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Models and Modelling: Routes to More Authentic Science Education

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Abstract

It is argued that a central role for models and modelling would greatly increase the authenticity of the science curriculum. The range of ontological states available for the notion of ‘model’ is outlined, together with the modes available for their representation. Issues in the selection of models for and the development of modelling skills within the model-based curriculum are presented. It is suggested that learning within such a curriculum entails: acquiring an acceptable understanding of what a model is and how modelling takes place; having a developed capacity to mentally visualise models; understanding the natures of analogy and of metaphor, processes which are central to models and modelling. The emphases required in teaching for this learning to be supported are discussed. Finally, implications of the model-based curriculum for teacher education are evaluated. It is concluded that a great deal of detailed research and development will be needed if the potential of this change in emphasis within the science curriculum is to be realised.

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References

  • Adey, P.S. & Shayer, M. (1994). Really raising standards: Cognitive acceleration and academic achievement. London: Routledge.

    Google Scholar 

  • Arnold, M. & Millar, R. (1996). Learning the scientific ‘story’: A case study in the teaching and learning of elementary thermodynamics. Science Education, 80(3), 249–281.

    Article  Google Scholar 

  • Bailer-Jones, D.M. (1999). Tracing the development of models in the philosophy of science. In Magnani, Nersessian & Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 23–40). New York: Kluwer Academic Publishers.

    Google Scholar 

  • Barab, S.A., Hay, K.E., Barnett, M. & Keating, T. (2000). Virtual solar system project: Building understanding through model building. Journal of Research in Science Teaching, 37(7), 719–756.

    Article  Google Scholar 

  • Barnea, N. (2000). Teaching and learning about chemistry and modelling with a computer-managed modelling system. In Gilbert & Boulter (Eds.), Developing models in science education (pp. 307–324). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Black, M. (1979). More about metaphor. In Ortony (Ed.), Metaphor and thought, 1st edn (pp. 19–43). Cambridge: Cambridge University Press.

    Google Scholar 

  • Buckley, B.C. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education, 22(9), 895–935.

    Article  Google Scholar 

  • Christopherson, J.T. (1997). The growing need for visual literacy at the university. Paper presented at the 28th annual conference ‘Visionquest: Journeys towards Visual Literacy’ of the International Visual Literacy Association, Cheyenne, Wyoming.

  • Coleman, S.L. & Gotch, A.J. (1998). Spatial perception skills of chemistry students. Journal of Chemical Education, 75, 206–209.

    CAS  Google Scholar 

  • Coll, R.K. & Treagust, D.F. (2001). Learners’ mental models of ionic bonding: A cross-age study. Paper presented at the National Association for Research in Science Teaching, St. Louis, Missouri.

  • DfEE. (1999). Science: The National Curriculum for England. London: Department for Education and Employment.

    Google Scholar 

  • Dori, Y.J. & Barak, M. (2001). Virtual and physical molecular modelling: Fostering model perception and spatial understanding. Educational Technology and Society, 4(1), 61–74.

    Google Scholar 

  • Ealy, J.B. (1999). A student evaluation of molecular modelling in first year college chemistry. Journal of Science Education and Technology, 8(4), 309–321.

    Article  CAS  Google Scholar 

  • Francoeur, E. (1997). The forgotten tool: The design and use of molecular models. Social Studies of Science, 27, 7–40.

    Google Scholar 

  • Frederiksen, J.R., White, B.Y. & Gutwill, J. (1999). Dynamic mental models in learning science: The importance of constructing derivational links among models. Journal of Research in Science Teaching, 36(7), 806–836.

    Article  Google Scholar 

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

    Article  Google Scholar 

  • Gentner, D. (1988). Analogical inference and analogical access. In Prieditis (Ed.), Analogia (pp. 63–88). London: Pitman.

    Google Scholar 

  • Gentner, D. (1989). The mechanism of analogical learning. In Vosniadou & Ortony (Eds.), Similarity and analogical reasoning (pp. 199–241). Cambridge: Cambridge University Press.

    Google Scholar 

  • Giere, R. (1988). Explaining science. Chicago: University of Chicago Press.

