle, 1961 Achieve specific goals Social activities For the sake of pure learning Economic Valuation Applicable To Science Education and Practical Work Direct, Indirect, Option, and Existence Values Bateman & Willis, 1999
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Chapter.VI
Knowledge.Transfer.and. Collaboration.Structures. for.Online.Science
Great discoveries and improvements invariably involve the cooperation of many minds. – Alexander Graham Bell Technological innovations in the area of digital media have opened up the possibility for a great number of inventive ways to share and transfer knowledge in online science learning environments. Knowledge transfer may involve interaction between a learner and learning resources such as learning objects or, conversely, knowledge transfer and sharing may be social, that is to say between individuals and/or groups. The types of knowledge transfer that can now be hybridized in educational settings are delineated by Puntschart (2005) as follows: (1) technology-enhanced versus face-to-face, (2) asynchronous versus synchronous, (3) voluntary versus obligatory, (4) self-directed versus externally controlled, (5) learning object transfer versus person-to-person, and (6) open versus closed communities. Such a wide variety of interaction options gives way to a variety of communication and collaboration approaches in online science education. Many of these options prospectively support more individualized learning. For example, learning scenarios are now possible where a science student conducts online remote experiments sponsored by another institution in the dead of night in the absence of an instructor. Moreover, a student may opt out of attending Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Knowledge Transfer and Collaboraton Structures for Onlne Scence
an onsite class session in favor of a streaming video lecture where they still contribute to the discussion through a mobile learning, or m-learning, device. Alternatively, a student may pursue learning at his or her own pace and learning style by reviewing relevant digital library learning objects on a science subject. Of course, innovative technology for knowledge transfer has its benefits, requirements, and compromises. On one hand, technology-enhanced science learning environments offer students and teachers exciting opportunities to join together and collaborate with learning communities beyond the classroom as well as utilize m-learning options and devices to accept knowledge transfer anytime or anywhere (considered in depth in Chapter VIII). For instance, Web seminars sponsored by the National Science Teachers Association through the association’s learning center resources (see http://learningcenter.nsta.org/) facilitate K-12 instructor ties to online communities of practice who share an interest in furthering humanity’s knowledge in general or particular science areas, such as the organizations NSF, NOAA, NASA, and FDA. Then again, the removal of time and place constraints required of a face-to-face classroom by online technologies places a greater demand on a student’s self-direction skills such as setting goals, time management, motivation, online interpersonal competence, and assessing their own learning progress.
Collaborative.vs..Cooperative.Online.Learning Learning configurations enabling students/groups to learn online and capitalize on the chief online social interactions fall into two major categories, cooperative or collaborative (Haythornthwaite, 2006; Williams, Duray, & Reddy, 2006). Cooperative (or coordinated) learning advances through instructor-delegated assignments with individual efforts assembled into a whole (e.g., readings and discussion questions). In contrast, collaborative learning evolves as groups of students address problems/issues regarding the course subject matter. In collaborative learning students are a source of authority and knowledge on assignments, and direct a significant amount of the learning. Adhering to these categorical distinctions, a cooperative learning paradigm online is less team-centered and more instructor-driven. Conversely, achieving veritable collaborative learning is a more complex process accentuating the development of team roles and processes with respect to learning objectives. Bermejo (2005) characterizes the components of online collaboration as: (1) positive interdependence, where respective student roles are defined within the team, (2) promotive interaction, where mutual help, feedback, and information exchange occurs between students, (3) individual accountability, and (4) group processing, where an instructor monitors and provides in-progress feedback to individuals and groups, and the whole-class reviews all team-learning. Therefore, collaborative learning requires that the learner be responsible for both their learning and the learning of others (Pimental, Gerosa, Fuks, & de Lucena, 2005). Both types of social interaction, cooperative or collaborative, can be readily facilitated in online science learning through current learning management systems (LMS) that offer basic discussion forums and communication tools, however they can be enhanced further by other technological tools such as Web-conferencing and virtual classrooms.
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Online.Collaboration Online collaboration, also known as “computer supported/mediated collaborative learning” (CSCL), differs from face-to-face collaboration chiefly in the affordances and constraints posed on it by technology and pedagogy. Collaboration is a fundamental component of the science profession as it can uniquely address complex problems (e.g., Sonnewald, 2007) by bringing together conventionally compartmentalized knowledge bases and experts. Online collaboration can be an important instructional strategy because it can promote learning synergy and authenticity as well as support the affective needs of learners. That is to say, if structured suitably collaboration can: (1) promote the active construction of knowledge, (2) model the real world (i.e., workplace) fostering group and project management skills, and (3) address a student’s need for social interaction and sense of belonging (Haythornthwaite, 2006). Kreijns and Kirschner (2001) recognize two categories of social affordances from online collaboration. The first of these is the educational dimension and the second area is the social psychological dimension (Figure 6.1). The educational dimension propels the learning performance on the part of the student and is influenced by feedback loops from the social psychological dimension. An implication of this model for online social interaction is that social space and processes are a necessary part of online design considerations, as they will support additional learning outcomes. For example, with adult learners, supporting social space is consistent with providing for their commonly held educational objective of partaking in social activity noted by Houle (1961). For many online students a key dissatisfaction with online learning is the feeling of disconnection from peers and instructor. Collaboration is an important means to mitigate the possible feeling of disconnection of distance students to one another and their instructor. Studies indicate that collaboration does not have to be elaborate to be effective and indicate that a
Figure 6.1. “Social interaction from two different dimensions”, from Kreijns and Kirschner (2001), showing the co-dependence of learning and social space. Used by permission from IEEE.
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sense of belonging is fostered in online environments even with basic communication tools such as discussion forums. For example, Mahoney (2006) reports in a study that compared online versus face-to-face nursing graduate students that there was no appreciable difference in the sense of belonging between the groups when communication and cooperation measures are incorporated methodically in each format. Likewise, Tilley (2006) determined that knowledge acquisition for online nursing students is jointly enhanced by strategically building a collaborative environment of supportiveness, “open sharing” of oneself, and customary socialization.
Collaborative.Learning.and.Online.Science The pervasiveness of the constructivist paradigm in online learning as well as its epistemological basis in social construction has elevated the importance of collaboration, if not placed it at the nucleus of online learning. While potentially effective for online science, collaborative learning is not an obligatory instructional strategy for every learning objective or effective for every blend of students. Collaboration will be particularly synergistic to online science course learning objectives when a student can gain knowledge from other students because of differences in situated cognition between them (i.e., a student has enhanced background in the subject area or has more thoroughly previewed course materials). Likewise, collaborative learning in online science will be effective when a group of students collectively has a diverse set of abilities and knowledge base that are mutually beneficial to hypothetico-predictive learning activities. On the other hand, collaboration is not necessarily an online learning strategy embraced by all online students because of personal learning styles and/or the dynamics of specific collaborative experiences. Individualized factors may make collaboration adverse to learning such as the lack of time to nurture a collaboration, preference to read and learn rather than learn through online discussion, preference to work alone and interact through oneto-one communications with an instructor, and unhelpful or inequitable peer impacts that actually create antergy (Fung, 2004; Harris, 2003). In addition, collaboration has temporal constraints that can be overwhelming if too much sharing is required in the time allotted, objectives are too wide ranging, and group interaction time is too short term to be meaningful (Haythornthwaite, 2006).
Stages.and.Models.of.Online.Collaboration For a team of students, the process of collaboration online is an evolving one with several identifiable stages. In an examination of student collaboration within an artificial neural networks course in the electrical engineering field, Bermejo (2005) identifies five stages: (1) access and motivation, (2) online socialization, (3) information sharing, (4) knowledge construction/collaborative interaction, and (5) development, where students extend their learning beyond the course. Then again, Haythornthwaite (2006) suggests three chief Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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stages in the collaborative process: (1) coming together—how to communicate, common understandings of objectives and concepts, (2) maintaining presence, and (3) disengaging. In each of these characterizations of the online collaborative process, key aspects are to have a strong interactive beginning, a sustained and structured intermediate phase, and a strategy for suitable completion of the group endeavor, either through its termination or through its planned sustenance beyond the class duration. Another way to view and model collaboration online is through the concept of a community of learners, often called a community of practice (CoP). An online community of practice (OCoP) is a group of people that deepen their knowledge around a subject of shared interest by persistent computer facilitated communication and interaction. Learners in a science course or even more broadly pursuing the disciplinary portion of a curriculum are a community of practice. Lin and Lin (2006) have conceptualized a creative and useful way of visualizing an OCoP, through the paradigm of an ecological system. In this model (Figure 6.2), the vitality of the ecosystem (i.e., collaboration) is dependent upon the “energy” of online activities, specifically the interactions of the organisms (members), the “nutrients” which are the stored and added materials/experiences of the members, and the presence of organisms themselves (e.g., instructor and students). The proposition of the ecosystem approach OCoP model is that the endurance of the community of practice (e.g., vigor of
Figure 6.2. An online community of practice model based on an ecological system (from Lin & Lin, 2006). Used by permission from IEEE.
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Knowledge Transfer and Collaboraton Structures for Onlne Scence 0
course collaboration) and output (e.g., achieving learning outcomes) relies on the successful interactions and persistent cycles of interaction between student, learning activity, and course materials. For online science instructors, conceptualizing their online course as an ecological system is a useful mental construct for managing the complexity and nurturing the interactions of the learning environment. An ideal conclusion to a course should promote sustained interactions with the subject matter through consequent resources or lasting connections to an online community of practice, such as a professional society. An additional model of online collaboration is that of Garrison, Anderson, and Archer (2000) for a community of inquiry (COI) that involves three essentials for educational functioning: cognitive presence, social presence, and teaching presence (Figure 6.3). In the COI model the student’s educational experience is formed by the confluence of these three variables. Collaboration is epitomized by social presence in the model and is responsible for setting the ambiance of the learning environment along with the teacher’s presence. Moreover, the social and cognitive presence of students influences the quality of the supporting discourse of the learning environment. The COI is a useful paradigm construct for conceptualizing online science courses with project or problem-based learning activities emphasizing team investigation of phenomena such as virtual and remote laboratories. We characterize the chief types of social interactions for knowledge transfer and sharing in online science educational environments as follows: student-to-student, student-to-instructor, student-to-team, student to OCoP, instructor-to-team, instructor-to-OCoP, team-to-team, and team to OCoP (Table 6.1). By team, we refer to a structured learning group of students linked by common educational objectives. From an instructional design perspective, each of these social interactions and their combinations can feasibly contribute to meeting an online
Figure 6.3. A community of inquiry model based on cognitive, teaching and social presence (adapted from Garrison et al., 2000)
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Table 6.1. Chief social interactions online
Student Instructor Team
Student •
Instructor •
Team • • •
OCoP • • •
science learning objective. Interactions designed to involve OCoP are a possible means to facilitate and sustain a student’s engagement with a science knowledge area even after they have completed a course thus promoting lifelong learning.
Effective.Approaches.to.Collaboration.and. Group.Structures How effective a collaborative approach will be in achieving learning objectives is dependent on several factors including team dynamics and instructional design strategies. For instance, based on their observations of student collaboration in online MBA courses, Williams et al. (2006) determined that teamwork orientation (i.e., student values towards teamwork) and group cohesiveness (i.e., interpersonal ties) predict the measure of student learning in collaborative/team environments. The implied consideration for online science is that student predisposition to teamwork should be a consideration in structuring learning activities. As well, a further implication is that collaboration will be more effective later in a course when students know each other or when students have advanced as an educational cohort. In terms of instructional design strategies, many approaches will improve the effectiveness of online collaborations. For example, based on a study of eight health studies courses, Kennedy and Duffy (2004) concluded that effective collaboration has seven characteristics. Collaboration should be managed, team focused, and started early in the course induction phase. Moreover, collaboration protocols should be introduced (i.e., ground rules), instructors should be versed in effective collaboration techniques, there should be technical support in place to assist in the collaboration, and there should be institutional support for collaboration systems. There are many other effective collaboration and communication approaches and best practices described for general online and online science learning environments (e.g., Bower, 2007; Haythornthwaite, 2006; Rutkowski, Vogel, Van Genuchten, Bemelmans, & Favier, 2002; Williams et al., 2006). A compilation of the best practices in collaboration/ group strategies from these resources applicable to online science learning environments is as follows:
Predetermine the outcomes of collaboration and whether teams will use applied knowledge (requiring previous knowledge) or generate new/“constructed” knowledge.
Select optimal Internet computer technology (ICT) for collaboration purposes and learning objectives.
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Preferentially design group processes, problems, and the team decision making process beforehand.
Build trust between students early on.
Put in deliberate interventions to encourage interactions.
Temporally coordinate collaborative activities towards synchronicity.
Factor in extra time for collaboration.
Provide fast feedback so groups can compare and contrast different approaches to problems and research.
Provide for public, private, and social communication.
Build incremental interactions to approach difficult tasks.
Use explicit (modeled or direct instruction) to support competence development.
Provide detailed and scaffolded task specifications.
For distance mathematics and the sciences courses specifically, Offenholley (2006) asserts that well-posed discussion questions in the areas of: (1) community building (i.e., course introductions), (2) science philosophy, (3) course pedagogy, and (4) problem solving are essential for generating a quality collaborative environment. Such strategic questions will enhance the development of a learning community by helping keep students punctual with assignments, fostering scientific analysis and synthesis, and supporting student-to-student assistance. Lastly, for implementation of a COI strategy for online collaboration, Garrison (2005) places teaching considerations into three categories design, discourse, and direct instruction. Within these categories, teaching presence should set in motion a climate of inquiry, build group cohesion, facilitate the progression of inquiry, promote self-direction and sustain metacognitive development (Table 6.2).
Table 6.2. Functions of teaching presence in a community of inquiry (after Garrison, 2005) Design
Facilitating Discourse
Direct Instruction
Social Presence
Establish a climate for community of inquiry
Sustain community through expression of group cohesion
Evolve collaborative relationships where students are increasingly responsible for their learning
Cognitive Presence
Establish critical reflection and discourse that will support systematic inquiry
Encourage and support the progression of inquiry through to resolution
Ensure resolution of metacognitive development
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Social.Software.for.Online.Science Social software enables students to interact with one another, their instructor or even with virtual people such as virtual classmates (e.g., Matsuura, Hesse, Ochi, Ogata, & Yano, 2002). There are many kinds of social software in common use today for online interaction including: instant messaging, e-mail, chat rooms, Internet forums, Blogs, Wikis, Web conferencing systems such as Elluminate Live (Becker, 2007), virtual reality (VR) applications, and learning management systems (LMS). Learning management systems, which are becoming ubiquitous at universities, are effectively social and administrative software that combine selected features of these other kinds of software systems. An LMS may be selected primarily to assist in administering a course, but many universities have also relied heavily on LMS systems for developing a social environment for their online courses and choose commercial (WebCT, Blackboard, ANGEL) or open source varieties (Moodle, Sakai) in part on the basis of collaboration features (Wainwright, Osterman, Finnerman, & Hill, 2007). Based on a graduate level course on distance education, Anderson (2006a) advises that educational social software, at the very least, should provide the following functions for the learner: a sense of presence, notification, information filtering tools, learning support, referring trails, individualized student modeling, introductions tools, cooperative help between students, object sharing, and documentation. In the selection of an LMS for online science learning a key additional consideration is the LMS platform’s ability to integrate practical work design requirements for activities such as multimedia, virtual labs, fieldtrips, simulations, and remote experimentation. Dalsgaard (2006) has pointed out that current LMS can be very limiting to a student’s ability to self-govern the learning process. As case in point, student learning and group learning in science, whether onsite or online, does not regularly persist or extend beyond the course’s duration. Dalsgaard (2006) suggests that personal software tools should be regularly available to students to extend on the learning achieved in courses as well as providing students with networks that engage students with experts in the subject area. In a sense, what this author is calling for is lifelong learning software that will permit a student to build learning interconnections that sustain and build upon the immediate learning endeavor. With this goal in mind, adaptive educational hypermedia systems (AEHS) are now emerging that provide users with customized views and navigation taking into consideration an individual’s learning goals, style, and progress (Kanzanidis & Satratzemi, 2007). There are many examples of LMS and some are customized to serve a particular pedagogical philosophy such as scientific inquiry. However, most of these target schools and not university-level online science learning environments. For instance, the European Union has sponsored the development of an LMS for schools, modeled to support the scientific inquiry, under the Innovative Technologies for Collaborative Learning and Knowledge Building (ITCOLE) project. ITCOLE software follows seven pedagogical principles to support the development of scientific inquiry skills. The chief principles include: (1) designing for flexibility and modularity, (2) facilitating knowledge building by storing and sharing information as a collective memory, (3) scaffolding progressive inquiry by having students label messages according to level of inquiry, (4) providing instructor summary tools to encourage progressive inquiry, (5) providing tools for structuring and coordinating activities, (6) provide designing tools for process analysis so students and instructors can follow learning
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progress, and (7) providing support for community building such as pictures of individuals and active user visibility (Rubens, Emans, Leinonen, Gomez Skarmeta, & Simons, 2005). The ITCOLE framework is exemplary and a best practice in scaffolding progressive inquiry in school science and to our knowledge has no university-level analogy. The Future Learning Environment or Fle3 server software (at http://fle3.uiah.fi/), also a product of the ITCOLE initiative, is an example of Web-based software supporting collaborative learning in schools, including around the scientific method. The collaborative tools available for students are a customizable “WebTop” to manage and share documents, files, links to resources and course notes; a “knowledge building” section for group work that is scaffolded by knowledge types, and a “jamming section” for collaborative generation of digital artifacts (Leinonen, Kligyte, Toikkanen, Pietarila, & Dean, 2003). Providing students with more elaborate software tools and options for collaboration is not necessarily going to be received in a welcoming manner by all learners. In a comparative study of student use of multiple types of social software, Anderson (2006b) determined that unless there was a compelling perceived value for newer technologies (e.g., blogs and Wikis) students would opt for e-mail as their chief form of communication. This result emphasizes the idiom to keep the simple things simple and the complicated things possible when designing online science collaboration.
Role.of.the.Instructor.in.Online.Collaboration In most contemporary online learning environments, a traditional classroom lecture format is not modeled closely, with the exception of advanced virtual classroom systems with greater synchronous delivery options (reviewed in the next chapter). Therefore, the idea of constructivist learning with its emphasis on collaboration, self-directed learning, and greater authority on the part of the student and relegation of the instructor to a facilitator has been a pedagogical necessity, if not an educational “movement” for distance education. In fact, in a study of the role of the instructor as a factor in student satisfaction in online courses, Eom (2006) concluded that the instructor’s knowledge was not a determinant of student satisfaction nor was the instructor’s role of facilitator or “stimulator”, or the instructor’s timely and useful feedback. Rather, instructor care and responsiveness (i.e., personalization) was determined to be the critical factor in student satisfaction. Of course, there is a pointed distinction between the instructor’s role in student learning and the instructor’s role in student satisfaction.
The.Sage.on.the.Stage.Lives? Much is written about the changing role of professor in the virtual arena of online learning and the inevitable shift to a more Socratic and multilogue interaction format with students (e.g., Coppola, Hiltz, & Rotter, 2002). In the absence of technology that reestablishes the qualities of onsite learning in virtual space through real-time Web-conferencing and lecturCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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ing, a future where the online professor’s main functions are sifting and sorting information, facilitation of discussion, and learning assessment is easy to deduce. However, many qualities of lecture pedagogy enhance the transfer of knowledge from the instructor to students. The lecture is a historic and proven style of teaching because of many factors, including: (1) the differential content knowledge and cognitive level on a subject between the instructor and students (i.e., expertise), (2) the requirement to simultaneously transfer knowledge to many in an efficient and cost effective way, (3) the differential ability of the student versus instructor to analyze, synthesize, and impart learning (i.e., the instructor as the model of a lifelong learner and teacher), (4) this is a preferred style of learning (auditory-visual-behavioral) for many students, and (5) for many students self-direction and autonomy in learning process is difficult and in some cases woefully ineffective. For online science learning, the ability to present a lecture and concurrent demonstration of principle through a live experiment is a vital instructional technique. Therefore, the lecture format in science is not likely to disappear altogether and may in fact see the “sage on the stage” resurge online as multimedia features of learning platforms continue to improve.
E-Moderating Whether interaction to support collaboration will be synchronous or asynchronous, facilitated or non-facilitated, two-way or one-way, or combinations of these must be decided during instructional design (Nicholson, 2005). Variations in interaction type will have an effect on the function of the instructor, in some cases making them effectively an e-moderator. For example, the role and responsibilities of the instructor is inherently different in asynchronous distance learning settings employing discussion forums versus synchronous online classrooms. In strongly collaborative activities, students use the collaborative responses from each other and corresponding supplemental information as an essential part of their learning, in addition to feedback from an instructor. Therefore, in these online learning settings the instructor must not only design, moderate, and scaffold the conference space, but must also take into account and monitor the developing structure of the student-to-student conference space (Ross, Kukulska-Hulme, Chappel, & Joyce, 2004). These interaction considerations and choices are universal and are applicable to online science learning as they are for other disciplines.
Gesture.and.Silence.in.the.Online.Science.Classroom In a face-to-face classroom, physical gestures such as using hands to illustrate a concept are a form of communication that can support learning. However, in online environments gestures are hardly ever used (Suthers, Girardeau, & Hundhausen, 2003) and are only possible in learning systems that include expansive forms viewing such as Web video-conferencing or superior systems. In view of the fact that most online interaction replaces physical
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gestures with verbal deixis and direct manipulation of representations it is important that instructors have tools for easy insertion of visual learning aids as well as display of prior course information (Suthers et al., 2003). Likewise, in asynchronous environments that rely on text chat exclusive of the subtle and silent student-to-student gestures possible in a face-to-face environment, it is difficult for students to interpret whether a peer who does not reply to a posting has anxiety about responding or whether they have simply not read the communication (Graham & Misanchuk, 2004). Therefore, online science instructors have to be particularly astute to subtle written clues in their learning environment including the meaning of silence. As more complex systems of synchronous video and audio with large display become more commonplace in the delivery of the online teaching environment it remains to be seen whether subtle social interactions such as gesture will re-emerge into part of the learning standard.
Using.Collaboratories.to.Enrich.and. Sustain.Science.Knowledge Expanding on early efforts by the U.S. Department of Energy over two decades ago (see discussions by Bly, Keith, & Henline, 1997; Wulf, 1989), bench scientists have been taking advantage of online environments to accomplish research-related collaboration though the e-science infrastructure of a collaboratory. Dove et al. (2005a) describe a collaboratory as, “an environment where researchers at distant locations work together at tackling important scientific and industrial problems” (p. 329). Bos et al. (2007) provide a more expanded definition, …an organizational entity that spans distance, supports rich and recurring human interaction oriented to a common research area, and fosters contact between researchers who are both known and unknown to each other, and provides access to data sources, artifacts, and tools required to accomplish research tasks (para. 15). There are a wide variety of collaboratories, including those that arise via the sharing of a scientific facility/instrument (e.g., remote experimentation) to others that originate as a cooperative effort to produce a scientific literature database. An example of the latter type is the digital library of scientific literature or CiteSeerX (at http://citeseer.ist.psu.edu/) sponsored by the National Science Foundation and Microsoft Research (Farooq, Ganoe, Carroll, & Giles, 2007). Based on a landscape sampling approach modeled from ecological examples, collaboratories are situated into seven-categories by Bos, Olson, and Olson (2005). The types include: •
Shared instrument: to increase access to expensive scientific instrumentation.
•
Community data systems: a semi-public information resource database of wide interest.
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•
Open community contribution system: to aggregate the work efforts of many, including the public, towards the solution of a problem.
•
Virtual community of practice: individuals who share a research interest and communicate about it.
•
Virtual learning community: to increase the knowledge of participants.
•
Distributed research center: to aggregate individual researchers and resources into a cooperative, but physically dispersed center.
•
Community infrastructure project: in a distributed approach, develops new resources to further work in a research domain.
Another example of a contemporary collaboratory infrastructure is the eMinerals project, which uses molecular simulation models to explore environmental processes. eMinerals is facilitated by the following online tools: e-mail, instant messaging, Wiki Web sites, videoconferencing in online rooms (virtual venues) provided through a high performance access grid, a personal access grid, e-newsletters, helpdesk software, and workstation tools to permit the simultaneous multicasting of video/audio streams for large numbers of research participants (Dove et al., 2005b). At the undergraduate level, collaboratories are already directly supporting online science education, particularly as virtual learning communities, community data systems, and shared instrument communities to support practical work through remote laboratories. Collaboratories can be an exceptionally powerful tool in online science learning, but notwithstanding these examples, collaboratories are largely an underused resource with regard to their regular incorporation in online science instructional design. The Science of Collaboratories Organization (http://www.scienceofcollaboratories.org/) provides a detailed list and bibliography of collaboratories across many science disciplines.
Collaboration.in.Virtual.Worlds.to. Support.Science.Learning Although most of the collaboration that occurs in online science environments still relies on the exchange of information through text-based asynchronous discussion forums or e-mail, the technological possibilities for online collaboration in science are growing. Much of the reliance on these basic online learning features has been a product of technological limitations, particularly on the student side because of hardware, software, and connection speed issues. As key thresholds in these areas are surpassed in the coming years, it is very likely that there will be a shift towards more visual and audio (e.g., real time and embedded faceto-face) communication in both synchronous and asynchronous formats. Already, there are examples of how students are accepting and utilizing virtual environments in educational settings. For example, Jones (2006) found that once a virtual world was established to support discussion for distance students taking computer courses at the University of North Texas, communication between students increased, their reliance on e-mail decreased, and there was greater student satisfaction.
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Another intriguing development in collaboration that can impact the way online science learning is accomplished, one taking place in virtual environments, is the massive multi-player online gaming environments (MMOG) that support thousands of players at one time, such as the fantasy stylized Everquest and World of Warcraft. These very popular MMOGs are revolutionizing the way that interaction and collaboration takes place at a distance (Steinkuehler, 2004). MMOGs are a promising area for education too as they afford the opportunity to leverage online game interaction to foster scientific habits of the mind. Steinkuehler and Chmiel (2006) assessed the scientific habits of the mind cultivated through the World of Warcraft and place them into three categories: (1) scientific discursive processes such as building on other’s ideas and multiple forms of argument, (2) model-based reasoning such as systems analysis and mathematical modeling and (3) understanding theory and evidence including testing and revising theory and linking theory to data (i.e., hypothetico–predictive reasoning). Likewise, Mayo (2007) argues that MMOG games can be an important tool for teaching science and engineering because of their ability to reach significant numbers of people anytime. Also, the massive player games are compelling to students, better than a lecture, and even stimulate brain chemistry changes conducive to learning. Whether MMOG virtual environments are more broadly utilized in science education and to promote scientific reasoning will depend ultimately on whether the cost justifies the learning objectives as game development is expensive and commercial companies have little financial incentive at present (FAS, 2006). An alternative strategy is for science learning activities to be adapted to previously constructed games through “modding” where popular game engines are used to create novel worlds and situations (El-Nasr & Smith, 2006).
Live.Online.Classrooms The emergence of multi-faceted online classroom software such as Adobe Connect, Centra 7, Horizon Wimba Live Classroom, and Elluminate Live that permit significant synchronous interaction and collaboration between students and the instructor akin to a face-to-face classroom is necessitating specific pedagogical frameworks. For example, Bower (2006) employed the Acrobat Connect Professional (formerly Macromedia “Breeze”) software to establish a virtual classroom environment for a computer-programming course. The features of this software permit a variety of knowledge transfer modalities: (1) presentation delivery, (2) screen-sharing, (3) Webcam, (4) VoIP, (5) text chat, (6) whiteboard, (7) file management, (8) polling, (9) attendee verification, (10) group Web launching, and (11) a notepad. Based on this course experience, Bower’s study (2006) established a number of general pedagogical best practices to optimize learning in the virtual classroom environment, each of which are applicable to other online science courses. We group the Bower (2006) strategies into three main pedagogical areas that involve course preparation, course pace and proportionality, and learning personalization. These are summarized in Table 6.3 and highlight the complexity of skills required to carry off an effective online classroom. Additionally, many other collaboration strategies typical of onsite classrooms were found achievable in a graduate level online computing course including problem-solving tasks,
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Table 6.3. Online classroom best practices (after Bower, 2006) Teaching Pedagogy Preparation
Pace and Proportionality
Personalization
Online Practice Deploy metacommunicative strategies such as smooth use of classroom tools, quick response time to chat, appropriate fonts, and emoticons Defer to course resources directly during instruction Rearrange/customize the virtual classroom environment for a specific learning activity Provide advance organizers for each activity Record sessions for later use by students (i.e., lesson artifacts) Pose questions reasonable for a short text chat answer Stage different levels of communication appropriate to the activity such as a dominant instructor presence or dual coding using voice and text chart simultaneously Elicit multiple simultaneous responses to accelerate the pace of collaboration Where voice response is used have other students respond with text chat Promote “anytime interjection” and concurrent questions Send personalized text chat as appropriate during presentation Provide scaffolded supplementary materials (e.g., examples) Place multiple information sources concurrently into the browser
brainstorming, exchanging information between groups, peer review tasks, and evaluation tasks involving debate on a particular issue (Bower, 2006, 2007)
Laboratory.E-Notebooks The electronic laboratory notebook (ELN) is a technology to share research notes and data to promote collaboration between distributed science students or researchers. In the past few years, several varieties of ELN have been designed for use in chemistry, biology, and physics laboratory settings, including those that have hierarchical chapter and page structures to organize information, automated data submission, and new entry notification (Myers, Mendoza, & Hoopes, 2001). An example of ELN free software is provided by The Pacific Northwest National Laboratory (at http://collaboratory.emsl.pnl.gov/software/eln/eln_list. shtml) and there are a large number of commercial varieties. Abari, Pierre, and Saliah-Hassane (2006) describe an ELN system and strategy where teams are structured as learning communities and corresponding information for teams is organized by the subjects of a course using each paragraph entry as a separate learning object consistent with metadata standards and repositories, such as the learning object metadata (LOM) standards. In this way, learning groups share data across learning platforms. For online science learning, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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electronic laboratory notebooks have potential for simultaneously coordinating field and laboratory work and data between spatially separated groups.
Science.Collaboration.Miscellany Another approach to strengthen collaborative learning is the use of document annotation. Annotations from peers serve to provide additional information on a subject and in some cases function as a form of peer review. Annotation systems have some features common to the mark-up features (e.g., comment tools) of word processing programs, plus document sharing and filtering options. Nokelainen, Miettinen, Kurhila, Floreen, and Tirri (2005) report on the EDUCOSM system that provides pop-up annotations in small color-coded widows reflecting user and peer comments. EDUCOSM features permit the user to add to a document pool, highlight, search, comment, filter, review document history, and explore the document pool. In a study of the effectiveness of EDUCOSM for adult and adolescent students in a statistics course, it was determined that annotations favorably supported learner-centered collaborative learning and that students benefited from their own annotations and those of peers (Nokelainen et al., 2005). However, underlining by peers and peer annotation were also found to be a distraction to some learners. Findings by Bohne et al. (2004) indicate that using a collaboration server and distance tutors in remote laboratory situations enhances student learning outcomes. Students with support from peers and tutors efficiently prevail over procedural and conceptual hurdles associated with completing the remote laboratory tasks. Finally, in an example of instructor to student discourse to refine courses, Wagner et al. (2005) report on the successful use of chat tools and e-interviewing to ascertain student understandings of physics topics acquired from prior experience (e.g., optical fibers). Even the use of simple listserves early on in a course, in the context of a mandatory assignment exchanged between students using the listserve, is an effective way of initiating an online community as shown for a preservice science course for teachers (Khan, 2005).
Evaluating.Online.Science.Collaboration In order for collaboration to be beneficial in the online science environment, students need to see that there is a tangible value to it. The most direct way to build a value for collaboration is to have an explicit framework for assessment that will comprise a percentage of a learner’s evaluation in a course. Swan (2004) and Swan, Shen, and Hiltz (2006) have described the process steps to develop an assessment of online collaboration as it is broken into three categories: collaborative discussion, small group discussion, and the collaborative design of assessments. In this model, the steps are: (1) specifying the learning goals to be accomplished with collaboration, (2) identifying specific behaviors that will be evidence of competence, (3) designating proportional values to the specific collaborative behaviors (e.g., rubrics with value levels), and (4) optionally, have students create their own assessments. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Such steps are reasonable in character and could form the basis of establishing both value and accountability in online science courses. Assessing the effectiveness of online collaboration towards student learning is a multi-faceted undertaking involving the determination of both individual and group performance. Daradoumisa, Martınez-Mones, and Xhafa (2006) report on an multi-layered model for assessing online collaboration with the following components: (1) an initial layer that qualitatively evaluates group and individual functioning, scaffolding, and performance, (2) an interaction analysis layer that applies a social network analysis of the group’s activity as well as individual participation behavior, and (3) quantitative analysis of the groups learning effectiveness as a product of the extent of collaboration involvement. The tracking tools of learning management systems are making such assessments of collaboration possible. However, the use of such an exhaustive assessment of online science learning would only be necessary and justified where there is an especially high value on team processes such as collaborative engineering projects. Since much of the collaboration in online courses still takes the form of students reacting to instructor or peer-posed questions or responses in discussion forums, another way to evaluate collaboration is through the analysis of argumentative discourse, particularly for its influence on and relationship to students’ development of knowledge. For the purpose of assessing argumentative discourse in online courses Weinberger and Fischer, (2006) break it down into the following four dimensions: participation (quantity and heterogeneity), epistemic (content of contributions), argument ( construction of arguments and sequences of arguments), and social modes of co-construction (the extent to which a learner relies/refers to peer contributions). The Weinberger and Fischer (2006) dimensions form a sound basis for developing a rubric for discussions they are a significant part of the online science learning process. Another area of interest in the evaluation of online teams is determining their efficacy. Efficacy can be evaluated by having students review their own contributions, the contributions of others or a combination of each. For example, in measuring group efficacy of virtual teams based on group consensus, Hardin, Fuller, and Valacich (2006) determined that social influence (i.e., group consensus) caused an inflation of team efficacy estimates and that fieldspecific learning measures should be used rather general measures. One way to undertake an efficacy assessment in an online science course is to include a self-assessment and team assessment of contributions near the end of the learning activity.
Conclusion Innovative Web technologies are opening up new possibilities for knowledge sharing and collaboration in online science education. Social interaction to support collaboration occurs as combinations between students, instructor, groups/teams, and online communities of practice. Communication structures to support learning may be cooperative and directed primarily by the instructor or collaborative where science students mutually assume a degree of responsibility for the learning. Social interaction in online science supports both
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educational and psycho-social dimensions. Online collaboration in online science is not a panacea and has its limitations and problems such as temporal, technological, and practical constraints to its implementation; potential student preferences for other styles of learning; and situations where collaboration results in antergy rather than synergy. In science courses there are progressive stages in online course collaboration that move from group familiarization to sustained interaction to wrapping up group efforts. Developing effective science collaboration involves combining many demonstrated strategies, among the most important are predetermining the outcomes for collaboration, managing the ongoing collaboration, and placing a direct value on it. Evaluating online science collaboration is inherently complex as it is difficult to monitor and assess interactions and individual/group efficacy without detailed tools provided by a learning management system. Emerging varieties of social software can help support collaborative efforts in online science education, however if students do not perceive the new tools as helpful then they will opt for more straightforward forms. As most current online learning environments do not approximate the characteristics or nuances of a face-to-face classroom, the traditional role of the instructor unavoidably changes towards that of a course author, facilitator, and/or e-moderator. However, as online science classroom technologies become more sophisticated and ubiquitous, synchronous teaching and lecture and their affordances will be increasingly re-engaged as instructional strategies. Educational games using the collaborative features of virtual worlds and virtual classrooms are two forms of promising online learning environments for science and mathematics that can promote complex as well as more traditional forms of interaction and collaboration. While a hopeful way to make online science learning more effective, virtual science classroom teaching elevates the instructor’s need to prepare, to control pace and interaction, and to create opportunities for learning personalization. There are several sundry tools and approaches such as laboratory e-notebooks, annotation systems, and e-interviewing that can foster communication and collaboration in online science courses. In summation, our review of collaboration in online science environments indicates its utility for seven key areas: (1) solving complex problems, (2) understanding theory and evidence, (3) supporting interdisciplinary efforts and knowledge acquisition, (4) conveying model-based reasoning and modeling the real-world workplace (i.e., authentic practical work), (5) discursive science and promoting hypothetico-predictive learning activities, (6) building communities of practice such as collaboratories that extend beyond classroom space and time, and (7) conducting group practical work.
References Abari, A., Pierre, S., & Saliah-Hassane, H. (2006). Laboratory e-notebooks: A learning object-based repository. Journal of STEM Education, 7(1/2), 15-23. Anderson, T. (2006a). Social software applications in formal online education. Proceedings of the Sixth International Conference on Advanced Learning Technologies, Kerkrade, The Netherlands, July 5-7. Los Alamitos, CA: IEEE.
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Anderson, T. (2006b). Distance learning—Social software’s killer ap?.Retrieved May 1, 2007, from http://www.unisa.edu.au/odlaaconference/PPDF2s/13%20odlaa%20%20Anderson.pdf Becker, K. (2007). Using Elluminate in CS. Journal of Computing in Small Colleges, 23(2), 73-75. Bermejo, S. (2005). Cooperative electronic learning in virtual laboratories through forums. IEEE Transactions on Education, 48(1), 140-149. Bly, S., Keith, K. M., & Henline, P. A. (1997, November). The work of scientists and the building of collaboratories. Paper presented at the Group 97 International Conference on Supporting Group Work, Phoenix, AZ. Bohne, A., Faltin, N., & Wagner, B. (2004). Synchronous tele-tutorial support in a remote laboratory for process control. In W. Aung (Ed.), INNOVATIONS 2004: World Innovations in Engineering Education and Research. Arlington, VA: Ineer. Bos, N., Olson, J. S., & Olson, G. M. (2005). Science on the net. Retrieved May 1, 2007, from http://www.scienceofcollaboratories.org/Resources/SOC-HCIC-Paper.pdf Bos, N., Zimmerman, A., Olson, J., Yew, J., Yerkie, J., & Dahl, E. et al. (2007). From shared databases to communities of practice: A taxonomy of collaboratories. Journal of Computer-Mediated Communication, 12(2). Retrieved July 12, 2007, http://jcmc. indiana.edu/vol12/issue2/bos.html Bower, M. (2006). Virtual classroom pedagogy..ACM SIGCSE Bulletin, 38(1), 148-152. Bower, M. (2007). Groupwork activities in synchronous online classroom spaces..ACM SIGCSE Bulletin, 39(1), 91-95. Coppola, N. W., Hiltz, S. R., & Rotter, N. G. (2002). Becoming a virtual professor: pedagogical roles and asynchronous learning networks. Journal of Management Information Systems, 18(4), 169-189. Dalsgaard, C. (2006). Social software: E-learning beyond learning management systems. Retrieved May 1, 2007, from http://www.eurodl.org/materials/contrib/2006/Christian_Dalsgaard.htm Daradoumisa, T., Martınez-Mones, A., & Xhafa, F. (2006). A layered framework for evaluating on-line collaborative learning interactions. International Journal of Human-Computer Studies, 64, 622-635. Dove, M. T., Calleja, M., Bruin, R., Wakelin, J., Tucker, M. G., & Lewis, G. J. et al. (2005a). The eMinerals collaboratory: Tools and experience. Molecular Simulation, 31(5), 329-337. Dove, M. T., White, T. O., Bruin, R., Tucker, M. G., Calleja, M., & Artacho, E. et al. (2005b). eScience usability: The eMinerals experience. Retrieved May 1, 2007, from http://archive.niees.ac.uk/documents/AHM_usability_2005.pdf El-Nasr, M. S., & Smith, B. K. (2006). Learning through game modding. ACM Computers in Entertainment, 4(1), 7. Eom, S. (2006). The role of the instructors as a determinant of students’ satisfaction in university online education. Proceedings of the Sixth International Conference on
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Advanced Learning Technologies, Kerkrade, The Netherlands, July 5-7. Los Alamitos, CA: IEEE. Farooq, U., Ganoe, C. H., Carroll, J. M., & Giles, C. L. (2007). Supporting distributed scientific collaboration: Implications for designing the CiteSeer collaboratory. Proceedings of the 40th Hawaii International Conference on System Sciences, Waikoloa, Big Island, Hawaii, January 3-6. Los Alamitos, CA: IEEE. FAS. (2006). R&D challenges for games in learning. Summit on Educational Games: Harnessing the power of video games for learning (p. 17), Washington, DC: Federation of American Scientist. Retrieved October 1, 2007, from http://www.fas.org/gamesummit/Resources/R&D_Challenges.pdf Fung, Y. Y. H. (2004). Collaborative online learning: Interaction patterns and limiting factors. Open Learning, 19(2), 15(135-149). Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: computer conferencing higher education. The Internet and Higher Education, 2(2-3), 87-105. Garrison, R. (2005). Online collaboration principles. Calgary: University of Calgary. Graham, C. R., & Misanchuk, M. (2004). Computer-mediated learning groups: Benefits and challenges to using group work in online learning environments. In T. S. Roberts (Ed.), Online collaborative learning: Theory and practice (pp. 181-202). London: Information Science Publishing. Hardin, A. M., Fuller, M. A., & Valacich, J. S. (2006). Measuring group efficacy in virtual teams: new questions in an old debate. Small Group Research, 37(1), 65-85. Harris, R. (2003). Building a GIScience community in cyberspace: Reflections on GIScOnline. Journal of Geography in Higher Education, 27(3), 17. Haythornthwaite, C. (2006). Facilitating collaboration in online learning. Journal of Asynchronous Learning Networks, 10(1). Retrieved May 1, 2007, from http://www.sloanc.org/publications/jaln/v10n1/v10n1_2haythornthwaite.asp Houle, C. O. (1961) The inquiring mind: A study of the adult who continues to learn. Madison, WI: University of Wisconsin Press. Jones, J. G. (2006). Accelerating online text-based discourse via 3D online learning environments. Retrieved May 1, 2007, from http://courseweb.unt.edu/gjones/pdf/ Jones_site_2006.pdf Kazanidis, I., & Satratzemi, M. (2007). Combine adaptive education hypermedia and SCORM compliant learning management systems. Seventh IEEE International Conference on Advanced Learning Technologies, Niigata, Japan, July 18-20 (pp. 330-331). Los Alamitos, CA: IEEE. Kennedy, D., & Duffy, T. (2004). Collaboration—A key principle in distance education. Open Learning, 19(2), 203-211. Khan, S. (2005). Listservs in the college science classroom: Evaluating participation and “richness” in computer-mediated discourse. Journal of Technology and Teacher Education, 13(2), 325-351.
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Kreijins, K., & Kirschner, P. A. (2001). The social affordances of computer-supported collaborative learning environments. Proceedings of the 31st Annual Frontiers in Education Conference, Reno, Nevada, October 10-13 (T1F-12-17). Los Alamitos, CA: IEEE. Leinonen, T., Kligyte, G., Toikkanen, T., Pietarila, J., & Dean, P. (2003). Learning with collaborative software: A guide to Fle3. Helsinki: University of Art and Design. Lin, S. C., & Lin, F. (2006). Towards an ecological perspective on the evolution of online communities of practice. Proceedings of the 39th Hawaii International Conference on System Sciences, Kauai, Hawaii, January 4-7 (pp. 1530-1605). Los Alamitos, CA: IEEE. Mahoney, J. (2006). Do you feel like you belong? An on-line versus face-to-face pilot study. Visions, 14(1), 16-26. Matsuura, K., Hesse, F. W., Ochi, Y., Ogata, H., & Yano, Y. (2002). Real/virtual classmates in an asynchronous distant learning environment. Proceedings of the International Conference on Computers in Education, Auckland, New Zealand, December 3-6 (pp. 792-796). Los Alamitos, CA: IEEE. Mayo, M. J. (2007). Games for science and engineering education. ACM Communication, 50(7), 30-35. Myers, J. D., Mendoza, E. S., & Hoopes, B. (2001). A collaborative electronic laboratory notebook. Retrieved May 1, 2007, from http://collaboratory.emsl.pnl.gov/resources/ publications/papers/ELN.IMSA.final.pdf Nicholson, S. (2005). A framework for technology selection in a Web-based distance education environment: Supporting community-building through richer interaction opportunities. Journal of Education for Library and Information Science, 46(3), 217-223. Nokelainen, P., Miettinen, M., Kurhila, J., Floreen, P., & Tirri, H. (2005). A shared document-based annotation tool to support learner-centred collaborative learning. British Journal of Educational Technology, 36(5), 757-770. Offenholley, K. (2006). Successful discussions in online mathematics and science courses. Online Classroom: Ideas for Effective Online Instruction, 17(2), 1-4. Pimental, M., Gerosa, M. A., Fuks, H., & de Lucena, J. P. (2005). Assessment of collaboration in online courses. Proceedings of the 2005 Conference on Computer Support for Collaborative Learning, Taipei, Taiwan, December 4-8 (pp. 494-498). Florence, KY: Lawrence Erlbaum Associates, Inc. Puntschart, I. (2005). Hybrid forms of knowledge sharing and transfer in higher education: A case study. Proceedings of I-KNOW ’05, Graz, Austria. Retrieved May 1, 2007, from http://64.233.167.104/search?q=cache:qxPQvNC-7nAJ:i-know.know-center. tugraz.at/content/download/532/2033/file/KSR_Puntschart.pdf+Hybrid+Forms+of+ Knowledge+Sharing+and+Transfer+in+Higher+Education:+A+Case+Study&hl=en &ct=clnk&cd=1&gl=us Ross, S. M., Kukulska-Hulme, A., Chappel, H., & Joyce, B. (2004). Taking e-moderating skills to the next level: reflecting on the design of conferencing environments. JALN, 8(2), 115-137.
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Rubens, W., Emans, B., Leinonen, T., Gomez Skarmeta, A., & Simons, R. J. (2005). Design of Web-based collaborative learning environments. Translating the pedagogical learning principles to human computer interface. Computers & Education, 45, 276-294. Rutkowski, A. F., Vogel, D. R., Van Genuchten, M., Bemelmans, T. M. A., & Favier, M. (2002). E-collaboration: the reality of virtuality. IEEE Transactions on Professional Communication, 45(4), 219-230. Sonnewald, D. H. (2007). Scientific collaboration: A synthesis of challenges and strategies.. Retrieved May 1, 2007, from http://www.ling.gu.se/~pierreg/sonnenwald-chapterdist.pdf Steinkuehler, C. A. (2004). Learning in massively multiplayer online games. Retrieved May 1, 2007, from http://www.academiccolab.org/resources/documents/SteinkuehlerICLS2004.pdf Steinkuehler, C. & Chmiel, M. (2006) . Fostering scientific habits of mind in the context of online play. In S. A. Barab, K. E. Hay, N. B. Songer, & D. T. Hickey (Eds.), Proceedings of the International Conference of the Learning Sciences (pp. 723-729). Mahwah NJ: Erlbaum. Suthers, D., Girardeau, L., & Hundhausen, C. (2003). Deictic roles of external representations in face-to-face and online collaboration. Proceedings of the International Conference on Computer Support for Collaborative, Bergen, Norway, June 14-18 (pp. 173-182). Dordrecht: Kluwer Academic Publishers. Swan, K. (2004). Relationships between interactions and learning in online environments. Needham, MA: The Sloan Consortium. Swan, K., Shen, J., & Hiltz, S. R. (2006). Assessment and collaboration in online learning. Retrieved May 1, 2007, from http://www.sloan-c-wiki.org/JALN/v10n1/pdf/ v10n1_5swan.pdf Tilley, D. (2006). Developing and establishing online student learning communities. Nursing, 24(3), 144-149. Wagner, D. J., Rivera, J. J., Mateycik, F., & Jennings, S. (2005). Using electronic interviews to explore student understanding. AIP Conference Proceedings, 790(1), 201-204. Wainwright, K., Osterman, M., Finnerman, C., & Hill, B. (2007). Traversing the LMS terrain. In Proceedings of the 35th Annual ACM SIGUCCS Conference on User Services, Orlando, Florida, October 7-10 (pp. 355-359). New York, NY: ACM Press. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46, 71-95. Williams, E., Duray, R., & Reddy, V. (2006). Teamwork orientation, group cohesiveness, and student learning: A study of the use of teams in online distance education. Journal of Management Education, 30(4), 592-616. Wulf, W. A. (1989). The national collaboratory—A white paper. In J. Lederberg & K. Uncaphar (Eds.), Towards a National Collaboratory: Report of an Invitational Workshop at the Rockefeller University, March 17-18 (Appendix A) (pp. 27-35). Washington, DC: National Science Foundation.
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Section II Online Science Instructional Strategies and Technologies
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Chapter.VII
Online.Science: Contemporary.Approaches.to. Practical.Work
Philosophers say a great deal about what is absolutely necessary for science, and it is always, so far as one can see, rather naive, and probably wrong. – Richard Feynman (1918-1988) Distance learning modalities in the natural sciences range from simple notes and discussion online (e.g., PowerPoint and asynchronous discussion threads), to remarkably sophisticated multimedia applications that enable students to explore complex systems such as the human body (e.g., The Visible Human Project, National Library of Medicine). In medical and engineering fields, the vanguard for testing the feasibility of learning technologies in online science, students both with time constraints or those far from educational institutions all benefit from sharing resources such as remote laboratories and virtual field trips, resources that are becoming increasingly sophisticated. Even so, the vast majority of Web-based science courses still fail to take full advantage of these interactive and push-pull technologies, relying on the learner’s desire to dig further (pull) within the course resources, rather than automatically delivering content at key points (push). In many cases, components/modules of courses are non-interactive and often resemble Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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nothing more than lectures adapted to the basic learning tools of learning management systems (LMS). In some cases, courses are technology-centered and basic pedagogical concepts (e.g., scaffolding) are not used. Even the modest incorporation of emerging innovations in information and communication technologies (ICT) can provide affordances to the student including: (1) promoting cognitive development, (2) enabling students to relate science to real-world experiences, (3) increasing students’ self-management, and (4) facilitating data collection and data interpretation (Webb, 2005). Despite the affordance of ICT, a general and strategic national vision, set of best practices, and coordinated effort for online science instruction is noticeably lacking throughout K-16. Among the key forms of contemporary online instructional design and practical work approaches reviewed here are simulations, remote laboratories, and virtual field trips. In addition, we review the idea of learning objects, elaborate on the use of multimedia in online science instruction and describe how home laboratories, educational games, puzzles, digital libraries, and blended sciences courses fit into emerging online science pedagogical practice. The discussion in this chapter is interwoven with examples that are a prelude to the more exhaustive treatment of mathematics and natural science examples reviewed in Chapters X-XII.
