Appendixes



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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Appendixes

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary This page in the original is blank.

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Appendix A Workshop Background Paper: Excerpts from a Document Prepared by the Committee on Information Technology BACKGROUND: THE COMMITTEE ON INFORMATION TECHNOLOGY IN UNDERGRADUATE SCIENCE EDUCATION The Committee on Information Technology in Undergraduate Science Education was organized in December, 1995 and completed its work in November of 1999. The committee’s charge was to improve undergraduate science education through the use of information technology. Nicholas Turro of Columbia University chaired the committee. Committee members included: Stephen Ehrmann, American Association for Higher Education; Bernard Gifford, Academic Systems Corporation; Steven Gilbert, American Association for Higher Education; B. James Hood, University of Central Arkansas; Deborah Hughes Hallett, University of Arizona; John Jungck, Beloit College; Stephen Lerman, Massachusetts Institute of Technology; Ronald Stevens, University of California at Los Angeles; and Jack Wilson, Rensselaer Polytechnic Institute. Committee members who served limited terms included Linda Chaput, Interactive Sciences, (December, 1995— October, 1996); Stephen Hurst, University of Illinois at Urbana-Champaign (February, 1996—November, 1999); C. Bradley Moore, Ohio State University (December, 1995—April, 1997); Dorothy Stout (February, 1996-November, 1999); and James Whitesell, University of Texas at Austin (May, 1997— November, 1999). Nancy Devino served as Staff Officer to the committee from December, 1995 through February, 1999. Jay Labov served as Staff Officer from March, 1999 until the committee completed its work in November, 1999. Other staff included: Gail Pritchard, Research Associate; Terry Holmer, Project Assistant; Stacy Lucas, Consultant; Steven Olson, Editor; and James Lawson, Editor.

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary APPENDIX A: TABLE OF CONTENTS     TRANSFORMING PEDAGOGY   37     INTRODUCTION,   37     CURRENT PRACTICES,   38     APPLICATIONS OF IT ACROSS SME&T DISCIPLINES,   50     ASSESSING STUDENT LEARNING,   50     CONCLUSION,   61     LEARNING USING INFORMATION TECHNOLOGY   62     INTRODUCTION,   62     EDUCATIONAL RESEARCH AND CURRICULUM REFORM,   62     LEARNING THEORIES AND EDUCATIONAL TOOLS,   64     CONCLUSION,   79     REFERENCES & BIBLIOGRAPHY,   79

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary TRANSFORMING PEDAGOGY INTRODUCTION Information technology (IT) has dramatically transformed scientific practice in the last 50 years. The ability to handle massive amounts of data, collect data continuously in extraordinarily fine detail, perform millions of computations per second, visualize information in three and four dimensions with rotations and translations, and simulate immensely complex phenomena with multivariate, multidimensional models has become commonplace. Computers are ubiquitous in scientific workplaces, whether in offices, laboratories, or field settings. Electronic preparation of manuscripts has allowed professionals greater control over layout, graphics, and symbols, such that many now use publishers as distributors rather than as editors, layout specialists, or typesetters. The World Wide Web, faxes, and e-mail allow scientists, mathematicians, and engineers to collaborate on a daily basis over long distances and across many time zones. New technologies allow scientists to control and collect real-time data from distant locations with expensive, highly specialized research instruments. This information technology revolution also has changed the undergraduate learning environment. Students are as likely to “surf the Web” from their dormitory rooms or teaching laboratories as they are to visit a library. They know that a portable computer or handheld calculator can perform almost all of the calculations expected in exams or problem sets. Having used computers extensively in high school, some students now arrive at college already knowing how to enter and analyze scientific data in spreadsheets and statistical graphing packages. Many more students who enter college are comfortable using these tools for a variety of other purposes. Reflecting these changes, many faculty members have begun to post lectures and reading lists on the Internet, create on-line discussion groups, and to use IT in other ways to deliver courses. However, to be truly effective, information technology needs to be embedded in instruction, not just provided as an additional activity to a standard course or program. For example, in several science, mathematics, engineering, and technology (SME&T) disciplines, professors and instructors are using more than one technological innovation to help students learn. Earth scientists employ geographic information systems (GIS), global positioning systems (GPS), and remote sensing. When teaching calculus classes, some faculty use information technology to emphasize multiple representations of mathematical ideas (symbolic, verbal, numerical, and graphical) and realistic problem solving. Graphing calculators and spreadsheets have made numerical and graphical representa-

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary BOX A-1 Expanded Educational Experiences Advocated by Reform Initiatives Education reformers anticipate that science, mathematics, engineering, and technology faculty will recast their curricula to tap the broad range of deep content accessible through IT. Such changes would reflect and encourage the learning opportunities available outside the classroom, such as ubiquitous access to digital libraries, the Internet, databases of raw information, GIS, GPS, simulations, and other IT tools. However, the goal of these reforms is not merely to add a topic to the syllabus of a course or curriculum, but to transform the curriculum so that students are well-prepared to enter the SME&T professional arena or participate in meaningful ways in lifelong learning about science and technology. tions easily accessible to undergraduate and precollege students. This means that most students—though not yet all faculty—expect technology to be part of a mathematics classroom (see Box A-1). However, the challenge to rethink the mathematics curriculum has just begun. The emergence of calculators that can do symbolic manipulation (computer algebra systems, or CAS) will push faculty and administrators to consider which topics in algebra and calculus they need to teach with pencil and paper—even though machines can do them—because pencil and paper develop understanding. In addition, faculty need to learn how to use computer algebra systems to promote understanding. Can they be used to illuminate topics that students have in the past learned by rote, without much understanding? Mathematicians, scientists, engineers, teachers, and educational researchers will need to reach a consensus on such questions. Both the power and widespread availability of these new technological capabilities, and recent scientific findings about how people learn are forcing science, mathematics, and engineering faculty members to rethink what and how they teach. Many have begun to use new pedagogical practices. The committee found that IT can play a particularly effective role in supporting six such practices: visualization simulation real-world problem solving collaboration inquiry design. The following section describes current technological tools that enable and support each of these pedagogical practices. CURRENT PRACTICES Visualization Computers are powerful tools to help

