Investigating pre-service science teachers’ metaknowledge about the modelling process and its relation to metaknowledge about models

References

• 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(3), 571–601. https://doi.org/10.1007/s10763-010-9209-4
   Web of Science ®Google Scholar
• American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (Eds.). (2014). Standards for educational and psychological testing. American Educational Research Association.
   Google Scholar
• Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4(3), 114–123.
   Google Scholar
• Bielik, T., Damelin, D., & Krajcik, J. S. (2019). Shifting the balance: Engaging students in using a modeling tool to learn about ocean acidification. EURASIA Journal of Mathematics, Science and Technology Education, 15(1). http://doi.org/10.29333/ejmste/99512
   Google Scholar
• Birenbaum, M., & Tatsuoka, K. K. (1987). Open-Ended versus multiple-choice response formats—It does make a difference for diagnostic purposes. Applied Psychological Measurement, 11(4), 385–395. https://doi.org/10.1177/014662168701100404
   Web of Science ®Google Scholar
• Bobek, E., & Tversky, B. (2016). Creating visual explanations improves learning. Cognitive Research: Principles and Implications, 1(1), 1–14. https://doi.org/10.1186/s41235-016-0031-6
   PubMedGoogle Scholar
• Brennan, R. L., & Prediger, D. J. (1981). Coefficient kappa: Some uses, misuses, and alternatives. Educational and Psychological Measurement, 41(3), 687–699. https://doi.org/10.1177/001316448104100307
   Web of Science ®Google Scholar
• Brooke, J. (1996). Sus: A “quick and dirty” usability scale. In usability evaluation In industry (pp. 207–212). CRC Press.
   Google Scholar
• 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(15), 2453–2475. https://doi.org/10.1080/09500693.2015.1082671
   Web of Science ®Google Scholar
• Cheng, M.-F., Wu, T.-Y., & Lin, S.-F. (2021). Investigating the relationship between views of scientific models and modeling practice. Research in Science Education, 51(1), 307–323. https://doi.org/10.1007/s11165-019-09880-2
   Web of Science ®Google Scholar
• Chiu, M.-H., & Lin, J.-W. (2019). Modeling competence in science education. Disciplinary and Interdisciplinary Science Education Research, 1(1), 12. https://doi.org/10.1186/s43031-019-0012-y
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum.
   Google Scholar
• Cook, D. A., Zendejas, B., Hamstra, S. J., Hatala, R., & Brydges, R. (2014). What counts as validity evidence? Examples and prevalence in a systematic review of simulation-based assessment. Advances in Health Sciences Education, 19(2), 233–250. https://doi.org/10.1007/s10459-013-9458-4
   PubMed Web of Science ®Google Scholar
• Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251. https://doi.org/10.1037/0033-295X.87.3.215
   Web of Science ®Google Scholar
• Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications. https://books.google.de/books?id=srb0a9fmMEoC
   Google Scholar
• Giere, R. (2010). An agent-based conception of models and scientific representation. Synthese, 172(2), 269. https://doi.org/10.1007/s11229-009-9506-z
   Web of Science ®Google Scholar
• Giere, R., Bickle, J., & Mauldin, R. (2006). Understanding scientific reasoning. Thomson/Wadsworth.
   Google Scholar
• Gilbert, J. K., & Justi, R. S. (2016). Approaches to modelling-based teaching. In J. K. Gilbert (Ed.), Modelling-based teaching in science education. Springer International Publishing.
   Google Scholar
• Göhner, M. F., Bielik, T., & Krell, M. (2022). Investigating the dimensions of modeling competence among preservice science teachers: Meta‐modeling knowledge, modeling practice, and modeling product. Journal of Research in Science Teaching, 59(8), 1354–1387. http://doi.org/10.1002/tea.v59.8
   Web of Science ®Google Scholar
• Gouvea, J., & Passmore, C. (2017). ‘Models of’ versus ‘models for’. Science & Education, 26, 49–63. https://doi.org/10.1007/s11191-017-9884-4
   Web of Science ®Google Scholar
• Greve, W., & Wentura, D. (1997). Wissenschaftliche beobachtung. Eine einführung [scientific observation. An introduction]. Beltz.
