Advertisement

Modeling for Meaningful Learning

Chapter

Abstract

In the first part of the chapter, we argue that the goal of formal education should be meaningful learning. Meaningful learning is necessarily social, collaborative, intentional, authentic, and active. The result of meaningful learning lies in its cognitive residue, the learner’ mental model.

In the second part of this chapter, we describe different components of individual mental models and collaborative mental models. Mental models are rich, complex, interconnected, interdependent, multi-modal representations of what someone or some group knows.

Perhaps the most effective means for fostering the development of mental models is the construction of computational models. We argue that modeling is an essential skill for all disciplines engaging students in meaningful learning. So, the third part of the chapter focuses on how technologies can be used to support students’ construction of their own models and theories of how phenomena work. Students can build models of domain knowledge, problems, systems, semantic structures, and thinking while studying. In addition to distinguishing between what is modeled, we also distinguish between kinds of modeling systems (deductive simulations, inductive simulations, qualitative causal models like expert systems, and semantic modeling tools), and their affordances for supporting the construction of mental models.

Keywords

modeling model-based reasoning constructivism problem solving mental models conceptual change cognitive tools Mindtools expert systems systems modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams-Webber, J. (1995). Constructivist psychology and knowledge elicitation. Journal of Constructivist Psychology, 8(3), 237–249.Google Scholar
  2. Confrey, J., & Doerr, H. M. (1994). Student modelers. Interactive Learning Environments, 4(3), 199–217.Google Scholar
  3. DiSessa, A., & Abeson, H. (1986). Boxer: A reconstructible computational medium. Communications of the ACM, 29, 859–868.CrossRefGoogle Scholar
  4. Dole, J.A., Sinatra, G.M. (1998). Reconceptualizing change in the cognitive construction of knowledge. Educational Psychologist, 33, 109–128.CrossRefGoogle Scholar
  5. Durkheim, Émile. (1915) The Elementary Forms of the Religious Life. Translated by Joseph Ward Swain. New York and London: The Free pressGoogle Scholar
  6. Engeström, Y. (1987). Learning by expanding: An activity theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit Oy.Google Scholar
  7. Frederiksen, J. R., White, B. Y. (1998). Teaching and learning generic modeling and reasoning skills. Journal of Interactive Learning Environments, 55, 33–51.Google Scholar
  8. Gentner, D., & Stevens, A.L. (1983). Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  9. Johnson-Laird, P.N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.Google Scholar
  10. Jonassen, D.H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology: Research and Development, 45(1), 65–95.CrossRefGoogle Scholar
  11. Jonassen, D.H. (2000). Computers as Mindtools for schools: Engaging critical thinking. Columbus, OH: Merrill/Prentice-Hall.Google Scholar
  12. Jonassen, D.H. (2003). Using cognitive tools to represent problems. Journal of Research on Technology in Education, 35(3), 362–381Google Scholar
  13. Jonassen, D.H., Beissner, K., & Yacci, M.A. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  14. Jonassen, D.H., & Henning, P. (1999). Mental models: Knowledge in the head and knowledge in the world. Educational Technology, 9(3), 37–42.Google Scholar
  15. Jonassen, D.H., Howland, J., Moore, J., & Marra, R.M. (2003) Learning to solve problems with technology: A constructivist perspective, 2nd. Ed. Columbus, OH: Merrill/Prentice-Hall.Google Scholar
  16. Jonassen, D.H. & Wang, S. (2003) Using expert systems to build cognitive simulations. Journal of Educational Computing Research, 28(1), 1–13.CrossRefGoogle Scholar
  17. Kraiger, K., & Salas, E. (1993, April). Measuring mental models to assess learning during training. Paper presented at the Annual Meeting of the Society for Industrial/Organizational Psychology, San Francisco, CA.Google Scholar