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary learners visualize, simulate, and construct objects in the created or natural worlds. Improvements in technology and significant decreases in the price of high-end computers mean that molecular modeling workstations now can be bought for as little as $2,000 compared to ten or more times that much only several years ago. Molecular modeling offers science educators a vehicle for helping students understand difficult abstract concepts. For example, students often have difficulty understanding the relationship between the macroscopic appearance of a substance, its symbolic representation as a chemical formula, and its molecular shape and structure as predicted by quantum mechanics (Gabel, 1990). Talley (1973) documented student difficulties in understanding the particulate nature of matter and proposed three-dimensional visualization to overcome the problem. Technology offers a way to move beyond static two-dimensional representations of three-dimensional phenomena into a format that more closely simulates reality. Current molecular modeling software can alternate images of a given object at rates approaching thousands of times per second, creating a realistic three-dimensional effect. The rapid increase in computer processing speeds means that software developers now are producing pedagogical software that was mere fantasy not long ago. While the full impact and influence of this technology on science education are not yet known, preliminary results are encouraging. Williamson and Abraham (1995), for example, developed computer animations using molecular modeling images. They found that students who used these tools developed better conceptual understanding of phenomena such as diffusion of intermolecular forces than did students who received conventional instruction. Molecular modeling also can improve the linkage between lecture and laboratory. Crouch, Holden, and Samet (1996) described an innovative instructional sequence in which students learn about nucleophiles and electrophiles in a lecture, construct electron density maps of assigned compounds using molecular modeling software, and then use these maps to predict the outcome of the laboratory experiment they are about to undertake. In contrast to visualization tools that exploit the computational capabilities of up-to-date computers, a GIS uses powerful information databases to construct detailed, precise computer maps. According to the U.S. Geological Survey,1 “a GIS is a computer system capable of assembling, storing, manipulating, and displaying geographically referenced information (i.e., data identified according to their locations).” A GIS works by relating information from different sources for the same location. Different kinds of data in map form can be entered into a GIS, and a GIS can also convert existing digital information into maps. There are numerous GIS applications in wide- 1   Available: http://info.er.usgs.gov/research/gis/title.html. [7/25/01].

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary spread use in many different fields, including: Agriculture (crop monitoring; land use management; commodity forecasting; soil studies; irrigation planning; and water resource assessment); Environment (pollution, weather, and climate monitoring; cause-effect studies; landscape assessment; conservation planning; and biodiversity libraries); Health care (asset management; ambulance routing and mobilization; epidemiological studies; and road traffic accident analysis); and Local government (planning and building control; land searches; boundary change modeling; property and highways maintenance; crime analysis; natural disaster management; and police and fire service command and control). This list, while not exhaustive, illustrates the range of disciplines that can be expanded and deepened with GIS technology. According to the U.S. Geological Survey, an active GIS market has resulted in lower costs and continual improvements in hardware and software components. Thus, GIS tools that only recently were restricted to use by professional scientists and engineers, are now becoming part of undergraduate science and engineering programs and courses.2 These developments are likely to result BOX A-2 Examples of Visualization Practice At the University of Michigan, students in Movement Science 303 learn to analyze human movements on videotape, transferring the images to digital format on the computer and conducting biomechanical analyses. Visualizing the Changing Night Sky with SKYGLOBE at the Eastern New Mexico University is a shareware program that shows the night sky at any moment between 30,000 BC and 30,000 AD. It can jump through time in various increments (minutes, hours, days, months, years, and centuries) to indicate how the sky changes as a function of time. Seasonal changes, retrograde motion, and conjunctions are easily displayed. in much wider GIS application in both the public and private sectors, which in turn will pressure higher education institutions to provide instruction in this kind of learning to science and engineering students. This example also indicates how information technology costs can be shared among the public and private sectors to everyone’s benefit. Many other visualization tools are finding a place in the postsecondary SME&T curriculum (see Box A-2). Spreadsheets and graphing calculators are now widely used for mathematical modeling. Large databases of all types of images are accessible to students and faculty from all over 2   Available: http://www.ehr.nsf.gov/EHR/DUE/web/ate/atelist.htm#geos. [7/25/01].

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary the world. The National Library of Medicine’s Visible Human Project contains thousands of digitized cryosection photographs, magnetic resonance images (MRI), and transverse computerized tomography (CT) images, which together comprise anatomically correct, three-dimensional representations of the male and female human body. Although used heavily in clinical medicine and biomedical research, these images also are used widely in undergraduate courses. The Visible Human World Wide Web site3 lists dozens of applications of the image data sets, most of which were developed by and are used at colleges and universities around the country. Other databases contain images taken by the Hubble Space Telescope of planets, stars, asteroids, and comets,4 which can be downloaded for use in astronomy courses. Digitized video can help students understand the physics involved in sporting events (e.g., pole-vaulting), transportation (a plane take-off), and nature (river flow). Technologies that rely on interactivity with the World Wide Web are still developing and hold promise as yet another avenue for pedagogical change. Small Java applets5 make it possible for a Web page user to perform interactive animations, immediate calculations, and other simple tasks without having to send a user request back to the server. Wolfgang Christian at Davidson College has created a Web site with numerous Java applets for physics, which he calls Physlets.6 The array of computer-based visualization tools available can dramatically affect not just how postsecondary SME&T educators teach their courses, but how they select the topics to cover. Simulation Simulations let students view multiple aspects of complex systems simultaneously or sequentially. For example, an ecosystem or an architectural development can be constructed from many components with various connections of material and energy flow. Output data can be displayed in scatter plots, histograms, pie charts, and on maps concurrently and can be linked so that all representations change simultaneously when variables are manipulated. Similarly, 3   Available: http://www.nlm.nih.gov/research/visible/visible_human.html. [7/25/01]. 4   Available: http://www.jpl.nasa.gov/pictures/. [7/ 25/01]. 5   Java is a programming language expressly designed for use in the distributed environment of the Internet. It was designed to have the “look and feel” of the C++ language, but it is simpler to use than C++ and enforces a completely object-oriented view of programming. Java can be used to create complete applications that may run on a single computer or be distributed among servers and clients in a network. It can also be used to build small application modules (known as applets) for use as part of a Web page. Applets make it possible for a Web page user to interact with the page. (Available: http://www.whatis.techtargetcom/. [7/25/01]. 6   See http://WebPhysics.Davidson.Edu/Applets/Applets.html. [7/25/01].

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary BOX A-3 Features and Advantages of Simulations encourage student ownership of a research or problem-solving agenda; allow students access to large, complex data sets; allow students to conduct the same experiment multiple times; draw on multidisciplinary learning, such as mathematics, in scientific exploration; help students develop quantitative skills as they engage in successive rounds of hypothesizing and logical and numerical testing of hypotheses; require interpretation of multiple representations of data; provide novel experiences each time the simulation is run; offer flexibility in classroom contexts (e.g., case studies, investigative labs, homework, distance education, local context, background and talents of students and teachers); and allow the use of many tools at once. SOURCE: The BioQUEST Curriculum Consortium, available at http://bioquest.org/. [7/25/01]. a transformation of the data (e.g., to log-log scales) can be reflected in all displays in a linked manner.7 Picture a situation where students are collaborating with each other or with research scientists from remote sites and are able to illustrate alternative graphical and iconic representations of the same data to highlight patterns in that data. The integration of simulations with real-world data allows the learner to incorporate both theoretical and empirical data to understand a complex phenomenon. Many simulations have been constructed to help students learn long-term research strategies. These micro worlds engage students in the exploration of open-ended problems and complex data sets, giving them numerous modes and opportunities for data analysis, visualization, and the ability to explore multiple variables using tools from many disciplines. Simulations allow students to investigate contemporary research problems, explore “what-if” questions, pose new problems, and examine fundamental concepts and classical experiments. These learning environments are content- and process-rich, since these types of simulations are open to such diverse manipulation for long periods of investigation. These elaborate simulations help students learn which tools are appropriate in which contexts and why one tool is more powerful than another in a given context. Several features of currently available simulation tools offer advantages to faculty interested in enhancing student mastery of scientific and technical disciplines. These features and advantages are listed in Box A-3. Because of these features and advan- 7   For example, see BioQUEST, available at http://bioquest.org/. [7/25/01].

