Beyond Frequency: Using Epistemic Network Analysis and Multimodal Traces to Understand Temporal Dynamics of Self-Regulated Learning

Self-regulated learning (SRL) process is temporally bounded and unfolds over time. Achieving insights into temporal dynamics in SRL processing, however, requires not only fine-grained data collection and methods but also modeling approaches that accurately account for the sequential structure of the events during the SRL process. Addressing this, we investigate an understudied aspect of SRL theory: how temporal SRL processing, as captured by multimodal traces, differs between mastery and progressing groups in the context of technology-enhanced biology learning in a lab setting. We examine count-based, duration-based, and sequence-based models of SRL processing to unveil diverse dimensions of the process across different achievement levels. Specifically, for the sequence-based model, we employed a multi-method approach, incorporating epistemic network analysis (ENA) to analyze the temporal and sequential dynamics of multimodal events during SRL processing and then qualitative analysis to delve deeper into those dynamics. Our study offered valuable methodological contributions by empirically comparing different methods for modeling temporality and by utilizing a novel analytic approach (i.e., ENA) to model the temporal dynamics of SRL processes. Additionally, it makes theoretical contributions to advancing the understanding of the dynamic interplay between SRL processing and academic performance, thereby informing targeted interventions to support students' learning processes, ultimately enhancing their learning outcomes in technology-enhanced STEM education. Future researchers can leverage these contributions to advance models of the dynamic and temporal nature of SRL.