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Abstract Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected fromn = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control,n = 48), (2) perform poorly and received intervention (treatment,n = 95) and (3) perform well (not flagged,n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. Practitioner notesWhat is already known about this topicSelf‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.SRL is a dynamic, temporal process that leads to purposeful student engagement.Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.What this paper addsA Markov process for measuring dynamic SRL processes using log data.Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.Evidence that SRL processes can be meaningfully impacted through educational intervention.Implications for theory and practiceComplexity approaches inform theory and measurement of dynamic SRL processes.Static representations of dynamic SRL processes are promising learning analytics metrics.Engineered features of LMS usage are valuable contributions to AI models.
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This content will become publicly available on October 1, 2026
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.
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- Award ID(s):
- 1920756
- PAR ID:
- 10654997
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Journal of Science Education and Technology
- Volume:
- 34
- Issue:
- 5
- ISSN:
- 1059-0145
- Page Range / eLocation ID:
- 1110 to 1127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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