Governed Early-Warning Analytics for Student Success in Digital Higher
Education: A Business-Oriented Evidence Model
Nahla Moussa1,∗, Low Hon Loon Alfred2
1Associate Professor, Amity University Dubai, UAE
2Senior Manager (Instructional Design), Higher Colleges of Technology, Abu Dhabi, UAE
Emails: nahlamoussa2020@gmail.com; lalfred@hct.ac.ae
Abstract
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.
Keywords: Education technology; Student success; Learning analytics; Dropout prediction; Early-warning systems; Higher
education; Predictive governance