Balancing Contextualization and Generalization in Trace-based Measurement of Self-Regulated Learning: A Systematic Review of Theoretically Grounded Studies

Trace-based measurements in self-regulated learning (SRL) require a careful balance between context and generalizability, while ensuring robust validity. This review of 58 theoretically grounded articles addresses three prevalent issues in SRL: contextualization, generalization, and validation. We seek to objectively define a context and examine how the design of software systems can facilitate generalizable trace capture and alignment of theoretical constructs. The learning environment, task type, and task duration are the key factors to consider while defining a context. We identify four categories of trace data useful for informing learning system design. Theoretical generalization is demonstrated by mapping trace data to constructs from two distinct SRL models—Zimmerman’s and Winne & Hadwin’s. We highlight that multichannel and multimodal data help capture enhanced trace data, while continuous measures, such as think-aloud protocols, are better suited than questionnaires to validate traces. We outline what validation in trace-based research should entail from the perspective of a modern validation framework and provide the best practices for balancing contextual depth with theoretical generalization in SRL research.