Abstract
Students’ as well as pre-service teachers’ and in-service teachers’ modeling competence is an important issue of research in science education due to its influence on both assessment and teaching. A large number of studies have used different methodological approaches, ranging from interviews to closed-ended tasks. In this chapter, we aim to provide an overview of the studies that have employed either open-ended tasks or closed-ended tasks as a way to elicit students’, pre-service teachers’, and in-service teachers’ understanding of models and modeling. We present different assessment instruments that contain, for example, multiple-choice, forced-choice, or rating scale tasks and summarize the main findings of studies on these instruments. As a second step, the results across these studies are compared, and, based on current standards for educational testing, the advantages and limitations of each of the instruments regarding the purpose of assessing and diagnosing perspectives on models and modeling in science are discussed. Bringing all aspects together, a variety of approaches for task and test development are illustrated, including concepts with regard to validation methods in particular.
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AERA, APA, & NCME (Eds.). (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.
Al-Balushi, S. M. (2011). Students’ evaluation of the credibility of scientific models that represent natural entities and phenomena. International Journal of Science and Mathematics Education, 9, 571–601. https://doi.org/10.1007/s10763-010-9209-4
Bamberger, Y. M., & Davis, E. A. (2013). Middle-school science students’ scientific modelling performances across content areas and within a learning progression. International Journal of Science Education, 35, 213–238. https://doi.org/10.1080/09500693.2011.624133
Borrmann, J. R., Reinhardt, N., Krell, M., & Krüger, D. (2014). Perspektiven von Lehrkräften über Modelle in den Naturwissenschaften: Eine generalisierende Replikationsstudie [Teachers perspectives’ on models in science: A generalizing replication study]. Erkenntnisweg Biologiedidaktik, 13, 57–72.
Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: pedagogical functions, discursive acts, and technology in modeling instruction. EURASIA Journal of Mathematics, Science & Technology Education, 11, 159-176. https://doi.org/10.12973/eurasia.2015.1314a
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. https://doi.org/10.1080/09500693.2015.1082671
Chittleborough, G. D., Treagust, D. F., Mamiala, T. L., & Mocerino, M. (2005). Students’ perceptions of the role of models in the process of science and in the process of learning. Research in Science & Technological Education, 23, 195–212. https://doi.org/10.1080/02635140500266484
Crawford, B. A., & Cullin, M. J. (2004). Supporting prospective teachers’ conceptions of modelling in science. International Journal of Science Education, 26, 1379–1401. https://doi.org/10.1080/09500690410001673775
Crawford, B. A., & Cullin, M. J. (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, The Netherlands, Springer. https://doi.org/10.1007/1-4020-3673-6_25
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. https://doi.org/10.1080/09500690902833221
Derman, A., & Kayacan, K. (2017). Investigation of the relationship between the views of the prospective science teachers on the nature of scientific models and their achievement on the topic of atom. European Journal of Education Studies, 3, 541–559.
Everett, S. A., Otto, C. A., & Luera, G. R. (2009). Preservice elementary teachers’ growth in knowledge of models in a science capstone course. International Journal of Science and Mathematics Education, 7, 1201–1225. https://doi.org/10.1007/s10763-009-9158-y
Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education. Cham, Switzerland: Springer.
Gobert, J. D., O’Dwyer, L., Horwitz, P., Buckley, B. C., Levy, S. T., & 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. https://doi.org/10.1080/09500691003720671
Gogolin, S. (2017). Diagnosing students’ meta-modelling knowledge: Gathering validity evidence during test development. Doctoral dissertation. Freie Universität, Berlin, Germany. Retrieved from http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000105919
Gogolin, S., Krell, M., Lange-Schubert, K., Hartinger, A., Upmeier Zu Belzen, A., & Krüger, D. (2017). Erfassung von Modellkompetenz bei Grundschüler/innen [Assessment of elementary students’ modeling competence]. In H. Giest, A. Hartinger, & S. Tänzer (Eds.), Vielperspektivität im Sachunterricht (pp. 108–115). Bad Heilbrunn, Germany: Klinkhardt.
