Using Epistemic Networks to Analyze Self-Regulated Learning in an Open-Ended Problem-Solving Environment

The micro-level analyses of how students’ self-regulated learning (SRL) behaviors unfold over time provides a valuable framework for understanding their learning processes as they interact with computer-based learning environments. In this paper, we use log trace data to investigate how students self-regulate their learning in the Betty’s Brain environment, where they engage in three categories of open-ended problem-solving actions: information seeking, solution construction and solution assessment. We use Epistemic Network Analysis (ENA) to provide us with an overall understanding of the co-occurrences between action types both within and between the three action categories. Comparisons of epistemic networks generated for two groups of students, those with low and high performance, provided us with insights into their self-regulated behaviors. © 2021, Springer Nature Switzerland AG.