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Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

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Abstract

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students’ reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

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References

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–609.

    Article  Google Scholar 

  • Barsalou, L. W. (2005). Abstraction as dynamic interpretation in perceptual symbol systems. In L. Gershkoff-Stowe & D. Rakison (Eds.), Building object categories in developmental time (pp. 389–431). Mahwah: Lawrence Erlbaum Associates.

  • Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.

    Article  Google Scholar 

  • Barsalou, L. W. (2009). Situating concepts. In P. Robbins & M. Aydede (Eds.), The Cambridge handbook of situated cognition (pp. 236–263). Cambridge: Cambridge University Press.

    Google Scholar 

  • Barsalou, L. W., Kyle Simmons, W., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7(2), 84–91. https://doi.org/10.1016/s1364-6613(02)00029-3.

    Article  Google Scholar 

  • Barsalou, L. W., Breazeal, C., & Smith, L. B. (2007). Cognition as coordinated non-cognition. Cognitive Processing, 8(2), 79–91. https://doi.org/10.1007/s10339-007-0163-1.

    Article  Google Scholar 

  • Brown, D. E., & Hammer, D. (2008). Conceptual change in physics. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 127–154). New York, NY: Routledge.

    Google Scholar 

  • Campbell, T., & Oh, P. S. (2015). Engaging students in modeling as an epistemic practice of science: an introduction to the special issue of the journal of science education and technology. Journal of Science Education and Technology, 24(2), 125–131. https://doi.org/10.1007/s10956-014-9544-2.

    Article  Google Scholar 

  • Capstick, S., Whitmarsh, L., Poortinga, W., Pidgeon, N., & Upham, P. (2015). International trends in public perceptions of climate change over the past quarter century. Wiley Interdisciplinary Reviews: Climate Change, 6(1), 35–61. https://doi.org/10.1002/wcc.321.

    Article  Google Scholar 

  • Choi, S., Niyogi, D., Shepardson, D. P., & Charusombat, U. (2010). Do earth and environmental science textbooks promote middle and high school students’conceptual development about climate change? Bulletin of the American Meteorological Society, 91(7), 889–898.

    Article  Google Scholar 

  • diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2/3), 105–225.

    Article  Google Scholar 

  • diSessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191.

    Article  Google Scholar 

  • DiSessa, A. A. (2000). Changing minds: computers, learning, and literacy. Cambridge: The MIT Press.

  • diSessa, A. (2002). Why “conceptual ecology” is a good idea. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: issues in theory and practice (pp. 28–60). Dordrecht: Kluwer.

    Chapter  Google Scholar 

  • diSessa, A. A., Elby, A., & Hammer, D. (2003). J’s epistemological stance and strategies. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional conceptual change (pp. 237–290). Mahwah: Lawrence Erlbaum Associates.

  • Elby, A., & Hammer, D. (2010). Epistemological resources and framing: a cognitive framework for helping teachers interpret and respond to their students’ epistemologies. In L. D. Bendixen & F. C. Feucht (Eds.), Personal epistemology in the classroom: theory, research, and implications for practice (pp. 209–234). Cambridge: Cambridge University Press.

    Google Scholar 

  • Etkin, D., & Ho, E. (2007). Climate change: perceptions and discourses of risk. Journal of Risk Research, 10(5), 623–641. https://doi.org/10.1080/13669870701281462.

    Article  Google Scholar 

  • Gilbert, J. K., Bulte, A. M., & Pilot, A. (2011). Concept development and transfer in context-based science education. International Journal of Science Education, 33(6), 817–837.

    Article  Google Scholar 

  • Greeno, J. G., & Engestrom, Y. (2015). Learning in activity. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 128–149). Cambridge: Cambridge University Press.

  • Goldstone, R. L., & Barsalou, L. W. (1998). Reuniting perception and conception. Cognition, 65(2–3), 231–262. https://doi.org/10.1016/s0010-0277(97)00047-4.

    Article  Google Scholar 

  • Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. Journal of the Learning Sciences, 17(4), 465–516. https://doi.org/10.1080/10508400802394898.

    Article  Google Scholar 

  • Gupta, A., Hammer, D., & Redish, E. F. (2010). The case for dynamic models of learners’ ontologies in physics. Journal of the Learning Sciences, 19(3), 285–321.

    Article  Google Scholar 

  • Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. P. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich: Information Age Publishing.

    Google Scholar 

  • Harlow, D. B., Bianchini, J. A., Swanson, L. H., & Dwyer, H. A. (2013). Potential teachers’ understanding of model-based science instruction: a knowledge in pieces approach. Journal of Research in Science Teaching, 50(9), 1098–1126.

  • Hernández, M. I., Couso, D., & Pintó, R. (2015). Analyzing students’ learning progressions throughout a teaching sequence on acoustic properties of materials with a model-based inquiry approach. Journal of Science Education and Technology, 24(2), 356–377. https://doi.org/10.1007/s10956-014-9503-y.

    Article  Google Scholar 

  • Hodson, D. (2003). Time for action: science education for an alternative future. International Journal of Science Education, 25(6), 645–670.

