Abstract
Learning analytics approaches to profiling students based on their study behaviour remain limited in how they integrate temporality and change. To advance this area of work, the current study examines profiles of change in student study behaviour in a blended undergraduate engineering course. The study is conceptualised through complex dynamical systems theory and its applications in psychological and cognitive science research. Students were profiled based on the changes in their behaviour as observed in clickstream data. Measure of entropy in the recurrence of student behaviour was used to indicate the change of a student state, consistent with the evidence from cognitive sciences. Student trajectories of weekly entropy values were clustered to identify distinct profiles. Three patterns were identified: stable weekly study, steep changes in weekly study, and moderate changes in weekly study. The students with steep changes in their weekly study activity had lower exam grades and showed destabilisation of weekly behaviour earlier in the course. The study investigated the relationships between these profiles of change, student performance, and other approaches to learner profiling, such as self-reported measures of self-regulated learning, and profiles based on the sequences of learning actions.
Original language | English |
---|---|
Pages (from-to) | 197-207 |
Number of pages | 11 |
Journal | Proceedings of Learning Analytics and Knowledge Conference |
DOIs | |
State | Published - 13 Mar 2023 |
Event | 13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023 - Arlington, United States Duration: 13 Mar 2023 → 17 Mar 2023 |
Keywords
- complex dynamical systems
- learning analytics
- self-regulated learning