Development and Application of a Category System to Describe Pre-Service Science Teachers’ Activities in the Process of Scientific Modelling

Abstract

As part of their professional competencies, science teachers need an elaborate meta-modelling knowledge as well as modelling skills in order to guide and monitor modelling practices of their students. However, qualitative studies about (pre-service) science teachers’ modelling practices are rare. This study provides a category system which is suitable to analyse and to describe pre-service science teachers’ modelling activities and to infer modelling strategies. The category system was developed based on theoretical considerations and was inductively refined within the methodological frame of qualitative content analysis. For the inductive refinement, modelling practices of pre-service teachers (n = 4) have been video-taped and analysed. In this study, one case was selected to demonstrate the application of the category system to infer modelling strategies. The contribution of this study for science education research and science teacher education is discussed.

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References

  1. Ainsworth, S., Prain, V., & Tytler, R. (2011). Science education: drawing to learn in science. Science, 333, 1096–1097.

    Article  Google Scholar 

  2. Bailer-Jones, D. (2003). When scientific models represent. International Studies in the Philosophy of Science, 17, 59–74.

    Article  Google Scholar 

  3. Black, M. (1962). Models and metaphors. Ithaca: Cornell U.P.

    Google Scholar 

  4. Boatwright, A., Puttick, S., & Licence, P. (2011). Can a siphon work in vacuo? Journal of Chemical Education, 88, 1547–1550.

    Article  Google Scholar 

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

    Google Scholar 

  6. Brennan, R., & Prediger, D. (1981). Coefficient kappa. Educational and Psychological Measurement, 41, 687–699.

    Article  Google Scholar 

  7. Bybee, R. (2014). NGSS and the next generation of science teachers. Journal of Science Teacher Education, 25, 211–221.

    Article  Google Scholar 

  8. Campbell, T., Oh, P., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies. Eurasia Journal of Mathematics, Science & Technology Education, 11, 159–176.

    Google Scholar 

  9. Capps, D., & Crawford, B. (2013a). Inquiry-based instruction and teaching about nature of science. Journal of Science Teacher Education, 24, 497–526.

    Article  Google Scholar 

  10. Capps, D., & Crawford, B. (2013b). Inquiry-based professional development. International Journal of Science Education, 35, 1947–1978.

    Article  Google Scholar 

  11. Chandrasekharan, S., & Nersessian, N. (2011). Building cognition: the construction of external representations for discovery. In L. Carlson, C. Hoelscher, & T. Shipley (Eds.) Proceedings of the Cognitive Science Society 33 (pp. 267–277). Cognitive Science Society.

  12. Cheng, M.-F., & Lin, J.-L. (2015). Investigating the relationship between students’ views of scientific models and their development of models. International Journal of Science Education, 37, 2453–2475.

    Article  Google Scholar 

  13. Clement, J. (1989). Learning via model construction and criticism. In J. Glover, C. Reynolds, & R. Royce (Eds.), Handbook of creativity (pp. 341–381). Berlin: Springer.

    Google Scholar 

  14. Clement, J. (2009). Creative model construction in scientists and students. Dordrecht: Springer.

    Google Scholar 

  15. Clement, J., & Williams, G. (2013). Parallel roles for nonformal reasoning in expert scientific model construction and classroom discussions in science. Paper presented at NARST 2013. Retrieved from http://people.umass.edu/~clement/pdf/Clement_2013NARST_Paper.pdf.

  16. Elo, S., Kaariainen, M., Kanste, O., Polkki, T., Utriainen, K., & Kyngas, H. (2014). Qualitative content analysis. SAGE Open, 4, 1–10.

    Article  Google Scholar 

  17. Ericsson, K., & Simon, H. (1980). Verbal reports as data. Psychological Review, 87, 215–251.

    Article  Google Scholar 

  18. Ericsson, K., & Simon, H. (1998). How to study thinking in everyday life. Mind, Culture, and Activity, 5, 178–186.

    Article  Google Scholar 

  19. Friege, G., & Lind, G. (2003). Allgemeine und fachspezifische Problemlösekompetenz [General and discipline-specific problem solving competence]. Zeitschrift für Didaktik der Naturwissenschaften, 9, 63-74.

    Google Scholar 

  20. Frigg, R., & Hartmann, S. (2017). Models in science. In E. Zalta (Ed.), The Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/archives/spr2017/entries/models-science/.

  21. Gayford, C. (1992). Patterns of group behaviour in open-ended problem solving in science classes of 15-year-old students in England. International Journal of Science Education, 14, 41–49.

    Article  Google Scholar 

  22. Giere, R. (1999). Using models to represent reality. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 40–57). New York: Kluwer.

    Google Scholar 

  23. Giere, R., Bickle, J., & Mauldin, R. (2006). Understanding scientific reasoning. London: Thomson Learning.

    Google Scholar 

  24. Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education. Cham: Springer.

    Google Scholar 

  25. Glanville, R. (1982). Inside every white box there are two black boxes trying to get out. Behavioral Science, 27, 1–11.

    Article  Google Scholar 

  26. Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21, 725–740.

