Online Science Learning: Best Practices and Technologies Kevn F. Downng DePaul Unversty, USA Jennfer K. Holtz DePaul Unversty, USA
Information Science Publishing Hershey • New York
Acquisition Editor: Senior Managing Editor: Managing Editor: Assistant Managing Editor: Managing Development Editor: Assistant Managing Development Editor: Assistant Development Editor: Editorial Assistant: Copy Editor: Typesetter: Cover Design: Printed at:
Kristin Klinger Jennifer Neidig Jamie Snavely Carole Coulson Kristin M. Roth Jessica Thompson Deborah Yahnke Rebecca Beistline Amanda Appicello Amanda Appicello Lisa Tosheff Yurchak Printing Inc.
Published in the United States of America by Information Science Publishing (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail:
[email protected] Web site: http://www.igi-global.com and in the United Kingdom by Information Science Publishing (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 3313 Web site: http://www.eurospanbookstore.com Copyright © 2008 by IGI Global. All rights reserved. No part of this book may be reproduced in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this book are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Online science learning : best practices and technologies / Kevin F. Downing and Jennifer K. Holtz, authors. p. cm. Summary: “This book reviews trends and efforts in web-based science instruction and evaluates contemporary philosophies and pedagogies of online science instruction. This title on an emergent and vital area of education clearly demonstrates how to enrich the academic character and quality of web-based science instruction”--Provided by publisher. ISBN 978-1-59904-986-1 (hardcover) -- ISBN 978-1-59904-987-8 (e-book) 1. Science--Study and teaching (Higher) 2. Web-based instruction. 3. Education--Computer network resources. 4. Education, Higher--Computer-assisted instruction. 5. Education, Higher--Effect of technological innovations on. I. Downing, Kevin F. II. Holtz, Jennifer K. Q179.97.O55 2008 507.8’5--dc22 2007049561
British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is original material. The views expressed in this book are those of the authors, but not necessarily of the publisher.
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
v
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
v
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
v
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
vii
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
v
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
x
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
x
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
x
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
x
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
x
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
xv
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.
xv
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,
xv
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.
xv
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.
xv
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.
Downng & Holtz
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.
Downng & Holtz
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.
Onlne Scence
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.
Downng & Holtz
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…
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
•
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Downng & Holtz
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-
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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:
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
•
Enhance the U.S.’s research capacity in order to sustain scientific and technical innovation.
•
Cultivate U.S. talent to enhance the nation’s math, science, engineering, and foreign language expertise.
•
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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.
Downng & Holtz
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 ● ● ● ● ● ●
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Controverses and Concurrence n Scence Educaton
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.
References Association of American Colleges and Universities. (2007). College learning for the new global century. National Leadership Council for Liberal Education & America’s Promise (LEAP). Washington, DC: Association of American Colleges and Universities. Association of American Universities. (2006). National Defense Education and Innovation Initiative: Meeting America’s economic and security challenges in the 21st century. Washington, DC: Association of American Universities. Altschud, R. A. (2003). US science education: The view from a practicing scientist. Review of Policy Research, 20(4), 635-645. Bapi, R. S., Pammi, V. S. C., Miyapuram, K. P., & Ahmed, A. (2005). Investigation of sequence learning: A cognitive and computational neuroscience perspective. Current Science, 89, 1690-1698. Beghetto, R. (2003). Scientifically based research. ERIC Clearinghouse on Educational Management. Retrieved September 10, 2007, from http://www.ericdigests.org/20035/based.htm
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Controverses and Concurrence n Scence Educaton
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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.
Vrtual School Scence
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.
Downng & Holtz
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.
Vrtual School Scence
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;
•
Broadening course offerings to include outside experts and resources;
•
Supporting particular student needs (at-risk, rural, special needs, accelerated learners);
•
Providing high quality courses consistent with standards (e.g., state);
•
Providing flexibility such as asynchronous learning;
•
Optimizing facilitation of learning with top qualified teachers;
•
Cost effectiveness;
•
Pedagogical benefits of adding multimedia;
•
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.
Downng & Holtz
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.
Vrtual School Scence
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);
•
Absence of curriculum guidance;
•
Inappropriate classroom materials and pedagogical content knowledge (PCK) for NOS;
•
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.
Downng & Holtz
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.
