Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

Abstract

The ability to regulate one’s own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students’ efficacy when studying and academic performance, for better or worse. However, neither students or instructors generally have proper understanding of what self-regulated learning is, the impact that it has or how to assess it. This paper has the purpose of showing how learning analytics can be used in order to generate simple metrics related to several areas of students’ self-regulation, in the context of a first-year university course. These metrics are based on data obtained from a learning management system, complemented by more specific assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation profiles are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.

Keywords

• Data analysis
• data processing
• engineering education
• learning management systems
• linear regression
• self-regulated learning