    Google Scholar 

  • Gilbert, S.W. (1991). Model building and a definition of science. Journal of Research in Science Teaching, 28(1), 73–79.

    Google Scholar 

  • Gilbert, J. & Watts, D. (1983). Concepts, misconceptions, and alternative conceptions: Changing perspectives in science education. Studies in Science Education, 10, 61–98.

    Google Scholar 

  • Gilbert, J.K. & Boulter, C. (1998). Learning science through models and modelling.In Fraser & Tobin (Eds.), International handbook of science education, Vol. 2(pp. 53–66). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Gilbert, J.K., Boulter, C.J. & Elmer, R. (2000). Positioning models in science education and in design and technology education. In Gilbert & Boulter (Eds.), Developing models in science education (pp. 3–18). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Gilbert, J.K., Boulter, C. & Rutherford, M. (1998). Models in explanations, part 1: Horses for courses. International Journal of Science Education, 20(1), 83–97.

    Google Scholar 

  • Grosslight, L., Unger, C., Jay, E. & Smith, C.L. (1991). Understanding models and their use in science: Conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28(8), 799–822.

    Google Scholar 

  • Gunstone, R.F. (1994). The importance of specific science content in the enhancement of metacognition. In Fensham, Gunstone & White (Eds.), The content of science: A constructivist approach to its teaching and learning (pp. 131–146). London: Falmer.

    Google Scholar 

  • Halloun, I. (1996). Schematic modeling for meaningful learning in physics. Journal of Research in Science Teaching, 33(9), 1019–1041.

    Article  Google Scholar 

  • Halloun, I. (1998). Schematic concepts for schematic models of the real world: The Newtonian concept of force. Science Education, 82(2), 241–263.

    Article  Google Scholar 

  • Harrison, A.G. & Treagust, D.F. (1996). Secondary students’ mental models of atoms and molecules: Implications for learning chemistry. Science Education, 80(5), 509–534.

    Article  Google Scholar 

  • Harrison, A.G. & Treagust, D.F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026.

    Article  Google Scholar 

  • Hesse, M. (1966). Models and analogies in science. London: Sheen and Ward.

    Google Scholar 

  • Hodson, D. (1992). In search of a meaningful relationship: An exploration of some issues relating to integration in science and science education. International Journal of Science Education, 14(5), 541–562.

    Google Scholar 

  • Holton, G. (1995). Imagination in science. In Holton (Ed.), Einstein, history, and other passions (pp. 160–184). Woodbury, NJ: American Institute of Physics.

    Google Scholar 

  • Ingham, A.M. & Gilbert, J.K. (1991). The use of analogue models by students of chemistry at higher education level. International Journal of Science Education, 22(9), 1011–1026.

    Google Scholar 

  • Justi, R. (2001). Teachers’ views about models and modelling in science education. Paper presented at the annual conference of the National Association for Research in Science Teaching, 26–28 March, St. Louis, Missouri.

  • Justi, R. & Gilbert, J.K. (1999). History and philosophy of science through models: The case of chemical kinetics. Science and Education, 8(2), 287–307.

    Article  Google Scholar 

  • Justi, R. & Gilbert, J.K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369–387.

    Article  Google Scholar 

  • Justi, R. & Gilbert, J.K. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25(11), 1369–1386.

    Article  Google Scholar 

  • Justi, R. & Gilbert, J.K. (In preparation). On the notion of ‘level’ in the understanding of the nature of ‘model.’

  • Kosma, R., Chin, E., Russell, J. & Marx, N. (2000). The role of representations and tools in the chemistry laboratory and their implications for chemistry learning. The Journal of the Learning Sciences, 9(2), 105–143.

    Article  Google Scholar 

  • Lakoff, G. & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press.

    Google Scholar 

  • Luisi, P.L. & Thomas, R.M. (1990). The pictographic molecular paradigm: Pictorial communication in the chemical and biological sciences. Naturwissenschaften, 77, 67–74.

    Article  CAS  PubMed  Google Scholar 

  • Mainzer, K. (1999). Computational models and virtual reality: New perspectives on research in chemistry. Hyle, 5(2), 117–126.

