Chapter 3: Using Analytics to Improve Academic Persistence

This chapter addresses the question of how analytics can be used to support the improvement of academic persistence, defined as the focused pursuit of both short- and long-term academic goals while avoiding seductive distractions. To improve motivation to persist, measures, analytics, and interventions can be organized to focus on the three factors that operationalize motivation at the task level: starting, persisting, and investing adequate mental effort (Pintrich & Schunk, 2002, Schunk, Pintrich & Meece, 2008). Using this threepart framework helps bring some coherence to the daunting array of both formal research and informal constructs surrounding persistence and motivation, and also provides a target for analytic possibilities. We present examples of validated measures and research-tested interventions for persistence motivation for students who need them most. Online instructional technologies have opened many new opportunities to capture information about students’ persistence motivation and learning as they work, and to provide personalized support to individual students. New analytic approaches will need to be validated, however, to show that they are actually providing accurate and reliable info that can be used to improve academic persistence. In addition, learning analytics may discover new insights about student differences and environmental factors that impact persistence and add to the interventions currently available. The growth of online instruction across the world also makes it possible to conduct rapid experimentation on new analytic strategies and the measures and interventions linked to them.