Background: Learning analytics dashboards (LAD) have been developed as feedback tools to help students self-regulate their learning (SRL) by 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. Objectives: 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 lecture to a control group. Methods: After receiving feedback, students were asked to write down how they planned to adjust their study habits. These reflection texts are the target of this study. Three human coders analysed 1251 self-reflection texts from 417 students at three different times, using a coding system that categorised learning strategies, metacognitive strategies and learning materials. Results and Conclusions: 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 LAD influences how students formulate their self-reflection texts. Furthermore, the extent to which students incorporated suggested behavioural changes into their reflections was predicted by state measures like perceived helpfulness of the feedback. Our findings outline areas where support is needed to improve learners' sense-making of feedback on LADs and self-reflection.