Students’ understanding of the purpose of models in different biological contexts.

Abstract

The present article analyses context dependencies in students’ ranking of three perspectives on the purpose of biological models, i.e. to show, to explain, or to predict. German students (N = 1,207; 11 to 18 years old; secondary schools) have been assessed using one decontextualized forced choice task (i.e. without referring to a specific model) as well as six contextualized forced choice tasks (each presenting a different biological model in the task stem). Students’ responses have been compared using the Wilcoxon test as well as within an IRT approach. The findings show that the respondents systematically preferred more elaborated perspectives concerning the purpose of models in biology in the contextualized tasks than in the decontextualized task. Further, students’ answers were slightly inconsistent even within the contextualized tasks. Based on these findings, implications for assessment in science education and science teaching are discussed

Keywords

• Purpose of models, biology teaching, science education

References

1. Al-Balushi, S. (2011). Students’ evaluation of the credibility of scientific models that represent
2. natural entities and phenomena. International Journal of Science and Mathematics
3. Education, 9, 571–601.
4. Arvisenet, G., Billy, L., Poinot, P., Vigneau, E., Bertrand, D., & Prost, C. (2008). Effect of apple
5. particle state on the release of volatile compounds in a new artificial mouth device.
6. Journal of Agricultural and Food Chemistry, 56, 3245–3253.
7. Bailer-Jones, D. (2003). When scientific models represent. International Studies in the Philosophy of Science, 17, 59–74.
8. Black, M. (1962). Models and metaphors. Ithaca, NY: Cornell U.P.

