Using learning analytics to measure self‐regulated learning: A systematic review of empirical studies in higher education

Background Measuring students' self‐regulation skills is essential to understand how they approach their learning tasks in order to identify areas where they might need additional support. Traditionally, self‐report questionnaires and think aloud protocols have been used to measure self‐regulated learning skills (SRL). However, these methods are based on students' interpretation, so they are prone to potential inaccuracy. Recently, there has been a growing interest in utilizing learning analytics (LA) to capture students' self‐regulated learning (SRL) by extracting indicators from their online trace data. Objectives This paper aims to identify the indicators and metrics employed by previous studies to measure SRL in higher education. Additionally, the study examined how these measurements were validated. Methods Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA), this study conducted an analysis of 25 articles, published between 2015 and 2022, and sourced from major databases. Results and Conclusions The results showed that previous research used a variety of indicators to capture learners' SRL. Most of these indicators are related to time management skills, such as indicators of engagement, regularity, and anti‐procrastination. Furthermore, the study found that the majority of the reviewed studies did not validate the proposed measurements based on any theoretical models. This highlights the importance of fostering closer collaboration between learning analytics and learning science to ensure the extracted indicators accurately represent students' learning processes. Moreover, this collaboration can enhance the validity and reliability of data‐driven approaches, ultimately leading to more meaningful and impactful educational interventions. Lay Description What is already known about this topic? Measuring students' self‐regulation skills is essential for understanding their learning approaches and identifying areas requiring further support. Traditionally, questionnaires and interviews have been used to measure learners’ self‐regulation; however, these methods rely on students' own views, which can sometimes be unreliable. Recently, there has been more interest in using learning analytics to measure students’ self‐regulation based on data extracted from their online activities. What this paper adds? This paper systematically analyses how previous studies used learning analytics to measure students’ self‐re