International Journal of Artificial Intelligence and Education Technology
2835-2432ISSN (Online)
Volume 4 , Issue 2 , PP: 11–18, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nahla Moussa 1 * , Low Hon Loon Alfred 2
Student-success analytics has moved from experimental prediction toward an institutional capability for reducing attrition, allocating support resources, and improving digital learning governance. This paper develops a business oriented early-warning model for education technology environments in which predictive performance, interpretability, intervention priority, and governance are treated as joint design requirements. The study uses a public student-success dataset from a higher education institution and evaluates decisive outcome prediction for dropout and graduation, while preserving a wider discussion of the enrolled group as an unresolved operational state. The proposed model combines a transparent predictive layer, a risk-to-action prioritization layer, and a governance layer that restricts how predictions are translated into student support decisions. The results show that a parsimonious logistic specification can provide competitive performance compared with more complex tree based models, while producing clearer accountability for academic advising and digital student-success units. The discussion argues that student-success technology should not be judged by accuracy alone, but by whether the analytics pipeline produces timely, explainable, privacy-aware, and operationally usable support signals.
Education technology , Student success , Learning analytics , Dropout prediction , Early-warning systems , Higher education , Predictive governance
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