Personal Analytics Explorations to Support Youth Learning

Personal Analytics Explorations to Support Youth Learning

Victor R. Lee (Utah State University, USA)
DOI: 10.4018/978-1-5225-3940-7.ch007

Abstract

While personalized learning environments often include systems that automatically adapt to inferred learner needs, other forms of personalized learning exist. One form involves the use of personal analytics in which the learner obtains and analyzes data about himself/herself. More known in informatics communities, there is potential for use of personal analytics for design of instruction. This chapter provides two cases of personal analytics learning explorations to demonstrate their range and potential. One case is of a high school student examining how sleep influences her mood. The other case is of a sixth-grade class of students examining how deviations from typical walking behavior change distributional shape in plotted step data. Both cases show how social support and direct experience with data correction are intimately involved in how youth can learn through personal analytics activities.
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Introduction

One widely recognized model of personalized learning involves using computational tools to exogenously recognize and respond to the immediate conceptual needs of a learner and automatically adjusting the learning environment accordingly; for instance, an intelligent tutoring system can infer the knowledge state of a user and then present appropriate tasks and instructional content to move them closer to a more expert-like understanding (e.g., Graesser, Chipman, Haynes, & Olney, 2005). Yet there are still several other ways in which personalization could take form in a technology-supported learning environment. As examples, consider how using personalized story scenarios that map onto students’ out-of-school interests can boost student learning in mathematics (Walkington, 2015; Walkington, Petrosino, & Sherman, 2013) or how personalization can also be achieved and supported by providing youth with generative and expressive computational media where they can design and engineer artifacts that are specific to their own interests and prior expertise (Peppler & Kafai, 2007). In this chapter, my goal is to further expand the space of personalized learning by highlighting what opportunities exist when students are able to explore “personal analytics”.

Personal analytics (Ruckenstein, 2014; Wolfram, 2012) refers to a set of data-related practices that have been associated with the area of personal informatics (Li, Dey, Forlizzi, 2010) and the Quantified Self (Nafus & Sherman, 2014). All typically involve collecting and analyzing aggregates of data obtained through “self-tracking” (Lee, 2017). The use of the term has generally varied depending on the scholarly community (i.e., personal informatics is more common parlance in the information sciences). Arguably, there are nuances that distinguish the terms, but for current purposes, it is acceptable to think of personal analytics as being comparable to the kinds of analytics that one might do with website data but instead do so with data about one’s own self. At its core, personal analytics is self-inquiry using data. While data collection and inquiry about one’s own self has been practiced for several years in a range of communities (e.g., Kopp, 1988; Lee & Drake, 2013; Wallace, 1977, Wheeler & Reis, 1991), the widespread availability of consumer-level mobile and wearable devices and automated data collection systems (such as clickstream recording) has reduced some of the initial barriers associated with analyzing data about one’s self (Lee, 2013) and popularized this approach. This has thus enabled some initial pioneering work to integrate personal analytics routines and practices into the design of learning environments. Some noteworthy examples include asking youth to use personal analytics data and gaming environments to motivate healthier lifestyle behaviors (Ching & Schaefer, 2015) or to support interactive exhibit design at settings such as zoos and museums (Lyons, 2015).

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