Learning.Objects Online learning environments and the object-oriented paradigm of computer programming have given rise to the idea of the learning object (LO) which Wiley (2000) defines as “any entity, digital or non-digital, which can be used, re-used or referenced during technology supported learning” (p. 4). Subsequently, the IEEE standard has been developed and defined so that the LO is now considered as any digital entity that can be used and re-used in a context of technological learning (IEEE, 2002). Thus, reusability has become part of the LO standard. In an applied sense, instructors who are designing online materials for a science course that can be reused in the future following reusability standards are building a LO. Learning objects range from very simple entities, for instance a digital photograph or e-mail, to complex multimedia entities such as streaming video with sound or an interactive 3-D model of human anatomy. Hence, for online science, learning objects are the stuff courses are made of. Haughey and Muirhead (2005) consider the chief importance for learning objects to be assisting in the introduction of new topics and skills, and providing reinforcement to existing skills. As well, they extend learning by providing new means for presenting curricular material and illustrate concepts less easily explained through traditional teaching methods. Finally, learning objects support new types of learning opportunities not available in a classroom environment and provide enrichment activities for gifted and highly motivated students (Haughey & Muirhead, 2005).
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Learning Objects Classification There have been several recent classifications of learning objects, beginning with Wiley’s five categories (Wiley, 2000): (1) fundamental uses one learning object (e.g., digital photo), (2) combined-closed uses two or more types of learning objects but is not reusable (e.g., video clip with audio), (3) combined-open uses two or more types of learning objects but has reusable elements (e.g., a Web page), (4) generative-presentation combines and/or generates the first two categories of LO and passes message to other objects (e.g., a Java applet demonstrating a science principle), and (5) generative-instruction instructs learner and provides practice on procedures (e.g., a virtual reality simulation on a science subject). Focusing on the educational-level components of learning objects, Redecker (2003) established an alternative hierarchy and four categories for LO including: (1) knowledge units, the smallest elements of knowledge, (2) learning units, contextual holders of thematically related knowledge units, (3) partial course, comprised of knowledge and learning units, and (4) course, a sequence or network of learning units. We can envision at least two other levels in an educational context of LO, such as curriculum units that represent combinations of reusable LO from multiple courses at a program level and communities of practice LO units that represent LO external of an institution at the professional society level. Redecker (2003) specifically breaks knowledge units down into receptive (learner passively receives them), internally interactive (there is human-computer interaction), and cooperative (require communicative activities by the learner). In contrast, Duval and Hodgins (2003) devised a LO taxonomy and hierarchy, which they termed a content object model that is Figure 7.1. The content object model of Duval and Hodgins, 2003, depicting the relationships between learning object hierarchy and reusability. Used by permission.
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broken into raw assets, information blocks, application objects (learning objects), aggregate assemblies (lessons), and collections (courses) (Figure 7.1). Their model shows the relationships between rank of learning object, extent of reusability, and degree of context. We favor the Duval and Hodgins (2003) framework for use in online science learning and develop an extended version of this learning object hierarchy further in Chapter XIII.
Learning.Object.Repositories In order for identification by search engines, learning objects follow standards such as the learning object metadata standard (IEEE LOM) or sharable content object reference model (SCORM). There are a growing number of repositories for learning objects. A key function of these repositories is to host and help organize the metadata of the learning object such as the U.S. and Canadian Multimedia Educational Resource for Learning and Online Teaching (MERLOT) at http://taste.merlot.org/index.html or the European Community’s Sharing Learning Objects in an Open Perspective (SLOOP) at http://sloop.tes.mi.it/sloop/. Other repositories actually host the metadata and the actual learning objects themselves such as DSpace http://www.dspace.org/. In addition, LO taxonomies are being developed consistent with their functions in online education. For example, Convertini, Albanese, Marengo, Marengo, and Scalera (2006) have developed a taxonomy of learning objects structured as a hybridization of the categories of the Wiley (2000) and Redecker (2003) so that LO may be more easily placed into repositories within learning and content management system (LCMS) platforms. As well, there are an increasing number of efforts by campuses and institutions to develop or purchase LCMS that permit faculty and staff to build, store, reuse, manage, and deliver e-learning content in a more integrated and efficient fashion (Cohen & Nyce, 2006).
Multimedia There are various definitions for multimedia; in general, the term is equated with the combinational use of text, sound, animation, video, and/or graphics. Consequently, multimedia are the raw assets for developing learning objects. In science instruction, multimedia-based learning objects can be passive where a student just receives information (one-way interaction). On the other hand, the LO may involve interactive multimedia (IMM) or rich media with complex, two-way interactions between the learner and the multimedia learning object, such as defined in the combined-open, generative-presentation, and generative-instruction categories of Wiley (2000), described previously. For example, Frear and Hirschbuhl (1999) report on the incorporation of IMM into an environmental geology course with modules using video representative of actual field studies. In this example, the learning objectives are provided as an environmental problem, students play the role as the investigator, there is a
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scenario for data collection, a modeling book is used to analyze data and develop conclusions, and student learning is assessed at the end of the module. The results of the Frear and Hirschbuhl (1999) study for in-class use of this particular IMM software, is that it can be an effective tool to foster learning through engaging students in simulated field trips. More recently there have been thorough tomes on the use of multimedia in learning that describe prevailing principles to design multimedia learning environments along with contextual examples (see, for example, Mayer, 2005). Naturally, many of the benefits and affordances of interactive multimedia demonstrated for the face-to-face classroom are also extendible to online environments, spatially distributed students, and the various science disciplines. In the followings sections of this chapter we examine the chief forms of learning objects used in online science instruction emphasizing instructional strategies that are becoming more commonplace.
Streaming.Digital.Video.in.Online.Science Streaming digital video is a useful multimedia tool that can be employed to create online learning objects in science. It is effective for both synchronous and asynchronous learning. Synchronous streaming media represents images, text, and slides that are delivered over the Internet with synchronized audio or video narration (Tront, 2001). Several projects have tested the effectiveness and reusability of using asynchronous digital video applications in distance learning, including the Digital Video Applications Project (DiVA) of the Open University (Kukulska-Hulme, Foster-Jones, Anne, Mallett, & Holland, 2004). The findings of this study, which employed a repository of learning objects, indicated generally positive feedback from students although the perceived limitations noted by them centered on their preparation for the activity and glitches in the technology, such as unclear terminology, irregular quality of video stream, and inaccurate text to audio narration. Uskov and Uskov (2004) describe a more advanced form of Web-based learning using streaming video and Web-lecturing technology applied to courses in electrical engineering, nursing, and computer science. The system developed at Bradley University’s Interlabs Research Institute (at http://www.interlabs.bradley.edu/) integrates communications technology, and streaming media with presentation, animation, and graphics software for synchronous or asynchronous interactive delivery. The user-view in the Interlabs system has a window for streaming video of the instructor, active links to other resources as well as a separate window for the presentation (Figure 7.2). Uskov and Uskov (2004) identify the benefits of this system for students as the attractiveness to learner, better interactivity, easy information searches, unlimited replay, and on-demand personalized education with no geographical boundaries. For the university the benefits of the system are increased marketability to users favoring technology mediated learning skills, unlimited number of users, low production cost, easy updates and security for Web casts.
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Figure 7.2. Screen capture of the Interlabs course delivery system that employs streaming video and presentation software from Uskov and Uskov (2004). Used by permission.
Typologies.for.Web-Enabled.Science.Laboratories A science laboratory is a facility with controlled conditions where practical learning, demonstration, and research can take place. Similarly, a Web-enabled or online laboratory refers to practical learning, demonstration, and research that is conducted by emulating part or all of a scientific experiment online. Based on a study of 100 Web-enabled laboratories, Amigud, Archer, Smith, Szymanski, and Servatius (2002) categorized them into four categories as: (1) active simulation (learner can change parameters for unique experience), (2) remote manipulation (learner remotely manipulates real equipment), (3) game-like laboratories (learner advances through sequential and increasingly complex activities), and (4) passive demonstration (learner reviews static content). Trgalova (2003) provides a more expansive typology for remote laboratories. The Trgalova typology, further discussed by d’Ham, de Vries, Girault, and Marzin (2004), is centered on the nature of science data utilized by the learner, whether real or simulated, and the degree of control of the experiment by the learner (see Table 7.1 translated from Trgalova, 2003, Table 1). This typology recognizes three degrees of learner control, from none to partial to total. Additionally, remote science Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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experiments are divided into remote sensing, simple remote operation devices, and distance laboratories. In this typology, simulations, animations, and viewed experiments (access to science) are included as remote experimentation categories. Simulations require partial or total control by the learner and do not involve real data. Animations may express real or simulated data, but are not learner-controlled. Finally, the access to science category (e.g., watching an experiment) includes real data but entails no learner control.
Table 7.1. Trgalova (2003) typology of remote experiments
Access to Science (Demonstrations) Animations Simulations Remote Remote Sensing Experimentation Simple Remote Operation Devices Distance Laboratory
Nature of Data Real Real or Simulated Simulated Real Real
Control By Learner None None Total or Partial None Partial
Real
Total
Table 7.2. Unified typology of Web-enabled science laboratories Lab Type Summary
Hands-on Distance Demonstration Animation Active Simulation Game Simulation Remote Sensing Simple Remote
Distance Laboratory
Amigud et al. (2002)
Studies Scanlon et Trgalova al. (2004) (2003)
Passive Demonstration
Active Simulation Game-like Laboratories Remote Manipulation
Ma & Nickerson (2006) Hands-on
Access to Science Animation Remote Simulation
Simulation
Simulation
Qualities Nature of Control Data By Learner Real Partial or Total Real None Real or Simulated Simulated
None Partial or Total
Simulated Remote Real
Remote Sensing Simple Remote Operation Devices Distance Laboratory
Remote
Real
None
Real
Partial
Real
Total
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Other notable typologies advanced to group the various design characteristics of remote experiments are given by Scanlon, Colwell, Cooper, and Di Paolo (2004) and Ma and Nickerson (2006). Scanlon et al. (2004) distinguish remote experimentation as real and simulated. Simulated remote experimentation involves control of a simulated laboratory environment usually by a graphic interface that mimics a real apparatus or other practical learning environment. By contrast, real remote experimentation refers to a learning environment where students have the ability to manipulate an actual experiment and corresponding instrumentation at a distance through a computer interface. Similarly, Ma and Nickerson (2006) recognized three categories of laboratories: hands-on, simulations, and remote. In comparison to one another, hands-on labs involve spatial closeness to the instrumentation, virtual labs simulate the lab environment with a computer, and remote labs have actual apparatus, but are mediated at a distance with computers. The key distinction identified between a hands-on laboratory and a remote experimentation is not that a computer interface is between the student and the instrumentation, as this is becoming increasingly common, rather the former is collocated with the student (Ma & Nickerson, 2006). We have correlated the Web-enabled laboratory typologies discussed, applying Trgalova’s (2003) qualities of nature of data and learner control. We append the category of handson distance for laboratories where procedure is conveyed through a Web-enabled learning platform (e.g., LMS), but distance students utilize real objects for study at their location. Our unifying typology of Web-based science laboratories for consideration in the development of online science activities is given in Table 7.2. Overall, the current use of Web-enabled science laboratories is uneven across science areas and for some forms such as remote laboratories is sparse, probably owing to the potentially high cost of development and instrumentation. In a recent comprehensive survey of articles pertaining to their defined laboratory categories (Table 7.2), Ma and Nickerson (2006) concluded that most articles and efforts on Web-enabled laboratories are in the engineering domain and that the literature is scattered among disciplines and journals. In our own survey of online science learning (Chapter IV), we had an analogous result finding that instructional strategies were sporadic across disciplines and institutions. These authors also concluded that standard criteria is not available to evaluate the efficacy of different types of lab work and there is differential emphasis on conceptual understanding, design skills, social skills and professional skills between hands-on, virtual, and remote labs. For example, remote and virtual labs have emphasized design skills less and there is a shortage of data in this area to determine their effectiveness for this learning objective. In terms of Web-enabled laboratory pedagogy, Ma and Nickerson (2006) assert that student presence, interactivity, and beliefs should be addressed in laboratory design. Moreover, where design skills are a learning objective, a hybrid laboratory approach including a hands-on component should be the norm. As with general best practices for online collaboration in online science learning, collaboration in distance labs should be planned and strategic. Consequently, their study suggests that the relative effectiveness of the various Web-enabled labs is in need of further study. Moreover and in particular, remote experimentation efforts are not distributed widely among science disciplines, but as might be expected, tend to be in areas where the production of scientific instrumentation is foundational (i.e., engineering).
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Benefits of Simulations.in.Online.Science.Learning Simulated learning settings are a complex form of learning object environment that model a system such that learners can change variables and make hypothetic predictions. A simulation imitates one process with another process. Hartmann (1996) soundly outlined the benefits of a simulation in the natural sciences as: (1) a technique to investigate the detailed dynamics of a system, (2) a heuristic tool to develop hypotheses, models, and theories, (3) a substitute for a numerical experiment, (4) a tool for experimentation, and (5) a pedagogical tool to help understand a process. What is more, Baggott (1998) demonstrated the effectiveness and benefits of multimedia simulations to enhance online practical work in science a decade ago for an interactive online microscope that simulates the key functional components of the instrument. From this experience, Baggott (1998) highlighted additional key learning benefits of simulations as, providing instruction with the ability to continuously assess and evaluate student learning, providing expanded (i.e., strategic) options for the student, permitting effective skill transfer, promoting discussion and student interaction, focusing tasks for efficient time use and providing edutainment value. In the next sections, we review various types of simulations and their benefits for online science learning.
3-D.Learning.Objects.as.Simulated.Specimens The presence and use of 3-D learning objects (or 3-D knowledge objects) and multimedia as learning tools on the Web is rapidly escalating as the power of the personal computer reaches critical thresholds of memory, speed, and graphical ability. Moreover, as limitations in bandwidth for graphics transfer continues to decrease in the next few years, the use of interactive 3-D objects on the Web for educational purposes will be commonplace. 3-D learning objects are digital representations of the surface morphology of objects (real or unreal) constructed of a mesh of polygons in various 3-D file formats (e.g., VRML) that are viewed using 3-D browsers. Alternatively, they may be layers of rich 3-D data as in CT and MRI scans. By way of the tools provided in the 3-D browser, the learner is often afforded the ability to manipulate the object such as size, perspective, and lighting or even analyze it via linear measurements, basic volume analysis, and so on. One of the pioneer institutions in placing 3-D learning objects (e.g., trilobites) on the Web for educational purposes was the Natural History Museum of London (NHM). Various 3-D objects are now presented in the NHM Virtual Wonders exhibit at http://www.nhm. ac.uk/nature-online/virtual-wonders/. Early on Downing (2001) demonstrated a virtual fossil cabinet for use in a virtual paleontology course where specimens could be downloaded into a 3-D browser and inspected and compared by students including assemblages of fossils for investigation of ecological and taphonomic relationships (Figure 7.3a). In addition, the study demonstrated how additional information can be situated in 3-D space during the instructional design phase by layering key concepts, such as the Y-5 molar pattern of hominoids, onto the basic 3-D learning object (Figure 7.3b).
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Figure 7.3a. Screen capture of a 3+D fossil assemblage allowing students to evaluate the paleoecology of fossil invertebrates in situ. K. Downing, personal image.
Figure 7.3b. An example of information layered on a navigable 3-D learning object. The Y-5 molar pattern of hominoids as demonstrated on Sivapithecus indicus. K. Downing, personal image.
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Animations A different example and somewhat more limited interaction style in the use of 3-D learning objects is the Human Evolution: The Fossil Evidence in 3D Web site by Phillip Walker and Edward Hagen (University of California Santa Barbara) at http://www.anth.ucsb.edu/projects/human/#. In this example, the 3-D representation of modern and fossil primate crania are recorded as a Shockwave movie, such that the learner can rotate and inspect features by advancing or reversing the movie. A similar example is provided by the Southwestern Adventist University’s online fossil museum at http://geology.swau.edu/fossil/. In this exhibit, archival 3-D copies of the museum’s specimens are viewed via QuickTime movies permitting, as in the previous example, the learner to rotate and inspect specimens through a 360o rotation. More recently, Ryosuke Motani of the University of California Davis has initiated a 3-D museum with a variety of fossil specimens at http://www.3dmuseum.org/ that are inspected in a 3-D viewer.
3-D.Anaglyphs A low tech, user-end means to represent 3-D objects online is by using an anaglyph, which is a stereographic image formed by offsetting and superposing two different color images representing the foreground and the background respectively. For a student to observe the effect of depth in the anaglyph, special low cost glasses with separate red and green lens are used. Anaglyphs are used extensively in geology and geography to represent the topography
Figure 7.4. An example of an anaglyph of Druid Arch (United States Geological Survey (USGS). *Requires color version of this chapter and 3-D glasses for viewing.
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of earth’s physical features. Examples of anaglyphs showing prominent landforms in the Western U.S. are provided at North Dakota State University’s Application of 3D Anaglyph Technologies in Geologic and Geographic Education Web site at http://www.ndsu.nodak. edu/instruct/schwert/anaglyph/ and the United States Geological Survey (USGS) Geology of the National Parks Web site at http://wrgis.wr.usgs.gov/parks/project/index.html. The anaglyph pictured here (Figure 7.4) is Druid Arch in Canyonlands National Park courtesy of the USGS. The anaglyph type of 3-D object has no interactivity and alone is not as useful for conceptual understanding, but is useful in visualization of complex objects.
Additional.3-D.Learning.Objects.for. Online.Science.Learning Advanced concepts, such as the structure of atoms are effectively conveyed using 3-D-animations and simulations, particularly those that permit manipulation of variables and repetition so that a student can refine their understanding (Combs, 2004). Moreover, commercial 3-D anatomy models such as those by Anatomium™ 3-D at http://www.anatomium.com/ are also becoming commonplace now. Likewise, sophisticated anatomy simulations that incorporate virtual tools for surgical simulation are available for online science application as in Figure 7.5 (from Dev, Montgomery, Senger, Heinrichs, Srivastava, & Waldron, 2002). In the geosciences area, Hsu and Thomas (2002) describe a mountain simulation (MtnSim) that permits a student to manipulate climate variables (e.g., temperature and precipitation) in conjunction with the elevation profile of a mountain. This simulation allows students to improve their weather forecasting predictions through iteration. In this way, students can use the simulator to learn through hypothetico-predictive reasoning.
Figure 7.5. An example of a 3-D anatomy model with virtual tools (e.g., forceps) from Dev et al. (2002). Used by permission of Elsevier.
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Figure 7.6. An example of a flyover interface that simulates travel over the surface of Mars. Used by permission of Marsquest Online.
Another popular type of graphic simulation technique used in science instruction, particularly for the study of landforms, is the flyover. In a flyover, the user’s browser provides an interface for navigation, much like a video game, to explore detailed features of a surface. An example of a flyover of mars using a 3-D model of the planet’s surface based upon satellite data has been developed by Marsquest Online at http://www.marsquestonline. org/tour/floods/flyin3d/index.html. A screen capture of the Mars Flyover interface including the steering tools, vertical exaggeration parameters, map view, and navigation view is shown in Figure 7.5.
3-D.Virtual.Worlds 3-D virtual worlds are networked, desktop virtual reality environments that make available an illusion of space, avatars that represent the user and interactive communication (i.e., chat) environments (Dickey, 2003). Typical examples of virtual worlds are found at the Active Worlds browser at www.activeworlds.com/ where users can assemble their own worlds. They differ from other more complex forms of virtual reality (VR) in that as a desktop entity they are not as fully immersive and multisensory as more complex VR systems (i.e., those that use wall size displays or haptic-enabled systems). There are many educational affordances Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 7.7. An example of the virtual cell interface on the topic of photosynthesis. Used by permission from Dr. Phil E. McClean.
provided by VR and 3-D worlds in building learning environments for teaching, including for online science learning. Dickey (2003) describes the beneficial features of 3-D virtual worlds for science education as: (1) graphically rich and compelling 3-D contexts for situated learning, (2) views of objects from multiple perspectives, (3) potential learner interactions with objects, (4) safety from danger experiments, (5) experiential, learn by doing opportunities, (6) personalization of learning environments, (7) interactive and collaborative learning across time and space, and (8) a self-selected identity (Dickey, 2003). An early and exemplary initiative to develop virtual reality environments for learning in geology and biology was that accomplished by North Dakota State University’s World Wide Web Instruction Committee (WWWIC) (Slator, Saini-Eidukat, & Schwert, 1999). The WWWIC produced the Geology Explorer where students learn geological principles on a mythical planet (http://oit.ndsu.edu/~mooadmin/PLANET/) and the virtual cell where students learn basic physical and chemical properties of the cell (http://vcell.ndsu.edu/). An animation frame from the virtual cell incorporating additional scaffolded information about photosynthesis is shown in Figure 7.7 (McClean et al., 2005). The virtual worlds designed by WWWIC follow a specific set of learning strategies and pedagogy; they are role-based, goal-oriented, learn by doing, spatially oriented, immersive, exploratory, game-like, highly interactive, and multi-user/player. In addition, varieties of tutors are used to interact with the learners in the WWWIC virtual words.
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Caveats.of.Using.Virtual.Worlds While there is much educational promise in the use of virtual worlds to advance the learning objectives of an online science course/curriculum, conversely there are potential limitations. Some of the caveats raised for employing virtual worlds are: (1) the effort required to develop the world may be disproportionate to the learning objectives, (2) student exploration of the virtual world may be inefficient without clear guidance for tasks, (3) students might be sidetracked from carrying out the learning objectives, (4) the possible elevated technological requirements and corresponding staff burden, and (5) VR may be intimidating to non-technical disciplines (The Educause Learning Initiative, 2006). What is more, there are also further institutional issues such as the development time and dollar costs to generate a VR learning environment, building supporting delivery structures, and the requisite pedagogical transitions for instructors (Jones, 2006). An additional concern is whether the highly visual-spatial environment of a virtual world will be conducive to all learners. A case in point is the 3-D virtual world, Visual Water developed as classroom-use software to support visual-spatial learning of atomic and molecular structures in physics and chemistry (Trindade, Fiolhais, & Almeida, 2002). Using Visual Water students can explore such things as the relation of atomic orbitals with respect to phases and phase transitions between gas, liquids, and solids. A conclusion drawn from this research is that immersion into the 3-D animations improves the conceptual understanding of students with high spatial abilities, but that such related techniques tried such as using stereoscopic glasses (i.e., 3-D anaglyphs) do not show appreciable gains. For online science learning, the implication and cautionary note of this study is that 3-D virtual worlds may be less effective for those students with lower spatial abilities necessitating scaffolding and training in visualization.
Affordances.of.Virtual.Science.Environments Virtual reality is a form of information visualization that allows the learner to see and often manipulate objects represented in 3-D coordinates on the screen. Virtual learning environments provide many affordances that make them effective for science education. Virtual 3-D environments may enhance a student’s aptitude to develop conceptual models in disciplines such as physics and chemistry that require high spatial-visual spatial ability as the virtual settings promote immersion into otherwise intangible worlds, experiential interaction with representations of phenomena, and learner-controlled engagement in the learning process (Trindade et al., 2002). Summarizing from Slator et al. (1999) and Walczak, Cellary, and White (2006), comparable and additional affordances of virtual science environments are the ability to control virtual space and time (very fast and slow), the ability to create shared spaces that are unfeasible in the real world (dangerous, macroscopic and microscopic, inner workings that are inaccessible, abstract concepts). Moreover, virtual science environments provide the ability to support shared experiences for distance learners, the ability to implement learning objects according to specific pedagogical goals, and the ability to support collaboration and competitive educational game play. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Examples.of.Online.Virtual.Science. Learning.Environments Many efforts have employed interactive media and simulations to promote teaching science online. An exemplary project is Virtual Labs Project at SUMMIT on fundamental concepts of biology (Huang, 2004). In this simulation laboratory environment, students can interact with parts of the human body. The Virtual Labs Project at SUMMIT is accessible through the Howard Hughes Medical Institute’s biointeractive virtual labs on medical topics at http://www.hhmi.org/biointeractive/vlabs/index.html. In an example from the physical sciences, the Virtual Physics Lab (VPLab) at http://www. vplab.co.uk/ employs a realism standard for its user interface. It includes 2-D depictions of traditional physics experiments with tools such as an onscreen calculator, stopwatch, ruler, protractor, tape measures, lab notebook, inset camcorder, and display monitor for data collection by students (Couture, 2004). The experiments in VPLab are supplemented by additional resources that include online help, video clips, and multimedia. While Couture (2004) found the use of realism in the experimental depiction of the VPLab increased the perceived degree of verisimilitude of students to the learning environment, it was also concluded that some depictions (e.g., the camcorder) actually added to student confusion. Hence, conveying the use of each virtual instrument to students prior to initiation of the
Figure 7.8. Simulation interface from the VPLab on elasticity showing key features and tools to conduct experiments on different wire types. Used by permission of Dr. J. Nunn.
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experiment (i.e., pedagogical content knowledge) is a best practice for these types of simulated laboratories. An example of the simulation window from the VPLab on elasticity of metal wires showing key features and tools to conduct experiments on different wire types is shown in Figure 7.8. A virtual experiment environment (VE environment) built using VRML has been developed and described by Shin (2004) to support the teaching of earth (seismic waves, isostacy, ocean movements) and planetary science (solar system and telescope movements) in middle schools. The simulated experiments in this case are exemplary in following a model of 3tiers to match a student’s situated cognition in both the conceptual and inquiry areas. In a concluding example, Spanias and Atti (2005) describe an online virtual science environment for electrical engineers that employs an interactive Web-based simulation tool called Java DSP. The DSP tool permits distance learners studying digital signal processing to perform both simulations and demonstration visualizations.
Educational.Science.Games Over 25 years ago, Games and Simulations in Science Education was published by Ellington (1981) demonstrating comprehensively how educational science games and simulations, including rudimentary computer-based varieties, could be an instructional tool to support science education. In the intervening years, computer games have often been the scorn of parents and the public. However, improvements in graphics technology and the sophistication of gaming software has resulted in a renewed interest and acknowledgement that if targeted towards learning objectives, video games, and Web-enabled games have the potential to substantially contribute to education. The key rationale and affordances of games for education are that the strategies, tactics, and methods of games can involve the learner in analyzing, synthesizing, and critical thinking skills and thus they are capable of providing a platform for the production of educational media and interactive learning environments (Dickey, 2005). Moreover, games also have attributes of play that promote social, research, problem solving, and transfer of learning skills (Oblinger, 2006). In spite of these potential benefits for learning, as Shaffer, Squire, Halverson, and Gee (2004) have advised, contemporary educational games have not overall, been founded on a coherent theory of learning. However, this is changing and there are a growing number of instructional design models for educational game use as well as university consortium efforts focused on pursuing the educational application of games. Two notable examples are Canada’s Simulation and Advanced Gaming Environments for Learning (SAGE) and The Education Arcade a joint effort by MIT and the University of Wisconsin.
Models.for.Online.Learning.Game.Development A range of models have been developed to engage student learning through games including those based on interaction, hypothetico-predictive pedagogy, and the narrative structure Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 7.9. The MR ELL model for using games and free play in conjunction with scientific inquiry to teach science at museums (from Stapleton et al., 2005). Used by permission from IEEE.
of adventure games. Iverson (2004) has developed a general model for e-learning games, which anchors off four key types of online interaction. The four interaction modes: learnerto-interface, learner-to-content, learner-to-facilitator, and learner-to-learner are coupled to specific instructional design theories that emphasize motivation, specifically Gagne’s nine events of instruction (Gagne, 1985) and Keller’s ARCS Model (Keller, 1987). Stapleton, Smith, and Hughes (2005) follow an alternative approach to online learning game development based more closely on a hypothetico-predictive learning model. These researchers developed a mixed reality experiential learning landscape model (MR ELL) to enhance citizen science education at the Orlando Science Center for the Sea Creatures exhibit. The objective of the model is to increase the entertainment value of learning (i.e., edutainment) without compromising core-learning objectives. The MR ELL model (Figure 7.9) provides patrons with games, stories, and free-play comparable to the process of scientific inquiry and hypothetico-predictive pedagogy. In this model, the learning objective (question) is generated from the initial immersion into the story of the exhibit. This is followed by experimentation that occurs as free-play followed by application of relevant natural laws via a game format. The model is structured to be Web-enabled so that it could be useful in formal education as well as home schooling. We consider this model to be a useful general framework for developing game storylines to support online science learning across the natural science disciplines. Lastly, Dickey (2006) provides a model of how online games can be planned for educational purpose using the narrative structure of adventure games. The typical adventure game narrative suggests the following steps: presenting the initial challenge, identifying potential obstacles, identifying and establishing roles, establishing the dimensions of the learning environment, creating a back-story to reinforce the characteristics of the environment, and developing cut scenes to provide feedback of how a student has done and to set up the next learning activity (Dickey, 2006). A key message of this report is that the narrative game sequence parallels a science research investigation involving asking questions, seeking data, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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receiving feedback and iterative refinement of concept. Promising formats for online science learning games are the multiplayer online gaming environments (MMOG) discussed in the previous chapter on collaboration, as well as MUD (multi-user dungeon) platforms where users can create lasting interactive objects.
Remote.Experimentation An advanced way to provide practical work experiences with high interactivity at a distance is through remote experiments. Remote experimentation, also known as remote access or mediated reality experimentation, involves the actual physical manipulation of an experiment through the Web by the learner. In this way, a remote experiment differs from a virtual experiment whose instrumentation is a simulation. Faltin, Böhne, Tuttas, and Wagner (2002) point out that remote experiments permit the testing and verification of scientific principles, as they are indeed real experiments and students who like the perception and influence of reality on their observations value this authenticity. Remote experiments ordinarily involve a virtual instrument interface on the user’s screen that simulates the control functions of a real piece of equipment housed away from the user. For example, the commercial product LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) has long been a popular choice to bridge real instrumentation to the distance learner through user-friendly interfaces called front panels. Ertugrul (1999) provides a thorough review of examples of LabView’s use in the engineering field where it has garnered the most application. While remote experiments share several key characteristics, including the learner interface, a facilitating learning management system (LMS), and a real experimental apparatus, until recently there has been no standardized architecture that builds off of the e-learning infrastructure and interoperability standards such as SCORM (addressed previously). This lack of interoperability for remote experiments is shifting and standardization to provide universal online access is in the works. For example, Duan, Ling, Hosseini, and Gay (2006) describe a learning system called OnlineLab built to include the SCORM standards. Their architecture for a universal online learning laboratory includes: (1) a user browser with a virtual interface, learning resources, and communication mechanism between shareable courseware objects (SCO) and the learning management system, (2) learning management system with a SCORM run-time environment and apparatus run-time environment, and (3) an instrument apparatus with controllers and communication applications.
Remote.Experimentation:.Design.Approaches and.Considerations .. The general design of a remote experiment is depicted effectively in Navarathna, Fedulov, Martin, and Fransson’s (2003) example of a controlled aerodynamics lab. In this remote Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 7.10. The general design of a remote experiment showing the instrument, monitoring systems, control system computer, and Internet infrastructure (from Navarathna, 2003). Used by permission from T. Fransson.
experiment (Figure 7.10), users connect through the internet to local area networks and a local computer that controls the remote experiment. Remote experiments often have cameras and audio feeds that permit the learner to see and hear the instrumentation as they conduct an experiment. Based on their experience with developing physics remote laboratories, Forinash and Wisman (2005) have identified key design principles for remote labs. To begin with, the experimental controls and results should be highly visible and learners require thorough guidelines regarding the use of the remote laboratory model. Each task should be linked to learning objectives and procedures. Moreover, the learner should understand the constraints on knowledge required to conduct the experiment, the conceptual model of the experiment and the particular approach taken. Finally, there should be concurrent feedback to learners as they work on the remote laboratory. Amigud et al. (2002) describe similar best practices for effective instructional design of a remote experiment. There should be clear learning objectives, support for a variety of learning styles, interactivity, a user guide, quick download of resources, easy to navigate Web site interface, chat function, aesthetic presentation of resources, links to additional resources, and an associated means for assessment of student learning. Another important consideration for a remote experiment is the responsiveness of the interface. Cartwright and Valentine (2002) point out that remote experimentation interfaces should offer: (1) starting, stopping, and adjusting the virtual instrument, (2) responsive feedback to the student, and (3) an interface representation of the equipment no more difficult than if the physical equipment were present. Remote experiments may involve sophisticated and valuable equipment with elevated logistical requirements relative to virtual experiments. Consequently, there are additional design requirements for a remote experiment Web site. For example, based on their remote experiment developed for study of chromatography and spectroscopy, Kennepohl, Baran, Connors, Quigley, and Currie (2005) identify the key components of Web site for a remote laboratory as: (1) a public information section, (2) password protection, (3) FAQ and help Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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sections, (4) a synchronous chat connection with the instructor, (5) a tutorial describing the principles of the experiment, (6) instrument qualifying exercises, (7) a scheduler function to put learners in a queue, (8) instrument access for experimentation, (9) an instrument monitoring camera, (10) a database of standards for comparison, (11) e-logbooks for data recording, and (12) supplemental resources. The comprehensive list resultant from Kennepohl et al. (2005) serves as a best practices framework for remote experiment Web site features. In constructing remote and virtual laboratories, it is useful if students experience a sense of presence and this can come about in several ways. As categorized by Sheridan (1992), presence may be fostered either as physical presence, telepresence (the emotional feeling of presence formed by adequate communication of the environmental character of the remote location), and/or virtual presence (the presence created by the computer interface and associated learning activities). In particular, best practice design of remote laboratories will accentuate telepresence and virtual presence. Another crucial part of laboratory work and experience is realizing the intricacies and fallibilities of experimental design and the commonness of procedural errors; hence, opportunities to encounter these quandaries should be a part of the design of remote experiments (Faltin et al., 2002; Forinash & Wisman, 2005).
Examples.of.Remote.Experiments One of the most adept projects in the remote experiment area is the UK Practical Experimentation by Access to remote Learning (PEARL) project which allows students to conduct
Figure 7.11. An example of a remote experiment system for spectroscopy form the PEARL Cooper et al. (2002). National Science Foundation funded study.
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traditional experiments with sophisticated instrumentation (e.g., electron microscope, optical spectrometers) employing a computer (Di Paolo & Scanlon, 2004). Another example of remote experimentation design in this program and its iterative refinement is described by Cooper, Donnelly, and Ferreira (2002) (Figure 7.11) and Scanlon et al. (2004) for the spectroscopy component of the Practical Experimentation by Accessible Remote Learning (PEARL) project at the Open University (UK). The PEARL experiments at http://iet.open. ac.uk/PEARL/ employ extensive video camera feeds and corresponding streaming media technology for students to observe, collaborate with peers, and control the ongoing remote experiment using equipment control technologies through a Web-hosted interface. A key conclusion of the authors was that remote experiments may not be directly comparable to their traditional counterparts and as a result may require special modifications during implementation. A further example of remote experimentation by Chu (2004) describes a comprehensive virtual instrument system environment to support students in learning digital electronics online that combines the features of remote experimentation with facilitative multimedia. This system has many of the remote features described earlier and includes online background information on the concepts that substitute for a textbook, material describing how to use the interface (in this case LabVIEW), and the real-time remote laboratory hardware and interface. However, this system also includes facilitative multimedia such as a complimentary set of demonstration video clips that explain the experimentation motif, corresponding sheets to record experimental data, and software simulation using animation clips to demonstrate examples of circuits. Student feedback about the digital electronics remote experimentation system was favorable but key limitations noted were the high set-up cost, limitations on concurrent use by students, and student concern that they needed hands-on experience on the subject.
Remote.Experiment.Affordances The general advantages of such remote experiment systems are potential financial savings over the duration of the system, the enhancement of distance education courses, safe student access to potentially hazardous environments, increasing the variety of possible available experiments, and familiarization with expensive equipment (Cartwright & Valentine, 2002; Forinash & Wisman, 2005). That is, a potential key institutional benefit of remote experimentation is the ability to share expensive equipment for both teaching and research (Kennepohl et al., 2005). Nevertheless, remote experiments do not always have to involve costly or complex instrumentation to be effective in online science instruction. For example, the remote physics labs by Forinash and Wisman (2005) demonstrate that it is possible to design inexpensive and diverse remote experiments. Their remote physics labs provide the opportunity to access unknown remote information as an experiment, allow multiple students to use a remote experiment asynchronously, allow multiple students to synchronously use a remote experiment, and permit multiple instrument setups to be used simultaneously by an individual student. Although the efficacy of different types of remote experimentation is an area for further study, in the case of remote access chemistry instrumentation versus
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comparable onsite access to chemistry instrumentation, no appreciable difference was indicated in a comparison of grades for students (Kennepohl et al., 2005).
Hands-on.Laboratory.Approaches.for. Online.Students Lab.Kits Most of the recent efforts to engage students in distance practical work have centered on employing technological advances in home computing to simulate or remotely replicate laboratory conditions, instrumentation, and study objects or specimens. Despite this emphasis on virtuality, a long-standing and viable instructional strategy for the distance science learner is the Home Experiment Kit (HEK). For instance, the United Kingdom’s Open University has employed HEK in many varieties from its inception in the early 1970’s including one kit with a notable 272 items to support a suite of experiments (Ross & Scanlon, 1995). Science kits are best utilized for students in introductory science courses that investigate everyday phenomena and that do not require expensive instrumentation to reinforce learning outcomes. The simplest HEK are those that can be assembled from household or store bought items by the student, the so-called kitchen chemistry kits. More sophisticated HEK are comprised of materials (e.g., microscopes, chemicals, specimens) to enable a student to undertake experiments on par with an introductory college course in the sciences. Some colleges chose to assemble their own science kits and provide them to students as a part of required course materials. An example of a fossil kit for college students is given in Figure 7.12. Alternatively, there are commercially available kits that span biology, anatomy and physiology, geology, chemistry and physics (see for example At Home Science Inc. at http://www.athomescience. com/index.html). A detailed laboratory guide presented in a digital format or patterned after the activities of a recent textbook typically accompanies the experiment kits of today. The efficacy of kits as means of teaching chemistry and physics to distance learners has been argued by Jeschofnig (2004) who, based on his experience primarily with micro-sample chemistry kits, concluded a laboratory kit “satisfies academic experimentation objectives better than any other substitute for traditional lab work” (p. 4). Even sophisticated topics in analytical chemistry such as spectroscopy and liquid chromatography are taught through inexpensive home kits (Hoole & Sithambaresan, 2003). In describing their experience with university sponsored introductory biology kits, Holmberg and Liston (1998) found merit in HEK but also described several difficulties. The key trouble issues noted were time limitations for development of kits when courses are offered continually, repeatability of results with varying samples and specimens, safety and liability concerns, lack of student input and testing of HEK lab design, and disagreements by multiple instructors/authors on the lab and kit design. For home experiment kits, Ross and Scanlon (1995) have described the best practices in practical work as: (1) appropriate time and logistics, home setting/space
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Figure 7.12. An example of a uniform fossil kit used for teaching introductory concepts of paleontology. K. Downing, personal image.
considerations, (2) integration with other materials, (3) links to assessment, (4) safety, (5) user-friendliness, and (6) cost effectiveness.
Local.Arrangements.for.Laboratory.or.Field.Work Another logistically difficult but low-tech way to provide online science students with selected hands-on lab experiences (or field work) in the natural sciences is to partner with and make local arrangements with institutions (colleges, universities, museums, etc.) that have the science resources to support learning. For example, most large metropolitan areas have science museums with educational divisions and many of these have instructional materials for loan or onsite use such as the Harris Educational Loan Center at the Field Museum in Chicago, the Western Australian Museum—Loan Centre in Perth, The Educators Loan Program of the Natural History Museum, Los Angeles, and others. Therefore, in some instances it may be possible to tap a student’s local resources to supplement their online science learning. A Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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coordinated national effort by such institutions to avail their resources to distance students would be a worthwhile pursuit.
Virtual.Field.Trips Field trips are one of the hallmark activities of the natural sciences and serve the key learning and practical work functions of (1) bringing students to a place that is not a part of the normal class environment and (2) providing learners the opportunity to observe a process or entity in its uncontrolled, natural state. Foley (2003) described a virtual field trip as “a guided exploration through the Web that organizes a collection of pre-screened, thematically based Web pages into a structured online learning experience” (p. 16). However, with the advent of technologies supporting live chat and streaming video, the more static Web field trip can be extended to include real-time interactive field work activities in which the distance learner can engage. Hence, the designation virtual field trip for online learning activities has expanded in meaning and practice to include a broader group of pedagogical and technological strategies to serve the field observation component of practical work. In the broadest sense, a virtual field trip represents a learner’s exploration of online resources and activities outside of the normal class environment that the instructor has determined will enrich their learning of a subject. Virtual field trips may seem like a compromise to actual field trips however, there are some qualities of virtual field trips such as their affordability, safety, time efficiency, scalability, distance curbing, and reliable and focused demonstration of critical phenomena that make them comparable in utility to actual field experiences in fostering inquiry through observation (Ramasundaran, Grunwald, Mangeot, Comerford, & Bliss, 2005). In spite of this, there are still undeniable limitations to virtual field trips. Qui and Hubble (2002) summarize the limitations of virtual field trips in the context of their use in the geosciences and from a student’s view. We present their detailed summary as Figure 7.13. Ultimately, an instructor or instructional design team must weigh how and whether learning objectives can be achieved with a virtual field trip. A simple variety of a virtual field trip, sometimes called a virtual tour, is one that involves no multimedia beyond graphics. These virtual field trips typically have a 2-D graphic (e.g., geographic map, geologic map, biological specimen) with labeled parts linked to additional information. Examples of these are the Dark Peak Virtual Tour course at http://www.art. man.ac.uk/Geog/fieldwork/virtual_tour_home.htm, which prepares geography students for a one-day physical geography field (McMorrow, 2005), Geological Tours of Southern California by California State University at Long Beach at http://seis.natsci.csulb.edu/VIRTUAL_FIELD/vfmain.htm, and the Virtual Cell described by White, McClean, and Slator (1999) at http://www.ibiblio.org/virtualcell/tour/cell/ cell.htm. We note that term virtual tour has more recently become synonymous with walkthrough virtual 3-D technologies that permit the user to navigate through a virtual world as in the case of virtual home tours in real estate. Virtual field trips are common now in K-12 education and have an increasing incorporation of multimedia such as NASA’s Quest interactive exploration Web site (http://quest. nasa.gov/) built around authentic problems. Other examples are those sponsored by e-field Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 7.13. Comparison of the advantages and the disadvantages of virtual field trips from Qui and Hubble (2002). Used by permission from Dr. Tom Hubble and UniServe Science.
trips.org (http://www.efield trips.org/) that include a field trip journal, a Flash movie science subject, ask the experts discussion boards, and a scheduled Web chat with experts. Likewise, the NASA LIVE system at http://nasadln.nmsu.edu/dln/ provides educators and their students with virtual field trips to scientific institutions where they can talk to experts through videoconferencing techniques (Townes-Young & Ewing, 2005). An advanced example of a virtual field trip that also involves simulation is the environmental virtual field laboratory developed using the virtual reality modeling language (VRML) by Ramasundaran et al. (2005). In this laboratory for field study, students apply exploration, analogy, scientific inquiry, and abstraction-based learning. The field laboratory depicts the Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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environmental conditions for a 42 hectare site in central Florida has several tiers of situated cognition (i.e., abstraction) including raw numerical data, 2-D maps, static 3-D maps, interactive 3-D amps, and space-time (i.e., 4-D) models. Student interactivity with the virtual field lab is accomplished by animations, focus questions, hyperlink navigation, 3-D model exploration, and selective dynamic simulation for changing soil landscape and water tables. This example is further described and illustrated in Chapter X.
Actual.Field.Study.to.Support.Distance.Education The heritage of science advanced from personal study of the environment and the outdoors where science can come alive for students (Slingsby, 2006). Individualized and even group field study may be a suitable instructional strategy for distance students where practical work learning objectives can be satisfied. An actual field study strategy can be particularly useful for distance education courses in disciplines such as geology, geography, biology, and ecology where natural environments are the basis for observation and outdoor resources are ubiquitous. The natural resources applicable to structured field study are such entities as scientific reserves, national parks, natural monuments, managed nature reserves, protected landscapes as well as representative forms of nature such as zoos, botanical gardens, aquariums, museums, and others. Akin to home experiment kits discussed previously, careful planning and design is a necessity of such an effort. There are two chief ways to structure actual field study at a distance: (1) instructor-designed study and (2) self-designed study. Vasalla (2006) describes a 3-stage model for instructor-designed outdoor learning for distance education. In synopsis, it includes: •
Instructor preparation: selects materials and subjects for study with student’s locality in mind; research and/or visit study area (where practical), design learning activities, secure cooperative agreements (where necessary); engage in a contact session with learner to review the activity, learning process, and means of assessment.
•
Student work in the field: individually or in groups, students undertake the learning activity collecting requested data within the methodology framework devised (e.g., observation notes, sampling, photographs, etc.).
•
Student composition and presentation: students analyze and interpret data; prepare requested deliverables; publicly present their work (e.g., electronically).
An alternative strategy for making use of actual field study is to design a common framework with which all students can self-design a plan of study based upon the resources accessible to them, where they live or within reasonable travel distance. We have employed this strategy for several science courses at DePaul University. For instance, an abbreviated example of the stages of a self-directed field trip from a physical geography/geology course called Interpreting Earth’s Physical Features is provided in Table 7.3.
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Table 7.3. Stages of a self-directed distance education field trip from a physical geography/ geology course called Interpreting Earth’s Physical Features Stages.of.a.Self-Directed.Field.Trip 1)
Instructor designs the learning objectives:.Can describe, differentiate, and explain form, function, and variation of a physical feature of earth through both research and direct experience.
2)
Instructor explains and illustrates the selection of the field study area:.(1) Select a key physical feature of earth to visit near where you live..Illinois Example: Visit a glacial feature, a plains-prairie feature, a lake, a bog, or a road-cut showing soil features.
3)
Instructor conveys the parameters of the assignment and deliverables: Develop a methodology/procedure for observing and characterizing your chosen physical feature. Take detailed notes for observations you make during your visit to the selected field area and physical feature. If the feature is associated with an information center (national, state, county parks and preserves, etc.) visit the center and its exhibits and learn as much as you can about the feature’s origin, importance, and evolution over time.
4)
Complete the fieldtrip summary form and develop a 2.0 page self-assessment of your learning. Provide specific examples from your visit. Topics that you should consider in your visit are: • What kind of physical feature(s) did you observe? • How are these features differentiated from one another? • How do such features originate? Are they found elsewhere? • How long lasting are such features on earth’s surface (i.e., do they evolve? disappear? • Are there any controversies concerning the origin of the physical feature(s) you observed? Student Conducts preliminary research:.Conduct background research on the physical feature you wish to observe before traveling to it. This can involve an internet search, viewing topographical maps of the feature(s), viewing satellite pictures of the feature, and/or reading scholarly books or literature about the feature(s).
5)
Student consults with Instructor: Student and Instructor confer and agree on the subject of study as well as the specific methods of study selected by the student.
6)
Student conducts the field study:.Visit the feature of your choice and complete the required assignment.
7)
Assessment:.Student completes assignment and conveys the results to.the class via a discussion board including corresponding visuals and links to additional resources. Peers review posted materials and submit questions to learner. Instructor assesses deliverables based on a rubric.
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Virtual.Puzzles.for.Learning.Science In many areas of science and engineering, the assembly of objects/parts into a functioning whole like a jigsaw puzzle is a critical, abstract, visual, and applied skill. Many examples come to mind such as composite maps and 3-D reconstructions of geomorphological features, the spatial fit of molecules in chemistry, anatomy reconstruction in biology and paleontology, and piecing together artifacts in archeology. Using online 2-D or 3-D puzzles is a straightforward way of enhancing the spatial visualization skills of online learners to support assembly and reconstruction skills. An example incorporating this learning strategy is the United States Geological Survey’s Tapestry of Time and Terrain Website at http://tapestry. usgs.gov/puzzle/default.html. This Web site utilizes a 2-D online puzzle to compliment other learning resources in the explanation of physical geography and geological provinces of the United States. An example of a 3-D jigsaw puzzle was constructed by Ritter et al. (2001, 2002) to help teach the anatomy of the foot and it was determined that this strategy was an effective way of improving the analysis of spatial relationships, correlations between parts, functional interdependencies of parts, and student motivation to learn. The 3-D puzzle for foot anatomy is illustrated in Figure 7.14. An additional puzzle-like system that has applicability to online learning is augmented reality (AR), where virtual representations of objects are overlain digitally with physical models by way of a camera tracking system. Augmented reality essentially lets the user manipulate and fit their object/piece with virtual objects in the computer. In this way the physical model held by the user acts as both a haptic interface and learning object with respect to a companion
Figure 7.14. An example of a 3-D jigsaw puzzle for learning foot anatomy from Ritter et al. (2001, 2002). Used by permission of IEEE.
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virtual object. Gillet (2001) has effectively employed AR in structural molecular biology to study molecular properties. One final virtual puzzle technique, primarily for use in research, is auto-assembling software, such as the virtual archaeologist (Papaioannou, Karabassi, & Theoharis, 2001), that will piece together the fragments of broken artifacts based on surface matching geometry. Such auto-assembling software has applicability for distributed lab work and research in paleontology, physical anthropology, and archaeology.
