TechTrends

, Volume 59, Issue 4, pp 46–53 | Cite as

Let's Get Physical: K-12 Students Using Wearable Devices to Obtain and Learn About Data from Physical Activities

Article

Abstract

Accessibility to wearable technology has exploded in the last decade. As such, this technology has potential to be used in classrooms in uniquely interactive and personally meaningful ways. Seeing this as a possible future for schools, we have been exploring approaches for designing activities to incorporate wearable physical activity data tracking technologies to help students learn how to interpret data. This article describes four instances of designed learning activities in which wearable physical activity data tracking devices in use with K-12 students. Of special note is how the devices could be used to help students learn both content related to statistics and about physical activities in general. We also identify some of the challenges associated with the use of such devices that others who may use wearable technology in the classroom may wish to consider.

Keywords

activity trackers sensors statistics education wearable computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cai, J., Lo, J., & Watanabe, T. (2002). Intended treatments of arithmetic average in U.S. and Asian school mathematics textbooks. School Science and Mathematics, 102(8), 391-403.CrossRefGoogle Scholar
  2. Ching, C. C., & Hunicke, R. (2013). GETUP: Health Gaming for “the Rest of Your Life”. Paper presented at the Games, Learning & Society 9.0, Madison, WI.Google Scholar
  3. Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. Journal of the Learning Sciences, 9(4), 471-500.CrossRefGoogle Scholar
  4. Hug, B., & McNeill, K. L. (2008). Use of First-hand and Second-hand Data in Science: Does data type influence classroom conversations? International Journal of Science Education, 30(13), 1725-1751.CrossRefGoogle Scholar
  5. Ito, M. (2010). Hanging out, messing around, and geeking out: Kids living and learning with new media. Cambridge, MA: MIT press.Google Scholar
  6. Klopfer, E., Yoon, S., & Perry, J. (2005). Using palm technology in participatory simulations of complex systems: A new take on ubiquitous and accessible mobile computing. Journal of Science Education and Technology, 14(3), 285-297.CrossRefGoogle Scholar
  7. Lee, V. R., & DuMont, M. (2010). An exploration into how physical activity data-recording devices could be used in computer-supported data investigations. International Journal of Computers for Mathematical Learning, 15(3), 167-189. doi: 10.1007/s10758-010-9172-8CrossRefGoogle Scholar
  8. Lee, V. R., & Drake, J. (2013a). Digital physical activity data collection and use by endurance runners and distance cyclists. Technology, Knowledge and Learning, 18(1-2), 39-63. doi: 10.1007/s10758-013-9203-3CrossRefGoogle Scholar
  9. Lee, V. R., & Drake, J. (2013b). Quantified recess: Design of an activity for elementary students involving analyses of their own movement data. In J. P. Hourcade, E. A. Miller & A. Egeland (Eds.), Proceedings of the 12th International Conference on Interaction Design and Children 2013 (pp. 273-276). New York, NY: ACM.CrossRefGoogle Scholar
  10. Lehrer, R., & Schauble, L. (2004). Modeling Natural Variation Through Distribution. American Education Research Journal, 41(3), 635-679.CrossRefGoogle Scholar
  11. Lyons, L., Silva, B. L., Moher, T., Pazmino, P. J., & Slattery, B. (2013). Feel the burn: exploring design parameters for effortful interaction for educational games. In J. P. Hourcade, E. A. Miller & A. Egeland (Eds.), Proceedings of the 12th International Conference on Interaction Design and Children (pp. 400-403). New York, New York: ACM.Google Scholar
  12. Murray, O., & Olcese, N. (2011). Teaching and Learning with iPads, Ready or Not? TechTrends, 55(6), 42-48. doi: 10.1007/s11528-011-0540-6CrossRefGoogle Scholar
  13. Nemirovsky, R. (2011). Episodic feelings and transfer of learning. Journal of the Learning Sciences, 20(2), 308-337.CrossRefGoogle Scholar
  14. Resnick, M., Berg, R., & Eisenberg, M. (2000). Beyond black boxes: Bringing transparency and aesthetics back to scientific investigation. Journal of the Learning Sciences, 9(1), 7-30.CrossRefGoogle Scholar
  15. Sherin, M. G., Russ, R. S., Sherin, B. L., & Colestock, A. (2008). Professional vision in action: An exploratory study. Issues in Teacher Education, 17(2), 27-46.Google Scholar
  16. Taylor, K., & Hall, R. (2013). Counter-Mapping the Neighborhood on Bicycles: Mobilizing Youth to Reimagine the City. Technology, Knowledge and Learning, 18(1-2), 56-93. doi: 10.1007/s10758-013-9201-5CrossRefGoogle Scholar
  17. Waltz, E. (2012). How I quantified myself. IEEE Spectrum, 49(9), 42-47. doi: 10.1109/MSPEC.2012.6281132CrossRefGoogle Scholar
  18. Watson, J. M., & Moritz, J. B. (2000). The longitudinal development of understanding of average. Mathematical Thinking and Learning, 2(1&2), 11-50.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2015

Authors and Affiliations

  1. 1.Utah University State UniversityLoganUSA

Personalised recommendations