9. Böckenholt, U. (2004). Comparative judgments as an alternative to ratings: Identifying the scale origin. Psychological Methods, 9, 453–465.
10. Braithwaite, R. (1962). Models in the empirical science. In E. Nagel, P. Suppes, & A. Tarski (Eds.), Logic, methodology and philosophy of science. Proceedings of the 1960 international congress (pp. 224–231). Stanford: Stanford University Press.
11. Boulter, C. & Buckley, B. (2000). Constructing a typology of models for science education. In J. Gilbert & C. Boulter (Eds.), Developing models in science education (pp. 41-57). Dordrecht: Kluwer Academic.
12. Chabalengula, V. & Mumba, F. (2012). Promoting biological knowledge generation using model-based inquiry instruction. International Journal of Biology Education, 2, 1-24.
13. Chittleborough, G., Treagust, D., Mamiala, T., & Mocerino, M. (2005). Students’ perceptions of the role of models in the process of science and in the process of learning. Research in Science and Technological Education, 23, 195–212.
14. Clough, E. & Driver, R. (1986). A study of consistency in the use of students’ conceptual frameworks across different task contexts. Science Education, 70, 473–496.
15. Cochran, W. & Cox, G. (1957). Experimental designs. New York, NY: John Wiley & Sons.
16. Crawford, B. & Cullin, M. (2005). Dynamic assessments of preservice teachers’ knowledge of models and modelling. In K. Boersma, M. Goedhart, O. de Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 309–323). Dordrecht: Springer.
17. 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, 871–905.
18. Fritz, C., Morris, P., & Richler, J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141, 2–18.
19. Gentner, D. & Stevens, A. (Eds.). (1983). Mental models. Hillsdale & London: Erlbaum.
20. Giere, R. (2002). Models as parts of distributed cognitive systems. In L. Magnani & N. Nersessian (Eds.), Model-based reasoning. Science, technology, values (pp. 227–241). New York, NY: Kluwer Academic.
21. Giere, R., Bickle, J., & Mauldin, R. (2006). Understanding scientific reasoning. London: Thomson Learning.
22. Gilbert, J., Boulter, C., & Elmer, R. (2000). Positioning models in science education and in design and technology education. In J. Gilbert & C. Boulter (Eds.), Developing models in science education (pp. 3–18). Dordrecht: Kluwer Academic.
23. Gilbert, J. (2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2, 115–130.
24. Gilbert, J. (2006). On the nature of “context” in chemical education. International Journal of Science Education, 28, 957–976.
25. Gilbert, S. (1991). Model building and a definition of science. Journal of Research in Science Teaching, 28, 73–79.
26. Gobert, J., O'Dwyer, L., Horwitz, P., Buckley, B., Levy, S., & Wilensky, U. (2011). Examining the relationship between students’ understanding of the nature of models and conceptual learning in biology, physics, and chemistry. International Journal of Science Education, 33, 653–684.
27. 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.
28. Grünkorn, J., Upmeier zu Belzen, A., & Krüger, D. (2011). Design and test of open-ended tasks to evaluate a theoretical structure of model competence. In A. Yarden & G. Carvalho (Eds.), Authenticity in biology education: Benefits and challenges (pp. 53–65). Braga: CIEC, Universidade do Minho.
29. Guala, F. (2002). Models, simulations, and experiments. In L. Magnani & N. Nersessian (Eds.), Model-based reasoning. Science, technology, values (pp. 59–74). New York, NY: Kluwer Academic.
30. Guerra-Ramos, M. (2012). Teachers’ ideas about the Nature of Science: A critical analysis of research approaches and their contribution to pedagogical practice. Science & Education, 21, 631–655.
31. Halloun, I. (2006). Modeling theory in science education. Dordrecht: Springer.
32. Halloun, I. (2007). Mediated modeling in science education. Science & Education, 16, 653-697.
33. Harré, R. (1970). The principles of scientific thinking. London & Basingstoke: Macmillan and Co Ltd.
34. Harré, R. (2009). Pavlov's dogs and Schrödinger's cat: Tales from the living laboratory. Oxford: Oxford University Press.
35. Hesse, M. (1966). Models and analogies in science. Notre Dame, IN: Notre Dame University Press.
36. Hestenes, D. (1992). Modeling games in the Newtonian world. American Journal of Physics, 60, 732–748.
37. Hicks, L. (1970). Some properties of ipsative, normative, and forced-choice normative measures. Psychological Bulletin, 74, 167–184.
38. Ingham, A. & Gilbert, J. (1991). The use of analogue models by students of chemistry at higher education level. International Journal of Science Education, 13, 193–202.
39. Justi, R. & Gilbert, J. (2003). Teacher’s views on the nature of models. International Journal of Science Education, 25, 1369–1386.
40. Kandel, E. (1983). From metapsychology to molecular biology: Explorations into the nature of anxiety. American Journal of Psychiatry, 140, 1277–1293.
41. Kleickmann, T., Hardy, I., Möller, K., Pollmeier, J., Tröbst, S., & Beinbrech, C. (2010). Die Modellierung naturwissenschaftlicher Kompetenz im Grundschulalter: Theoretische Konzeption und Testkonstruktion. [The modeling of scientific competence in primary school age: Theoretical conception and test construction.] Zeitschrift für Didaktik der Naturwissenschaften, 16, 265–283.
42. Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2012). Assessment of students’ concepts of models and modeling: Empirical evaluation of a model of model competence. In C. Bruguière, A. Tiberghien, & P. Clément (Eds.), Ebook proceedings of the ESERA 2011 conference. (pp. 68-74). Retrieved from: http://lsg.ucy.ac.cy/esera/e_book/base/ebook/ebook-esera2011.pdf.
43. Laubichler, M. & Müller, G. (Eds.). (2007). Modeling biology. Cambridge, MA: MIT.