Hybrid.or.Blended.Science.Courses Within colleges and universities, hybrid models that employ both classroom and online practical work are becoming commonplace and may be an effective way to support science learning as suggested by Combs (2004) for Web chemistry courses. A hybrid or blended online course is one in which a substantial portion (30-79%) of the content is delivered online but still requires some face-to-face meetings between the instructor and student (Allen & Seaman, 2005). A blended course relies upon the Web-learning environment and its imbedded resources to contribute substantially to learning outcomes, in contrast to a Web-facilitated course that uses a course management system to convey just the fundamental information about the course (syllabus and assignments). A blended course differs from a fully online course for the most part in the degree of required direct face-to-face contact time. Blended science courses typologies are reviewed further in Chapter XIII. A common blended form of college science course is that which meets on alternating sessions (or weeks) and facilitated with online instruction in the interim. In a comparison of a hybrid and traditional formats for a course in environmental biology, Riffell and Sibley (2005) determined that the hybrid students scored better on tests and that this was likely attributable to the quality of the interaction with the instructor, more deliberate reading of text material, and student engagement in extra group study. This finding is expected in that a well-structured blended course has interactivity points that require more regular accountability than an onsite format and this can enhance the learning outcomes. In an earlier study, Riffell and Sibley (2005) determined that the blended model increased attendance and corresponding performance in their large lecture environmental biology course, particularly for upperclassmen. The rationale for a course to be of blended type rather than fully online is a consequence of pedagogical, technological, and/or institutional considerations. For instance, the pedagogical-rationale may be that physical presence is a requirement for more effective learning of a task such as a tactile experience generating a chemical reaction. Likewise, a determination that the online environment and associated technology cannot well represent the instructor’s learning goals or the learning dynamic, such as collaboration during a frog dissection, may necessitate blending a science course. Even an institution’s assets in bricks and mortar lab space may explain a course blending strategy. Rossett, Douglis, and Frazee (2003) review other considerations of importance in determining the choice for and character of blended courses and training. These include such variables as the time to implement learning (short = days to long = years), desire for content stability (volatile to stable), cost considerations,
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degree and character of interactivity desired (touch from systems versus humans), character of learning resources (direct instruction to extensions such as a link to resources), and the social character of learning (independent to social). As pointed out by Clark and James (2005), another potential consideration to promote the favorable reception of a blended learning course by the learner is to provide an explanation and pedagogical justification as to why certain aspects of the course are viable through the online format and visa versa. As a case in point, Combs (2004) describes a hybrid science course in chemistry that is lectureless and involves instead a student response system (SRS) providing the instructor strategic feedback indicative of where students are having difficulties with the course materials. This course provides an optional weekly face-to-face contact meeting to assist students in their previously identified unsure areas of learning. The value of blending this chemistry course is to take advantages of the Web format’s strength in repetitive practice. In particular, 3-D animations and simulations that can demonstrate complex geometries such as the Bohr atom and the ability to make obvious the effects of dependent variables on changing independent variables effectively conveyed through online repetition (Combs, 2004). Locatis et al. (2006) describe an alternative blended strategy for a science course for health science topics taught to students at a magnet high school. In this example, videoconferencing and asynchronous streaming video-on-demand delivery is used to send real time presentations of National Library of Medicine (NLM) experts to a group of collocated students.
Digital.Libraries.and.Repositories.for. Science.Education Science digital libraries and repositories serve as cognition-leveraging Web sites for creating, exchanging, managing, and presenting information (in the sense of Fulker, 2003). Government agencies, universities, and other scientific institutions with an educational purpose typically sponsor science digital libraries. A primary source for digital science resources for instruction can be found at the National Science Digital Library (NSDL) http://nsdl. org/ established by the National Science Foundation (NSF) in 2000. The NSDL serves as a general portal for K-16 educators to access exemplary online instructional materials in the areas of science, technology, engineering, and mathematics (STEM). There are many allied programs and portals connected with NSDL targeting specific learners such as the Applied Math and Science Education Repository (AMSER) at http://amser. org/SPT--Home.php for community and technical schools as well as subject-specific efforts at the Digital Library for Earth System Education (DLESE) at http://www.dlese.org/library/index.jsp. DLESE provides collections of earth science educational resources, earth data sets and imagery, support services to help create, use, and share educational resources and community networks. Likewise, the Communities for Physics and Astronomy Digital Resources in Education Pathway (ComPADRE) at http://www.compadre.org/portal/ is a repository project of the NDSL with targeted collections in the educational areas of: highschool physics, introductory astronomy, quantum physics, undergraduate students, public education, physics education research, teacher education, and preparation (Mason, 2006).
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For a digital library to facilitate the development of effective online science instructional materials, Recker et al. (2004) advise it should have the following qualities: ease of use, time efficient surfing, value-added components for instruction, fit easily into existing learning contexts plus permit the learning designer to access accurate, age-appropriate, and up-to-date resources. Whereas the LOM and SCORM standards use metadata to organize learning objects in repositories, specialized repositories are now also being designed using standards such as the IMS-Learning Design Information Model to store and convey the learning processes themselves. Saliah-Hassane, Kourri, and de la Teja (2006) describe the application of the IMS-Learning Design Information Model to build a repository of online laboratory learning objectives.
Conclusion As those who have attempted it know well, the development of materials for online science instruction can be time-consuming and bear a high upfront cost, well beyond the normal face-to-face course efforts and budget. The outcome of this reality has been desire for reusability and interoperability for instructional materials such as learning objects. In turn, reusability and interoperability is being realized through corresponding learning objects standards that allow fundamental instructional pieces to be stored, accessed, reconfigured, and reused in a strategic way. Like many other disciplines, the key form of learning objects in online science are built on multimedia variously combining text, 2-D and 3-D graphics, animation, streaming video and audio. However, the assemblages of learning objects in science are governed significantly by the pedagogical need to facilitate practical work online. Practical work is accomplished through Web-enabled labs that may be structured as simulations or involve real instrumentation. Simulations are diverse and can involve simple 2-D animations, 3-D virtual objects, or even 3-D worlds. Simulations may incorporate the affordances of games and effectively become edutainment. Web-enabled laboratories may also take the form of remote experiments such as remote sensing with passive data collection or very sophisticated distance laboratories, where students manipulate and collect data from research quality remote instrumentation via interfaces available directly on their home personal computer. Likewise, online practical work in science must also support the field work component of learning central to many disciplines. Practical online field work is accomplished through simple virtual tours all the way through sophisticated 3D virtual field trips. Online science students can also undertake hands-on and more traditional activities for both laboratory and field work using home kits, learning by using local institutional resources coordinated through the student’s institution, or through self-directed learning experiences. Web-enabled labs and field work may form the basis of an entire course. However, they may also act as a sophisticated supplement to face-to-face forms as in a blended course. In our chapters on disciplinary efforts in online practical work, we provide many more examples of the use of Web-enabled laboratories and field work. Our next chapter looks ahead to emerging technologies and pedagogical approaches that have potential to become conventional parts of online science instruction and elements of best practice. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Saliah-Hassane, H., Kourri, A., & de la Teja, I. (2006). Building a eepository for online laboratory learning scenarios. Paper presented at the ASEE/IEEE Frontiers in Education Conference, San Diego, CA. Scanlon, E., Colwell, C., Cooper, M., & Di Paolo, T. (2004). Remote experiments, re-versioning and re-thinking science learning. Computers & Science, 43, 153-163. Shaffer, D. W., Squire, K. R., Halverson, R., & Gee, J. P. (2004). Video games and the future of learning. Retrieved May 1, 2007, from http://tabuladigita.com/files/Theory_2004_ 12_GamesFutureLrng.pdf Sheridan, T. B. (1992). Musings on telepresence and virtual presence. Presence: Teleoperators and Virtual Environments, 1(1), 120-126. Shin, Y. S. (2004). Virtual experiment environments design for science education. Proceedings of the 2003 International Conference on Cyberworlds, Singapore, December 3-5 (pp. 1922-1929). Los Alamitos, CA: IEEE. Slator, B., Saini-Eidukat, B., & Schwert, D. (1999). A virtual world for earth science education in secondary and post-secondary environments: The geology explorer. International Conference on Mathematics/Science Education and Technology, 1(7), 519-525. Slingsby, D. R. (2006). Editorial: The future of school science lies outdoors. Journal of Borderland Education, 40(2), 51-52. Spanias, A., & Atti, V. (2005). Interactive online undergraduate laboratories Using J-DSP. IEEE Transactions on Education, 48(4), 735-749. Stapleton, C., Smith, E., & Hughes, C. H. (2005). The art of nurturing citizen scientists through mixed reality. Proceedings of the International Symposium on Mixed and Augmented Reality, Athens, Greece, October 5-8 (pp. 2-11). Los Alamitos, CA: IEEE. Townes-Young, K. L., & Ewing, V. R. (2005). NASA LIVE creating a global classroom. T H E Journal, 33(4), 43-45. Trgalová, J. (2003). Systèmes de formation à distance. Proposition d’une typologie [Distance learning systems, a typology]. In C. Desmoulins, P. Marquet, & D. Bouhineau (Eds.), EIAH2003 Environnements Informatiques pour l’Apprentissage Humain (pp. 563-566). Paris: INRP. Trindade, J., Fiolhais, C., & Almeida, L. (2002). Science learning in virtual experiments: A descriptive study. British Journal of Educational Technology, 33(4), 471-488. Tront, J. G. (2001). Synchronized streaming media in VLSI education. Proceedings of the 2001 International Conference on Microelectronic Systems Education, Las Vegas, Nevada, June 17-18 (pp. 75-76). Los Alamitos, CA: IEEE. Uskov, V., & Uskov, A (2004). Streaming media and advanced Web-based education. Proceedings of the IASTED International Conference, Innsbruck, Austria, February 16-18 (pp. 122-127). Calgary, Alberta, Canada: ACTA Press. Retrieved May 1, 2007, from http://www-ice.upc.es/butlleti/innsbruck/416-064.pdf Vasalla, P. (2006). The field study as an educational technique in open and distance learning. Turkish Online Journal of Distance Education, 7(4). Retrieved May 1, 2007, from http://tojde.anadolu.edu.tr/tojde24/pdf/article_1.pdf
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Walczak, K., Cellary, W., & White, M. (2006). Virtual museum exhibits. Computers, March, 93-95. Webb, M. E. (2005). Affordances of ICT in science learning: Implications for an integrated pedagogy. International Journal of Science Education, 27(6), 705-735. White, A. R., McClean, P. E., & Slator, B. M. (1999, June). The virtual cell: An interactive, virtual environment for cell biology. Paper submitted to the World Conference on Educational Media, Hypermedia and Telecommunications, Seattle, WA. Wiley, D. A. (2000). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. Retrieved May 1, 2007, from www.elearning-reviews. org/.../learning-objects/2001-wiley-learning-objects-instructional-design-theory.pdf
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Chapter.VIII
The.Cutting.Edge: Promising.Technologies.and. Strategies.for. Online.Science.Education
Are these the shadows of things that Will be, or are they shadows of things that May be, only? – Ebenezer Scrooge, A Christmas Carol The evolution of online education will continue to be coupled to and constrained by innovations in communication and information technologies (CIT). Only a few years ago, Web-based courses were characterized by slow data transmission, at 56 kbps with dial-up lines that dramatically limited the styles of communication and the amount of multimedia that could be incorporated directly through the Internet to support learning. As a result, course development was regularly compromised by technical limitations. For online education, the ideal threshold in data transmission speed is that point at which an author’s course vision and creativity is unrestricted by the instructional hardware and software permitting the effortless incorporation of interactivity styles and multimedia. This ideal will be met at different times by particular course authors, institutions, and even between disciplines (e.g., English versus science).
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A related constraint on innovation in online science learning is the cost of technology for both the student and the institution. It is reasonable to assume that the cost of technology to support online learning will decrease in the vein of Moore’s “Law” where transistor and integrated circuit improvements have consistently lowered computer costs and increased their capabilities. However, online science instructional designers still need to be cognizant of and avoid overshooting the average learner’s ability to pay for elevated technology requirements to support core-learning activities such as practical work. In many cases, it will be incumbent on institutions to either support the technology requirements of online-facilitated practical work themselves or collaborate with other institutions as in the case of remote experiment consortiums. What is more, online science learning practice will recurrently take advantage of popular technologies that emerge initially for personal use where pedagogical application is sensible and cost is low as it has recently for podcasting or SciVee at http://www.scivee. tv/ , a YouTube-like entity for scientists. Using “everyday” technology will enhance online science learning without adding extraordinary cost. Distance education programs in the United States now routinely assume in their course design that students have digital subscriber lines (DSL) capable of smoother multimedia transmission and telephony. What is more, mobile communications are rapidly evolving and will also be able to provide Internet service downloads of media-rich information at high-speeds with concurrent advances in cellular communications such as third generation digital technology (3G) or the evolving “4G” (Lawton, 2005) and the rapid wireless broadband structure of evolution data only/evolution data optimized (EVDO) “Revision B” (Williams, 2005). Additionally, there is a trend towards the merging of today’s TV’s with Internet features into a common digital network (Walczak et al., 2006) such that there may be a corresponding common hardware for computing and learning/entertainment media. This has the potential of markedly enhancing the visualization capabilities of home learning systems. Beyond raw increases in transmission speed, many emerging CIT innovations promise to have a substantial impact on the character of online-based science education. For example, the Learning Federation, a consortium of higher education institutions, businesses, government agencies, and foundations (http://www.learningfederation.org/index.html), has been endeavoring to set forth a roadmap for future research and development efforts in educational software in NSF’s Science, Technology, Engineering and Math Education (STEM) categories. The five critical areas identified by the Learning Federation are: (1) instructional design for learning games and simulations, (2) intelligent question and answer systems to individualize learning, (3) learner modeling and assessment, (4) building virtual learning environments, and (5) integration tools for building and maintaining advanced learning systems (van Dam, 2005). These stated goals provide an indication of the future of online science and reflect many of the promising technologies and strategies discussed next. In this chapter, we examine enhancements and/or innovations in learning systems that can affect the quality and character of online science education including virtual classrooms and Web-broadcasting studios, remote laboratories, mobile science learning, visualization, virtual reality, advanced educational games, haptic design, virtual instructors, and virtual museums.
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Emerging.Learning.Systems.for. Online.Science.Education. The online learning environment for science will continue to undergo dramatic changes as technological innovations afford refinements in practice and pedagogy. The Learning Federation described earlier has also advanced eight goals for the next generation of educational software. They recommend that educational software be: (1) interoperable, (2) modular, (3) just in time, (4) have multiple levels of detail, (5) foster interactive development, (6) be useable in both synchronous and asynchronous learning modes, (7) address different learning styles and learning objectives, and (8) satisfy different interactivity demands (van Dam, 2005). Many of these goals are already happening in conjunction with the maturation of Web 2.0. For instance, social software (elucidated in Chapter VI) is a core technological innovation already impacting the character of interaction through the Internet via social networks such as wikis, blogs, podcasting, videoblogs, as well as user editing (Alexander, 2006). Likewise, there have been considerable efforts to develop more effective and diverse learning systems for online instruction such as reconfigurable systems (El Saddik, 2001; Sklyarov & Skliarova, 2005), multi-user virtual space systems (Endo, Yasuda, & Yoko, 2003), distributed systems (Madhavan, Arns, & Bertoline, 2005), and universal provider systems (Stevens, Appaerts, Van Vlierberghe, & Boutsen, 2001). The National Science Foundation’s initiative (NSF, 2003) to develop a cyberinfrastructure for higher education has the potential to dramatically change and affect the quality of science instruction in the next decade. Although the primary focus is on research with goals of higher: (1) online storage, (2) speedier computers, (3) interactivity, and (4) rich discipline specific data collections and advanced instruments (Freeman & Almes, 2005), this advanced capacity should have a spin-off effect in science education and online science education too.
Virtual.Classrooms By way of virtual classrooms, emerging technologies make it possible to add back useful affordances such as the onsite classroom’s simultaneous, multi-user, visual communication lost in the early translation of learning to online formats. For example, the Network EducationWare (NEW) is an inexpensive open-source Web-based course delivery system that allows the learner both the active synchronous participation in classes and asynchronous review of recorded presentations (Snow, Pullen, & McAndrews, 2005). This system allows for simulteaching, supporting real-time reception of presentations (instructor’s graphics and annotations plus optional video feed), audio communications, and interaction between students and with the instructor, plus a floor control for the instructor that both indicates who is attending the class and who has the floor for questions. Another comprehensive virtual classroom system is the Cisco® Virtual Classroom by Cisco Systems® that blends the features of voice conferencing, Web conferencing, video conferencing, security and administration systems (Educause, 2005). Several other Web-conferencing systems are available including Acrobat Connect Professional used effectively as a virtual classroom tool by Bower (2006) and others such as Centra 7, Horizon Wimba Live Classroom and Elluminate Live noted in Chapter VI. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Webcasting.Studios Dolgovesov, Morozov, Shevtsov, Debelov, and Zhmulevskaya (2003) describe the virtual studio system that generates a real-time virtual environment allowing the instructor to demonstrate interaction with both virtual and real objects. The system is capable of online delivery and may be especially effective for distance interactive video courses. The benefits of such a system in education are the ability to demonstrate and exchange, 3-D objects and phenomena in scales from atomic to galactic, as well as record and archive the presentations. In virtual studio, real objects, including the instructor are stationed in a monochrome room (e.g., blue) thus permitting chroma key separation of the background with replacement
Figure 8.1. Examples of the virtual studio teaching environment from Dolgovesov et al. (2003). a) An instructor adjusts interactions with the virtual object (globe) based on animation track. b) The instructor is embedded in representation of geometric principle. Used by permission of V. A. Debelov.
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by the desired learning setting (Figure 8.1a and b). In a way, the system is similar to a TV network’s weather casting technique, but without the need for the full studio infrastructure. The virtual studio system uses trackers to detect the motion of the instructor so that the avatar of the instructor accurately depicts the instructional interaction. The supporting focus system that drives the production includes multi-format video, 3-D rendering, and static and robotized cameras, all managed by a hotactions interface. The disadvantages of this system are the developmental costs of the system, the amount of rehearsal to perform the lecture, the creation of the instructional materials (i.e., 3-D animation and scene materials) and potential need for studio operators.
Remote.and.Virtual.Experimentation.to. Support.Student.Research A common goal of colleges and universities and one funded extensively by the National Science Foundation is undergraduate research. Undergraduate research serves many purposes including engaging and immersing promising students in mentored research situations who will go on to advanced degrees and become the next generation of bench scientists. It also provides authentic experiences in research, akin to an internship, that will characterize a student’s later work life (e.g., engineering). Research fundamentally supports the hypothetico-predictive reasoning, the basis of human learning (reviewed in Chapter V). As more aspects of scientific research become networked online through the cyberinfrastructure and as an increasing number of students undertake online science courses, the importance of undergraduate research will remain, but there will be a growing need by degree-granting undergraduate institutions to consider innovative ways to capitalize on the online learning environment for undergraduate research. Remote experimentation is one way in which online undergraduate students can become deeply involved in research. This concept is not new; for example, astronomical observatories such as the MIT Haystack Observatory have engaged undergraduate students in research for many years, including the regular involvement of 150 students from about 20 colleges (Pratap & Salah, 2004). Virtual research laboratories are also a possible means to encourage undergraduate research. Way (2006) has developed a virtual laboratory environment, complete with an organizational hierarchy and participation from faculty, for applied computing technology (ACT). In this ACT virtual laboratory example, students work to determine ways to use computers to solve problems of their own interest. In this way, the laboratory environment is open to learners from a large variety of academic backgrounds, levels, and interests. The benefits described for the ACT project are numerous and include: (1) providing an appealing focal point for undergraduate research, (2) fostering collaboration between students, faculty and industry, (3) creating stakeholders in research results, (4) lowering expenses, and (5) providing flexible organization for varied research topics. Moreover the ACT virtual laboratory (6) allows for public dissemination, (7) provides a format for continuity of projects, (8) allows for dissemination of research artifacts, (9) reinforces course lessons, (10) provides for career networking, (11) promotes entrepreneurship, (12) promotes self-directed learning, and (13) generates publications. Thus, this online laboraCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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tory concept is modeled closely to an onsite research laboratory and provides comparable learning opportunities. Although this virtual laboratory concerns the software development area, it is straightforward how a similar virtual research laboratory could be developed for disciplines in the natural sciences. Two other key developments in remote experimentation are interoperability and collaboration. Standardization of the software architecture for the purpose of interoperability and unified access is a broad goal for future remote labs. An example of an effort in this area is the LABNET project that seeks to build an integrated laboratory environment for heterogeneous remote lab equipment (Davoli, Spanô, Vignola, & Zappatore, 2006). Another approach is to have cooperative collaboratories (see Chapter VI) that share of apparatus online between institutions (Bos et al., 2007).
Mobile.Technologies.for.Online.Science.Education Mobile learning or m-learning is considered the next frontier in interactive, multimedia education (Mikic & Anido-Rifón, 2006). Georgiev, Georgieva, and Smrikarov (2004, IV28-2) define m-learning as “the ability to learn everywhere at every time without permanent physical connection to cable networks” and see it as an extension of distance learning. The growing use of m-learning by colleges and universities is made possible by innovations in the kinds and capabilities of synchronously Internet-connected wireless devices such as notebook computers, tablet PCs, personal digital assistants (PDAs), and cell phones. Added to these are iPods® (Apple Computers, Cuppertino, CA) and other devices that permit asynchronous downloading of information in support of learning activities. Some contemporary examples are shown in Figure 8.2 from Mikic and Anido-Rifón (2006).
Figure 8.2. Examples of contemporary mobile devices from Mikic and Anido-Rifón (2006). Clockwise from left are notebook computer, Blackberry, cell phone, PDA, and Nokia phone (unlocked). Used by permission from F. Mikic.
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Figure 8.3. A comparison of the dominant wired virtual learning environment today, e-learning, versus the emerging wireless m-learning environment of tomorrow. Used by permission from D. Keegan.
Keegan (2001) distinguishes the virtual learning environment of today versus tomorrow’s m-learning environment by the wireless connections and reductions in the size of the communication device in the latter, as depicted in Figures 8.3a and 8.3b from http://learning. ericsson.net/mlearning2/project_one/elearnmlearn.html. A central aspiration and technical problem for m-learning equipment is to retain all of the functionality of the computer in the mobile device while limiting trade-offs in ergonomics such as viewing, sound and keyboarding, or other data input. The emerging use of mobile technologies to provide anytime-anywhere learning has generated investigation into standardizing models for this technology (e.g., Mikic & Anido-Rifón, 2006), as well as review of the effectiveness of this mode of information and knowledge transfer. Koole and Ally (2006) describe the framework for the rational analysis of mobile education (FRAME) for assessing the effectiveness of mobile learning. In their model (see Figure 8.4), the mobile learning process conceptually lies at the convergence of the learning aspect (characteristics of individual learners), device usability aspect (characteristics unique to networked mobile technologies), and social aspect (mechanisms of interaction). In this way, the FRAME model provides a structure for designing mobile learning through consideration of three key learning learning areas. The first of these is context learning (AB) involving the intersection a learner’s needs and learning activities to mobile device capabilities. A second circumstance is social computing (AC) involving the ability of a student to use the device to communicate with students and data course materials. Last is interaction learning (BC) where students construct knowledge through social interaction with others. The balance of these three conjunctions of learning aspects will establish the mobile learning experience for the student. Of particular concern in the design of mobile online science
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Figure 8.4. The FRAME mobile learning model of Koole and Ally (2006) depicting the interrelationships between the learner aspect, device usability aspect, and social aspect. Used by permission from IEEE.
learning activities in the frame paradigm is context learning and whether device usability will adequately support practical work.
Using.PDAs.and.iPods®.in.Online.Science While the use of PDAs in m-learning instruction is relatively recent, Segall, Doolen, and Porter (2005) demonstrated for a group of engineering students, that PDAs are effective for quizzes with the benefits of real-time scoring for students and faster grading for the instructor. Moreover, Mikleia (2006) found that PDAs were useful in helping students in a research methods course to adhere to deadlines and the organizational structure of the course through strategic reminders made possible by the devices. The advent of mobile computing and m-learning also opens a number of new possibilities for structuring field work for online science students. One of the key benefits of mobile learning is the enhanced ability to interact with authentic environments while at a distance. It is now possible to direct and/or augment the field work that students conduct at a distance with stored information and/or real-time interactive information in PDAs and other similar devices. This type of computer use for fieldwork has been called ubiquitous computing (UbiComp) by Rogers, Price, Randell, Fraser, Weal, and Fitzpatrick (2005) and may have positive attributes for learners, including the ability to: access, compare and input information in the field, collaborate at a distance, and provide opportunities for on-demand information. For example, beyond their application to student assessment and prompting students about course activities, PDAs can be successfully applied as portable research instruments. As a Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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case in point, Skalsky and Pastel (2004) developed a PDA-based physics laboratory (PDL) that combines PDAs with telemetry and kinematic sensors into a portable research instrument for use in studying low-altitude rocket telemetry and mass-spring experiments. iPods® and podcasts are also used to facilitate online science instruction in a variety of ways. Science professors are supplementing course learning with podcasted science issues available online, provide their podcasted lectures as review materials, provide supplementary pre- and post laboratory information and review sessions, and even extend their intellectual property as open content for public benefit (McLaughlin, 2006). Organizations such as National Geographic are now podcasting selected science resources at www.nationalgeographic. com/podcasts including featured science news of the week. Likewise, the podcasting portal NASAcast at http://www.nasa.gov/multimedia/podcasting/ permits users to download video and audio from NASA’s TV and Web site sources.
Mobile.Learning.Objects Just as the learning object paradigm has become commonplace for online learning, the m-learning environment is now seeing the incorporation of learning objects. For mobile phones, Holzinger, Nischelwitzer, and Meisenberger (2005) have erected a specific category of learning object called mobile interactive learning objects (MILOs) which are rendered through the mobile learning engine (MLE), a multimedia-based application for cell phones. The MLE of Holzinger et al. (2005) is capable of supporting: (1) formatted continuous text, (2) integrated images with text, (3) hyperlinks, (4) streaming audio and video within a playback interface (see Figure 8.5), and (5) interactive questions and intelligent help. An example of a MILO showing human internal anatomy in given in Figure 8.5. Strategies are also being pursued to facilitate the streaming of complex 3-D graphics to handheld devices in order to permit sophisticated simulations, virtual worlds, and CAD
Figure 8.5. An example of a screenshot of a mobile interactive learning object (MILO) showing internal organs and with playback control features. Used by permission from IEEE.
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Figure 8.6. a) Demonstration of different types of mobile devices participating in collaborative remote visualization session. b) A PDA receiving a reduced pixel image (from Lamberti & Sanna, 2007). Used by permission from IEEE.
representations to be used for collaborative remote visualizations. Lamberti and Sanna (2007) describe a system along these lines utilizing remote servers to carry out the largest part of the computations, which are a current limitation of smaller mobile devices. A collaborative remote visualization session using three different devices is shown in Figure 8.6a and a downloaded anatomical 3-D learning object is shown in Figure 8.6b. As computational limitations are overcome on the smaller mobile devices, 3-D visualization environments applicable to science, including remote experiments and multi-user virtual environments, become possible anywhere and anytime. The remaining limitation of such mobile learning devices will be the scale of images and the practicality of viewing them.
Advances.in.Visualization Scientific visualization and information visualization (also called graphics presentation) are areas of computer information technology with considerable recent achievement and potential for Web-based science instruction. Scientific visualization refers to the graphical presentation of scientific data to improve its interpretation, while information visualization is an allied field, with the broader objective of communicating information and learning through graphics (see Rhyne, 2000, 2003). There are many benefits gained from the application of visualization in science education such as enhancing learning and practice plus to open up new ways to view science and go about solving problems (McGrath & Brown, 2005). In addition, visualization in science education promotes the development of mental models, metavisual capacity, and deeper understanding of scientific models through 2-D and 3-D representations (Gilbert, 2005). Visualizations can be direct, structural, analytical, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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explanatory, or synthesized representations, and can vary in their interactivity from none (still images) to very high, where students generate the visualizations themselves (El Saddik, 2001). Interactive visualization activities can be a very effective means of teaching science online. The affordances of interactive visualization as an instructional tool is summarized by El Saddik (2001) as: (1) individualized improvement of understanding, (2) skill mastery through additional practice, (3) development of analytical skills, (4) motivation through aesthetic presentation, and (5) a presentation aid for the instructor. Much like an online game environment, a student can immerse deeply into an interactive visualization environment, manipulating the perspective of a study object to achieve better understanding. A growing number of studies across science disciplines have documented how teaching abstract concepts can be enhanced with quality 2-D and 3-D visualizations for instance electromagnetism (Dori & Belcher, 2005), genomics (Takayama, 2005), and geology (Reynolds, Johnson, Piburn, Leedy, Coyan, & Busch, 2005). An excellent example of how visualization can enhance understanding of complex principles is provided by Weiskopf, Borchers, Ertl, Falk, Fechtig, and Frank (2006) in their example of a wormhole model based on the general relativity theory. In this example (Figure 8.7), students can visually explore spacetime characteristics near a wormhole between a city (i.e., Tübingen) and the surface of Mars.
Figure 8.7. Visualization of general relativity theory. A wormhole between Tübingen and Mars (from Weiskopf et al., 2006, and courtesy of M. Borchers and T. Müller, ©2005). Used by permission of IEEE.
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Instructional visualization in the science classroom setting has advanced to sophisticated representations of subjects such as geological structures, molecular models, mathematical models, and anatomical systems (McGrath & Brown, 2005). One of the refined examples of visualization in the sciences is the GeoWall system, successor to the CAVE system at the University of Illinois at Chicago. The relatively inexpensive GeoWall system is comprised of a computer, two monitors, and two projectors with polarizing filters, a black screen, and polarizing glasses (see Figure 8.8). The GeoWall provides learners with an opportunity to immerse themselves in stereoscopic 3-D representations of earth science information (Johnson, Leigh, Morin, & Van Keken, 2006). The standard software of the system allows for 3-D terrain viewing, 3-D object viewing, and a stereo-image pair viewer. Johnson et al. (2006) also describe personal GeoWalls (e.g., PG-2) that built on emerging flat screen technologies. How will this kind of visualization technology impact distance learners? In the short run, they can visit many of the GeoWall examples at museums such as Chicago Adler Planetarium exhibit for the Martian Rovers or may have access an institution that uses them. However, individualized GeoWalls are prohibitively expensive for individual students at this time. There are many other dramatic changes in technology that will affect information visualization and corresponding online science learning environments over the next decade. Two key changes described by Eick (2005) are: (1) real-time visualization where data will be displayed as it arrives and (2) improvements in display technology such as megapixel flat
Figure 8.8. Depiction of the GeoWall visualization system combining dual projectors and dual computer displays to generate stereoscopic views and simulate 3D terrain (from Johnson et al., 2006, and courtesy of Electronic Visualization Lab, University of Illinois at Chicago and the GeoWall Consortium). Used by permission from IEEE.
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screens with wide aspect ratios, video walls, and as described earlier sophisticated small PDAs displays. For the future, it is conceivable that innovations in TV and projection technology and lowered costs will permit a student to have a home TV wall that serves as a space for 3-D learning immersion. Visualization as a means of facilitating the acquisition of scientific knowledge is well explored in K-12 contexts. For example, the efficacy of visualization applied to teaching middle and high school students earth science subjects (e.g., climate, weather, earth structure, environmental science and geographic data) has been demonstrated by the Geographic Data in Education Project (GEODE) at http://www.worldwatcher.northwestern.edu/index.html as well as its predecessor projects Supportive Scientific Visualization Environment for Education (SSciVEE) and WorldWatcher (Brown & Edelson, 1998; Gordin, Edelson, & Gomez, 1996). Another more recent example is the Adviser System for Planetary Geoscientists that provides immersive field study of planetary surfaces (Forsberg et al., 2006). The ability to use and share complex instructional visualizations will improve online science instruction as shared reusable visualization-based modules becomes more prevalent. Current obstacles to employing visualization more thoroughly for interactive multimedia learning are the lack of standardization of software for reusability, platform and context dependencies, collaboration is rarely supported, and multiple views for collaborators are not supported such as perspectives for each learner during collaboration (El Saddik, 2001). Brodlie, Duce, Gallop, Walton, and Wood (2004) categorize three types of Web-based distributed visualization types: full service—where the visualization is developed through software at the server and sent to the learner for use in a 3-D browser, software delivery—where software with aspects of the design itself (i.e., a JAVA applet) is transferred to the learner, and data only—where just the data for the visualization is sent to the learner. An ideal visualization learning environment would be one that also permits collaboration. The openVisaar system by Goetz et al. (2006) is just such an example of real-time distributed and collaborative visualization system developed for use with geographical, meteorological, geological, and medical data. The openVisaar system combines the portable scene graph system (openSG), computer-supported cooperative work systems, and shared whiteboard (e.g., Eclipse). The resulting collaborative visualization space can permit cooperative learning of complex visual objects. An example of openVisaar 3-D terrain with richly embedded information for multiuser visualization is shown in Figure 8.9a while the collaboration interface with whiteboard, navigation tools, and visualization tools is provided in Figure 8.9b.
Emerging.3-D.Learning.Environments Web.3-D Developments are underway to dramatically improve the interoperability of 3-D objects that support interactive 3-D virtual learning environments also known as educational virtual environments or EVEs (Chittaro & Ranon, 2007). Chittaro and Ranon (2005) characterized the current limitations of Web3D technologies as: (1) difficult navigation of 3-D user interfaces, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 8.9. a) An example of the openVisaar system showing a landscape with added visualization features. b) The cooperative interface with navigation tools. Figures created by and used with permission from F. Goetz.
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(2) educational context (such as the inabilities of the instructor to use the virtual environment effectively, (3) lack of realism, and (4) the ergonomics may be difficult for student (e.g., special glasses). Emerging technologies are addressing issues such as standardized interfaces, realistic portrayals of objects, and standardized formats (MPEG-4, VRML and extensible 3-D or X3-D) as well as improvements in visualization hardware. The Web3D consortium (http://www.Web3d.org/) has been researching and moving towards standards for 3D content so that users will be able to employ complex 3-D objects in a variety of applications. Below we describe some of the interesting innovations in Web3D learning environments that have the potential to impact the character and quality of online science learning in upcoming years.
Autosteroscopic.Displays The use of special glasses for immersion into 3-D environments has been the norm to view anaglyphs, stereo-pairs of pictures, and complex data and images within advanced CAVE environments. Recently, autostereoscopic display (ASD) systems have been developed that require no glasses. There are now 3-D displays on laptop computers such as the Sharp Corporation’s Actius RD3D and prototypes of 3D-HDTVs have already been demonstrated with 2-D to 3-D conversion chips and other ASD display technologies (Delaney, 2005).
Mixed.Reality Another form of visualization technique that may be of eventual application to online instruction is mixed reality (Tamura, Yamamoto, & Katayama, 2001) where virtual objects are placed into real environments by way of see through head mounted displays (ST-HMD). In a mixed reality learning environment an instructor can potentially embed virtual objects for educational purposes such as features from the past (e.g., the former positions of stars, real scale dinosaurs) or even information about processes (water cycles, rock cycles). With mixed reality, students could undertake a virtual field trip where they follow a prescribed path through the real world but with strategic virtual information and learning objects imbedded to facilitate their learning.
3-D.Audio. Similarly, technologies and strategies are also being developed to provide for 3-D audio experience to amalgamate with a virtual reality visual environment. One example is described by Doerr, Rademacher, Huesgen, and Kubbat (2007) that utilizes a head mounted display (HMD), dataglove, stereo headset, and a magnetic tracking system that detects the position of the glove and headset. Sounds for the virtual world are stored and computed rapidly by a second computer taking into account the motion of the user (Figure 8.10). Beyond a more realistic virtual experience, 3-D audio may enhance online science activities requiring elevated auditory acuity (e.g., nature sounds) or the study of sound waves in physics.
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Figure 8.10. A virtual reality environment with a system for creating 3-D audio effects (from Doerr et al., 2007). Used by permission from IEEE.
Figure 8.11. An example of a portable 3-D virtual reality system, the PI-casso system (from Hoffmann et al., 2006). Used by permission from Elsevier.
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Portable.3-D.Virtual.Reality Ultimately, if these sorts of innovative immersive VR systems are to be used for teaching students online, they will need to be relatively inexpensive, compact and portable. This possibility is not so far off as it may seem as portable VR systems are now in development. One example is the PI-casso system (Hoffmann, Stefani, & Patel, 2006) that has a foldable and retractable screen, dual projectors, an infrared camera-based optical tracking system, wireless navigation using dual two-handed interaction (a dragonfly for navigation and a bug for selection of objects), and two modes of interface (list style and color 3-D). This system (Figure 8.11) has both a portable version and a desktop version; it could be a indication of future 3-D learning station for distance learners.
Computer.Generated.Holography An emerging technology for immersive visual display is computer-generated holography (CGH). CGH systems at their most refined capability can produce an interactive holographic 3-D image for the observer in the space in front of the computer screen. Slinger, Cameron, and Stanley (2005) describe how this is accomplished by taking a laser-produced wavefront, forming a computer-calculated holographic fringe pattern, and sending this pattern through a spatial light modulator (SLM) which in turn diffracts the light into an interactive 3-D image (Figure 8.12). CGH systems are still far from broad use because of their cost, primarily as they require enormous computing power to generate streams of billions of pixels, as well as remaining technical hurdles. However, as these costs come down they are considered the ultimate display technology by many and, in addition to generating interactive 3-D im-
Figure 8.12. An example of computer-generated holography (CGH) from Slinger et al. (2005). Used by permission from IEEE.
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Figure 8.13. An example of both photorealistic renderings of a circuit board in 3-D (top) and a corresponding standard 3-D model (below) (from Adamo-Villani et al., 2006). Used by permission from N. Adamo-Villani.
ages, will also support 2-D, stereoscopic, and volumetric visualizations in online science learning.
3-D.Photorealism Lack of realism in the visual presentation of information is one of the expressed limitations of 3-D learning environments. This is rapidly changing as inexpensive desktop 3-D color scanners become available and photorealistic representations of physical objects can be rendered more easily with software. For example, Adamo-Villani, Richardson, Carpenter, and Moore (2006) built a system for teaching the design of microcontrollers by using high-end graphics using Maya 3-D animation software coupled with openGL and C++ programming to generate interactive computer animation techniques (Figure 8.13). Employing these realistic 3-D images, true-to-life laboratory scenes can be developed giving students a more authentic laboratory or simulation experience.
Multimedia.Hypertextbooks A further innovation to improve the distance learning environment is modernizing the online textbook to a visualization-based hypertextbook. A visualization-based textbook model is described by Rößling, Naps, Hall, Karavirta, Kerren, and Leska (2006) that embeds Web links, video, flash animations, and JAVA applets while still containing the characteristic textbook features (e.g., table of contents, chapters, index) and text-based structure. Prospectively, authors of science hypertextbooks will be able to embed even more-complex immersive features such as interactive virtual reality and perhaps someday even the interfaces to control remote experiments. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Advanced.Educational.Games There is growing evidence that students who have regularly played video games are different learners cognitively or at least have dissimilar preferred learning styles than previous generations (Morales, Maicher, Pedela, & Weaver, 2006; Prensky, 2000). Emerging research based on contemporaneous MRI studies of neural patterns of the brain while students are playing video games indicate that the neural patterns are parallel between highly immersive virtual world experiences and real world experience (Weber, Ritterfeld, & Mathiak, 2006). While it is a troublesome conclusion that synthetic experiences, such as violent games, affect the brain in the same way as real experiences, this conclusion positively affirms the potential of well-structured VR environments for online science learning. In fact, Shaffer in Halverson, Shaffer, Squire, and Steinkuehler (2006) has appealed for a new model of learning termed games for thought, that emphasizes immersion in virtual worlds (i.e., epistemic games) as preparation and training for the professions and careers associated with the technological post-industrial world. Moreover, process models are already emerging to describe the design of serious games by interjecting pedagogical steps into the normal structure of game development involving story, art, and software (Zyda, 2005). Although the field of educational science games is embryonic, there are already some good examples providing a glimpse of where the future of advanced educational games may be moving. A form of advanced game style virtual world is a MUVE (multi-user-virtual environment). It is possible to develop effective role-playing games with MUVES that can support situated cognition learning structures to enhance science instruction. An example of the application of a MUVE to science is the River City simulation relating water quality to the surroundings of a city described by Dede, Nelson, Ketelhut, Clarke, and Bowman (2004) and Dieterle
Figure 8.14. A screen capture of a MUVE (multi-user-virtual environment) that teaches student about water quality (from Dede et al., 2004). Used by permission from C. Dede.
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and Clarke (2007). In this simulation promoting scientific inquiry skills, students are able to play the role of scientists taking on authentic tasks, scientific procedures, and self-designed experiments impractical in the real world. The River City interface showing an avatar of a student collecting a water sample is shown in Figure 8.14. Another example of educational gaming software is Immune Attack at http://www.fas.org/immuneattack/ that teaches immunology through different levels of complexity (i.e., situated cognition levels). This game was cooperatively developed by the Federation of American Scientists, Brown University, and University of Southern California. In Immune Attack, the objective is to use nanobots to fight off infections and alter the immunodeficiency of the central character. Likewise, another multi-player science 3-D video game is The Chemistry Game, in development at Purdue University, which requires knowledge of chemistry to advance to different levels (Morales et al., 2006). One additional approach in the instructional design of educational games is to alter existing computer game environments taking advantage of their reusable architectures to serve an educational purpose. This approach, called modding has been used to teach students game design techniques (El-Nasr & Smith, 2006), but could also be employed to assemble educational science games with diminished overhead and development time. Lastly, to help students navigate in complex virtual environments, efforts have also been made to design intelligent frequent wayfinding-sequences (FWS) that model the routes of more experienced users (Sadeghian, Kantardzic, Lozitskiy, & Sheta, 2006).
Haptic.Design In contrast to remote experiments that allow the learner to actually monitor and manipulate the conditions of a remotely situated physical experiment, virtual and remote experiments with haptic design permit the online learner to have a tactile experience by adding a simulated response, such as pressure to a hand. Marshall (2007) categorizes the learning activities of haptic learning environments as exploratory, where learners explore an existing representation, or expressive, where learners create the representation. The potential educational benefits of haptic or tangible learning systems are summarized by Marshall (2007) as: (1) physical materials and 3-D forms may be perceived or understood more readily, (2) playful learning with greater engagement and reflection may occur, and (3) collaboration reinforcing situated learning can develop where a multi-user system is present. For online science learning exploratory haptic learning can be applied across many of the natural sciences such as its use to investigate molecules (Davies, John, MacDonald, & Hughes, 2006) and for many medical applications including simulated surgery. Expressive use of haptic learning is ideal in situations where sculpting models or structural design is an important part of the learning (e.g., Creighton, 2004). Perhaps the most familiar form of a haptic response system is that of a wired glove generating a response from the user’s finger movements/pressures which are duplicated either in 3-D on the screen to maneuver a virtual object or to a remote location to control the actions of a robotic arm. The digital reproduction of touch, to improve human-computer interaction and to express content, is in its formative stage. However, progress is being made to incorporate Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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realistic tactile and force feedback such as the European Union project Haptic Sensing of Virtual Textiles (HAPTEX) described by Magnenat-Thalmann and Bonnani (2006). So far, the HAPTEX project has been able to achieve realistic single point feedback, but ultimately the goal is to refine the system so that multi-point haptic feedback will be possible and as a result, in this example, a person will experience the tactile sensation of grasping a particular cloth type and texture. In science and mathematics education, haptic enabled 3-D visualization systems are being developed for a variety of subjects. Davies et al. (2006) describe a system that permits 3-D molecular, potential energy surface, and wavepacket visualization all of which can be delivered to a Web browser. The haptic portion of this system uses a relatively expensive Reachin Display (Figure 8.15) which is at this point is not feasible for implementation into distance learning without modification to less expensive haptic systems. Another system intended for schools, virtual geometry, uses a Cyberglove™ to model the movements of a virtual hand that can modify the geometries of the virtual scene (Moustakas, Nikolakis, Tzovaras, & Strintzis, 2005). Other examples include a virtual laboratory instrument using a haptic control to direct a virtual physics pulley designed by Inoue, Matsubara, Iwane, Nakamura,
Figure 8.15. Molecular visualization using the Reachin 3-D system with haptic capability (from Davies et al., 2006). Used by permission from N. W. John.
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Figure 8.16. An example of an inexpensive haptic device for home use called the Falcon. Used by permission from Novint Technologies, http://www.novint.com/falcon.htm
and Ichitsubo (2006) and a system that teaches about bioelasticity for surgical procedures by Nakao, Minato, Kuroda, Komori, Oyama, and Takahashi (2006). If the potential benefits of haptic design are to be realized in the distance education environment, a key will be the development of adaptable and reasonably priced haptic systems for learners. This need is not far off as inexpensive haptic response systems are now finding their way to the general public. For example, Krikke (2006) reports on the development of an inexpensive 3-D haptic device for gaming called the Falcon developed by Novint Technologies (Figure 8.16). Other devices and strategies for designing haptic devices to control virtual environments are discussed in detail by Patel et al. (2006). Innovations in haptic devices open the possibility for more realistic online learning games with touch sensitivity.
Virtual.Instructors,.Classmates,.and.Tutors Taylor and Mohr (2001) characterized the emerging model of distance education, the 5th generation, to be one characterized by intelligent flexible learning involving interactive online multimedia, Internet-based access to Web resources, campus portals that access institutional resources and computer-mediated communications using automated response systems. The key component of automated response systems are intelligent objects databases that store such things as student questions and instructor responses so that this information can either be accessed by students through key word searches or provided to a student via an intelligent tutor system that decides whether a question requires novel instructor input or an existing response. This sort of intelligent feedback system has been around for many Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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years on corporate Web sites for just-in-time responses, but has not been brought into play regularly in educational settings. Is it possible that a credible, “professorless” university will emerge before long, built upon innovations in artificial intelligence, vast inventories of e-learning objects, and structured self-directed learning? Such a university would seem as implausible as degree programs offered fully through an interconnected computer system may have seemed 25 years ago. However, emerging virtual instructor systems and steady improvements in the kinds and quality of online learning objects suggest that but for acquiescence of such an enterprise by learners, such a university may in fact be realized. Engelhardt, Hildebrand, Lang, Schmidt, and Werlitz (2004) raise the possibility that the purchase of a large number of e-learning objects could give rise to the offering of automated courses with minimal instructor input. Of course a key assessment question to be addressed along the way to a professorless college science experience surrounds whether and in what circumstances the learning outcomes of a science course are more efficiently and agreeably met with human intervention. Teacherless learning systems, such as those involving intelligent tutors or virtual instructors, can be made to work with an individual learner or as a part of a collaborative team. The key roles of an intelligent tutor (i.e., agent) on a virtual team are outlined by Marin, Hunger, Werner, Meila, and Schuetz (2005) as: (1) an interrogator who poses problems, (2) a reviewer who can assess, compare, and contrast results of team members and guide revisions, (3) a monitor/administrator who keep records for team activities, and (4) an instructor who coaches team members in underperforming areas. Beyond the direct interactive role an intelligent tutor will play with a student or team, it should also have the capacity to individualize and scaffold learning. For example, Tsinakos (2006) concludes that an intelligent instructor system is optimized by employing a student model that takes into account: (1)
Figure 8.17. An example of a virtual instructor system where a chatterbot works individually with a student. Use by permission from T. Arthanari.
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planning individualized education based upon student’s previous knowledge on the subject, (2) planning delivery that optimizes learning activities that play off of a learner’s previous experience and cognitive level, (3) monitoring a student’s learning paths and generating feedback that is consistent with experience and the current subject, and (4) addressing student misconceptions as they are revealed. Maghsoudi and Arthanari (2004) describe an example of a virtual instructor, one that is used to assist in teaching statistics to graduate students in a research methods course. The virtual instructor for this course is a chatterbot, a computer program that can use a database of knowledge to conduct a realistic vocal or textual conversation combined with a studentselected animated character to play the role of teacher in the interface. The intelligent instructor scheme in this example (Figure 8.17) is built using a 3-tiered knowledge base (student profile, course specific knowledge, and general knowledge using the artificial intelligence markup language (AIML) and the artificial linguistic Internet computer entity (ALICE) with approximately 24,000 categories. Other architectures to develop virtual humans exist such as those described by Ieronutti and Chittaro (2007) for the development of an informal coach or formal instructor. In a contrasting example provided by Marin et al. (2005) for a software engineering course, an intelligent tutor is used broadly as an agent including as a team member, as well as an instructor. Similarly, Matsuura, Hesse, Ochi, Ogata, and Yano (2002) have created virtual classmates for asynchronous learning using software agents that utilize the past class behaviors and activities of students and Kim, Hamilton, Zheng, and Baylor (2006) have evaluated the efficacy of virtual peers with varying gender, competence, and emotional expression to scaffold learner motivation.
Figure 8.18. An example of an immersive 3-D haptic environment with an avatar that teaches table tennis (Rusdorf et al., 2007). Used by permission from IEEE.
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Figure 8.19. The system architecture for a virtual presenter system (from van Welbergen et al., 2006). Used by permission from IEEE.
It is also possible to build humanoid avatars that can teach interactively with students. For example, Rusdorf, Brunnett, Lorenz, and Winkler (2007) have developed an immersive table tennis simulation, similar to the less complex XaviXTechnology™ baseball, bowling, and tennis games for TV, where the player and the avatar interact in the game based on programmed laws of physics for ball motion (Figure 8.18). It can be envisioned from this type of example that it will be possible to build haptic connections between intelligent tutors and learners in the future, where useful interactions that promote virtual authentic learning will be possible (e.g., the correct way to take a measurement or retrieve and process a sample). Sophisticated 3-D virtual presenters are also being developed, so even the venerated area of a professor’s presentation style is now being modeled. Van Welbergen, Nijholt, Reidsma, and Zwiers (2006) review an example of an anthropomorphic 3-D presenter. The presenter model (Figure 8.19) takes into account verbal, deitic, pose and sheet control expressions, as well as voluntary and involuntary movements. It is therefore becoming increasingly possible to model a science instructor’s teaching expressions and employ this reproduction as a virtual presenter in an online course. The possibility of a virtual teaching assistant who is a facsimile of the instructor is one of the multitude of possibilities that come to mind for this technology. Lastly, another commonsense strategy that is growing in use for distance learning environments is standard online tutoring using VOIP telephony or videoconferencing. In distance Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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engineering education, Böhne, Faltin, and Wagner (2004) describe how video chat is used at the University of Hanover to effectively assist students conduct Web-based remote experiments on picture generation by laser deflection.