   Google Scholar
• Halloun, I. A. (2007). Mediated modeling in science education. Science & Education, 16(7-8), 653–697. http://doi.org/10.1007/s11191-006-9004-3
   Google Scholar
• Harlow, D. B., Bianchini, J. A., Swanson, L. H., & Dwyer, H. A. (2013). Potential teachers’ appropriate and inappropriate application of pedagogical resources in a model-based physics course: A “knowledge in pieces” perspective on teacher learning. Journal of Research in Science Teaching, 50(9), 1098–1126. https://doi.org/10.1002/tea.21108
   Web of Science ®Google Scholar
• Hubley, A. M., & Zumbo, B. D. (2017). Response processes in the context of validity: Setting the stage. In B. D. Zumbo, & A. M. Hubley (Eds.), Understanding and investigating response processes in validation research (pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-319-56129-5_1
   Google Scholar
• Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369–387. https://doi.org/10.1080/09500690110110142
   Web of Science ®Google Scholar
• Justi, R. S., & 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(5), 549–573. https://doi.org/10.1080/0950069042000323773
   Web of Science ®Google Scholar
• KMK. (2019). Standards für die lehrerbildung [standards for teacher education]. Luchterhand.
   Google Scholar
• 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(2), 160–173. http://doi.org/10.1080/00219266.2015.1028570
   Web of Science ®Google Scholar
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
   PubMed Web of Science ®Google Scholar
• Lazenby, K., Rupp, C. A., Brandriet, A., Mauger-Sonnek, K., & Becker, N. M. (2019). Undergraduate chemistry students’ conceptualization of models in general chemistry. Journal of Chemical Education, 96(3), 455–468. https://doi.org/10.1021/acs.jchemed.8b00813
   Web of Science ®Google Scholar
• Lazenby, K., Stricker, A., Brandriet, A., Rupp, C. A., Mauger-Sonnek, K., & Becker, N. M. (2020). Mapping undergraduate chemistry students’ epistemic ideas about models and modeling. Journal of Research in Science Teaching, 57(5), 794–824. https://doi.org/10.1002/tea.21614
   Web of Science ®Google Scholar
• Lee, S., & Kim, H.-B. (2014). Exploring secondary students’ epistemological features depending on the evaluation levels of the group model on blood circulation. Science & Education, 23(5), 1075–1099. https://doi.org/10.1007/s11191-013-9639-9
   Web of Science ®Google Scholar
• Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. In R. M. Lerner, L. S. Liben, & U. Mueller (Eds.), Handbook of child psychology and developmental science: Cognitive processes (Vol. 2 (7th ed, pp. 671–714). John Wiley.
   Google Scholar
• Leighton, J. P. (2017). Collecting and analyzing verbal response process data in the service of interpretive and validity argument. In K. Ercikan, & J. W. Pellegrino (Eds.), Validation of score meaning for the next generation of assessments (pp. 25–38). Routledge.
   Google Scholar
• Leiner, D. J. (2019). SoSci Survey (Version 3.1.06). www.soscisurvey.de
   Google Scholar
• Loftus, G. R. (1993). A picture is worth a thousandp values: On the irrelevance of hypothesis testing in the microcomputer age. Behavior Research Methods, Instruments, & Computers, 25(2), 250–256. https://doi.org/10.3758/BF03204506
   Google Scholar
• Lucas, L., Helikar, T., & Dauer, J. (2022). Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics. International Journal of Science Education, 44(13), 2152–2179. https://doi.org/10.1080/09500693.2022.2114815
   Web of Science ®Google Scholar
• Mahr, B. (2009). Information science and the logic of models. Software & Systems Modeling, 8(3), 365–383. https://doi.org/10.1007/s10270-009-0119-2
   Web of Science ®Google Scholar
• Mathesius, S., & Krell, M. (2019). Assessing modeling competence with questionnaires. In A. Upmeier zu Belzen, D. Krüger, & J. van Driel (Eds.), Towards a competence-based view on models and modeling in science education (pp. 117–129). Springer International Publishing.
