Technology, Knowledge and Learning

, Volume 17, Issue 1–2, pp 23–42 | Cite as

From Agents to Continuous Change via Aesthetics: Learning Mechanics with Visual Agent-based Computational Modeling

Article

Abstract

Novice learners find motion as a continuous process of change challenging to understand. In this paper, we present a pedagogical approach based on agent-based, visual programming to address this issue. Integrating agent-based programming, in particular, Logo programming, with curricular science has been shown to be challenging in previous research on educational computing. We present a new Logo-based visual programming language—ViMAP—and, a sequence of learning activities involving programming and modeling, designed specifically to support seamless integration between programming and learning kinematics. We describe relevant affordances of the ViMAP environment that supports such seamless integration. We then present ViMAP-MoMo, a curricular unit designed in ViMAP for modeling kinematics, for a wide range of students (elementary—high school). Finally, we describe in detail a sequence of learning activities in three phases, discuss the underlying rationale for each phase, and where relevant, report results in the form of observational data from two studies.

Keywords

Visual programming Multi-agent-based modeling Mechanics Physics education Learning sciences NetLogo ViMAP Agent-based modeling Computational thinking 

References

  1. Abelson, H., & diSessa, A. (1981). Turtle geometry: The computer as a medium for exploring mathematics. Cambridge, MA: MIT Press.Google Scholar
  2. diSessa, A. A. (1985). A principled design for an integrated computational environment. Human Computer Interaction, 1, 1–47.CrossRefGoogle Scholar
  3. diSessa, A., & Abelson, H. (1986). Boxer: A reconstructible computational medium. Communications of the ACM, 29, 859–868.CrossRefGoogle Scholar
  4. diSessa, A. A., Abelson, H., & Ploger, D. (1991a). An overview of boxer. Journal of Mathematical Behavior, 10(1), 3–15.Google Scholar
  5. diSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991b). Inventing graphing: Children’s meta-representational expertise. Journal of Mathematical Behavior, 10(2), 117–160.Google Scholar
  6. Dyskra, D. I., Jr, & Sweet, D. R. (2009). Conceptual development about motion and force in elementary and middle school students. American Journal of Physics, 77(5), 468–476.CrossRefGoogle Scholar
  7. Eisenberg, M., & Buechley, L. (2008). Pervasive fabrication: Making construction ubiquitous in education. Journal of Software, 3(4), 62–68.CrossRefGoogle Scholar
  8. Elby, A. (2000). What students’ learning of representations tells us about constructivism. Journal of Mathematical Behavior, 19, 481–502.CrossRefGoogle Scholar
  9. Guzdial, M. (1994). Software‐realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1).Google Scholar
  10. Halloun, I. A., & Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043–1056.CrossRefGoogle Scholar
  11. Hestenes, D. (1993). MODELING is the name of the game. A presentation at the NSF Modeling Conference.Google Scholar
  12. Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Cambridge, MA: Harvard University Press.Google Scholar
  13. Lehrer, R. (2009). Designing to develop disciplinary dispositions: Modeling natural systems. American Psychologist, 64(8), 759–771.CrossRefGoogle Scholar
  14. Leinhardt, G., Zaslavsky, O., & Stein, M. M. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60, 1–64.Google Scholar
  15. McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. Stevens (Eds.), Mental models (pp. 299–324). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  16. McDermott, L. C., Rosenquist, M. L., & van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55, 505–513.CrossRefGoogle Scholar
  17. Miller, A. I. (1978). Visualization lost and regained: The genesis of the quantum theory in the period 1913–27. In J. Wechsler (Ed.), On aesthetics in science (pp. 73–102). Cambridge: MIT Press.Google Scholar
  18. Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3–45). Minneapolis, MN: University of Minnesota Press.Google Scholar
  19. Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York, NY: Basic Books.Google Scholar
  20. Piaget, J. (1957). Logic and psychology. New York: Basic Books.Google Scholar
  21. Sengupta, P., & Hubbell, W. (in review). Integrating computational thinking and modeling with high school physics using agent-based visual programming.Google Scholar
  22. Sengupta, P. (2011). Design principles for a visual programming language to integrate agent-based modeling in K-12 science. In Sayama, H., Minai, A. A., Braha, D., & Bar-Yam, Y. (Eds.), Unifying themes in complex systems volume VIII: proceedings of the eighth international conference on complex systems (ICCS 2011) (pp 1636–1638). New England Complex Systems Institute Series on Complexity (NECSI Knowledge Press, ). ISBN 978-0-9656328-4-3.Google Scholar
  23. Sengupta, P., & Farris, A. V. (2012). Learning kinematics in elementary grades using agent-based computational modeling: A visual programming based approach. In Proceedings of the 11th international conference on interaction design & children.Google Scholar
  24. Sengupta, P., & Wright, M. (2010). ViMAP [Computer software]. Mind, Matter & Media Lab, Vanderbilt University, USA.Google Scholar
  25. Sherin, B. L. (2000). How students invent representations of motion: A genetic account. Journal of Mathematical Behavior, 19, 399–441.CrossRefGoogle Scholar
  26. Sherin, B., diSessa, A. A., & Hammer, D. M. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91–118.CrossRefGoogle Scholar
  27. Tanimoto, S. L. (1990). VIVA: A visual language for image processing. Journal of Visual Languages and Computing, 1, 127–139.CrossRefGoogle Scholar
  28. Tisue, S., & Wilensky, U. (2004). NetLogo: A simple environment for modeling complexity. In Proceedings of the international conference on complex systems, Boston, May 16–21.Google Scholar
  29. Tufte, E. R. (1990). Envisioning information. Cheshire, CT: Graphics Press, LLC.Google Scholar
  30. Wechsler, J. (Ed.). (1978). On aesthetics and science. Boston: Birkhäuse.Google Scholar
  31. Wilensky, U. (1991). Abstract meditations on the concrete and concrete implications for mathematics education. In Constructionism. Norwood, NJ: Ablex Publishing Corp.Google Scholar
  32. Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Pratim Sengupta
    • 1
  • Amy Voss Farris
    • 1
  • Mason Wright
    • 1
  1. 1.Mind, Matter & Media LabVanderbilt University, Peabody CollegeNashvilleUSA

Personalised recommendations