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Reflective writing is part of many higher education courses across the globe.It is often considered a challenging task for students as it requiresself-regulated learning skills to appropriately plan, timely engage and deeplyreflect on learning experiences. Despite an advance in writing analytics andthe pervasiveness of human feedback aimed to support student reflections,little is known about how to integrate feedback from humans and analytics toimprove students' learning engagement and performance in reflective writingtasks. This study proposes a personalised behavioural feedback interventionbased on students' writing engagement analytics utilising time-series analysisof digital traces from a ubiquitous online word processing platform. In asemester-long experimental study involving 81 postgraduate students, its impacton learning engagement and performance was studied. The results showed that theintervention cohort engaged statistically significantly more in theirreflective writing task after receiving the combined feedback compared to thecontrol cohort which only received human feedback on their reflective writingcontent. Further analyses revealed that the intervention cohort reflected moreregularly at the weekly level, the regularity of weekly reflection led tobetter performance grades, and the impact on students with low self-regulatedlearning skills was higher. This study emphasizes the powerful benefits ofimplementing combined feedback approaches in which the strengths of analyticsand human feedback are synthesized to improve student engagement andperformance. Further research should explore the long-term sustainability ofthe observed effects and their validity in other contexts.

Impact of combining human and analytics feedback on students' engagement with, and performance in, reflective writing tasks