Examining university teachers’ self-regulation in using a learning analytics dashboard for online collaboration

Learning analytics dashboards (LADs) are often used to display real-time data indicating student learning trajectories and outcomes. Successful use of LADs requires teachers to orient their dashboard reviews with clear goals, apply appropriate strategies to interpret visualized information on LADs and monitor and evaluate their interpretations to meet goals. This process is known as self-regulated learning (SRL). Critical as it is, little research investigates teachers’ SRL in LAD usage. The present study addressed the gap by examining teachers’ SRL and sought to understand how teachers’ SRL relates to their use of LADs. To this end, a case study was designed in which ten participants were invited to use a LAD for asynchronous online problem-based learning. Think-aloud techniques and process mining methods were applied. The findings show that teachers were cognitive regulation in the early stage of LAD usage and became more metacognitive regulated later. The comparison of SRL between the good and the weak regulators indicates that the good self-regulators enacted more monitoring and evaluation events. Thus their regulator pattern was more non-linear. The qualitative analysis of think-aloud protocols reveals that teachers with good SRL are more likely to use the LAD to diagnose issues in student learning and collaboration. The study highlights the importance of SRL for teachers’ success in using LAD for data-driven instructions. The study also reinforces the importance of fostering teachers’ SRL, which accounts for teachers’ professional success in the digital era. Copyright © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.