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Abstract

The studies reported in this paper are an initial effort to explore the applicability of computational models in introductory science learning. Two instructional interventions are described that use a molecular dynamics model embedded in a set of online learning activities with middle and high school students in 10 classrooms. The studies indicate that middle and high schools students can acquire robust mental models of the states of matter through guided explorations of computational models of matter based on molecular dynamics. Using this approach, students accurately recall arrangements of the different states of matter, and can reason about atomic interactions. These results are independent of gender and they hold for a number of different classroom contexts. Follow-up interviews indicate that students are able to transfer their understanding of phases of matter to new contexts.

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REFERENCES

  • Berkheimer, G. D., Anderson, C. W., Lee, O., and Blakeslee, T. S. (1988). Matter and Molecules Teacher's Guide, The Institute for Research on Teaching College of Education, Michigan State University.

  • Bodner, G. M., and Domin, D. S. (1996). The role of representations in problem solving in chemistry. Paper presented at the New Initiative in Chemical Education, ChemConf '96.

  • Burke, K. A., Greenbowe, T. J., and Windschitl, M. A. (1998). Developing and using conceptual computer animations for chemistry instruction. Journal of Chemical Education 75: 1658-1661.

    Google Scholar 

  • Chi, M., Glaser, R., and Farr, M. (Eds.). (1988). The Nature of Expertise, Erlbaum, Hillsdale, NJ.

    Google Scholar 

  • Colella, V. S., Klopfer, E., and Resnick, M. (2001). Adventures in Modeling; Exploring Complex, Dynamic Systems With Star Logo, Teachers College Press, New York.

    Google Scholar 

  • Copolo, C. F., and Hounshell, P. B. (1995). Using three-dimensional models to teach molecular structures in high school chemistry. Journal of Science Education and Technology 4: 295-305.

    Google Scholar 

  • Driver, R. (1985). Changing perspectives on science lessons. British Journal of Educational Psychology Monograph, no. 2.

  • Glaser, R. (1989). Expertise and learning: How do we think about instructional processes now that we have dis-covered knowledge structures? In Klahr, D., and Kotovsky, K. (Eds.), Complex Information Processing, Erlbaum, Hillsdale, NJ, pp. 269-282.

    Google Scholar 

  • Griffiths, A. K., and Preston, K. R. (1992). Grade 12 students misconceptions rekating to fundamental characteristics of atoms and molecules. Journal of Research in Science Teaching, 29: 611-628.

    Google Scholar 

  • Hickey, D., Kindfield, A. C. H., and Wolfe, E. (1999). Assessment-oriented scaffolding of student and teacher performance in a technology-supported genetics environment. Paper presented at the Annual Meeting of the American Educational Research Association, Montreal, Canada.

  • Horwitz, P. (1996). Linking models to data: Hypermodels for science education. The High School Journal 79: 148-156.

    Google Scholar 

  • Horwitz, P., and Christie, M. (1999). Hypermodels: Embedding curriculum and assessment in computer-based manipulatives. Journal of Education 181: 1-23.

    Google Scholar 

  • Horwitz, P., Neumann, E., and Schwartz, J. (1996). Teaching science at multiple levels: The GenScope program. Communications of the ACM 39.

  • Horwitz, P., and Tinker, R. (2001). Pedagogica to the rescue: A short history of hypermodels. CONCORD (The Concord Consortium) 5: 12-13.

    Google Scholar 

  • Jackson, S. L., Krajcik, J. S., and Soloway, E. (1998). The design of guided learner-adaptable scaffolding in interactive learning environments. In Karat, C. M., Lund, A., Coutaz, J., and Karat, J. (Eds.), ACM CHI 98. Human Factors in Computing Systems, April 18–23, Addison-Wesley, Los Angeles, CA, pp. 187-194.

    Google Scholar 

  • Jackson, S. L., Stratford, S., Krajcik, J. S., and Soloway, E. (1996). A learner-centered tool for students building models. Communications of the ACM 39: 48-49.

    Google Scholar 

  • Johnston, K., and Driver, R. (1991). A Case Study of Teaching and Learning about Particle Theory, University of Leeds: Center for Studies in Science and Mathematics Education (Children's Learning in Science Project), Leeds, England.

    Google Scholar 

  • Kozma, R., and Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching 34: 949-968.

    Google Scholar 

  • Krajcik, J. S. (1991). Developing students' understanding of chemical concepts. In Glynn, S., Yeany, R., and Britton, B. (Eds.), The Psychology of Learning Science, Hillsdale, NJ, Erlbaum, pp. 117-147.

