Technology, Knowledge and Learning

, Volume 18, Issue 1–2, pp 9–37 | Cite as

Biking with Particles: Junior Triathletes’ Learning About Drafting Through Exploring Agent-Based Models and Inventing New Tactics



The present research addresses a curious finding: how learning physical principles enhanced athletes’ biking performance but not their conceptual understanding. The study involves a model-based triathlon training program, Biking with Particles, concerning aerodynamics of biking in groups (drafting). A conceptual framework highlights several forms of access to understanding the system (micro, macro, mathematical, experiential) and bidirectional transitions among these forms, anchored at the common and experienced level, the macro-level. Training was conducted separately with two groups, both 14–17 years old youth: an elite junior triathletes team (experts; 4 male, 3 female) and a local team (hobbyists; 6 male, 3 female). The goal was to explore whether agent-based models of bikers and air particles could be used to enhance athletes’ understanding and performance, and whether this depends on expertise. The study lasted 3 days and included lectures, discussions, guided exploration of models, inventing new tactics, and biking in practice. Data included questionnaires, interviews, videotapes, and performance measures of heart-rate and biking duration. Athletes’ designs were innovative and diverse, expressing well-known and new features in the sport. Local features were more dominant than global features. Their performance in bicycle drafting increased dramatically, with a gain of 20 %, at both individual and group levels. The experts mainly reduced their times. Hobbyists mainly reduced their effort. Some conceptual change was evidenced for the complex systems components but not for drafting. Results are discussed in light of learning about complex systems and the balance between cognitive-verbal and motor learning within competitive sports.


Agent-based models Complex systems Competitive sports Physical education Model-based learning 



We are particularly indebted to Ronnie Lidor, currently president of Wingate College, the main Israeli sports academic and Olympic center, who has provided much support in spirit, in designing the study and thinking through the theory. We thank Uri Zilberman, the elite team’s coach, who supported, accompanied and encouraged us in this study. We are grateful to the athletes in the two teams that participated in the study and engaged with all their hearts in the training, providing thoughtful reflection and advice. We thank Wingate College and an anonymous northern local school for providing us with their facilities and support during the study. We gratefully acknowledge Osnat Gal, Asher Kakoon, Yoni Bacalo, Ariel Asaf and Lital Zar who worked with us on creating the computer models.


