Enhancing student teachers’ epistemological beliefs about models and conceptual understanding through a model-based inquiry process

References

• Andersson, B., & Bach, F. (2005). On designing and evaluating teaching sequences taking geometrical optics as an example. Science Education, 89(2), 196–218. doi:10.1002/sce.20044
   Web of Science ®Google Scholar
• Barab, S., Hay, K., Barnett, M., & Squire, K. (2001). Constructing virtual worlds: Tracing the historical development of learner practices. Cognition and Instruction, 19(1), 47–94. doi:10.1207/S1532690XCI1901_2
   Web of Science ®Google Scholar
• Bromme, R., Pieschl, S., & Stahl, E. (2010). Epistemological beliefs are standards for adaptive learning: A functional theory about epistemological beliefs and metacognition. Metacognition Learning, 5, 7–26. doi:10.1007/s11409-009-9053-5
   Web of Science ®Google Scholar
• 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(1), 159–176. doi:10.12973/eurasia.2015.1314a
   Web of Science ®Google Scholar
• Campbell, T., Oh, P. S., & Neilson, D. (2013). Reification of five types of modeling pedagogies with model-based inquiry (MBI) modules for high school science classrooms. In M. S. Khine & I. M. Saleh (Eds.), Approaches and strategies in next generation science learning (pp. 106–126). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-2809-0.ch006
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York, NY: Lawrence Erlbaum Associates.
   Google Scholar
• Crawford, B., & Cullin, M. (2005). Dynamic assessments of preservice teachers’ knowledge of models and modeling. In K. Boersma, M. Goedhart, O. De Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 309–323). Dordrecht: Springer. doi:10.1007/1-4020-3673-6_25
   Google Scholar
• Galili, I., & Hazan, A. (2000). Learner’s knowledge in optics: Interpretation, structure and analysis. International Journal of Science Education, 22(1), 57–88. doi:10.1080/095006900290000
   Web of Science ®Google Scholar
• Gilbert, K. J., & Boulter, J. C. (Εds). (2000). Developing models in science education. Dordrecht: Kluwer Academic. doi:10.1007/978-94-010-0876-1
   Google Scholar
• Gobert, J., & Discenna, J. (1997). The relationship between students’ epistemologies and model-based reasoning. Kalamazoo: Department of Science Studies, Western Michigan University (ERIC Document Reproduction Service No. ED409164)
   Google Scholar
• 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(5), 653–84. doi:10.1080/09500691003720671
   Web of Science ®Google Scholar
• 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. doi: 10.1080/09500693.2013.873155
   Web of Science ®Google Scholar
• Halloun, I. (2006). Modeling theory in science education. Dordrecht: Springer.
   Google Scholar
• Hatzikraniotis, E., Bisdikian, G., Barbas, A., & Psillos, D. (2007). Optilab: Design and development of an integrated virtual laboratory for teaching optics. In C. P. Constantinou, Z. C. Zacharia, & M. Papaevripidou (Eds.), Proceedings of the 7th international conference on computer based learning in science, CBLIS, (pp. 523–530). Heraclion, Greece: Technological Educational Institute of Crete.
   Google Scholar
• Heywood, D. S. (2005). Primary trainee teachers’ learning and teaching about light: Some pedagogic implications for initial teacher training. International Journal of Science Education, 27(12), 1447–1475. doi:10.1080/09500690500153741
   Web of Science ®Google Scholar
• Hodson, D. (2014). Learning science, learning about science, doing science: Different goals demand different learning methods. International Journal of Science Education, 36(15), 2534–2553. doi:10.1080/09500693.2014.899722
   Web of Science ®Google Scholar
• Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67(1), 88–140. doi:10.3102/00346543067001088
   Web of Science ®Google Scholar
• Hofer B. K., & Sinatra G. M. (2010). Epistemology, metacognition, and self-regulation: Musings on an emerging field. Metacognition Learning, 5,113–120. doi:10.1007/s11409-009-9051-7
   Web of Science ®Google Scholar
• Holliday, W. G. (2006). A balanced approach to science inquiry teaching. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science (pp. 201–217). Springer. doi: 10.1007/978-1-4020-5814-1_10
   Google Scholar
• de Jong, T. (2011). Instruction based on computer simulations. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 446–466). New York, NY: Routledge.
