Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T06:02:47.158Z Has data issue: false hasContentIssue false

33 - Multimedia Learning in Games, Simulations, and Microworlds

Published online by Cambridge University Press:  05 June 2012

By
Lloyd P. Rieber
Edited by
Richard Mayer
Lloyd P. Rieber
Affiliation:
The University of Georgia
Richard Mayer
Affiliation:
University of California, Santa Barbara
Get access

Summary

Abstract

This chapter reviews and critiques the scientific evidence and research methods studying the use of games, simulations, and microworlds as multimedia learning tools. This chapter focuses on interactive educational multimedia, which is distinguished from scripted forms of educational multimedia by the degree to which users participate in and control the multimedia software. This chapter also uses the distinction between explanation and experience to understand the unique design opportunities of interactive educational multimedia. The strongest empirical evidence comes from the simulation literature, especially that related to questions about how to design a simulation's interface to provide feedback and questions about students engaged in discovery learning activities. Microworld research is less empirically rigorous with evidence continuing to remain largely anecdotal based on implementation reports. Research on gaming is the most transitory, ranging from early research on learning from playing games to learning from designing games. Current debates among educational researchers about what constitutes scientific research are particularly relevant to anyone interested in research about interactive multimedia due to the increased use of qualitative research methodologies and the newly emerging trend toward design experiments.

What Is Multimedia Learning in Games, Simulations, and Microworlds?

The purpose of this chapter is to review the scientific evidence on the use of games, simulations, and microworlds as multimedia learning tools. This is a tall order. All three have very distinctive design and research pedigrees resulting in many cases from very distinctive philosophies about education.

Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 549 - 568
Publisher: Cambridge University Press
Print publication year: 2005

