Skip to main content
SpringerLink
Log in

Models in Science and in Learning Science: Focusing Scientific Practice on Sense-making

  • Chapter
  • First Online:
International Handbook of Research in History, Philosophy and Science Teaching

Abstract

The central aim of science is to make sense of the world. To move forward as a community endeavor, sense-making must be systematic and focused. The question then is how do scientists actually experience the sense-making process? In this chapter we examine the “practice turn” in science studies and in particular how as a result of this turn scholars have come to realize that models are the “functional unit” of scientific thought and form the center of the reasoning/sense-making process. This chapter will explore a context-dependent view of models and modeling in science. From this analysis we present a framework for delineating the different aspects of model-based reasoning and describe how this view can be useful in educational settings. This framework highlights how modeling supports and focuses scientific practice on sense-making.

Note: The first two authors contributed equally to the creation of this manuscript.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 749.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 949.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 949.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See, for example, Giere (1988), Nersessian (1992, 1999, 2002), Morrison and Morgan (1999) from philosophy of science and Duschl (2008), Gilbert (2004), Matthews (1992), Osborne et al. (2003), and Hodson (1992) from science education.

  2. 2.

    See Duschl (2008), Hodson (1996, 2008), Rudolph (2005), and Windschitl et al. (2008b).

  3. 3.

    See, for example, Stewart et al. (2005), Duschl (2008), Engle and Conant (2002), Ford (2008), Duschl and Grandy (2008), Lehrer and Schauble (2004), and Roth and Roychoudhury (1993).

  4. 4.

    See Adúriz-Bravo (2012), Bottcher (2010), (Develaki 2007), and Koponen (2007 and this volume). Please also see a special issue of Science & Education (Matthews 2007) for a careful treatment of models and modeling for the education audience.

  5. 5.

    In biology, see Cooper (2003), Lloyd (1997), and Odenbaugh (2005, 2009); in chemistry see Suckling et al. (1980); in physics see Cartwright (1997, 1999), Hughes (1999), and Nersessian (1999, 2002); in economics see Boumans (1999) and Morrison (1999). See also Auyang (1998) for comparison across biology, physics, and economics.

  6. 6.

    There are two recent books that develop the “patchwork” idea in quite rich directions for those readers who might want an even more sophisticated version of these ideas: Mark Wilson, Wandering Significance, Oxford Univ Press, 2006, and William Wimsatt, Re-engineering philosophy for limited beings, Harvard Univ Press, 2007.

  7. 7.

    See, for example, Boulter and Buckley (2000), Coll and Lajium (2011), Gilbert (2004), and Harrison and Treagust (2000).

  8. 8.

    See Svoboda and Passmore (2011) for a much more thorough treatment of Odenbaugh’s framework.

  9. 9.

    A major caution about the business of categorizing: We do this for the purpose of discussion and because we believe that a consideration of these different cognitive aims is potentially fruitful in the context of education. However, whenever something is presented in a list of categories, a common interpretation is that that format implies an order. This is not our intention. The point here is that models organize a broad array of cognitive aims beyond representing and explaining which seem to be the two most commonly associated with models (Odenbaugh 2005; Knuuttila 2005).

  10. 10.

    See, for example, Baek et al. (2011), Clement (1989, 2000), Gilbert et al. (1998a, b), Hogan and Thomas (2001), Passmore and Stewart (2002), Schwarz et al. (2009), Svoboda and Passmore (2011), and White (1993).

  11. 11.

    See, for example, Danusso et al. (2010), Nelson and Davis (2012), and Schwarz and Gwekwerere (2006).