Virtual.Science.Museums.and.Science.Centers Museums have evolved from grand spaces to exhibit artifacts to places promoting learning through active exploration of objects. Museums have also extending their educational outreach by capitalizing on the Internet’s capacity to connect learners with useful museum repositories of online learning objects. Internet repositories, such as virtual museums, develop and store considerable numbers of learning objects and/or learning interfaces that can be incorporated as part of online science course activities. Science museums have especially capitalized on the development of the Internet to extend their resources to online audiences as “virtual science centers” (Jones, 2005). There are fine examples of virtual science exhibits found at the Exploratorium of San Francisco at http://www.exploratorium.edu/, Boston’s Museum of Science at http://www.mos.org/, and University of California Museum of Paleontology at http://www.ucmp.berkeley.edu/exhibits/index.php. Museums have begun initiatives to
Figure 8.20. A virtual planetarium interface (from Chan et al., 2003). Used by permission from IEEE.
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develop online games that incorporate the richness of their collections (Din, 2006). There are also national efforts to build systems to unite collections of large repositories of digital specimens such as China’s University Digital Museum Grid that connects about 100,000 digital specimens from universities in 10 different cities (Chen et al., 2006) and others that specialize on specific disciplines for instance the Earth Science Digital Museum—ESDM (Dong, Xu, & Wu, 2005). Virtual science centers in cyberspace without a corresponding presence in a physical space are taking their place among the online learning resources of landmark museums. Examples of virtual science centers include examples such as the Leonardo Interactive Virtual Science Museum at http://www.ba.infn.it/~zito/museo/leonardoen.html, the Virtual Museum of Canada at http://www.virtualmuseum.ca/, and the IEEE Virtual Museum at http://ieeevirtual-museum.org/exhibit/index.php. Virtual wildlife parks and planetariums have also been developed using the VRML 3-D programming language to teach young students such as the immersive planetarium described by Chan, Wu, Jong, and Lin (2003) depicted in Figure 8.20. State of the art forms of virtual museums are emerging employing dynamic interactive virtual reality network services and realistic objects that can be viewed in 3-D (Walczak, Wojciechowski, & Cellary, 2006a; Walczak, Cellary, & White 2006b) such as a realistic museum interiors with objects. Still others are providing photorealistic objects combined with haptic design features (Figure 8.21) that can be directed and manipulated with control cubes (Huang, Chen, & Chung, 2005).
Figure 8.21. A virtual museum system with haptic features to inspect artifacts (from Huang, 2005). Used by permission from IEEE.
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Resources from virtual museums can be a good way to develop science learning activities for students. Absence of long-term archiving of virtual exhibits and time consuming searches to locate appropriate materials/learningobjects to support a particular science topic have been key difficulties of relying of virtual museums to structure online activities. The digital library and virtual museum concepts should relieve some of this site evaporation as standardized metadata is used for virtual exhibits
Trends Advances in communication and information technologies suggest the following about the possible future of learning science online. Learning systems will have vastly expanded capacity and speed supporting the inclusion of copious media-rich resources, diverse interactivity options, sophisticated visualizations, realistic/immersive virtual environments, and learning objects with multiple layers of detail (i.e., scaffolding). Science students will have expanded opportunities to learn anytime-anywhere through mobile learning technologies with mobile learning engines capable of delivering complex 3-D learning objects based around science themes. A student’s learning may be supplemented and personalized using intelligent tutors and/or virtual instructors, constructed via artificial intelligence systems that will be available to answer questions anytime and anywhere. Student collaboration will be supported by social software such as multi-user environments that may be virtual, as in a scientific learning game, or real, as in the case of a virtual classroom. It is possible that long-established affordances of a face-to-face classroom will be used more frequently as technologies for virtual classrooms and Webcasting becomes more available. Science students will have expanded opportunities to use and share institutional resources online such as remote experiments and rich disciplinary data from virtual museums. Moreover, learning at a distance may be increasingly tactile, employing haptic technologies to engage students in the simulated manipulation of learning objects.
Impact.of.Online.Technological.Innovation.to.the. Science.Professoriate Emerging technologies are already having a significant impact on the role of the science professoriate. Very few have not already experienced the greater demand on time to interact with students via e-mail. In our experience, we have also seen a shift to more of a facilitator role with increased online science formats at our university and a greater need to be broadly versed in online technologies including those involving multifaceted online presentation. It is a growing necessity that science faculty be informed about the affordances of online learning systems, the emerging cyberinfrastructure that can support student research, remote experiment possibilities, and the possibilities of connecting with their students through mlearning. Pedagogical content knowledge of online instructional approaches is elevated for science faculty as they conceive their courses and teaching approach. For example, whether Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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they should employ promising online-facilitated learning approaches such as collaboratories that can engage students in science beyond the duration of a course or Web3D virtual environments that can improve visualization and tactile (i.e., haptic) skills or then again immerse students in scientific reasoning through educational games. We have also witnessed a faculty workload shift to online course authorship and corresponding requirements to extensively search multimedia science learning objects to support course development. While some online tools can create teaching efficiencies such as automated test and quiz assessment, elevated use of written assignments for sustained accountability with students as well as the intensity of discussion forum interactions has increased this workload. It is not clear whether the intensity of written online interactions will wane as traditional classroom interactions become increasing possible through Web-classrooms and like technologies. Moreover, whether universities will capitalize on virtual science instructors and tutors and if so whether this will adversely impact the professoriate (using virtual resources rather than human resources) or positively impact workload by freeing up time for other responsibilities such as research or other aspects of teaching is uncertain.
Conclusions As we have expressed earlier, learning should not be utterly technology driven but must be grounded in fundamental pedagogical principles. Technology as described earlier will only see broad use if it is seen by learners as beneficial, if it is cost effective for the institution/ nation, and if it is determined to be effective in producing the desired learning outcomes. However, it is straightforward to imagine a future scenario where a learner at home collaborates synchronously with peers through a 3-D multi-user educational science game environment with haptic interaction capabilities. This same student seeks assistance late at night from an intelligent tutor in preparation for a virtual classroom activity broadcast by the instructor the next day. To finish, the student plans to use their mobile education device to participate in a remote experiment when they are traveling the subsequent week. These are the shadows of things that may be.
References Adamo-Villani, N., Richardson, J., Carpenter, E. & Moore, G. (2006). A photorealistic 3D virtual laboratory for undergraduate instruction in microcontroller technology. Retrieved May 1, 2007, from http://portal.acm.org/citation.cfm?id=1179317&jmp= abstract&coll=GUIDE&dl=&CFID=7878894&CFTOKEN=20130255 Alexander, B. (2006). Web 2.0: A new wave of innovation for teaching and learning? Educause Review, 41(2), 32-44. Böhne, A., Faltin, N., & Wagner, B. (2004). Synchronous tele-tutorial support in a remote laboratory for process control. In W. Aung, R. Altenkirch, T. Cermak, R. W. King, & Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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L. M. S. Ruiz (Eds.), Innovations 2004: World innovations in engineering education and research (pp. 317-329). Arlington, VA: iNEER. Bos, N., Zimmerman, A., Olson, J., Yew, J., Yerkie, J., & Dahl, E. et al. (2007). From shared databases to communities of practice: A taxonomy of collaboratories. Journal of Computer-Mediated Communication, 12(2). Retrieved July 21, 2007, from http://jcmc. indiana.edu/vol12/issue2/bos.html Bower, M. (2006). Virtual classroom pedagogy. Retrived May 1, 2007, from http://portal. acm.org/citation.cfm?id=1121390 Brodlie, K. W., Duce, D. A., Gallop, J. R., Walton, J. P. R. B., & Wood, J. D. (2004). Distributed and collaborative visualization. Retrieved May 1, 2007, from http://www. comp.leeds.ac.uk/vvr/gViz/publications/CGF_dcvstar.pdf Brown, M., & Edelson, D. C. (1998). Software in context: Designing for students, teachers, and classroom enactment. In A. S. Bruckman, M. Guzdial, J. L. Kolodner, & A. Ram (Eds.), Proceedings of ICLS 98: International Conference on the Learning Sciences, Atlanta, Georgia, December 3-5 (pp. 63-69). Charlottesville, VA: AACE. Chan, T. Y., Wu, Y. L., Jong, B. S., & Lin, T. W. (2003). Interactive and collaborative virtual learning environment: Virtual wildlife park. Proceedings of the 23rd International Conference on Distributed Computing Systems Workshops, Providence, Rhode Island, May 19-22 (pp. 622-626). Los Alamitos, CA: IEEE. Chen, X., Ou, H., Luo, X., Chen, M., Zhang, Y., Hao, K., & Mi, S. (2006)..The progress of university digital museum grid. Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops, Hunan, China, October 21-23 (pp. 78-85). Los Alamitos, CA: IEEE. Chittaro, L., & Ranon, R. (2005). Web3D technologies in learning, education and training: Motivations, issues, opportunities. Retrieved May 1, 2007, from http://hcilab.uniud. it/publications/2007-03/LearningEducationTraining_ComputersEducation07.pdf Chittaro, L., & Ranon, R. (2007). Web3D technologies in learning, education and training: Motivations, issues, opportunities. Computers & Education Journal, 49(2), 3-18. Creighton, I., & Ho-Stuart, C. (2004). A sense of touch in online sculpting. In Proceedings of the 2nd International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia, Singapore, November 2-7 (pp. 118-122). New York, NY: ACM Press. Davies, R. A., John, N. W., MacDonald, J. N., & Hughes, K. H. (2006). Visualization of molecular quantum dynamics—A molecular visualization tool with integrated Web3D and haptics. Retrieved May 1, 2007, from https://portal.acm.org/poplogin. cfm?dl=GUIDE&coll=GUIDE&comp_id=1050512&want_href=delivery%2Ecfm% 3Fid%3D1050512%26type%3Dpdf%26CFID%3D18668325%26CFTOKEN%3D23 941260&CFID=18668325&CFTOKEN=23941260&td=1179201742328. Davoli, F., Spanô, G., Vignola, S., & Zappatore, S. (2006). LABNET: Towards remote laboratories with unified access. IEEE Transactions on Instrumentation & Measurement, 55(5), 1551-1558.
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Dede, C., Nelson, B., Ketelhut, D. J., Clarke, J., & Bowman, C. (2004). Design-based research strategies for studying situated learning in a multi-user virtual environment. Paper presented at the International Conference on Learning Sciences, Mahweh, NJ. Delaney, B. (2005). Forget the funny glasses. IEEE Computer Graphics and Applications, May/June, 14-19. Dieterle, E., & Clarke, J. (2005). Multi-user virtual environments for teaching. Retrieved October 20, 2007, from http://muve.gse.harvard.edu/rivercityproject/documents/ MUVE-for-TandL-Dieterle-Clarke.pdf Din, H. W. (2006). Play to learn: Exploring online educational games in museums. Proceedings of the International Conference on Computer Graphics and Interactive Techniques, Boston, MA, August 1-3. New York: ACM Press. Doerr, K. U., Rademacher, H., Huesgen, S., & Kubbat, W. (2007). Evaluation of a low-cost 3D sound system for immersive virtual reality training systems. IEEE Transactions on Visualization and Computer Graphics, 13(2), 204-212. Dolgovesov, B. S., Morozov, B. B., Shevtsov, M. Y., Debelov, V. A., & Zhmulevskaya, D. R. (2003). The system for interactive virtual teaching based on “focus” virtual studio. Retrieved May 1, 2007, from http://www.graphicon.ru/2003/Proceedings/Multimedia/VirtualTeaching.pdf Dong, S., Xu, S., & Wu, G. (2005). Earth science digital museum (ESDM): Toward a new paradigm for museums. Computers & Geosciences, 32(6), 793-802. Dori, Y. J., & Belcher, J. W. (2005). Learning electromagnetism with visualizations and active learning. In J. K. Gilbert (Ed.), Visualization in science education (pp. 187-216). Dordrecht, Netherlands: Springer. Educause. (2005). Cisco Systems®. Retrieved May 28, 2007, from http://newsroom.cisco. com/dlls/2005/prod_101905b.html Eick, S. G. (2005). Information visualization at 10. IEEE Computer Graphics and Applications, 3, 12-14. El-Nasr, & Smith, B. (2006). Learning through game modding. ACM Computers in Entertainment, 4(1). Retrieved May 12, 2007, from http://portal.acm.org/toc.cfm?id=111 1293&type=issue&coll=GUIDE&dl=GUIDE&CFID=3761867&CFTOKEN=9207 8654#1111299 El Saddik, A. (2001). Interactive multimedia learning: Shared reusable visualization-based modules. Dordrecht, Netherlands: Springer. Endo, M., Yasuda, T., & Yoko, S. (2003). A distributed multiuser virtual space system. IEEE Computer Graphics and Applications, 8, 50-57. Retrieved May 12, 2007, from http://doi.ieeecomputersociety.org/10.1109/MCG.2003.1159613 Engelhardt, M., Hildebrand, A., Lang, A., Schmidt, T. C., & Werlitz, M. (2004). A constructivist content exploration based on a hypermedia e-learning object system. In M. E. Auer & U. Auer (Eds.), Proceedings of the International Conference, Interactive Computer Aided Learning, Villach, Austria, July 5-8. Villach, Austria: Kassel University Press.
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Forsberg, A., Prabhat, (n.i.), Haley, G., Bragdon, A., Levy, J., & Fassett, C. I. et al. (2006). Adviser: Immersive field work for planetary geoscientists. IEEE Computer Graphics and Applications, July/August, 46-54. Freeman, P. A. & Almes, G. T. (2005). Campus cyberinfrastructure: A crucial enabler for science. EDUCAUSE Review, January/February, 64-65. Georgiev, T., Georgieva, E., & Smrikarov, A. (2004). M-Learning—A new stage of e-learning. Retrieved May 1, 2007, from http://ecet.ecs.ru.acad.bg/cst04/Docs/sIV/428.pdf Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (Ed.), Visualization in science education (pp. 1-27). Boston: Kluwer Academic Publishers. Goetz, F., Eßmann, B., & Hampel, T. (2006). Collaboration by illustration: Real-time visualization in Web3D. In Proceedings of the Eleventh International Conference on 3D Web Technology, Web3D ‘06, Columbia, Maryland, April 18-21 (pp. 47-56). New York, NY: ACM Press. Gordin, D. N., Edelson, D. C., & Gomez, L. M. (1996). Scientific visualization as an interpretive and expressive medium. In D. C. Edelson & E. A. Domeshek (Eds.), Proceedings of the International Conference on the Kearning Sciences, Evanston, IL, July 24-27 (pp. 409-414). Charlottesville, VA: AACE. Halverson, R., Shaffer, D., Squire, K., & Steinkuehler, C. (2006). Theorizing games in/and education. Proceedings of the 7th International Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 1048-1052). Bloomington, IN. Retrieved May 1, 2007, from http://portal.acm.org/citation.cfm?id=1150034.1150231 Hoffmann, H., Stefani, O., & Patel, H. (2006). Extending the desktop workplace by a portable virtual reality system. International Journal of Human-Computer Studies, 64(3), 170-181. Holzinger, A., Nischelwitzer, A., & Meisenberger, M. (2005). Mobile phones as a challenge for m-learning: Examples for mobile interactive learning objects (MILOs). Proceedings of the 3rd International Conference on Pervasive Computing and Communications Workshops, Kauai, Hawaii, March 8-12 (pp. 307-311). Los Alamitos, CA: IEEE. Huang, C.-R., Chen, C.-S., & Chung, P.-C. (2005). Tangible photorealistic virtual museum. IEEE Computer Graphics and Applications, 25(1), 15-17. Ieronutti, L., & Chittaro, L. (2007). Employing virtual humans for education and training in X3D/VRML worlds. Computers & Education Journal, 49(1), 93-109. Inoue, M., Matsubara, Y., Iwane, N., Nakamura, M., & Ichitsubo, M. (2006). VR-based dynamics learning system using haptic device and its evaluation. Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies, Kaohsiung, Taiwan, July 5-8 (pp. 917-921). Los Alamitos, CA: IEEE. Johnson, A., Leigh, J., Morin, P., & Van Keken, P. (2006). GeoWall: Stereoscopic visualization for geoscience research and education. IEEE Computer Graphics and Applications, November/December, 10-14. Jones, B. (2005). Establishing identification in virtual science museums: Creating connections and community. In L. T. Wee & R. Subramaniam (Eds.), E-learning and virtual science centers (pp. 327-346). Hershey, PA: Information Science Publishing. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Keegan, D. (2001). The future of learning: From eLearning to mLearning. Retrieved July 17, 2007, from http://learning.ericsson.net/mlearning2/project_one/book.html Kim, Y., Hamilton, E. R., Zheng, J., & Baylor, A. L. (2006). Scaffolding learner motivation through a virtual peer. In Proceedings of the 7th International Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 335-341). Alpharetta, GA: International Society of the Learning Sciences. Koole, M., & Ally, M. (2006). Framework for the rational analysis of mobile education (FRAME) model: Revising the ABCs of educational practices. Retrieved May 1, 2007, from http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/10841/34159/01628461. pdf Krikke, J. (2006). Bringing haptics to consumers. Pervasive Computing, 5(4), 8-9. Lamberti, F., & Sanna, A. (2007). A streaming-based solution for remote visualization of 3D graphics on mobile devices. IEEE Transactions on Visualization and Computer Graphics, 13(2), 247-260. Lawton, G. (2005). What lies ahead for cellular technology? Computer, June, 14-17. Madhavan, K. P. C., Arns, L. L., & Bertoline, G. R. (2005). A distributed rendering environment for teaching animation and scientific visualization. IEEE Computer Graphics and Applications, September/October, 32-38. Maghsoudi, S., & Arthanari, T. (2004). Learning interface for virtual education. Paper presented at the 8th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, Florida. Retrieved July 15, 2007, from www.alicebot.org/articles/learningInterface.pdf Magnenat-Thalmann, N., & Bonnani, U. (2006). Haptics in virtual reality and multimedia. IEEE Multimedia, July/September, 6-11. Marin, B. F., Hunger, A., Werner, S., Meila, S., & Schuetz, C. (2005). Roles of an intelligent tutor agent in a virtual society. In Proceedings of the 2005 Symposium on Applications and the Internet, Orlando, FL, January 27-31 (pp. 237-244). Los Alamitos, CA: IEEE. Marshall, P. (2007). Do tangible interfaces enhance learning? Proceedings of the First International Conference on Tangible and Embedded Interaction, Baton Rouge, LA, February 15-17 (pp. 163-170). New York: ACM Press. Matsuura, K., Hesse, F. W., Ochi, Y., Ogata, H., & Yano, Y. (2002). Real/virtual classmates in an asynchronous distant learning environment. Proceedings of the International Conference on Computers in Education, Auckland, New Zealand, December 3-6 (pp. 792-796). Los Alamitos, CA: IEEE. McGrath, M. B., & Brown, J. R. (2005). Visual learning for science and engineering. IEEE Computer Graphics and Applications, 56-63. McLaughlin, L. (2006). What the Web’s new trend means to you. Pervasive Computing, October/December, 7-11. Mikic, F., & Anido-Rifón, L. (2006). M-learning standardization: Concepts and new ideas about learner profile. In IFIP (Ed.), International Federation for Information Processing: Education for the 21st century—Impact of ICT and digital resources (pp. 375-379). Boston: Springer. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Mikleia, E. (2006). Mobile devices to be applied as supporting tools in research methods class for undergraduate students. Proceedings of the 7th International Conference on Learning Sciences, Bloomington, IN, June 27-July 1 (pp. 918-919). Bloomington, IN: International Society of the Learning Sciences. Morales, C., Maicher, K., Pedela, R., & Weaver, G. (2006). Immersive chemistry video game. In Proceedings of International Conference on Computer Graphics and Interactive Techniques, Boston, MA, August 1-3. New York: ACM Press. Moustakas, K., Nikolakis, G., Tzovaras, D., & Strintzis, M. G. (2005). A geometry education haptic VR application based on a new virtual hand representation. Proceedings of IEEE Virtual Reality, Bonn, Germany, March 12-16 (pp. 249-252). Los Alamitos, CA: IEEE. Nakao, M., Minato, K., Kuroda, T., Komori, M., Oyama, H., & Takahashi, T. (2006). Transferring bioelasticity knowledge through haptic interaction. IEEE Multimedia, July/September, 50-60. NSF. (2003). Revolutionizing science and engineering through cyberinfrastructure. Report of the National Science Foundation blue ribbon advisory panel on cyberinfrastructure. Retrieved March 30, 2007, from http://www.nsf.gov/od/oci/reports/toc.jsp Patel, K., Bailenson, J. N., Hack-Jung, S., Diankov, R., & Bajcsy, R. (2006). The effects of fully immersive virtual reality on the learning of physical tasks. Proceedings of the 9th Annual International Workshop on Presence, Cleveland, Ohio, August 24-26. Retrieved July 17, 2007, from http://vhil.stanford.edu/pubs/2006/patel-physical-tasks.pdf Pratap, P., & Salah, J. (2004). The effectiveness of Internet-controlled astronomical research instrumentation for education. Journal of Science Education and Technology, 13(4), 473-484. Prensky, M. (2000). Digital game-based learning. New York: McGraw Hill. Reynolds, S. J., Johnson, J. K., Piburn, M. D., Leedy, D. E., Coyan, J. A., & Busch, M. M. (2005). Visualization in undergraduate geology courses. In J. K. Gilbert (Ed.). Visualization in science education (pp. 253-266). Boston: Kluwer Academic Publishers. Rhyne, T. (2000). Scientific visualization in the next millennium. IEEE Computer Graphics and Applications, 20(1), 20-21. Rhyne, T. M. (2003). Does the difference between information and scientific visualization really matter? IEEE Computer Graphics and Applications, 23(5), 6-8. Rogers, Y., Price, S., Randell, C., Fraser, D. S., Weal, M., & Fitzpatrick, G. (2005). Ubilearning integrates indoor and outdoor experiences. Communications of the ACM, 49(1), 55-59. Rößling, G., Naps, T., Hall, M. S., Karavirta, V., Kerren, A., & Leska, C. (2006). Merging interactive visualizations with hypertextbooks and course management. ACM SIGCSE Bulletin, 38(4), 17-22. Rusdorf, S., Brunnett, G., Lorenz, M., & Winkler, T. (2007). Real-time interaction with a humanoid avatar in an immersive table tennis simulation. IEEE Transactions on Visualization and Computer Graphics, 13(1), 15-25.
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Sadeghian, P., Kantardzic, M., Lozitskiy, O., & Sheta, W. (2006). The frequent wayfindingsequence (FWS) methodology: Finding preferred routes in complex virtual environments. International Journal of Human-Computer Studies, 64(4), 356-374. Segall, N., Doolen, T. L., & Porter, J. D. (2005). A usability comparison of PDA-based quizzes and paper-and-pencil quizzes. Computers and Education, 45, 417-432. Skalsky, N., & Pastel, R. (2004). Transportable research instrument: A PDA-based laboratory for science experiments..Proceedings of the 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, Jhongli, Taiwan, March 23-24. Los Alamitos, CA: IEEE. Sklyarov, V., & Skliarova, A. (2005). Teaching reconfigurable systems: Methods, tools, tutorials and projects. IEEE Transactions on Education, 48(2), 290-300. Slinger, C., Cameron, C., & Stanley, M. (2005). Computer-generated holography as a generic display technology. Computer, 8, 46-53. Snow, C., Pullen, J. M., & McAndrews, P. (2005). Network EducationWare: An open-source Web-based system for synchronous distance education. IEEE Transactions on Education, 48(4), 705-712. Stevens, K., Appaerts, F., Van Vlierberghe, I., & Boutsen, J. (2001). Hyperlab: A universal provider system and an interactive test bench for distance learning. European Journal of Engineering Education, 26(4), 13-14. Takayama, K. (2005). Visualizing the science of genomics. In J. K. Gilbert (Ed.). Visualization in science education (pp. 217-252). Boston: Kluwer Academic Publishers. Tamura, H., Yamamoto, H., & Katayama, A. (2001). Mixed reality: Future dreams seen at the border between real and virtual worlds. IEEE Computer Graphics and Applications, 21(6), 64-70. Taylor, J. A., & Mohr, J. (2001). Mathematics for math anxious students studying at a distance. Journal of Developmental Education, 25(1), 30. Tsinakos, A. A. (2006). Virtual instructor and pedagogical issues. Proceedings of the Sixth International Conference on Advanced Learning Technologies, Kerkrade, The Netherlands, July 5-7. Los Alamitos, CA: IEEE. van Dam, A. (2005). Visualization research problems in next-generation educational software. IEEE Computer Graphics and Animation, 25(5), 88-92. van Welbergen, H., Nijholt, A., Reidsma, D., & Zwiers, J. (2006). Presenting in virtual worlds: An architecture for a 3D anthropomorphic presenter. IEEE Intelligent Systems, 21(5), 47-53. Walczak, K., Wojciechowski, R., & Cellary, W. (2006a). Dynamic interactive VR network services for education. Proceedings of the ACM Symposium on Virtual Reality Software and Technology, Limassol, Cyprus, November 1-3 (pp. 277-286). New York: ACM Press. Walczak, K., Cellary, W., & White, M. (2006b). Virtual museum exhibits. Computers, 3, 93-95. Way, T. P. (2006). A virtual laboratory model for encouraging undergraduate research. ACM SIGCSE Bulletin, 38(1), 203-207. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Weber, R., Ritterfeld, U., & Mathiak, K. (2006)..Does playing violent video games induce aggression? Empirical evidence of a functional magnetic resonance imaging study. Media Psychology, 8, 39-60. Weiskopf, D., Borchers, M., Ertl, T., Falk, M., Fechtig, O., & Frank, R. (2006). Explanatory and illustrative visualization of special and general relativity. IEEE Transactions on Visualization and Computer Graphics, 12(4), 522-534. Williams, M. (2005). CDMA2000 to offer 73Mbit/sec. downloads in 2008. ComputerWorld Mobile and Wireless. Retrieved July 3, 2006, from http://www.computerworld.com/ mobiletopics/mobile/story/0,10801,106269,00.html?source=x10. Zyda, M. (2005). From visual simulation to virtual reality to games. Computer, 9, 25-32.
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Section III Assessing Online Science Learning
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Chapter.IX
Assessing.Science. Competence.Achieved. at.a.Distance
As in the case of all branches of art, success depends in a very large measure upon individual initiative and exertion, and cannot be achieved except by dint of hard work. – Anna Pavlova (1881-1931) Assessment of student learning is integral to design of curricular experiences, a reality which is often purported to be more complex in online learning environments (Wijekumar, Ferguson, & Wagoner, 2006) and characteristic of constructivist praxis (Comeaux, 20052006; Segall, Doolen, & Porter, 2005). However, the framework within which evaluation occurs—with assessment as one form of evaluation, unique to the learning environment—is constant (Williams, 2004). Moreover, learning assessment was a meaningful topic pre-constructivism and assessment literature in online learning is nearly identical to that of assessment in general, especially as computerized assessment methods become more popular for all learners (Holtz & Radner, 2005; Robles & Braathen, 2002). There are differences and specific challenges; however, in practice those challenges need not be onerous. This chapter describes current practices in online learning assessment, notes similarities in on-site and online methods and explores the differences and how those differences are or can be addressed for online science learning. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Assessng Scence Competence Acheved at a Dstance
Assessment.Standards.and.Science.Assessment Assessment in online learning actually increases accountability for real learning, simply because the controls that instructors typically use (e.g., face-to-face monitoring, synchronicity) are, at best, ephemeral in online learning (Holtz & Radner, 2005; Lorenzetti, 2003). Routine, surface assessments, while nowhere near ideal, may be employed in both environments, but need not occur in distance any more so than classroom-based. To review briefly, learning assessment is categorized as formative or summative. Formative assessment is ongoing and serves to inform both the instructor and the student where the student stands, in terms of achievement of course objectives (e.g., quizzes, laboratory reports). Summative assessment is the penultimate evaluation of the student’s achievement throughout the course (e.g., final exam, project or paper) (Holtz & Radner, 2005). Wiggins and McTighe (1998) maintain that the use of both formative and summative assessment remains standard best practice (after Bloom, Hastings, & Madaus, 1971), although the authors have both experienced science courses where summative assessment was not used. Perhaps the most important idea for the typical instructor to remember is that learning assessment is a well-developed field of study in itself, with substantial literature, an extensive knowledge base and specialized vocabulary. While some readers may consider these characteristics self-evident, default educators—those who teach within their field of specialty without the benefit of formal education theory-to-practice background—often assess as they were assessed, simply because those techniques are what they know and because they are unaware of the options (Holtz & Radner, 2005). Yet, in Against Method, Feyerabend (1993) maintains that rigid adherence to a canon, tenet, or ideology often impedes knowledge development and nowhere is this more obvious than in assessment of science learning. Aikenhead (2000), elaborating on Ryan (1988, in Aikenhead) summarizes the approach to assessment of science learning as traditionally empirical, although unnecessarily so. Empirical-analytic assessment is essentially positivistic, quantitative and focused on the product—that is, student work—negating the process of product development, wherein interpretive assessment is more process-oriented, as Aikenhead describes. It embraces nonquantitative assessment techniques, such as rubrics, concept mapping, check lists, and authentic assessment (Black, 1993, 1997). The critical-theoretic paradigm gives special attention to the social or cultural context in which assessment takes place, a context that greatly influences both the process and the product of a student’s work (Roth & McGinn, 1998). The critical-theoretic paradigm focuses on the product, process, and context of student learning (Aikenhead, 2000, p. 79). Aikenhead could have added iterative to this description, as assessment theorists from Bloom to the present have advocated providing students with multiple opportunities for demonstrating eventual understanding within a given term (Holtz & Radner, 2005). Clearly and understandably, assessment appears to echo learning theories’ increasing movement toward student-centeredness with a distinct parallel between the critical-theoretic model and constructivism (Aikenhead, 2000). However, the influence of less radical proponents of constructivism have been heard (Edelson, 2002; Sherin, Reiser, & Edelson, 2004), as has Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 9.1. Changes in science assessment emphasis (New Jersey Science Curriculum Framework, 1999) Less Emphasis Assessing what is easily measured Assessing discrete knowledge Assessing scientific knowledge Assessing to learn what students do not know Assessing only achievement End-of-term assessments by teachers Development of external assessments by experts alone
More Emphasis Assessing what is most highly valued Assessing rich, well-structured knowledge Assessing scientific understanding and reasoning Assessing to learn what students do understand Assessing achievement and opportunity to learn Ongoing assessment by students of their work and that of others Involvement of teachers in the development of external measurement
research in neurological aspects of learning (e.g., Lawson, 2003). The emphasis in science assessment has become more inclusive and less discrete, yet thoughtful application retains the importance of correctness and incorrectness, a distinction of considerable importance in the sciences. Table 9.1 summarizes those differences. Important to note is the comparative verbiage: less, rather than eliminate, more rather than focus. For example, while all students can benefit from authentic and iterative assessment, what is most highly valued by students in a career-preparation science program will vary from that of non-majors or non-career oriented students, where the emphasis on discrete knowledge and achievement will be greater for the career oriented students (Osborne & Hennessy, 2006). For those students, problem-based learning, a form of performance-based learning, may be preferable (Lijnse, 2005; Oktac, 2004), although there is increasing debate on the veracity of problem-based learning at lower levels of sequential instruction (e.g., introductory microbiology versus an advanced course in pathogenic microbiology). The crux of the debate is novice-to-expert knowledge differences.
Novice-to-Expert.Knowledge Performance-based assessment requires students to apply a knowledge base and skills in realworld situations, with the expectation of what Reeves (2000) calls, “judgment in ill-defined realistic contexts” (p. 105). In such assessment, students must use higher-order cognitive skills in multidimensional and multi-step tasks that, ideally, are integrated into learning activities (MacDonald & Twining, 2002). Yet, such metacognitive skills are characteristic of increasingly expert levels of knowledge organization, levels that students may not be able to achieve in a given course, as Niemi (1997) explains. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Assessng Scence Competence Acheved at a Dstance
The organization of expert knowledge around concepts and principles accounts for many of the performance differences that have been found to distinguish experts from novices in different subject areas (e.g., Bereiter & Scardamalia, 1986; Chi, Glaser, & Rees, 1982; Glaser & Chi, 1988). Studies in many different subject areas show that experts perceive more patterns among the pieces of information they know, recall relevant information more quickly, and spend more time trying to analyze nonroutine problems in terms of domain principles (Glaser & Chi, 1988). All of these differences spring from differences in knowledge organization (Niemi, 1997, p. 243). It is generally accepted that information and skill sets that cannot be linked to existing knowledge are more difficult to learn that those that can be linked in such a manner (Holtz, 2002; Niemi, 1997). Similarly, Sternberg (1984) describes the difference between novice and expert learning skills as “the extent to which one is able to link knowledge on either of two processing systems; global and local. The global system is where new information or knowledge that has not been integrated into one’s existing knowledge base is processed. Local processing is automatic and ‘of almost unlimited capacity’ (p. 94)” (Holtz, 2002, p. 29). Thus, expertise is characterized by greater cognitive fluency. Learners with more integrated knowledge—that is, knowledge linked via the local processing system—are better able to further integrate new learning in their areas of expertise. Novices, including subject matter experts learning outside their field of expertise, must rely to a greater extent on the less welldeveloped and more deliberate global processing system, and can become overwhelmed with new information, a concept represented by the theory of cognitive load (Sweller, 1999). As early as 1984, Sternberg recognized that the theory of local and global processing contradicts “certain theorists who seem to believe in the primacy of knowledge itself in intelligent functioning (e.g., Chi, Glaser, & Rees, 1982; Keil, 1984)” (p.95). Yet Jarvis (1992) maintained that Sternberg’s theory represented the development of heuristics, “which relies on a sequence of practice, repeated failures and learning when to break the rules, a sequence that moves quickly beyond factual knowledge and into the realm of reflection and experience (Schön, 1983, in Jarvis, 1992)” (Holtz, 2002, p. 30), a sequence that bears a remarkable resemblance to the hypothetico-predictive model supported by contemporary research in the neurology of learning (Lawson, 2003). Thus, the best elements of constructivism—as originally conceived of and implemented by Vygotsky and Piaget—are blended under the moderating influence of advances in the neurology of learning to promote interpretive assessment in the online science learning environment. Interpretive assessment values the importance of the discrete and, in the online environment, seeks to engage students in a more flexible instructional model that integrates instruction and assessment.
Aligning.Content,.Instruction,.and.Assessment Bloom’s Taxonomy of Educational Objectives remains a standard in assessment, with its emphasis on the types of thinking necessary in order for students to respond to increasingly complex questions or tasks. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Knowledge: recall of specifics and patterns or generalizations Comprehension: restating, interpreting, extrapolating Application: using abstractions in specific situations Analysis: identifying and interpreting relationships and principles Synthesis: creating a new and unique combination or product Evaluation: appraising with logic (Bloom et al., 1971, pp. 271-273) In the Handbook on Formative and Summative Evaluation of Student Learning (1971), Bloom and colleagues developed an assessment design process that linked these types of thinking with instructional objectives, as illustrated in Table 9.2. Using this format, instructors can assess an existing course or plan a new course. While Bloom maintained that instructors must first evaluate course content, then identify the types of thinking required of students in demonstrating mastery of course objectives and subsequently design assessment tools, Wiggins and McTighe (1998) build on Bloom’s seminal work and reverse the process in their three-step backward design process, which has arguably supplanted Bloom as the contemporary standard. Instructors identify what they want students to understand at the end of the course, decide what evidence they need to see in order to demonstrate understanding, and only then plan learning and instructional activities. Integral to the backward design process is filtered content and the six facets of understanding model. Filtered content refers to careful, thorough treatment of select subject matter, determined through the application of four filtering questions. •
To what extent does the idea, topic, or process represent a “big idea” having enduring value beyond the classroom?
•
To what extent does the idea, topic, or process reside at the heart of the discipline?
•
To what extent does the idea, topic, or process require uncoverage?
•
To what extent does the idea, topic, or process offer potential for engaging students? (Wiggins & McTighe, 1998, p. 6-7).
Table 9.2. Assessment design process (adapted from Bloom et al., 1971) Knowledge
Comprehension
Application
Analysis
Synthesis
Evaluation
Patterns Ideas Relationships Principles Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Assessng Scence Competence Acheved at a Dstance 0
Table 9.3. The six facets of understanding rubric, with comparison to Bloom’s Taxonomy level (adapted from Wiggins & McTighe, 1998) (Holtz & Radner, 2005, p. 281). Facet Explanation Interpretation Application Perspective Empathy SelfKnowledge
Naïve Literal Novice Uncritical Egocentric Innocent
Levels of Understanding Intuitive Developed In-depth Interpreted Apprentice Aware Developing Unreflective
Perceptive Able Considered Aware Thoughtful
Revealing Skilled Thorough Sensitive Circumspect
Sophisticated Profound Masterful Insightful Mature Wise
Bloom’s Level Knowledge Comprehension Application Analysis Synthesis Evaluation
Uncoverage, as used in the third bullet, refers to the constructivist concept of discovery, wherein students are not provided all information, but are presented with learning activities that require them to discover, or uncover, related content, content that often falls outside the parameters of the four filtering questions. It is our contention that this concept is misinterpreted by proponents of radical constructivism, especially in science learning, resulting in calls for largely inductive learning activities. Correct, limited application is in keeping with the true constructivist theory of Vygotsky and Piaget (e.g., experiential learning, practical work). Filtering content enables students to reach a higher level of understanding, as exemplified in the six facets of understanding model (Wiggins & McTighe, 1998). Table 9.3 correlates the six facets with Bloom’s taxonomy. Wiggins and McTighe also delineate levels of understanding within the six facets model, on a continuum from superficial to well-developed. In keeping with constructivist norms, the levels of understanding are somewhat ephemeral, leaving the distinction between considered and thorough, for example, to curriculum planners and instructors.
Interpretive.Assessment.Online Wiggins and McTighe’s (1998) concept of filtered content parallels calls for a reduction in curriculum content mass, with deeper and more meaningful exploration of key content and processes (New Jersey Science Curriculum Framework, 1999; Valverde & Schmidt, 1997). Ideally, science educators who work with online students will integrate that call with assessment measures that are, of course, meaningful, and that meet the concerns of educators about the online environment. They will develop courses using the backward design process: know what they want students to walk away knowing, limit content to cognitively reasonable levels, encourage uncoverage—but not solely inductive discovery, then use authentic, iterative assessments to gauge student progress. It is undeniable that sufficient research has not been done on the validity and reliability of online assessment techniques, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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yet no assessment technique was first employed with full validity and reliability established. Table 9.4 includes techniques with related sources that address the documented online assessment concerns of educators.
Table 9.4. Documented educator concerns about online assessment with solutions and resources
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For high-stakes assessment (e.g., medical or nursing licensure, or financial certifications) and for assessment in learning environments that choose to maintain the empirical paradigm, the issue of primary importance is accountability and security. As Shepherd (2003) explains, high stakes assessment is typically most secure in a dedicated assessment center, which is often the most expensive option, as well. As the stake level declines, the less efficient a dedicated center becomes. Table 9.5 summarizes Shepherd’s (2003) findings, where the letters represent traditional grade levels of achievement.
Table 9.5. Relationship between security and assessment level in designated assessment sites (adapted from Shepherd, 2003)
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Online.Science.Assessment.Cases The cases that follow are innovative to a degree, some more than others. Where published examples could not be found, the authors’ own efforts are described. As noted throughout the book, cutting edge technologies are not routinely applied anywhere; thus, the assessment examples are representative of current, as opposed to possible, practice.
Case.1..Assessing.Discrete.Knowledge Arizona’s Mesa Community College compared two subsequent online biology courses designed for non-majors to two on-campus sections of the same course. Learning objectives were identical: “the ability to apply scientific reasoning skills; store, retrieve, ad interpret data using standard graphing techniques; design controlled experiments; and identify causal relationships between independent and dependent variables” (Johnson, 2002, p. 312-313). The course is inquiry-based, with integrated laboratory experiences. Distance students use purchased kits, a few household items and a printed lab manual in each of 14 modules, communicating with each other and the instructor and completing Web modules via learning management software. Some modules included CD-based animations of abstract concepts to accompany text descriptions. Assessments include weekly quizzes based on the laboratory exercises, “a proctored midterm and comprehensive final examination” (Johnson, 2002, p. 313). Students in each of the online and on-campus courses were “pre- and post-tested for knowledge of biology, graphing skills, reasoning ability, and attitudes toward science. Data were also gathered on four descriptive variables: number of previous biology courses completed, race, age, and gender. Withdrawal rates were tabulated” (Johnson, 2002, p. 313). Post-tests and subsequent ANOVA demonstrated no significant differences between online and oncampus students for any of the learning objectives. The course in question was assessed largely through the use of test-bank items and a final exam that the instructor neither designed nor saw prior to use. In effect, this course followed a traditional, empirical paradigm assessment plan, but met each of the standards recommended for science learning: students’ preconceptions were addressed during modules and discussion of laboratory results; information was organized around factual knowledge and concepts; from both pre-tests and introductory work, students were made aware of their knowledge and misunderstandings, thus supporting metacognition; and students were encouraged to learn cooperatively through discussion of their work, as well as ungraded laboratory reports that supported graded, weekly quizzes. A few online practices for science learning were also in evidence: students were actively engaged in practical work that was inquiry-based and communication was continuous. Assessment measures were not integrated into learning activities and collaboratory work was not included, but the hypothetico-predicative model was, whether or not intentionally, the structure on which the course appears based. If Johnson had chosen to integrate such assessment methods, options could include selfexplanatory rubrics, such as those in Figure 9.1. Based on the work of Pelz (2004), each rubric simply states the quality characteristics expected at each level.
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Figure 9.1. Examples of online science rubrics for laboratory and field work experiences, based on Pelz (2004). K. Downing, personal images. Virtual.Laboratory.Report.Rubric Level 4
Interpretation Excellent
The lab report has the following qualities: 1. Your observations (main principles and or terms learned while completing this lab; the characteristics or features of objects/specimens/data you studied in this lab (e.g., fossils etc.), and/or any patterns you may have noticed while making your observations. 2. Your methodology to address the objectives of this lab exercise (how you deduced/reasoned an explanation for what you observed, what evidence you specifically employed to reach your lab exercise conclusion. 3. Your lab conclusions. Depending on the kind of lab, this may include: (1) providing accurate answers for questions asked, or (2) providing the best possible explanation for data provided. 4. Your lab is accurate to the theories, principles
3
Above Average
2
Average
1
Minimal
0
Unacceptable
5. Information is accurately communicated and report is well written. The report lacks at least one of the above qualities, but is above average in quality. A level 3 report demonstrates a strong understanding of the issue being discussed. The report lacks two or three of the required qualities. A level 2 report demonstrates a reasonable understanding of the issue being discussed. The report presents little evidence of the above qualities. A level 1 report demonstrates a nominal understanding of the issue being discussed. The report does not demonstrate understanding of the lab topics.
Case.2..Assessing.Discussion Vonderwell, Liang and Alderman (2007) analyzed, perhaps more thoroughly than did prior authors, the theoretical constructs considered necessary for assessment of online discussion. Discussion assessment clearly falls within formative evaluation, although the aggregate can be examined for evidence of summative achievement. The case study was conducted with Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 9.1. Examples of online science rubrics for laboratory and field work experiences, based on Pelz (2004). K. Downing, personal images. (continued) Virtual.Field.Trip.and.Self-Directed.Field.Trip.Report.Rubric Level 4
Interpretation Excellent
The field trip report summarizes, addresses and/or has the following qualities: 1. Your observations (what you observed and/or read about during your field trip). 2. Specific examples of what you observed (e.g., species, habitats, etc.) 3. How this field trip helped you to gain a better understanding of the module topic. 4. Connection to the theories, principles and information reviewed.
3
Above Average
2
Average
1
Minimal
0
Unacceptable
5. Information is accurately communicated and report is well written. The report lacks at least one of the above qualities, but is above average in quality. A level 3 report demonstrates a strong understanding of the issue being discussed. The report lacks two or three of the required qualities. A level 2 report demonstrates a reasonable understanding of the issue being discussed. The report presents little evidence of the above qualities. A level 1 report demonstrates a nominal understanding of the issue being discussed. The report does not demonstrate understanding of the fieldtrip topics.
master’s-levels students and consisted of thematic data analysis by Vonderwell, Liang and Alderman of online, asynchronous discussion, an open-ended online survey of students and a transcript of an asynchronous discussion from one of the courses. Results identified five key aspects for assessment, which led to the assessment rubric in Figure 9.2. •
Structure: Threaded discussions are preferable to non-threaded and are more likely to occur as a result of both open-ended initiatory questions and a clear assessment rubric that clarified for students what format their responses should ideally take.
•
Learning community: Another way of referring to what Brookfield (2005) calls group dynamic, the interactivity between students within a learning environment.
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•
Self-regulatory cognitions: “Self regulation refers to the degree to which ‘students are metacognitively, motivationally, and behaviorally active participants in their own learning process’ (Zimmerman, 2001, p. 5)…Asynchronous discussions enable and encouraged monitoring and assessment of learning. This was illustrated when Joe expressed that he could review the responses that he had submitted and see ‘where he might have weakness and strengths.’” (Vonderwell, Liang, & Alderman, 2007, p. 319).
•
Learner autonomy: Closely related to self-regulatory cognitions, learner autonomy manifests in online learning environments when students reconsider their work. It also refers to the desire of students to have latitude in the subjects discussed and the direction in which conversations proceed.
•
Student writing skills: Poor writing skills can be indicative of a lack of understanding, while even the bet writing skills can sometimes not do justice to an idea.
With the lack of conversation (e.g., classroom) to help clarify thoughts, writing becomes that much more important. Worth noting is that, expanding on Brookfield (2005), instructors may distinguish clearly between an assignment posting and a conversational posting. As described in one of our own courses,
Figure 9.2. Asynchronous discussion assessment criteria (from Vonderwell, Liang, & Alderman, 2007). Used by permission from AECT.
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An assignment posting is one in which your discussion is centered on submission of your assignment. Each assignment posting is graded, so you must read the discussion assignment, draft your submission (to be certain that you address each component of the assignment), post your submission, and respond to questions/discussion. Your submissions should clearly indicate that you have read and thought about the related material, as well as any discussion already ongoing, as appropriate…However, that does not mean that every posting must be formally written or include repetition of substantial detail. In subsequent postings,
Figure 9.3. Rubric for assessing online discussion (from Hazari, 2004). Used by permission from Journal of Information Systems Education.
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whether you are responding to another student’s assignment posting or discussing your own or others’, conversational discussion is fine. As long as you distinguish between an assignment posting, in which detail is required, and conversational discussion, you’ll be fine. (Holtz, 2006, p. 7-8). A more proscriptive rubric is provided by Hazari (2004, Figure 9.2), whose approach was noticeably interpretive, rather than the heavily critical-theoretic approach described by Vonderwell, Liang, and Alderman (2007). Content and discrete knowledge are emphasized to a greater degree than interactivity and conversation-based knowledge creation, which predominate in the rubric in Figure 9.3.
Case.3..Assessing.Metacognitive.Skills Design of the Fittest is an advanced elective seminar in DePaul University’s adult learning program; only one student in a recent term was in a science-oriented vocation, but all students had completed the science requirements in the competence-based program. Metacognitive skills, which are refined through a carefully designed core series in the program, enable students to understand what they both know and do not know, why they believe as they do, why they think as they do, and how to work past those facets to attempt a more holistic approach to epistemic understanding. The advanced elective seminar is designed to cap development of those skills. Using eugenics as the focus, Design of the Fittest incorporates the theory of unintended consequences; a review of human subjects history and law, previously covered in depth during Research Seminar (a basic research methods course); and an overview of the actual science of genetics. Learning activities included educational modules from Cold Spring Harbor Laboratory and development of a final project that explored an aspect of eugenics either previously identified by the instructor or chosen by the student. Assessment is fully integrated into course activities, rather than existing in parallel. After introductions, students begin with a core anchoring assignment, Center Yourself. However, the assignment is designed to necessitate iterative examination of their hypothesized concepts of the issues under discussion, deliberately integrating an original—that is, Vygotskian and Piagetian—constructivist tenet with contemporary understanding of neurological learning’s hypothetico-predictive model, as seen in the following excerpt from the student course guide. This course focuses on characteristics that societies, from time to time, have considered undesirable. In order to center your attention on your own experiences related to these characteristics—an exercise that educators refer to as “anchoring,” and that will be used again later in the course—this assignment asks that you develop two lists that will be shared only with your instructor, and even then in edited format, if you wish. For the first list, describe yourself. Think in terms of identifiable characteristics (e.g. blue eyes), and identity characteristics (e.g. religious affiliation). Do a thorough job—try to describe yourself in sufficient detail that someone reading only the list would have a good sense of who you are. However, personality traits aren’t important here. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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For the second list, spend some time thinking about characteristics that you consider undesirable. Again, think of identifiable and identity characteristics, and in terms of what you would not find desirable in a mate, a son- or daughter-in-law, or a supervisor, for example. Try not to limit yourself by engaging in political correctness. If you would not want your child to marry into a particular religion, for example, document it. Remember, no one else needs to see this. When you finish, email your lists to your instructor as an attachment (.doc, .wpd, .rtf or .pdf). You may choose to edit the lists you submit, if you wish, to remove any items you do not want to share. However, do send a representative list, so that the instructor can be certain that you’re on the right track (Holtz, 2006). Students revisit and, typically, revise this assignment after completing learning activities on the theory of unintended consequences and human subjects history and law. After completing the Pence readings, return to Assignment 2, “Center Yourself,” and reflect on how each characteristic you listed could be perceived as fitting into each Delimiting category. You need to stretch with this assignment, doing your best to take on another worldview. How do you justify inclusion in each category? After you have documented your reasoning, consider the Common Mistakes again—which, if any, “mistakes” do you believe you have to make in order to justify inclusion? Finally, consider the principles of autonomy, beneficence (benefit), nonmaleficence (lack of harm) and justice. How is each demonstrated in your justification? (Holtz, 2006, p. 23). Incorporated into the final project, which, in turn, incorporates assignments related to the science of genetics, is a reflective paragraph that explores student perceptual changes compared to the initial assignment, with discussion of why the changes did or did not occur. Student responses demonstrate retention of discrete knowledge (e.g., legal parameters), as well as integration of genetic and eugenic concepts introduced in the course. Most important for the purposes of the course, each was able to identify latent prejudices and the origins of those perspectives, how conceptual analysis uncovered flaws that they had not considered previously and how the exercise related back to the core competence areas on which the assignments were built. It should be noted that this course was developed using Wiggins and McTighe’s (1998) backward design process, with careful attention to filtered content, appropriate uncoverage, and learning activities designed to elicit sophisticated levels of understanding.
