Learning Analytics and Academic Support Strategies in Higher Education
DOI:
https://doi.org/10.63345/Keywords:
Learning Analytics, Higher Education, Academic Support, Artificial Intelligence, Student Engagement, Early-Warning SystemsAbstract
Learning analytics has become an important mechanism for converting heterogeneous educational data into evidence that can inform academic support in digitally mediated higher education. However, many existing systems remain prediction-centred, identifying academic risk without determining which support strategy is most appropriate for a particular learner or whether the recommendation remains reliable across different student groups. This study addresses this gap by proposing an intervention-oriented learning analytics framework that links multidimensional indicators of engagement, assessment progression, temporal study behaviour, and support history with personalized academic support recommendations. The proposed research conceptualizes academic assistance as a dynamic decision-support problem rather than a conventional binary prediction task. A temporally aware machine-learning architecture is designed to distinguish emerging disengagement patterns from ordinary fluctuations in learning-management-system activity while generating interpretable risk and support profiles.
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