  18. Larkin, J.H. (1983). The role of problem representation in physics. In D. Gentner & A.L. Stevens (Eds.). Mental models (pp. 75–98). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  19. Lehrer, R., & Schauble, L. (2000). Modeling in mathematics and science. In R. Glaser (Ed.) Advances in instructional psychology: volume 5. Educational design and cognitive science (pp. 101–159). New Jersey: Lawrence Erlbaum.Google Scholar
  20. Lippert, R. C. (1988). An expert system shell to teach problem solving. Tech Trends, 33(2), 22–26.Google Scholar
  21. McGuinness, C. (1986). Problem representation: The effects of spatial arrays. Memory & Cognition, 14(3), 270–280.Google Scholar
  22. Mellar, H., Bliss, J., Boohan, R., Ogborn, J., & Tompsett, C. (1994). Learning with artificial worlds: Computer-based modeling in the curriculum. London: Falmer Press.Google Scholar
  23. Penner, D.E., Giles, N.D., Lhrer, R., & Schauble, L. (1997). Buildig functional models: designing and elbow. Journal of Research in Science Teaching, 34(2), 125–143.CrossRefGoogle Scholar
  24. Ploetzner, R., & Spada, H. (1998). Constructing quantitative problem representations on the basis of qualitative reasoning. Interactive Learning Environments, 5, 95–107.Google Scholar
  25. Ploetzner, R., Fehse, E., Kneser, C., & Spada, H. (1999). Learning to relate qualitative and quantitative problem representations in a model-based setting for collaborative problem solving. Journal of the Learning Sciences, 8(2), 177–214.CrossRefGoogle Scholar
  26. Rips, L.J. (1986). Mental muddles. In M. Brand & R.M. Harnish (Eds.), The representation of knowledge and beliefs (pp. 258–286). Tuscon, AZ: University of Arizona Press.Google Scholar
  27. Salomon, G., Perkins, D.N. & Globerson, T. (1991). Partners in Cognition: Extending Human Intelligence with Intelligent Technologies. Educational Researcher, 20(3), 2–9.CrossRefGoogle Scholar
  28. Schank, R.C. (1994). Goal-based scenarios. In R.C. Schank & E. Langer (eds.), Beliefs, reasoning, and decision making: Psycho-logic in honor of Bob Abelson. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  29. Schwartz, J.L., & Yerulshalmy, M. (1987). The geometric supposer: Using microcomputers to restore invention to the learning of mathematics. In D. Perkins, J. Lockhead, & J.C. Bishop (Eds.), Thinking: The second international conference (pp. 525–536). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  30. Schwarz, C.V., & White, B. (2005). Metamodeling Knowledge: Developing Students’ Understanding of Scientific Modeling. Cognition and Instruction, 23(2), 165–205.CrossRefGoogle Scholar
  31. Schwarz, C.V., & White, B.Y. (in press). Developing a model-centered approach to science education. Journal of Research in Science Teaching.Google Scholar
  32. Shavelson, R.J. (1972). Some aspects of the correspondence between content structure and cognitive structure in physics instruction. Journal of Educational Psychology, 63, 225–234.CrossRefGoogle Scholar
  33. Spector, J. Michael; Christensen, Dean L; Sioutine, Alexei V; McCormack, Dalton (2001) Models and simulations for learning in complex domains: Using causal loop diagrams for assessment and evaluation, in: Computers in Human Behavior. Vol 17(5–6) Sep–Nov 2001, 517–545CrossRefGoogle Scholar
  34. Taylor, H.A., & Tversky, B. (19920. Spatial mental models derived from survey and route descriptions Journal of Memory and Language, 31, 261–292.Google Scholar
  35. van der Veer, G.C. (1989). Individual differences and the user interface. Ergonomics, 32(11), 1431–1449.Google Scholar
  36. Vosniadou, S. (1999). Conceptual change research: The state of the art and future directions In W. Schnotz, S. Vosniadou, & M. Carretero (Eds.), New perspectives on conceptual change (pp. 1–13). Amsterdam: Pergamon.Google Scholar
  37. White, B. (1993a). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10(1), 1–100.CrossRefGoogle Scholar
  38. Wittgenstein, L. (1922). Tractatus logico-philosophicus. London: Routledge.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.University of MissouriUSA
  2. 2.Concordia UniversityCanada

Personalised recommendations