References

  • Adúriz-Bravo, A. (2012). A “Semantic” View of Scientific Models for Science Education. Science & Education. doi:10.1007/s11191-011-9431-7

    Google Scholar 

  • Auyang, S. Y. (1998). Foundations of complex-system theories: in economics, evolutionary biology, and statistical physics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Baek, H., Schwarz, C. V., Chen, J., Hokayem, H., & Zhan, L. (2011). Engaging elementary students in scientific modeling: The MoDeLS fifth-grade approach and findings. In M. S. Khine & I. M. Saleh (Eds.), Models and Modeling in Science Education: Cognitive Tools for Scientific Enquiry (pp. 195–218). Dordrecht: Springer Netherlands. doi:10.1007/978-94-007-0449-7

    Chapter  Google Scholar 

  • Bauer, H. H. (1992). The so-called scientific method. Scientific Literacy and the Myth of the Scientific Method (pp. 19–41).

    Google Scholar 

  • Berland, L. K., & Reiser, B. J. (2011). Classroom communities’ adaptations of the practice of scientific argumentation. Science Education, 95(2), 191–216. doi:10.1002/sce.20420

    Article  Google Scholar 

  • Boulter, C.J., & Buckley, B.C. (2000). Constructing a typology of models for science education. In J.K. Gilbert & C.J. Boulter (Eds.) Developing models in science education. (pp. 41–57). Dordrecht: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Bottcher, F. & Meisert, A. (2010). Argumentation in science education: A model-based framework. Science & Education 20(2) 103–140.

    Article  Google Scholar 

  • Boumans, M. (1999). Built-in justification. In Models as mediators: Perspectives on natural and social science (Vol. 52). Morgan, M. S., & Morrison, M. (Eds.). Cambridge University Press.

    Google Scholar 

  • Cartwright, N. (1997). Models: The blueprints for laws. Philosophy of Science, 64(4), S292–S303. University of Chicago Press.

    Google Scholar 

  • Cartwright, N. (1999) The dappled world: A study of the boundaries of science. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Clement, J. J. (1989). Learning via model construction and criticism. In G. Glover, R. Ronning, & C. Reynolds (Eds.), (pp. 341–381). New York: Plenum Publishers.

    Google Scholar 

  • Clement, J. J. (2000). Model Based Learning as a Key Research Area for Science Education. International Journal of Science Education, 22(9), 1041–1053.

    Article  Google Scholar 

  • Coll, R. K., & Lajium, D. (2011). Modeling and the Future of Science Learning. In M. S. Khine & I. M. Saleh (Eds.), Models and Modeling in Science Education: Cognitive Tools for Scientific Enquiry (pp. 3–21). Dordrecht: Springer Netherlands. doi:10.1007/978-94-007-0449-7

    Chapter  Google Scholar 

  • Coll, R. K., & Treagust, D. F. (2003). Learners’ mental models of metallic bonding: A cross-age study. Science Education, 87(5), 685–707. doi:10.1002/sce.10059

    Article  Google Scholar 

  • Cooper, G. J. (2003). The science of the struggle for existence: On the foundations of ecology. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Danusso, L., Testa, I., & Vicentini, M. (2010). Improving prospective teachers’ knowledge about scientific models and modelling: Design and evaluation of a teacher education intervention. International Journal of Science Education, 32(7), 871–905.

    Article  Google Scholar 

  • Develaki, M. (2007). The Model-Based View of Scientific Theories and the Structuring of School Science Programmes. Science & Education, 16(7), 725–749. doi:10.1007/s11191-006-9058-2

    Article  Google Scholar 

  • Downes, S. (1992). The Importance of models in theorizing: a deflationary semantic view. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association (Vol. 1992, pp. 142–153). Chicago: The University of Chicago Press.

    Google Scholar 

  • Duschl, R. A. (2008). Science Education in Three-Part Harmony: Balancing Conceptual, Epistemic, and Social Learning Goals. Review of Research in Education, 32(1), 268–291. doi:10.3102/0091732X07309371

    Article  Google Scholar 

  • Duschl, R. A., & Grandy, R. E. (2008). Teaching scientific inquiry: Recommendations for research and implementation. Sense Publishers.