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary BOX A-4 Examples of Simulation Practices The Virtual Genetics Lab at the University of Pittsburgh offers a course in conjunction with an honors course in genetics, and the NextStep-based simulator completely replaces lab experiments. At James Madison University, a four-year integrated science and technology (ISAT) curriculum examines issues and scientific content in biotechnology, energy, environmental issues, information and knowledge management, and manufacturing. Students use the computer as a productivity tool, simulator, model builder, laboratory, and communication medium to concentrate on the connections between science and the technology rather than disciplinary differences. tages, simulations probably will play an increasingly important role in undergraduate SME&T classrooms and laboratories. For example, in some fields, certain experiments may be too risky for novices to perform, they might be too advanced for a student’s laboratory skills, or the necessary equipment might be too costly. Another possibility is that conducting a real-world experiment would require more time than is available in a course (e.g., multiple crosses or crosses of organisms with long breeding cycles in a genetics experiment). Simulations are a reasonable alternative (see Box A-4). However, wet labs offer an indispensable experience to students that often cannot be replaced by the use of information technology. Simulations can provide critical support for education based around realistic problem solving, such as the programs described earlier. By linking to research databases, simulations may enhance data mining8 and, in some cases, allow students to do original, open-ended research. Using simulations, students can replicate aspects of historically important models and classical experiments. They also can learn long-term strategies of scientific research using strategic simulations. Finally, simulations are a means of enhancing conceptual integration and conceptual change. Real-World Problem Solving Our society faces challenges that were unknown to our parents and grandparents. Increases in global atmospheric carbon dioxide may be linked to a gradual warming of the planet. Run-off of fertilizers used to grow crops and fecal waste from livestock can contaminate drinking water supplies hundreds of miles away. The safety of the food supply, the advantages and disadvantages of genetic testing, and the development of reproductive technologies to benefit infertile couples all pose difficult 8   Data mining is the analysis of data for relationships that have not previously been discovered. Data mining results can include forecasting or simply discovering patterns in the data that can lead to predictions about the future.

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary knowledge. Baltimore: The Johns Hopkins University Press. Bybee, R.W. (1997). Achieving science literacy: From purposes to practice. Portsmouth, NH: Heinemann. Carr, A.A., Jonassen, D.H., and Litzinger, M.E. (1998). Good Ideas to Foment Educational Revolution: The Role of Systemic Change in Advancing Situated Learning, Constructivism, and Feminist Pedagogy. Educational Technology, 38(1):5-15. Carraher, T.N., Carraher, D.W., and Schliemann, A.D. (1985). Mathematics in the Streets and in Schools. British Journal of Developmental Psychology, 3:21-29. Casti, J.L. (1997). Would-be worlds: How simulation is changing the frontiers of science. New York: J. Wiley. Cech, T.R. (1999). Science at Liberal Arts Colleges: A Better Education? Daedalus, 128 (1), Winter:195-216. Cennamo, K.S. and Dawley, G.W. (1995). Designing Interactive Video Materials for Adult Learners. Performance & Improvement, 34(1):14-19. Chase, W.G. and Simon, H.A. (1973). Perception in chess. Cognitive Psychology, 4:55-81. Chi, M.T.H., Feltovich, P.J., and Glaser, R. (1981). Categorization and Representation of Physics Problems by Experts and Novices. Cognitive Science, 5:121-152. Chickering, A.W. and Gamson, Z.E. (1987). Seven Principles for Good Practice in Undergraduate Education. American Association for Higher Education Bulletin, 39(7):3-7. Choi, J.I. and Hannafin, M. (1995). Situated cognition and learning environments: Roles, Structures, and implications for design. Educational Technology, Research, and Development, 43(2):53-69. Chonacky, N. and Myers, J. (1997). Exploring Collaboratory Partnerships for Interdisciplinary Undergraduate Science Education Reform. Council on Undergraduate Research Quarterly, 18(1):18-23. Claxton, C.S. and Murrell, P.H. (1987). Learning Styles: Implications for Improving Educational Practices. ASHE-ERIC Higher Education Report No. 4. Washington, DC: George Washington University. Clinton, W.J. (1997). Opening College Doors to All Americans: Excerpts from Remarks at San Jacinto Community College. Journal of Chemistry Education, 74(12):1392-1393. Cognition and Technology Group at Vanderbilt University. (1993). Anchored Instruction and Situated Cognition Revisited. Educational Technology, 33(3):52-70. Cole, M. and Engestrom, Y. (1993). A cultural-historical approach to distributed cognition. Pp. 1-46 in G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press. Coley, R.J., Cradler, J., and Engel, P.K. (1997). Computers and classrooms: The status of technology in U.S. schools. Princeton, NJ: Educational Testing Service. Collins, A. (1988). Cognitive apprenticeship and instructional technology (Report No. BBN-R-6899). Cambridge, MA: BBN Laboratories. (ERIC Document Reproduction Service No. ED 331 465). Collis, B. (1997). Cooperative Learning on the World Wide Web. Selected Papers from the Eighth National Conference on College Teaching and Learning. Jacksonville, FL: Florida Community College at Jacksonville. Consumer Electronics Association. (1996). Senior market potential. Arlington, VA: Author. Available: http://www.ebrain/org/ers/crs_all.asp. [October 24, 2001]. Crouch, R.D., Holden, M.S., and Samet, C. (1996). CAChe Molecular Modeling: A Visualization Tool Early in the Undergraduate Curriculum. Journal of Chemistry Education, 73(10):916-917.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Curry, L. (1983, April). An Organization of Learning Styles Theory and Constructs. Paper presented at the American Educational Research Association annual meeting, Montreal. Daniel, J.S. (1996). Mega-universities and knowledge media: technology strategies for higher education. London: Kogan Page. Davis, P. (1997). What Computer Skills do Employers Expect from Recent College Graduates? Technological Horizons in Education, Sept:74-78. Department for Education and Employment (UK). (1997). Connecting the learning society. London: Author. Derry, S.J., DuRussel, L.A., and O’Donnell, A.M. (1997). Individual and distributed cognitions in interdisciplinary teamwork: A developing case study and emerging theory. Madison, WI: National Institute for Science Education. DiAmico, M., Baron, L.J., and Sissons, M.E. (1995). Gender Differences in Attributions about Microcomputer Learning. Sex Roles, 33:353-385. Doerr, John. (1998, March 16). Business Week, p 29. Druger, M. (1997). Motivating the Unmotivated. In E. Siebert, M. Caprio, and C. Lyda (Eds.), Effective teaching and course management. Dubuque, IA: Kendall-Hunt. Duderstadt, J.J. (1997). The Future of the University in an Age of Knowledge. Journal of Asynchronous Learning Networks, 1(2):78-88. Dufresne, R.J., Gerace, W.J., Leonard, W.J., Mestre, J.P., and Wenk, L. (1996). Classtalk: A Classroom Communication System for Active Learning in the College Lecture Hall. Journal of Computing and Higher Education, 7:3-47. Dyson, E. (1997) Release 2.0. New York: Broadway Books, Bantam Doubleday Dell Publishing Group. Ehrmann, S.C. (1988). Improving a Distributed Learning Environment with Computers and Telecommunications. Pp. 255-259 in R. Mason and A.Kaye (Eds.), Mindweave: Communication, computers and distance education. New York: Pergamon Press. Ehrmann, S.C. (1990). Reaching Students, Reaching Resources: Using Technologies to Open the College. Academic Computing, IV(7):10-14, 32-34. Ehrmann, S.C. and Balestri, D. (1992). Learning to Design, Designing to Learn: A More Creative Role for Technology. pp. 1-20. In D. Balestri, S. Ehrmann, and D. Ferguson (Eds.), Learning to design, designing to learn: Using technology to transform the curriculum. New York: Taylor & Francis. Ellis, J.D. (1990). Preparing Science Teachers for the Information Age. Journal of Computers in Mathematics and Science Teaching, 9(4):55-70. Ercolano, V. (1998). Students, Start Your Designs! American Society for Engineering Education Prism, 7(5):16. Esiobu, G.O. and Soyibo, K. (1995). Effects of concept and vee mapping under three learning modes on students’ cognitive achievement in ecology and genetics. Journal of Research in Science Teaching, 32(9): 971-95. Faison, C.L. (1996). Modeling Instructional Technology Use in Teacher Preparation: Why We Can’t Wait. Educational Technology, XXXVI(5):47-59. Felder, R.M. (1993). Reaching the Second Tier: Learning and Teaching Styles in College Science Education. Journal of College Science Teaching, 22(5):286-290. Felder, R.M. (1996). Matters of Style. American Society for Engineering Education Prism, 6(4):18-23. Fifield, S.J. and Peifer, R.W. (1994). Enhancing Lecture Presentations in Introductory Biology with Computer-Based Multimedia . Journal of College Science Teaching, 23:235-239. Fischer, G. (1995). Distributed Cognition, Learning Webs, and Domain-Oriented Design Environments. Pp. 125-129 in Proceedings of the conference on computer supported for collaborative learn-

