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