44. Leach, J., Millar, R., Ryder, J., & Séré, M.-G. (2000). Epistemological understanding in science learning: The consistency of representations across contexts. Learning and Instruction, 10, 497–527.
45. Leonelli, S. (2007). What is in a model? Combining theoretical and material models to develop intelligible theories. In M. Laubichler & G. Müller (Eds.), Modeling biology (pp. 15–35). Cambridge, MA: MIT.
46. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.
47. MacLellan, H. (1996). Situated learning perspectives. Englewood Cliffs, NJ: Educational Technology.
48. Magnani, L. (1999). Model-based creative abduction. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (p. 219–238). New York, NY: Kluwer Academic.
49. Magnani, L. & Nersessian, N. (Eds.). (2002). Model-based reasoning. New York, NY: Kluwer Academic.
50. Mahr, B. (2008). Ein Modell des Modellseins. [A model of model-being.] In U. Dirks & E. Knobloch (Eds.), Modelle (pp. 187–218). Frankfurt am Main: Peter Lang.
51. Masters, G. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174.
52. Masters, G. (2010). The partial credit model. In M. Nering & R. Ostini (Eds.), Handbook of polytomous item response theory models (pp. 109–122). New York, NY: Routledge.
53. Morgan, M. & Morrison, M. (Eds.). (1999). Models as mediators. Cambridge: Cambridge University Press.
54. Murcia, K. & Schibeci, R. (1999). Primary student teachers' conceptions of the nature of science. International Journal of Science Education, 21, 1123–1140.
55. Nehm, R. & Ha, M. (2011). Item feature effects in evolution assessment. Journal of Research in Science Teaching, 48, 237–256.
56. Odenbaugh, J. (2005). Idealized, inaccurate but successful: A pragmatic approach to evaluating models in theoretical ecology. Biology & Philosophy, 20, 231–255.
57. Oh, P. & Oh, S. (2011). What teachers of science need to know about models: An overview. International Journal of Science Education, 33, 1109–1130.
58. Penner, D., Giles, N., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34, 125–143.
59. Prins, G., Bulte, A., Van Driel, J., & Pilot, A. (2009). Selection of authentic modelling practices as contexts for chemistry education. International Journal of Science Education, 30, 1867–1890.
60. Schwartz, R. & Lederman, N. (2005). What scientists say: Scientists’ views of models. Paper presented as part of the symposium, “Learning about models and modeling in science: International views of research issues” at the annual meeting of the American Educational Research Association, April 12, 2005. Montreal, Canada.
61. Schwarz, C. & White, B. (1998). Fostering middle school students’ understanding of scientific modeling. Paper presented at the Annual Meeting of the American Educational Research Association. San Diego, CA.
62. Schwarz, C., Reiser, B., Davis, E., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46, 632–654.
63. Smith, R. (2000). Fit analysis in latent trait measurement models. Journal of Applied Measurement, 1, 199–218.
64. Son, J. & Goldstone, R. (2009). Contextualization in perspective. Cognition and Instruction, 27, 51–89.
65. Song, J. & Black, P. (1991). The effects of task contexts on pupils' performance in science process skills. International Journal of Science Education, 13, 49–58.
66. Suckling, C., Suckling, K., & Suckling, C. (1978). Chemistry through models: Concepts and applications of modelling in chemical science and industry. Cambridge, MA: Cambridge University Press.
67. Suppes, P. (1962). Models of data. In E. Nagel, P. Suppes, & A. Tarski (Eds.), Logic, methodology and the philosophy of science: Proceedings of the 1960 international congress (pp. 252–261). Stanford: Stanford University Press.
68. Svoboda, J. & Passmore, C. (2011). The strategies of modeling in biology education. Science & Education, Advance online publication. doi: 10.1007/s11191-011-9425-5
69. Terzer, E., Krell, M., Krüger, D. & Upmeier zu Belzen, A. (2011). Assessment of students’ concepts of models and modelling using multiple- and forced-choice items [Abstract]. In C. Bruguière (Ed.), Science learning & citizenship. 9th international conference ESERA 2011 (p. 220). Lyon: Lyon Ingenierie Projets.
70. Treagust, D., Chittleborough, G., & Mamiala, T. (2002). Student's understanding of the role of scientific models in learning science. International Journal of Science Education, 24, 357–368.
71. Treagust, D., Chittleborough, G., & Mamiala, T. (2004). Students’ understanding of the descriptive and predictive nature of teaching models in organic chemistry. Research in Science Education, 34, 1–20.
72. Upmeier zu Belzen, A. & Krüger, D. (2010). Modellkompetenz im Biologieunterricht. Zeitschrift für Didaktik der Naturwissenschaften, 16, 41–57. Retrieved from: http://www.ipn.uni-kiel.de/zfdn/pdf/16_Upmeier.pdf.
73. Urhahne, D., Kremer, K., & Mayer, J. (2011). Conceptions of the nature of science - Are they general or context specific? International Journal of Science and Mathematics Education, 9, 707–730.
74. Van Der Valk, T., Van Driel, J., & De Vos, W. (2007). Common characteristics of models in present-day scientific practice. Research in Science Education, 37, 469–488.
75. Van Driel, J. & Verloop, N. (1999). Teachers’ knowledge of models and modelling in science. International Journal of Science Education, 21, 1141–1153.
76. Van Driel, J. & Verloop, N. (2002). Experienced teacher’s knowledge of teaching and learning of models and modeling in science education. International Journal of Science Education, 24, 1255–1277.
77. Van Oers, B. (1998). From context to contextualization. Learning and Instruction, 8, 473-488.
78. Wimsatt, W. (1987). False models as means to truer theories. In M. Nitecki & A. Hoffman (Eds.), Neutral models in biology (pp. 23–55). New York, NY: Oxford University Press.
79. Wu, M., Adams, R., & Wilson, M. (2007). ACER ConQuest version 2.0: Generalised item response modelling software. Camberwell, Vic: ACER Press.