Students’ sense-making of personalised feedback based on learning analytics
Keywords:learning analytics, feedback, self-regulated learning, student perspectives, epistemic network analysis
Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students’ sense-making of learning analytics–based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students’ current learning strategies and achievement of associated learning goals.
Implications for practice or policy:
- Personalised feedback based on learning analytics should be informed by an understanding of students’ self-regulated learning.
- Instructors implementing personalised feedback should align this closely with the course curriculum.
- Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone.
- Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.
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