Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning

Abstract

The temporal and sequential nature of learning is receiving increasing focus in Learning Analytics circles. The desire to embed studies in recognised theories of self-regulated learning (SRL) has led researchers to conceptualise learning as a process that unfolds and changes over time. To that end, a body of research knowledge is growing which states that traditional frequency-based correlational studies are limited in narrative impact. To further explore this, we analysed trace data collected from online activities of a sample of 239 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We employed SRL categorisation of micro-level processes based on a recognised model of learning, and then analysed the data using: 1) simple frequency measures; 2) epistemic network analysis; 3) temporal process mining; and 4) stochastic process mining. We found that a combination of analyses provided us with a richer insight into SRL behaviours than any one single method. We found that better performing learners employed more optimal behaviours in their navigation through the course's learning management system.