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Enhancing Undergraduate Learning with Information Technology: A Workshop Summary ing. Available: http://www-cscl95.indiana.edu/cscl95/fischer.html. [7/27/01]. Fischer, G. (1996, June). Making Learning a Part of Life: Beyond the Gift Wrapping Approach to Technology. Paper presented at the National Science Foundation Symposium, Learning and Intelligent Systems. Available: http://www.cs.colorado.edu/~l3d/presentations/gf-wlf/. [7/27/01]. Flavell, J.H. (1979). Metacognition and Cognitive Monitoring: A New Era of Cognitive-Development Inquiry. American Psychologist, 34:906-911. Floyd, B.P. (1998, September 11). Professor’s Web Site Makes Her “Dear Abby of Math.” Chronicle of Higher Education, p. A31. Gabel, D.L. (1990). Students’ Understanding of the Particle Nature of Matter and its Relationship to Problem-Solving. Pp. 92-105 in Empirical research in mathematics and science education. Proceedings of the International Seminar, University of Dortmund, Germany. Garmer, A.K. and Firestone, C.M. (1996). Creating a learning society: Initiatives for technology and education. Washington, DC: The Aspen Institute. Garnett, P.J., Hacking, M., and Oliver, R. (1995). Refocusing the Chemistry Lab: A Case for Laboratory-Based Investigations. Australian Science Teachers Journal, 41(2):26-32. The Gartner Group, Inc. (1993). Management strategies: PC cost/benefit and payback analysis. Stamford, CT: Author. Gearhart, M., Novak, J.R., and Herman, J.L. (1996). Issues in Portfolio Assessment: The Scorability of Narrative Collections. CSE Tech. Report No. 410. Los Angeles: University of California, National Center for Research on Evaluation, Standards, and Student Testing. Gick, M.L. and McGarry, S.J. (1992). Learning From Mistakes: Inducing Analogous Solution Failures to a Source Problem Produces Later Successes in Analogical Transfer. Journal of Experimental Psychology: Learning, Memory and Cognition, 18:623-639. Gilbert, S.W. (1996). Posted to the American Association for Higher Education information technology listserve (aahesgit@list.cren.net), 1/ 23/96. Subject: AAHESGIT: 10th of 14; Support Service Crisis. Glanz, J. (1998). Cosmos in a Computer. Science, 280:1522-1523. Glaser, R. (1991). Expertise and assessment. Pp. 17-30 in M.C. Wittrock and E.L. Baker (Eds.), Testing and cognition. Englewood Cliffs, NJ: Prentice Hall. Glassick, C.E., Huber, M.T., and Maeroff, G. I. (1997). Scholarship assessed: Evaluation of the professoriate. San Francisco: Jossey-Bass. Glennan, T.K. and Melmed, A. (1996). Fostering the use of educational technology: Elements of a national strategy. Santa Monica, CA: Rand. Gobbo, C., and Chi, M.T.H. (1986). How knowledge is structured and used by expert and novice children. Cognitive Development, 1:221-237. Godleski, E. (1984). Learning Style Compatibility of Engineering Students and Faculty. Proceedings, Annual Frontiers in Education Conference. American Society for Engineering Education/ Institute of Electronic and Electrical Engineers, Philadelphia, PA. Goodsell, A., Maher, M., Tinto, V., Smith, B.L., and MacGregor, J., (Eds.) (1990). Collaborative learning: A sourcebook for higher education. University Park, PA: National Center on Postsecondary Teaching, Learning, and Assessment. Graves, W.H. (1997). A Framework for Universal Intranet Access. CAUSE/EFFECT, 20(2):48-52. Available: http://cause-www.colorado.edu/. [7/27/01]. Green, K.C. (1996a). Campus Computing Survey. Pomona, CA: Claremont Graduate School. Griffin, M.M., and Griffin, B.W. (1996). Situated cognition and cognitive style: Effects on students’ learning as measured by conventional