    Article  Google Scholar 

  27. Gouvea, J., & Passmore, C. (2017). ‘Models of’ versus ‘Models for’. Science & Education, 26, 49-63. doi:10.1007/s11191-017-9884-4.

  28. Grünkorn, J., Upmeier zu Belzen, A., & Krüger, D. (2014). Assessing students’ understandings of biological models and their use in science to evaluate a theoretical framework. International Journal of Science Education, 36, 1651–1684.

    Article  Google Scholar 

  29. Günther, S., Fleige, J., Upmeier zu Belzen, A., & Krüger, D. (2017). Interventionsstudie mit angehenden Lehrkräften zur Förderung von Modellkompetenz im Unterrichtsfach Biologie [Intervention study with pre-service teachers for fostering model competence in biology education]. In C. Gräsel & K. Trempler (Eds.), Entwicklung von Professionalität pädagogischen Personals (pp. 215–236). Wiesbaden: Springer.

    Google Scholar 

  30. Henze, I., Van Driel, J., & Verloop, N. (2007). Science teachers’ knowledge about teaching models and modelling in the context of a new syllabus on public understanding of science. Research in Science Education, 37, 99–122.

    Article  Google Scholar 

  31. Henze, I., van Driel, J., & Verloop, N. (2008). Development of experienced science teachers’ pedagogical content knowledge of models of the solar system and the universe. International Journal of Science Education, 30, 1321–1342.

    Article  Google Scholar 

  32. Hodson, D. (2014). Learning science, learning about science, doing science. International Journal of Science Education, 36, 2534–2553.

    Article  Google Scholar 

  33. Hughes, S., & Gurung, S. (2014). Exploring the boundary between a siphon and barometer in a hypobaric chamber. Scientific Reports, 4, 4741. doi:10.1038/srep04741.

    Article  Google Scholar 

  34. Jong, J.-P., Chiu, M.-H., & Chung, S.-L. (2015). The use of modeling-based text to improve students’ modeling competencies. Science Education, 99, 986–1018.

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Justi, R., & Van Driel, J. (2006). The use of the interconnected model of teacher professional growth for understanding the development of science teachers’ knowledge on models and modelling. Teaching and Teacher Education, 22, 437–450.

    Article  Google Scholar 

  37. Khan, S. (2008). What if scenarios for testing student models in chemistry. In J. Clement & M. Rea-Ramirez (Eds.), Model based learning and instruction in science (pp. 139–150). Dordrecht: Springer.

    Google Scholar 

  38. Khan, S. (2011). What’s missing in model-based teaching. Journal of Science Teacher Education, 22, 535–560.

    Article  Google Scholar 

  39. Koch, S., Krell, M., & Krüger, D. (2015). Förderung von Modellkompetenz durch den Einsatz einer Blackbox [Fostering model competence using a black-box]. Erkenntnisweg Biologiedidaktik, 14, 93-108.

  40. Krell, M., & Krüger, D. (2016). Testing models: A key aspect to promote teaching-activities related to models and modelling in biology lessons? Journal of Biological Education, 50, 160-173.

  41. Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2012). Students’ understanding of the purpose of models in different biological contexts. International Journal of Biology Education, 2(2), 1-34. Retrieved from http://dergipark.ulakbim.gov.tr/ijobed/article/view/5000115839/5000107805 

  42. Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014). How year 7 to year 10 students categorise models. In D. Krüger & M. Ekborg (Eds.), Research in biological education (pp. 117-131). Retrieved from http://www.bcp.fu-berlin.de/biologie/arbeitsgruppen/didaktik/eridob_2012/eridob_proceeding/8-How-year.pdf?1389177404 

  43. Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2016). Modellkompetenz im Biologieunterricht [Model competence in biology education]. In A. Sandmann & P. Schmiemann (Eds.), Biologiedidaktische Forschung. Schwerpunkte und Forschungsgegenstände (pp. 83-102). Berlin: Logos.

  44. Laubichler, M. D., & Müller, G. B. (Eds.). (2007). Modeling biology. Cambridge: MIT.

    Google Scholar 

  45. Lederman, N., & Abd-El-Khalick, F. (2002). Avoiding de-natured science. In W. McComas (Ed.), The Nature of Science in science education (pp. 83–126). Dordrecht: Kluwer.

    Google Scholar 

  46. Leighton, J., & Gierl, M. (2007). Verbal reports as data for cognitive diagnostic assessment. In J. Leighton & M. Gierl (Eds.), Cognitive diagnostic assessment for education (pp. 146–172). Cambridge: Cambridge U.P.

    Google Scholar 

  47. Leisner-Bodenthin, A. (2006). Zur Entwicklung von Modellkompetenz im Physikunterricht [On the development of modelling competence in physics education]. Zeitschrift für Didaktik der Naturwissenschaften, 12, 91–109.

    Google Scholar 

  48. Louca, L., & Zacharia, Z. (2012). Modeling-based learning in science education. Educational Review, 64, 471–492.

    Article  Google Scholar 

  49. Louca, L., & Zacharia, Z. (2015). Examining learning through modeling in K-6 science education. Journal of Science Education and Technology, 24, 192–215.

