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Embodied Perspective Taking in Learning About Complex Systems

, University of Alabama, United States ; , Teachers College, Columbia University, United States ; , Vanderbilt University, United States ; , Northwestern University, United States

Journal of Interactive Learning Research Volume 28, Number 3, ISSN 1093-023X Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

Abstract

In this paper we present a learning design approach that leverages perspective-taking to help students learn about complex systems. We define perspective-taking as projecting one’s identity onto external entities (both animate and inanimate) in an effort to predict and anticipate events based on ecological cues, to automatically sense the affordances of objects in the environment and take advantage of these affordances, and to understand the mental states of other individuals; an ability crucial for socialization and communication. We introduce one key construct; “phenomenological connectors”, which are essentially embodied perspective-taking activities across micro and macro levels of a system. Phenomenological connectors enable the learner to step inside the system at various levels, thereby having first-person experiences of multiple agents and components, as they attempt to make sense of the system. We consider agent-based modeling activities as exemplar of this design approach. We argue that agent-based models (ABMs) present unique perspective-taking challenges, and learning opportunities, to learners. Informed by the learning design approach proposed, we present a curricular agent-based modeling unit on Particulate Nature of Matter (PNoM) and present data from the implementation of this unit in two 10th grade science classes. The discussion of data focuses on how students make sense of their first-hand experiences in a diffusion of odor experiment, where students collectively act as sensors to track the diffusion of odor, by building and reasoning with agent-based models, and taking perspectives of different agent- and aggregate-level elements of the system.

Citation

Soylu, F., Holbert, N., Brady, C. & Wilensky, U. (2017). Embodied Perspective Taking in Learning About Complex Systems. Journal of Interactive Learning Research, 28(3), 269-303. Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved September 21, 2018 from .

