• Victor R. Lee
  • Joel Drake
  • Jeffrey Thayne
  • Ryan Cain


Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the quantified self—in which data about one’s own activities are collected and made into objects of study—provides inspiration for one such approach. By drawing on what one already knows about their self and their prior experiences, it may be possible to bootstrap students’ abilities to interpret and make sense of data. Taking that possibility seriously, this article describes some of the gains observed in students’ statistical reasoning following a quantified self, wearables-based elementary statistics unit and provides a theoretical framework drawing from cognitive psychology, embodiment, and situative perspectives to characterize how prior experience is used as a resource in data sense-making when the data are about students’ own physical experiences. This framework centralizes and interrogates the work of “remembering” prior experiences and articulates how remembering is involved in interpreting quantified self data. Specifically, the framework emphasizes that remembering in service of data interpretation is a reconstructive act that draws from both general and specific embodied resources and that the work of reconstructive remembering in the classroom is both individual and multi-participant work. To demonstrate measured learning gains and illustrate the framework, written assessment results and descriptive cases of student and teacher discussions about quantified self data from two sixth-grade classes participating in a classroom design experiment are provided. Both a discussion of and recommendations for ethical considerations related to quantified self data in education are also provided.


The SELF Institute