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22 - Cultivating Model-Based Reasoning in Science Education

Published online by Cambridge University Press:  05 June 2012

By
Richard Lehrer  and
Leona Schauble
Edited by
R. Keith Sawyer
Richard Lehrer
Affiliation:
Vanderbilt University
Leona Schauble
Affiliation:
Vanderbilt University
R. Keith Sawyer
Affiliation:
Washington University, St Louis
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Summary

Social studies of scientific practice reveal considerable diversity in the methods and material means of production across scientific disciplines (e.g., Galison & Stump, 1995). Yet, in spite of this diversity and regardless of their domain, scientists' work involves building and refining models of the world (Giere, 1988; Hestenes, 1992; Stewart & Golubitsky, 1992). Scientific ideas derive their power from the models that instantiate them, and theories change as a result of efforts to invent, revise, and stage competitions among models. These efforts are mobilized to support socially grounded arguments about the nature of physical reality, so model-based reasoning is embedded within a wider world that includes networks of participants and institutions (Latour, 1999); specialized ways of talking and writing (Bazerman, 1988); development of representations that render phenomena accessible, visualizable, and transportable (Gooding, 1989; Latour, 1990); and efforts to manage material contingency, because no model specifies instrumentation and measurement in sufficient detail to prescribe practice (Pickering, 1995).

Studies of modeling practices and model-based reasoning encompass a wide spectrum of approaches and disciplines. Some investigators rely on laboratory tasks that are intended to identify important aspects of model-based reasoning (e.g., Craig, Nersessian, & Catrambone, 2002; Gentner & Gentner, 1983). For example, Gentner and Gentner (1983) investigated how different analogical models of electricity influenced participants' reasoning about circuits. Participants who employed an analogy that emphasized the similarities of electricity flow to fluid flow made predictions about the consequences of different arrangements of batteries in a circuit that were more accurate than their predictions about the effects of resistors.

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The Cambridge Handbook of the Learning Sciences , pp. 371 - 388
Publisher: Cambridge University Press
Print publication year: 2005

