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Article
Appropriating quantified self technologies to support elementary statistical teaching and learning
IEEE Transactions on Learning Technologies
  • Victor R. Lee, Utah State University
  • Joel R. Drake, Utah State University
  • Jeffrey L. Thayne, Utah State University
Document Type
Article
Publisher
Institute of Electrical and Electronics Engineering
Publication Date
10-1-2016
Disciplines
Abstract
Wearable activity tracking devices associated with the Quantified Self movement have potential benefit for educational settings because they produce authentic and granular data about activities and experiences already familiar to youth. This article explores how that potential could be realized through explicit acknowledgment of and response to tacit design assumptions about how such technologies will be used in practice and strategic design for use in a classroom. We argue that particular practical adaptations that we have identified serve to ensure that the classroom and educational use cases are appropriately considered. As an example of how those adaptations are realized in actual elementary classrooms, we describe an effort to provide fifth-grade students each with their own Fitbit activity trackers in the context of a multi-week unit exploring core ideas in elementary statistics. Observational descriptions and transcript excerpts of students and teachers discussing their own Fitbit data are presented to illustrate what opportunities exist to leverage youth familiarity with daily activities in a way that targets development of statistical thinking. Quantitative written test results showing learning gains and differences between traditional and wearable device-enhanced instruction are also presented. Improvement on several statistical thinking constructs is identified, including in the areas of data display, conceptions of statistics, modeling variability, and informal inference.
DOI
doi:10.1109/TLT.2016.2597142
Citation Information
Lee, V. R., °Drake, J. R., & °Thayne, J. L. (2016). Appropriating quantified self technologies to support elementary statistical teaching and learning. IEEE Transactions on Learning Technologies, 9(4), 354-365. doi:10.1109/TLT.2016.2597142