    Article  Google Scholar 

  • Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2(4), 705–715. https://doi.org/10.1111/j.1756-8765.2010.01089.x.

    Article  Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2013). Model-based reasoning. Computers & Education, 64, 131–142. https://doi.org/10.1016/j.compedu.2012.11.014.

    Article  Google Scholar 

  • Jacobson, M., Markauskaite, L., Kelly, N., & Stokes, P. (2012). Model based learning about climate change with productive failure: preliminary findings. Paper presented at the Annual Meeting of the American Educational Research Association, Vancouver, Canada, 13–17 April.

  • Jacobson, M. J., Markauskaite, L., Portolese, A., Kapur, M., Lai, P. K., & Roberts, G. (2017). Designs for learning about climate change as a complex system. Learning and Instruction, online first. https://doi.org/10.1016/j.learninstruc.2017.03.007.

    Article  Google Scholar 

  • Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L., Braman, D., & Mandel, G. (2012). The polarizing impact of science literacy and numeracy on perceived climate change risks. Nature Climate Change, 2(10), 732–735. https://doi.org/10.1038/nclimate1547.

    Article  Google Scholar 

  • Kamarainen, A. M., Metcalf, S., Grotzer, T., & Dede, C. (2014). Exploring ecosystems from the inside: how immersive multi-user virtual environments can support development of epistemologically grounded modeling practices in ecosystem science instruction. Journal of Science Education and Technology, 24(2), 148–167. https://doi.org/10.1007/s10956-014-9531-7.

    Article  Google Scholar 

  • Kelly, N., Jacobson, M., Markauskaite, L., & Southavilay, V. (2012). Agent-based computer models for learning about climate change and process analysis techniques. In J. van Aalst, K. Thompson, M.J. Jacobson & P. Reimann (Eds.), The 10th international conference of the learning sciences. ICLS 2012 Proceedings (Vol. 1, pp. 25–32). Sydney, Australia, 2–6 July.

  • Khine, M. S., & Saleh, I. M. (Eds.). (2011). Models and modeling: cognitive tools for scientific enquiry. Netherlands: Springer.

    Google Scholar 

  • Kukkonen, J. E., Kärkkäinen, S., Dillon, P., & Keinonen, T. (2014). The effects of scaffolded simulation-based inquiry learning on fifth-graders’ representations of the greenhouse effect. International Journal of Science Education, 36(3), 406–424.

    Article  Google Scholar 

  • Leiserowitz, A. (2006). Climate change risk perception and policy preferences: the role of affect, imagery, and values. Climatic Change, 77(1), 45–72.

    Article  Google Scholar 

  • Levy, S. T., & Wilensky, U. (2010). Mining students’ inquiry actions for understanding of complex systems. Computers & Education, 56(3), 556–573.

    Article  Google Scholar 

  • Lobato, J., Rhodehamel, B., & Hohensee, C. (2012). “Noticing” as an alternative transfer of learning process. Journal of the Learning Sciences, 21(3), 433–482. https://doi.org/10.1080/10508406.2012.682189.

    Article  Google Scholar 

  • Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency and professional education: innovation, knowledgeable action and actionable knowledge. Dordrecht: Springer.

    Book  Google Scholar 

  • Markauskaite, L., & Jacobson, M. (2016). Tracking and assessing students’ learning strategies in model-based learning environments. In P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu, & B. Wasson (Eds.), Measuring and visualising learning in the information-rich classroom (pp. 137–153). London: Routledge.

    Google Scholar 

  • McElhaney, K. W., & Linn, M. C. (2011). Investigations of a complex, realistic task: intentional, unsystematic, and exhaustive experimenters. Journal of Research in Science Teaching, 48(7), 745–770. https://doi.org/10.1002/tea.20423.

    Article  Google Scholar 

  • Minsky, M. (1988). The society of mind. New York, NY: Simon & Schuster.

    Google Scholar 

  • Minsky, M. (2006). The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. New York, NY: Simon & Schuster.

    Google Scholar 

  • Nersessian, N. J. (2005). Interpreting scientific and engineering practices: integrating the cognitive, social, and cultural dimensions. In M. E. Gorman, R. D. Tweney, D. C. Gooding, & A. P. Kincannon (Eds.), Scientific and technological thinking (pp. 17–56). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Nersessian, N. J. (2008). Mental modeling in conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 391–416). New York, NY: Routledge.

    Google Scholar 

  • Nersessian, N. J. (2012). Engineering concepts: the interplay between concept formation and modeling practices in bioengineering sciences. Mind, Culture, and Activity, 19(3), 222–239. https://doi.org/10.1080/10749039.2012.688232.

    Article  Google Scholar 

  • Pallant, A., & Lee, H.-S. (2014). Constructing scientific arguments using evidence from dynamic computational climate models. Journal of Science Education and Technology, 24(2), 378–395. https://doi.org/10.1007/s10956-014-9499-3.