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References

  1. Abelson, H., & DiSessa, A. (1986). Turtle geometry: The computer as a medium for exploring mathematics. MIT press.
  2. Abrahamson, D. (2009). Embodied design: constructing means for constructing meaning. Educational Studies in Mathematics, 70(1), 27–47.
  3. Abrahamson, D. (2013). Toward a taxonomy of design genres: Fostering mathematical insight via perception-based and action-based experiences. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 218–227). Http://doi.org/10.1145/2485760.2485761Ackermann,E.K.(1996).Perspective-takingand object construction: Two keys to learning. In Y. Kafai& M. Resnick (Eds.), Constructionism in Practice: Designing, Thinking, and Learning in a Digital World (pp. 25–37). Lawrence
  4. Ahn, A.C., Tewari, M., Poon, C.S., & Phillips, R.S. (2006). The limits of reductionism in medicine: Could systems biology offer an alternative? PLoS Medicine. ., & Corness, G. (2008). Playing with the sound maker: do embodied metaphors help children learn? In Proceedings of the 7th international conference on Interaction design and children (pp. 178–185).
  5. Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews. Genetics, 12(1), 56–68.
  6. Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015). Sandboxes for Model-Based Inquiry. Journal of Science Education and Technology, 24(2–3), 265–286. Http://doi.org/10.1007/s10956-014-9506-8Bullmore,E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186–98. Http://doi.org/10.1038/nrn2575
  7. Carbonneau, K.J., Marley, S.C., & Selig, J.P. (2013). A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives. Journal of Educational Psychology, 105, 380–400. Http://doi.org/10.1037/a0031084Chialvo,D.R.(2010).Emergentcomplex neural dynamics. Nature Physics, 6(10), 744–750. Http://doi.org/10.1038/nphys1803
  8. Cook, S.W., Mitchell, Z., & Goldin-Meadow, S. (2008). Gesturing makes learning last. Cognition, 106(2), 1047–1058.
  9. Corballis, M.C. (2010). Mirror neurons and the evolution of language. Brain and Language, 112(1), 25–35. , J., & Grèzes, J. (2006). The power of simulation: Imagining one’s own and other’s behavior. Brain Research, 1079(1), 4–14. Http://doi.
  10. Dickes, A.C., & Sengupta, P. (2012). Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models. Research in Science Education (Vol. 43). ., & Ubuz, B. (2009). Effects of drama-based geometry instruction on student achievement, attitudes, and thinking levels. The Journal of Educational Research, 102(4), 272–286. Http://doi.org/10.3200/
  11. Foster, J. (2005). From simplistic to complex systems in economics. Cambridge Journal of Economics, 29(6), 873–892. ., & Goldman, A. (1998). Mirror neurons and the simulation theory of mind-reading. Trends In Cognitive Sciences, 2(12), 493.
  12. Gallese, V., & Lakoff, G. (2005). The brain’s concepts: the role of the sensorymotor system in conceptual knowledge. Cognitive Neuropsychology, 22, 455–79. ., & Sinigaglia, C. (2011). What is so special about embodied simulation? Trends in Cognitive Sciences, 15(11), 512–519. Http://doi.
  13. Gibson, J.J. (1986). The theory of affordances. In The Ecological Approach to Visual Perception (pp. 127–136). Lawrence Erlbaum Associates.
  14. Goldstone, R., & Wilensky, U. (2008). Promoting Transfer by Grounding Complex Systems Principles. Journal of the Learning Sciences, 17, 465–516.
  15. Hmelo-Silver, C.E., & Pfeffer, M.G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), 127–138. Http://doi.org/10.1016/S0364-0213(03)00065-X
  16. Howison, M., Trninic, D., Reinholz, D., & Abrahamson, D. (2011). The Mathematical Imagery Trainer: From Embodied Interaction to Conceptual Learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1989–1998). ACM.
  17. Jacobson, M. (2001). Problem Solving, Cognition, and Complex Systems: Differences between Experts and Novices. Complexity, 6(3), 41–49.
  18. Jacobson, M., Kapur, M., & Reimann, P. (2016). Conceptualizing debates in learning and educational research: Toward a complex systems conceptual framework of learning. Educational Psychologist, 1520(September), 1–9.
  19. Kapon, S., & DiSessa, A. (2012). Reasoning through instructional analogies. Cognition and Instruction, 30(3), 261–310.
  20. Klopfer, E., Scheintaub, H., Huang, W., Wendel, D., & Roque, R. (2009). The simulation cycle: Combining games, simulations, engineering and science using StarLogo TNG. E-Learning, 6(1), 71–96.
  21. 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.
  22. Lindgren, R. (2012). Generating a learning stance through perspective-taking in a virtual environment. Computers in Human Behavior, 28(4), 1130–1139.
  23. Modell, H.I. (2007). Helping students make sense of physiological mechanisms: the “view from the inside”. Advances in Physiology Education, 31(2), 186– 92. Http://doi.org/10.1152/advan.00079.2006Moher,T.(2006).Embeddedphenomena: supporting science learning with classroom-sized distributed simulations. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 691–700. Http://doi.
  24. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
  25. Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36(2), 1–11.
  26. Piaget, J., & Inhelder, B. (1969). The psychology of the child. New York: Basic Books.
  27. Resnick, M. (1996). Beyond the Centralized Mindset. Journal of the Learning Sciences, 5(1), 1–22. Http://doi.org/10.1207/s15327809jls0501_1Resnick,M.(1997).Turtles,termites,andtraffic jams: Explorations in massively parallel microworlds. Cambridge: MIT Press.
  28. Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. The Journal of the Learning Sciences, 7, 153–172.
  29. Rizzolatti, G., & Arbib, M. (1998). Language within our grasp. Trends in Neurosciences, 21(5), 188–194.
  30. Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3(2), 131–141.
  31. Salk, J. (1983). Anatomy of reality. New York: Columbia University Press. Embodied Perspective Taking in Learning About Complex Systems grating computational thinking with K-12 science education using agentbased computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. Http://doi.org/10.1007/s10639-012-9240-xSoylu,F.(2016).Anembodied approach to understanding: Making sense of the world through simulated bodily activity. Frontiers in Psychology, 7(December), 1–10. Http://doi.org/10.3389/fpsyg.2016.01914
  32. Stroup, W.M., & Wilensky, U. (2014). On the embedded complementarity of agent-based and aggregate reasoning in students’ developing understanding of dynamic systems. Technology, Knowledge and Learning, 19(1–2), 19–52. Http://doi.org/10.1007/s10758-014-9218-4Studdert-Kennedy,M.(2002).Mirrorneurons, vocal imitation, and the evolution of particulate speech. Mirror Neurons and the Evolution of Brain and Language.
  33. Tomasello, M., Kruger, A.C., & Ratner, H.H. (1993). Cultural learning. Behavioral and Brain Sciences, 16, (April), 495–552.
  34. Van Schaik, C.P., Deaner, R.O., & Merrill, M.Y. (1999). The conditions for tool use in primates: implications for the evolution of material culture. Journal of Human Evolution, 36(6), 719–741.
  35. Wilensky, U. (1999). NetLogo. Http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.
  36. Wilensky, U. (2003). Statistical mechanics for secondary school: The gaslab multi-agent modeling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1–41. ., & 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.
  37. 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.
  38. Wilkerson-Jerde, M., Wagh, A., & Wilensky, U. (2015). Balancing Curricular and Pedagogical Needs in Computational Construction Kits: Lessons From the DeltaTick Project. Science Education, 99(3), 465–499. Http://doi.org/10.1002/sce.21157

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