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Epistemic gameplay and discovery in computational model-based inquiry activities

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Abstract

In computational modeling activities, learners are expected to discover the inner workings of scientific and mathematical systems: First elaborating their understandings of a given system through constructing a computer model, then “debugging” that knowledge by testing and refining the model. While such activities have been shown to support science learning, difficulties building and using computational models are common and reduce learning benefits. Drawing from Collins and Ferguson (Educ Psychol 28(1):25–42, 1993), we conjecture that a major cause for such difficulties is a misalignment between the epistemic games (modeling strategies) learners play, and the epistemic forms (model types) a given modeling environment is designed to support. To investigate, we analyzed data from a study in which ten groups of U. S. fifth graders (n = 28) worked to create stop motion animations and agent-based computational models (ABMs) to discover the particulate nature of matter. Content analyses revealed that (1) groups that made progress—that is, that developed increasingly mechanistic, explanatory models—focused on elements, movement, and interactions when developing their models, a strategy well-aligned with both animation and ABM; (2) groups that did not make progress focused on sequences of phases, a strategy well-aligned with animation but not with ABM; and (3) struggling groups progressed when they received guidance about modeling strategies, but not when they received guidance about model content. We present summary analyses and three vignettes to illustrate these findings, and share implications for research and curricular design.

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References

  • Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and Practice in Technology Enhanced Learning, 11(1), 13. doi:10.1186/s41039-016-0036-2.

    Article  Google Scholar 

  • Bollen, L., & van Joolingen, W. R. (2013). SimSketch: Multiagent simulations based on learner-created sketches for early science education. IEEE Transactions On Learning Technologies, 6(3), 208–216. doi:10.1109/TLT.2013.9.

    Article  Google Scholar 

  • Chang, H. Y., Quintana, C., & Krajcik, J. S. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(1), 73–94. doi:10.1002/sce.20352.

    Google Scholar 

  • Chin, D., Dohmen, I. M., Cheng, B. H., Oppezzo, M., Chase, C. C., & Schwartz, D. L. (2010). Preparing students for future learning with teachable agents. Educational Technology Research and Development, 58, 649–669. doi:10.1080/10494820.2013.803125.

    Article  Google Scholar 

  • Clark, D., Nelson, B., Sengupta, P., & D’Angelo, C. (2009). Rethinking science learning through digital games and simulations: Genres, examples, and evidence. In Learning science: Computer games, simulations, and education workshop sponsored by the National Academy of Sciences. Washington, DC: National Research Council.

  • Cobb, P., Confrey, J., DiSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. doi:10.3102/0013189X032001009.

    Article  Google Scholar 

  • Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42. doi:10.1207/s15326985ep2801_3.

    Article  Google Scholar 

  • Danish, J. (2014). Applying an activity theory lens to designing instruction for learning about the structure, behavior, and function of a honeybee system. Journal of the Learning Sciences, 23(2), 100–148. doi:10.1080/10508406.2013.856793.

    Article  Google Scholar 

  • Dickes, A. C., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multilevel explanations of ecology in third grade using agent-based models. Science Education, 100(4), 734–776. doi:10.1002/sce.21217.

    Article  Google Scholar 

  • diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.

    Google Scholar 

  • Doerr, H. M. (1996). Stella ten years later: A review of the literature. International Journal of Computers for Mathematical Learning, 1(2), 201–224.

    Article  Google Scholar 

  • Fretz, E. B., Wu, H. K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modeling practices. Research in Science Education, 32(4), 567–589. doi:10.1023/A:1022400817926.

    Article  Google Scholar 

  • Gravel, B. E., Scheuer, N., & Brizuela, B. M. (2013). Using representations to reason about air and particles. In B. M. Brizuela, & B. E. Gravel (Eds.), Show me what you know: Exploring student representations across STEM disciplines (pp. 163–182). New York, NY: Teachers College Press.

  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., et al. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987–991. doi:10.1126/science.1116681.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. Journal of the Learning Sciences, 15(1), 53–61. doi:10.1207/s15327809jls1501_7.

    Article  Google Scholar 

  • Jackson, S. L., Stratford, S. J., Krajcik, J., & Soloway, E. (1996). A learner-centered tool for students building models. Communications of the ACM, 39(4), 48–49. doi:10.1145/227210.227224.

    Article  Google Scholar 

  • Kaput, J., Noss, R., & Hoyles, C. (2002) Developing new notations for a learnable mathematics in the computational era. In L. D. English (Ed.), Handbook of international research in mathematics education (pp. 51–75). New York, NY: Routledge.

  • Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11(1), 65–100. doi:10.1111/j.1551-6708.1987.tb00863.x.

    Article  Google Scholar 

  • Levy, S. T., & Wilensky, U. (2008). Inventing a “mid level” to make ends meet: Reasoning between the levels of complexity. Cognition and Instruction, 26(c), 1–47. doi:10.1080/07370000701798479.

    Article  Google Scholar 

  • Löhner, S., van Joolingen, W. R., & Savelsbergh, E. R. (2003). The effect of external representation on constructing computer models of complex phenomena. Instructional Science, 31(6), 395–418. doi:10.1023/A:1025746813683.

    Article  Google Scholar 

  • Louca, L. T., & Zacharia, Z. C. (2008). The use of computer-based programming environments as computer modelling tools in early science education: The cases of textual and graphical program languages. International Journal of Science Education, 30(3), 287–323. doi:10.1080/09500690601188620.

