Leveraging Learning Analytics to Foster Self-Regulated Learning: A Multi-Semester Design-Based Research Study

• In Event: Virtual Poster Session
  In Virtual iPresentation Only: SIG-Instructional Technology Virtual Poster Session

Abstract

This design-based research study explored how learning analytics (LA) can support self-regulated learning (SRL) across three iterative interventions in a fully online course. Study 1 tested the addition of an LA-based visual with peer comparison but found no significant effects beyond a baseline self-assessment and peer-sharing activity. Study 2 introduced individualized feedback, though low participation limited interpretation. Study 3 implemented an early, sustained intervention aligned with SRL phases, resulting in significant improvements in exam performance and engagement behaviors. Findings highlight the importance of timing, structure, and sustained support in SRL interventions, and demonstrate the potential of combining theory-driven design with LA to enhance student learning in online contexts.

Authors

• Kun Huang, University of Kentucky

• Anita Lee-Post, University of Kentucky

• Michael Stacy, University of Kentucky

• Ryan Flores, University of Kentucky