    Google Scholar 

  • Engle, R. A., & Conant, F. R. (2002). Guiding Principles for Fostering Productive Disciplinary Engagement: Explaining an Emergent Argument in a Community of Learners Classroom. Cognition and Instruction, 20(4), 399–483. Lawrence Erlbaum Associates (Taylor & Francis Group). doi:10.2307/3233901

  • Ford, M. (2008). Disciplinary authority and accountability in scientific practice and learning. Science Education, 92(3), 404–423.

    Article  Google Scholar 

  • Giere, R. N. (1988). Explaining Science: A Cognitive Approach. University of Chicago Press.

    Google Scholar 

  • Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71, 742–752.

    Article  Google Scholar 

  • Gilbert, J. K. (2004). Models and Modelling: Routes to More Authentic Science Education. International Journal of Science and Mathematics Education, 2(2), 115–130. doi:10.1007/s10763-004-3186-4

    Article  Google Scholar 

  • Gilbert, J. K., Boulter, C., & Rutherford, M. (1998a). Models in explanations, Part 2: Whose voice? Whose ears? International Journal of Science Education, 20(2), 187–203. doi:10.1080/0950069980200205

    Article  Google Scholar 

  • Gilbert, J. K., Boulter, C., & Rutherford, M. (1998b). Models in Explanations, Part 1: Horses for Courses? Science Education, 20(1), 83–97. [Part I should be (a), no?]

    Google Scholar 

  • Gobert, J. D. (2005). The Effects of Different learning Tasks on Model-building in Plate Tectonics: Diagramming Versus Explaining. Journal of Geoscience Education, 53(4), 444–455.

    Google Scholar 

  • Hammer, D. (1996). Misconceptions or P-Prims: How May Alternative Perspectives of Cognitive Structure Influence Instructional Perceptions and Intentions. Journal of the Learning Sciences, 5(2), 97–127. doi:10.1207/s15327809jls0502_1

    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 

  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.

    Article  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, 541–562. doi:10.1080/0950069920140506

    Article  Google Scholar 

  • Hodson, D. (1996). Laboratory work as scientific method: three decades of confusion and distortion. Journal of Curriculum Studies, 28(2), 115–135.

    Article  Google Scholar 

  • Hodson, D. (2008). Towards scientific literacy. Rotterdam, The Netherlands: Sense Publishers.

    Google Scholar 

  • Hogan, K., & Thomas, D. (2001). Cognitive comparisons of students’ systems modeling in ecology. Journal of Science Education and Technology, 10(4).

    Google Scholar 

  • Hughes, R. I. G. (1999). The Ising Model, Computer Simulation, and Universal Physics. In M. Morrison & M. S. Morgan (Eds.), Models as Mediators: Perspectives on Natural and Social Science (pp. 97–145). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Knuuttila, T. (2005). Models as epistemic artefacts: Toward a non-representationalist account of scientific representation. Philosophical Studies. Helsingin yliopisto.

    Google Scholar 

  • Koponen, I. (2007). Models and Modelling in Physics Education: A Critical Re-analysis of Philosophical Underpinnings and Suggestions for Revisions. Science & Education,16, 751–773.

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (2004). Modeling Natural Variation Through Distribution. American Educational Research Journal, 41(3), 635–679. doi:10.3102/00028312041003635

    Article  Google Scholar 

  • Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421–431.

    Google Scholar 

  • Lloyd, E. A. (1997). The structure and confirmation of evolutionary theory. Princeton University Press.

    Google Scholar 

  • Matthews, M. R. (1992). History, philosophy, and science teaching: The present rapprochement. Science & Education, 1(1), 11–47. doi:10.1007/BF00430208

    Article  Google Scholar 

  • Matthews, M. R. (Ed.). (2007). Models in Science and Science Education. [Special Issue] Science & Education, 16(7–8).

    Google Scholar 

  • Manz, E. (2012). Understanding the codevelopment of modeling practice and ecological knowledge. Science Education, 96(6) 1071–1105. doi: 10.1002/sce.21030

    Article  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. doi:10.1207/s1532690xci2202

    Article  Google Scholar 

  • Metz, K. E. (2006). The knowledge building enterprises in science and elementary school science classrooms. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science (pp. 105–130). Springer.

