Using Logfile Data to Evaluate Motivational Processes in University Students' Exam Preparation

• In Event: Emotions and Motivation in Technology-Based Environments

Abstract

Digital learning environments provide easy access to educational resources in higher education. Besides personal beliefs (expectancy-value theories), oftentimes neglected temporal factors explain differences in (digital) self-regulated learning. Temporal Motivation Theory (TMT) combines both approaches in a formalized manner. We evaluated the predictive power of the TMT on N = 2,351 learning days of 127 psychology students’ self-regulated exam preparation for statistics over one semester using logfile data of an intelligent tutoring system. The TMT score predicted students’ achievement-motivated behavior (β = .33, p < .001). Further analyses revealed that not the trait compositions of the TMT, but the temporal proximity of the statistics exam was the main driver. Thus, we propose a revised version of TMT incorporating intraindividual situational variability.

Authors

• Marc Philipp Janson, Karlsruhe University of Educatiopm

• Theresa Schnettler, University of Mannheim

• Lisa Baeulke, University of Tübingen