Case.4..Assessing.Large-Group.Science.Classes While the first three cases presented have obvious application to small and moderate-sized classes, each—while valuable—would be onerous or expensive with larger groups. Formative assessment in large groups is often dismissed as such, despite the promotion of small group work (Center for the Study of Higher Education, 2002) and computerized feedback systems (Dopper & Sjoer, 2004). However, use of audience response technology has been found to be extremely effective for formative assessment with large science classes (Herreid, 2006; Moredich & Moore, 2007; Salmon & Stahl, 2005; Zielinska & Pawlina, 2007). Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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One of the authors (J.H.) used an early version of the technology with medical residents and hospital staff in preparatory sessions for accreditation review and found that novelty was not the only attraction for participants. In high-stakes assessment, committing a mistake openly is avoided at all costs, often at the expense of participation. Whether the respondent was a senior resident, nursing administrator or maintenance staff member, participation was both lively and well received when data were composite. Quiet discussion among resident physicians after results were displayed confirmed that misconceptions were corrected. Because of the high-stakes nature of the environment, educational research was not allowed, although anecdotal feedback supported observations. The early version described was both rudimentary, interfacing only with the DOS operating system, and quite expensive. Groups of three respondents shared a feedback module, a “clicker” in contemporary parlance, and were asked to reach consensus before submitting an answer, although it was understood that the person holding the clicker was the person whose answer was often submitted. Newer models are available at educational discounts and are designed for very large groups, up to 1000 participants according to merchant information (Turning Point, 2007). While Salmon and Stahl (2005) found that the audience response technology had no effect on either immediate post-test or follow-up test results with their cooperative extension students, they did note that they could make better use of real-time feedback in structuring material coverage and integrating review activities. However, Zielinska and Pawlina (2007) found a positive correlation (RSQ=0.711) between first-year medical students’ “average daily performance and their final assessment scores at the end of the 6-week long block” (para. 12). Use of audience response technology allows real-time formative feedback, which facilitates hypothetico-predictive learning, even as it engages students and addresses concerns of constructivist educators (e.g., student preconceptions and misunderstandings). Undoubtedly, such technology can be used in poorly constructed learning environment, just as any assessment tool can be. In fact, a poorly designed course might be more readily identified and such identification documented by a substantial disconnect between course activity and audience response feedback, although research is needed into that premise. Other case examples of large lecture approaches in the sciences are explored in subsequent chapters.
Conclusions As illustrated clearly in Chapter IV, assessment of online science learning is currently little different from that occurring in the physical classroom and, with a few exceptions (e.g., where security is paramount), need not be. Formative and summative assessment remain appropriate, as do assessments that are authentic and iterative. Even the standard, empirical assessments common to science education work in the online environment, as Case 1 illustrates, although there is little need to rely on them. In fact, those not familiar with contemporary assessment literature are encouraged to explore further ideas presented in the cases.
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Perhaps the most promising advances in assessment of science learning, whether or not online, build on advances in functional neuroimaging and neurological research, as described in Chapter II. We now understand that learners naturally engage in hypothetico-predictive behavior and that such behavior is vital in development of novice-to-expert cognitive functioning. To that end, contemporary science assessment is more inclusive and less discrete, while still retaining the important distinctions of correctness and incorrectness. These distinctions are essential to both science as a content field and to those engaged in hypothetico-predictive learning, where accurate feedback plays an integral role in the iterative process.
References Aikenhead, G. S. (2000). STS Science in Canada: From policy to student evaluation. In D. D. Kumar & D. E. Chubin (Eds.), Science, technology, and society: A sourcebook on research and practice (pp. 49-90). New York: Kluwer Academic. Bloom, B., Hastings, J., & Madaus, G. (1971). Handbook on formative and summative evaluation of student learning. New York: McGraw-Hill. Brookfield, S. D. (2005). Discussion as a way of teaching. San Francisco: Jossey-Bass. Center for the Study of Higher Education. (2002). Assessing large classes. Retrieved July 21, 2007, from http://www.cshe.unimelb.edu.au/assessinglearning/03/large.html Comeaux, P. (2005-2006). Assessing students’ online learning: Strategies and resources. POD Network Essays on Teaching Excellence, 17. Retrieved May 27, 2007, from http://teaching.uchicago.edu/pod/pod2/05-06/Comeaux.htm Comeaux, P. (2005). Assessing online learning. San Francisco: Jossey-Bass. Dopper, S. M., & Sjoer, E. (2004). Implementing formative assessment in engineering education: The use of the online assessment system Etude. European Journal of Engineering Education, 29(2), 259-266. Edelson, D. C. (2002). Design research: What we learn when we engage in design. The Journal of the Learning Sciences, 11(1), 105-121. Feyerabend, P. (1993). Against method. New York: Verso. Hazari, S. (2004). Strategy for assessment of online course discussion. Journal of Information Systems Education, 15(4), 349 - 355. Herreid, C. F. (2006). “Clicker” cases: Introducing case study teaching into large classrooms. Journal of College Science Teaching, 36(2), 43-48. Holtz, J. K. (2002). Effect of graduate medical education on the problem finding and divergent thinking abilities of resident physicians. Unpublished Doctoral, Kansas State University, Manhattan. Holtz, J. K. (2006). Design of the fittest: Course learning guide. Chicago: DePaul University. Holtz, J. K., & Radner, B. (2005). Assessment and college progress: Capacity building through formative and summative program evaluation. In D. D. Williams, S. L. Howell, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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& M. Hricko (Eds.), Online assessment, measurement and evaluation (pp. 275-291). Hershey, PA: Information Science Publishing. Jarvis, P. (1992). Paradoxes of learning: On becoming an individual in society. San Francisco: Jossey-Bass. Johnson, M. (2002). Introductory biology online: Assessing outcomes of two student populations. Journal of College Science Teaching, 31(5), 6. Lawson, A. E. (2003). The neurological basis of learning, development and discovery. Boston: Kluwer Academic Publishers. Lijnse, P. (2005). Reflections on a problem posing approach. In K. Boersma, M. Goedhart, O. de Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 15-26). Dordrecht, The Netherlands: Springer. Lorenzetti, J. P. (2005). Beyond multiple choice: Assessment for online learning. Distance Education, 9(18), 4. MacDonald, J., & Twining, P. (2002). Assessing activity-based learning for a networked course. British Journal of Educational Technology, 33(5), 603-618. Moredich, C., & Moore, E. (2007). Engaging students through the use of classroom response systems. Nurse Educator, 32(3), 113. New Jersey Science Curriculum Framework. (1999). Retrieved May 28, 2007, from http:// www.state.nj.us/education/frameworks/science/ Niemi, D. (1997). Cognitive science, expert-novice research, and performance assessment. Theory into Practice, 36, 239-246. Oktac, A. (2004). Student discussions on a linear algebra problem in a distance-education course. Linear Algebra & its Applications, 379, 439-455. Osborne, J., & Hennessy, S. (2006). Report 6: Literature review in science education and the role of ICT: Promise, problems and future directions. Bristol, U. K.: Futurelab. Pelz, W. (2004). (My) three principles of effective online pedagogy. Journal of Asynchronous Learning Networks, 8(3), 33-46. Reeves, T. C. (2000). Alternative assessment approaches for online learning environments in higher education. Journal of Educational Computing Research, 23(1), 101-111. Robles, M., & Braathen, S. (2002). Online assessment techniques. The Delta Pi Epsilon Journal, XLIV(1), 39-49. Salmon, T. P., & Stahl, J. N. (2005). Wireless audience response system: Does it make a difference? Journal of Extension, 43(3). Retrieved July 21, 2007, from http://www. joe.org/joe/2005june/rb10.shtml Segall, N., Doolen, T. L., & Porter, J. D. (2005). A usability comparison of PDA-based quizzes and paper-and-pencil quizzes. Computers and Education, 45, 417-432. Shepherd, E. (2003). Delivering computerized assessments safely and securely. The e-Learning Developers’ Journal, October 20, 1-9. Sherin, B., Reiser, B. J., & Edelson, D. C. (2004). Scaffolding analysis: Extending the scaffolding metaphor to learning artifacts. The Journal of the Learning Sciences, 13(3), 387-421. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Sternberg, R. J. (1984). Beyond IQ. New York: University of Cambridge. Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: Australian Council for Educational Research. Turning Point. (2007). Interactive student response. Retrieved July 21, 2007, from http:// www.turningtechnologies.com/highereducationinteractivelearning.cfm Valverde, G. A., & Schmidt, W. H. (1997). Refocusing U. S. math and science education. Issues Online in Science and Technology, Winter. Retrieved May 27, 2007, from http://www.issues.org/14.2/schmid.htm Vonderwell, S., Liang, X., & Alderman, K. (2007). Asynchronous discussions and assessment in online learning. Journal of Research on Technology in Education, 39(3), 309-328. Wiggins, G., & McTighe, J. (1998). Understanding by design. Alexandria, VA: Association for Supervision and Curriculum Development. Wijekumar, K., Ferguson, L., & Wagoner, D. (2006). Problems with assessment validity and reliability in Web-based distance learning environments and solutions. Journal of Educational Multimedia and Hypermedia, 15(2), 199-215. Williams, S. L. (2004). A meta-analysis of the effectiveness of distance education in Allied health science programs. Cincinnati: University of Cincinnati. Zielinska, W., & Pawlina, W. (2007). Experience with the audience response system as a formative assessment tool in first year medical curriculum. Retrieved July 21, 2007, from http://slice.utah.edu/2007/schedule/documents/35WeronikaZielinska1.pdf
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Section IV Disciplinary Examples in Online Science Courses
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Chapter.X
Online.Mathematics.and. Physical.Science. (Mathematics,.Astronomy,. Chemistry,.and.Physics)
We could use up two eternities in learning all that is to be learned about our own world and the thousands of nations that have arisen and flourished and vanished from it. Mathematics alone would occupy me eight million years. – Mark Twain Our focus in this chapter is to present a more discipline-centered review of representative and sound practices published examples from math and the physical sciences. This builds on to the previous chapters on Contemporary Approaches and Promising Technologies and Strategies and compliments the mathematics and physical science examples reviewed earlier. We group the examples into the chief areas: courses, simulations, virtual laboratories, collaborations, virtual science museums, and digital libraries. Our goal is to provide the reader with a surveyed appreciation of the many recent innovations in online mathematics, physics, astronomy, and chemistry. In addition, this chapter provides a foundation of approaches and other resources to consider in the development of new online or blended math and physical science courses, or then again to support the systematic improvement of existing online courses. Effective online Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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science instruction integrates best practices in online learning reviewed in earlier chapters, particularly abundant scaffolding. It also integrates practical work and departs from textcenteredness in the presentation of abstract concepts. With that acknowledged, our primary impetus here is to highlight the particular requirements achieve learning outcomes in an online science course from published research, such as the design of practical work. Our discussion begins with general best practices for online mathematics and physical science courses recently established in the literature.
Designing.Online.Math.Learning.Activities Engelbrecht and Harding (2005a) have recently published a detailed appraisal of teaching undergraduate mathematics online. These authors conclude that mathematics has lagged many other disciplines in online delivery most likely because of the inherent difficulty of symbol representation in the predominant language for building Web pages, HTML. They also note that this shortcoming is surmounted by special Java® applets that permit the insertion of symbols and by modified mark-up languages specific to mathematics such as MathML. In addition to a user-friendly language to convey mathematics online, Engelbrecht and Harding (2005b) identify seven other desired components of a Web-based math course. The best practice components described are not exclusive to mathematics as they are generally desirable for online courses and have been touched upon in earlier chapters. They include the following: (1) wide-ranging instructor facilitation of the course (i.e., interaction, collaboration, administration), (2) asynchronous and synchronous communication opportunities, (3) collaboration opportunities for students, (4) cognitive visualization and information gathering tools, (5) supplementary Internet resources, (6) a robust graphical interface, and (7) online assessment (i.e., student work and interactions). As well, the affordances and disadvantages of teaching mathematics online posited by Engelbrecht and Harding (2005b) are common broadly to online instruction. We summarize these in Table 10.1.
Table 10.1. Affordances and drawbacks of online mathematics (after Engelbrecht & Harding, 2005b) Affordances Range of enriching resources Convenience, flexibility, and accessibility Dynamic and updatable learning environment Diverse communication and interaction possibilities Self-directed learning opportunities Compatibility with the contemporary student preferred learning styles
Disadvantages Faculty reluctance to online format Necessary technological training of students and faculty Adverse aspects to asynchronous communication and coordinating synchronous communication Cost to develop materials and systems Immaturity of traditional age students
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Similar to many of the disciplines of science, there is a gamut of online efforts in mathematics, some of which are reviewed in the following cases. They range from solitary online learning activities designed by an individual faculty member to enhance an onsite course to an intense effort on the part of a university department to offer courses fully online. Engelbrecht and Harding (2005a) have classified the approaches to online mathematics as: (1) mathematical resources (e.g., digital library), (2) notice boards for class administration, (3) content sites with links and hypertext, (4) exploration and demonstration activities including visual illustrations and animations), (5) exercises for practice and quizzes, (6) online communication, and (7) online courses from facilitated to entirely online formats. Regrettably, the effectiveness of these online approaches as applied to mathematics requires much greater attention. Engelbrecht and Harding (2005a) recommend future research to analyze math courses based on the richness and usage of the online learning environment. They encourage investigation of the frequency of Web access by math students, online assessment frequency, online communication frequency, amount of course content online, richness (i.e., use of online learning objects), and degree of face to face contact versus online presence (Engelbrecht & Harding, 2005a). One area of online math studied for its usefulness is collaborative learning and group cognition. For example, Stahl, Zemel, Sarmiento, Cakir, Weimar, Wessner, and Muhlpfordt (2006) studied how collaboration occurs in open-ended inquiry of non-traditional forms of geometry by teams of math students. They conclude that joint referencing is a central part of math conceptualization, but in doing so employed a solely inductive decidedly constructivist discovery approach.
On.the.Design.of.Physical.Science. Learning.Activities A recent broad discussion of how the physical sciences, specifically astronomy, can be effectively taught online is presented by Radnofsky and Bobrowsky (2005), who argue that a fundamental paradigm shift in teaching is required involving educational philosophy, writing style, and attending to a contemporary audience (i.e., students). For instance, in terms of instructional design approach there is a need to replace lectures with cooperative learning activities, incorporate interactive multimedia lessons using real data, and develop alternative means of assessment. Likewise, they note that the online learning environment in physical sciences requires that students become proficient self-directed learners (i.e., in organization and study habitats). Radnofsky and Bobrowsky (2005) see the key advantages of online astronomy instruction as increased student participation (asking questions, seeking help, responding to one-another) and an ability to reach the underserved (e.g., rural areas and the disabled). Their corresponding instructional design model for constructing an astronomy course involves consideration to the following subjects: •
Selecting content and resources: Use existing online course sharing materials, Web site lists, online science centers, and ask the astronomer Web sites.
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•
Class design: Assume that an online course takes twice the amount of time to construct as a face-to-face course. Content can be modularized into major units.
•
Communication skills: Since educational achievement is related more to facilitation than instructor’s knowledge emphasis should be on reading rather than listening and writing rather than speaking.
•
Developing a classroom culture: Begin collaborative exercises early; employ strategic discussion; use current topics and news; provide a means for online self-evaluation and evaluation of team peers; and incorporate problem-solving lessons.
•
Class management: Provide intermittent feedback on assignments; make sure students do not monopolize discussion; monitor and promote the importance of participation levels.
•
Timing of feedback: Maintain a regular faculty presence online and provide clear guidance on the character and pace of feedback to students.
•
Assessment: Incorporate grade-weighted discussions; consider having students develop portfolios for assignments; consider regular homework and open book test formats; incorporate group projects, and provide examinations with enough assessment value as to make students accountable and the occurrence of academic integrity issues rare.
Just as the best practices for mathematics described earlier reflected general ways to make online learning effective, those too described by Radnofsky and Bobrowsky (2005) for the physical sciences are for the most part core best practices of online learning. Therefore, as online learning in mathematics and the physical sciences maturates, there will be a need to develop another stratum of online best practices specific to the character of a discipline (e.g., kinds of remote experiments, virtual labs, simulations, and so on). Our following review contributes to developing that second stratum taking into account published best practices and technologies of courses, virtual laboratories, collaboration, virtual science museums, and digital libraries in the physical sciences and mathematics.
Courses In this section, we summarize courses in mathematics and the physical sciences that employ online components. Online features of the courses vary from simple use of online quizzes, and use of household materials to conduct practical work, to sophisticated learning objects and software that support course delivery in a completely online format. We describe the instructional strategies, components of the online courses, and design methods and evaluations of courses (where available). A summary of the cases reviewed is provided in Table 10.2.
Case.1..NetMath.Courses The classic example of online instruction in the mathematics area is the NetMath course system sponsored by a consortium of universities led by the University of Illinois at UrCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 10.2. Courses Learning Category Fully Online Course
Blended Course
Case Number and Subject 1. Online Math Courses from Algebra to Probability Theory 2. Contemporary Physics Online 3. Introductory Physics for non-science majors 4. General Chemistry Course 5. Chemistry 6.
Web-Assisted
7.
Digital Chemistry Project for Large Lecture Physics
Instructional Strategy Calculus and Mathematica© courseware
Reference Engelbrecht & Harding, 2005a and the NetMath Web site at http://netmath.uiuc.edu/
Experiment Kits
Rebello et al., 2003
Online and Authentic Labs
Adams, 2003
‘Kitchen Chemistry’
Casanova et al., 2006
Collaborative Design PRISM system using streaming media for student interaction Online Quizzes/ Assessment
Barack, 2007 Cuthbert et al., 2005
Toback et al., 2005
bana-Champaign. NetMath at http://netmath.uiuc.edu/index.php began operating in 1991 and currently consists of 11 courses, from algebra through probability theory. The courses utilize the Calculus WIZ® and Mathematica® (Wolfram Research, Champaign, IL) software programs that incorporate hands-on and math visualization approaches. Students are introduced to the courses and software through video tutorials and the duration of the course is flexible. Assessment is based on timed tests and quizzes that can be completed remotely with a proctor. Students have access to program-sponsored mentors via e-mail, phone, and synchronous online discussion to address course questions. NetMath emphasizes moderate student interaction (i.e., “cooperation”) through Mathematica® notebooks (Engelbrecht & Harding, 2005a).
Case.2..Contemporary.Physics.Online Rebello, Zollman, Hoagg, and Itza-Ortiz (2003) modified a course on contemporary physics from a face-to-face version to an online format. The course is intended for in-service middle and high school physics teachers to improve their pedagogical and content knowledge of contemporary physics content. Learning strategies that the authors wished to preserve in Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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the online format were student collaboration, hands-on practical work, computer activities and timely feedback from the instructor. The course is self-paced and modular with several topics in each module. Practical work is accomplished with experiment kits that are sent to students and these kits include descriptions of how the laboratory materials can be used in school classrooms. Students also use interactive simulations and review QuickTime movies of actual experiments. Online students submit their numerical and written answers on a standard form, which are sent to a database for assessment by the instructor. The general response to the course was highly positive. However, areas identified for improvement include supporting collaboration through chat environments and building a threaded discussion environment so that faculty would not have to generate individualized responses for general questions.
Case.3..Introductory.Physics.for.Non-Science.Majors Adams (2004) describes the development of two online introductory physics courses for non-majors at a community college. The courses use the WebCT® learning management system; a textbook with CD-Rom and built-in learning objectives; quizzes and videoclips; non-proctored, randomized questions, and time-limited online exams; homework via e-mail; asynchronous discussion forums; and synchronous chat online discussion. The course provides a start-up week for students to become acclimated to the online environment. Course materials included regular lectures, solutions to exercises, laboratories and supplemental links. Laboratory exercises were modeled after traditional activities including hands-on experiences using store bought items, data analysis (e.g., free-fall experiments), and Java applets for electrical, optics and rectilinear motion experiments. Additional laboratory exercises were incorporated from online resources available online from another university. The students considered the quality of textbook materials high, as well as the instructor-generated materials; 78% of the class rated the course as excellent. The success rate, as assessed from students who received a C grade or better, was comparable to the face-to-face version; in fact, student online presence was correlated to success.
Case.4..Kitchen.Chemistry.in.a.General.Chemistry.Course One of the challenges of establishing an online format for chemistry courses is the difficulty of providing hands-on chemistry labs. One approach (described next in the simulation section) is to develop virtual reality environments and conduct simulated chemistry experiments. Another approach is to address the learning objectives by incorporating at-home materials to conduct authentic chemistry experiments (Hoole & Sithambaresan, 2003). Casanova, Civelli, Kimbrough, Heath, and Reeves (2006) discuss an example of using the “kitchen chemistry” methodology to support the laboratory portion of an online general chemistry course offered by community colleges. In this general chemistry course, the syllabus, outlines, lecture notes, homework, laboratory reports, weekly quizzes, and exams were conducted online. The laboratory experiment topics included density and volume determination, chromatography, reactions, solubility-conductivity measurements, and estimates of burning calories. Exercises were designed to be no more dangerous than cooking. An inexpensive balance Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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had to be purchased, but other materials could be obtained at a grocery store. Evaluation of the course through comparative assessments indicated that distance students performed equally to onsite counterparts on final exams, lab procedures, and data presentation. Overall, the kitchen chemistry approach appears to have been a rigorous, quantitative and effective introduction to general chemistry for those students who selected this format.
Case.5..Collaborative.Design.and.Implementation.of. Chemistry.Courses Barack (2007) summarizes the advantages of using information communication technologies (ICT) in chemistry instruction as: (1) individualized learning, (2) visualization of the macro- and micro-world, and (3) dynamic and interactive 3-D visualization of molecules. Barack (2007) provides an example of an integrated process between educational researchers, teachers and students to build blended chemistry courses as well as transitioning faculty to teach them. The collaborative model components (Figure 10.1) are “knowledge sharing”, a “community of learners”, “inquiry-based learning”, and “visualization of molecules”. These strategies and outcomes are accomplished respectively by utilizing chemistry course Web sites, electronic forums, Web-based projects and molecular modeling tools. During course design to develop the four blended courses in this study, instructors and educational researchers met weekly to collaborate concerning pedagogy and technology. The transition of traditional chemistry instructors to ICT enhanced learning involved four identifiable steps: “non-active”, “support dependant”, “partial independent”, and “totally independent”.
Figure 10.1. An integrated process for the collaborative design of chemistry courses from Barack (2007). Used by permission from Elsevier.
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By the end of the courses, instructors were able to capably upload learning materials, add links to useful chemistry Web sites, add 3-D molecules, and send messages. Initially faculty participants had little familiarity with the use of ICT, but their perception of employing ICT was very favorable by the end of the study. All told, the Barack (2007) model provides a solid framework for the team design and implementation of a chemistry course.
Case.6..Digital.Chemistry.for.Large.Lecture The Digital Chemistry project at the University of California, Berkeley was initiated to provide students participating in a large lecture introductory chemistry course with elevated interactivity, supplemental Web-based learning materials, and immediate feedback within the context of a lecture, whether synchronous or asynchronous (Cuthbert, Kubinec, Tanis, Leong, Wei, & Schlossberg, 2005). A premise of the project (details at http://digitalchem. berkeley.edu/) is to meet emerging technology affordances as well as the wishes of the contemporary student to have ready access to their assessed work and grades, personalized feedback from the instructor, and high quality learning resources at whatever time or place they are needed. At the heart of the Digital Chemistry project is the Presentation Replay and Interaction with Streaming Media (PRISM) system that allows students to view lectures live through stream-
Figure 10.2. Screen capture of the PRISM Digital Chemistry Project in live lecture mode (from Cuthbert et al., 2005). Used by permission from A. Cuthbert and ACM Publications.
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ing video and interact with peers in a discussion room (Figure 10.2). Quizzes are provided as a pop-up window at key points in the lecture and individual answers are tabulated in relation to the authentic answer. Discussions between students center on their answers to quizzes. Students have the ability to annotate lecture content and are provided a personal electronic portfolio of their course activities. Much of the digital content of the course such as lectures on PowerPoint are stored as learning objects. Although currently targeted at the resident student body, another goal of this research is to make delivery of the course and/or its learning objects available to other institutions online.
Case.7..Online.Assessment Toback, Mershin, and Novikova (2005) describe the incorporation of three “teaching while quizzing” online tools for a large introductory lecture in physics. These spontaneous assessment tools include: (1) the automated mathematics evaluation system (AMES) guiding students through basic integration and differentiation, (2) the computerized homework assignment grading system (CHAGS) that provides homework questions paralleling those that were answered previously by hand, and (3) quizzes intended to consolidate knowledge (QUICK) utilizing intermediate problems drawn from a pool of questions. Preliminary evidence suggests the benefits of these systems for students are the following: students keep up with course content more consistently; they spend more time on problem solving rather than on formula memorization; their readiness for exams is increased, and their complaints about exam preparedness is decreased.
Simulations.and.Virtual.Labs There are many fine examples of science simulations, virtual labs, and remote experiments that have been designed for online delivery. At this time, most of the published accounts of learning objects, learning environments, and activities support blended course models. However, they have the potential to be applied to facilitated, blended or fully online courses. The drawbacks of developing this type of interface for wide-ranging use in online science settings are the technological complexity of designing the simulation, time to develop the system, necessity for pedagogical content knowledge training for faculty and students to implement them, and the associated overhead cost. A synopsis of the best practices cases we review for these instructional approaches is provided in Table 10.3.
Case.8..Virtual.Chemistry.for.Schools A multi-faceted simulation environment to teach school students principles of chemistry and to provide practical experiments was developed by Morozov, Tanakov, Gerasimov, Bystrov, and Cvirco (2004). The Virtual Chemistry Laboratory for Schools environment is interactive and multimedia rich with over 150 experiments supported by 3-D graphics, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 10.3. Simulations and virtual labs Learning Category Simulations
Virtual Labs
Remote Experimentation
Case Number and Subject 8. Virtual Chemistry Lab for Schools 9. Virtual Chemistry Lab for College 10. Astronomical Visualization 11. Molecular Visualization 12. Circuitry Lab for Large Introductory Physics course 13. Introductory and Survey Physics 14. Astronomical Observations
Instructional Strategy VRML simulation with animated pedagogical agent VRML simulation
15. NanoVisualization 16. Physics within a computer science course 17. Spectrophotometry
Reference Morozov et al., 2004
Dalgarno, 2005; Dalgarno et al., 2003
VR representations of astronomical entities Generate molecules in a Web-based format Simulating physics laboratory equipment
Barnett et al., 2005; Magnor et al., 2005 Bender et al., 2000
Java and Shockwavebased applets
Sethi, 2001
Virtual Telescope
Marschall et al., 2000
Remote Laboratory IN-VSEE Remote Laboratory Experiments
Luckenbill et al., 1999
Remote Laboratory Educaffix
d’Ham et al., 2004
Finkelstein et al., 2005
Forinash & Wisman, 2005
pre-rendered animation and a pedagogical agent called the “chemist”. Particular affordances of this learning environment for teaching chemistry are identified by Morozov et al. (2004) as: providing students with practice on laboratory techniques, learning the assembly of laboratory apparatus, providing a safe environment, developing note taking and analysis skills, honing manipulative skills, and working in a “fun” environment. Many laboratory objects are pre-rendered, but glassware, chemicals and activity interactions of the learner are supported by real time animations. Students can take pictures (screen captures) and record data in electronic notebooks. There is also a chemical formula editor resource and a 3-D molecule builder based on the VRML language (Figure 10.3a & b). Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 10.3. Screen captures of the Virtual Chemistry Laboratory for Schools. a) the chemical mixing environment with the “chemist” pedagogical agent, and b) 3-D molecular visualization. Used by permission from IEEE.
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Case.9..Virtual.Chemistry.for.Colleges Another example of a thorough simulation of a virtual chemistry laboratory environment using VRML is represented in the work of Dalgarno, Bishop, and Bedgood (2003) and Dalgarno (2005). This virtual chemistry laboratory is for use by university students to familiarize them with approaching laboratory sessions. It takes the form of a replica of the Charles Stuart University’s undergraduate teaching laboratory for chemistry (Figure 10.4). In this single-user environment with first-person perspective, students can assemble and “carry” apparatus as well as move freely around to review laboratory procedures. The user interface has motion control and object manipulation features and various reusable object features such as drawers, lockers, and cabinet doors. Based on a post-test of student recall of the virtual laboratory, it was determined that where students had been provided with specific tasks, student’s knowledge of the lab environment was improved. Based upon Likert scale scoring, students considered the virtual laboratory a useful tool to introduce them to the configuration of the chemistry lab and its apparatus. The future goal of the project is to incorporate virtual experiments themselves into the simulation environment.
Figure 10.4. A virtual chemistry laboratory simulation constructed to familiarize students with approaching face-to-face laboratory sessions (from Dalgarno et al., 2003). Used by permission from B. Dalgarno.
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Case.10..Astronomy.Visualization.Using.Virtual.Reality The inaccessible nature of astronomical phenomena has long made it necessary for physical scientists to generate visualizations to explore complex phenomena. Astronomy visualization has moved from an effort primarily directed towards research to one that is now a useful part of education, particularly as its incorporation in advanced display technologies (Fluke, Bourke, & O’Donovan, 2006). One of the difficulties that students encounter in learning astronomy is visualizing the spatial relationships and perspectives of astronomical objects. Barnett, Yamagata-Lynch, Keating, Barab, and Hay (2005) explored the effectiveness of having undergraduate students in an introductory astronomy course develop 3-D models of the solar system and the impact of this VR activity towards changing perspectives and improving spatial learning skills. Students in this astronomy course progressively develop a VRML-based geocentric earth sun system, a dynamic model of the sun-earth-moon system, and finally, a dynamic model for the entire solar system (Figure 10.5). This study demonstrated that student understanding of the solar system significantly improved because of this sequence of 3-D activities. Moreover, this approach promoted an improved understanding of principles not represented well in 2-D graphical depictions, such as the causality of an eclipse. While this example was not directly employed in an online setting, it highlights the potential instructional use of having students develop and/or “sculpt” 3-D representations of scientific principles and phenomena as a part of their online learning. Magnor, Kindlmann, Hansen, and Duric (2005) describe a similar example of realistic 3+D astronomy visualizations applicable for illustration and interactive use in astronomy courses for planetary nebulae. The rendering model in this visualization system permits the reconstruction of the spatial-volume relationships of ionized gas clouds from optical im-
Figure 10.5. Student interface for constructing and navigating through their own 3-D models of the solar system (from Barnett et al., 2005). Used by permission from M. Barnett and Ed/ITLib.
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ages. Lastly, 3-D visualizations established for supernovae (Turnage, 2003) and black holes (Weiskopf, Borchers, Ertl, Falk, Fechtig, & Frank, 2006) are fine examples of an incipient trove of rich learning objects for astronomy education.
Case.11..Molecular.Visualization Molecular visualization is a powerful instructional tool for teaching chemistry (Trindade, Fiolhais, & Almeida, 2002). As innovations in home computers and Web capacity improve, molecular simulations are now easily incorporated into distance learning environments. Bender, Klein, Disch, and Ebert (2000) describe a framework for the design of Web-based 3-D visualization system for chemistry and physics. Using this framework, several 3-D visualization environments including MOLVIS (Figure 10.6) were developed permitting the learner to download and study the chemical and physical properties of molecules. A companion project, DYNAVIS, simulates the visual and vibrational characteristics of machines. As discussed in previous chapters, visualization is an important tool for scientists to study complex phenomena. These highly immersive interfaces are exemplary best practices for accomplishing this learning objective. Korkmaz and Hardwood (2004) have developed another example and tutorial for visualizing molecules to support students learning about molecular symmetry and point groups in a 3-D environment. Students are able to manipulate molecules to investigate spatial relationships between atoms and bonds. Overall, there are a growing number of Web-accessible molecular visualizations. Representative examples, collected by Dr. Eric Martz of
Figure 10.6. The molecular visualization interface MOLVIS for studying the chemical and physical properties of molecules (from Bender et al., 2000). Used by permission from IEEE.
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the University of Massachusetts, are found at the MolViz.org Web site at http://www.umass. edu/microbio/chime/.
Case.12..Simulating.Circuitry.in.Physics In order to determine whether the conceptual learning objectives and manipulative skills required of a second semester large scale physics courses could be successfully substituted with computer simulations, Finkelstein, Martin, Bhushan, Arora, and Joshi (2005) evaluated a simulation environment for circuitry (Figure 10.7). Their effort is a part of the broader Physics Education Technology Project (PhET), available at http://phet.colorado.edu/Webpages/index.html, which has developed over 50 simulation environments for common topics in introductory physics. The PhET pedagogical model links everyday experience to physical principles through an inquiry-based dynamic simulation. In the circuitry example, a learner can alter and measure the current and voltage with respect to resistors, wires, batteries, and lighting sources. In an evaluation of students who had utilized the online circuitry simulations, conducted real experimentation in a lab, or had not partaken in a lab at all, it was determined that students using the simulations performed better on conceptual questions and achieved greater competence in manipulating real equipment. The conclusion of this research is that in the right circumstances, a well-designed simulation can produce better learning results and is preferable to the actual environment particularly because: (1) students are more efficiently introduced to productive concepts and representations (e.g., concepts are more visual) and
Figure 10.7. Screenshot of the PhET simulation for circuit construction (from Finkelstein et al., 2005). Used by permission of APS Journals.
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(2) the simulation environment affords experimental constraints that are productive (e.g., in the circuitry example the distraction of wire colors is eliminated).
Case.13..Introductory.and.Survey.Physics Straightforward virtual experiments using Java and Shockwave-based applets have been developed by Sethi (2001) for an online physics laboratory course and are available at http://www.sethi.org/classes/elabs/. The affordances of the virtual laboratory exercises are: (1) a personalized learning experience that is fun and self-paced and (2) virtual instruments with minimized experimental errors appropriate for introductory level students. Five virtual labs corresponding to the following topics have been developed: measurement with a Vernier caliper, fluid behavior, and density using a graduated cylinder, measuring heat transfer with a metal heating coil and thermometer (Figure 10.8), using a hanging object to determine centripetal force and motions, and determining buoyancy by submerging objects in fluids. Student reaction to the online virtual physics labs was generally positive however, a few students indicated that they would have preferred to use actual objects. This last point highlights a common criticism of the use of virtual environments, namely that there is an aesthetic/emotional dimension of interacting with real objects that may enhance learning. It also suggests that the more sophisticated virtual environments using advanced design techniques such as haptic interfaces and remote experimentation may be more aesthetically appealing to students then “flat” simulations.
Figure 10.8. Virtual experiments using Java and Shockwave-based applets for an online physics laboratory (from Sethi, 2001). Used by permission of R. J. Sethi and the Journal of Physics Teacher Education Online.
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Case.14..Virtual.Telescope Marschall, Snyder, and Cooper (2000) describe the Contemporary Laboratory Exercises in Astronomy (CLEA) project to develop simulated laboratory environments to demonstrate research techniques in astronomy. The NSF and Gettysburg College sponsor this long established project. CLEA learning activities are topic-based (e.g., Hubble Red Shifts, the Astronomy of Asteroids) and scalable in complexity to support a variety of college level astronomy courses. Materials include guides and software that simulate virtual radio and optical telescopes as well as associated instrumentation including cameras, spectrometers and photometers. These online instructional materials (available for use at http://public.gettysburg.edu/~marschal/clea/CLEAhome.html) have been used extensively both nationally and internationally as supportive modules in astronomy courses.
Case.15..Remote.Experimentation:.Nano-Visualization Luckenbill, Hintze, Ramakrishna, and Pizziconi (1999) developed an innovative and pioneering example of remote experimentation using a scanning probe microscopy (SPM) for application in education. The project, called Nano-Visualization in Science and Engineering Education (IN-VSEE), has the following components and online affordances: (1) use of a research grade scanning probe microscope, (2) an online interface for remote operation (Figure 10.9), (3) observer pages for those not operating the instrument, (4) instrument operator switch over
Figure 10.9. Screen capture of the IN-VSEE interface for remote experimentation using scanning probe microscopy (from Luckenbill et al., 1999). Used by permission from IMEJ.
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between students, (5) synchronous interaction by chat, (6) real-time viewing by camera of the IN-VSEE instrument and laboratory, (7) viewing natural and manmade materials from chemistry, physics, engineering, and biology at the atomic scale, (8) interactive/discovery based instructional modules with supportive introductory animations, and (9) a database of related SPM images. The IN-VSEE system details are found at http://invsee.asu.edu/Invsee/invsee.htm. Early on evaluation of this remote experiment system suggested that both technical limitations and the amount of time teachers needed to learn about the technology hindered its use in physical science contexts at schools. However, after additional training and hands-on use of the instructional modules was undertaken, there was a higher interest in integrating such technology into the curriculum by teachers. This experience highlights the importance of developing an instructor-training plan emphasizing pedagogical content knowledge for complex remote experiments.
Case.16..Physics.Remote.Laboratory Examples of authentic remote experimentation in the physical sciences are not as common as virtual reality environments simulating physical processes. It is probable as more students are engaged in online science that many more examples will emerge. Forinash and Wisman (2005) discuss one straightforward set of remote experiments in physics. The basis and structure of their remote experiment design is: (1) visibility of experiment controls and results, (2)
Figure 10.10. An example of authentic remote experimentation using a spreadsheet interface to analyze a wave patterns collected with a microscope (from Forinash & Wisman, 2005). Used by permission from Inderscience Subs.
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making available guidelines of how the remote laboratory model is to be used, (3) providing task mapping linking learning objectives to procedures, (4) placing constraints on knowledge required as well as the approach to be taken on an experiment, (5) developing a thorough conceptual model for the experiment, and (6) supplying concurrent feedback to the learner. Based on these criteria, four Internet-based remote experiments were developed. The first has students collect sounds with a microscope attached to their computer. They then determine the frequency and waveform components of the sound via a spreadsheet database (Figure 10.10). Other related experiments are conversion of analog to digital signal and display in a JAVA-based oscilloscope, analysis of low frequency electromagnetic signals from antennas, and detecting light variations using remote sensors at multiple distance sites.
Case.17..Chemistry:.Spectrophotometry A commercial remote experiment environment called “educaffix.net” at http://www.educaffix. com/ has been built to foster student competence in the design of chemical experiments. The remote experiment centers on having students determine the concentration of a compound through spectrophotometry (d’Ham et al., 2004). In preparation for initiating the robotically operated dilution instrument, students must first determine the order of eight procedural steps involving such things as selecting a compound, preparing standard solutions, constructing calibration curves, and determining the chemical concentration of a compound. An artificial tutor assesses the proposed experimental steps and provides the learner feedback as to their order and general viability, including indicating where corrections to steps are needed. When the experiment is carried out, the user interface permits viewing of the particular actions of the diluting robot supporting the experiment. d’Ham et al. (2004) note differences in the fidelity of the experimental environment to an actual environment as: (1) the reduction of human gestures to control the experiment, (2) reliance on discrete experimental steps, (3) non-simultaneous student conduction of experiments, and (4) safety checks of the apparatus and chemicals are not performed by the students. Thus, this example expresses the trade-off between remote lab authenticity and providing a resource for mass use.
Collaborations,.Virtual.Science.Museums,. and.Digital.Libraries An important part of successful online learning is developing an environment conducive to the exchange of ideas. For online science instruction, effective collaboration in support of learning should consider the most effective social interactions to enhance learning transfer and sharing, such as student-to-student, instructor to student, team interactions and/or students to external experts (e.g., community of learners). While ordinary learning management systems provide discussion boards or more advanced audiovisual capabilities for asynchronous and/or synchronous communication and collaboration, they are infrequently used to strategically connect students with external communities of learners to support lifelong learning in a professional or personal interest area. Two key types of external forms Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 10.4. Collaboration, virtual science museums, and the cyberinfrastructure Learning Category Collaboration
Virtual Science Museum Digital Libraries
Case Number and Subject 18. Subatomic Particles and High Energy Physics 19. Cosmic Rays 20. Physics
21.
Instructional Strategy Collaboratory for the EuroTev project
Grid Computing Applets and Shockwave Animations
Reference Gabrielli et al., 2006
Bardeen et al., 2005 Zito, 2007
Compadre, MathDL, and JCE-DLib
of collaboration that can be added to enhance the online science learning environment are “collaboratories”, where inter-institutional research and education take place at a distance, and “grid computing”, where computer resources are shared such as between schools or colleges. In the first part of this section, we review case examples of collaboratory and grid computing structures in the physical sciences. In order for the rapidly increasing number of learning objects and learning environments to be used productively for online science instruction, it is requisite that entities that consolidate resources are fashioned so that instructors and instructional designers can quickly access and employ them. In this section, we also review examples of a virtual science museums and digital libraries, which are two emerging centralized repositories of learning objects. A synopsis of cases reviewed in these areas is provided in Table 10.4.
Case.18..The.EuroTev.Collaboratory The EuroTev venture is a design study project by 28 European institutions to assist in the development of the next generation high-energy linear collider, the “International Linear Collider”. In order to support collaboration between these institutions, a collaboratory called the global accelerator network multipurpose virtual laboratory (GAN-MVL) is being developed. The key types of knowledge forms addressed in the GAN-MVL are described by Gabrielli, Hodapp, and Ranon (2006) and include what information is necessary to address a problem, how information should be applied, when the information should be applied (judgment) and why the information should be used. Moreover to address these knowledge sharing requirements GAN-MVL will use: (1) communication and groupware support such as videoconference tools and electronic logbook, (2) remote instruments controls for the physics instruments as well as 3-D visualizations of instrument parts, and (3) high resolution video-capture of equipment and operation to archive critical incidents for system improveCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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ment. The GAN-MVL is expected to also serve an educational function as the products of the collaboration (i.e., digital content such as 3-D visualizations and video streaming archives) can be reconfigured and simplified to use in support of school and university curricula in physics (Gabrielli et al., 2006). In addition, virtual communication between students and experts on the collider project would be possible with the planned collaboration tools. This example demonstrates how ‘big” science can supply learning objects into the educational stream.
Case.19..Grid.Computing:.QuarkNet/Grid Bardeen et al. (2005) describe how students from geographically separated high schools are able to collaborate in an investigation of cosmic rays using grid-computing techniques. Students at different schools have a detection system that collects and stores data on cosmic ray fluxes (Figure 10.11). Detectors can be coordinated in order that array data can be collected and shared through a virtual portal. To address different levels of situated cognition, the Web site of the QuarkNet/Grid uses a scaffolded approach with introductory animations, examples of final products, a study guide, a team e-logbook, ask the expert, and a variety of asynchronous communication and database tools to promote collaboration. The results of student investigations are disseminated through online posters and papers. Schools without detection systems can also be a part of the collaboration by analyzing and commenting on other students’ data. Hence, QuarkNet/Grid establishes a collaboratory between schools as they collect authentic data on physical phenomena. Assessment of the QuarkNet/Grid learning system is ongoing.
Figure 10.11. Detector set-up for a collaborative remote experiment on detecting cosmic rays between schools (from Bardeen et al., 2005). Used by permission from IEEE.
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Case.20..Virtual.Science.Museums.(Physical.Sciences) As discussed previously in the Online Science Strategies section of this book, virtual science museums exist solely in cyberspace to provide useful learning objects and activities for educational purposes. A useful Web site for obtaining interactive learning objects of physics principles and phenomena for incorporation into an online course is the Leonardo: Interactive Virtual Science Museum at http://www.ba.infn.it/~zito/museo/leonardoen.html (Zito, 2007). The Leonardo Interactive Virtual Science Museum brings together quality applets and shockwave representations now numbering over 200 physics topics. A companion Website with additional physical science learning objects organized by topic is the Online Educational Resources for Physics Teachers at http://www.ba.infn.it/www/didattica.html. As both of these resources do not have a virtual museum interface, they could also be positioned in the digital library category.
Case.21..Digital.Libraries There are a number of fine repositories of digital resources available to instructors in the physical sciences. They are particularly useful for those instructors adding online components to their blended or fully online courses. Several of the key resources are from affiliate projects to the National Digital Science Library (NDSL) such as Compadre, the Portal for Physics and Astronomy; the Mathematical Digital Science Library; and the Journal of Chemical Education Digital Library. In Table 10.5, we provide the links for these resources and a few others that are generally informative.
Table 10.5. Digital library resources Digital Library Name “Compadre” Portal for Physics and Astronomy Physics to go Hyperphysics Astronomical Society of the Pacific: Website lists for college astronomy instructors Mathematical Sciences Digital Library (MathDL) Journal of Chemical Education Digital Library (JCE DLib) Engineering Subject Centre
Related URL http://www.compadre.org/portal/ http://www.compadre.org/informal/index.cfm http://hyperphysics.phy-astr.gsu.edu/hbase/ hframe.html http://astrosociety.org/education/resources/ educsites.html http://mathdl.maa.org/mathDL/ http://jchemed.chem.wisc.edu/JCEDLib/index. html http://www.engsc.ac.uk/index.asp
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Trends.and.Conclusion Our survey of recent online efforts in mathematics and the physical sciences instruction suggests the following trends and developments: •
An increasing number of online learning objects available for use;
•
An increasing number of remote experiments;
•
Greater attention to collaboration between students and among institutions;
•
Greater attention to using online graphics and visualization;
•
Improved symbolic representation (i.e., math);
•
Progress to centralize learning resources into digital repositories for instructional use;
•
Expanding use of online affordances to improve large-lecture courses;
•
Expanding use of online assessment strategies such as quizzes and tests; and
•
Expanding use of online simulations as preparation for class and laboratories.
As might be expected at this point in the evolution of online science learning in the physical sciences, fully online courses tend to be at the undergraduate level and typically represent introductory or survey classes. There have been relatively few efforts published that describe upper division and graduate online mathematics and physical science courses or even individual practical work exercises. Many of the best practice approaches described and utilized for mathematics and physical sciences courses are consistent with and employ instructional design principles commonly identified for effective online learning. However, more study is required to develop online best practices specific to disciplines and their characteristic practical work. Well-designed online courses and/or activities in the physical sciences regularly receive favorable reception by students. Teaching physical science online does require a paradigm shift that moves the instructor towards a more facilitative role as learning becomes more student-centered, collaborative, and self-directed. It is our inference from a review of the literature in the physical sciences and mathematics, that there is a significant amount “raw assets” produced by research products (e.g., visualizations and simulations) that could be transformed into online learning objects for educational use in college mathematics and physical sciences, but are seldom realized for this purpose. Efforts to convey the products of research to digital libraries for educational use should be a national priority. Lastly, the affordances provided by online learning environments, such as virtual reality simulations, can often be preferable to actual practical work as they are an efficient means of implementing learning objectives and have been demonstrated to produce as good or better educational results in the physical sciences.
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References Adams, A. M. (2004). Pedagogical underpinnings of computer-based learning. Journal of Advanced Nursing, 46(1), 5. Barack, M. (2007). Transition from traditional to ICT-enhanced learning environments in undergraduate chemistry courses. Computers & Education, 48, 30-43. Bardeen, M., Gilbert, E., Jordan, T., Nepywoda, P., Quigg, E., Wilde, M., & Zhao, Y. (2005). The QuarkNet/Grid collaborative learning e-lab. Intemational Symposium on Cluster Computing and the Grid, Singapore, May 16-19. Los Alamitos, CA: IEEE. Barnett, M., Yamagata-Lynch, L., Keating, T., Barab, S. A., & Hay, K. E. (2005). Using virtual reality computer models to support student understanding of astronomical concepts. Journal of Computers in Mathematics and Science Teaching, 24(4), 333-356. Bender, M., Klein, R., Disch, A., & Ebert, A. (2000). A functional framework for Web-based information visualization systems. IEEE Transactions on Visualization and Computer Graphics, 6(1), 8-23. Casanova, R. S., Civelli, J. L., Kimbrough, D. R., Heath, B. P., & Reeves, J. H. (2006). Distance learning: A viable alternative to the conventional lecture-lab format in general chemistry. Journal of Chemical Education, 83(3), 501-507. Cuthbert, A., Kubinec, M., Tanis, D., Leong, F., Wei, L., & Schlossberg, D. (2005). Advanced technology for streamlining the creation of ePortfolio resources and dynamicallyindexing digital library assets: A case study from the Digital Chemistry project. In Proceedings of CHI ‘05 Extended Abstracts on Human Factors in Computing Systems, Portland, Oregon, April 2-7 (pp. 972-987). New York: ACM Press. Dalgarno, B., Bishop, A. G., & Bedgood, Jr., D. R. (2003). The potential of virtual laboratories for distance education science teaching: Reflections from the development and evaluation of a virtual chemistry laboratory. In Proceedings of the Improving Learning Outcomes Through Flexible Science Teaching Symposium, Sydney, Australia, October 2-3 (pp. 90-95). Sydney: University of Sydney. Dalgarno, B. (2005). A VRML virtual chemistry laboratory incorporating reusable prototypes for object manipulation. Retrieved May 12, 2007, from http://www.siaa.asn. au/get/2411855836.pdf d’Ham, C., de Vries, E., Girault, I., & Marzin, P. (2004, December). Exploiting distance technology to foster experimental design as a neglected learning objective in labwork in chemistry. Journal of Science Education and Technology, 13(4). Engelbrecht, J., & Harding, A. (2005a). Teaching undergraduate mathematics on the Internet. PART 1: Technologies and taxonomy. Educational Studies in Mathematics, 58(2), 235-252. Engelbrecht, J., & Harding, A. (2005b). Teaching undergraduate mathematics on the Internet. Part 2: Attributes and Possibilities. Educational Studies in Mathematics, 58(3), 253-276.