    Google Scholar 

  • Metz, K. E. (2008). Narrowing the gulf between the practices of science and the elementary school science classroom. The Elementary School Journal, 109(2), 138–161.

    Article  Google Scholar 

  • Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators: Perspectives on natural and social science (Vol. 52). Cambridge University Press.

    Google Scholar 

  • National Research Council (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: The National Academies Press.

    Google Scholar 

  • National Research Council; Committee on Conceptual Framework for the New K-12 Science Education Standards. (2011). A Framework for K-12 Science Education : Practices, Crosscutting Concepts, and Core Ideas. Washington D.C.: The National Academies Press. Retrieved from http://www.nap.edu/catalog.php?record_id=13165

  • Nelson, M. M., & Davis, E. A. (2012). Preservice Elementary Teachers’ Evaluations of Elementary Students’ Scientific Models: An aspect of pedagogical content knowledge for scientific modeling. International Journal of Science Education, 34(12), 1931–1959.

    Article  Google Scholar 

  • Nersessian, N. J. (1989). Conceptual change in science and in science education. Synthese, 80(1), 163–183. doi:10.1007/BF00869953

    Article  Google Scholar 

  • Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R.N. Giere (Ed) Cognitive models of science: Minnesota studies in the philosophy of science, Vol XV. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Nersessian, N. J. (1995). Should Physicists Preach What They Practice? Constructive Modeling in Doing and Learning Physics. Science & Education, 4, 203–226.

    Article  Google Scholar 

  • Nersessian, N. J. (1999). Model-based Reasoning in Conceptual Change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), (pp. 5–22). New York: Kluwer Academic/Plenum Publishers.

    Google Scholar 

  • Nersessian, N. J. (2002). The cognitive basis of model-based reasoning. The cognitive basis of science (pp. 133–153). Cambridge University Press.

    Google Scholar 

  • Odenbaugh, J. (2005). Idealized, Inaccurate but Successful: A Pragmatic Approach to Evaluating Models in Theoretical Ecology. Biology & Philosophy, 20(2–3), 231–255. doi:10.1007/s10539-004-0478-6

    Article  Google Scholar 

  • Odenbaugh, J. (2009). Models in biology. In E. Craig (Ed.), Routledge Encyclopedia of Philosophy. London: Routledge.

    Google Scholar 

  • Osbeck, L., Nersessian, N. J., Malone, K. R., & Newstetter, W. (2010). Science as Psychology: Sense-making and identity in science practice. New York: Cambridge University Press.

    Book  Google Scholar 

  • Osborne, J., Collins, S., Ratcliffe, M., Millar, R., & Duschl, R. A. (2003). What “ideas-about-science” should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40(7), 692–720. doi:10.1002/tea.10105

    Article  Google Scholar 

  • Passmore, C., & Stewart, J. (2002). A modeling approach to teaching evolutionary biology in high schools. Journal of Research in Science Teaching, 39(3), 185–204. doi:10.1002/tea.10020

    Article  Google Scholar 

  • Passmore, C., & Svoboda, J. (2011). Exploring Opportunities for Argumentation in Modelling Classrooms. International Journal of Science Education, (October), 1–20. doi:10.1080/09500693.2011.577842

  • 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.

    Article  Google Scholar 

  • Penner, D. E., Lehrer, R. & Schauble, L. (1998) From physical models to biomechanics: A design modeling approach. The Journal of the Learning Sciences, 7 (3 & 4), 429–449.

