Measuring (meta)emotion, (meta)motivation, and (meta)cognition using digital trace data: A systematic review of K-12 self-regulated learning

Artificial intelligence (AI) has demonstrated significant potential in enhancing digital learning by offering personalized and adaptive experiences that meet learners' individual needs. However, while self-regulated learning (SRL) skills are critical for success in digital environments, AI-driven learner models mainly focus on cognitive processes, with limited integration of SRL skills. This systematic review synthesizes research from 1990 to 2024, analyzing digital trace data from various learning platforms to identify which data serve as indicators of the three phases and areas of SRL in K-12 digital learning. Our findings highlight digital trace data that measure (meta)emotion, (meta)motivation, and (meta)cognition across the three SRL phases, while also revealing significant gaps in tracing (meta)motivation and (meta)emotion, particularly in the preparatory and appraisal phases. Although a variety of data traces address the (meta)cognitive area, the results underscore the challenges of meaningfully interpreting the learning process. Despite these challenges, the evolving research on trace data demonstrates substantial potential for integrating adaptive SRL scaffolding into digital learning environments.