Examining the Relationship Between Comparative and Self-Focused Academic Data Visualizations in At-Risk College Students' Academic Motivation

Abstract

This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory—a prominent theory of students' academic motivation—and utilizes interviews (n = 60) to investigate how students at risk of college failure interpret visualizations of their potential academic achievement. Findings suggest that visualizations only containing information about students themselves (i.e., self-focused affordances) evoked statements centered on mastering material. Visualizations containing information about students and a class average (i.e., comparative information), on the other hand, evoked responses that disheartened students and/or made them feel accountable to do better. Findings from this study suggest the following guidelines for designing visualizations for learning analytics-based educational technologies: (1) Never assume that more information is better; (2) anticipate and mitigate against potential misinterpretations—or harmful alternative interpretations—of visualizations; and (3) always suggest a way for students to improve. These guidelines help mitigate against potential unintended consequences to motivation introduced by visualizations used in learning analytics-based educational technologies. (Keywords: motivation, visualizations, learning analytics, Achievement Goal Theory, college students, educational technologies)

 Acknowledgments. The author thanks the University of Southern California's Provost's Postdoctoral Scholars Program for its support, as well as Dr. Stuart Karabenick, Dr. Barry Fishman, Dr. Steven Lonn, and Dr. Stephanie Teasley, who nurtured an early interest in learning analytics applications.

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Stephen J. Aguilar

Dr. Stephen J. Aguilar is a Provost Postdoctoral Scholar at the USC Rossier School of Education. His areas of expertise include motivation and self-regulated learning as they relate to the design and implementation of educational technologies. He specializes in learning analytics. Please address correspondence regarding this article to Stephen J. Aguilar, Rossier School of Education, University of Southern California, 3470 Trousdale Parkway, 600B Waite Phillips Hall, Los Angeles, CA 90089–4036, USA. E-mail: [email protected]