Science & Education

, Volume 22, Issue 1, pp 119–142 | Cite as

The Strategies of Modeling in Biology Education

  • Julia SvobodaEmail author
  • Cynthia Passmore


Modeling, like inquiry more generally, is not a single method, but rather a complex suite of strategies. Philosophers of biology, citing the diverse aims, interests, and disciplinary cultures of biologists, argue that modeling is best understood in the context of its epistemic aims and cognitive payoffs. In the science education literature, modeling has been discussed in a variety of ways, but often without explicit reference to the diversity of roles models play in scientific practice. We aim to expand and bring clarity to the myriad uses of models in science by presenting a framework from philosopher of biology Jay Odenbaugh that describes five pragmatic strategies of model use in the biological sciences. We then present illustrative examples of each of these roles from an empirical study of an undergraduate biological modeling curriculum, which highlight how students used models to help them frame their research question, explore ideas, and refine their conceptual understanding in an educational setting. Our aim is to begin to explicate the definition of modeling in science in a way that will allow educators and curriculum developers to make informed choices about how and for what purpose modeling enters science classrooms.


Science Education Measle Scientific Practice Scientific Reasoning Vaccination Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the National Science Foundation Interdisciplinary Training for Undergraduates in Biological and Mathematical Sciences (UBM) program under Grant No. 0531935. We would like to acknowledge CLIMB mentors and the students of the 2008–2009 CLIMB cohort for allowing us to bare witness to their learning process. We also thank Richard Lehrer and four anonymous reviewers for their helpful suggestions for improving this manuscript.


