Appropriating quantified self technologies to support elementary statistical teaching and learning

Victor R. Lee, Utah State University
Joel R. Drake, Utah State University
Jeffrey L. Thayne, Utah State University


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.