Lee, Victor R


The Quantified Self (QS) movement is a growing global effort to use new mobile and wearable technologies to automatically obtain personal data about everyday activities. The social and material infrastructure associ- ated with the Quantified Self (QS) movement provides a number of ideas that educational technologists should consider incorporating and using. This article discusses some recent efforts to bring the movement to the practices of the educational technology field and presents some issues to consider in the future.


  1. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: SRI International. 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. Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., … Landay, J. A. (2008). Activity sensing in the wild: A field trial of Ubifit Carden. Paper pre-sented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy. Gee, J. P. (2007). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan. Lee, V. R., & Drake, J. (2013). 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 72th International Conference on Interaction Design and Children 2073 (pp. 273-276). New York: ACM. Lee, V. R., & Thomas, J. M. (2011). Integrating physical activity data technologies into elementary school classrooms. Educational Technology Research and Development, 59(6), 865-884; doi: 70.7007/577423-077-9270-9. Wolf, G. (2010). The data-driven life. The New York Times Magazine; http://www.nytimes.com120 70/0S/02/maga-zine/ 02self-measurement-t.html? J= 7 &ref=magazine .
The SELF Institute