From Clicks to Insights: Exploring SRL Behaviors Longitudinally using Institutional Data

• In Event: Self-Regulated Learning and Motivation in Technology-Enhanced Environments

Abstract

The collection and use of student data in higher education institutions have increased in recent years with the widespread adoption of learning management systems (LMSs). These large-scale data have the potential to provide scholarly and practical insights about student learning and how their learning evolves over time. We used a learning analytics approach on an institutional dataset to cluster students (N=2293) based on behavioral characteristics, such as engagement and self-regulated learning (SRL), and observed how these clustering patterns changed over time and influenced academic performance and student retention. Our results highlight the importance of long-term SRL behaviors for academic success and demonstrate that SRL behaviors can act as early predictors of at-risk students.

Authors

• Heeryung Choi, University of Minnesota

• Chris Steadman, University of Minnesota

• Caitlin Mills, University of Minnesota

• Panayiota Kendeou, University of Minnesota