The Quantified Self Goes to School

• In Event: Connecting Learners to Place, Space, and Personal Experience With Technology and New Media

Abstract

Objective: With increased pervasiveness of wearable technologies that passively and continuously obtain movement data from everyday activities, we are beginning to access an abundance of “self-data”. What opportunities do self-data present for youth and classrooms? The purpose of this poster is to show, when supportive curricula exist, (a) what fifth-grade students in classrooms will voluntarily interrogate with self-data and (b) the range of measurable gains in statistical reasoning appear from use of self-data.

Perspectives: Two perspectives inform this work. First is recognition that the “quantified self” (QS) movement (Wolf, 2010), which involves extensive collection, analysis, and sharing of data from everyday activities and experiences, represents a new technologically-supported way to engage with data (Lee, 2013). While QS practices have largely been the purview of adult hobbyists (Lee, 2014) and human-computer interaction researchers (Choe, Lee, Lee, Pratt, & Kientz, 2014), learning scientists are beginning to recognize that some aspects of QS may be relevant to youth and educational settings (Ching & Schaefer, 2015; Lee, 2013).

Second, increased interest in embodiment within education and the learning sciences specifically (Abrahamson & Lindgren, 2014; Hall & Nemirovsky, 2012) highlights the importance of bodily experience and how it can be positioned in service of learning. Differently stated, there are some contexts where it is fruitful to take what a student feels or remembers about an experience (e.g., when something felt tiring or when something felt atypical) and reconcile that with content they are being asked to learn (e.g., when a difference is significant or when a measure deviates from center).

Methods and Data Sources: The findings shared here involve design-based research methods (The Design-Based Research Collective, 2003), including 4 years of iteration in classrooms coupled with collection of video, pre and post-test measures of statistical reasoning (Lehrer, Kim, Ayers, & Wilson, 2014), and interviews with students and teachers. Three cases of student self-data investigations are identified from the larger video corpus that show the range of what students will investigate including: the accuracy of commercial activity trackers and how the apparent difference in exertion across four different activities is reflected in student step data. Written test results are also presented.

Results: This poster serves as a demonstration case for how wearable activity trackers can be repurposed in elementary schools and how youth can become critical consumers of self-data, as evidenced by the cases. We also present significant increases in four out of five measurement constructs to show that progress can be made on ambitious statistical reasoning goals. Still, we observe our data and necessary iterations that researcher-designed support is still needed in the form of new curriculum and professional development.

Scholarly Significance: As QS becomes more common, it is important that educational technologists seriously explore the learning possibilities that are entailed. Consistent with forward-looking math and science standards (e.g., Achieve, 2013), this work extends earlier models for data-intensive elementary classroom learning (e.g., Lehrer & Schauble, 2004) and shows how cyberlearning technology can be of value.

Authors

• Victor R. Lee, Utah State University

• Joel Drake, Utah State University

• Jeffrey Thayne, Utah State University