    Google Scholar 

  • Linn, M. C., and Songer, N. B. (1991). Teaching thermodynamics to middle school students: What are appropriate cognitive demands? Journal of Research in Science Teaching 28: 885-918.

    Google Scholar 

  • Metcalf, S. J. (1999). The Design of Guided Learner-Adaptable Scaffolding in Interactive Learning Environment, Unpublished PhD Dissertation, University of Michigan, Ann Arbor, MI.

    Google Scholar 

  • Millar, R. (1990). Making sense: What use are particles to children? In Lijnse, P., Licht, P., de Vos, W., and Waarlo, A. J. (Eds.), Relating Macroscopic Phenomena to Microscopic Particles, Utrecht, Holland, pp. 283-293.

    Google Scholar 

  • Nakhleh, M. B. (1992). Why some students don't learn chemistry. Journal of Chemical Education 69: 191-196.

    Google Scholar 

  • Novick, S., and Nussbaum, J. (1978). Junior high school pupils' understanding of the particulate nature of matter: An Interview Study. Science Education 62: 273-281.

    Google Scholar 

  • Nussbaum, J. (1985). The particulate nature of matter in the gaseous phase. In Driver, R., Guesne, E., and Tiberghien, A. (Eds.), Children's Ideas in Science, Open University Press, Milton Keynes, UK, pp. 124-144.

    Google Scholar 

  • Nussbaum, J. (1997). History and philosophy of science and the preparation for constructivist teacher: The case of particle theory. In Mintzes, J. J., Wandersee, J. H., and Noval, J. D. (Eds.), Teaching Science for Understanding: A Human Constructivist View, Academic Press, New York.

    Google Scholar 

  • Nussbaum, J., and Novick, S. (1981). Brainstorming in the classroom to invent a model: A case study. School Science Review 62: 771-778.

    Google Scholar 

  • Repenning, A. (1993). Agentsheets: A Tool for Building Domain-Oriented Dynamic, Visual Environments, University of Colorado, Boulder, CO.

    Google Scholar 

  • Roberts, N., Feurzeig, W., and Hunter, B. (1999). Computer Modeling and Simulation in Science Education, Springer–Verlag, Berlin.

    Google Scholar 

  • Schank, P., and Kozma, R. (2002). Learning chemistry through the use of a representation-based knowledge building environment. Journal of Computers in Mathematics and Science Teaching 21: 253-279.

    Google Scholar 

  • Smith, M., Grosslight, L., and Davis, E. (1997). Teaching for understanding: A study of students pre-instruction theories of matter and comparison of the effectiveness of two approaches to teaching about matter and density. Cognition &; Instruction 15: 317-393.

    Google Scholar 

  • StatView (1999). SAS Institute Inc., 3rd edn., Cary, NC.

  • Stavy, R. (1990). Children's conception of changes in the state of matter: From liquid (or solid) to gas. Journal of Research in Science Teaching 27: 247-266.

    Google Scholar 

  • The National Research Council (1995). National Science Education Standards, National Academy of Sciences, Washington, DC.

  • Tinker, R. (2001a). Molecular dynamics hypermodels: Supporting student inquiry across the sciences. Paper presented at the Gordon Research Conference on Science Education and Visualizations, Mt Holyoke College, August 9, 2001.

  • Tinker, R. (2001b). The Molecular Workbench Project. Paper presented at the Molecular Visualization and Science Education Workshop, Arlington, VA, January 12–14, 2001.

  • Trunfio, P. (1990). Visualization Technologies as a Tool for Science Education, ACM SIGGRAPH '90 Panel Proceedings, Dallas. (ACM SIGGRAPH, New York, 1990).

  • White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction 10: 1-100.

    Google Scholar 

  • Wilensky, U. (1999). GasLab—An extensible modeling toolkit for exploring micro-and macro-views of gases. In Roberts, N., Feurzeig, W., and Hunter, B. (Eds.), Computer Modeling and Simulation in Science Education, Springer-Verlag, Berlin, pp. 151-178.

    Google Scholar 

  • Wilensky, U., and Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology 8(1): 3-18.

    Google Scholar 

  • Wu, H. K., Krajcik, J. S., and Soloway, E. (2001). Promoting conceptual understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching 38: 821-842.

    Google Scholar 

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Correspondence to Amy Pallant.

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Pallant, A., Tinker, R.F. Reasoning with Atomic-Scale Molecular Dynamic Models. Journal of Science Education and Technology 13, 51–66 (2004). https://doi.org/10.1023/B:JOST.0000019638.01800.d0

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