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary tests and performance assessments. Journal of Experimental Education, 64(4):293-308. Hake, R.R. (1998). Interactive Engagement Versus Traditional Methods: A Six-Thousand Student Survey of Mechanics Test Data for Introductory Physics Courses. American Journal of Physics, 66:64-75. Hall, R H. and Hickman, L.L. (1997, March). The Effect of Contiguity and Complexity of Web Page Displays on Subjective Ratings: The Role of Gender. Paper presented at the annual meeting of the American Educational Research Association, Chicago IL. Available: http://www.umr.edu/~rhall/research/aera/aera97.html. [7/27/01]. Halpern, D. (1992). Sex differences in cognitive abilities. 2nd Ed. Hillsdale, NJ: Erlbaum. Hamilton, C.J. (1995). Beyond Sex Differences in Visuo-Spatial Processing: The Impact of Gender Trait Possession. British Journal of Psychology, 86:1-20. Harley, S. (1993). Situated Learning and Classroom Instruction. Educational Technology, 33(3):46-51. Helgeson, S.L. and Kumar, D.D. (1993). A Review of Educational Technology in Science Assessment. Journal of Computers in Mathematics and Science Teaching, 12(3/4):227-243. Hellige, J. (1993). Hemispheric asymmetry: What’s right and what’s left. Cambridge, MA: Harvard University Press. Herreid, C.F. (1997/1998). What Makes a Good Case? Journal of College Science Teaching, 27(3):163-165. Herron, J.D. (1978). Piaget in the Classroom: Guidelines for Applications. Journal of Chemistry Education, 55(3):165-170. Hilosky, A., Sutman, F., and Schmuckler, J. (1998). Is Laboratory-Based Instruction in Beginning College-Level Chemistry Worth the Effort and Expense? Journal of Chemistry Education, 75(1):100-104. Holyoak, K.J. (1991). Symbolic Connectionism: Toward Third-Generation Theories of Expertise. Pp. 301-336 in K.A. Ericsson and J. Smith (Eds.), Toward a general theory of expertise. Cambridge, UK: Cambridge University Press. Howard Hughes Medical Institute. (1996). New tools for science education, Undergraduate Program Directors’ Meeting, October 25-27, 1995. Chevy Chase, MD: Author. Howe, A.C. and Dowdy, W. (1989). Spatial Visualization and Sex-Related Differences in Science Achievement. Science Education, 73(6):703-709. Hunt, W.T. (1992). Shared Understanding: Implications for Computer-Supported Cooperative Work. Qualifying exam paper, Department of Computer Science, University of Toronto. Available: http://www.dgp.utoronto.ca/people/WilliamHunt/qualifier.html. [7/27/01]. Iaccino, J.F. (1993). Left brain-right brain differences: Inquiries, evidence, and new approaches. Hillsdale, NJ: Lawrence Erlbaum. International Technology Education Association. (2000). Standards for technology education. Reston, VA: Author. Available: http//www.itea.www.org. [7/27/01]. Johnson, D.W., Johnson, R.T., and Smith, K.A. (1998). Cooperative Learning Returns to College: What Evidence is there that it Works? Change, 30(4):27-35. Johnson, J. (1997). It Takes a (Global) Village to Prepare Teachers: Teaching/Technology Reflection. Selected Papers from the Eighth National Conference on College Teaching and Learning. Jacksonville, FL: Florida Community College at Jacksonville. Johnson, S.D. and Thomas, R.G. (1994). Implications of Cognitive Science for Instructional Design in Technology Education. Journal of Technology Studies, 20(1):33-45. Jonassen, D.H., Campbell, J.P., and Davidson, M.E. (1994). Learning with Media: Restructuring the