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Finkelstein, J., Martin, C., Bhushan, A., Arora, M., & Joshi, A. (2005). Feasibility of computer-assisted education in patients with multiple sclerosis. In Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, Bethesda, MD, September 5-7 (pp. 254-261). Los Alamitos, CA: IEEE. Fluke, C. J., Bourke, P. D., & O’Donovan, D. (2006). Future directions in astronomy visualization. Retrieved on May 1, 2007, from http://adsabs.harvard.edu/abs/2006astro. ph..2448F Forinash, K., & Wisman, R. (2005). Building real laboratories on the Internet. International Journal of Continuing Engineering Education and Life-Long Learning, 15(1/2), 5666. Gabrielli, S., Hodapp, M., & Ranon, R. (2006). Designing a multipurpose virtual laboratory to support communities of practice in physics. In Proceedings of the Second lEEE International Conference on e-Science and Grid Computing, Amsterdam, The Netherlands, December 4-6. Los Alamitos, CA: IEEE. Hoole, D., & Sithambaresan, M. (2003). Analytical chemistry labs with kits and CD-based instructions as teaching aids for distance learning. Journal of Chemical Education, 80(11), 1308-1311. Korkmaz, A., & Harwood, W. S. (2004). Web-supported chemistry education: Design of an online tutorial for learning molecular symmetry. Journal of Science Education and Technology, 13(2), 243-253. Luckenbill, L. A., Hintze, K., Ramakrishna, B. L., & Pizziconi, V. B. (1999). Interactive nano-visualization in science and engineering education: Conforming technology to transform education the IN-VSEE project. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1(2). Retrieved October 20, 2007, from http://imej. wfu.edu/articles/1999/2/09/index.asp Magnor, M., Kindlmann, G., Hansen, C., & Duric, N. (2005). Reconstruction and visualization of Planetary Nebulae. IEEE Transactions on Visualization and Computer Graphics, 11(5), 485-496. Marschall, L. A., Snyder, G. A., & Cooper, P. R. (2000). A desktop universe for the introductory astronomy laboratory. The Physics Teacher, 38, 536-537. Morozov, M., Tanakov, A., Gerasimov, A., Bystrov, D., & Cvirco, E. (2004). Virtual chemistry laboratory for school education. In Proceedings of IEEE International Conference on Advanced Learning Technologies, Helsinki, Finland, August 30-September 2 (pp. 605-608). Los Alamitos, CA: IEEE. Radnofsky, M. L., & Bobrowsky, M. (2005). Teaching astronomy online. The Astronomy Education Review, 3(2), 148-169. Rebello, S., Zollman, D. A., Hoagg, K., & Itza-Ortiz, S. F. (2003). Enhancing the teaching of contemporary physics through online instruction for teachers. Retrieved on May 1, 2007, from http://Web.uniud.it/cird/girepseminar2003/abstracts/pdf/sanjay.pdf Sethi, P. (2001). Infusing interactive, multimedia CD-ROM technology into the first-year college-level geology curriculum: Recent example from Radford University, United States. In Proceedings of the World Conference on E-Learning in Corporate, Govern-
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ment, Healthcare, & Higher Education, Vienna, Austria, October 2-6 (pp. 1690-1695). Charlottesville, VA: AACE. Stahl, G., Zemel, A., Sarmiento, J., Cakir, M., Weimar, S., Wessner, M., & Muhlpfordt, M. (2006). Shared referencing of mathematical objects in online chat. Paper presented at the International Conference of the Learning Sciences (ICLS 2006), Bloomington, Indiana. Toback, D., Mershin, A., & Novikova, I. (2005). Integrating Web-based teaching tools into large university physics courses. The Physics Teacher, 43, 594-597. Trindade, J., Fiolhais, C., & Almeida, L. (2002). Science learning in virtual experiments: A descriptive study. British Journal of Educational Technology, 33(4), 471-488. Turnage, A. (2003). Modeling supernovae: Braving a bold new frontier applications. IEEE Computer Graphics and Applications, 6, 6-11. Weiskopf, D., Borchers, M., Ertl, T., Falk, M., Fechtig, O., & Frank, R. (2006). Explanatory and illustrative visualization of special and general relativity. IEEE Transactions on Visualization and Computer Graphics, 12(4), 522-534. Zito, G. (2007). Leonardo: Interactive virtual science museum. Retrieved February 5, 2007, from www.ba.infn.it/~zito/museo/leonardoen.html
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Chapter.XI
Online.Geoscience
I speak of that learning which makes us acquainted with the boundless extent of nature, and the universe, and which even while we remain in this world, discovers to us both heaven, earth, and sea. – Cicero Complimenting the geoscience examples reviewed in the Online Science Strategies section of this book, our focus in Chapter XI is to present a more discipline-centered review of representative published examples from the geosciences. Our review takes account of courses, virtual field trips, virtual laboratories, collaboration, virtual science museums, and the relationship of the emerging cyberinfrastructure to the geosciences. Our goal is to provide the reader with a diversity of models and resources to consider in the development of new online or blended geoscience courses or to support the systematic improvement of existing ones. Additionally, our impetus here is to highlight the particular design requirements to achieve learning outcomes in an online science course, such as the design of practical work. Our discussion begins with a review of recent trends in undergraduate geoscience education.
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Courses In their expansive survey of teaching methods in undergraduate geoscience courses (n=932) for 2004, Macdonald, Manduca, Mogk, and Tewksbury (2005) indicate that online activities are more commonly used in both larger introductory and geology major courses than those with smaller enrollment. The primary affordances cited are easier collection of data by the instructor and deliverables, increased student collaboration with peers, and enhanced personalization of learning experiences. Students reported that each of these affordances lead to greater satisfaction with the course. Furthermore, this survey determined that use of online activities in geoscience courses was minor, with weekly and every-class use of online activities less than 18% for all course categories. . In the Macdonald et al. (2005) study, everyday class use of online activities is a likely indicator of hybrid and/or fully online courses and was less than 5% for all categories. Hence, it can be concluded from this survey, albeit from a few years ago, that undergraduate geoscience programs have not hurried to take advantage of the affordances of online-facilitated activities and are unremarkable in offering their students distance and hybrid course options. Libarkin and Anderson’s (2005) survey of a diverse sample of introductory geoscience courses also supports this supposition. In their study, only one in 43 geology courses were fully online. Despite the measured pace that geoscience programs are taking to incorporate online strategies, there are a variety of examples in the literature that provide insight on ways to effectively integrate online learning experiences. In the remainder of this chapter, we review online learning activities from examples of fully online geoscience courses as well as blended varieties. A summary of the course cases reviewed in the first section is provided in Table 11.1.
Table 11.1. Online geoscience courses Learning Category Course
Case Number and Subject 1. Oceanography 2.
3. Blended Course
4.
5.
Survey of Geology Course for Teachers Virtual Paleontology Introduction to Geology Soil Science
Instructional Strategy Inquiry-based using online resources. Design-study (i.e., iterative) approach to course development. Competence-based with Virtual Learning objects Just-in-time, where student feedback informs instruction Guided instruction with online presentations, discussions and assignments
Reference Geer et al., 2003 Veal et al., 2004
Downing, 1999, 2001 Linneman & Plake, 2006 Clark & James, 2005a, 2005b
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Case.1..Oceanography In this first example by Geer, Moran, Blair, Weinbeck, Smith, and Hopkins (2003), a graduate course for K-12 teachers called Datastreme Ocean (companion to Datastreme Atmosphere and Datastreme WES) has been developed as a joint initiative of the American Meteorological Society and National Oceanic and Atmospheric Administration. The Datastreme Ocean course focus is the world’s oceans as a part of the earth’s biogeochemical system. The key ocean variables reviewed are biodiversity, energy and materials budget. These variables are reviewed in relation to the atmosphere, lithosphere, atmospheric-oceanic circulation, physical and chemical composition of the ocean, and the impact of humans on the ocean system. Students use a custom textbook and progress through 12 common themes and three optional themes based on physical laws that govern the oceans. Students work on bi-weekly benchmark investigations that are derived from the textbook and contemporary environmental/oceanographic data as well as imagery accessed through the course Web site. The learning format is project-based, driven by inquiry into case studies and corresponding driving questions that require review and analysis of real-time environmental data. Students collaborate, share interpretations with their cohort, and place their weekly learning artifacts into an “ocean bag” for assessment by an instructor. Based on pre and post-test science content questions, Weinbeck et al. (2006) concludes that the Datastreme courses are highly effective in enhancing teacher competence in earth system science.
Case.2..Survey.of.Geology.for.Teachers Veal, Brantley, and Zulli (2004) have developed an online graduate geology course for pre-service and in-service teachers following a two-phase design-study approach. In the first phase, a pilot course is developed and then revised in Phase 2 by implementing needed enhancements revealed by phase 1. The majority of content of the course is derived from a textbook augmented by a group of external Web sites (>300) chosen for their quality, multimedia, and interaction value. Learning topics of the course are guided by North Carolina standards in the area of geology. The key benchmarks employed by Veal et al. (2004) in development and implementation of the course were: (1) content, development and structure (e.g., graphical organizers, visual imagery of linked Web sites, flowcharts), (2) pedagogy (e.g., asynchronous e-mail and discussions and synchronous virtual chats), and (3) feedback, assessment, and evaluation (e.g., conceptual questions, objectives and concept maps, content exams, and daily quizzes). Students first read a short background on the subject then expand their learning by reviewing selected Web sites. Online discussion is required and there are daily quizzes and more lengthy exams upon completion of the major topics. The authors conclude that the design-study approach was effective for course development and that content knowledge was effectively transferred in the online format. Feedback from students indicated that daily quizzes were excessive, but regular feedback from the instructor and collaboration with peers contributed to their learning. Pedersen (2004) provides additional details on the Veal et al. (2004) example centering on the importance of determining delivery mode early on, the comparative affordances between face-to-face and online approaches, and the general utility of the design-study methodology.
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Case.3..Virtual.Paleontology Designed as an introductory course for non-science majors to support core science requirements in the competence-based program for adults at DePaul University, Virtual Paleontology (Downing, 1999) was likely the first paleontology course offered fully online. In its most recent configuration, there are 16 process-centered learning modules beginning with scientific reasoning and progressing through the history of life. Student learning is facilitated by a textbook, introduction to key principles via a course Web site, case studies of exemplary fossil groups, and links to online resources supporting the learning objectives of each module, such as fossil preservation, sedimentation, origin of life, evolution, biodiversity, extinction, ecology, functional morphology, and adaptive radiation. Course modules are comprised of four integrated activity areas: (1) Concepts, Processes and Examples; (2) Virtual Field trips; (3) Virtual Labs; and (4) Online Discussions. Instructional materials include original varieties, such as 2-D depictions of stratigraphic processes and 3-D fossils (Downing, 2001) as well as others drawn from the emerging paleontology learning objects at museum, university, and community of practice (i.e., professional society) Web sites. Assessment of student learning in the course is customized corresponding to a student’s selected competence activity. Virtual paleontology’s custom user interface was initially sent to students via CD-Rom, was later embedded into a stand-alone Web site, and will be integrated with a learning management system (LMS) at its next offering.
Case.4..Introduction.to.Geology.(Blended-Large.Lecture) Many universities are taking advantage of selected affordances of online instruction in blended course models. For example, large group class settings appear to be well suited for the incorporation of online activities as a way of providing a more connected and interactive experience for learners. Linneman and Plake (2006) describe a blended approach for an introductory geology class that employs a just-in-time learning approach, where student learning drives instruction through a rapid feedback loop. The just-in-time approach for this geology course has three distinct parts: (1) Web-based warm-up exercises that provide an indication of student preparation with topics, (2) problem-based and just-in-time interactive lectures derived from warm-up exercise feedback, and (3) Web-based critical thinking questions as a wrap-up to the topic. In this case, lectures are modified just in time, based on the warm-up exercises. Specific online methods used are: presentation of learning objectives and course logistics, automatically graded warm-up exercises, open-ended conceptual questions as puzzle exercises, online grade books, and group and individual response tracking (Linneman & Plake, 2006). Student evaluations of the just-in-time approach were positive, as was the stated learning experience in this introductory geology course.
Case.5..Soil.Science.(Blended.and.Just-in-Time) Another example of a blended learning strategy in a course is an introductory soil science course described by Clark and James (2005a, 2005b). This course transitioned from a faceCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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to-face format emphasizing lecture, to a more interactive and student-centered blended format. Specific online methods employed are course outlines, PowerPoint slides from the face-to-face lectures, readings and corresponding questions, a discussion page, and an assignment submission tool. Much like the previous example that used just-in-time, lectures are modified to address the general misunderstandings of students revealed in their answers to posed questions from a few days earlier. Students found most of the online components of the course useful, but were less satisfied with the online readings and guided questions.
Virtual.Field.Trips Geoscience instructors undertake various virtual field trip approaches to supplement courses with online resources. In the most basic form of a virtual field trip, an instructor can provide an organized and previously reviewed set of hot links that take the learner to a Web site with rich information, interactivity, or multimedia learning objects on the target subject. This sort of virtual field trip is a largely passive production on the part of the instructor with externally shaped instructional design dominant. On the other hand, a more customized approach is for the instructor or instructional design team to develop a virtual field trip to meet the learning objectives for a course. In either design approach, a virtual field trip can involve a basic succession of general Web links to gain information, a virtual tour where links center on one topic with nested links, or more multi-faceted characteristics such as learning objectives, geological images, movie clips, and online assessment tools such as quizzes or field trip reports (Qui & Hubble, 2002). To develop virtual field trips with enhanced immersive, visualization, and interactive qualities, advanced instructional design techniques can be employed such as 3-D simulations
Table 11.2. Geoscience virtual field trips Learning Category Virtual Field Trip
Case Number and Subject 6. Ecology and Environmental Issues across Estonia 7. Volcanoes and Catastrophies
Instructional Strategy Simulation of Virtual Hike via InquiryLearning Online Interactive Maps
8. Paleontology
Autonomous Distance Fieldwork Representation of Landscape in 3D
Clary & Wandersee, 2006 Donert, 2003
Inverse Distance Learning
Hasse & Colvard, 2006
9. Geography of European Mountain Ranges 10. Geography of National Parks in U.S.
Reference Pedaste & Sarapuu, 2006 Turney et al., 2004
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of geological field settings. Table 11.2 provides a synopsis of the virtual field trips cases reviewed in this section.
Case.6..Environmental.Issues To teach primary and secondary students ecological and environmental issues, Pedaste and Sarapuu (2006) have developed a learning simulation that takes the form of a hike across Estonia (at http://bio.edu.ee/tour/). Students on the virtual hike volunteer themselves into small teams of three and then undertake 25 tasks/stops centered on environmental problems about soil, air, and water pollution plus waste management. The hike path takes place in five terrestrial ecosystem settings. To formulate hypotheses and solve the inquiry-based
Figure 11.1. Screen capture of Northern Cascades National Park virtual field trip. Each stop is an active link providing information about the geology of that section of the park (from http://www2.nature.nps.gov/GEOLOGY/usgsnps/noca/nocaft.html). Public access.
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problems, the simulation provides virtual tools such as microscope, magnifier, ruler, and virtual measuring instruments including a pH meter to evaluate soil, water, and air. The data is provided in tables, graphs, figures, and photos and supplemental information on over 200 of the key plants and animals of Estonia are provided through linked windows of the simulation. Based on pre- and post-tests for two sequential clusters of students, the authors of this study determined that the small collaborative groups of students could effectively learn the intended environmental principles. However, the virtual hike had to be modified in its first revisions to provide targeted support to students on developing certain analytical skills (abstraction and the concept of multiple hypotheses) that were not successfully achieved in the first version. There are many examples of online virtual hikes with a geosciences focus. The typical interface for a virtual hike is a map with stops hot-linked to additional Web pages with scientific information. For example, the Northern Cascades National Park virtual field trip (Figure 11.1) has links to geological information for each stop.
Case.7..Volcanoes One of the affordances of virtual field trips is that they provide an inexpensive means of providing students with concentrated learning experiences to real locations that would be impractical in the regular implementation of a course field trip. For instance, Turney, Robinson, Lee, and Soutar (2004) have developed an undergraduate earth science module to teach principles of volcanology called Volcanoes, Humans and Environmental Catastrophes. The module has five virtual field trips to volcanoes in geographically distributed areas (Italy, New Zealand, England, and Australia). Lectures are accessed online and are coordinated with interactive maps with varying scales of information from the plate tectonic relationships of the example volcanoes to specifics about the impact of eruptions in human history and associated archaeology. Linked resources include film footage, images, and text; collaboration between students is accomplished by online discussion pages. Student learning is evaluated through practical exams and a computer assisted assessment system that evaluates a student’s answers to field trip questions posed prior to the beginning of the field trip. Overall, the impact of the virtual field trips on student learning was considered effective, but one limitation uncovered in study was that students would move on before completing the exercise once they were able to find information on the questions provided in advance.
Case.8..Paleontology One of the characteristics of fully online degree programs is that their students are likely to be distributed nationally or even internationally. If an instructor prefers that learning be accomplished by actual (e.g., direct field collection and observations) rather than by virtual means, this presents a challenging dilemma for designing practical work activities. For instance, it would be ideal in paleontology if students could all go to a uniform field area and collect and analyze similar fossil specimens or alternatively evaluate comparable specimens in a museum. However, this is unrealistic for distributed students as the field study areas and museum possibilities will rarely be uniform. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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One design strategy that works around this problem is to provide distance learners with individualized field trip options based on where they live (instructor designated) or conversely to facilitate a process of self-directed research to locate a suitable area for field work. Clary and Wandersee (2006) describe an example of the latter (i.e., self-directed) strategy to facilitate autonomous geoscience field work activities for a graduate paleontology course for teachers. To accomplish this distributed field trip learning activity, students are required to apply their subject knowledge of paleontology to identify and investigate a local fossil area. Subsequently, the graduate students use this hands-on field experience to generate a paleontology exercise for their own students. Feedback from students on the effectiveness on the exercise was very positive and the assessment of student learning indicated that students accomplished this exercise with higher scores than other online activities. The strategy of autonomous field work has the potential of supporting online learning with authentic features, but obviously requires a high degree of prerequisite background knowledge and research skills to secure an appropriate field area. Therefore, these sorts of autonomous activities may be best placed as a concluding online course activity.
Case.9..Geography.of.Mountain.Ranges. Developing virtual field trips can be a labor-intensive activity and in many cases may come about as a part of a collaborative effort. A case in point is Virtual Montana, a UK, French and Romanian effort to provide region—specific learning activities to open and distance learning students living in European mountain areas (Donert, 2003). The outcome of Virtual Montana is a Web site that includes online tools for the development of field work skills, virtual visits to selected mountain areas, collaboration with fellow students, assessing student
Figure 11.2. Screen capture of Virtual Montana Project’s Snowdonia 3-D tour tool to assist students learn about mountain regions (from Donert, 2003). Used by permission of K. Donert.
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learning, providing feedback to students, and a searchable research database on mountains. For instance, the virtual sightseeing tool, Snowdonia, provides 3-D tours of the topography of mountain areas such as North Wales (Figure11.2). Virtual Montana provides both disabled students and those with financial difficulties to have a field work experience highlighting additional affordances of virtual field trips.
Case.10..National.Park.Geography.(United.States) The experiences and insights of learners on actual field trips are rich sources of information and can be distributed for the mutual benefit of learners and/or research colleagues that are collaborating at a distance. Hasse and Colvard (2006) review their field course in geography that enables such a collaborative learning approach that they designate as inverse distance learning. The course begins with a pre-course discussion that establishes student projects and research questions. Students then travel to their pre-selected field areas along predetermined routes in the western United States and commence the key learning activity, to interview resource managers working in national parks and national forests. The daily learning goal for students while in the field is to review and to develop online presentations of the physical geography on their field area, the human history, and summarize critical resources management issues based upon the daily interviews. The inverse distance learning is accomplished through daily Web page updates to peers back at the host college. Updates typically include photos and other information from observations for that day. Collaboration between distributed students centers on how to interpret and present the daily geographical information. This study highlights the potential value of authentic student field experiences being turned into virtual field work experiences in near real-time for peers not undertaking the fieldwork.
Virtual.Laboratories The development of virtual laboratories in support of geoscience education has largely involved the development of 2-D and 3-D representations of objects and all-encompassing virtual reality environments. Virtual learning environments in geology typically attempt to replicate the visual aspects of a field area (e.g., topography, outcrop), or to a lesser extent a laboratory instrument (e.g., virtual microscope; see Jaksa, James, Davison, & Toll, 2000, for example). As might be expected, the tendency in the geosciences is to replicate outdoor field activities that are commonplace practical work. To an extent, this is in contrast to the other knowledge areas such as the physical sciences, engineering and medical sciences, where the development of actual remote instrumentation and experiments to replicate an interior laboratory setting is more common. The principle virtual laboratory infrastructure available for geoscience education is remote sensing and geographic information systems (GIS) respectively for data collection and analysis. These virtual laboratory resources permit data generation and hypothesis testing and are effective form of remote experimentation for geoscientists as well as students. In Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Table 11.3. Geoscience virtual laboratories Learning Category Virtual Laboratory
Case Number and Subject 11. Fossilization 12. Stream Erosion 13. Geological Visualization
Instructional Strategy VR Simulation VR Simulation Virtual Reality Movies and Graphics
Reference
14. Environmental Virtual Field Laboratory 15. GIS
VR Simulation
Ramasundaram et al., 2005
Simulations and Interactive Virtual Environments Authentic, VR. and GIS
Metternicht, 2003; Veenendaal et al., 2005
Wang & Yang, 2005 Fung-Chen et al., 2002 Piburn et al., 2002, 2005
the next section in our survey of online geosciences, we review examples of virtual reality based laboratories, approaches to visualization, and the vital emerging area of GIS. Table 11.3 provides a synopsis of the virtual laboratories cases reviewed.
Case.11..Fossilization An important consideration and strategy in the design of virtual environments in science education is making them user-centered and engaging. To teach high school students about fossilization, Wang and Yang (2005) have developed a Web-based learning environment conforming to the following user-centered interface principles: (1) crucial information is conspicuous, (2) a visual order of importance preserves the hierarchy of learning objectives, (3) controls are consistent for Web layers, and (4) visual feedback is provided to the learner. In addition, fossilization, the key physical-chemical-biological process being investigated, is made to be as realistic as possible incorporating pictures of actual fossils; realistic scenes built using 3-D software supplemented by relevant movies. To keep track of variables controlling the fossilization process (organism, ecological system and burial type), this simulation tracks a student’s learning pathway, as well as how variables change in kind and combination according to student choices. Students found the simulation engaging and considered it helpful in their learning. In a follow-up study, Wang and Reeves (2007) examined how the four themes: challenge, control, curiosity, and fantasy influenced the motivation of students using the fossilization simulation. They determined that their Web learning environment (Web-LE) engaged previously unmotivated students and improved inattentive behavior. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 11.3. Stream erosion 3-D simulation for southeastern Taiwan (from Fung-Chen et al., 2002). Used by permission from IEEE.
Case.12..Stream.Erosion To teach students about the very high rates of stream erosion in southeastern Taiwan, FungChen et al. (2002) developed a 3-D simulation based upon actual stream erosion data from 124 monitoring stations for water discharge, sediment content, and suspended sediment load. Basic topographic information for the 3-D renderings were based on remote sensing images (SPOT satellite images) and placed into a GIS context (Figure 11.3). The erosion simulation changes in appearance as gradients are varied using a model of landform change based on statistical analysis and fractal geometry. Post-test results from students demonstrated that those using the visualization simulation had a higher understanding of stream erosion than those students that learned in a traditional fashion. Such simulations, whether originally intended or not for online use, are readily adapted to that purpose.
Case.13..Geological.Visualization One of the key affordances of virtual reality environments in education is improving student visualization skills. In geology in particular, visualization skills are very important to investigate such subjects as 3-D topography, maps, inferred subsurface structural relationships of rocks, reconstructions of ancient environments (i.e., paleoecology), and abstract graphical representations of physical process such as Mohr’s circle for stresses and orientation data plotted on stereonets. Piburn, Reynolds, Leedy, McAuliffe, Birk, and Johnson (2002) and Piburn, Reynolds, McAuliffe, Leedy, Birk, and Johnson (2005) describe an introductory geology laboratory environment with enhanced visualization activities. In this example, Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 11.4. 3-D visualization for folded and faulted strata. Used by permission of Stephen J. Reynolds, GeoBlocks3D at http://reynolds.asu.edu/blocks/home.htm.
geology laboratory exercises are designed as computer-based modules employing Quicktime Virtual Reality Movies. The topical focus is on earth’s surface geomorphology and interior features of the lithosphere such as folded and faulted strata (Figure 11.4). A key objective of this effort is to train and improve student spatial skills by incorporating learning objects that permit the manipulation and transformation of images. In comparison to control groups, students completing the visualization-enhanced modules demonstrated an improvement in spatial abilities (i.e., spatial reasoning could be taught). In addition, dramatic increases were observed for female students suggesting that learning embedded with visualization activities could minimize initial differences in geovisualization among the sexes.
Case.14..Environmental.Virtual.Field.Laboratory A fine example of a virtual laboratory is described by Ramasundaram, Grunwald, Mangeot, Comerford, and Bliss (2005) to teach students about the interrelationships of soils, landscapes, and hydrologic patterns. The details of this graduate level course on GIS in land resource management can be found at http://3dmodel.ifas.ufl.edu/. This virtual laboratory uses animations, focus questions, 3-D models, and adaptive simulations to achieve the primary learning objectives of this virtual laboratory, both to have students study environmental principles and to enhance their higher cognitive skills. The 3-D simulations draw on authentic data from soil profiles and are modeled after water tables values of actual well data. This permits the generation of a sophisticated hierarchy of abstraction spaces through 4-D (i.e., space-time) visualizations (Figure 11.5).
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The researchers conclude that their virtual laboratory design permits learning that is not possible in a customary approach on this environmental topic. By both mimicking an actual field landscape in Florida and providing a complex virtual reality simulation environment, a variety of learning approaches can be achieved including: exploration, scientific inquiry, analogies, and abstraction. Moreover, this virtual laboratory is deliverable online.
Figure 11.5. Composite pictures showing the hierarchy of data and visualization abstraction spaces for a virtual field laboratory on soils, landscapes and hydrology (from Ramasundaram et al., 2005). Used by permission from Elsevier.
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Figure 11.6. Virtual assembly of GPS instrumentation as an activity in a GIS laboratory (from Veenendaal et al., 2005). Used by permission from B. Veenendaal.
Case.15..GIS An area of geoscience instruction that has seen extensive growth in the last two decades is geographic information systems (GIS). It is estimated that over 100,000 students per year are now taking a GIS course (Kerski, 2007) either as a part of the Environmental Systems Research Institute (ESRI) instruction program or within college and university settings. A substantial amount of GIS instruction is carried out online. The challenges of incorporating the theoretical and practical aspects of GIS to an online format are described by Metternicht (2003) for an undergraduate course that includes a virtual laboratory paradigm. This course is delivered through an LMS with a course-specific interface comprised of the following online components: (1) a study plan (i.e., syllabus), (2) a study guide with content in the form of lecture notes, references, and links, (3) laboratory practical work that includes materials, activities, data, and field work assignments, and (4) a discussion forum and e-mail. Virtual GIS laboratories in this course are conducted through interactive virtual environments with lecture and center on the use and operation of surveying equipment. In a related project, Veenendaal, Gulland, and Hall (2005) describe the virtual assembly of GPS instrumentation as an activity in a GIS laboratory (Figure 11.6). Student responses from a composite evaluation of the course indicated that the practical laboratory exercises were valuable to their learning.
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Collaboration,.Virtual.Science.Museums,.and. the.Cyberinfrastructure As discussed in Chapter VI on Knowledge Transfer and Collaboration Structures for Online Science an important part of successful online learning is developing an environment conducive to the exchange of ideas. To compliment regular course interactions and collaboration, a geoscience instructor can additionally foster student interaction with communities of learners such as those interconnected in a collaboratory. In this way, students will have the opportunity to connect as lifelong learners to the discussions and/or inter-institutional research of professional scientists taking place in a distributed format. Another collaboration infrastructure being used in the geosciences is grid computing, where computer resources are shared between schools, colleges, or other institutions. In this section, we review an example of a collaboratory in the geosciences. Additional information and examples of collaboratories are reviewed in other chapters. Another important development in online science education is the consolidation of digital resources such as learning objects. In order for the rapidly increasing quantity of learning objects and learning environments to be applied productively in online-facilitated science instruction, there have to be repositories that consolidate resources so that instructors and instructional designers can quickly access and employ them. In this section, we review examples of a virtual science museum and digital library, which are two emerging forms of central repositories for learning objects in the geosciences. Lastly, we examine the role of the emerging geoscience cyberinfrastructure for its potential to impact online geoscience instruction. A synopsis of reviewed cases in these areas is provided in Table 11.4.
Table 11.4. Collaboration, virtual science museums, and the cyberinfrastructure in the geosciences Learning Category
Case Number and Subject
Collaboration
16. Seismology
Virtual Science Museums
17. Geology, Paleontology, and Evolution 18. Cyberinfrastructure
Cyberinfrastructure
Instructional Strategy Distributed Collaborative Experimentation
Reference Baloian et al., 2006
Linked Web pages
Schimmrich, 1995
New approaches to data services to solve geological problems
Ramamurthy, 2006
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Collaboration Case.16..Seismology Baloian, Buschmann, Breuer, and Matsumoto (2006) demonstrate an example of authentic online collaboration between six high schools in Chile around the topic of earthquakes. In this example, students from each high school are responsible for monitoring their own seismograph, and obtaining and analyzing data from frequent regional earthquakes. The central task is to determine the epicenter of the earthquake, but in order to do that accurately, the students of each school must cooperatively share and discuss their results. Local collaboration and determination of the arrival time of seismic waves between students is accomplished asynchronously by recording each participant’s work followed by collaboration between groups to determine the best estimate for the hypocenter of the earthquake (Figure 11.7). This example could also be considered a form of mutual remote experimentation.
Figure 11.7. Authentic and distributed collaborative experimentation and student collaboration with earthquake data (from Baloian et al., 2006). Used by permission from IEEE.
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Virtual.Science.Museums Case.17..Geology,.Paleontology,.and.Evolution Virtual museums are good sources of information for teaching geosciences online. Virtual museums in the geosciences and natural history can be roughly sorted into two types: (1) those that have been built to be standalone sources of online information in the natural sciences and (2) those that primarily emphasize the display of artifacts, exhibits, and research endeavors of an actual natural history museum. An example of the former type is Hooper Virtual Natural History Museum at http://hoopermuseum.earthsci.carleton.ca/lobby.html that has been constructed by students and faculty in the Department of Earth Sciences of Carleton College. In this example, a floor plan-styled interface takes the learner to different natural history topics (Figure11.8). Individual topics covered at the Hooper museum are typically of broad interest (e.g., climate change), hot topics (e.g., Velociraptor), and reviews
Figure 11.8. The museum interface for the Hooper Virtual Natural History Museum courtesy of the Department of Earth Sciences of Carleton College. Used by permission from T. Patterson.
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of major taxonomic groups (e.g., mammals) or virtual field trips to interesting natural areas. Hence, this virtual museum parallels the floor plan of traditional natural history museums. In this example, subjects are reviewed in summary form, have links to other information and are accompanied by static 2-D graphics. Most of the reknowned natural history museums around the world provide online resources and fall into the second type of virtual museum described above as virtually extended natural history museums. A substantial example of an extended natural history museum is the University of California’s Museum of Paleontology Website at http://www.ucmp.berkeley. edu/exhibits/index.php. Initiated over a decade ago (Schimmrich, 1996), the UCMP Web site also covers topics of broad interest including evolution, the world’s biomes, geologic time change, and the history of life through time including detailed reviews of major taxonomic groups. In addition, this virtual museum has special exhibits, K-12 resources, and collaboratively sponsors the Paleontology Portal which itself provides a suite of resources (Figure 11.9). Most subjects at the Museum of Paleontology are reviewed in summary form, have links to other information and are accompanied by graphics. There are also a few multimedia learning objects including Quicktime movies of tectonic plate movements.
Figure 11.9. Screen capture of the Paleontology Portal sponsored by the University of California’s Museum of Paleontology. Used by permission from the University of California Museum of Paleontology and The Paleontology Portal project, www.paleoportal.org.
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Cyberinfrastructure Case.18..Cyberinfrastructure The impact of the evolving cyberinfrastructure on geoscience research and education is and will continue to be profound. Instructors and students will see computer technology thresholds surpassed permitting easy transfer and use of complex learning objects. Moreover, real-time or asynchronous interactivity and collaboration in online education settings will involve rich discipline-specific geoscience data collections and advanced instruments such as those used for remote sensing. Ramamurthy (2006) characterizes the emerging cyberinfrastructure in geoscience research and education as a revolution. We highlight some of the revolutionary changes described by Ramamurthy (2006) that may potentially affect the character of online geoscience instruction as: •
Development of extensive digital libraries such as Digital Library for Earth System Education (DLESE) with learning objects to augment Web-based learning resources;
•
Vastly increased remote data for educational use (real-time, archived, field and demonstration projects, case studies, GIS and interdisciplinary resources);
•
Distributed computing, data access, and collaboration such as exemplified by the NSF sponsored Unidata enterprise at http://www.unidata.ucar.edu/;
•
Open access to real world data (e.g., for students and faculty at small colleges);
•
Multidisciplinary integration of earth science data to address problems and to employ in courses; and
•
Curricular changes towards active learning strategies utilizing real world earth and space data.
Trends.and.Conclusion Despite the general revolution going on in the cyberinfrastructure in geosciences, there are surprisingly few published examples of online or hybrid geoscience courses. This parallels data reviewed earlier that indicates that geoscience departments have in general been slow to experiment with and/or incorporate many of the proven affordances of online learning into their classes. In addition, the effectiveness of various online approaches to specific geoscience disciplines, or in specific learning contexts, remains an area of necessary research. We expect that a natural sequence of curricular evolution is going on in the geosciences, much like has been observed in other disciplinary areas. First, geoscience programs will utilize learning management systems (LMS) for course administrative purposes followed by an increasing use of online resources for direct instruction. This contention is supported by the strong individual examples and efforts available in the literature for virtual field trips, virtual laboratories, virtual science museums, and collaboratories. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Other developments and trends observed for online geoscience in our review of recent literature are: •
Significant sponsorship of online geoscience courses by governmental agencies (e.g., NOAA) and private organizations (e.g., ERSI);
•
Expanding use of online affordances to improve large-lecture courses;
•
High use of virtual field trips for practical work at all levels (grade school to university);
•
Virtual laboratories that replicate field areas;
•
Realistic simulations employing actual field data;
•
Greater attention to incorporating geovisualization;
•
An increasing number of online learning objects available for use via digital repositories (e.g., virtual museums);
•
Greater attention to collaboration between students and among institutions; and
•
Growing importance of the cyberinfrastructure for geoscience education.
Finally, there has been a keen and growing interest to coordinate and provide professional development for geoscientists in the online teaching area. This is apparent in such projects as On the Cutting Edge: Professional Development for Geoscience Faculty, which includes resources to assist instructors in developing materials for online use as well as advice to effective online teaching. Presumably, such initiatives are in response to the growing use of online environments and the demand by faculty for training and resources in this area. We list selected professional development links in Table 11.5 that can assist geoscience instructors to broaden their online teaching practice and instructional design.
Table 11.5. Professional development resources for geoscience instructors Resource Name On The Cutting Edge: Professional Development for Geoscience Faculty, Geoscience Education Resource Center Designing Principles for Creating Effective Web-Based Learning Resources in the Geosciences Teaching Geoscience with Visualizations: Using Images, Animations, and Models Effectively Virtual Courseware Geoscience Evolution and the Nature of Science Institutes: Lesson Plan
Related URL http://serc.carleton.edu/NAGTWorkshops/ index.html http://serc.carleton.edu/NAGTWorkshops/ webdesign/references.html http://serc.carleton.edu/NAGTWorkshops/ visualization/index.html http://www.sciencecourseware.org/eecindex. php http://www.indiana.edu/~ensiweb/less.fs.html
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References Baloian, N., Buschmann, S., Breuer, H., & Matsumoto, M. (2006). Implementing authentic activities for supporting learning through remote monitoring of earthquakes. In Proceedings of the 4th IEEE International Workshop on Technology for Education in Developing Countries, Iringa, Tanzania, July 10-12 (pp. 46-50). Los Alamitos, CA: IEEE. Clark, I., & James, P. (2005a). Blended learning: An approach to delivering science courses online. Paper presented at the 2005 Breaking Down Boundaries: A Conference on the International Experience in Open, Distance and Flexible Learning, Adelaide, South Australia. Clark, I., & James, P. (2005b). Blending what? An approach to delivering earth science courses online. CAL-laborate. Retrieved July 12, 2007, from http://science.uniserve. edu.au/pubs/callab/vol14/cal14_clark_james.pdf Clary, R. M., & Wandersee, J. H. (2006). Earth science teachers’ perceptions of an autonomous fieldwork assignment in a nationwide online paleontology course. Geological Society of America Abstracts with Programs, 38(7), 80. Donert, K. (2003). The Virtual Montana project: Using open and distance learning to support fieldwork-based activities. International Journal of Fieldwork Studies, 1. Downing, K. F. (1999). Virtual paleontology: A Web-based course for undergraduates. Geological Society of America, Abstracts and Programs, 29(7). Downing, K. F. (2001). Incorporating 3-D virtual fossils to enhance Web-based learning experiences in paleontology. National Association of Geology Teachers, Symposium on Technology in Geology Instruction. North-Central Section, Geological Society of America Abstracts and Programs. Retrieved May 1, 2007, from http://gsa.confex. com/gsa/2001NC/finalprogram/abstract_5470.htm Fung-Chen, L., Angelier, J., Deffontaines, B., Jyr-Ching, H., Shih-Hao, H., & Chin-Hui, L. et al. (2002). A virtual reality application for distance learning of Taiwan stream erosion in geosciences. In Proceedings of the International Conference on Computers in Education, Auckland, New Zealand, December 3-6 (pp. 1156-1160). Los Alamitos, CA: IEEE. Geer, I. W., Moran, J. M., Blair, B. A., Weinbeck, R. S., Smith, D. R., & Hopkins, E. J. (2003). DataStreme Ocean: A new distance-learning course for precollege teachers on the basics of oceanography. Paper presented at the 13th Symposium on Education, Seattle, WA. Hasse, J., & Colvard, C. (2006). Inverse distance learning: Digitally enhancing a geography field-course. Journal of Geography, 105(4), 167-174. Jaksa, M. B., James, P. R., Davison, L. R., & Toll, D. G. (2000). Computer aided learning in geoengineering education: Current resources and future trends. Paper presented at the Geoengineering Education 2000 conference, Melbourne, Australia. Kerski, J. J. (2007). Why and how is GIS used in education? ArcWatch, 2. Retrieved May 1, 2007, from http://www.esri.com/news/arcwatch/0207/feature.html
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Libarkin, J. C., & Anderson, S. W. (2005). Assessment of learning in entry-level geoscience courses: Results from the geoscience concept inventory. Journal of Geoscience Education, 53(4), 394-401. Linneman, S., & Plake, T. (2006). Searching for the difference: A controlled test of just-intime teaching for large-enrollment introductory geology courses. Journal of Geoscience Education, 54(1), 18-24. Macdonald, R. H., Manduca, C. A., Mogk, D. W., & Tewksbury, B. J. (2005). Teaching methods in undergraduate geoscience courses: Results of the 2004 On the Cutting Edge Survey of U.S. faculty. Journal of Geoscience Education, 53(3), 237-252. Metternicht, G. (2003). Expanding distance education in the spatial sciences. Retrieved May 1, 2007, from http://lazarus.elte.hu/cet/publications/095.pdf Pedaste, M., & Sarapuu, T. (2006). Developing an effective support system for inquiry learning in a Web-based environment. Journal of Computer Assisted Learning, 22(1), 47-62. Pedersen, S. (2004). Designing and researching enhancements for online learning: A commentary on Veal, Brantley, and Zulli. Contemporary Issues in Technology and Teacher Education, 4(2), 163-172. Piburn, M. D., Reynolds, S. J., Leedy, D. E., McAuliffe, C. M., Birk, J. P., & Johnson, J. K. (2002, April). The hidden Earth: Visualization of geologic features and their subsurface geometry. Paper presented at the annual meeting of the National Association for Research in Science Teaching, New Orleans, LA. Piburn, M. D., Reynolds, S. J., McAuliffe, C., Leedy, D. E., Birk, J. P., & Johnson, J. K. (2005). The role of visualization in learning from computer-based images. International Journal of Science Education, 27(5), 513-527. Qui, W., & Hubble, T. (2002). The advantages and disadvantages of virtual field trips in geoscience education. Retrieved May 1, 2007, from http://science.uniserve.edu. au/pubs/china/vol1/weili.pdf Ramamurthy, M. K. (2006). A new generation of cyberinfrastructure and data services for earth system science education and research. Advances in Geosciences, 8, 69-78. Ramasundaram, V., Grunwald, S., Mangeot, A., Comerford, N. B., & Bliss, C. M. (2005). Development of an environmental virtual field laboratory. Computers and Education, 45, 21-34. Schimmrich, S. H. (1996). Exploring geology on the world-wide Web—Invertebrate paleontology and evolution. Journal of Geoscience Education, 44, 95-98. Turney, C., Robinson, D., Lee, M., & Soutar, A. (2004). Bringing the mountain to the student: Developing a fully integrated online volcano module. Retrieved May 1, 2007, from http://science.uniserve.edu.au/pubs/callab/vol12/Cal12_turney.pdf Veal, W., Brantley, J., & Zulli, R. (2004). Developing an online geology course for preservice and inservice teachers: Enhancements for online learning. Contemporary Issues in Technology and Teacher Education, 3(4), 382-411. Veenendaal, B., Gulland, E.-K., & Hall, D. (2005). Developing authentic and virtual elearning environments. Retrieved May 1, 2007, from http://www.igg.tu-berlin.de/ISPRS/proceedings/Paper/eLearnWS_Potsdam2005_Veenendaal.pdf
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Wang, S. K., & Reeves, M. E. (2007). The effects of a Web-based learning environment on student motivation in a high school earth science course. Educational Technology Research and Development, 55, 169-192. Wang, S. K., & Yang, C. (2005). The interface design and the usability testing of a fossilization Web-based learning environment. Journal of Science Education and Technology, 14(3), 305-313. Weinbeck, R. S., Geer, I. W., Moran, J. M., Smith, D. R., Hopkins, E. J., Mills, E. W., & Blair, B. A. (2006). A decade of AMS DataStreme courses—Teachers using Earth System Information. Paper presented at the15th Symposium on Education at the 86th AMS Annual Meeting, Atlanta, GA. Retrieved May 1, 2007, from http://ams.confex. com/ams/pdfpapers/102324.pdf
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Chapter.XII
Online.Life.Sciences
The capacity to blunder slightly is the real marvel of DNA. Without this special attribute, we would still be anaerobic bacteria and there would be no music. – Lewis Thomas (1913-1993) In some ways, the life sciences have surpassed other fields in adoption of instructional technologies, although coverage is by no means uniform. In many cases found, textbooks are posted in traditional, linear format, slides accompany simple audio taped lectures or lectures are videotaped with slides as background. While useful, these resources differ substantially from those that address the best practices described in earlier chapters. Several better sites that used to offer free-use educational programs have now gone commercial, requiring purchase of their programs. Applications in the medical sciences tend to predominate somewhat, although excellent examples of nonmedical programs are available. Our focus in Chapter XII is to review representative published examples from the life sciences, including medical sciences. Our review takes into account courses, virtual field trips, virtual laboratories, collaboration and
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virtual science museums. Our goal is to provide the reader with an appreciation of the many innovations and initiatives in general biology, biological fields and the medical sciences.
Courses In this section, we summarize courses in biological and medical sciences that are either fully online or blended. As in Chapters X and XI, online features of the courses vary from simple use of online quizzes and home-based practical work, to simulations and use of sophisticated software that facilitates a fully online format. We describe instructional strategies and components of interest for each. A summary of the cases reviewed is provided in Table 12.1.
Table 12.1. Online life science courses Learning Category Course
Case Number and Subject 1.
Interactive Flash Learning Games
2.
Introductory Biology Medical Simulations
Inquiry-based learning
Small-Group Distance Continuing Medical Education Immunology Education
Case-based learning
Surgery Simulation for Pharmacy Education Hybrid Distance Continuing Nurse Education Web-based Oral Surgery for Distance Undergraduates
Case-based learning Practical work Patient simulation Case-based learning Practical work
3.
4.
5.
6.
Blended Course
Instructional Strategy Game-based learning
7.
8.
Case-based learning Practical work Patient simulation
Case-based learning Practical work
Practical work
Reference/URL Microbiology instructor uses Internet-based “hybrid” course, 2002 Johnson, 2002 Medical simulations in wound care management, 2006, http://www. medicalsimulations.com Allen, Sargeant, Mann, Fleming, & Premi, 2003
Debard, Py, Kraehenbuhl, & Fuchs, 2005 Via, Kyle, Trask, Shields, & Mongan, 2004 Alexander et al., 2005
Corrêa, de Campos, Souza & Novelli, 2003
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Case.1:.Interactive.Flash.Learning.Games Faculty concerns about the ability of traditional-aged college students to be sufficiently self-directed to learn well via distance technologies led to collaboration on a collection of learning games (Microbiology instructor uses Internet-based “hybrid” course, 2002). Some games are roughly similar to the television game show, Jeopardy!© (Sony Pictures Digital Inc.), while others are of simple multiple-choice design. Questions are developed from course content, which now extends beyond the sciences to others fields (e.g., business, history, music). Figure 12.1 shows a food science game in mid-completion. Each of the five columns contains four questions worth between 100 and 400 points. A correct answer adds points to the player’s score and, as illustrated, the player is cued that the answer is correct. An incorrect answer results in subtracted points and a cue that the answer is incorrect. The correct answer is not indicated, nor is the player able to select a different answer. Only after ending and restarting the game can the player change the answer, if the question is randomly selected. There is no feedback at the end of the game.
Figure 12.1. Food Science, a Jeopardy!©-like learning game developed at the University of Minnesota. Used with permission from the University of Minnesota.
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This game and those similar to it address two of the best practices discussed in previous chapters. Students are active and somewhat engaged, and the games can be used in cooperative, competition-based learning activities.
Case.2:.Introductory.Biology Johnson (2002) presents an outcomes assessment designed to determine whether a basic online learning approach was sufficiently effective to warrant substantial institutional investment in more sophisticated approaches. On-campus course outcomes were compared with those from a subsequent online section of the same course, a nonmajor, inquiry-based, introductory biology course. Identical learning objectives applied to both sections. Online students used a physical textbook, completed online modules using WebCT© and performed kit-based laboratory work at home. High-resolution microscopic images were provided as needed. Students took weekly quizzes online, and completed proctored midterms and final, comprehensive exams. Students also completed pre- and post-tests, and course evaluations. No significant differences were found in either student assessment between the on-campus and online sections, or in evaluation scores (Johnson, 2002).
Case.3:.Medical.Simulations These wound care and transplantation case simulations, authored by clinicians, mimic case management. Designed for nurse and physician continuing education, each consists of a case presentation and levels of decision making based on sequential addition of narrative history, imitating case progression. Simulated laboratory, radiographic and pathology reports are not accessible by the user—data is provided in the narrative for user interpretation—and user decisions do not change the course of the pre-determined case progressions, as with the adaptive technologies discussed in earlier chapters. As Figure 12.2 illustrates, the developers use push-pull technology to provide immediate feedback on all decisions, with supporting discussion. As is the norm in continuing medical education, there is no actual assessment monitor; the site specifically states that, “Should you decide to apply for continuing education credit, you will NOT be penalized for having selected incorrect options” (Medical simulations in wound care management, 2006). However, users who return to a case are recognized and their previous answers are both visible and unchangeable. The case studies address several best practices discussed in Chapter II. Student preconceptions are tested by the decision making, especially effective with sequential changes introduced to the cases; more effective would be an expert system that altered case progression based on decision making. The feedback provided to both correct and incorrect answers supports conceptual understanding and knowledge organization around the diagnostic and therapeutic concepts addressed. Student activity levels in the cases are predetermined, but representative of actual activity, if not outcome.
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Figure 12.2. Medical case study of wound care management, illustrating push-pull technology use for comprehensive feedback on correct and incorrect answers. Used with permission from T. Treadwell, M.D. and InterMed Press.
Case.4:.Small-Group.Continuing.Medical.Education While computer-based continuing medical education (CME) can be both sophisticated and pedagogically sound, as demonstrated in this chapter, learner isolation is one of the modality’s disadvantages. An option for circumventing isolation is the videoconferenced learning experience, which is especially effective for health care practitioners in remote or small-practice environments, where practice coverage limits travel. As both of the authors experienced, early attempts at videoconferencing often included nothing more than two or three groups or individuals linked by clunky precursors to modern audiovisual teleconferencing equipment. Sound was poor and the instructor had to work at a frenetic pace to adjust
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and coordinate cameras between campuses while lecturing. In addition, materials needed to be sent prior to the conference, which precluded spontaneity. However, a 2003 study of 10 learners in three locations who were linked via contemporary videoconferencing demonstrated the success of the approach for small-group CME (Allen, Sargeant, Mann, Fleming, & Premi, 2003). Both video and audio quality and transmission lag time continue to be hindrances to discussion, although the facilitator in this study was more sensitive to these drawbacks than were the learners. Most important, “there was evidence of knowledge gain, with post-test scores being 20% higher than pretest scores (p = .006). Learners reported nine practice changes from taking the modules” (p. 38).
Case.5:.Immunology.Education Debard, Py, Kraehenbuhl, and Fuchs (2005) describe Immunology Online, an originally Swiss project that, with World Health Organization backing, is now used by students in Africa, Asia, and South America. Development brought together the five Swiss medical schools in a collaborative effort to achieve common learning goals without duplicative effort. The interface uses text, sophisticated animation and images to support clinical, case-based learning, incorporating a sophisticated annotation system for scientific publications, and what the authors call a structure-function approach, “detailed animation to provide an in-depth exploration of the structural organization of lymphoid organs and their respective roles in the development of an immune response” (Debard, Py, et al., 2005, p. 3).