   Google Scholar
• Mühling, A. (2014). CoMapEd. https://www.comaped.de/
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Nielsen, S. S., & Nielsen, J. A. (2021). A competence-oriented approach to models and modelling in lower secondary science education: Practices and rationales Among danish teachers. Research in Science Education, 51(2), 565–593. https://doi.org/10.1007/s11165-019-09900-1
   Web of Science ®Google Scholar
• NRC. (2012). A framework for K-12 science education. The National Academy Press.
   Google Scholar
• Oh, P. S. (2019). Features of modeling-based abductive reasoning as a disciplinary practice of inquiry in earth science. Science & Education, 28(6), 731–757. https://doi.org/10.1007/s11191-019-00058-w
   Web of Science ®Google Scholar
• Opitz, A., Heene, M., & Fischer, F. (2017). Measuring scientific reasoning – a review of test instruments. Educational Research and Evaluation, 23(3-4), 78–101. https://doi.org/10.1080/13803611.2017.1338586
   Web of Science ®Google Scholar
• Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), 177–196. https://doi.org/10.1007/s10972-014-9384-1
   Google Scholar
• 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). Springer.
   Google Scholar
• Ropohl, M., Walpuski, M., & Sumfleth, E. (2015). Welches Aufgabenformat ist das richtige? – Empirischer Vergleich zweier Aufgabenformate zur standardbasierten Kompetenzmessung. Zeitschrift für Didaktik der Naturwissenschaften, 21(1), 1–15. https://doi.org/10.1007/s40573-014-0020-6
   Google Scholar
• Rost, M., & Knuuttila, T. (2022). Models as epistemic artifacts for scientific reasoning in science education research. Education Sciences, 12(4), 276. https://doi.org/10.3390/educsci12040276
   Web of Science ®Google Scholar
• Schwarz, C. V., Ke, L., Salgado, M., & Manz, E. (2022). Beyond assessing knowledge about models and modeling: Moving toward expansive, meaningful, and equitable modeling practice. Journal of Research in Science Teaching, 59(6), 1086–1096. https://doi.org/10.1002/tea.21770
   Web of Science ®Google Scholar
• Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, 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(6), 632–654. https://doi.org/10.1002/tea.20311
   Web of Science ®Google Scholar
• Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205. https://doi.org/10.1207/s1532690xci2302_1
   Web of Science ®Google Scholar
• Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout-Wolters, B. H. A. M. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modelling task. International Journal of Science Education, 31(9), 1205–1229. https://doi.org/10.1080/09500690802192181
   Web of Science ®Google Scholar
• Treagust, D., Chittleborough, G., & Mamiala, T. (2002). Students' understanding of the role of scientific models in learning science. International Journal of Science Education, 24(4), 357–368. https://doi.org/10.1080/09500690110066485
   Web of Science ®Google Scholar
• Upmeier zu Belzen, A., Engelschalt, P., & Krüger, D. (2021). Modeling as scientific reasoning—the role of abductive reasoning for modeling competence. Education Sciences, 11(9), 495. http://doi.org/10.3390/educsci11090495
   Web of Science ®Google Scholar
• 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(4), 469–488. https://doi.org/10.1007/s11165-006-9036-3
   Web of Science ®Google Scholar
• van Driel, J. H., & Verloop, N. (2002). Experienced teachers’ knowledge of teaching and learning of models and modelling in science education. International Journal of Science Education, 24(12), 1255–1272. https://doi.org/10.1080/09500690210126711
   Web of Science ®Google Scholar
• Vo, T., Forbes, C. T., Zangori, L., & Schwarz, C. V. (2015). Fostering third-grade students’ Use of scientific models with the water cycle: Elementary teachers’ conceptions and practices. International Journal of Science Education, 37(15), 2411–2432. https://doi.org/10.1080/09500693.2015.1080880
   Web of Science ®Google Scholar
• Walpuski, M., & Ropohl, M. (2011). Einfluss des Testaufgabendesigns auf Schülerleistungen in Kompetenztests [Influence of test item design on student performance in competency tests]. Naturwissenschaften Im Unterricht Chemie, 22, 82–86.
   Google Scholar
• White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to All students. Cognition and Instruction, 16(1), 3–118. doi:10.1207/s1532690xci1601_2
   Web of Science ®Google Scholar