Vrtual School Scence
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.
Downng & Holtz
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.
Vrtual School Scence
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.
0 Downng & Holtz
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.
Vrtual School Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Vrtual School Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Vrtual School Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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,
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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,
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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%).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
(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.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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.
Takng Unversty Scence Educaton Onlne
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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 )
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Takng Unversty Scence Educaton Onlne
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%
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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%
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
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.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
The Role of Practcal Work n Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
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
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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.
The Role of Practcal Work n Onlne Scence
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.
Downng & Holtz
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.
The Role of Practcal Work n Onlne Scence
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.
Downng & Holtz
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.
The Role of Practcal Work n Onlne Scence
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.
Downng & Holtz
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)
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
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
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
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.,
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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.
The Role of Practcal Work n Onlne Scence
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.
Downng & Holtz
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.
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
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
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
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
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
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
00 Downng & Holtz
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.
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 0
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.
0 Downng & Holtz
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.
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 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)
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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.
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 0
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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
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 0
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.
0 Downng & Holtz
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
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 0
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
•
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.
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
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,
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Knowledge Transfer and Collaboraton Structures for Onlne Scence
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.
Downng & Holtz
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
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
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
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
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
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
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Section II Online Science Instructional Strategies and Technologies
Onlne Scence
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.
Downng & Holtz
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Onlne Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Downng & Holtz
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.
Onlne Scence
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.
0 Downng & Holtz
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.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Onlne Scence
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.
Downng & Holtz
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.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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.
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
0 Downng & Holtz
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,
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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).
