Triggers for self-regulated learning: A conceptual framework for advancing multimodal research about SRL

Abstract

This paper introduces a theory-driven trigger regulation framework for advancing multimodal analytical approaches to research about self-regulated learning. Events and/or situations that may inhibit learning processes and, thus, require regulatory responses are defined as trigger events. Empirically identifying trigger signals in multimodal data as markers for the regulation of cognition, motivation, emotion, and behavior has great potential for advancing the field. We propose a trigger regulation framework and explain how it can be leveraged in multimodal research for detecting trigger signals focusing analysis on meaningful regulatory responses. This conceptual framework offers potential to guide methodological and analytical advances in research to examine the situated nature of regulatory responses and within-person individual differences in SRL as they play out during complex task work and teamwork.

Educational relevance and implications statement

The trigger regulation framework contributes to advancing multimodal approaches to the study of SRL. It presents a theory driven analytical approach for detecting, modeling, and interpreting adaptive and maladaptive regulation during individual or collaborative work. Grounding analytical approaches to multimodal data analysis in this framework has potential to increase the quality and accuracy of research findings and interpretations and inform the development of interventions and AI systems.