  1. Anchwer, H. (1998). The complete guide to triathlon training. Aachen, Germany: Meyer & Meyer Sport.Google Scholar
  2. Bacalo, Y., Kakoon, A., Asaf, A., Zar, L., Hirsh, A. & Levy S. T. (2012). Of particles and bikers model. Completed as part of two undergraduate projects at the Computer Science department led by Hananel Hazan. Faculty of Education, University of Haifa.Google Scholar
  3. Bacalo, Y., Kakoon, A., Hirsh, A. & Levy S. T. (2011). Of particles and bikers model. Completed as part of an undergraduate project at the Computer Science department led by Hananel Hazan. Faculty of Education, University of Haifa.Google Scholar
  4. Bailer-Jones, D. M. (2003). Scientists’ thoughts on scientific models. Perspectives on Science, 10, 275–301.Google Scholar
  5. Bar-Yam, Y. (1997). Dynamics of complex systems, Addison-Wesley, The Advanced Book Program, Reading, MA.Google Scholar
  6. Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9–13.Google Scholar
  7. Casti, J. L. (1994). Complexification: Explaining a paradoxical world through the science of surprise. New York, NY: Harper Collins.Google Scholar
  8. Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. Journal of the Learning Sciences, 1, 69–105.CrossRefGoogle Scholar
  9. Gilbert, J. K., & Boulter, C. (Eds.). (2000). Developing models in science education. The Netherlands: Kluwer Academic Publishers.Google Scholar
  10. Hausswirth, C., & Brisswalter, J. (2008). Strategies for improving performance in long duration events: Olympic distance triathlon. Sports Medicine, 38(11), 881–891.CrossRefGoogle Scholar
  11. Hausswirth, C., Lehenaff, D., Dreano, P., & Savonen, K. (1999). Effects of cycling alone or in a sheltered position on subsequent running performance during a triathlon. Medicine & Science in Sports & Exercise, 31(4), 599–604.Google Scholar
  12. Hausswirth, C., Vallier, J.-M., Lehenaff, D., Brisswalter, J., Smith, D., Millet, G., et al. (2001). Effect of two drafting modalities in cycling on running performance. Medicine and Science in Sports and Exercise, 33(3), 485–492.CrossRefGoogle Scholar
  13. Hirsh, A., Haviv-Gal, O., & Levy, S. T. (2011). Aerodynamics of flocking birds model. Based on the above Flocking model (Wilensky, 1998). Faculty of Education, University of Haifa.Google Scholar
  14. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Cambridge, MA: Helix Books/Addison-Wesley.Google Scholar
  15. Holland, J. H. (1998). Emergence: From chaos to order. MA: Addison-Wesley.Google Scholar
  16. Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15(1), 11–34.CrossRefGoogle Scholar
  17. Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. Oxford, NY: Oxford University Press.Google Scholar
  18. Levy, S. T., & Lahav, O. (2011). Enabling blind people to experience science inquiry learning through sound-based mediation. Journal of Computer Assisted Learning, 28(6), 499–513.Google Scholar
  19. Levy, S. T., Novak, M., & Wilensky, U. (2006). Connected chemistry curriculum, CC1. Evanston, IL. Center for Connected Learning and Computer Based Modeling, Northwestern University. Download at
  20. Levy, S. T., & Wilensky, U. (2009a). Students’ learning with the connected chemistry (CC1) curriculum: navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243–254.CrossRefGoogle Scholar
  21. Levy, S. T., & Wilensky, U. (2009b). Crossing levels and representations: The connected chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 223–242.Google Scholar
  22. Mann, D., Williams, M., Ward, P., & Janelle, C. (2007). Perceptual-cognitive expertise in sport: A meta-analysis. Journal of Sport and Exercise Psychology, 29, 457–478.Google Scholar
  23. Mayer-Kress, G., & Newell, K. M. (2002). Stochastic iterative maps with multiple time-scales for modelling human motor behavior. Nonlinear Phenomena in Complex Systems, 5(4), 418–427.Google Scholar
  24. McCole, S. D., Claney, K., Conte, J. C., Anderson, R., & Hagberg, J. M. (1990). Energy expenditure during bicycling. Journal of Applied Physiology, 68, 748–753.Google Scholar
  25. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.CrossRefGoogle Scholar
  26. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. NY: Basic Books.Google Scholar
  27. Schmidt, R. A., & Lee, T. D. (2011). Motor control and learning: A behavioral emphasis (5th ed.). Champaign, Illinois, US: Human Kinetics.Google Scholar
  28. Schmidt, R. A., & Wrisberg, C. A. (2008). Motor learning and performance: A situation-based learning approach. US: Human Kinetics Publishers.Google Scholar
  29. Strogatz, S. (2003). Sync: The emerging science of spontaneous order. Theia.Google Scholar
  30. Town, G. P. (1985). The science of triathlon training and competition. Champaign, IL: Human Kinetic.Google Scholar
  31. USA Triathlon (2012). Complete triathlon guide. Champaign, IL: Human Kinetics.Google Scholar
  32. Vicsek, T. (2002). The bigger picture. Nature, 418, 131.CrossRefGoogle Scholar
  33. Wilensky, U. (1998). NetLogo flocking model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  34. Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  35. Wilensky, U. (2005). NetLogo connected chemistry 3 circular particles model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  36. Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.Google Scholar
  37. Williams, A. M., & Ford, P. R. (2008). Expertise and expert performance in sport. International Review of Sport and Exercise Psychology, 1(1), 4–18.Google Scholar
  38. Williams, A. M., Ford, P. R., Eccles, D. W., & Ward, P. (2010). Perceptual-cognitive expertise in sport and its acquisition: Implications for applied cognitive psychology. Applied Cognitive Psychology, 25, 432–442.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Faculty of EducationUniversity of HaifaHaifaIsrael

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