Exploring Learners' Self-reflection and Intended Actions After Consulting Learning Analytics Dashboards in an Authentic Learning Setting

Learning Analytics Dashboards (LAD) have been developed as feedback tools to help students self-regulate their learning (SRL), using the large amounts of data generated by online learning platforms. Despite extensive research on LAD design, there remains a gap in understanding how learners make sense of information visualised on LADs and how they self-reflect using these tools. We address this gap through an experimental study where a LAD delivered personalised SRL feedback based on interactions and progress to a treatment group, and minimal feedback based on the average scores of the class to a control group. Following the feedback, students were asked to state in writing how they would change their study behaviour. Using a coding scheme covering learning strategies, metacognitive strategies and learning materials, three human coders coded 1,251 self-reflection texts submitted by 417 students at three time points. Our results show that learners who received personalised feedback intend to focus on different aspects of their learning in comparison to the learners who received minimal feedback and that the content of the dashboard influences how students formulate their self-reflection texts. Based on our findings, we outline areas where support is needed to improve learners' sense-making of feedback on LADs and self-reflection in the long term.