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
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.
Onlne Scence
References Allen, I. E., & Seaman, J. (2005). Growing by degrees: Online education in the United States. Washington, D.C.: The Sloan Consortium. Amigud, Y., Archer, G., Smith, J., Szymanski, M., & Servatius, B. (2002). Assessing the quality of Web-enabled laboratories in undergraduate education. In Proceedings of the Frontiers in Education Conference (F3E-12-F3E-16). Los Alamitos, CA: IEEE. Baggott, L. M. (1998). Multimedia simulation: A threat or an enhancement of practical work in science education. In J. Wellington (Ed.), Practical work in school science—Which way now? (pp. 252-270). London: Routledge. Cartwright, H. M., & Valentine, K. (2002). A spectrometer in the bedroom—The development and potential of Internet-based experiments. Computers & Education, 38(1-3), 53. Chu, K. C. (2004). Using virtual instrument to develop a real time Web-based laboratory. International Journal of Distance Education Technologies, 2(1), 13. Clark, I., & James, P. (2005). Blended learning: An approach to delivering science courses online. Paper presented at 2005 Breaking Down Boundaries Conference, Adelaide, South Australia. Cohen, E. B., & Nyce, M. (2006)..Learning objects and e-learning: An informing science perspective..Interdisciplinary Journal of Knowledge and Learning Objects, 2, 23-34. Combs, L. L. (2004). Science education in the Web era. Journal of Computers in Mathematics and Science Teaching, 23(2), 139-149. Convertini, V. N., Albanese, D., Marengo, A., Marengo, V., & Scalera, M. (2006).. The OSEL taxonomy for the classification of learning objects. Interdisciplinary Journal of Knowledge and Learning Objects, 2, 125-138. Cooper, M., Donnelly, A., & Ferreira, J. (2002). Remote controlled experiments for teaching over the Internet: A comparison of approaches developed in the PEARL project. Retrieved May 1, 2007, from http://discoverlab.com/References/112.pdf Couture, M. (2004). Realism in the design process and credibility of a simulation-based virtual laboratory. Journal of Computer Assisted Learning, 20, 40-49. d’Ham, C., de Vries, E., Girault, I., & Marzin, P. (2004). Exploiting distance technology to foster experimental design as a neglected learning objective in labwork in chemistry. Journal of Science Education and Technology, 13(4), 425-434. Dev, P., Montgomery, K., Senger, S., Heinrichs, L., Srivastava, S., & Waldron, K. (2002). Simulated medical learning environments on the Internet. Journal of the American Medical Informatics Association, 9(5), 437-447. Dickey, M. D. (2003). Teaching in 3D: Pedagogical affordances and constraints of 3D virtual worlds for sychronous distance learning. Distance Education, 24(1), 17. Dickey, M. D. (2005). Engaging by design: How engagement strategies in popular computer and video games can inform instructional design. Educational Technology, Research and Development, 53(2), 67-83. Dickey, M. D. (2006). Game design narrative for learning. Educational Technology, Research and Development, 54(3), 245-263. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
Di Paolo, T., & Scanlon, E. (2004). Mediating science learning through information and communications technology. London: Routledge Press. Downing, K. F. (2001). Incorporating 3-D virtual fossils to enhance Web-based learning experiences in paleontology. NAGT Symposium on Technology in Geology Instruction. North-Central Section, Geological Society of America Abstracts and Programs. Duan, B., Hosseini, H. M. M., Ling, K. V., & Gay, R. K. L. (2006). An architecture for online laboratory e-learning system. International Journal of Distance Education Technologies, 4(2), 87-101. Duan, B., Ling, K. V., Hosseini, H. M. M., & Gay, R. K. L. (2005). An e-learning model for lab-based courses. International Journal of Engineering Education 21(6), 10681075. Duval, E., & Hodgins, W. (2003). A LOM research agenda. Paper presented at the WWW2003 Conference, Budapest, Hungary. Retrieved May 28, 2007, from http://www2003. org/cdrom/papers/alternate/P659/p659-duval.html.html Educause. (2006). 7 things you should know about virtual worlds. Retrieved April 22, 2007, from http://www.educause.edu/ir/library/pdf/ELI7015.pdf Ellington, H. (1981). Games and simulations in science education. New York: Nichols Publishing. Ertugrul, N. (1999). Towards virtual laboratories: A survey of LabVIEW-based teaching/learning tools and future trends. Retrieved May 1, 2007, from http://discoverlab. com/References/Ertugrul.pdf Faltin, N., Böhne, A., Tuttas, J., & Wagner, B. (2002, August). Distributed team learning in an Internet-assisted laboratory. Paper presented at International Conference on Engineering Education, Manchester, United Kingdom. Foley, K. (2003). The big pocket guide to using & creating virtual field trips. Kirkland, WA: Tramline. 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. Frear, V., & Hirschbuhl, J. J. (1999). Does interactive multimedia promote achievement and higher level thinking skills for today’s science students? British Journal of Educational Technology, 30(4), 323-329. Fulker, D. (2003, June). Thoughts on the NSF role: Network-enabled frameworks for knowledge leveraging. Paper presented at the NSF Post Digital Library Futures Workshop, Chatham, MA. Retrieved May 1, 2007, from http://www2.sis.pitt.edu/~dlwkshop/ paper_fulker.html Gagné, R. M. (1985). The conditions of learning and theory of instruction. New York: Holt, Rinehart, and Winston. Gillet, D. (2001). Hand-on laboratory experiments in flexible and distance learning. Journal of Engineering Education, 90(2), 367-385. Hartmann, S. (1996). The world as a process: Simulations in the natural and social sciences. In R. Hegselmann, U. Mueller, & K. G. Troitzsch (Eds.), Modelling and simulation Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