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. https://doi.org/10.1002/tea.3660280907
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. https://doi.org/10.1080/09500693.2013.873155
Justi, R., & Gilbert, J. K. (2005). Investigating teachers’ ideas about models and modelling: Some issues of authenticity. In K. Boersma, M. Goedhart, O. de Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 325–335). Dordrecht, The Netherlands: Springer.
Justi, R., & van Driel, J. (2005). The development of science teachers’ knowledge on models and modelling: Promoting, characterizing, and understanding the process. International Journal of Science Education, 27, 549–573. https://doi.org/10.1080/0950069042000323773
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50, 1–73. https://doi.org/10.1111/jedm.2013.50.issue-1
Kane, M. (2015). Validation strategies. Delineating and validating proposed interpretations and uses of test scores. In M. Raymond, S. Lane, & T. Haladyna (Eds.), Handbook of Test Development (pp. 64-80). New York: Routledge.
KMK (Ed.). (2005). Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss [Biology education standards for the Mittlere Schulabschluss]. München, Neuwied: Wolters Kluwer.
Krell, M. (2012). Using polytomous IRT models to evaluate theoretical levels of understanding models and modeling in biology education. Science Education Review Letters. Theoretical Letters 2012, 1–5.
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. https://doi.org/10.1080/00219266.2015.1028570
Krell, M., & Krüger, D. (2017). University students’ meta-modelling knowledge. Research in Science & Technological Education, 35, 261–273. https://doi.org/10.1080/02635143.2016.1274724
Krell, M., Reinisch, B., & Krüger, D. (2015). Analyzing students’ understanding of models and modeling referring to the disciplines biology, chemistry, and physics. Research in Science Education, 45, 367–393. https://doi.org/10.1007/s11165-014-9427-9
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, 1–34. Retrieved from http://www.ijobed.com/2_2/Moritz-2012.pdf
Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014a). Students’ levels of understanding models and modelling in biology: Global or aspect-dependent? Research in Science Education, 44, 109–132. https://doi.org/10.1007/s11165-013-9365-y
Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014b). Context-specificities in students’ understanding of models and modelling: An issue of critical importance for both assessment and teaching. In C. Constantinou, N. Papadouris, & A. Hadjigeorgiou (Eds.), E-book proceedings of the ESERA 2013 conference: Science education research for evidence-based teaching and coherence in learning (pp. 1024–1035). Nicosia, Cyprus: European Science Education Research Association. Retrieved from https://www.esera.org/publications/esera-conference-proceedings/esera-2013
Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2016). Modellkompetenz im Biologieunterricht [Modeling competence in biology education]. In A. Sandmann, & P. Schmiemann (Eds.), Biologiedidaktische Forschung: Schwerpunkte und Forschungsstände (pp. 83–102). Berlin, Germany: Logos.
Lee, S. W.-Y. (2017). Identifying the item hierarchy and charting the progression across grade levels: Surveying Taiwanese students’ understanding of scientific models and modeling. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-017-9854-y
Lee, S. W.-Y., Chang, H.-Y., & Wu, H.-K. (2017). Students’ views of scientific models and modeling: Do representational characteristics of models and students’ educational levels matter? Research in Science Education, 47, 305–328. https://doi.org/10.1007/s11165-015-9502-x
Leighton, J. P. (2004). Avoiding misconception, misuse, and missed opportunities. The collection of verbal reports in educational achievement testing. Educational Measurement: Issues and Practice, 23, 6–15. https://doi.org/10.1111/j.1745-3992.2004.tb00164.x
Lin, J.-W. (2014). Elementary school teachers’ knowledge of model functions and modeling processes: A comparison of science and non-science majors. International Journal of Science and Mathematics Education, 12, 1197–1220. https://doi.org/10.1007/s10763-013-9446-4
Martinez, M. E. (1999). Cognition and the question of test item format. Educational Psychologist, 34, 207–218.