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Debate. Educational Technology Research and Development, 42(2):31-39. Jones, B.F., Valdez, G., Nowakowski, J., and Rasmussen, C. (1996). Plugging in: Choosing and using educational technology. Oak Brook, IL: North Central Regional Educational Laboratory. Jones, J.B. (1998). The Non-Use of Computers in Undergraduate Engineering Courses. Journal of Engineering Education, 87(1):11-14. Jones, L.M. and Kane, D.J. (1994). Student Evaluation of Computer-Based Instruction in a Large University Mechanics Course. American Journal of Physics , 62(9):832-836. Keefe, J. (1987). Learning style: Theory and practice. Reston, VA: National Association of Secondary School Principals. Keller, J.M. (1987). Development and use of the ARCS model of motivational design. Journal of Instructional Development, 10(3):2-10. Kershaw, A. (1996). People, Planning, and Process: The Acceptance of Technological Innovation in Post-Secondary Organizations. Educational Technology, XXXVI(5):44-48. Khan, B.H. (Ed.) (1997). Web-based instruction. Englewood Cliffs, NJ: Educational Technology Publications Inc. Kimura, D. (1987). Are Men and Women’s Brains Really Different? Canadian Psychology, 28:133-147. Kimura, D. (1992). Sex Differences in the Brain. Scientific American, 267(3):118-125. King, J.E. (1998, May 1). Too Many Students Are Holding Jobs for Too Many Hours. Chronicle of Higher Education, p. A72. Kolb, D. (1984). Experiential learning: Experience as the source of learning and development . Englewood Cliffs, NJ: Prentice Hall. Kotovsky, K., Hayes, J.R., and Simon, H.A. (1985). Why Are Some Problems Hard? Evidence from the Tower of Hanoi. Cognitive Psychology, 17:248-294. Kouzes, M., and Wulf, W.A. (2001). EMSL Collaborative. Available: http://www.emsl.pnl.gov:2080/docs/collab/CollabHome.html. [7/27/01]. Kozma, R.B., and Quellmalz, E.S. (1995). Issues and needs in evaluating the educational impact of the national information infrastructure. Menlo Park, CA: Center for Technology in Learning, SRI International. Paper presented at workshop sponsored by the Office of Educational Technology, U.S. Department of Education. Available: http://www.ed.gov/Technology/Futures/kozma.html [7/27/01]. Lanctot, R.C. (1998, March 11). U.S. Home PC Penetration Tops 45 Percent. Computer Retail Week. Available: http://www.techweb.com/wire/story/TWB19980311S0025 [7/27/01]. Larkin, J.H., McDermott, J., Simon, D., and Simon, H. (1980). Expert and Novice Performance in Solving Physics Problems. Science, 208:1335-1342. Laurillard, D. (1993). Rethinking university teaching: A framework for the effective use of technology. London: Routledge. Lave, J. and Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press. Laws, P.W. (1991). Calculus-Based Physics Without Lectures. Physics Today, Dec:24-31. Laws, P.W. (1999). New Approaches to Science and Mathematics Teaching at Liberal Arts Colleges. Daedalus, 128(1):217-240. Lehman, J.R. (1986). Microcomputer Offerings in Science Teacher Training. School Science and Mathematics, 86(2):119-125. Levy, J. (1976). Cerebral Lateralization and Spatial Ability. Behavior Genetics, 6:171-188. Linn, R.L., Baker, E.L., and Dunbar, S.B. (1991). Complex, Performance-Based Assessment: Expectation and Validation Criteria. Educational Researcher, 20:15-21. Litzinger, M.E., and Osif, B. (1993). Accommodating diverse learning styles: Designing instruction for electronic information sources. In L.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Shirato, (Ed.) What is good instruction now? Library instruction for the 90s. Ann Arbor, MI: Pierian Press. Macdonald, R.H. and Korinek, L. (1995). Cooperative Learning Activities in Large Entry-Level Geology Courses. Journal of Geology Education, 43:341-345. MacLeod, C.M., Hunt, E.B., and Mathews, N.N. (1978). Individual Differences in the Verification of Sentence-Picture Relationships. Journal of Verbal Learning and Verbal Behavior, 17:493-508. Massy, W.F., and Zemsky, R. (1996). Using Information Technology to Enhance Academic Productivity. Educom Review, 31(1):12-14. Available: http://www.educause.edu/nlii/keydocs/massy.html. [7/27/01]. Mayer, M. (1987). Common Sense Knowledge Versus Scientific Knowledge: The Case of Pressure, Weight and Gravity. Pp. 299-310 in Proceedings of the second international seminar: Misconceptions and educational strategies in science and mathematics, Vol. 1. Ithaca, NY: Cornell University Press. Mazur, E. (1997). Peer instruction. Upper Saddle River, NJ : Prentice-Hall. McCandless, G. (1998). Creating a Level Playing Field for Campus Computing: Universal Access. Syllabus, 11(6):12-14, 29. Available: http://www.syllabus.com [7/27/01]. McDermott, L.C. (1991). What we teach and what is learned-closing the gap. American Journal of Physics, 59:301-315. McDermott, L.C., Shaffer, P., and Somers, M. (1994). Research as a guide for curriculum development: an illustration in the context of the Atwood’s machine. American Journal of Physics, 62:46-55. McLellan, H. (1993). Evaluation in a Situated Learning Environment. Educational Technology, 33(3):39-45. McLellan, H. (1994). Situated Learning: Continuing the conversation. Educational Technology, 34(8):7-8. McMillan, J.H., and Forsyth, D.R. (1991). What Theories of Motivation Say about How Learners Learn. Pp. 39-52 in R.J. Menges and M.D. Svinicki, (Eds.) College teaching: From theory to practice . New Directions in Teaching and Learning, No. 45. San Francisco: Jossey-Bass. McNeal, A.P. and D’Avanzo, C. (Eds.) (1997). Student-active science: Models of innovation in college science teaching. Fort Worth, TX: Saunders College Publishing. Means, B., and Olson, K. (1995). Technology’s role in education reform: Findings from a national study of innovating schools. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement. Millar, S.B., Kosciuk, S., Penberthy, D. and Wright, J.C. (1996). Faculty Assessment of a Freshman Chemistry Course. Proceedings of the American Society for Engineering Education Annual Conference. Washington, DC: American Society for Engineering Education, Session Number 2530. Mislevy, R.J., and Gitomer, D. (1996). The Role of Probability-based Inference in an Intelligent Tutoring System. User modeling and user-adapted interaction, 5: 253-282. Mitchell, W.J and Dertouzos, M.L, (Eds.) (1997). MIT Educational Technology Council Report. Cambridge, MA: MIT. Morris, P.M., Ehrmann, S.C., Goldsmith, R.B., Howat, K.J., and Kumar, M.S.V. (Eds.) (1994). Valuable, viable software in education: Case studies and analysis. New York: PRIMIS-McGraw-Hill. Mullis, I.V.S., Martin, M.O., Beaton, A.E., Gonzalez, E.J., Kelly, D.L., and Smith, T.A. (1998). Mathematics achievement in the final year of secondary school: IEA’s third international mathematics and science study. Chestnut Hill, MA: Boston College, Center for the Study of Testing, Evaluation, and Educational Policy.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Munro, B.C. (1984). B. F. Skinner. British Columbia Journal of Special Education, 8(1):45-60. Nakhleh, M.B. (1992). Why Some Students Don’t Learn Chemistry. Journal of Chemistry Education, 69(3):191-196. National Academy of Sciences. (1996). Careers in science and engineering: A student planning guide to grad school and beyond. Washington, DC: National Academy Press. National Center for Education Statistics. (1998). The condition of education, 1998. Washington, DC: Author. National Center for Improving Science Education. (1991). The high stakes of high school science. Washington, DC: Author National Council for the Accreditation of Teacher Education. (1997). Technology and the new professional teacher: Preparing for the 21st century classroom. Washington, DC: Author. Available: http://www.ncate.org/pubs/m_pubs.htm#tech_prof_teach. [7/27/01]. National Council of Teachers of Mathematics. (1989). Curriculum and evaluation standards for school mathematics. Reston, VA: Author. National Council of Teachers of Mathematics. (1991). Professional standards for teaching mathematics. Reston, VA: Author. National Institute for Science Education. (1998). Proceedings of the NISE forum on assessment. Madison, WI: Author. National Research Council. (1993). National collaboratories: Applying information technology for scientific research. Committee Toward a National Collaboration: Establishing the User Developer Partnership. Washington, DC: National Academy Press. National Research Council. (1994a). Information technology in the service society: A twenty-first century lever. Committee to Study the Impact of Information Technology on the Performance of Service Activities. Washington, DC: National Academy Press. National Research Council. (1994b). Realizing the information future: The internet and beyond. NRENAISSANCE Committee. Washington, DC: National Academy Press. National Research Council. (1995). Reshaping the graduate education of scientists and engineers. Committee on Science, Engineering, and Public Policy. Washington, DC: National Academy Press. National Research Council. (1996a). From analysis to action: Undergraduate education in science, mathematics, engineering, and technology. Center for Science, Mathematics, and Engineering Education. Washington, DC: National Academy Press. National Research Council. (1996b). National science education standards. National Committee on Science Education Standards and Assessment. Washington, DC: National Academy Press. National Research Council. (1996c). The unpredictable certainty: Information infrastructure through 2000. NII 2000 Steering Committee. Washington, DC: National Academy Press. National Research Council. (1997a). More than screen deep: Toward every-citizen interfaces for the nation’s information infrastructure. Toward an Every-Citizen Interface to the NII Steering Committee. Washington, DC: National Academy Press. National Research Council. (1997b). Adviser, teacher, role model, friend: On being a mentor to students in science and engineering. Committee on Science, Engineering, and Public Policy. Washington, DC: National Academy Press. National Research Council. (1998). Developing a digital national library for undergraduate science, mathematics, engineering, and technology education . Center for Science, Mathematics, and Engineering Education. Washington, DC: National Academy Press. National Research Council. (1999a). Being fluent with information technology. Committee on