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barab, S. A., Evans, M. A., & Baek, E. (2003). Activity theory as a lens for characterizing the participatory unit. In Jonassen, D. (Ed.), Handbook of research for educational communications and technology (2nd ed., pp. 199–214). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Berliner, D.C. (2002). Educational research: The hardest science of all. Educational Researcher, 31(8), 18–20CrossRefGoogle Scholar
Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178CrossRefGoogle Scholar
Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children's cognition. Journal of Educational Psychology, 76(6), 1051–1058CrossRefGoogle Scholar
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13CrossRefGoogle Scholar
Collins, A. (1999). The changing infrastructure of education research. In Lagemann, E. C. & Shulman, L. B. (Eds.), Issues in educational research: Problems and possibilities (pp. 289–298). San Francisco: Jossey-BassGoogle Scholar
Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University PressGoogle Scholar
Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: Explaining an apparent paradox. American Educational Research Journal, 38(4), 813–834CrossRefGoogle Scholar
Jong, T., & Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201CrossRefGoogle Scholar
Dempsey, J., Lucassen, B., Gilley, W., & Rasmussen, K. (1993–1994). Since Malone's theory of intrinsically motivating instruction: What's the score in the gaming literature?Journal of Educational Technology Systems, 22(2), 173–183CrossRefGoogle Scholar
Dick, W., Carey, L., & Carey, J. O. (2001). The systematic design of instruction (5th ed.). New York: LongmanGoogle Scholar
diSessa, A. (1997). Twenty reasons why your should use Boxer (instead of Logo). In Turcsányi-Szabó, M. (Ed.), Learning and exploring with Logo: Proceedings of the Sixth European Logo Conference, Budapest, Hungary (pp. 7–27)Google Scholar
diSessa, A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: The MIT PressGoogle Scholar
diSessa, A., & Abelson, H. (1986). Boxer: A reconstructible computational medium. Communications of the ACM, 29(9), 859–868CrossRefGoogle Scholar
diSessa, A., Abelson, H., & Ploger, D. (1991). An overview of Boxer. Journal of Mathematical Behavior, 10, 3–15Google Scholar
diSessa, A., Hoyles, C., Noss, R., & Edwards, L. D. (Eds.). (1995). Computers and exploratory learning. New York: SpringerCrossRefGoogle Scholar
Edwards, L. D. (1995). Microworlds as representations. In diSessa, A. A., Hoyles, C., Noss, R., & Edwards, L. D. (Eds.), Computers and exploratory learning (pp. 127–154). New York: SpringerCrossRefGoogle Scholar
Erickson, F., & Gutierrez, K. (2002). Culture, rigor, and science in educational research. Educational Researcher, 31(8), 21–24CrossRefGoogle Scholar
Feuer, M. J., Towne, L., & Shavelson, R. J. (2002). Scientific culture and educational research. Educational Researcher, 31(8), 4–14CrossRefGoogle Scholar
Gee, J. P. (2003). What video games have to teach us about learning and literacy. New York: Palgrave MacMillanGoogle Scholar
Gentner, D., & Stevens, A. (Eds.). (1983). Mental models. Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Gredler, M. E. (1996). Educational games and simulations: A technology in search of a (research) paradigm. In Jonassen, D. (Ed.), Handbook of research for educational communications and technology (pp. 521–540). Washington, DC: Association for Educational Communications and TechnologyGoogle Scholar
Gredler, M. E. (2003). Games and simulations and their relationships to learning. In Jonassen, D. (Ed.), Handbook of research for educational communications and technology (2nd ed., pp. 571–581). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1, 1–32CrossRefGoogle Scholar
Harel, I., & Papert, S. (1991). Software design as a learning environment. In Harel, I. & Papert, S. (Eds.), Constructionism (pp. 41–84). Norwood, NJ: AblexGoogle Scholar
Horwitz, P. (1999). Designing computer models that teach. In Feurzeig, W. & Roberts, N. (Eds.), Modeling and simulation in science and mathematics education (pp. 179–196). New York: Springer-VerlagCrossRefGoogle Scholar
Jih, H. J., & Reeves, T. C. (1992). Mental models: A research focus for interactive learning systems. Educational Technology Research and Development, 40(3), 39–53CrossRefGoogle Scholar
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Research, 33(7), 14–26CrossRefGoogle Scholar
Jonassen, D. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm?Educational Technology Research and Development, 39(3), 5–14CrossRefGoogle Scholar
Jonassen, D., & Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61–79CrossRefGoogle Scholar
Kafai, Y. (1994). Electronic play worlds: Children's construction of video games. In Kafai, Y. & Resnick, M. (Eds.), Constructionism in practice: Rethinking the roles of technology in learning. Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Kafai, Y. (1995). Minds in play: Computer game design as a context for children's learning. Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Kafai, Y., Ching, C., & Marshall, S. (1997). Children as designers of educational multimedia software. Computers and Education, 29, 117–126CrossRefGoogle Scholar
Kafai, Y., & Harel, I. (1991). Learning through design and teaching: Exploring social and collaborative aspects of constructionism. In Harel, I. & Papert, S. (Eds.), Constructionism (pp. 85–106). Norwood, NJ: AblexGoogle Scholar
Kirriemuir, J., & McFarlane, A. (2004). Literature review in games and learning: A report for NESTA Futurelab. Retrieved September 1, 2004, from http://www.nestafuturelab.org/research/reviews/08_01.htmGoogle Scholar
Lajoie, S. (Ed.). (2000). Computers as cognitive tools, volume two: No more walls: Theory change, paradigm shifts, and their influence on the use of computers for instructional purposes (2nd ed.). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Lajoie, S. P., & Derry, S. J. (Eds.). (1993). Computers as cognitive tools. Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59, 43–64CrossRefGoogle Scholar
Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University PressCrossRefGoogle Scholar
National Research Council. (2002). Scientific research in education. Washington, DC: National Academy Press
Newman, D. (1990). Opportunities for research on the organizational impact of school computers. Educational Researcher, 19(3), 8–13CrossRefGoogle Scholar
Newman, D. (1992). Formative experiments on the coevolution of technology and the educational environment. In Scanlon, E. & O'Shea, T. (Eds.), New directions in educational technology (pp. 61–70). New York: Springer-VerlagCrossRefGoogle Scholar
Norman, D. A. (2002). The design of everyday things. New York: BasicBooksGoogle Scholar
Olive, J. (1998). Opportunities to explore and integrate mathematics with “The Geometer's Sketchpad” in designing learning environments for developing understanding of geometry and space. In Lehrer, R. & Chazan, D. (Eds.), Designing learning environments for developing understanding of geometry and space (pp. 395–418). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Paivio, A. (1990). Mental representations: A dual coding approach (2nd ed.). New York: Oxford University PressCrossRefGoogle Scholar
Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287CrossRefGoogle Scholar
Papert, S. (1980a). Computer-based microworlds as incubators for powerful ideas. In Taylor, R. (Ed.), The computer in the school: Tutor, tool, tutee (pp. 203–210). New York: Teacher's College PressGoogle Scholar
Papert, S. (1980b). Mindstorms: Children, computers, and powerful ideas. New York: BasicBooksGoogle Scholar
Papert, S. (1987). Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30Google Scholar
Pea, R., & Kurland, M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2, 1137–1168CrossRefGoogle Scholar
Pellegrini, A. D. (Ed.). (1995). The future of play theory: A multidisciplinary inquiry into the contributions of Brian Sutton-Smith. Albany, NY: State University of New York PressGoogle Scholar
Penner, D. E. (2000–2001). Cognition, computers, and synthetic science: Building knowledge and meaning through modeling. Review of Research in Education, 25, 1–35Google Scholar
Poole, S. (2000). Trigger happy: Videogames and the entertainment revolution. New York: ArcadeGoogle Scholar
Randel, J. M., Morris, B. A., Wetzel, C. D., & Whitehill, B. V. (1992). The effectiveness of games for educational purposes: A review of recent research. Simulation and gaming, 23, 261–276CrossRefGoogle Scholar
Resnick, M. (1994). Turtles, termites, and traffic jams. Cambridge, MA: MIT PressGoogle Scholar
Richey, R. C., & Nelson, W. A. (1996). Developmental research. In Jonassen, D. (Ed.), Handbook of research for educational communications and technology (pp. 1213–1245). Washington, DC: Association for Educational Communications and TechnologyGoogle Scholar
Rieber, L. P. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82, 135–140CrossRefGoogle Scholar
Rieber, L. P. (1991). Animation, incidental learning, and continuing motivation. Journal of Educational Psychology, 83, 318–328CrossRefGoogle Scholar
Rieber, L. P. (1992). Computer-based microworlds: A bridge between constructivism and direct instruction. Educational Technology Research and Development, 40(1), 93–106CrossRefGoogle Scholar
Rieber, L. P. (1996a). Animation as feedback in a computer-based simulation: Representation matters. Educational Technology Research and Development, 44(1), 5–22CrossRefGoogle Scholar
Rieber, L. P. (1996b). Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games. Educational Technology Research and Development, 44(2), 43–58CrossRefGoogle Scholar
Rieber, L. P. (2003). Microworlds. In Jonassen, D. (Ed.), Handbook of research for educational communications and technology (2nd ed., pp. 583–603). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Rieber, L. P., Boyce, M., & Assad, C. (1990). The effects of computer animation on adult learning and retrieval tasks. Journal of Computer-Based Instruction, 17(2), 46–52Google Scholar
Rieber, L. P., Davis, J., Matzko, M., & Grant, M. (2001, April). Children as multimedia critics: Middle school students' motivation for and critical analysis of educational multimedia designed by other children. Paper presented at the annual meeting of the American Educational Research Association, Seattle, WAGoogle Scholar
Rieber, L. P., & Kini, A. (1995). Using computer simulations in inductive learning strategies with children in science. International Journal of Instructional Media, 22(2), 135–144Google Scholar
Rieber, L. P., Luke, N., & Smith, J. (1998). Project KID DESIGNER: Constructivism at work through play. Meridian: Middle School Computer Technology Journal 1(1). Retrieved May 19, 2004 from http://www.ncsu.edu/meridian/archive_of_meridian/jan98/index.htmlGoogle Scholar
Rieber, L. P., & Noah, D. (1997, March). Effect of gaming and graphical metaphors on reflective cognition within computer-based simulations. Paper presented at the annual meeting of the American Educational Research Association, Chicago, ILGoogle Scholar
Rieber, L. P., Noah, D., & Nolan, M. (1998, April). Metaphors as Graphical Representations within Open-Ended Computer-Based Simulations. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CAGoogle Scholar
Rieber, L. P., & Parmley, M. W. (1995). To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 13(4), 359–374CrossRefGoogle Scholar
Rieber, L. P., Smith, M., Al-Ghafry, S., Strickland, W., Chu, G., & Spahi, F. (1996). The role of meaning in interpreting graphical and textual feedback during a computer-based simulation. Computers and Education, 27(1), 45–58CrossRefGoogle Scholar
Rieber, L. P., Tzeng, S., & Tribble, K. (2004). Discovery learning, representation, and explanation within a computer-based simulation: Finding the right mix. Learning and Instruction, 14, 307–323CrossRefGoogle Scholar
Roschelle, J., & Jackiw, N. (2000). Technology design as educational research: Interweaving imagination, inquiry and impact. In Kelley, A. & Lesh, R. (Eds.), Handbook of research design in mathematics and science education (pp. 777–797). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Roschelle, J., Kaput, J., & Stroup, W. (2000). SimCalc: Accelerating student engagement with the mathematics of change. In Jacobson, M. J. & Kozma, R. B. (Eds.), Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 47–75). Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Sadoski, M., & Paivio, A. (2001). Imagery and text: A dual coding theory of reading and writing. Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2–9CrossRefGoogle Scholar
Sutton-Smith, B. (1997). The ambiguity of play. Cambridge, MA: Harvard University PressGoogle Scholar
White, B. Y. (1984). Designing computer games to help physics students understand Newton's laws of motion. Cognition and Instruction, 1(1), 69–108CrossRefGoogle Scholar
White, B. Y. (1992). A microworld-based approach to science education. In Scanlon, E. & O'Shea, T. (Eds.), New directions in educational technology (pp. 227–242). New York: Springer-VerlagCrossRefGoogle Scholar
White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10(1), 1–100CrossRefGoogle Scholar
White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118CrossRefGoogle Scholar
White, B. Y., & Frederiksen, J. R. (2000). Technological tools and instructional approaches for making scientific inquiry accessible to all. In Jacobson, M. J. & Kozma, R. B. (Eds.), Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 321–359). Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
White, B. Y., & Horowitz, P. (1987). ThinkerTools: Enabling children to understand physical laws (No. 6470). Cambridge, MA: Bolt, Beranek, and NewmanGoogle Scholar
White, B. Y., & Schwarz, C. V. (1999). Alternative approaches to using modeling and simulation tools for teaching science. In Feurzeig, W. & Roberts, N. (Eds.), Modeling and simulation in science and mathematics education (pp. 226–256). New York: Springer-VerlagCrossRefGoogle Scholar
Zhao, Y., & Frank, K. A. (2003). Factors affecting technology uses in schools: An ecological perspective. American Educational Research Journal, 40(4), 807–840CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×