NGSS Lead States (Eds.). (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
Nicolaou, C. T., & Constantinou, C. P. (2014). Assessment of the modeling competence: A systematic review and synthesis of empirical research. Educational Research Review, 13, 52–73. https://doi.org/10.1016/j.edurev.2014.10.001
Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models. An overview. International Journal of Science Education, 33, 1109–1130. https://doi.org/10.1080/09500693.2010.502191
Osborne, J. (2013). The 21st century challenge for science education: Assessing scientific reasoning. Thinking Skills and Creativity, 10, 265–279. https://doi.org/10.1016/j.tsc.2013.07.006
Patzke, C., Krüger, D., & Upmeier zu Belzen, A. (2015). Entwicklung von Modellkompetenz im Längsschnitt [Development of modeling competence in a longitudinal study]. In M. Hammann, J. Mayer, & N. Wellnitz (Eds.), Lehr- und Lernforschung in der Biologiedidaktik: Vol. 6. Theorie, Empirie & Praxis: Internationale Tagung der Fachsektion Didaktik der Biologie im VBIO, Kassel 2013 (pp. 43–58). Innsbruck, Austria: Studienverlag.
Pintó, R., & Gutierrez, R. (2005). Teachers’ conceptions of scientific models. In R. Pinto & D. Couso (Eds.), Proceedings of the 5th international ESERA conference on contributions of research to enhancing students’ interest in learning science (pp. 866–868). Barcelona, Spain: ESERA.
Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23, 165–205. https://doi.org/10.1207/s1532690xci2302_1
Shavelson, R. J. (2013). On an approach to testing and modeling competence. Educational Psychologist, 48, 73–86. https://doi.org/10.1080/00461520.2013.779483
Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout Wolters, B. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modelling task. International Journal of Science Education, 31, 1205–1229. https://doi.org/10.1080/09500690802192181.
Terzer, E. (2013). Modellkompetenz im Kontext Biologieunterricht: Empirische Beschreibung von Modellkompetenz mithilfe von Multiple-Choice Items [Modeling competence in the context of biology education]. Doctoral dissertation. Humboldt-Universität, Berlin, Germany. Retrieved from: http://edoc.hu-berlin.de/dissertationen/terzer-eva-2012-12-19/PDF/terzer.pdf
Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2002). Students’ understanding of the role of scientific models in learning science. International Journal of Science Education, 24, 357–368. https://doi.org/10.1080/09500690110066485
Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2004). Students’ understanding of the descriptive and predictive nature of teaching models in organic chemistry. Research in Science Education, 34, 1–20. https://doi.org/10.1023/B:RISE.0000020885.41497.ed
Upmeier zu Belzen, A., & Krüger, D. (2010). Modellkompetenz im Biologieunterricht [Model competence in biology education]. Zeitschrift für Didaktik der Naturwissenschaften, 16, 41–57.
Van Der Valk, T., Van Driel, J. H., & De Vos, W. (2007). Common characteristics of models in present-day scientific practice. Research in Science Education, 37, 469–488. https://doi.org/10.1007/s11165-006-9036-3
Van Driel, J. H., & Verloop, N. (1999). Teachers’ knowledge of models and modelling in science. International Journal of Science Education, 21, 1141–1153. https://doi.org/10.1080/095006999290110
VCAA. (2016). Victorian certificate of education. Biology. Melbourne, VIC: VCAA.
Wei, S., Liu, X., & Jia, Y. (2014). Using Rasch measurement to validate the instrument of students’ understanding of models in science (SUMS). International Journal of Science and Mathematics Education, 12, 1067–1082. https://doi.org/10.1007/s10763-013-9459-z
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Mathesius, S., Krell, M. (2019). Assessing Modeling Competence with Questionnaires. In: Upmeier zu Belzen, A., Krüger, D., van Driel, J. (eds) Towards a Competence-Based View on Models and Modeling in Science Education. Models and Modeling in Science Education, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-30255-9_7
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