    Article  Google Scholar 

  • Louca, L. T., & Zacharia, Z. C. (2012). Modeling-based learning in science education: Cognitive, metacognitive, social, material and epistemological contributions. Educational Review, 64(4), 471–492. doi:10.1080/00131911.2011.628748.

    Article  Google Scholar 

  • Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2010). Finding out how they find it out: An empirical analysis of inquiry learners’ need for support. International Journal of Science Education, 32(15), 2033–2053. doi:10.1080/09500690903289993.

    Article  Google Scholar 

  • Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2011). Comparing two types of model progression in an inquiry learning environment with modelling facilities. Learning and Instruction, 21(5), 614–624. doi:10.1016/j.learninstruc.2011.01.003.

    Article  Google Scholar 

  • [NGSS] NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: Achieve, Inc.

    Google Scholar 

  • [OECD] Organization of economic co-operation and Development (2016) OECD science, technology and innovation outlook 2016. OECD Publishing, Paris. Retrieved from http://dx.doi.org/10.1787/sti_in_outlook_2016-en.

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books Inc.

    Google Scholar 

  • Penner, D. E. (2000). Cognition, computers, and synthetic science: Building knowledge and meaning through modeling. Review of Research in Education, 25(1), 1–35. doi:10.3102/0091732X025001001.

    Article  Google Scholar 

  • Riley, D. (1990). Learning about systems by making models. Computers & Education, 15(1), 255–263. doi:10.1016/0360-1315(90)90155-Z.

    Article  Google Scholar 

  • Russ, R. S., Scherr, R. E., Hammer, D., & Mikeska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education, 92(3), 499–525. doi:10.1002/sce.20264.

    Article  Google Scholar 

  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., et al. (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.

    Article  Google Scholar 

  • Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.

    Article  Google Scholar 

  • Sherin, B., diSessa, A. A., & Hammer, D. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91–118. doi:10.1080/1049482930030201.

    Article  Google Scholar 

  • Shwartz, Y., Weizman, A., Fortus, D., Krajcik, J., & Reiser, B. (2008). The IQWST experience: Using coherence as a design principle for a middle school science curriculum. The Elementary School Journal, 109(2), 199–219.

    Article  Google Scholar 

  • Sins, P. H., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The difficult process of Scientific modelling: An analysis of novices’ reasoning during computer-based modelling. International Journal of Science Education, 27(14), 1695–1721. doi:10.1080/09500690500206408.

    Article  Google Scholar 

  • Soloway, E. (1986). Learning to program = learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850–858. doi:10.1145/6592.6594.

    Article  Google Scholar 

  • Van Joolingen, W. R., De Jong, T., & Dimitrakopoulou, A. (2007). Issues in computer supported inquiry learning in science. Journal of Computer Assisted Learning, 23(2), 111–119.

    Article  Google Scholar 

  • VanLehn, K. (2013). Model construction as a learning activity: A design space and review. Interactive Learning Environments, 21(4), 371–413. doi:10.1080/10494820.2013.803125.

    Article  Google Scholar 

  • White, B. Y., Collins, A., & Frederiksen, J. R. (2011). The nature of scientific meta-knowledge. In Models and modeling: Cognitive tools for scientific enquiry (pp. 41–76). Dordrecht: Springer.

  • White, B. Y., & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42(1), 99–157. doi:10.1016/0004-3702(90)90095-H.

    Article  Google Scholar 

  • Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. In J. E. Clayson & I. Kalaš (Eds.), Constructionism 2010: Constructionist approaches to creative learning, thinking and education: Lessons for the 21st century. Paris: American University of Paris.

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171–209. doi:10.1207/s1532690xci2402_1.

    Article  Google Scholar 

  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19. doi:10.1023/A:1009421303064.

    Article  Google Scholar 

  • Wilkerson-Jerde, M., Gravel, B., & Macrander, C. (2013). SiMSAM: An integrated toolkit to bridge student, scientific, and mathematical ideas using computational media. In Proceedings of the International Conference of Computer Supported Collaborative Learning (CSCL 2013) (Vol. 2, pp. 379–381). Madison, WI, USA.

  • Wilkerson-Jerde, M. H., Gravel, B. E., & Macrander, C. A. (2015a). Exploring shifts in middle school learners’ modeling activity while generating drawings, animations, and simulations of molecular diffusion. Journal of Science Education and Technology, 24(2–3), 204–251. doi:10.1007/s10956-014-9497-5.

    Google Scholar 

  • Wilkerson-Jerde, M. H., Wagh, A., & Wilensky, U. (2015b). Balancing curricular and pedagogical needs in computational construction kits: Lessons from the DeltaTick project. Science Education, 99(3), 465–499. doi:10.1002/sce.21157.

    Article  Google Scholar 

  • Xiang, L., & Passmore, C. (2015). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, 24(2–3), 311–329. doi:10.1007/s10956-014-9534-4.

    Article  Google Scholar 

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Funding was provided by National Science Foundation (Grant No. IIS-1217100).

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Correspondence to Michelle Hoda Wilkerson.

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Wilkerson, M.H., Shareff, R., Laina, V. et al. Epistemic gameplay and discovery in computational model-based inquiry activities. Instr Sci 46, 35–60 (2018). https://doi.org/10.1007/s11251-017-9430-4

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