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Information Technology Literacy. Washington, DC: National Academy Press. National Research Council. (1999b). How people learn: Brain, mind, experience and school. Committee on Developments in the Science of Learning, J.D. Bransford, A.L. Brown, and R.R. Cocking (Eds.). Washington, DC: National Academy Press National Research Council. (1999c). Transforming education in science, mathematics, engineering, and technology. Center for Science, Mathematics, and Engineering Education. Washington, DC: National Academy Press. National Science Foundation. (1993). Indicators of science and mathematics education. Arlington, VA: Author. National Science Foundation. (1996). Shaping the future: New expectations for undergraduate education in science, mathematics, engineering, and technology. Arlington, VA: Author. National Science Foundation. (1998). Information technology: Its impact on undergraduate education, science, mathematics, engineering and technology. Available: http://www.nsf.gov/pubs/1998/nsf9882/nsf9882.pdf. [7/27/01]. National Science Teachers Association. (1998). NSTA standards for science teacher education. Arlington, VA: Author. Naour, P. (1985). Brain/Behavior Relationships, Gender Differences, and the Learning Disabled. Theory into Practice, 24(2):100-105. Neal, E. (1998, June 19). Using Technology in Teaching: We Need to Exercise Healthy Skepticism. Chronicle of Higher Education, p. B4. Newell, A. and Simon, H.A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall. Niaz, M. (1991). Reasoning strategies of students in solving chemistry problems as a function of developmental level, functional M-capacity and disembedding ability. (ERIC Document Reproduction Service No. ED360180). Norman, D.A. (1991). Cognitive Artifacts. Pp. 17-38 in John M. Carroll (Ed.), Designing interaction: Psychology at the human-computer interface. Cambridge, UK: Cambridge University Press. Norman, D.A. (1992). Turn signals are the facial expressions of automobiles. Reading, MA: Addison-Wesley. Norman, D.A. (1993). Things that make us smart. Reading, MA: Addison-Wesley. Novak, G.M., Patterson, E.T., and Gavrin, A.D. (1999). Just-in time teaching: Blending active learning with Web technology. Upper Saddle River, NJ: Prentice Hall. Novak, G.M., Patterson, E.T., and Gavrin, A.D. (2001). A Brief Overview: What Is JiTT? Available: http://webphyics.iupui.edu/jitt/whatOVR.html. [9/25/01]. Novak, J.D. and Gowin, D.B. (1984). Learning how to learn. New York: Cambridge University Press. Oberlin, J.L. (1996). The Financial Mythology of Information Technology: The New Economics. Cause/Effect, Spring: 21-29. Okebukola, P.A. and Jegede, O. J. (1988). Cognitive preference and learning mode as determinants of meaningful learning through concept mapping. Scientific Educator, 74:489-500. O’Loughlin, M. (1992). Rethinking Science Education: Beyond Piagetian Constructivism toward a Sociocultural Model of Teaching and Learning. Journal of Research in Science Teaching, 29(8):791-820. Orlansky, J. and String, J. (1979). Cost-effectiveness of computer-based instruction in military training. Washington, DC: U.S. Department of Defense. Osborne, J.F (1996). Beyond Constructivism. Science Education, 80(1):53-82. Panitz, B. (1998). Learning on Demand. American Society for Engineering Education Prism, 7(8):18-24. Pea, R. (1993). Practices of distributed intelligence and designs for education. Pp. 47-87 in G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Pence, H.E. (1997). Are Simulations Just a Substitute for Reality? Paper #9, Summer On-Line Conference on Chemical Education, June 1-August 1. Peters, P.C. (1982). Even Honors Students Have Conceptual Difficulties with Physics. American Journal of Physics, 50(6):501-508. Pew Research Center for The People and The Press. (1995). Technology in the American Household. Available: http://www.people-press.org/tech.htm. [7/27/01]. Phillips, D.C. (1995). The Good, the Bad, and the Ugly: The Many Faces of Constructivism. Educational Researcher, 24(7):5-12. Poole, B.J. and Kidder, S.Q. (1996). Making Connections in the Undergraduate Laboratory. Journal of College Science Teaching, 26(1):34-36. Powers, P. (1998). One Path to Using Multimedia in Chemistry Courses: Enlivening Students’ Learning Through Visual Presentations. Journal of College Science Teaching, 27:317-318. President’s Committee of Advisors on Science and Technology. Panel on Educational Technology. (1997). Report to the President on the use of technology to strengthen K-12 education in the United States. Washington, DC: U.S. Government Printing Office. Project Kaleidoscope. (1991). What works: Building natural science communities: A plan for strengthening undergraduate science and mathematics. Volume I. Washington, DC: Author. Project Kaleidoscope. (1994). What works. Leadership: Challenges for the future. Volume II. Washington, DC: Author. Project Kaleidoscope. (1997). The question of reform: Report on Project Kaleidoscope 1996-1997. Washington, DC: Author. Project Kaleidoscope. (1998). Shaping the future of undergraduate science, mathematics, engineering and technology education: Proceedings and recommendations from the PKAL day of dialogue. Washington, DC: Author. Project Kaleidoscope. (1999). Steps toward reform: Report on Project Kaleidoscope, 1997-1998. Washington, DC: Author. Rogoff, B. and Gardner, W. (1984). Adult Guidance of Cognitive Development. Pp. 95-116 in B. Rogoff and J. Lave (Eds.), Everyday cognition. Cambridge, MA: Harvard University Press. Ruiz-Primo, M.A., Schultz, S.E., and Shavelson, R.J.. (1997). Concept Map-based Assessment in Science: Two Exploratory Studies. CSE Technical Report 436. Los Angeles: National Center for Research on Evaluation, Standards, and Student Testing. Salomon, G. (1993). Editor’s introduction. Pp. xi-xxi in G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press. Schoenfeld, A.H. (1990). On mathematics as sense-making: An informal attack on the unfortunate divorce of formal and informal mathematics. In D.N. Perkins, J. Segal. and J. Voss (Eds.), Informal reasoning and education. Hillsdale, NJ: Erlbaum. Schwartz, A.T., Bunce, D.M., Silberman, R.G., Stanitski, C.L., Stratton, W.J., and Zipp, A.P. (1997). Chemistry in context. 2nd ed. New York: McGraw-Hill. Seymour, E. and Hewitt, N.M. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press. Seymour, E. and Hunter, A-B. (1998). Talking about disability: The education and work experience of graduates and undergraduates with disabilities in science, mathematics, and engineering majors. Washington, DC: American Association for the Advancement of Science. Shaywitz, B.A., Shaywitz, S.E., Pugh, K.R., Constable, R.T., Skudlarskl, P., Fulbright, R.K., Bronen, R.A., Fletcher, J.M., Shankweller, D.P., Katz, L., and Gore, J.C. (1995). Sex Differences in the Functional Organization of the Brain for Language. Nature, 33:607-609.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Sherald, M., and Ward, S. (1994). Market Predictions with Backprop Neural Nets. AI in Finance, Fall 1994:25-30. Shymansky, J.A., Yore, L.D., Treagust, D.F., Thiele, R.B., Harrison, A., Waldrip, B.G., Stocklmayer, S.M., and Venville, G. (1997). Examining the Construction Process: A Study of Changes in Level 10 Students’ Understanding of Classical Mechanics. Journal of Research in Science Teaching, 34(6):571-593. Simpson, R.D. and Frost, S.H. (1993). Inside college: Undergraduate education for the future. New York: Insight Books. Sinnott, J. and Johnson, L. (1996). Reinventing the university: A radical proposal for a problem-based university. Norwood, NJ: Ablex Publishing Company. Slavin, R.E. (1995). Cooperative learning: Theory, research, and practice (Second ed.). Boston: Allyn and Bacon. Smith, S.G., Jones, L.L., and Waugh, M.L. (1986). Production and Evaluation of Interactive Videodisc Lessons in Laboratory Instruction. Journal of Computer-Based Instruction, 13:117-121. Society for Industrial and Applied Mathematics. (1995). The SIAM report on mathematics in industry. Philadelphia, PA: Author. Available: http://www.siam.org/mii/miihome.htm. [7/27/ 01]. Software Publishers Association. (1997). The effectiveness of technology in schools, ’90-’97. Washington, DC: Author. Spitulnik, M.W and Krajcik, J. (1998). Technological Tools To Support Inquiry in a Science Methods Course. Journal of Computers in Mathematics and Science Teaching, 17(1):63-74. Springer, L., Millar, S.B., Kosciuk, S. and Penberthy, D. (1997a). Relating Concepts and Applications through Structured Active Learning. Presented at the American Educational Research Association meeting, Chicago, IL. Springer, L., Stanne, M.E., and Donovan, S. (1997b). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Madison, WI: National Institute for Science Education (pre-publication manuscript). SRI Consulting. (1997). Digital literacy: survival skills for the information age. London, England: BIT3M Futurescript. State of California. (1997). Interim County Population Projections. Sacramento, CA: Department of Finance. Available: http://www.dof.ca.gov/html/demograp/post2nd.htm. [7/27/01]. Sternberg, R.J. and Rifkin, B. (1979). The Development of Analogical Reasoning Processes. Journal of Experimental Child Psychology, 27:195-232. Stevens, R.S., Lopo, A.C., and Wang, P. (1996). Artificial Neural Networks Can Distinguish Novice and Expert Strategies During Complex Problem Solving. Journal of the American Medical Informatics Association, 3:131-138. Strassman, P. (1996, April 15). Spending without results? Computerworld, p. 88. Sugrue, B. (1994). Specifications for the design of problem-solving assessments in science. CSE Technical Report No. 387. Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, and Student Testing. Talley, L.H. (1973). The Use of Three-Dimensional Visualization as a Moderator in the Higher Cognitive Learning of Concepts in College Level Chemistry. Journal of Research in Science Teaching, 10:263-269. Thomes, K. and Clay, K. (1998). University Libraries in Transition. American Society of Engineering Education Prism, 7(8):26-29. Tinker, R. (1997). The problem of extended inquiry in science teaching: Technology-rich curricula to the rescue. Concord, MA: The Concord Consortium. Available: http://concord.org/pubs/extinq.html. [7/27/01]. Tissue, B.M. (1997). The Costs of Incorporating Information Technology in Education. Paper presented in the Summer On-Line Conference on Chemical Education, June 1-August 1.

