Epistemic Network Analysis and Multigroup Modeling Reveals Domain Specific Self-Regulated and Active Learning in Biology

• In Event: Unpacking Self-Regulated Learning: Cognitive, Motivational, and Methodological Insights Across Developmental and Disciplinary Contexts

Abstract

We examined how students' adherence to high-structure active learning designs and students’ further self-regulated learning (SRL) processes emerged in a high-structure active learning STEM course and related to their academic outcomes. Epistemic network analysis revealed groups with distinct behavior patterns: adherent learners engaged in planning and domain-specific strategies; independent learners engaged in and monitored domain-general strategies. Two-group path modeling of these digital trace data further indicated group-specific predictive relationships between strategy use and learning outcomes; domain general strategy engagement predicted independent learners’ achievement and domain specific strategies predicted achievement for adherent learners. These findings suggest that SRL strategy use and its impact on academic success vary by active learning approach (adherence, independence), underscoring the need to tailor instructional support accordingly.

Authors

• Hanall Sung, University of Tennessee

• Jeff A. Greene, University of North Carolina - Chapel Hill

• Matthew L. Bernacki, University of North Carolina - Chapel Hill

• Michael Abdul Ghani Berro, University of North Carolina - Chapel Hill

• Linyu Yu, University of North Carolina - Chapel Hill

• Robert D Plumley, University of North Carolina - Chapel Hill