    Article  Google Scholar 

  • Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K., & Spivey, M. J. (2013). Computational grounded cognition: a new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3(612), 1–11. https://doi.org/10.3389/fpsyg.2012.00612.

  • Ratcliffe, M., & Grace, M. (2003). Science education for citizenship: teaching socio-scientific issues. Maidenhead: Open University Press.

    Google Scholar 

  • Roth, W.-M., & McGinn, M. K. (1998). Inscriptions: toward a theory of representing as social practice. Review of Educational Research, 68(1), 35–59. https://doi.org/10.3102/00346543068001035.

    Article  Google Scholar 

  • Ryu, S., Han, Y., & Paik, S.-H. (2015). Understanding co-development of conceptual and epistemic understanding through modeling practices with mobile internet. Journal of Science Education and Technology, 24(2), 330–355. https://doi.org/10.1007/s10956-014-9545-1.

    Article  Google Scholar 

  • Sadler, T. D. (2011). Socio-scientific issues-based education: what we know about science education in the context of SSI. In T. D. Sadler (Ed.), Socio-scientific issues in the classroom: teaching, learning and research (pp. 355–369). Dordrecht: Springer.

    Google Scholar 

  • Sadler, T. D., & Zeidler, D. L. (2005). Patterns of informal reasoning in the context of socioscientific decision making. Journal of Research in Science Teaching, 42(1), 112–138.

    Article  Google Scholar 

  • Schacter, D. (1987). Implicit memory: history and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(3), 501–518.

    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.

    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. https://doi.org/10.1007/s10758-009-9144-z.

    Article  Google Scholar 

  • Shepardson, D. P., Niyogi, D., Choi, S., & Charusombat, U. (2011). Students’ conceptions about the greenhouse effect, global warming, and climate change. Climatic Change, 104(3), 481–507.

    Article  Google Scholar 

  • Simon, H. A. (1979). Models of thought (Vol. 1–2). New Haven: Yale University Press.

    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.

    Article  Google Scholar 

  • Sterman, J. D. (2011). Communicating climate change risks in a skeptical world. Climatic Change, 108(4), 811–826.

    Article  Google Scholar 

  • Svihla, V., & Linn, M. C. (2012). A design-based approach to fostering understanding of global climate change. International Journal of Science Education, 34(5), 651–676.

    Article  Google Scholar 

  • Thompson, K., & Reimann, P. (2010). Patterns of use of an agent-based model and a system dynamics model: the application of patterns of use and the impacts on learning outcomes. Computers & Education, 54(2), 392–403. https://doi.org/10.1016/j.compedu.2009.08.020.

    Article  Google Scholar 

  • Vera, A. H., & Simon, H. A. (1993). Situated action: a symbolic interpretation. Cognitive Science, 17(1), 7–48.

    Article  Google Scholar 

  • Visintainer, T., & Linn, M. (2015). Sixth-grade students’ progress in understanding the mechanisms of global climate change. Journal of Science Education and Technology, 24(2), 287–310. https://doi.org/10.1007/s10956-014-9538-0.

    Article  Google Scholar 

  • Vygotsky, L. S. (1986). Thought and language. Cambridge, MA: MIT Press.

    Google Scholar 

  • Wagner, J. F. (2010). A transfer-in-pieces consideration of the perception of structure in the transfer of learning. Journal of the Learning Sciences, 19(4), 443–479.

    Article  Google Scholar 

  • Wartofsky, M. W. (1979). Models: representation and the scientific understanding. Dordrecht: D. Reidel Pub. Co..

    Book  Google Scholar 

  • 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.

    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. https://doi.org/10.1023/a:1009421303064.

    Article  Google Scholar 

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Acknowledgements

The research discussed in this paper has been funded by grants to the first and third authors from the Australian Research Council Linkage program, LP100100594, and from the Curriculum Learning and Innovation Centre in the New South Wales Department of Education and Communities. We acknowledge the support from the teachers in the collaborating school. Also, we thank Dr. Kate Thompson, Dr. Polly Lai and Dr. Paul Sokes for their assistance with various aspects of this project. Finally, we greatly appreciate the feedback from our international collaborators on this project, Professor Uri Wilensky and Dr. Sharona Levy, on the NetLogo agent-based models we developed and on the overall program of research we are conducting.

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  1. Faculty of Arts and Social Sciences, Sydney School of Education and Social Work, The University of Sydney, Education Building A35, Sydney, NSW, 2006, Australia

    Lina Markauskaite

  2. Science and Engineering Faculty, Queensland University of Technology; and Australian Digital Futures Institute, University of Southern Queensland, Toowoomba, Queensland, Australia

    Nick Kelly

  3. Faculty of Arts and Social Sciences, Sydney School of Education and Social Work, The University of Sydney, Sydney, Australia

    Michael J. Jacobson

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  1. Lina Markauskaite
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Correspondence to Lina Markauskaite.

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Markauskaite, L., Kelly, N. & Jacobson, M.J. Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?. Res Sci Educ 50, 53–77 (2020). https://doi.org/10.1007/s11165-017-9680-9

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