    Article  Google Scholar 

  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.

    Google Scholar 

  51. Mahr, B. (2012). On the epistemology of models. In G. Abel & J. Conant (Eds.), Rethinking epistemology (pp. 301–352). Berlin: De Gruyter.

    Google Scholar 

  52. MAXQDA (n.d.). (Version 12) [Computer software]. Berlin: VERBI Software GmbH.

  53. Mayring, P. (2000). Qualitative content analysis. Forum Qualitative Social Research, 1(2), Artikel 20. Retrieved from http://nbn-resolving.de/urn:nbn:de:0114-fqs0002204.

  54. Métrailler, Y., Reijnen, E., Kneser, C., & Opwis, K. (2008). Scientific problem solving in a virtual laboratory: a comparison between individuals and pairs. Swiss Journal of Psychology, 67, 71–83.

    Article  Google Scholar 

  55. Nersessian, N. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 5–22). New York: Kluwer.

    Google Scholar 

  56. Nicolaou, C., & Constantinou, C. (2014). Assessment of the modeling competence. Educational Research Review, 13, 52–73.

    Article  Google Scholar 

  57. NGSS Lead States (Ed.). (2013). Next Generation Science Standards: For states, by states. Washington, DC: The National Academies Press.

  58. Oh, P., & Oh, S. (2011). What teachers of science need to know about models. International Journal of Science Education, 33, 1109–1130.

    Article  Google Scholar 

  59. Orsenne, J., & Upmeier zu Belzen, A. (2012). Hands-On Aufgaben zur Erfassung und Förderung von Modellkompetenz im Biologieunterricht [Hands-on tasks for assessing and fostering model competence in biology education]. In U. Harms & F. Bogner (Eds.), Lehr- und Lernforschung in der Biologiedidaktik. Band 5 (pp. 29–44). Studienverlag: Innsbruck.

    Google Scholar 

  60. Orsenne, J., Upmeier zu Belzen, A. & Krüger, D. (2016). Drawing, constructing and verbalizing as modeling processes. Manuscript in preparation.

  61. Passmore, C., & Svoboda, J. (2012). Exploring opportunities for argumentation in modelling classrooms. International Journal of Science Education, 34, 1535–1554.

    Article  Google Scholar 

  62. Passmore, C., Gouvea, J., & Giere, R. (2014). Models in science and in learning science. In M. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 1171–1202). Dordrecht: Springer.

    Google Scholar 

  63. Schreier, M. (2012). Qualitative content analysis in practice. Thousand Oaks: Sage.

    Google Scholar 

  64. Schreier, M. (2014). Varianten qualitativer Inhaltsanalyse [Ways of doing qualitative content analysis]. Forum qualitative Sozialforschung, 15(1), Artikel 18. Retrieved from http://nbn-resolving.de/urn:nbn:de:0114-fqs1401185.

  65. Schwarz, C., Reiser, B., Davis, E., Kenyon, L., Achér, A., Fortus, D., et al. (2009). Developing a learning progression for scientific modeling. Journal of Research in Science Teaching, 46, 632–654.

    Article  Google Scholar 

  66. Suckling, C., Suckling, K., & Suckling, C. (1978). Chemistry through models. Cambridge: Cambridge U.P.

    Google Scholar 

  67. Upmeier zu Belzen, A., & Krüger, D. (2010). Modellkompetenz im Biologieunterricht [Model competence in biology teaching]. Zeitschrift für Didaktik der Naturwissenschaften, 16, 41-57.

  68. van Fraassen, B. (2008). Scientific representation. Oxford: Oxford U.P.

    Google Scholar 

  69. Wirtz, M., & Caspar, F. (2002). Beurteilerübereinstimmung und Beurteilerreliabilität [Rater agreement and rater reliability]. Göttingen: Hogrefe.

    Google Scholar 

  70. Wu, H.-K., & Puntambekar, S. (2012). Pedagogical affordances of multiple external representations in scientific processes. Journal of Science Education and Technology, 21, 754–767.

    Article  Google Scholar 

  71. Yenilmez Turkoglu, A., & Oztekin, C. (2016). Science teacher candidates’ perceptions about roles and nature of scientific models. Research in Science & Technological Education, 34, 219-236.

  72. Ziemek, H.-P., Keiner, K.-H., & Mayer, J. (2005). Problemlöseprozesse von Schülern der Biologie im naturwissenschaftlichen Unterricht: Ergebnisse qualitativer Studien [Problem solving processes of students in science education: Findings of qualitative studies]. In R. Klee, A. Sandmann, & H. Vogt (Eds.), Lehr- und Lernforschung in der Biologiedidaktik (Band 2, pp. 29-40). Innsbruck: Studienverlag.

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Krell, M., Walzer, C., Hergert, S. et al. Development and Application of a Category System to Describe Pre-Service Science Teachers’ Activities in the Process of Scientific Modelling. Res Sci Educ 49, 1319–1345 (2019). https://doi.org/10.1007/s11165-017-9657-8

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Keywords

  • Scientific modelling
  • Modelling strategy
  • Qualitative content analysis
  • Science education
  • Pre-service science teachers