    CAS  Google Scholar 

  • Millar, R. & Osborne, J. (2000). Beyond 2000: Science education for the future. London: School of Education, King’s College London.

    Google Scholar 

  • Miller, A.I. (1987). Imagery and scientific thought. Cambridge, MA: MIT Press.

    Google Scholar 

  • Oversby, J. (2000). Models and explanations in chemistry: The case of acidity. In Gilbert & Boulter (Eds.), Developing models in science education (pp. 227–252). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Penner, D.E. (2000). Explaining systems: Investigating middle school students’ understanding of emergent phenomena. Journal of Research in Science Teaching, 37(8), 784–806.

    Article  Google Scholar 

  • Piburn, M.D., Reynolds, S.J., Leedy, D.E., McAuliffe, C.M., Birk, J.P. & Johnson, J.K. (2002). The hidden Earth: Visualization of geologic features and their subsurface geometry. Paper presented at the annual meeting of National Association for Research in Science Teaching, New Orleans.

  • Raghaven, K. & Glaser, R. (1995). Model-based analysis and reasoning in science: The MARS curriculum. Science Education, 79(1), 37–61.

    Google Scholar 

  • Raghaven, K., Sartoris, M.L. & Glaser, R. (1998a). Impact of the MARS curriculum: The mass unit. Science Education, 82(1), 53–91.

    Article  Google Scholar 

  • Raghaven, K., Sartoris, M.L. & Glaser, R. (1998b). Why does it go up? The impact of the MARS curriculum as revealed through changes in student explanations of a helium ballon. Journal of Research in Science Teaching, 35(5), 547–567.

    Article  Google Scholar 

  • Rouse, W.B. & Morris, N.M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100(3), 349–363.

    Article  Google Scholar 

  • Shulman, L. (1987). Knowledge and teaching: Foundations of the new reforms. Harvard Educational Review, 57(1), 1–22.

    Google Scholar 

  • Stewart, J., Hafner, R., Johnson, S. & Finkel, E. (1992). Science as model building: Computers and high-school genetics. Educational Psychologist, 27(3), 317–336.

    Google Scholar 

  • Suckling, C.J., Suckling, K.E. & Suckling, C.W. (1980). Chemistry through models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Tomasi, J. (1988). Models and modelling in theoretical chemistry. Journal of Molecular Structure, 179, 273–292.

    Google Scholar 

  • Treagust, D.F. & Chittleborough, G. (2001). Chemistry: A matter of understanding representations, subject-specific instructional methods and activities, Vol. 8 (pp. 239–267). New York: Elsevier.

    Google Scholar 

  • Treagust, D.F., Harrison, A.G., Venville, G.J. & Dagher, Z. (1996). Using an analogical teaching approach to engender conceptual change. International Journal of Science Education, 18(2), 213–229.

    Google Scholar 

  • Tuckey, H. & Selvaratnam (1993). Studies involving three-dimensional visualisation skills in chemistry. Studies in Science Education, 21, 99–121.

    Google Scholar 

  • Van Driel, J.H. & Verloop, N. (1999). Teachers’ knowledge of models and modeling in science. International Journal of Science Education, 21(11), 1141–1154.

    Article  Google Scholar 

  • Van Driel, J.H., Verloop, N. & de Vos, W. (1998). Developing science teachers’ pedagogical content knowledge. Journal of Research in Science Teaching, 35(6), 673–695.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Education, University of Reading, Bulmershe Court, Woodlands Avenue, Earley, Reading, RG6 1HY, U.K.

    John K. Gilbert

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  1. John K. Gilbert
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Correspondence to John K. Gilbert.

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An earlier version of this paper was given at the International Conference on Science and Mathematics Learning held in Taipei, Taiwan, 16 December 2003.

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Gilbert, J.K. Models and Modelling: Routes to More Authentic Science Education. Int J Sci Math Educ 2, 115–130 (2004). https://doi.org/10.1007/s10763-004-3186-4

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  • DOI: https://doi.org/10.1007/s10763-004-3186-4

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