    Google Scholar 

  • Pluta, W. J., Chinn, C. A., & Duncan, R. G. (2011). Learners’ epistemic criteria for good scientific models. Journal of Research in Science Teaching, 48(5), 486–511. doi:10.1002/tea.20415

    Article  Google Scholar 

  • Roth, W.-M., & Roychoudhury, A. (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30, 127–152. doi:10.1002/tea.3660300203

    Article  Google Scholar 

  • Rudolph, J. L. (2005). Epistemology for the Masses: The Origins of “The Scientific Method” in American Schools. History of Education Quarterly, 45(3), 341–376. doi:10.1111/j.1748-5959.2005.tb00039.x

    Article  Google Scholar 

  • Schwarz, C. V., & Gwekwerere, Y. N. (2006). Using a guided inquiry and modeling instructional framework (EIMA) to support preservice K‐8 science teaching. Science Education, 91(1), 158–186.

    Article  Google Scholar 

  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L. O., Achér, A., Fortus, D., Shwartz, Y., et al. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. doi:10.1002/tea.20311

    Article  Google Scholar 

  • Smith, E., Haarer, S., & Confrey, J. (1997). Seeking diversity in mathematics education: Mathematical modeling in the practice of biologists and mathematicians. Science & Education, 6, 441–472.

    Article  Google Scholar 

  • Stewart, J., Cartier, J., & Passmore, C. (2005). Developing Understanding Through Model-based Inquiry. In M. S. Donovan & J. Bransford (Eds.), How Students Learn: History, Mathematics, and Science in the Classroom (pp. 515–565). Washington D.C.: National Research Council.

    Google Scholar 

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

    Google Scholar 

  • Svoboda, J., & Passmore, C. (2011). The strategies of modeling in biology education. Science & Education. doi:10.1007/s11191-011-9425-5

    Google Scholar 

  • Teller, P. (2001). Twilight of the perfect model model. Erkenntnis, 55, 393–415.

    Article  Google Scholar 

  • Vosniadou, S. (2001). Models in conceptual development. In L. Magnangi & N. Nersessian (Eds.) Model-based reasoning: Science, technology, values. New York: Kluwer Academic.

    Google Scholar 

  • Watson, J. D. and Crick, F. H. C. (1953). A structure for Deoxyribose Nucleic Acid. Nature, 171, 737–738.

    Article  Google Scholar 

  • White, B. Y. (1993). Thinker Tools: Causal models, conceptual change, and science education. Cognition and instruction, 10(1), 1–100. Routledge.

    Google Scholar 

  • Wimsatt, W. C. (1987). False models as means to truer theories. In M. Nitecki and A. Hoffman (Eds.), Neutral Models in Biology (pp. 23–55). New York: Oxford University Press.

    Google Scholar 

  • Windschitl, M., Thompson, J., & Braaten, M. (2008a). How Novice Science Teachers Appropriate Epistemic Discourses Around Model-Based Inquiry for Use in Classrooms. Cognition and Instruction, 26(3), 310–378. Routledge. doi:10.1080/07370000802177193

  • Windschitl, M., Thompson, J., & Braaten, M. (2008b). Beyond the Scientific Method : Model-Based Inquiry as a New Paradigm of Preference for School Science Investigations. Science Education, 1–27. doi:10.1002/sce

Download references

Author information

Author notes

    Authors and Affiliations

    1. School of Education, University of California, Davis, USA

      Cynthia Passmore & Julia Svoboda Gouvea

    2. Department of Philosophy (Emeritus), University of Minnesota, Minneapolis, USA

      Ronald Giere

    Authors
    1. Cynthia Passmore
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Julia Svoboda Gouvea
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Ronald Giere
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Cynthia Passmore .

    Editor information

    Editors and Affiliations

    1. School of Education, University of New South Wales, Sydney, New South Wales, Australia

      Michael R. Matthews

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2014 Springer Science+Business Media Dordrecht

    About this chapter

    Cite this chapter

    Passmore, C., Gouvea, J.S., Giere, R. (2014). Models in Science and in Learning Science: Focusing Scientific Practice on Sense-making. In: Matthews, M. (eds) International Handbook of Research in History, Philosophy and Science Teaching. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7654-8_36

    Download citation

    Publish with us

    Policies and ethics

    Search

    Navigation