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

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Abstract

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.

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Notes

  1. The first author is an ITU (International Triathlon Union) competitive triathlon level 2 coach, with many years of experience in training, was the triathlon national team head coach and today, trainer of coachers. This claim is based on his personal experience and through his many conversations with other coaches worldwide.

  2. The aerobic threshold is the minimum speed at which a person is performing in the aerobic zone (below 65% of maximal heart-rate). The anaerobic threshold is the fastest speed at which a person can perform at a steady state where oxygen supply is adequate to meet muscle demands (80–90 % of maximal heart-rate). At higher intensities, lactic acid levels in the blood rise sharply, interfering with aerobic metabolism and causing muscles to fatigue.

  3. RPE is a subjective sensation that athletes report on that describes the level of effort. It is a common measure for identifying exercise intensity levels.

  4. An elite youth athlete is a child between the ages of 7–17 that demonstrates above average performance and reaches regional, national or international competitions.

  5. The measurement of lactic acid is used to assess levels of stress. Lactic acid is found in the muscles and blood stream and is released when the body exerts itself beyond the anaerobic threshold.

  6. M and mM are units of concentration, describing the relative proportion of a substance dissolved in a solvent.

References

  • Anchwer, H. (1998). The complete guide to triathlon training. Aachen, Germany: Meyer & Meyer Sport.

    Google Scholar 

  • 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.

  • 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.

  • Bailer-Jones, D. M. (2003). Scientists’ thoughts on scientific models. Perspectives on Science, 10, 275–301.

    Google Scholar 

  • Bar-Yam, Y. (1997). Dynamics of complex systems, Addison-Wesley, The Advanced Book Program, Reading, MA.

  • 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.

  • Casti, J. L. (1994). Complexification: Explaining a paradoxical world through the science of surprise. New York, NY: Harper Collins.

  • Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. Journal of the Learning Sciences, 1, 69–105.

    Article  Google Scholar 

  • Gilbert, J. K., & Boulter, C. (Eds.). (2000). Developing models in science education. The Netherlands: Kluwer Academic Publishers.

  • Hausswirth, C., & Brisswalter, J. (2008). Strategies for improving performance in long duration events: Olympic distance triathlon. Sports Medicine, 38(11), 881–891.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Cambridge, MA: Helix Books/Addison-Wesley.

  • Holland, J. H. (1998). Emergence: From chaos to order. MA: Addison-Wesley.

  • 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.

    Article  Google Scholar 

  • Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. Oxford, NY: Oxford University Press.

  • 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 

  • Levy, S. T., Novak, M., & Wilensky, U. (2006). Connected chemistry curriculum, CC1. Evanston, IL. Center for Connected Learning and Computer Based Modeling, Northwestern University. http://ccl.northwestern.edu/curriculum/chemistry/. Download at http://mac.concord.org/downloads.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. NY: Basic Books.

    Google Scholar 

  • Schmidt, R. A., & Lee, T. D. (2011). Motor control and learning: A behavioral emphasis (5th ed.). Champaign, Illinois, US: Human Kinetics.

    Google Scholar 

  • Schmidt, R. A., & Wrisberg, C. A. (2008). Motor learning and performance: A situation-based learning approach. US: Human Kinetics Publishers.

  • Strogatz, S. (2003). Sync: The emerging science of spontaneous order. Theia.

  • Town, G. P. (1985). The science of triathlon training and competition. Champaign, IL: Human Kinetic.

    Google Scholar 

  • USA Triathlon (2012). Complete triathlon guide. Champaign, IL: Human Kinetics.

  • Vicsek, T. (2002). The bigger picture. Nature, 418, 131.

    Article  Google Scholar 

  • Wilensky, U. (1998). NetLogo flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

  • Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

  • Wilensky, U. (2005). NetLogo connected chemistry 3 circular particles model. http://ccl.northwestern.edu/netlogo/models/ConnectedChemistry3CircularParticles. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

  • 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 

  • 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 

  • 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 

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Acknowledgments

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.

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Correspondence to Sharona T. Levy.

Appendix

Appendix

1.1 Biking with Particles Training Program and Models

A triathlon training program named Biking with Particles was created. It is made up of some short lectures (e.g. on the relationship between pulse and effort), several discussions, exploring computer models of flocking birds and bikers in various configurations and then using the models to create new possibly more efficient configurations, testing familiar and invented tactics out on the road (five tests, four heats each) and collaborative analysis and discussion of the pulse, time and effort data (Appendix).

See Table 5 and Figs. 8, 9, 10, 11.

Table 5 Biking with particles training program
Fig. 8
figure 8

Flocking (Wilensky 1998)

Fig. 9
figure 9

Big Particles—adaptation of the “Connected Chemistry 3 Circular Particles” model (Wilensky 2005)

Fig. 10
figure 10

“Birds and particles”—adaptation of the flocking model above, to include air particles and their interactions (Hirsh et al. 2011)

Fig. 11
figure 11

“Of particles and bikers”: bikers (orange circles) move from left to right. One can adjust several features in the model, and most importantly change the spatial configuration of the bikers. On the right, one can observe the rate at which each biker is getting hit by particles, and how this changes over time. This rate is related to the effort expended by the moving through the air (Bacalo et al. 2011)

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Hirsh, A., Levy, S.T. Biking with Particles: Junior Triathletes’ Learning About Drafting Through Exploring Agent-Based Models and Inventing New Tactics. Tech Know Learn 18, 9–37 (2013). https://doi.org/10.1007/s10758-013-9199-8

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