OCR for page 33
Enhancing Undergraduate Learning with Information Technology: A Workshop Summary Available: http://www.chem.vt.edu/archive/chemconf97/paper04.html. [7/27/01]. Tobias, S. (1990). They’re not dumb, they’re different: Stalking the second tier. Tucson, AZ: Research Corporation. Tobias, S. and Raphael, J. (1997a). The hidden curriculum: Faculty-made tests in science. Part 1: Lower-division courses. New York: Plenum. Tobias, S. and Raphael, J. (1997b). The hidden curriculum: Faculty-made tests in science. Part 2: Upper-division courses. New York: Plenum. Trefil, J. and Hazen, R.M. (1995). The sciences: An integrated approach. New York: Wiley. Tripp, S.D. (1993). Theories, Traditions, and Situated learning. Educational Technology, 33(3):71-77. Uecker, A. and Obrzut, J.E. (1993). Hemisphere and Gender Differences in Mental Rotation. Brain and Cognition, 22:42-50. U.S. Department of Education. National Center for Education Statistics. (1996). The condition of education 1996. Washington, DC: U.S. Government Printing Office. Available: http://nces.ed.gov/pubs/ce/c9609a01.html. [7/27/01]. Van Dusen, G.C. (1997). The Virtual University: Technology and Reform in Higher Education. ASHE-ERIC Higher Education Report, Volume 25, No. 5. Washington, DC: George Washington University. von Glasersfeld, E. (1996). Footnotes to The Many Faces of Constructivism. Educational Researcher, 25(6):19. Wang, R. (1996). Learning chemistry in laboratory settings: A hands-on curriculum for non-science majors. (ERIC Document Reproduction Service No ED399189.) Watson, S.B. and Marshall, J.E. (1995). Effects of Cooperative Incentives and Heterogeneous Arrangement on Achievement and Interaction of Cooperative Learning Groups in a College Life Science Course. Journal of Research in Science Teaching, 32(3):291-299. Weiner, B. (1990). History of Motivational Research in Education. Journal of Educational Psychology, 82:616-622. Williamson, V.M. and Abraham, M.R. (1995). The Effect of Computer Animation on the Particulate Mental Models of College Chemistry Students. Journal of Research in Science Teaching, 32(5):521-534. Wilson, A.L. (1993). The Promise of Situated Cognition. New Directions for Adult and Continuing Education, 57(3):71-80. Wilson, B.J., Teslow, J., and Osman-Jouchoux, R. (1995). The Impact of Constructivism on Instructional Design Fundamentals. Pp. 137-157 in B.B. Seels (Ed.), Instructional design fundamentals: A review and reconsideration. Englewood Cliffs, NJ: Educational Technology Publications. Wilson, J.M. (1994). The CUPLE Physics Studio. The Physics Teacher, 32:518. Wilson, J.M. (1997). Distance Learning for Continuous Education. EDUCOM Review, 32(2):12-16. Available: http://educause.edu. [7/ 27/01]. Wittrock, M.C. (1974). Learning as a Generative Process. Educational Psychologist, 11:87-95. Wright, J.C. (1996). Authentic Learning Environment in Analytical Chemistry Using Cooperative Methods and Open-Ended Laboratories in Large Lecture Courses. Journal of Chemistry Education, 73(9):827-832. Wulf, W.A. (1989). The National Collaboratory: A White Paper. Appendix A in Towards a National Collaboratory, the unpublished report of an invitational workshop held at the Rockefeller University, March 17-18, 1989. Young, J.R. (1998, March 13). For Students with Disabilities, the Web Can Be Like a Classroom without a Ramp. The Chronicle of Higher Education, p. A31. Young, M.F. (1993). Instructional Design for Situated Learning. Educational Technology, Research and Development, 41(1):43-58. Zoller, U. (1996). The Use of Examinations for Revealing and Distinguishing between Students’ Misconceptions, Misunderstandings and “No Conceptions” in College Chemistry. Research in Science Education, 26(3):317-326.