   Google Scholar
• van Joolingen, W. (2004, August). Roles of modeling in inquiry learning. Paper presented at the IEEE International Conference on Advanced Learning Technologies (ICALT’04), Joensuu, Finland. Retrieved from http://www.computer.org/csdl/proceedings/icalt/2004/2181/00/21811096.pdf
   Google Scholar
• Justi, R. S., & Gilbert, J. K. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25(11), 1369–1386. doi:10.1080/0950069032000070324
   Web of Science ®Google Scholar
• Kistner, S., Rakoczy, K., Otto, B., Dignath-van Ewijk, C., Büttner, G., & Klieme, E. (2010). Promotion of self-regulated learning in classrooms: Investigating frequency, quality, and consequences for student performance. Metacognition and Learning, 5(2), 157–171. doi:10.1007/s11409-010-9055-3
   Web of Science ®Google Scholar
• Krell M., & Dirk Krüger, D. (2015). Testing models: A key aspect to promote teaching activities related to models and modelling in biology lessons? Journal of Biological Education. doi:10.1080/00219266.2015.1028570
   Google Scholar
• Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014). Students’ levels of understanding models and modelling in biology: Global or aspect-dependent? Research in Science Education, 44, 109–132. doi:10.1007/s11165-013-9365-y
   Web of Science ®Google Scholar
• Langley, D., Ronen, M., & Eylon, B. (1997). Light propagation and visual patterns: Preinstruction learners’ conceptions. Journal of Research in Science Teaching, 34(4), 399–424. doi:10.1002/(SICI)1098-2736(199704)34:4\399::AID-TEA8[3.0.CO;2-M
   Web of Science ®Google Scholar
• Mellar, H., & Bliss, J. (1994). Introduction: Modeling and education. In H. Mellar, J. Bliss, R. Boohan, J. Ogborn, & C. Tompsett (Εds.), Learning with artificial worlds: Computer based modeling in the curriculum (pp. 1–7). London: The Falmer Press.
   Google Scholar
• Nersessian, N. J. (2008). Model-based reasoning in scientific practice. In R. Duschl & R. Grandy (Εds.), Teaching scientific inquiry: Recommendations for research and implementation (pp. 57–79). Rotterdam: Sense.
   Google Scholar
• Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: An overview. International Journal of Science Education, 33(8), 1109–1130. doi:10.1080/09500693.2010.502191
   Web of Science ®Google Scholar
• Petridou, E., Psillos, D., Hatzikraniotis, E., & Kallery, M. (2013). A study on the exploratory use of microscopic models as investigative tools: The case of electrostatic polarization. In G. Tsaparlis & H. Sevian (Εds.), Concepts of matter in science education (pp. 199–212). Dordrecht: Springer. doi:10.1007/978-94-007-5914-5
   Google Scholar
• Petridou, E., Psillos, D., Hatzikraniotis, E., & Viiri, J. (2009). Design and development of a microscopic model in polarization. Physics Education, 44(6), 589–598. doi:10.1088/0031-9120/44/6/003
   Google Scholar
• Psillos, D. (2015). Teaching and learning sequences. In R. Gunstone (Ed.), Encyclopedia of science education (pp. 1035–1038). Dordrecht: Springer.
   Google Scholar
• Psillos, D., & Kariotoglou, P. (2016). Theoretical issues related to designing and developing teaching learning sequences. In D. Psillos & P. Kariotoglou (Eds.), Iterative design of teaching-learning sequences: Introducing the science of materials in European schools, (pp. 11–34). Dordrecht: Springer. doi:10.1007/978-94-007-7808-5
   Google Scholar
• Saari, H., Viiri, J. (2003). A research-based teaching sequence for teaching the concept of modeling to seventh-grade students. International Journal of Science Education, 25(11), 1333–1352. doi:10.1080/0950069032000052081
   Web of Science ®Google Scholar
• Schwarz, C., Reiser, B., Davis, E., Kenyon, L., Acher, A., Fortus, D., … 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. doi: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 & Instruction, 23(2), 165–205. doi:10.1207/s1532690xci2302_1
   Web of Science ®Google Scholar
• Sinatra, G. M. (2005). The ‘warming trend’ in conceptual change research: The legacy of Paul Pintrich. Educational Psychologist, 40(2), 107–115. doi:10.1207/s15326985ep4002_5
   Web of Science ®Google Scholar
• Singh, A., & Butler, P. H. (1990). Refraction: Conceptions and knowledge structure. International Journal of Science Education, 12(4), 429–442. doi:10.1080/0950069900120409
   Web of Science ®Google Scholar
• Sins, H., Savelsbergh, R., van Joolingen, W., & van Hout-Wolters, H. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modeling task. International Journal of Science Education, 31(9), 1205–1229. doi:10.1080/09500690802192181
   Web of Science ®Google Scholar
• Smith, C. L., Maclin, D., Houghton, C., & Hennessey, M. G. (2000). Sixth-grade students’ epistemologies of science: The impact of school science experiences on epistemological development. Cognition & Instruction, 18(3), 349–422. doi:10.1207/S1532690XCI1803_3
   Web of Science ®Google Scholar
• Testa, I., Lombardi, S., Monroy, G., & Sassi, E. (2011). An innovative context-based module to introduce students to the optical properties of materials. Physics Education, 46(2), 167–177. doi:10.1088/0031-9120/46/2/004
   Google Scholar
• Testa, I., & Monroy, G. (2016). The iterative design of a teaching-learning sequence on optical properties of materials to integrate science and technology. In D. Psillos & P. Kariotoglou (Eds.), Iterative design of teaching-learning sequences: Introducing the science of materials in European schools (pp. 233–286). Dordrecht: Springer.
   Google Scholar
• Vosniadou, S. (2007). The cognitive-situative divide and the problem of conceptual change. Educational Psychologist, 42(1), 55–66. doi:10.1080/00461520709336918
   Web of Science ®Google Scholar
• Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92(5), 941–967. doi:10.1002/sce.20259
   Web of Science ®Google Scholar