in the social sciences from the philosophy of science point of view (pp. 77-100). Dordrecht: Kluwer. Haughey, M., & Muirhead, B. (2005). Evaluating learning objects for schools. E-Journal of Instructional Science and Technology, 8(1). Retrieved May 28, 2007, from http://www. usq.edu.au/electpub/e-jist/docs/vol8_no1/fullpapers/eval_learnobjects_school.htm Holmberg, R. G., & Liston, M. L. (1998). Home lab activities for introductory biology delivered at-a-distance. Retrieved May 1, 2007, from http://www.zoo.utoronto.ca/ able/volumes/vol-19/mini.15.holmberg.pdf 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. Hsu, Y. S., & Thomas, R. A. (2002). The impacts of a Web-aided instructional simulation on science learning. International Journal of Science Education, 24(9), 955-979. Huang, C. (2004). Virtual labs: E-learning for tomorrow. PLoS Biology 2(6). Retrieved May 1, 2007, from http://biology.plosjournals.org/perlserv/?request=get-toc&issn=15457885&volume=2&issue=6 IEEE. (2002). Draft standard for learning objects metadata version. New York: Institute of Electrical and Electronics Engineers, Inc. Retrieved May 1, 2007, from http://itsc. ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf Iverson, K. M. (2004). E-learning games: Interactive strategies for digital delivery. New York: Prentice-Hall. Jeschofnig, P. (2004). Effective learning experiences for distance learning science courses with self-contained laboratory kits. Paper presented at the 20th Annual Conference on Distance Teaching and Learning, Madison, WI. Retrieved May 1, 2007, from http:// www.uwex.edu/disted/conference/Resource_library/proceedings/04-1260.pdf Jones, S. (2006). Reality and virtual reality: When virtual and real worlds collide. Cultural Studies, 20(2-3), 211-226. Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2-10. Kennepohl, D., Baran, J., Connors, M., Quigley, K., & Currie, R. (2005, November). Remote access to instrumental analysis for distance education in science. International Review of Research in Open and Distance Learning, 6(3). Retrieved May 28, 2007, from http://www.irrodl.org/index.php/irrodl/article/view/260/404 Kukulska-Hulme, A., Foster-Jones, J. J., Anne, Mallett, E., & Holland, D. (2004). Investigating digital video applications in distance learning. Journal of Educational Media, 29(2), 13. Locatis, C., Gaines, C., Liu, W. L., Gill, M., Carney, J., & Foster, J. et al. (2005). A blended training approach using videoconferencing for distance education. Journal of the Medical Library Association, 94(4), 464-468. Ma, J., & Nickerson, J. V. (2006). Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Computing Surveys, 38(3). Mason, B. (2006). Digital libraries in support of science education: a case for computational physics. Computing in Science and Engineering, July/August, 62-65. Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Downng & Holtz
Mayer, R. E. (2005). Introduction to multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning. New York: Cambridge University Press. McClean, P. E., Johnson, C., Rogers, R., Daniels, L. M., Reber, J., & Slator, B. M. et al. (2005). Molecular and cellular biology animations: Development and impact on student learning. Cell Biology Education, 4(2), 169-179. McMorrow, J. (2005). Using a Web-based resource to prepare students for fieldwork: Evaluating the dark peak virtual tour. Journal of Geography in Higher Education 29(2), 223-240. Navarathna, N., Fedulov, V., Martin, A. & Fransson, T. (2003). Design of a remotely-controlled laboratory exercise in aerodynamics. Retrieved May 1, 2007, from http://www.energy. kth.se/proj/projects/Remote_labs/Downloads/mekanisten-article-remotelab.pdf Oblinger, D. (2006). Simulations, games, and learning. Retrieved May 1, 2007, from http:// www.educause.edu/ir/library/pdf/ELI3004.pdf Papaioannou, G., Karabassi, E. A., & Theoharis, T. (2001). Virtual archaeologist: Assembling the past. IEEE Computer Graphics and Applications, 21, 53-59. 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 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. Recker, M., Dorward, J., & Nelson, L. (2004). Discovery and use of online learning resources: case study findings. Educational Technology & Society, 7(2), 93-104. Redecker, G. H. J. (2003). An educational taxonomy for learning objects. Proceedings of the 3rd IEEE International Conference on Advanced Learning Technologies, Athens, Greece, July 9-11. Los Alamitos, CA: IEEE. Riffell, S., & Sibley, D. (2005). Using Web-based instruction to improve large undergraduate biology courses: An evaluation of a hybrid course format. Computers & Education, 44, 217-235. Ritter, F., Berendt, B., Fischer, B., Richiter, R., & Preim, B. (2002). Virtual 3D jigsaw puzzles: Studying the effect of exploring spatial relations with implicit guidance. Retrieved May 1, 2007, from http://mc.informatik.uni-hamburg.de/konferenzbaende/mc2002/ konferenzband/mc2002_05_paper/mc2002-34-ritteretal.pdf Ritter, F., Strothotte, T., Deussen, O., & Preim, B. (2001). Virtual 3D puzzles: A new method for exploring geometric models in VR. IEEE Computer Graphics and Applications, 21(5), 11-13. Ross, S., & Scanlon, E. (1995). Open science: Distance teaching and open learning of science subjects. London: Paul Chapman Publishing Ltd. Rossett, A., Douglis, F., & Frazee, R. V. (2003). Strategies for building blended learning. ASTD: Learning Circuits. Retrieved May 1, 2007, from http://www.learningcircuits. org/2003/jul2003/rossett.htm
Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Onlne Scence
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., &