Case.6:.Surgery.Simulation.for.Pharmacy.Education Using mannequin patient simulation, Stanford medical students working at a distance from an anesthesiologist instructor’s simulated delivery of anesthetic medications. The Med-SimEagle simulator, invented at Stanford, consists of 12 diagnostic or therapeutic level features. Manipulatable variables include cardiac output, heart rate, mean arterial pressure and systemic vascular pressure (Via, Kyle, Trask, Shields, & Mongan, 2004). As illustrated at http://med. stanford.edu/VAsimulator/medsim.html, the system consists of three components: patient mannequin, interface cart, and simulation computer with control station. While the Web site illustrations show all components within close proximity, the research reported was done in a distance environment, using an Advanced Distance Education Network (ADEN).
Case.7:.Hybrid.Distance.Continuing.Nurse.Education Our chapters on practical work and collaboration illustrate that both can be essential to successful science education, which was confirmed by the research of Alexander et al. (2005) into hybrid distance continuing education for public health nurses. A total of 80 nurses in North Carolina’s 85 local health departments began the pilot course, which consisted of a preliminary computer-skills screening, four distance modules and a final, on-site module, which participants had to qualify for through successful completion of the initial four modCopyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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ules. The on-site module was held at three different locations, to ease travel requirements and allow for multiple calendar times (Alexander et al., 2005). Completion rates were similar to those of fully online courses, and overall satisfaction was very high, “with 69 percent of participants strongly agreeing and 29 percent agreeing that the course was excellent” (Alexander et al., 2005, p. S121). Participants also reported spending more time than they had anticipated on the course and feeling the need for online interaction with other participants, which was not part of the initial structure. This feedback led to restructuring of the course, emphasizing interactivity with other learners, enhanced access to instructors and a reduction in non-representative, repetitive tasks.
Case.8:.Web-Based.Oral.Surgery.for.Distance. Undergraduates The research reported by Correa et al. is unusual in that it is a simulation of a distance learning environment, used to determine whether distance students could function without simulations and other sophisticated computer equipment, in order to practice manipulation skills (Correa, de Campos, Souza, & Novelli, 2003). The Web site created was based on a Principles of Surgical Technique course, divided into three modules: •
Materials and Theory: names and functions of surgical equipment; review of select course material;
•
First Class: organization of the surgical environment, including health and safety rules; and
•
Second Class: anesthesia, incision and suture techniques (Correa, de Campos, Souza, & Novelli, 2003)
In order to simulate distance learning, a carefully constructed and fully monitored environment was developed. Because of comparative studies indicating significant similarities to the human mouth, chicken legs were used for the practice session. Students participated in the Web-based course in a multimedia lab equipped with computers, internal sound system and TV circuits…Data management and report issues were obtained using distance learning software (TopClass®, WBT Systems). The teacher and instructors were absent most of the time, intervening only in the case of difficulties presented by the students. In these situations, the correct action was shown using the TV circuit and cameras…Students’ difficulties were detected by means of micro cameras installed in the work-field of each student, and controlled by the teacher from a distance (Correa et al., 2003, p. 112). Results were largely positive and appropriate for the expertise level of the students. However, both faculty and students noted a considerable problem in transitioning from computer in-
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structions and resources to the surgical environment and back, vis-à-vis handling the mouse and keyboard while gloved and surgically engaged (Correa et al., 2003, p. 112).
Table 12.2. Life science virtual field work and laboratories Learning Category Virtual Field Work
Case Number and Subject 9. Autonomous Distance Field Work
Instructional Strategy Autonomous distance field work
Virtual Laboratory
10. Center for Human Simulation
VR simulation, 3D modeling
11. Stereoscopic Imaging in Anatomy and Physiology 12. Plant Pathology
3D imaging
13. Dolan DNA Learning Center
14. CompuCell3D 15. Centre for Biological Information Technology 16. Howard Hughes Medical Institute
17. Augmented Reality in Structural Molecular Biology 18. Web-Based Audiometric Simulation 19. Real-Time Intestinal Surgery Simulation
Reference/URL http://www. montana.edu/msse/ coursecatalog. html#bio519 Center for Human Simulation, 2007; Teran et al., 2005; Evesque, Gerlach, & Hersch, 2002 Perry, Kuehn, & Langlois, 2007
Interactive graphics, VR simulation Interactive graphics, VR simulations, streaming video, VR museum VR simulation, 3D modeling Interactive graphics, VR simulations
Schumann, 2003; Bruce et al., 2005 http://www.dnalc.org/ home.html
Interactive graphics, VR simulations, streaming video, VR museum Combination of 1D and 2D renderings
http://www.hhmi.org/ biointeractive/
Patient simulation, VR simulation
Liebarth & Martin, 2005
VR simulation
Raghupathi et al., 2004
Cickovski et al., 2005 http://www.cbit.uq.edu. au
Gillet, Sanner, Stoffler, & Olson, 2005
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Virtual.Field.Work.and.Laboratories As with the geosciences, virtual laboratories in the life sciences have relied heavily on 2-D and 3-D imaging, but differ in the extent to which instrumentation and simulations are used. Because of the influence of clinical applications, there is substantial emphasis on improving these to the level of diagnostic quality. However, even discounting the clinical, instrumentation and simulations remain the primary virtual formats.
Case.9:.Self-Directed.Field.Trips In addition to on-campus field experiences, Montana State University’s master’s program in science education provides equivalent self-directed field experiences courses for distance students, who can similarly engage their own students. For example, Terrestrial Ecology of Plains and Prairies structures learning experiences around the commonalities of grasslands, requiring students to locate grasslands in their areas and to investigate specified characteristics (Course Catalog, 2007). Students practice writing and quantitative skills, and develop learning tools for classroom use. It is noted that the course may be combined with another, “Biology of Riparian Zones and Wetlands, for observing similarities and differences between drier and wetter communities” (Course Catalog, 2007). While the master’s program sets prerequisites that limit the type of scaffolding necessary, distance students with less experience could be adequately supported to engage in this type of rich course, which addresses several of the best practices discussed in Chapter II. Whether or not at the master’s level, student preconceptions are engaged and tested by actual onsite visualization of the environment and comparison either to a standard description or to another local environment. Students are encouraged to think critically about any variations seen, addressing their preconceptions. The student is active and engaged in the practical work of applying learned skills, by which metacognition is developed. Interdependent collaborative learning could be arranged to further exercise those skills by pairing distance learners for comparative analysis.
Case.10:.Center.for.Human.Simulation,.University.of. Colorado Recent publications have detailed the effectiveness of multimedia and 3-D imaging modalities in medical education, specifically as it pertains to dissection and anatomy and physiology (Benton et al., 2007; Johns, 2007). Multiple imaging processes have been and continue to be tested for authenticity, manipulability by the learner and technical flexibility. One such example is that of the Center for Human Simulation (CHS) at the University of Colorado, “a synthesis of human anatomy and computed three-dimensional imaging. This synthesis resulted in a three-dimensional, high resolution database of human male and female anatomy (the Visible Human) as derived from direct analysis of anatomical specimens and radiological imaging” (http://www.uchsc.edu/sm/chs/overview/overview.html).
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Figure 12.3. Three rendered images illustrating layers of the human model. The left-most image shows shaded body shape contours, the skeleton, major vasculature and, less clearly, the heart and left kidney. Those organs are more clearly seen in the middle image, which omits the skeleton but retains vasculature and body contours. The right most image details vasculature and organs. Used by permission from the Center for Human Simulation, University of Colorado.
An interdisciplinary effort, the award-winning CHS is designed specifically for educational purposes, rather than commercial imaging. While the related educational programs are still under development, even current CHS material provides a rich interface for teachers seeking realistic, multidimensional adult models for their students. Most animations and some simulations are in place; others are anticipated, including virtual reality applications. Those renderings that exist are immensely preferable to textbook diagrams, despite the lack of uniform labeling. Figure 12.3 is such an example. As a resource, CHS does not actually address best practices; however, the publicized plans, if fully realized, should prove both effective and comprehensive.
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Case.11:.Stereoscopic.Imaging.in.Anatomy.and.Physiology As impressive as the virtual reality 3-D rendering is in Case 10, Perry, Kuehn, and Langlois (2007) raise very valid points in explaining their preference for stereoscopic imaging, an advanced form of what readers might remember as the View-Master® (Mattel, Inc., Segundo, CA) or, even earlier, card stereograms. Both basic and virtual reality 3-D are expensive to produce, requiring specialized software and appropriately trained developers. Furthermore, basic 3-D can lose the detail of surface textures, unless constructed by a rendering artist, and virtual reality 3-D may lose depth, a serious consideration in clinical and other careeroriented science courses. Conversely, stereoscopic imaging uses little more than normal, binocular human vision and a viewer. “When an object is viewed with both eyes, the gaze converges toward the object; however, the right and left eyes receive information that is specific to the right and left sides of the object, respectively. The eyes bring two slightly different representations of the object to the brain, where information is combined to give the object the senses of solidarity and depth,” resulting in normal vision (Perry et al., 2007, p. 19). In the case of images designed to be observed stereoscopically, the viewer imparts that solidarity and depth to an essentially 2-D object. Unfortunately, the 2% of the population with monocular vision—that is, whose right and left eyes see the same image, or who lack vision in one eye—are unable to benefit from stereoscopic imaging (Frisby, 1980, in Perry, Kuehn, & Langlois, 2007). When one of the authors used stereoscopic imaging in an anatomy and physiology course focusing on speech mechanisms, 96% of students responded favorably (e.g., helpful or very helpful), with the only concerns being technical issues that were readily corrected.
Case.12:.Plant.Pathology In Schumann’s (2003) update of teaching innovations in plant pathology, she found that, “how to incorporate new instructional technologies to improve learning” (p. 377) remains a theme of concern, despite advances made since the field’s last comprehensive review, in 1978, “but that the problems associated with them—cost, time for development, technical support, and the recognition that technology is not a substitute for teaching—remain the same” (p. 377). Similarly, even the best images do not substitute for live plants, considering the detail that live plants contain (Schumann, 2003; Taraban, McKenney, Peffley, & Applegarth, 2004) Even so, both Schumann and Bruce, Dowd, Eastburn, & D’Arcy, (2005) discuss simulations (e.g., DiagnosisTM), computer games (e.g., Applescab, LateBlight, Resistan), and decision making tools (e.g., TurfBlight) that students find challenging, despite the fact that most programs remained in DOS format in 2003. A check of these programs found that Java script is now being used to update Applescab, LateBlight and Resistan, but that DiagnosisTM is no longer being supported because it is incompatible with Version 7 of Microsoft Internet Explorer© and is being replaced with a PBL-Interactive platform. Activities at Plants, Pathogens and People (http://www.ppp.uiuc.edu/) emphasize late blight, Dutch elm disease and crown gall, using simulations and online lab notebooks to
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guide students through problem-based learning. Beginning with a question-based resource interface, the Web site uses extensive hyper linking to combine brief, illustrated readings with annotated photographs and glossary functions. A partially public, partially restricted dialogue section enables course-related chat, but also allows the public to discuss content or format. Multiple tools are publicly available: color microscopic slides, slide presentations, disease maps, a non-integrated dictionary and a calculator. Finally, a library section documents both print and hyperlinked resources (Bruce et al., 2005). The American Phytopathological Society sponsors APSnet Education Center (http://www. apsnet.org/education/), a free Web site of course resources that enables instructors to spend less time developing individual resources or duplicating what has been done by others (Education Center: The Plant Health Instructor, 2007). Figure 12.4 illustrates a LateBlight potato crop simulation, where the user has entered variables such as potato type, environmental descriptors and management decisions, resulting in the growth curve demonstrated. Modifying variables changes the growth curve. This and other simulation software addresses several of the best practices discussed in previous chapters. Student preconceptions can be engaged and tested by manipulation of the variables and examination of the resultant crop. Once students see the results of those preconceptions, they can apply factual knowledge developed from non-simulator coursework and revise the simulation variables, thus furthering their conceptual understanding and enhancing their knowledge organization around the core concepts addressed. Furthermore, the student is active and engaged in the practical work of applying learned skills, in a well-
Figure 12.4. Potato blight simulation using LateBlight simulation software, freely available from the American Phytopathological Society’s APSnet Education Center. Used by permission from the American Phytopathological Society.
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scaffolded environment, by which metacognition is developed. The learning activities can easily be arranged into interdependent collaborative learning by assignment of planting decisions and management tasks to individuals on the team, followed by a team debriefing and concluding report.
Case.13:.Cold.Spring.Harbor.Laboratory.and.Dolan.DNA. Learning.Center The home laboratory of Nobel Prize laureate Barbara McClintock (1983, individual award), Cold Spring Harbor Laboratory has developed a highly sophisticated, inter-related collection of Web resources appropriate for students at various levels. “The Leading Strand Web portal features a combination of freely available and private Web sites including meeting video archives, meeting photo albums, online courses and public lectures” (The Leading
Figure 12.5. The Dolan DNA Learning Center home page, with multiple interactive components. The center bar, DNALC Internet Sites, pans to provide access to 11 different applications. Resources, top right, provides a Media Showcase of key personalities and discoveries, many two-dimensional, and podcasts on various topics. Used by permission from Cold Spring Harbor Laboratory.
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Figure 12.6. The applications module at DNAinteractive, one of many learning tools on the Dolan DNA Learning Center site. This image is from analysis of the buried Romanov remains, by which students can explore skeletal morphology. Used by permission from Cold Spring Harbor Laboratory.
Strand, 2007), while publicly available real-time videos of symposia and interviews with researchers are available at specified days and times. Arguably the most popular resource is the Dolan DNA Learning Center (Figure 12.5), containing multiple resources for both teachers and students. Teaching resources include myDNAi customizable features, lesson plans, multimedia tools and directions on purchasing various resources. Additional links lead to interactive modules that incorporate streaming video, animations, and interactive components (Figure 12.6). These resources address each of the best practices discussed in previous chapters. Student preconceptions can be engaged and tested by manipulation of variables in several modules, with integrated access to scaffolding for different levels of students. Modules are self-contained, but reference each other to encourage cognitive linkages, furthering student conceptual understanding and enhancing knowledge organization around the core concepts addressed. Students can be as active and engaged as they choose, applying learned skills in practical work. The activities readily lend themselves to interdependent collaborative learning.
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Case.14:.Three-Dimensional.Simulation.of.Morphogenesis CompuCell3D is a modeling software platform being developed for the purpose of 3-D imaging of morphogenesis (Cickovski et al., 2005). Software function “uses design patterns for speed, efficient memory management, extensibility, and flexibility to allow an almost unlimited variety of simulations” (p. 273). By treating cells phenomenologically and ignoring intracellular behaviors, we can reduce multiple complex interactions to a small set of behaviors such as movement, division, death, differentiation, shape change, force exertion, secretion and uptake of molecules, and changes in the distribution of surface properties (p. 273). Current applications visible on the related Web site include a remarkable imitation of morphogenic processes; video of the process mimics actual cell development, but is currently limited primarily to small segments of avian limb. Even so, the potential for CompuCell3D is obvious. A popular and award-winning application is Lawrence Berkeley National Laboratory’s Whole Frog project (http://froggy.lbl.gov/), with its Virtual Frog Dissection Kit and Virtual Frog Builder Game, which “tests your knowledge of the 3D spatial relationships between the organs in the frog” (“Virtual Frog Builder Game,” para 1). However, neither application is truly three-dimensional; rather, it mimics three-dimension by emphasizing ordered placement of structures with a frog body cavity. CompuCell3D has the potential to transform such applications into actual three-dimension.
Case.15:.Centre.for.Biological.Information.Technology,. University.of.Queensland The Centre for Biological Information Technology, based in the departments of biology and chemistry at University of Queensland, produces applications designed for decision making and learning (Lucid digital identification keys: their role in on-line taxonomy, 2005). For example, On the Fly (Figure 12.7) is a comprehensive identification tool for the multitude of Australian flies, a multimedia atlas that “provides an easy-to-use interface, with comprehensive fact-sheets, diagnostic features for all fly families and numerous photographs of macroscopic and microscopic features” (On the Fly, para. 3). More student-centered is Grain Storage Tutor, (Figure 12.8), a multi-national product addressing the grain storage needs of the Australasia region. “Images, diagrams, video clips, text simulation models and Lucid® identification keys are used to provide information and learning material to increase the user’s knowledge and skills in commodity management” (Grain Storage Tutor, para. 1). As with LateBlight simulation software in Case 1, users can enter variables such as environmental descriptors and management decisions to evaluate changes in practice, resulting in the drying curves demonstrated.
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Figure 12.7. Multi-layered, interactive atlas, On the Fly, with visible zoom capability and detailed identifying characteristics. Used by permission from the Centre for Biological Information Technology, University of Queensland.
Figure 12.8. Multi-layered, interactive atlas, Grain Storage Tutor, with visible zoom capability and detailed identifying characteristics. Used by permission from the Centre for Biological Information Technology, University of Queensland.
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Because of this interactivity, Grain Storage Tutor meets more of the best practices identified in Chapter II than does On the Fly, which functions simply as a resource, albeit a sophisticated resource. With Grain Storage Tutor, student preconceptions can be engaged and tested by manipulation of the variables and examination of the resultant graphics. Once students see the results of those preconceptions, they can apply factual knowledge developed from non-simulator coursework and revise the simulation variables, thus furthering their conceptual understanding and enhancing their knowledge organization around the core concepts addressed. Furthermore, the student is active and engaged in the practical work of applying learned skills, by which metacognition is developed.
Case.16:.Howard.Hughes.Medical.Institutes HHMI has developed a highly sophisticated, inter-related collection of Web resources appropriate for students at various levels, in an extensive array of formats: a virtual museum, animations, virtual laboratories, videotaped expert lectures both with and without accompanying slides, and video clips. Perhaps most intriguing are Ask a Scientist and Click and Learn.
Figure 12.9. HHMI’s Virtual Museum display on Bubonic Plague. Used by permission from Howard Hughes Medical Institutes. Copyright 1999,2006.
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Figure 12.10. A click and learn topic designed for the public, but perhaps more appropriate for an advanced secondary school or college student. Used by permission from Howard Hughes Medical Institutes. Copyright 1999,2006.
Ask a Scientist is staffed by over 50 volunteers willing to answer biology questions from anyone. A searchable archive is maintained, as well. Representative sample questions include: •
What is the molecular mechanism for stripes in zebras?
•
I was told to put salt on cuts I get when I’m hiking outdoors. Why does it sting when you put salt on a cut?
•
If protein aggregation is the main cause of both prion diseases and Alzheimer disease, why are prion diseases transmissible and Alzheimer is not (Ask a Scientist, 2007)?
Click and Learn consists of approximately two dozen short lessons “illustrated with interactive Web animation” (Click and Learn, para. 1) and designed for non-scientists (Figure 12.9), although some are more appropriate for college-level students (Figure 12.10).
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Case.17:.Augmented.Reality.in.Structural.Molecular. Biology While molecular biologists rely heavily on computer-generated images, there are educational advantages to models that can be manipulated. Not only do all levels of learners tend to rely on multiple senses, including expert-level researchers, but sophisticated models also accurately represent “physical features such as shape, flexibility, and bonding capacity (for example, using magnets), represent molecular characteristics…both overview and detail simultaneously” (Gillet, Sanner, Stoffler, & Olson, 2005, p. 16). Furthermore, tangible models promote collaborative work more readily than sharing a computer image. These authors used augmented reality to develop computerized overlays of tangible models. As Figure 12.11 shows, a user manipulates a physical model while the “system tracks the model (with a video camera) and displays it on the computer screen. The system composites a virtual representation (such as another 3-D rendering of the same molecule, textual labels, or a 3D animation) with the video display and spatially registers it with the model as the user manipulates and explores the structure. The result is a compelling sense of virtual object realism” (p.14). While not all students use visual learning preferentially, 3-D models have long been shown to be highly effective in supporting conceptual understanding. Evaluation of the augmented reality models with high school students found that the students were both engaged and retained knowledge. Molecular biologists, too, found the models helpful in a variety of applications. Evaluation with college students is ongoing (Gillet et al., 2005).
Figure 12.11. A user manipulates a physical model while the camera just over his hands transmits the image to the computer monitor, where detailed virtual data is overlaid. Used by permission from IEEE.
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Case.18:.Web-Based.Audiometric.Simulation The effectiveness of clinical simulators is continuously being evaluated, especially as additional programs of study are introduced to distance formats (Lieberth & Martin, 2005). In this study, both undergraduate and graduate students in speech-language pathology performed hearing tests using a portable audiometer and a Web-based audiometer simulation. Both levels of learners performed similarly with each modality.
Case.19:.Real-Time.Intestinal.Surgery.Simulation The advantages of minimally invasive surgery (e.g., laparoscopy) over open surgery—less pain, fewer infections, and faster recovery—has resulted in a training challenge, as the learning curve is described as flat (Raghupathi et al., 2004). Attaining the volume of practice surgeries needed has been achieved using pigs, at much expense and considerable ethical concern. To alleviate these problems, the authors developed a simulation that focused strictly on mimicking the feel and mobility of intestinal tissue. The intermediate product was tested by surgical educators and received favorably. Research on this simulation and various rendering techniques continues (Garcıa-Perez, Munoz-Moreno, de Luis-Garcıa, & Alberola-Lopez, 2006).
Figure 12.12. Images of the intestinal surgery simulator. (a) Intestine (purple) and mesentery (orange), pulled by a probe on the left side. (b) Stable resting position, with no pulling and no mesentery visible. (c) Inside the abdominal cavity. Used by permission from IEEE.
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Table 12.3. Online resources in the life science Learning Category Online Resources
Case Number and Subject 20. Todar’s Online Textbook of Bacteriology 21. WNET New York— Ed Online
Instructional Strategy Nonlinear integration of multiple resources Multiple applications for existing software
Reference/URL http://www. textbookofbacteriology. net/ http://www.thirteen. org/edonline/software/ index.html#hyper
Online.Resources As noted in Chapters VII and VIII, consolidation of digital resources—which can be prohibitively expensive and time-intensive to develop—is important for expansion of online science education. Resources developed with support by some funding agencies, including the U.S. government, are, by definition, public access. However, several of the cases already reviewed, as well as the work on which each was built, demonstrate the willingness of researchers to also share privately funded resources. Some resources do not readily fit into the categories of course or virtual reality applications; those are reviewed in this section.
Case.20:.Todar’s.Online.Textbook.of.Bacteriology. Dr. Kenneth Todar, University of Wisconsin-Madison, has compiled his lectures into a series of chapters that integrate remarkable color and grayscale electron and standard micrograph images, environmental photographs, professional line drawings and instructional handdrawn images, as well as numerous tables (Todar, 2006). Content is comprehensive: general bacteriology (nine chapters), bacterial relationships with animals (six chapters), principles of bacterial pathogenesis (eight chapters), bacterial pathogens and diseases of humans (19 chapters), important groups of procaryotes (three chapters), and an additional chapter on emerging pathogens. The text is searchable, updated and revised regularly, and is linked to Todar’s original lecture notes and course readings. While not interactive, non-linear resources such as Todar’s text encourage students to integrate multiple treatments (e.g., Emerging Pathogens with Cholera and Bacterial Structure in Relationship to Pathogenicity) as their level of sophistication increases. The student is active and engaged in self-directed learning.
Case.21:.Thirteen.WNET.New.York—Ed.Online As a PBS station, WNET in New York provides access to the gamut of PBS resources (e.g., lesson plans, animations) for both students and teachers. It also offers an invaluable resource Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Figure 12.13. Scanning electron microscopy of Staphylococcus epidermidis cluster embedded in exopolysaccharide matrix, similar to images in Todar’s Online Textbook of Bacteriology (from http://www3.niaid.nih.gov/labs/aboutlabs/lhbp/pathogenMolecularGeneticsSection)
for teachers who have access to instructional and developmental software, but lack the time or ability to develop their own applications. Software supported are “HyperStudio, Claris Home Page, ClarisWorks, and other applications that have slide show, desktop publishing, and multimedia capabilities” (Software Samples and Projects, para. 2). Projects are identified by grade range, topic and platform. For example, Interactive Body Works is a science topic for grades nine through 12 that is developed using Hyperstudio©. Students will create an interactive, virtual human body. Each student will be assigned a part of the body. Their task is to create a HyperStudio© presentation on the part of the body which they are assigned. Student stacks will consist of a minimum of 5 cards. Each card will contain text, images, and sound. Each student’s work will be added to a central stack. The main card will contain an image of the human body. When a part of the body is clicked, the user will go to a specific student’s work. Upon completion, students will present their finished projects to the class in the form of a multimedia presentation (Interactive Body Works, 2005).
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Each project page includes descriptors of project length, curricular uses, learning objectives, teacher procedures, and student organizers.
Trends.and.Conclusion As with other areas explored, curricular enhancements are underway, although the time and expense inherent to sophisticated systems means that only well-funded programs, typically medical or affiliated with large, research universities, are truly innovative. The Howard Hughes Medical Institutes and Cold Spring Harbor Laboratory are two outstanding examples that remain available publicly, although equally sophisticated for-profit and subscriber programs exist. While the impetus to take sophisticated programs private is understandable, given the resources devoted to their development, doing so is unfortunate, in that it deprives learners from less well-endowed institutions from the benefits of sophisticated systems. One cannot help but wonder whether widespread availability of these innovations to undergraduate and even younger students might not be advantageous in drawing more students into the sciences. The ideal would be that such resources be archived as learning objects in national digital libraries, such as the National Science Digital Library (NSDL). As discussed in several other chapters, appealing to a variety of learning styles is beneficial, especially so when content is perceived as difficult or not intuitive. A primary affordance of technological innovations is the ability to integrate multiple levels of scaffolding, most clearly seen in adaptive systems, which can both simplify processes and approach those processes from points of view preferable to a variety of learners, while still maintaining academic rigor, a position that has not been the norm in science education either pre- or post-constructivism. We expect the biological sciences to use learning management systems (LMS) for course administrative purposes, as indicated for previous fields. Developments similar to those identified in the geosciences are expected: •
Continuing sponsorship of online courses by governmental agencies and private organizations, especially in the clinical sciences;
•
Expanding use of online modalities to enhance large-lecture courses;
•
Widespread use of virtual field trips for practical work and virtual laboratories, especially effective in conceptual courses;
•
Realistic simulations using actual data; and
•
Wider use of collaboration between students and among institutions.
References Alexander, L. K., Dail, K., Davis, M. V., Hajat, A., Rothney, E., Rybka, T., Kipp, A., & Maillard, J.M., et al. (2005). A pilot hybrid Internet/vlassroom-based communicable Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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disease continuing education course for public health nurses in North Carolina: Lessons learned. Journal of Public Health Management & Practice, 11, 119-122. Allen, M., Sargeant, J., Mann, K., Fleming, M., & Premi, J. (2003). Videoconferencing for practice-based small-group continuing medical education: Feasibility, acceptability effectiveness, and cost. Journal of Continuing Education in the Health Professions, 23(1), 38-47. Ask a Scientist. (2007). Retrieved May 28, 2007, from http://www.askascientist.org/ Benton, H., Hernandez, J., Bello, F., Strutton, P., Purkayasthna, S., Firth, T. & Darzi, A. (2007). Using multimedia and Web3D to enhance anatomy teaching. Computers & Education, 49, 32-53. Bruce, B. C., Dowd, H., Eastburn, D. M., & D’Arcy, C. J. (2005). Plants, pathogens, and people: Extending the classroom to the Web. Teachers College Record, 107(8), 17301753. Center for Human Simulation. (2007). Retrieved July 27, 2007, from http://www.uchsc. edu/sm/chs/open.html Cickovski, T. M., Huang, C., Chaturvedi, R., Glimm, T. H., Hentschel, G. E., Alber, M. S., Glazier, J. A., Newman, S. A., & Izaguirre, J. A. (2005). A framework for three-dimensional simulation of morphogenesis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(4), 273-288. Correa, L., deCampos, C. A., Souza, S. C. O. M., & Novelli, M. D. (2003). Teaching oral surgery to undergraduate students: a pilot study using a Web-based practical course. European Journal of Dental Education, 7, 5. Course Catalog. (2007). Retrieved July 17, 2007, from http://www.montana.edu/msse/coursecatalog.html#bio513 Debard, N., Py, P., Kraehenbuhl, J.-P., & Fuchs, J. (2005). Science and society: The influence of the Internet on immunology education. Nature Reviews Immunology, 5(9). Retrieved May 28, 2007, from http://www.cdc.gov/ncidod/diseases/index.htm Education Center: The Plant Health Instructor. (2007). Retrieved May 28, 2007, from http://www.apsnet.org/education/ Evesque, F., Gerlach, S., & Hersch, R. D. (2002). Building 3D anatomical scenes on the Web. The Journal of Visualization and Computer Animation, 13(1), 43-52. Garcıa-Perez, V., Munoz-Moreno, E., de Luis-Garcıa, R., & Alberola-Lopez, C. (2006). A 3D collision handling algorithm for surgery simulation based on feedback fuzzy logic. Paper presented at the IEEE International Special Topic Conference on Information Technology in Biomedicine, Ionnina, Greece. Gillet, A., Sanner, M., Stoffler, D., & Olson, A. (2005). Tangible augmented interfaces for structural molecular biology. IEEE Computer Graphics and Applications, 13-17. Interactive Body Works. (2005). Retrieved May 28, 2007, from http://www.thirteen.org/ edonline/software/bodyworksov.html Johns, N. (2007). The impact of Web3D technologies on medical education and training. Computers & Education, 49, 19-31.
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Onlne Lfe Scences
Johnson, M. (2002). Introductory biology online: Assessing outcomes of two student populations. Journal of College Science Teaching, 31(5), 217-223. The Leading Strand. (2007). Retrieved May 28, 2007, from http://leadingstrand.cshl.edu/ Lieberth, A. K., & Martin, D. R. (2005). The instructional effectiveness of a Web-based audiometry simulator. Journal of the American Academy of Audiology, 16(2), 79-84. Lucid digital identification keys: Their role in on-line taxonomy. (2005). Antenna, 29, 64-70. Retrieved July 12, 2007, from http://www.cbit.uq.edu.au/articles/article_ldik.htm Medical simulations in wound care management. (2006). Retrieved May 28, 2007, from http://www.medicalsimulations.com/sim00001/ Microbiology instructor uses Internet-based “hybrid” course. (2002, July 25). Retrieved July 12, 2007, from http://www.crk.umn.edu/newsevents/notices01-02/KnutsonClass. htm Perry, J., Kuehn, D., & Langlois, R. (2007). Teaching anatomy and physiology using computerbased, stereoscopic images. Journal of College Science Teaching, 36(4), 18-23. Raghupathi, L., Grisoni, L., Faure, F., Marchal, D., Cani, M.-P., & Chaillou, C. (2004). An intestinal surgery simulator: Real-time collision processing and visualization. IEEE Transactions on Visualization and Computer Graphics, 10(6), 708-718. Schumann, G. L. (2003). Innovations in teaching plant pathology. Annual Review of Phytopathol, 47(22), 377-398. Taraban, R., McKenney, C., Peffley, E., & Applegarth, A. (2004). Live specimens more effective than World Wide Web for learning plant material. Journal of Natural Resources and Life Sciences Education, 33(4), 106-111. Teran, J., Sifakis, E., Blemker, S. S., Ng-Thow-Hing, V., Lau, C., & Fedkiw, R. (2005). Creating and simulating skeletal muscle from the visible human data set. IEEE Transactions on Visualization and Computer Graphics, 11(3), 317-328. Todar, K. (2006). Todar’s online textbook of bacteriology. Retrieved May 28, 2007, from http://www.textbookofbacteriology.net Via, D. K., Kyle, R. R., Trask, J. D., Shields, C. H., & Mongan, P. D. (2004). Using high-fidelity patient simulation and an advanced istance education network to teach pharmacology to second-year medical students. Journal of Clinical Anesthesia, 16(2), 8-16.
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Section V Best Practice Model for Online Science Learning
A Ddactc Model for the Development of Effectve Onlne Scence Courses
Chapter.XIII
A.Didactic.Model.for.the. Development.of.Effective. Online.Science.Courses
Do not quench your inspiration and your imagination; do not become the slave of your model. – Vincent van Gogh (1853-1890) In our final chapter, we present a didactic model for online science instruction based upon best practices in both science education and online education coupled with insights from the diverse and substantial literature reviewed in previous chapters. Our goal is to present the reader a process flow through key course design steps bringing together original learning design structures with sensible paradigms from the literature. The general structure of our model is comparable to the three-part convention described by Hegarty-Hazel (1990) that includes planning, design and implementation phases. In our model (Figure 13.1), the planning phase involves three key components including consideration of the society-level values that guide science education, settling on and targeting the individual-specific purpose, and establishing learning activities that evaluate and accommodate learner profiles. In our second, design phase, learning objectives and science content are formulated and the key epistemological and pedagogical positions that Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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will guide implementation are resolved. In the third phase, a course model is implemented through the consideration and selection of appropriate and available technology, deciding on collaboration modalities, establishing assessment means for learners and assembling practical work activities. Our model’s fourth phase, redesign, emphasizes a design-study (i.e., iterative) approach to course development. We have designed worksheets to guide the use of this model and these are located in the chapter appendix. The worksheets can be used as both a checklist to identify the impetus of instructional design as well as an instrument to settle on key objectives for an online science course or activity. They can also be used to work out the specific approaches and content.
Figure 13.1. A didactic model for the development of effective online science courses (Copyright Downing & Holtz)
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Considerations Technological sophistication for its own sake is not the goal of our online science model. However, experimentation with new technologies is a necessity to explore the efficacy of their features for particular science learning objectives. Where educational fads may be gusts of wind that pushes the education wheel around, innovative technology properly applied can provide the tread to move online science education frontward. Hence, an institution’s best-practice model must be pedagogically sound, but foster the integrated technologies with probable benefit for learners. A related issue in best practice modeling is optimizing the technology available to both the instructor and students, whether or not the institution’s instructional technology is cutting edge. As we have described earlier, institutional constraints in the areas of available technology, support of faculty in their instructional design, and technical support for students can dramatically affect the ultimate character of a local best practice model for an online science course..Instructor preparation is a vital component of planning an online science course and should involve both familiarization with technology and pedagogical best practices (e.g., pedagogical content knowledge, collaboration, practical work, assessment of student learning)..Lastly, distance learning is ideally a medium of inclusiveness, making learning possible for those unable to attend an onsite school. Hence, excluding learners on the basis of available technology should be minimized to the greatest extent possible.
Phase.1:.Course.Planning
Values A first step in shaping the purpose of an academic science program, a particular course or even a particular learning activity is consideration of the value of the potential educational endeavor. What is the role of science education in a society? What is your college or program’s mission in the science area? Why are you going to teach this online science course? A useful structure to scrutinize the value of science education with respect to society, the learner, and philosophically is the contingent valuation method described by Bateman and Willis (1999); originally applied to the valuation of the environment. In this valuation plan, there are two major categories, the use value that includes the direct, indirect and option worth of an entity (or undertaking) and the corresponding conservation value that includes the bequest, existence and intrinsic worth.
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Figure 13.2. A contingent valuation model for science education (adapted from Bateman & Willis, 1999) (Copyright Downing & Holtz)
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Our schema for science education applying this framework is given in Figure 13.2. In the use value category, an individual’s use of science education for a profession in science, a vocation that applies science, to make informed decisions as a citizen or science for enjoyment are examples of direct value. Among other things, the direct value may be specifically to enhance the student’s long-term employment prospects, promote professional skills and personal knowledge useful to employer. By distinction, the indirect value is how one learner’s science education has an additional impact on others. For example, how scientific innovations affect the goods, services and well-being of a nation. Scientific innovation remains the key to America’s prosperity so it is a key consideration when structuring learning to foster the indirect value of science. The option value of science education is the untapped portion of knowledge that can be activated where and when needed by a society. The U.S. failure in science, as discussed early on in this book, certainly involves weaknesses in fostering direct, indirect, and option value (i.e., scientific capital) among its people. A matter in need of enduring study is the optimal skills, content and attitudes that create scientific capital and scientific innovation as well as how online learning can affect scientific capital. Although there have been exhaustive efforts to develop goals for science in America, such as the AAAS’s Project 2061—Science for All Americans put together almost two decades ago, these have resulted in little change in America’s decline in science. In the case of Science for All Americans, promoting innovation (i.e., indirect value) of science was not one of the key criteria for selecting learning goals. Can Americans afford to wait for science for all Americans? The other key valuation category is the conservation value of the science education enterprise, consisting of bequest, existence and intrinsic values. How future generations will benefit from today’s science is the bequest value of science education or our scientific legacy. For example, the computer age is a product (i.e., spin-off) of the space program legacy and its corresponding science educational system. Likewise, a concerted effort in renewable fuels science could yield an “energy independence age” by bequest for future U.S. citizens as a modern-day example. An additional conservation value is the existence value of science education that places worth on science as a force of reason in a complex world; that the presence of science is reassuring psychologically. For example, the presence of science is a hopeful stanchion for patients with serious diseases or societies confronting militant ideologies that contradict modern society and its underpinnings in science and reason. Lastly, science education serves an intrinsic value related to the natural quest of understanding of a thinking organism; as the AAAS (1989) stated, “to ponder the enduring questions of human meaning such as life and death, perception and reality, the individual good versus the collective welfare, certainty and doubt.”
Purpose A second key step in the course-planning phase is determining the individual-specific purpose that a course or learning activity serves. What is the prospective future use of science for the learners we serve? What useful competencies should they acquire from this learning? Chin, Munby, Hutchinson, Taylor, and Clark (2004) designate the three key dimensions that anchor the pragmatic purpose of science education for the individual as workplace science,
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Figure 13.3. Workplace science, citizen science, and bench science; the three key science dimensions that anchor individual-specific purpose in science education (after Chin et al., 2004)
citizen science, and bench science (Figure 13.3). In this scenario, workplace science is that used by an individual on a job without the associated purpose to establish new scientific knowledge (i.e., it is about applied science). In contrast, bench science reflects the dimension of learning purposed to have a student contribute to the expansion of knowledge in the future. These dimensions are clearly overlapping at times such as in engineering where applied science often yields new discoveries. The final dimension, citizen science, reflects learning targeted for students that have a low likelihood of pursuing science as a career, but will benefit as lifelong learners through the affordances provided by science education (e.g., Chapter V appendix). In introductory science courses, where it is likely that there will be a mix of students from these three dimensions, the difficulty in properly positioning instructional purpose is elevated. Does an instructor design learning activities for the “highest cognitive denominator”, namely the bench scientist or can learning activities and assignments in a course be varied to meet the needs and purpose of all three dimensions? Another difficulty in building a course for purpose is the alignment of societal need/purpose with an individual’s need/purpose. For example, a nation may decide that it is in its interest to develop scientific capital in physical science, however, the individual’s need and/or interest for learning physical science may be low or absent altogether for workplace, citizen, and bench science. In this instance, what is the responsibility of an institution to design and balance instruction for societal purpose and need versus the individual’s needs, interests or indulgence? One might argue that a reason for America’s failure in science, including the stagnation or relative declines in the physical
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A Ddactc Model for the Development of Effectve Onlne Scence Courses
sciences, mathematics and statistics, engineering etc., from the 1970’s to present (Digest of Education Statistics, 2005; Table 249), is that institutions have been consumed with an indulgence model of education emphasizing areas other than science.
Learner Profile Another key aspect of the planning phase is to understand the character of the learners within a course or program, both why they are there (motivation) and what kind of learners they are (learning styles). For instance, Houle’s (1961) classic study determined that three key motivating factors characterized the adult learner’s interest in further education, achieving specific goals, social activities and for the sake of pure learning. These factors should have a strong bearing on the design of online science courses for adults. For example in online science design, attention to developing a social environment is an important consideration as is recognizing that most adults will be in the citizen science dimension of purpose, so there should be sufficient learning activities and design structures to support those aims. Some knowledge about the diversity of learners typical of a course or program can be very useful in the design phase. A best practice online science model should consider that students will have varying learning styles; hence, activities and scaffolding should be in place to attend to these differences. As a case in point, learning styles for engineers were evaluated in detail by Felder (1993) and by Felder and Brandt (2005) by means of the Felder-Silverman Model of learning styles. Theses studies identify the key learning style differences as: •
Sensory vs. Intuitive: What type of information does the student preferentially perceive: sights, sounds, and psychomotor or memories, ideas, and insights?
•
Visual vs. Verbal: Through which modality is sensory information most effectively perceived: pictures, diagrams, graphs, and demonstrations or sounds, written and spoken words and formulas?
•
Actively vs. Reflectively: How does the student prefer to process information: through engagement in physical activity or discussion or through introspection?
•
Sequentially vs. Globally: How does the student progress toward understanding: in a logical progression of small incremental steps or in large jumps, holistically?
Therefore, online science activities should be varied to optimize individual learning styles. For instance, if a student’s preferred learning styles are for sensory psychomotor, visual demonstrations, active engagement and incremental steps, then a haptic remote experiment with a good visual interface and a detailed stepped instruction guide might provide an ideal learning environment. Again, the key is providing a suite of online activities across the expected learning styles. Many learning style inventories can be employed to investigate student distinction. For deeper consideration, see Coffield et al. (2004), who provide a useful critical review of learning styles.
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Phase.2:.Design
Learning.Objectives We have synthesized the chief learning objectives for science as expressed in the science literature and position them in the following categories: (1) general cognitive skills, (2) science abilities and skills, (3) science content knowledge, (4) interdisciplinary and bridge building, (5) communication and collaboration, (6) attitudes, and (7) psychomotor skills (Figure 13.4). Our divisions generally parallel Bloom’s 1956 taxonomy of educational objectives (cognitive, attitude, and psychomotor), however we break up the cognitive area into common learning objective themes for science and those particularly useful for developing online science activities. Our categorization for learning objectives at this level is not intended to comprehensively delineate all of the intended behaviors possible (i.e., the hierarchical details of the Bloom study) as these are elaborated in the context of pedagogy below.
Figure 13.4. Chief online science learning objectives (Copyright Downing & Holtz)
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A Ddactc Model for the Development of Effectve Onlne Scence Courses
To make these divisions useful in the design phase, we provide learning objective worksheets in the Appendix with selected objectives for each category drawn from the literature with specific concentration on the best practices science literature. The worksheets can be used as both a checklist to settle on key objectives of an online science activity or course as well as to begin the identification of the specific objectives themselves.
Content We consider the learning object paradigm reviewed in previous chapters to be a very useful way of visualizing the development of online science content. For that reason, we have developed the disciplinary content object model (DCOM) building on and extending the valuable “content object model” of Duval and Hodgins (2003). In the DCOM model, the hierarchy of content sequentially includes raw assets, information blocks, learning objects, modules, courses, programs, and disciplines (Figure 13.5). In the raw assets area, common multimedia (text, audio, video, graphics, animation) are accompanied by routine online science assets such as data, simulation, real objects, and scientific instrumentation (e.g., remote). In progression, raw assets contribute to the information blocks (facts, procedures, principles, concepts, processes, overviews and summaries) and in turn, these coalesce in objectives, competencies, themes, curricula and the body of knowledge of a discipline. The body of knowledge of a discipline is the composite collection of learning content and is continuously altered by new knowledge generated by workplace and bench scientific inquiry in both private and academic settings. Of course, knowledge transfer is significant between the disciplines too, but for brevity, our DCOM model is established through the idea of a discipline. Like the Duval and Hodgins (2003) model, we represent the corresponding usability, reusability and context trends of content objects, but we also signify the levels of new knowledge generation and the knowledge feedback loop that nourishes other parts of the content object hierarchy. To make the disciplinary content object model (DCOM) useful to the reader, we provide corresponding DCOM worksheets in the Appendix. The worksheets can be used as groundwork to the implementation phase described later in the chapter that more fully develops the details about raw assets. Most importantly, the worksheets can be used to address the following essential questions: •
What will be the theme of the course?
•
What are the competencies to be developed?
•
What are the specific learning objectives that support the development of competence?
•
What are the key information blocks of this subject associated with the learning objectives?
•
What are the preliminary kinds of raw assets that may be useful for imparting the key information blocks?
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Figure 13.5. The disciplinary content object model (DCOM) for online science showing the hierarchy from raw assets through disciplines (adapted and extended from Duval & Hodgins, 2003) (Copyright Downing & Holtz)
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Pedagogical.Considerations As explained more fully in earlier chapters, we reject strict constructivist formats in favor of what we term neuro-cognitive instrumentalism, which decisively incorporates advances in the neurology of learning to the central tenet of instrumentalism; that cognitive reality is linked to physical reality by hypothetico-predictive processes. Specifically, knowledge acquisition involves testing concepts and theories for how effective they are in predicting and explicating phenomena. Testing may be actual and targeted as in the case of practical work, however much of the testing is continuous and cerebral (e.g., learning while reading).
Figure 13.6. The neuro-cognitive instrumentalism model
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Knowledge: Knowledge questions: Students remember and recall factual information. Questions asked to prompt or assess a student’s thinking behavior at this lowest level in the hierarchy require only factual recall (”regurgitation”), are easy to formulate, and typically incorporate verbs or phrases such as Define, Describe, State, Name, How much is, How did, or What is.
Comprehension Comprehension questions: Students demonstrate understanding of ideas. Questions linked to this level of Bloom’s Taxonomy require students to show more in-depth understanding and typically use the verbs or phrases Explain, Summarize, Translate, Extrapolate, What is the main idea of, or Give an example of.
Synthesis Synthesis questions: Students transform ideas into something new.
Integrating and combining ideas to form a new product, pattern, plan, communication, or structure (including those for abstract relationships, such as classification schemes); solving problems involving creativity or originality. Questions that ask students to function at this cognitive level typically use the verbs Design, Develop, or Propose.
Analysis Analysis questions: Students break ideas down into parts.
Breaking a concept, statement, or question into its components (e.g., assumptions, hypotheses, and evidence) and explaining the relationships between the components and the organizational structures and principles involved. Analysis includes the ability to distinguish relevant information from irrelevant information and facts from inferences, and to recognize fallacies in reasoning. Questions that assess students at this level ask them to Compare, Contrast, Categorize, Discriminate, Question or Relate. Analysis of experimental data Requires functioning at this level.
Application Application questions: Students apply information to unfamiliar situations. Selecting and using information (such as rules, methods such as experimental approaches, and theories) in a new and concrete context (including solving problems and performing tasks). At this level, questions ask students to use what they know without telling them how to use it, and, in addition to Apply, use verbs such as Use, Demonstrate, Compute, Solve or Predict.
Using a specific set of internal or external criteria or standards to arrive at a reasoned judgment (decision, appraisal, or critique) about the value of material for a given purpose. Questions used to assess an individual’s level of competency in this category are typically open ended, with more than one correct answer or more than one path to an answer. They use verbs such as Judge, Appraise, Rate, Defend, Revise or Assess. Critical appraisal of research papers, particularly when the findings are controversial or inconsistent with previous findings, falls under this category.
Evaluation Evaluation questions: Students think critically and defend a position.
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Table 13.1. Questions associated with different cognitive levels (adapted from Allen & Tanner, 2003)
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Consequently, online science pedagogy should be fundamentally guided by activities that stimulate and optimize hypothetico-predictive reasoning as the mind is constantly applying this biological way of thinking as it encounters physical reality. We visually convey the neuro-cognitive instrumentalism paradigm of learning in Figure 13.6. We draw from the seminal work of Lawson (2003) and the insightful summary of the induction-deduction feedback cycle of science by Strahler (1992). We overlay the cognitive framework of the Bloom (1956) survey to the neuro-cognitive instrumentalist concept map to show how it integrates with this model. In our learning premise, there is a physical and cognitive reality, with one’s cognitive reality shaped by sensory perceptions of the real world combined with memory. Percepts are the root of the conceptualization process and are the brain’s mental images of the external environment (in the sense of Campbell, 1985, summarized in Strahler, 1992). Conceptualization involving knowledge (the recall of factual information) and comprehension (the recall and explanation of ideas) are the most basic cognitive levels (Bloom, 1956). Higher-order thinking such as application is directly actualized through the hypothetico-predictive reasoning process. Likewise, analysis is utilized to ascertain the qualities of experimental results and correspondingly, synthesis is utilized make sense of results and derive fresh insights and conclusions. Finally, evaluation involves choosing among and defending evidence through induction, such as a particular generalization or theory. Allen and Tanner (2003) provide a very useful discussion of how questions can be optimized to target various cognitive levels based on insights from cell biology courses. We summarize the Allen and Tanner (2003) strategy in Table 13.1. In addition to neuro-cognitive instrumentalist pedagogy, we anticipate that online science programs will seek to develop best practice models that provide students each of the four pedagogical components identified by Kim and Hannafin (2004) as enabling contexts, resources, tools, and scaffolds. We also expect that online science programs will join campus-based programs in addressing the well-documented disconnect between science reform efforts and science achievement data. For example as we have described earlier, the National Research Council (2005) identified four overriding best practices for science that encompass the needs of social, goal-directed, and enrichment-oriented students. •
Engaging resilient preconceptions;
•
Organizing knowledge around core concepts;
•
Supporting metacognition and student self-regulation; and
•
Cooperative learning.
Furthermore, these practices recognize the foundational need for factual knowledge and practical work, while respecting and considering variances in student knowledge acquisition. Therefore, they are extensively embedded into the model presented here.
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Phase.3:.Implementation
Implementation is the third phase of our didactic model and chiefly involves selecting and putting into operation an effective online science course based on the planning and design phases 1 and 2 described earlier. Following selection of course content using the DCOM approach described previously, the major components of implementation are the determination of knowledge transfer modes to be used, practical work activities, forms of collaboration to be employed and learning assessment strategies to be used. Foundational to implementation are the technologies and respective raw assets available and chosen to optimize learning in the online science course or learning activity.
