Transforming Self-regulated Learning – Multimodal Insights and Future Directions

Abstract Research on self-regulated learning (SRL) is undergoing a methodological and conceptual transformation from static, retrospective measures toward dynamic, multimodal, and temporally sensitive analyses. This discussion paper synthesizes and extends the contributions of a Topical Collection devoted to multimodal approaches in SRL research. It examines how diverse studies conceptualize and operationalize SRL as a complex interplay of cognitive, metacognitive, affective, and motivational (CAMM) processes. The Self-regulated learning, Multimodal data, and Analysis Grid (SMA Grid) serves as a shared framework for classifying and integrating different modalities and analytical designs. Across the reviewed contributions, a general shift from unimodal toward integrated multimodal approaches is evident, though motivational and affective dimensions remain underrepresented. The paper argues for expanding existing frameworks—particularly SMA and CAMM—toward explanatory models that account for social, contextual, and resource-based factors shaping regulatory processes. It also highlights persistent challenges in aligning data richness with theoretical depth, especially regarding temporal modeling and causal inference. A central concern is the translation of multimodal diagnostics into actionable pedagogical support, an area still underdeveloped despite the rise of AI-based analytics. Building on concepts such as cognitive load theory and resource-based perspectives, the paper proposes that SRL should be understood as a function of the dynamic balance between learners’ resources, task demands, and instructional context. Ultimately, it calls for a more integrated, theory-driven, and practice-oriented research agenda that connects analysis with support, and measurement with meaningful educational intervention.