Investigating Self-Regulated Learning Measurement Based on Trace Data: A Systematic Literature Review

Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and 2023. The goal of this review is to exhibit current research orientations, characteristics of their contexts, and the SRL theories they rely on. A part of this study is then dedicated to a categorization of the trace data and indicators used to capture SRL behaviors as well as automatic methods to leverage them. Finally, we describe how learning scaffolds are provided to support SRL. In this respect, current research has brought methodological and theoretical insights to the study of SRL, particularly through the analysis of student's behaviors and profiles, and their relationship to learning performance. Researchers guide their work by using recognized theoretical models of SRL to map trace data into SRL processes and dimensions. Future research is motivated by the development of learning platforms that go beyond data collection to incorporate learning analytics tools that provide personalized SRL support to students.