Online.Course.Typologies Because traditional and online science course formats are often blended or hybridized, typologies have been established to categorize the degree of format combination employed. A helpful typology defined on the basis of the percentage of Web use is given by the Sloan Consortium (2005). In this scenario (Table 13.3, redrafted from Sloan Consortium, 2005) traditional courses do not use any online technology, Web-facilitated courses are predominantly face-to-face courses employing some features of learning management systems, blended or hybrid courses have a substantial amount of content objects delivered online (e.g., 30-79%), and online courses are delivered fully online with no face-to-face meetings. A course model for online science should not be driven by such typologies but be pedagogically sound and hence, settle into one these categories on its own merits. Another typology for online course models is offered by Twigg (2003) that takes into consideration the character of the learning environment, but does not demarcate specific numeric levels of online use (Table 13.4 derived from discussion in Twigg, 2003). We have matched up the Sloan (2005) types to this typology and there is much overlap. The key distinctions of the Twigg (2003) convention are the buffet model (blended to fully online) that provides individualized learning via varied materials and interactivity styles, and the
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Table 13.3. Sloan Consortium (2005) typology of online learning Percentage of Web Use 0%
Type
Characteristics
Traditional
1 to 29%
Web-Facilitated
30 to 79%
Blended/Hybrid
80+%
Online
Course with no online technology used; content is delivered in writing or orally. Course which uses Web-based technology to facilitate what is predominantly a face-to-face course. Uses a course management system (CMS) or Web pages to post the syllabus and assignments, for example. Course that blends online and face-to-face delivery. Substantial proportion of the content is delivered online, typically uses online discussions, and typically has some face-to-face meetings. A course where most or all of the content is delivered online. Typically have no face-to-face meetings.
Table 13.4. Twigg (2003) models for distance courses Sloan (2005) Type Equivalents Supplemental
Replacement
Buffet
Emporium
Fully online
Retain traditional Course Structure, but use Web to supplement with additional materials. Reduction of class meeting times with replacement by online learning activities. Customize the learning environment to each student by using a variety of learning materials and interactivity styles.
Web-facilitated
Eliminates class meeting and replaces with learning resources center, online materials and on-demand personalized assistance. Heavy utilization of instructional software. Instructor is responsible for all interactions, personally answering every inquiry.
Online
Blended
Blended-Online
Online
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emporium model which replaces a class with on-demand service through use of a learning resources center and instructional software.
Knowledge.Transfer A pivotal step in developing an online science course is deciding on the optimal modes of knowledge transfer contingent upon available technologies and institutional infrastructure. Knowledge transfer to the learner in the online science environment occurs in one of four modes: (1) instructor to learner, (2) science learning objects to learner, (3) peers and teams to learner, and (4) knowledge transfer from the science community of learners/practice to the learner (Figure 13.6). Of course, there are multitudes of knowledge transfer possibilities between nodes before taking the four pathways to the learner. Instructor to learner knowledge transfer may be considered the traditional means of knowledge conveyance and is accomplished through such medium as live lecture, online notes, and individualized communication between the instructor and student. We include the possibility of virtual instructors and tutors in path 1. In contrast, science learning objects to learner knowledge transfer occurs in the absence of the instructor as a student utilizes linked information from Web sites, animations, simulations, VR environments and so on. This form of knowledge transfer is a chief mode for online science courses and typically places the instructor in an indirect role as a designer/facilitator of the learning environment. Peers and teams to learner knowledge transfer represents the main collaborative form of
Figure 13.6. Knowledge transfer modes for online science. Pathway options are numbered 1 through 4. (Copyright Downing & Holtz)
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learning in online science and is accomplished through such mechanisms as peer review, class discussion forums where students can present their experience or new learning, or in teams where group synergy or cognitive differences facilitate knowledge transfer. We include the possibility of virtual classmates in path 3. The fourth knowledge transfer mode, science community of learners to the learner takes place as students are connected to bench and workplace scientists through such entities as online communities of practice and collaboratories. This type of knowledge transfer mode holds promise as a means of establishing a lifelong learning connection between the student and the discipline’s dynamic body of knowledge.
Knowledge.Transfer.Attributes.and.Hybrids Knowledge transfer online can occur in a variety of ways including hybrid forms that mix attributes of online and conventional learning. Puntshart (2005) has reviewed and characterized the types of knowledge transfer hybrids that are possible with online learning. On one hand are the potential approaches typically associated with online learning: Web-enhanced learning, asynchronous delivery, voluntary forms of knowledge transfer (e.g., visiting a self-selected Web site), self-directed learning and an open learning community (Table 13.2 column 1). The counterparts to these approaches are the more conventional varieties of knowledge transfer characteristic of face-to-face learning (Table 13.2 column 2). The affordances of online environments have made possible, if not necessitated the realignment in knowledge transfer emphasis from the instructor-to-learner (path 1 in Figure 13.6) to other forms (paths 3 and 4). For instance, a greater emphasis is placed on the development and use of learning objects in instruction to support self directed and personalized learning in online science (path 4). Prospectively, science students can be more easily connected to and learn via communities of practice (path 2), including bench and workplace scientists thus supporting a more lasting connection to ongoing learning than the more ephemeral character of a course. Hybridization of conventional and online types of knowledge transfer is a viable and common strategy particularly in blended courses. Moreover, emerging information technologies such as real-time virtual classrooms, hint of a future where the knowledge transfer emphasis will reestablish stronger instructor to learner styles (path 1).
Table 13.2. Knowledge transfer attributes and hybrids (adapted from Puntshart, 2005) Online Web-enhanced Asynchronous Voluntary forms of knowledge transfer Self-directed Learning Open Learning Community
Conventional Face-to-face Synchronous Obligatory forms of knowledge transfer Externally Controlled Learning Closed Learning Community
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Technology.and.Practical.Work. What an online science course will ultimately look like, its functionality and effectiveness will depend to an extent on the technologies available and their deployment. Online science practical work and collaboration is inextricably tied to technology, so we treat them together in this section. New online technology options and to a lesser extent, practical work options, are emerging all of the time. Therefore, our model’s approach is to categorize and make perceptible the options, affordances and desired features of common technologies and practical work so that their qualities can be reviewed against learning objectives and pedagogy. Thus, the model permits a strategic decision-making process in technology and practical work selection. We accomplish the analysis and implementation of technology, practical work and collaboration features in support of the online science course model via corresponding worksheets provided in the Appendix.
Collaboration We regard the types of social interaction to support collaboration in an online science course to be six-fold, expressed by the axes combinations in Figure 13.7. Our model’s approach to establish collaboration strategies is analogous to that of the technology and practical work above. This approach is to categorize and make perceptible the options, affordances, desired features as well as selected strategies of collaboration modalities so that their qualities can be reviewed against learning objectives and pedagogy. In the implementation process, the most effective axes to carry out a learning objective need to be chosen. We accomplish the analysis and implementation collaboration features in support of the online science
Figure 13.7. Types of social interactions possible to support collaboration objectives (Copyright Downing & Holtz)
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course model via corresponding worksheets, also provided in the Appendix. As discussed in Chapter V, another important consideration for collaboration is staging its sequential roll out in a course. To recap, the first stage is to initiate presence, motivate students to access and use the chosen collaboration tools and approaches. This stage should be accompanied by presenting guidelines as well as teaching students how to collaborate consistent with the course strategy. The subsequent stages of collaboration typically involve: •
Online socialization and maintaining presence;
•
Information/knowledge transfer;
•
Jointly testing theories and concepts for theory predictive value; and
•
Disengaging with the course collaboration environment or sustaining it through communities of learners, such as collaboratories.
Assessment.of.Online.Learning This model calls for a combination of formative and summative assessment that is authentic, cumulative and integrated into course design. Discussion and collaboration rubrics are essential to both place value and assess interaction in the online science environment. For practical work, forms-based and multimedia laboratory reports and concept mapping are a few of the innovative approaches that we have used in meeting that goal; many other approaches and cases are documented in the literature and presented in this text. Research papers are a very dependable form of evidence for assessing competence and often demonstrate the higher cognitive levels of synthesis and evaluation. As contended in Chapter IX, the most pressing issues for assessing learning in online environments come down to only two: (1) instructor acceptance of the security limitations inherent to the format and (2) the need for instructors to use the full range of assessment options, rather than those that offer expediency alone. Campus-based assessment modalities are possible online; as noted, even interactive assessments are possible, with supportive technology and careful design. Our pilot study supported the trend noted in our literature review and content analysis towards expanded innovation in assessment methods, however: faculty still tend to use modalities that provide the highest level of security (e.g., proctoring of exams) or those that are the easiest (e.g., automated scoring quizzes).
Phase.4:.Course.Assessment.and.Redesign
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Course assessment for the purpose of redesign will tend to be case-specific and vary by institutional guidelines. However, there are some published models that serve as best practice guides for how to accomplish course redesign in a program. Skelton, West, and Zeff (2002) provide a good example for courses in a dentistry program. The general components and procedures are: •
Course documentation (syllabus, student evaluations, and other course materials);
•
Instructor self-evaluation (course effectiveness, outcomes assessment measures, faculty performance, overall evaluation);
•
Peer review (a review committee evaluates the materials in a structure paralleling the self-evaluation);
•
An agreed upon stratagem and schedule for course or learning activity revision; and
•
System assessment (review and improvement of the review process itself).
Naturally, for a blended or fully online science course, proportional attention should be given to reviewing the employed technologies, practical work, science content, collaboration effectiveness, and so on. The conclusions of the periodic course review and assessment should feed back into the design phase. For online science courses, this might result in a general repurposing of a course towards new or modified learning objectives, the development of a new suite of raw assets, or updating materials to take advantage of the affordances of emerging technology. The National Center for Academic Transformation (NCAT), whose focus is on employing information technology to improve learning outcomes for students, has a variety of useful resources on course redesign, including case studies and models (see resources available at http://www.center.rpi.edu/index.html).
Conclusion This chapter presented a didactic model for the design of online science courses based on the best practices expressed in recent online science literature as well as the general literature on science education and online learning. Our instructive model includes four phases: planning, design, implementation and redesign. In the planning stage, we advise that a thorough review of values of science education be considered to help frame the mission of a learning endeavor. Too often, science course and program development jumps over this reflective step and the broader result is that much of the potential use and conservation value of science education is undercapitalized by our society. Likewise, it is important to understand the purpose of a course in terms of the kind(s) of learning dimensions it will serve. Should course structure be focused specifically for bench, workplace, or citizen scientists? Is it possible to build course features and scaffolding to effectively teach and support combinations of these dimensions? In the planning stage, it is also vital to understand the characteristics of the learner including their motivation and student learning styles. As Rousseau wrote in Emile, “If you are a wise Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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man you will observe your pupil carefully before saying a word to him.” Whether it is for social activities with newly formed student friends, to achieve a degree for a specific job, or for the pure love learning, understanding student motivation and addressing it through the design elements of a course is beneficial to student learning and satisfaction. Likewise, providing learning activities that build on the breadth of learning styles present in a course, whether they cluster (e.g., engineering students) or not will lead to a greater impact in student learning outcomes. While learning style scrutiny is largely a programmatic level concern, these insights about students are rarely filtered down to instructors for instructional design. We suggest academic programs should be more thorough and systematic in assessing learning styles as they may influence best practice instructional design for online science. In the design phase of our model, learning objectives, content and pedagogy are key considerations. Building on the insights revealed in the planning phase, we have fashioned a seven-fold division of learning objectives compiled from the science literature. Our divisions, expressed in corresponding worksheets, identify general and science-specific cognitive objectives, science content objectives, interdisciplinary objectives, collaborative skills objectives, as well as psychomotor and attitudes objectives akin to the categories of the Bloom (1956) study. For content selection in the design phase, we advocate a content object model as a useful paradigm to work through the logic of how basic raw assets of scientific knowledge can be assembled to support information blocks and in sequential rank, learning objectives and competence. We have adapted a previous content object model and have established the disciplinary content object model (DCOM) to represent these interrelationships from raw assets through the body of knowledge of a discipline. Pedagogically, our model is placed firmly within what we term neuro-cognitive instrumentalism, described in earlier chapters and above, which incorporates the neurological learning-based, hypothetico-predictive model of Lawson (2003) and the empiricist school of instrumentalism that places emphasis on the effectiveness of theories and concepts to explain and predict phenomena. As described, this approach is completely amenable to the learning needs of social, goal-directed and enrichment-oriented students, as well as the designated contingent use-value (Bateman & Willis, 1999). It is our intent to urge reintroduction of conveyed structure and realism into science education, through this model and the hypothetico-predictive brain processes that form the basis of human learning. In our implementation phase, the final online science course model is a product of the planning and design phase; but also takes into consideration the styles of knowledge transfer, the employable technologies, practical work and collaboration activities to be utilized, plus strategies for assessment of student learning in the online environment. Assessment of learning activities designed to take advantage of such resources reinforces the need for instructors to use the full range of assessment options, rather than those that offer expediency alone. We view knowledge transfer as originating from four external sources to the learner: the instructor (real or virtual), peers and groups (real or virtual), the science community of learners, and science learning objects (real or virtual). How these transfer pathways will be implemented is strongly a function of pedagogical choice and the rationale supporting use of conventional and online knowledge transfer attributes and their hybrids; such combinations are codified in emerging distance course typologies.
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Our online science model’s approach is to select technology, design practical work, and establish collaboration schemes is to categorize and make perceptible the options, affordances and desired features a series of worksheets derived from best practice in these areas as expressed in recent literature in science. Lastly, our model supports an iterative approach to course design. It is our contention that online science courses should be reviewed regularly, particularly in light of changing technology, to generally improve their effectiveness to achieving online learning outcomes.
Epilogue Although online science learning is but a portion of the science education endeavor, it is a growing and useful component. If our current investigation of online science learning best practices and technologies contributes to stimulating a science instructor’s ingenuity in this area, exhilarating more students to pursue scientific knowledge, and/or assists in building national science capital we will have achieved our primary objectives.
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About the Authors
About the Authors
Kevin.F..Downing, PhD, is an associate professor at DePaul University’s college for adult learners, the School for New Learning. His research interests include the investigation of Miocene fossil mammals in the western United States and Pakistan, the character of small mammal diversity and paleoecology through volcanic disturbances and global cooling maxima, the record of stratigraphic and paleogeographic change during the Himalayan Orogeny, and the application of 3-D virtual laboratories for geological instruction in distance learning settings. He was one of the three faculty tasked with establishing the Distance Education program for adults at DePaul, is the author of several science course guides for the DE program, and regularly teaches online science courses. Dr. Downing has published widely in paleontology and stratigraphy, and has presented papers on the application of 3-D learning objects to online geoscience education. Dr. Downing received BS degrees in astronomy and geology from the University of Illinois at Urbana, a Master of Science in Teaching degree in geology from the University of Florida, and a PhD in geoscience with emphasis in paleobiology from the University of Arizona. Jennifer.K..Holtz, PhD, is an assistant professor at DePaul University’s college for adult learners, the School for New Learning. Her areas of interest include the actual act of research and characteristics of researchers, how creativity affects both teaching and learning in the sciences and the implications of brain research to learning. Since joining DePaul University, she has authored multiple distance course guides in the sciences and teaches almost exclusively online, including the mentoring of distance students. Dr. Holtz has published widely in clinical sciences, clinical education, and in distance learning and assessment in the sciences. Her PhD is in adult and continuing education with emphasis in research education, from Kansas State University; her Masters is in gerontology with clinical emphasis, from Wichita State University, and her Bachelors is in biology with emphasis in human biology, from Kansas Newman College (now Newman University).
Author.of.the.Foreword: W.. Franklin. Spikes, EdD,. is a professor of educational leadership and director of the Doctoral Program at Kansas State University. His research interests are related to program
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About the Authors
evaluation, human resource development and higher education. Spikes is a past president of the American Association for Adult and Continuing Education, a past American Council on Education Fellow in Academic Administration, and is a member of the inaugural class in the International Adult Education Hall of Fame. Currently he serves as the president of the Faculty Senate at Kansas State. Spikes has been actively involved in the field of higher and adult education for a number of years, has published widely and has presented scholarly works at venues throughout North America and abroad Dr. Spikes received his BS Ed, MS, and EdD from Northern Illinois University.
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0 Index
Index
Symbols 3-D anaglyphs 131 3-D animation software 176 3-D audio 173 3-D color scanners 176 3-D graphics 167 3-D haptic device 180 3-D haptic environment 182 3-D images 176 3-D learning environments 171 3-D learning immersion 171 3-D learning objects 129, 168 3-D model 122, 133, 147 3-D photorealism 176 3-D rendering 163 3-D virtual learning environments 171 3-D virtual presenters 183 3-D virtual reality, portable 175 3-D virtual worlds 133 3-D visualization environments 168 3G 160 4G 160 5th generation 180 A AAAS benchmarks 37 abstract graphical representation 252 abstraction 248, 253, 254 accountability 197, 203 achievement 33 active simulation 126, 127 actual classroom practice 35 adaptive radiation 245 advanced educational games 160, 177
advanced learning systems 160 advanced placement 32, 36, 37 AEHS 106 affective skills 80, 83 Africa 4, 7, 8, 9, 12, 270 capacity 7 AIML 182 al-Haitham, Ibn 76 ALICE 182 alignment with standards 39 American Phytopathological Society 276 AMSER 151 anaglyphs 131, 132, 135 3-D 131 analysis 200, 206, 210, 303, 308, 309, 314 anchored instruction 24 anchoring assignment 209 animations 50, 127, 131, 147, 204, 218, 225, 233, 236, 274, 278, 281, 285 2-D 152 3-D 132, 135, 151 annotation 113, 115, 270 system 270 Applescab 275 application 198, 200, 201, 210, 303, 313 applied computing technology (ACT) 163 Applied Math and Science Education Repository (AMSER) 151 APSnet Education Center 276 Aristotelian philosophy of logic and reason 76 artificial intelligence markup language (AIML) 182 artificial linguistic Internet computer entity (ALICE) 182 ASD 173
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Index Asia 270, 279 Ask a Scientist 281, 282, 288 assessment 49, 51, 52, 54, 55, 108, 113, 114, 118, 160, 217, 218, 219, 220, 221, 222, 224, 236, 238, 292, 293, 304, 309, 310, 311 center 203 of online learning 309 standards 197 strategies 39 assignment posting 207, 208, 209 astronomy 216, 218, 228, 229, 232, 237 visualization using virtual reality 228 asynchronous 52 communication 217, 234, 236 discussion forums 221 interaction 35 lecture 223 atmosphere 244 atmospheric-oceanic circulation 244 attitude 295, 298, 311 audience response technology 210, 211 augmented reality (AR) 149, 157, 283 in structural molecular biology 283 Australia 7, 11, 12 authentic assessment 49, 197, 309 authentic practical work 75, 82 authentic representation 35 authentic role-play 36 automated feedback 52 autonomous activities 249 autonomous field work 249 autonomy 54 autosteroscopic displays (ASD) 173 B backward design process 200, 201, 210 Bacon, Roger 76 baseline 55 basic education 6, 9 behaviorism 22, 23, 24, 29 behaviorist 23, 25 Bell, Alexander Graham 98 benchmark 50, 244 bench science 74, 296 Bequest conservation value 4 bequest value 295 best practices 50, 55, 104, 111, 217, 219, 224, 229, 238, 291, 293, 299, 303, 310, 312 biodiversity 244, 245 blended/hybrid format 51, 52 blended chemistry courses 222
blended course models 224 blended courses 31, 150, 216, 237 blended schools 37 blended science courses 150 blogs 106, 107 Bloom’s Taxonomy 79, 199, 201, 298 Boston’s Museum of Science 184 Brazil 9 Bruner, Jerome 23 Burkina Faso 8 C Calculus WIZ® 220 Canada 55 capacity 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Carleton College 258 case-based learning 270 case management 268 case simulations 268 case studies 244, 245, 260 CAVE system 170 Ceara Basic Education Quality Improvement Project 9 Center for Human Simulation, University of Colorado 273, 288 Center for Science Education 39 Central America 9 capacity 9 Centre for Biological Information Technology, University of Queensland 279 CGH 175 chemistry 216, 221, 222, 223, 224, 225, 227, 229, 233, 234 chemistry courses, design and implementation 222 Chile 9 China 7 Cicero 242 circuitry 230, 231 in physics 230 Cisco® Virtual Classroom 161 CIT 159 CiteSeerX 109 citizen science 18, 19, 20, 21, 50, 54, 296, 297 citizen scientist 35 Claris Home Page 286 ClarisWorks 286 Click and Learn 281, 282 clinical applications 273 clinical simulators 284 cognitive fluency 199 cognitive load 23, 25, 29, 199
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Index cognitivism 22, 23, 24, 25 COI 103 Cold Spring Harbor Laboratory 54, 209, 277, 287 collaboration 2, 3, 6, 10, 36, 37, 39, 52, 54, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 216, 217, 218, 219, 221, 234, 235, 236, 238, 242, 243, 244, 248, 249, 250, 256, 257, 260, 261, 265, 267, 270, 287, 292, 293, 308, 309, 310, 311, 312 approaches to group structures 104 in virtual worlds 110 structures for online science 98 collaborative learning 26 and online science 101 online learning 99 remote visualization 168 collaboratories 164 College Blue Book 51 Committee of Ten Report 78 commodity management 279 communication 50, 51, 52, 204, 298, 306, 313 and information technologies (CIT) 159 Communities for Physics and Astronomy Digital Resources in Education Pathway (ComPADRE) 151 community of inquiry (COI) 103, 105 community of practice (CoP) 34, 102, 103, 110, 245 ComPADRE 151 competence-based program 209 competition 268 comprehension 200, 201, 303 CompuCell3D 279 computer-assisted practical work 74 computer-generated holography (CGH) 175 computer information technology 168 computerized feedback 210 computerized overlay 283 computer supported/mediated collaborative learning (CSCL) 100 concept mapping 197 conceptual 52 analysis 210 understanding 268, 276, 278, 281, 283 conceptualization 303 Confucius 73 conservation value 3, 4, 293, 295, 310 constructivism 15, 19, 22, 23, 24, 27, 78, 83, 84, 196, 197, 199, 201
and practical work 83 constructivist 15, 19, 24, 25, 26, 27, 54, 196, 201, 209, 211 learning 107 paradigm 84, 101 consumers of science 35 contemporary physics online 220 content analysis 51, 54 content knowledge 19, 35, 39 content object model 123 context learning 165 contingent valuation 79 continual improvement 36 continuing medical education (CME), smallgroup 269 conversational posting 207 cooperative learning 26, 99 cooperative online learning 99 coordinated learning 99 CoP 102 correctness 198, 210, 212 course 216, 217, 218, 219, 220, 221, 222, 223, 224, 228, 230, 232, 237, 238, 242, 243, 244, 260, 261, 265, 266, 267, 268, 270, 271, 273, 275, 276, 277, 281, 285, 287 activities 51, 52 assessment and redesign 309 casting technology 52 in biological and medical sciences 266 course design 291, 298, 309, 312 content 299 learning objectives 298 pedagogical considerations 301 course implementation 304 assessment of online learning 309 collaboration 308 knowledge transfer 306 knowledge transfer attributes and hybrids 307 online course typologies 304 technology and practical work 308 course materials 50, 51 course model 292, 304, 308, 309, 311 course planning 293 learner profile 297 purpose 295 values 293 course typologies 304, 311 CREativity in Science and Technology (CREST) 82 CREST 82 CSCL 100
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Index Culture Demanded by Modern Life, The 78 cumulative assessment 309 curriculum reform efforts 35 Cyberglove™ 179 cyberinfrastructure 242, 256, 260 cyberschools 31 CyberTech 1 37 D Datastreme Atmosphere 244 Datastreme Ocean 244, 262 Datastreme WES 244 DCOM 299, 304, 311 debates about science 15 deep-knowledge approach 36 default educators 197 Delphi study 21, 27 DePaul University 51, 54, 209, 212 DESCANT 38 design approaches 51 Designing E-learning Systems to Celebrate and Nurture Teaching in Science and Technology 38 Design of the Fittest 209, 212 developmental software 286 Development Learning Center Project 8, 11 DiagnosisTM 275 DIC 5, 13 didactic model 50 digital chemistry for large lecture 223 digital divide 31 digital libraries 99, 109, 122, 151, 152, 155, 216, 219, 234, 235, 237, 238 Digital Library for Earth System Education (DLESE) 260 digital library resources 39, 40 digital literacy 1, 121, 159, 216, 265, 291 digital repositories 238 digital subscriber lines (DSL) 160 Digital Video Applications Project (DiVA) 125 direct field collection 248 direct use value 4 direct value 295 disciplinary content object model (DCOM) 299, 311 discovery 201, 213 discrete knowledge 198, 204, 209, 210 assessing 204 discussion 204, 205, 206, 207, 208, 209, 210, 211 assessing 205 board 52
disease maps 276 distance education 28, 49, 51, 54 distance learning 21, 28 distance science education 7, 10 distributed computing 260 DiVA 125 DLESE 260 Dolan DNA Learning Center 277, 278 Downing-Holtz model 292, 294, 298, 300, 306, 308 Dubai Internet City (DIC) 5 E e-designers 38 e-learning environments 38 e-learning objects 181 E-Learning Support Project 6, 7, 12 e-logbooks 141 e-mail 51, 52 e-moderating 108, 118 e-moderator 108, 115 e-schools 31 ecology 245, 252 educational games 33, 41, 122, 137 educational research 34, 50 educational researchers 34 educational science games 137 Education Quality Improvement Program (EQIP) 6 Education Reform for Knowledge Economy (ERfKE) 5 Education Reform Project 6, 11 education theory 14 EDUCOSM system 113 efficacy 114 Egypt 6 Einstein, Albert 1 electronic laboratory notebook (ELN) 112, 113, 118 electron micrograph images 285 eMinerals project 110 empirical-analytic assessment 197 enabling contexts 54, 303 engineering 17, 20, 21, 28 environmental descriptors 276, 279 Environmental Systems Research Institute (ESRI) 255 Environmental Virtual Field Laboratory 253 epistemology 14, 15, 35, 36, 291 EQIP 6 ERfKE 5, 6 ESRI 255, 262
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Index EuroTev Collaboratory, The 235 evaluation 196, 197, 200, 205, 212, 303, 309, 310 EVDO 160 evolution 245, 258, 259, 260 evolution data only/evolution data optimized (EVDO) 160 existence conservation value 4 existence value 295 experiential activities 49 experiential learning 25 experimentation 55 experiment kits 220, 221 exploration 254 Exploratorium of San Francisco 184 extinction 245 F face-to-face 39 classrooms 34 courses 34 K-16 science teachers 36 learning 33 factual knowledge 199, 204 failure in science 84 Faulkes Telescopes Project 7 feasibility 39 Feynman, Richard 121 field trip reports 246 field trips 54, 74, 88 field work 52, 54 film footage 248 filtered content 200, 201, 210 Flash learning games 267 interactive 267 Fle3 107, 118 flexibility 18, 21, 22, 26, 32, 33 floor control 161 formative assessment 197, 210, 309 fossilization 251 fossil preservation 245 fragmentation 34 FRAME 165 framework for the rational analysis of mobile education (FRAME) 165 France 249 full service 171 functional morphology 245 Future Learning Environment (Fle3) 107
G Galileo 76 game-like laboratories 126, 127 general cognitive skills 298 GEODE 171 Geographic Data in Education Project (GEODE) 171 geographic information systems (GIS) 250, 255 geography of mountain ranges 249 geological visualization 252 geology 243, 244, 245, 250, 252, 258 introduction to 245 geoscience virtual laboratories 251 GeoWall 170 Germany, former East 3 gesture 108, 109 in online science classroom 108 Gilbert, William 76 Girls Re-designing and Excelling in Advanced Technology [GREAT!] 19 GIS 255 global economy 3, 5 Global Environmental Projects 8 Global Learning Center 8 global processing 199 global science 2 global scientific capital 1 goal-oriented learner 20, 21 government guidelines 19 graduate course 38 Grain Storage Tutor 279, 281 graphical interface 217 graphics presentation 168 GREAT! 19 grid computing 236 group structures 104 H hands-on laboratory 128, 143 approaches for online students 143 Hands-On Universe, Russia 7 HAPTEX 179 haptic-enabled systems 133 haptic design 160, 178 haptic interface 149 Haptic Sensing of Virtual Textiles (HAPTEX) 179 head mounted display (HMD) 173 Hegarty-Hazel model 86
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Index HHMI 281 high-stakes assessment 203, 211 higher education 50 Higher Education Enhancement Project 6 HMD 173 Home Experiment Kit (HEK) 143 home laboratories 122 Hooper Virtual Natural History Museum 258 hotactions interface 163 Houle, Cyril 80, 90 Howard Hughes Medical Institutes (HHMI) 281 hybrid distance continuing nurse education 270 hybrid science courses 150 hydrologic patterns 253 HyperStudio 286 hypertextbooks 176 hypothetico-predictive 22, 25, 34, 84, 88, 97 model 25, 199, 209 theory 22 I ICT 122 IEEE LOM 124 IEEE standard 122 Illinois 51 virtual campus database 51 illustrative practical work 74, 85 IMM 124, 125 immunology education 270 Immunology Online 270 implementation 291, 292, 299, 304, 308, 310, 311 in-service teacher 33 incorrectness 198, 212 India 7 indirect use value 4 indirect value 295 individual-specific purpose 291, 295 individualized learning 37 induction-deduction feedback cycle 303 inductive discovery 201 inductive learning activities 201 information and communication technologies (ICT) 122 information visualization 168 infrared camera-based optical tracking system 175 infrastructure 2, 3, 4, 6, 7, 8, 10 initiative 33
innovations 295 Innovative Technologies for Collaborative Learning and Knowledge Building (ITCOLE) project 106 inquiry-based 268 course 204 learning 19, 28 inquiry empowering technologies 88 institutional constraints 293 instructional architect 39, 40 instructional design 21, 22, 28 instructor preparation 293 instrumentation 273 integrated assessment 293, 309 integrated laboratory 204 interaction learning 165 Interactive Body Works 286, 288 interactive Flash learning games 267 interactive modules 278 interactive multimedia (IMM) 124, 125, 154 interdisciplinary 298, 311 Internet for Technology, Science, and Mathematics (ITSAM) 34 interpretive assessment 197, 199, 201 online 201 intrinsic conservation value 4 intrinsic value 295 introduction to geology 245 introductory biology 268 introductory modules 33 introductory physics for non-science majors 221 inventive model 22, 29 inverse distance learning 250, 262 investigative phase 77, 94 investigative practical work 74 investigative skills 78 iPods® 164, 166 Israeli 38 Italy 55 ITCOLE project 106 iterative assessment 37, 198, 201, 292, 312 iterative evaluation 292, 312 ITSAM 34, 37 J Japan 7 Jordan 5, 6 just-in-time 246 learning 245
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Index K K-12 30, 31, 32, 33, 34, 35, 36, 37, 43 K-16 31, 34, 35, 36, 43, 122, 151 online science learning 36 science instruction, obstacles to 34 Kennesaw State University 37 KIE 36 kindergarten to university 36 kit-based laboratory 268 kitchen chemistry 143, 221, 222 knowledge 197, 198, 199, 200, 204, 209, 210, 293, 295, 296, 297, 298, 299, 301, 303, 304, 306, 307, 309, 311, 312 acquisition 301, 303 integration environment programs (KIE) 36 organization 198, 199 transfer 98, 99, 103, 111, 299, 304, 306, 307, 309, 311 transfer attributes 307 transfer hybrids 307 L lab kits 143 LABNET project 164 laboratory 74, 78, 79, 80, 81, 83, 85, 86, 88 e-notebooks 112 kits 204 Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) 139 LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) 139, 142, 154 land resource management 253 landscape 253, 254 language of science 81, 82, 95 large-group science classes, assessing 210 LateBlight 275, 276, 279 Lawrence Berkeley National Laboratory 279 LCMS 124 learner modeling 160 learner profiles 291 learning activities 51, 54, 198, 201, 204, 209, 210, 268, 277 learning and content management system (LCMS) 124 learning concepts 22 learning environments 26, 30, 33, 36, 37, 38, 39 learning games 160 learning management software 204
learning management systems (LMS) 39, 99, 106, 114, 115, 122 learning objectives 204, 291, 293, 298, 299, 308, 310, 311 learning object metadata (LOM) 112 standard 124 learning object repositories 33, 124 learning objects 98, 99, 122, 123, 124, 125, 135, 152 3-D 129, 131, 132 classification 123 learning science, issues in 22 learning systems 161 teacherless 181 learning theories 14, 15, 22, 24, 25, 27, 51 learning theory 19, 22, 23, 24, 25, 27 Lebanon 5, 6, 12 Life-long Learning and Training Project 9, 12 lifelong learners 55, 256 lifelong learning 2, 3, 12, 31, 34, 104, 106 life sciences, online 265 listserv 113, 117 lithosphere 244, 253 live online classrooms 111 LMS 99, 122 Local processing 199 LOM 112 long-term memory 25 Louis Stokes Louisiana Alliance for Minority Participation [LS-LAMP] 19 LS-LAMP 19 M m-learning 99, 164 Malaysia 7 management decisions 276, 279 management tasks 277 manipulation skills 271 massive multi-player online gaming environments (MMOG) 111 Mathematica® 220 mathematics 216, 217, 218, 219, 224, 238 math phobia 17 Mauritania 8 McClintock, Barbara 277 MDGs 2 meaningful questions 37 Med-Sim-Eagle 270 mediated reality experimentation 139 medical sciences 265, 266 medical simulations 268
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Index MEPI 6 MERLOT 124 Mesa Community College 204 metacognition 273, 277, 281 metacognitive skills 198, 209 assessing 209 metacognitive tasks 24 metavisual capacity 168 Michigan 31, 45 microscopic images 268 microscopic slides 276 Middle East 2, 4, 6, 7, 12 scientific capital 4 Middle East Partnership Initiative (MEPI) 6 Millennium Development Goals (MDGs) 2 Millennium Science Initiative 8, 12 MILOs 167 Minority K-12 Initiatives for Teachers and Students [MKITS] 19 mission 293 MIT Haystack Observatory 163 mixed reality experiential learning landscape model (MR ELL) 138 MKITS 19 MLE 167 MMOG 111, 139 mobile interactive learning objects (MILOs) 167 mobile learning 99, 164 engine (MLE) 167 objects 167 mobile science learning 160 mobile technologies 164 for online science education 164 modding 111, 116 model practical work 75 Mohr’s circle 252 molecular visualization 229 Montana State University 273 morphogenesis 279, 288 three-dimensional simulation of 279 Morrill Act 78 MR ELL 138 MUD 139 multi-user-virtual environment (MUVE) 177 multi-user dungeon (MUD) 139 multi-user virtual environments 168 multicasting 110 multimedia 30, 33, 35, 40, 121, 122, 124, 125, 129, 136, 142, 145, 152, 224 hypertextbooks 176 lessons 218
Multimedia Educational Resource for Learning and Online Teaching (MERLOT) 124 multiplayer online gaming environments (MMOG) 139 Museum of Paleontology 259 MUVE (multi-user-virtual environment) 177 myDNAi 278 N naïve inductionism 77 nano-visualization 232 National Defense Education Act (NDEA) of 1958 78 National Oceanic and Atmospheric Administration 244 National Park Geography, United States 250 National Research Council 54, 303, 314 National Science Digital Library (NSDL) 39, 82, 151 National Science Education Standards (NSES) 78 National Science Foundation 109, 119, 161 national science standards 31, 34 national standards 19, 26 nature of science (NOS) 18, 35, 79, 83, 89, 126 NCLB 18 NDEA 78 NetMath courses 219 Network EducationWare (NEW) 161 neuro-cognitive instrumentalism 301, 303, 311 neurological basis of learning 19, 23, 25, 28, 84, 91 neurological learning 209 theories 24 New Zealand 35 NGOs 8 Niger Basin Water Resources Development and Sustainable Ecosystems Management Project 8, 12 No Child Left Behind Act (NCLB) 18 non-government organizations (NGOs) 8 North Africa 4 North Carolina 270, 288 NOS 35 notebook computers 164 novice-to-expert 198, 212 knowledge 198 NSDL 39, 151 NSES 78 Nuffield Science Teaching Project 77
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Index O oceanography 244 OCoP 102 online-facilitated courses 31 online assessment 224 online collaboration 99, 100, 101, 103, 104 role of instructor 107 stages and models 101 online community of practice (OCoP) 102, 103 online geoscience 242 case 1 244 case 2 244 case 3 245 case 4 245 case 5 245 case 6 247 case 7 248 case 8 248 case 9 249 case 10 250 case 11 251 case 12 252 case 13 252 case 14 253 case 15 255 case 16 257 case 17 258 case 18 260 collaboration 256 courses 243 cyberinfrastructure 256, 260 environmental issues 247 trends 260 virtual field trips 246 virtual laboratories 250 virtual science museums 256 online learning 21, 31, 160 assessment of 309 game development, models for 137 pedagogy, considerations influencing 21 online life sciences 265 case 1 267 case 2 268 case 3 268 case 4 269 case 5 270 case 6 270 case 7 270 case 8 271 case 9 273
case 10 273 case 11 275 case 12 275 case 13 277 case 14 279 case 15 279 case 16 281 case 17 283 case 18 284 case 19 284 case 20 285 case 21 285 trends 287 online mathematics 216 online mathematics and physical science case 1 219 case 2 220 case 3 221 case 4 221 case 5 222 case 6 223 case 7 224 case 8 224 case 9 227 case 10 228 case 11 229 case 12 230 case 13 231 case 14 232 case 15 232 case 16 233 case 17 234 case 18 235 case 19 236 case 20 237 case 21 237 collaborations 234 courses 219 digital libraries 234 simulations 224 trends 238 virtual labs 224 virtual science museums 234 online math learning activities 217 online physical science 216 online practical work, critiques of 84 online professional development for science teachers 37 online quizzes 266 online resources 245, 246, 259, 260, 285 online science 1, 4, 15, 16, 19, 20, 22 approaches to practical work 121
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Index collaboration structures for 98 knowledge transfer for 98 role of practical work 73 social software for 106 online science assessment case 1 204 case 2 205 case 3 209 case 4 210 online science classroom, gesture and silence 108 online science collaboration, evaluation 113 online science courses, didactic model for development 291 online science education 2, 10 emerging learning systems 161 global implications for 10 mobile technologies 164 strategies for 159 technologies for 159 online science environment 51 online science learning 30, 31, 33, 36, 37, 38, 40, 43, 44, 51, 52 3-D learning objects for 132 benefits of simulations 129 contemporary approaches to 40 objects 38 online science resources 31 online technological innovation, impact of 186 online videoconferencing 37 online virtual science learning environments, examples of 136 On the Fly 279, 281 option use value 4 option value 18, 295 oral surgery 271, 288 organizational skills 33 origin of life 245 P paleontology 248, 258 Paleontology Portal 259 passive demonstration 126, 127 Pavlov, Ivan 22 Pavlova, Anna 196 PCK 35, 39 PDA-based physics laboratory (PDL) 167 PDAs 166 PDL 167 PEARL 55, 141, 142 pedagogical bridge 43 pedagogical content knowledge (PCK) 35, 39
pedagogy 33, 36, 39, 298, 303, 308, 311, 313 of inquiry 39 performance-based learning 198 personal digital assistants (PDAs) 164 personalized support 33 personal knowledge 81, 95 personal responsibility 33 Peru 9 Philippines 7 physical science 216, 217, 218, 219, 233, 235, 237, 238 learning activities 218 physics 216, 220, 221, 224, 229, 230, 231, 233, 235, 236, 237 introductory and survey 231 remote laboratory 233 Piaget, Jean 23, 199, 201, 209 planning and teaching strategies 39 planning phase 291, 295, 297, 311 plant pathology 275 Plants, Pathogens and People 275 podcasting 160 Poincaré, Jules Henri 14 policy 3, 5, 8, 9, 12 making 3 reform 5 portable 3-D virtual reality 175 portable audiometer 284 postmodernism 15 postmodernist 15 Poverty Reduction Strategy Paper (PRSP) 8 power science 18, 19, 20, 21, 27, 54 Practical Experimentation by Access to Remote Learning (PEARL) 55, 141 practical field work, value of 80 practical work 19, 25, 26, 37, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 122, 129, 139, 143, 145, 147, 150, 152, 217, 219, 221, 238, 266, 270, 273, 276, 278, 281, 287, 292, 293, 301, 303, 304, 308, 309, 310, 311, 312 additional purposes for 80 controversies 82 epistemological and procedural introduction 86 history in U.S. and UK 76 in online science 73 in science education, types of 75 learning outcomes 93 purpose and value 79 tasks, designing 85 United Kingdom 76
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0 Index United States 78 pre-course counseling 33 pre-service teacher 33 preconceptions 268, 273, 276, 278, 281 Presentation Replay and Interaction with Streaming Media (PRISM) 223 PRISM 220, 223 probeware 39 problem-based learning 198 process science 77 professional development 33, 36, 37, 38, 39, 43 progressive era 78 project-based learning format 244 PRSP 8 psychomotor skill 298 push-pull technologies 121 puzzles 122 2-D and 3-D 149 virtual 149 Q QuarkNet 236, 239 QuarkNet/Grid 236 QuickTime 131 QuikData2 51 quizzes 244, 246, 266, 268 R radical constructivism 19, 24, 27, 50, 84, 201 Reachin Display 179 real-time 278, 284, 289 intestinal surgery simulation 284 mentor 38 real-world data 36 real data 218 real remote experiment 128 real world data 260 redesign 292, 309, 310 remote data 260 remote experiment 33, 38, 127, 128, 139, 140, 141, 142, 152, 168 affordances 142 examples of 141 remote experimentation 127, 128, 139, 140, 142, 163, 231, 232, 233 design approaches 139 remote laboratories 121, 122, 126, 128, 140, 141, 160, 233, 234 remote manipulation 126, 127 remote sensing 250, 252, 260 research-practice gap 34
Resistan 275 resources 250, 256, 259, 260, 261, 303, 306, 310, 311 reusability 37, 38 Rogers, Will 30 Romania 249 rubric 197, 204, 206, 209, 309 Rural Education Project 9, 12 Russian Federation 6 scientific capital 6 S Scaffolded Knowledge Integration Framework for Instruction (SKI) 81 scaffolding 23, 24, 25, 29, 81, 87, 122, 135, 217, 297, 303, 310 in practical work 87 school-to-college interface 31 school science 74, 75, 90 science, technology, engineering, and mathematics (STEM) 151, 160 Science, Technology and Higher Education Reform Program 8 science, technology and innovation (STI) 2 science, U.S. failure in 16 science abilities and skills 298 science assessment 197 science capacity 1, 2, 3, 4, 9, 10, 11 science centers 184 science collaboration miscellany 113 science competence 196 science content 291, 298, 299, 310, 311 knowledge 298 science education 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 18, 19, 24, 26, 27, 30, 31, 32, 35, 36, 37, 39, 43, 50, 51, 54, 291, 293, 295, 310, 311, 312 additional factors influencing 19 concurrence in 14 controversies in 14 digital libraries and repositories 151 online 10 valuation 3 Science for All Americans 295, 312 science knowledge, collaboratories 109 science learning 12, 15, 19, 20, 22, 25, 26, 28, 29, 110 science literacy 15, 18, 19 Science of Collaboratories Organization 110 science phobia 17 science potential 36 science valuation 293, 295
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Index Science Wars 14 scientific capital 30, 43 scientific community 36 scientific inquiry 36, 38 scientific knowledge 34 scientific method 76, 77, 90 scientific reasoning 14 scientific visualization 168 SciVee 160 SCO 139 SCORM 124, 139, 152 Scrooge, Ebenezer 159 secondary education 9 security 203, 211 sedimentation 245 seismology 257 self-assessment 33, 37, 56 self-directed field experiences 273 self-directed field trips 273 self-directed learners 218 self-directed learning 238, 285 self-directed research 249 self-esteem 33 sequenced modules 37 sequencing activities 37 sharable content object reference model (SCORM) 124 shareable courseware objects (SCO) 139 shared resources 55 Sharing Learning Objects in an Open Perspective (SLOOP) 124 Shockwave 131 short-term memory 25 silence 109 in online science classroom 108 simulated distance learning 271 simulated laboratory 268 reports 268 simulated pathology 268 reports 268 simulated radiographics 268 reports 268 simulated remote experiment 128 simulations 30, 33, 39, 40, 50, 54, 74, 85, 88, 122, 127, 128, 129, 132, 136, 137, 151, 152, 160, 216, 219, 221, 224, 266, 268, 271, 273, 274, 275, 279, 287 in online science learning, benefits of 129 simulteaching 161 Singapore 7 situated cognition 37, 87
situated learning 24 six facets of understanding 200, 201 SKI 81 Skinner, B. F. 22 Sloan Consortium 51 SLOOP 124 Snowdonia 249, 250 social computing 165 social learner 20 social software 106, 107, 115 for online science 106 software delivery 171 soil 245, 247, 248, 253 soil science 243, 245 blended and just-in-time 245 South America 9, 270 capacity 9 Southeast Asia 2, 7 capacity 7 South Korea 7 space-time visualizations 253 spectrophotometry 225, 234 SPSS® Exact Module® 52 Sputnik era 78 SRS 151 SSciVEE 171 Stanford University 270 stature of science 15 STEM 151, 160 stereograms 275 stereoscopic imaging 275 in anatomy and physiology 275 STI 2 stream erosion 252 streaming digital video 125 in online science 125 streaming video 35, 99, 122, 125, 145, 151, 152, 278 structural molecular biology 283 structure-function approach 270 student attributes 33 student preconceptions 211 student research 163 student response system (SRS) 151 SUDSE© 50 background 50 methods 51 online survey 58 purpose 51 summative assessment 197, 211, 309 Supportive Scientific Visualization Environment for Education (SSciVEE) 171
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Index surgery simulation for pharmacy education 270 survey of geology for teachers 244 Survey of Undergraduate Distance Science Education (SUDSE©) 51 sustainability 3, 8 Swiss 270 symbol representation 217 synchronous chat online discussion 221 synchronous communication 52, 217, 234, 236 synchronous interaction by chat 233 synchronous lecture 223 synchronous online discussion 220 synchronous streaming media 125 synthesis 200, 201, 303, 309 T tablet PCs 164 tangible models 283 Tanzania 8 teacherless learning systems 181 teamwork orientation 104, 119 techniques 197, 201, 202, 204, 213 technological innovations 51 technology 292, 293, 304, 308, 309, 310, 312 capital 2 skills 33 Technology Enhanced Elementary Math and Science Project (TEEMSS2) 39 TEEMSS2 39 telescope 225, 232 tertiary education 2, 3, 8, 9 Thailand 7 third generation digital technology (3G) 160 Thomas, Lewis 265 Thorndike, Edward 22 Todar’s Online Textbook of Bacteriology 285 tools 303, 309, 315 topography 250, 252 tracking 21, 27 system 21 transition to teaching 36 Tunisia 6 TurfBlight 275 Turkle, Sherry 49 Twain, Mark 216 typologies for Web-enabled science laboratories 126 U U.S. failure in science 16, 18 U.S. virtual schools 31
UbiComp 166 ubiquitous computing (UbiComp) 166 ucmp 259 Uganda 8 UK 249 UK National Curriculum 77 uncoverage 200, 201, 210 understanding, six facets of 200 unidata 260 United Kingdom 55, 76 United States 78 United States Agency for International Development (USAID) 4 University of California 259 Museum of Paleontology 184, 259 University of Colorado 273 University of Queensland 279 University of Wisconsin-Madison 285 university science education online 49 USAID 4, 6 use value 3, 4, 293, 295 V validity of science 15 values 291, 293, 295, 310 Vanderbilt University 37 van Gogh, Vincent 291 video clips 279, 281 videoconferencing 183, 269 videotaped expert lectures 281 Vietnam 7 Vietnamese peninsula 7 virtual chemistry for colleges 227 virtual chemistry for schools 224 virtual classmates 180 virtual classrooms 160, 161 virtual experimentation 163 virtual field trips 30, 33, 40, 54, 121, 122, 145, 146, 152, 242, 245, 246, 247, 248, 249, 250, 259, 260, 261, 265, 287 virtual field work 273 Virtual Frog Builder Game 279 Virtual Frog Dissection Kit 279 virtual instructors 160, 180 virtual instrument 136, 139, 142, 153 virtual instrumentation 50 virtual laboratories 30, 33, 216, 219, 239, 242, 250, 251, 260, 261, 273 virtual learning environments 160 Virtual Montana 249, 250 virtual museums 160, 258, 261, 281 virtual objects 74
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Index virtual paleontology 245 virtual planetarium interface 184 virtual practical work 74 virtual puzzles 149 for learning science 149 virtual reality (VR) 106, 160, 274, 275, 285 virtual schools 31, 33, 37 virtual school science 30 virtual science centers 185 virtual science environments, affordances of 135 virtual science museums 216, 219, 234, 235, 237, 242, 256, 258, 260, 266 science centers 184 Virtual Scientist Program 37 virtual studio system 163 virtual telescope 232 virtual tour 145, 152 virtual tutors 180 virtual worlds, 3-D 133 virtual worlds, caveats of using 135 Visible Human Project, The 121 visualization 160, 217, 220, 222, 228, 229, 232, 235, 236, 238, 246, 251, 252, 253, 261 advances in 168 vocational education 6 vocational pathway 9 vocational training 82 VOIP telephony 183 volcanoes 248 VR 106 VS assistant 33 VS counselor 33 VS Designer and Teacher 33 VS Teacher 33 Vygotsky, Lev 23, 24, 199, 201
Web-based audiometric simulation 284 Web-based courses 159 Web-based distributed visualization, types of 171 Web-based inquiry science environment (WISE) 36, 81, 92 Web-based learning objects 30 Web-based oral surgery for distance undergraduates 271 Web-based science activities 30, 43 Web-based science instruction 30, 31, 168 Web-based training 37 Web-broadcasting studios 160 Web-enabled or online laboratory 126 Web-enabled science laboratories, typologies for 126 Web-facilitated format 52 Web 3-D 171 Webcasting Studios 162 Web conferencing 106 Whole Frog 279 Wikis 106, 107 wireless navigation 175 WISE 36, 81 WNET New York 285 Women on the Prairie: Bringing Girls into Science through Environmental Stewardship 19 working memory 25 workplace science 74, 75, 89, 295, 296, 313 worksheets 292, 299, 308, 309, 311 World Bank, The 2, 3, 4, 8, 11 World Health Organization 270 WorldWatcher 171
W
XaviXTechnology™ 183
X
Watson, John B. 22 Web-based audiometer simulation 284
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Web - Based Learning and Teaching Technologies
International Journal of
Web-Based Learning and Teaching Technologies Liliane Esnault, E.M.LYON, France ISSN: 1548-1093 EISSN: 1548-1107 Institution: US $395.00 Online Only (Institution): US $345.00 Individual: US $110.00
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