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References

Abelson, H., & diSessa, A. (1980). Turtle geometry: The computer as a medium for exploring mathematics. Cambridge, MA: MIT Press.Google Scholar
Bazerman, C. (1988). Shaping written knowledge: The genre and activity of the experimental article in science. Madison: University of Wisconsin Press.Google Scholar
Cartier, J. L., Barton, A. M., & Mesmer, K. (2001, March). Inquiry as a context for meaningful learning in a 9th-grade science unit. Paper presented at the annual meeting of the National Association for Research on Science Teaching, St. Louis, MO.Google Scholar
Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences, 14, 161–199.CrossRefGoogle Scholar
Collela, V. (2000). Participatory simulations: Building collaborative understanding through dynamic modeling. The Journal of the Learning Sciences, 9, 471–500.CrossRefGoogle Scholar
Craig, D. L., Nersessian, N. J., & Catrambone, R. (2002). Perceptual simulation in analogical problem solving. In Magnani, L. & Nersessian, N. J. (Eds.), Model-based reasoning. Science, technology, values. (pp. 167–189). Dordrecht, Netherlands: Kluwer Academic Press.CrossRefGoogle Scholar
Dawkins, R. (1996). Climbing mount improbable. New York: W. W. Norton.Google Scholar
DeLoache, J. S. (2004). Becoming symbol-minded. Trends in Cognitive Sciences, 8(2), 66–70.CrossRefGoogle ScholarPubMed
DeLoache, J. S., Pierroutsakos, S. L., & Uttal, D. H. (2003). The origins of pictoral competence. Current Directions in Psychological Science, 12(4), 114–118.CrossRefGoogle Scholar
diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22, 293–331.CrossRefGoogle Scholar
Doerr, H. M., & English, L. (2003). A modeling perspective on students' mathematical reasoning about data. Journal for Research in Mathematics Education, 34(2), 110–136.CrossRefGoogle Scholar
Driver, R., Leach, J., Millar, R., & Scott, P. (1996). Young people's images of science. Buckingham, England: Open University Press.Google Scholar
Dunbar, K. (1993). Scientific reasoning strategies for concept discovery in a complex domain. Cognitive Science, 17(3), 397–434.CrossRefGoogle Scholar
Dunbar, K. (1998). How scientists really reason: Scientific reasoning in real-world laboratories. In Sternberg, R. J. & Davidson, J. F. (Eds.), The nature of insight (pp. 265–395). Cambridge, MA: MIT Press.Google Scholar
Galison, P., & Stump, D. (1995). The disunity of science. Boundaries, context, and power. Stanford, CA: Stanford University Press.Google Scholar
Gentner, D., & Gentner, D. R. (1983). Flowing waters or teeming crowds: Mental models of electricity. In Gentner, D., & Stevens, A. L. (Eds.), Mental models (pp 99–129). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Gentner, D., & Toupin, C. (1986). Systematicity and surface similarity in the development of analogy. Cognitive Science, 10, 277–300.CrossRefGoogle Scholar
Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Gooding, D. (1989). “Magnetic curves” and the magnetic field: Experimentation and representation in the history of a theory. In Gooding, D., Pinch, T., & Schaffer, S. (Eds.), The uses of experiment. Studies on the natural sciences. (pp. 183–223). Cambridge: Cambridge University Press.Google Scholar
Gooding, D. (1990). Experiment and the making of meaning. London: Kluwer Academic Publishers.CrossRefGoogle Scholar
Greeno, J. G., & Hall, R. (1997). Practicing representation: Learning with and about representational forms. Phi Delta Kappan (January), 361–367.Google Scholar
Grosslight, L., Unger, C., Jay, E., & Smith, C. (1991). Understanding models and their use in science: Conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28, 799–822.CrossRefGoogle Scholar
Hesse, M. B. (1965). Forces and fields. Totowa, NJ: Littlefield, Adams & Co.Google Scholar
Hestenes, D. (1992). Modeling games in the Newtonian world. American Journal of Physics, 60(8), 732–748.Google Scholar
Jungck, J. R., & Calley, J. (1985). Strategic simulations and post-Socratic pedagogy: Constructing computer software to develop long-term inference through experimental inquiry. American Biology Teacher, 47, 11–15.CrossRefGoogle Scholar
Karmiloff-Smith, A. (1979). Micro- and macro-developmental changes in language acquisition and other representational systems. Cognitive Science, 3, 91–118.CrossRefGoogle Scholar
Karmiloff-Smith, A. (1992). Beyond modularity: A developmental perspective on cognitive science. Cambridge, MA: MIT Press.Google Scholar
Kline, M. (1980). Mathematics: The loss of certainty. Oxford: Oxford University Press.Google Scholar
Latour, B. (1990). Drawing things together. In Lynch, M. & Woolgar, S. (Eds.), Representation in scientific practice (pp. 19–68). Cambridge, MA: MIT Press.Google Scholar
Latour, B. (1993). We have never been modern. Cambridge, MA: Harvard University Press.Google Scholar
Latour, B. (1999). Pandora's hope: Essays on the reality of science studies. London: Cambridge University Press.Google Scholar
Lehrer, R., Carpenter, S., Schauble, L., & Putz, A. (2000). Designing classrooms that support inquiry. In Minstrell, J. & Zee, E. V. (Eds.), Inquiring into inquiry learning and teaching in science (pp. 80–99). Washington, DC: American Association for the Advancement of Science.Google Scholar
Lehrer, R., & Lesh, R. (2003). Mathematical learning. In Reynolds, W. & Miller, G. (Eds.), Handbook of psychology: Vol. 7, Educational psychology (pp. 357–391). New York: John Wiley.Google Scholar
Lehrer, R., & Pritchard, C. (2002). Symbolizing space into being. In Gravemeijer, K., Lehrer, R., Oers, B., & Verschaffel, L. (Eds.), Symbolization, modeling and tool use in mathematics education. (pp. 59–86). Dordrecht, Netherlands: Kluwer Academic Press.CrossRefGoogle Scholar
Lehrer, R., & Schauble, L. (2000). Modeling in mathematics and science. In Glaser, R. (Ed.), Advances in instructional psychology: Educational design and cognitive science. Vol. 5 (pp. 101–159). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Lehrer, R., & Schauble, L. (2004, April). Modeling aquatic systems: Contexts and practices for supporting inquiry, agency and epistemology. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.Google Scholar
