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

  • Lina Markauskaite
  • Nick Kelly
  • Michael J. Jacobson
Article

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.

Keywords

Model-based learning Socio-scientific issues Climate change Complex systems Grounded cognition Agent-based models Science education 

Notes

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.

References

  1. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  2. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–609.Google Scholar
  3. 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.Google Scholar
  4. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.CrossRefGoogle Scholar
  5. 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
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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
  9. 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.CrossRefGoogle Scholar
  10. 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.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2/3), 105–225.CrossRefGoogle Scholar
  13. diSessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191.CrossRefGoogle Scholar
  14. DiSessa, A. A. (2000). Changing minds: computers, learning, and literacy. Cambridge: The MIT Press.Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.Google Scholar
  17. 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
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.Google Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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
  25. 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.Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. Hodson, D. (2003). Time for action: science education for an alternative future. International Journal of Science Education, 25(6), 645–670.CrossRefGoogle Scholar
  28. Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2(4), 705–715.  https://doi.org/10.1111/j.1756-8765.2010.01089.x.CrossRefGoogle Scholar
  29. Ifenthaler, D., & Seel, N. M. (2013). Model-based reasoning. Computers & Education, 64, 131–142.  https://doi.org/10.1016/j.compedu.2012.11.014.CrossRefGoogle Scholar
  30. 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.Google Scholar
  31. 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.
  32. 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.
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. Khine, M. S., & Saleh, I. M. (Eds.). (2011). Models and modeling: cognitive tools for scientific enquiry. Netherlands: Springer.Google Scholar
  36. 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.CrossRefGoogle Scholar
  37. Leiserowitz, A. (2006). Climate change risk perception and policy preferences: the role of affect, imagery, and values. Climatic Change, 77(1), 45–72.CrossRefGoogle Scholar
  38. Levy, S. T., & Wilensky, U. (2010). Mining students’ inquiry actions for understanding of complex systems. Computers & Education, 56(3), 556–573.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency and professional education: innovation, knowledgeable action and actionable knowledge. Dordrecht: Springer.CrossRefGoogle Scholar
  41. 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
  42. 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.CrossRefGoogle Scholar
  43. Minsky, M. (1988). The society of mind. New York, NY: Simon & Schuster.Google Scholar
  44. Minsky, M. (2006). The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. New York, NY: Simon & Schuster.Google Scholar
  45. 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
  46. 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
  47. 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.CrossRefGoogle Scholar
  48. 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.Google Scholar
  49. 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.
  50. Ratcliffe, M., & Grace, M. (2003). Science education for citizenship: teaching socio-scientific issues. Maidenhead: Open University Press.Google Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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
  54. 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.CrossRefGoogle Scholar
  55. Schacter, D. (1987). Implicit memory: history and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(3), 501–518.Google Scholar
  56. 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.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. Simon, H. A. (1979). Models of thought (Vol. 1–2). New Haven: Yale University Press.Google Scholar
  60. 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.CrossRefGoogle Scholar
  61. Sterman, J. D. (2011). Communicating climate change risks in a skeptical world. Climatic Change, 108(4), 811–826.CrossRefGoogle Scholar
  62. 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.CrossRefGoogle Scholar
  63. 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.CrossRefGoogle Scholar
  64. Vera, A. H., & Simon, H. A. (1993). Situated action: a symbolic interpretation. Cognitive Science, 17(1), 7–48.CrossRefGoogle Scholar
  65. 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.CrossRefGoogle Scholar
  66. Vygotsky, L. S. (1986). Thought and language. Cambridge, MA: MIT Press.Google Scholar
  67. 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.CrossRefGoogle Scholar
  68. Wartofsky, M. W. (1979). Models: representation and the scientific understanding. Dordrecht: D. Reidel Pub. Co..CrossRefGoogle Scholar
  69. 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.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of Arts and Social Sciences, Sydney School of Education and Social WorkThe University of SydneySydneyAustralia
  2. 2.Science and Engineering Faculty, Queensland University of Technology; and Australian Digital Futures InstituteUniversity of Southern QueenslandToowoombaAustralia
  3. 3.Faculty of Arts and Social Sciences, Sydney School of Education and Social WorkThe University of SydneySydneyAustralia

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