  1. Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441.CrossRefGoogle Scholar
  2. Bechtel, W., & Abrahamsen, A. (2010). Complex biological mechanisms: Cyclic, oscillatory, and autonomous. Handbook of the Philosophy of Complex Systems, 10, 1–26.Google Scholar
  3. Chinn, C., & Malhotra, B. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86(2), 175–218.CrossRefGoogle Scholar
  4. Clement, J. (1989). Learning via model construction and criticism. In G. Glover, R. Ronning, & C. Reynolds (Eds.), Handbook of creativity: Assessment, theory and research (pp. 341–381). New York: Plenum Publishers.Google Scholar
  5. Clement, J. C. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053.CrossRefGoogle Scholar
  6. Cooper, G. J. (2003). The science of the struggle for existence: On the foundations of ecology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Darden, L. (1991). Theory change in science: Strategies from Mendelian genetics. New York: Oxford University Press.Google Scholar
  8. Darden, L. (2002). Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward/backward chaining. Philosophy of Science, 69(S3), S354–S365.CrossRefGoogle Scholar
  9. diSessa, A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.Google Scholar
  10. Downes, S. (1992). The importance of models in theorizing: A deflationary semantic view. In PSA: Proceedings of the biennial meeting of the philosophy of science association (Vol. 1, pp. 142–153). Chicago: The University of Chicago Press.Google Scholar
  11. Ford, M. J. (2008). Disciplinary authority and accountability in scientific practice and learning. Science Education, 92(3), 404–423.CrossRefGoogle Scholar
  12. Ford, M. J., & Forman, E. A. (2011). Redefining disciplinary learning in classroom contexts. Educational Research, 30(2006), 1–32.Google Scholar
  13. Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.Google Scholar
  14. Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71, 742–752.CrossRefGoogle Scholar
  15. 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
  16. Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21(5), 725–740.CrossRefGoogle Scholar
  17. Grandy, R., & Duschl, R. (2007). Reconsidering the character and role of inquiry in school science: Analysis of a conference. Science & Education, 16, 141–166.CrossRefGoogle Scholar
  18. 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, 799–822.CrossRefGoogle Scholar
  19. Hammer, D., Russ, R., Mikeska, J., & Scherr, R. (2008). Identifying inquiry and conceptualizing students’ abilities. In R. Duschl & R. Grandy (Eds.), Teaching scientific inquiry (pp. 138–156). Rotterdam: Sense Publishers.Google Scholar
  20. Harre, R. (1986). Varieties of realism: A rationale for the natural sciences. New York: Blackwell.Google Scholar
  21. Harrison, A., & Treagust, D. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026.CrossRefGoogle Scholar
  22. Hodson, D. (1996). Laboratory work as scientific method: Three decades of confusion and distortion. Journal of Curriculum Studies, 28(2), 115–135.CrossRefGoogle Scholar
  23. Hodson, D. (1998). Science fiction: The continuing misrepresentation of science in the school curriculum. Pedagogy, Culture and Society, 6(2), 191–216.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Keller, E. F. (2000). Models of and models for: Theory and practice in contemporary biology. Philosophy of Science, 67(S1), S72–S86.CrossRefGoogle Scholar
  26. Knorr-Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Cambridge: Harvard University Press.Google Scholar
  27. Knuuttila, T. (2005). Models, representation, and mediation. Philosophy of Science, 72(5), 1260–1271.CrossRefGoogle Scholar
  28. Koponen, I. (2007). Models and modelling in physics education: A critical re-analysis of philosophical underpinnings and suggestions for revisions. Science & Education, 16(7), 751–773.CrossRefGoogle Scholar
  29. Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp. 19–68). Cambridge, MA: MIT Press.Google Scholar
  30. Laubichler, M. D., & Müller, G. B. (2007). Modeling biology: Structures, behavior, evolution. Cambridge: MIT Press.Google Scholar
  31. Lehrer, R., & Schauble, L. (2005). Developing modeling and argument in the elementary grades. In T. A. Rombert, T. P. Carpenter, & F. Dremock (Eds.), Understanding mathematics and science matters (Part II: Learning with understanding). Mahway, NJ: Lawrence Erlbaum Associates.Google Scholar
  32. Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 371–388). New York: Cambridge University Press.Google Scholar
  33. Lehrer, R., Schauble, L., & Lucas, D. (2008). Supporting development of the epistemology of inquiry. Cognitive Development, 23, 512–529.CrossRefGoogle Scholar
  34. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421–431.Google Scholar
  35. Lloyd, E. A. (1994). The structure and confirmation of evolutionary theory. Princeton: Princeton University Press.Google Scholar
  36. Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.CrossRefGoogle Scholar
  37. Magnani, L., Nersessian, N. J., & Thagard, P. (Eds.). (1999). Model-based reasoning in scientific discovery. New York: Kluwer.Google Scholar
  38. Matthews, M. (1994). Science teaching: The role of history and philosophy of science. New York: Routledge.Google Scholar
  39. May, R. (1973). The stability and complexity of model ecosystems. Princeton: Princeton University Press.Google Scholar
  40. Metz, K. (2010). 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
  41. Morrison, M., & Morgan, M. (1999). Models as mediating instruments. In M. Morrison & M. Morgan (Eds.), Models as mediators: Perspectives on natural and social science (pp. 10–37). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  42. 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 (pp. 3–44). Minneapolis: University of Minnesota Press.Google Scholar
  43. Nersessian, N. J. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery. New York: Kluwer/Plenum Publishers.Google Scholar
  44. Nersessian, N. J. (2002). The cognitive basis of model-based reasoning. The cognitive basis of science (pp. 133–153). Cambridge: Cambridge University Press.Google Scholar
  45. Nersessian, N. J. (2008). Model-based reasoning in scientific practice. In R. Duschl & R. Grandy (Eds.), Teaching Scientific Inquiry: Recommendations for Research and Implementation (pp. 57–79). Rotterdam, the Netherlands: Sense Publishers.Google Scholar
  46. NRC. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.Google Scholar
  47. Odenbaugh, J. (2005). Idealized, inaccurate but successful: A pragmatic approach to evaluating models in theoretical ecology. Biology and Philosophy, 20(2–3), 231–255.CrossRefGoogle Scholar
  48. Odenbaugh, J. (2009). Models in biology. In E. Craig (Ed.), Routledge encyclopedia of philosophy. London: Routledge.Google Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. Rudolph, J. (2005). Epistemology for the masses: The origins of “The Scientific Method” in American schools. History of Education Quarterly, 45(3), 341–376.CrossRefGoogle Scholar
  52. Schwarz, C., Reiser, B., Davis, E., Kenyon, L., Acher, A., Fortus, D., 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.CrossRefGoogle Scholar
  53. Schwarz, C., & White, B. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.CrossRefGoogle Scholar
  54. 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.Google Scholar
  55. Tang, X., Coffey, J., Elby, A., & Levin, D. (2010). The scientific method and scientific inquiry: Tensions in teaching and learning. Science Education, 94(1), 29–47.Google Scholar
  56. White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and instruction, 10(1), 1–100.Google Scholar
  57. Wimsatt, W. C. (1987). False models as means to truer theories. In M. Nitecki (Ed.), Neutral models in biology (pp. 23–55). New York: Oxford University Press.Google Scholar
  58. Wimsatt, W. C. (2002). Using false models to elaborate constraints on processes: Blending inheritance in organic and cultural evolution. Philosophy of Science, 69(s3), S12–S24.CrossRefGoogle Scholar
  59. Windschitl, M., Thompson, J., & Braaten, M. (2008a). 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.
  60. Windschitl, M., Thompson, J., & Braaten, M. (2008b). How novice science teachers appropriate epistemic discourses around model-based inquiry for use in classrooms. Cognition and Instruction, 26(3), 310–378.CrossRefGoogle Scholar
  61. Wynne, C., Stewart, J., & Passmore, C. (2001). High school students’ use of meiosis when solving genetics problems. International Journal of Science Education, 23(5), 501–515.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of EducationUniversity of California, DavisDavisUSA

Personalised recommendations