Lehrer, R., & Schauble, L. (2005). Developing modeling and argument in the elementary grades. In Romberg, T. & Carpenter, T. P. (Eds.), Understanding mathematics and science matters. (pp. 29–53). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Lehrer, R., Schauble, L., Carpenter, S., & Penner, D. E. (2000). The inter-related development of inscriptions and conceptual understanding. In Cobb, P., Yackel, E., & McClain, K. (Eds.), Symbolizing and communicating in mathematics classrooms: Perspectives on discourse, tools, and instructional design (pp. 325–360). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Lesh, R., & Doerr, H. (2003). Foundations of a models and modeling perspective on mathematics teaching, learning, and problem solving. In Lesh, R. & Doerr, H. (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. (pp. 3–33). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Lesh, R., Hoover, M., Hole, B., Kelly, A., & Post, T. (2000). Principles for developing thought revealing activities for students and teachers. In Kelly, A. & Lesh, R. (Eds.). The handbook of research design in mathematics and science education. (pp. 591–646). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Leslie, A. M. (1987). Pretense and representation: The origins of “theory of mind.”Psychological Review, 94(4), 412–426.CrossRefGoogle Scholar
Lucas, D., Broderick, N., Lehrer, R., & Bohanan, R. (2005, November). Making the grounds of scientific inquiry visible in the classroom. Science Scope. Available at http://www.nsta.org/middleschool, accessed November 23, 2005.
Lynch, M. (1990). The externalized retina: Selection and mathematization in the visual documentation of objects in the life sciences. In Lynch, M. & Woolgar, S. (Eds.), Representation in scientific practice (pp. 153–186). Cambridge, MA: The MIT Press.Google Scholar
Mead, G. H. (1910). Social consciousness and the consciousness of meaning. Psychological Bulletin, 7, 397–405.CrossRefGoogle Scholar
Metcalf, S. J., Krajcik, J., & Soloway, E. (2000). Model-It: A design retrospective. In Jacobson, M. & Kozma, R. B., (Eds.), Innovations in science and mathematics education: Advanced designs for technologies of learning. (pp. 77–116). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
Metz, K. E. (2004). Children's understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22(2), 219–290.CrossRefGoogle Scholar
Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In Carruthers, P., Stich, S., & Siegal, M. (Eds.), The cognitive basis of science. (pp. 133–155). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Nersessian, N. J., Kurz-Milcke, E., Newsletter, W. C., & Davies, J. (2003). Research laboratories as evolving distributed cognitive systems. In Alterman, R. & Kirsh, D. (Eds.), Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society (pp. 857–862). Mahwah, NJ: Erlbaum.Google Scholar
Olson, D. R. (1994). The world on paper: The conceptual and cognitive implications of writing and reading. New York: Cambridge University Press.Google Scholar
Penner, D. E. (2000). Explaining systems: Investigating middle school students' understanding of emergent phenomena. Journal of Research in Science Teaching, 37, 784–806.3.0.CO;2-E>CrossRefGoogle Scholar
Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34(2), 125–143.3.0.CO;2-V>CrossRefGoogle Scholar
Penner, D. E., Lehrer, R., & Schauble, L. (1998). From physical models to biomechanics: A design-based modeling approach. Journal of the Learning Sciences, 7(3&4), 429–449.CrossRefGoogle Scholar
Petrosino, A. J., Lehrer, R., & Schauble, L. (2003). Structuring error and experimental variation as distribution in the fourth grade. Mathematical Thinking and Learning, 5(2&3), 131–156.CrossRefGoogle Scholar
Pickering, A. (1995). The mangle of practice: Time, agency, and science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.Google Scholar
Resnick, M. (1996). Beyond the centralized mindset. The Journal for the Learning Sciences, 5, 1–22.CrossRefGoogle Scholar
Resnick, M., & Wilensky, U. (1998). Diving into complexity: developing probabilistic decentralized thinking through role-playing activities. The Journal of the Learning Sciences, 7, 153–172.CrossRefGoogle Scholar
Schwartz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23, 165–205.CrossRefGoogle Scholar
Shapin, S., & Schaffer, S. (1985). Leviathan and the air pump. Princeton: Princeton University Press.Google Scholar
Stenning, K., Greeno, J. G., Hall, R., Sommerfield, M., & Wiebe, M. (2002). Coordinating mathematical with biological multiplication: Conceptual learning as the development of heterogeneous reasoning systems. In Brna, P., Baker, M., Stenning, K., & Tiberghien, A. (Ed.), The role of communication in learning to model. (pp 3–48). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Stewart, I., & Golubitsky, M. (1992). Fearful symmetry: Is God a geometer? London: Penguin Books.Google Scholar
Stewart, J., Hafner, R., Johnson, S., & Finkel, E. (1992). Science as model building: Computers and high-school genetics. Educational Psychologist, 27, 317–336.CrossRefGoogle Scholar
Stewart, S., Passmore, C., Cartier, J., Rudolph, J., & Donovan, S. (2005). Modeling for understanding in science education. In Romberg, T., Carpenter, T., and Dremock, F., (Eds.), Understanding mathematics and science matters (pp. 159–184). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Troseth, G. L. (2003). Getting a clear picture: Young children's understanding of a televised image. Developmental Science, 6(3), 247–253.CrossRefGoogle Scholar
Troseth, G. L., & DeLoache, J. S. (1998). The medium can obscure the message: Young children's understanding of video. Child Development, 69, 950–965.CrossRefGoogle ScholarPubMed
Troseth, G. L., Pierroutsakos, S. L., & DeLoache, J. S. (2004). From the innocent to the intelligent eye: The early development of pictoral competence. In Kail, R. (Ed.), Advances in child development and behavior, Vol. 32 (pp. 1–35). New York: Academic Press.Google Scholar
White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3–118.CrossRefGoogle Scholar
Wilensky, U. (1996). Modeling rugby: Kick first, generalize later? International Journal of Computers for Mathematical Learning, 1, 125–131.Google Scholar
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology. 8(1): 3–19.CrossRefGoogle Scholar
Wilensky, U., & Stroup, W. (1999). Learning through participatory simulations: Network-based design for systems learning in classrooms. Computer Supported Collaborative Learning Conference, Stanford University, California.Google Scholar

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