Explainable Early Warnings for Dropout in Distance Education Through Longitudinal Assessment Trajectories: Evidence from the Open University Learning Analytics Dataset

Early-warning models in distance education must distinguish future withdrawal from events that have already occurred and make the information available at prediction time explicit. Using the Open University Learning Analytics Dataset (OULAD), we constructed risk sets at days 28, 56 and 84 containing 27,515, 26,506 and 25,713 eligible enrollments, with future-withdrawal rates of 18.55%, 15.43% and 12.81%. Logistic regression, random forest and XGBoost were evaluated on the later 2014J presentation after student-grouped development and probability calibration. Logistic regression achieved ROC-AUCs of 0.687, 0.692 and 0.710. Specifically in the day-84 temporal holdout, its ROC-AUC was 0.7102 versus 0.7104 for random forest; the paired difference was 0.0002 (95% CI −0.0105 to 0.0119). A simultaneous regression identified independent associations for assessment completion, weighted performance and lateness, whereas submission inactivity added weaker evidence after mutual adjustment. Day-84 discrimination remained 0.708 in a student-disjoint test subset and 0.682 when score-derived predictors were removed. Adding basic virtual-learning-environment indicators improved earlier discrimination but provided no clear day-84 gain. At the day-84 F2-selected threshold, 427 alerts per 1000 enrollments included approximately 93 future withdrawals and 333 false positives. Subgroup error differences and imperfect calibration support capacity-aware human review, local recalibration and prospective evaluation of intervention effects.