Keywords
Abstract
This thesis develops a conceptual model for combining learning analytics and instructor-mediated adaptive feedback to support self-directed study in engineering courses. The proposed approach moves beyond simple risk dashboards by connecting trace indicators to pedagogical hypotheses, intervention rules, and student reflection. Its core innovation is a transparent feedback loop in which a teacher validates analytics-based signals before recommending targeted scaffolds. The paper defines the model, illustrates its application to engineering assignments, and outlines indicators for future evaluation. The study is conceptual; it reports no empirical performance gains and does not present a validated predictive algorithm.
References
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[3] UNESCO. (2023). Global education monitoring report 2023: Technology in education—A tool on whose terms? https://gem-report-2023.unesco.org/