A hybrid deep Kronecker and BN-eLU-AlexNet framework for early prediction of learner outcomes in e-learning platforms

Introduction Early prediction of learner outcomes in online learning environments can support timely academic intervention and learner support. Methods This study proposes DKBNE-AlexNet, a hybrid architecture combining a Deep Kronecker Network branch, a feature-token batch-normalized eLU AlexNet-inspired branch, gated residual fusion, and a wide linear residual path. Using the Open University Learning Analytics Dataset, 24,153 enrolments from 22 module presentations and 22,234 learners were analyzed. Eighty-four predictors were constructed from learner characteristics, course design, and engagement during the first 25% of each presentation. The target was the weighted mean of tutor-marked and computer-marked assessment scores. Models were evaluated using five-fold cross-validation grouped by module presentation and repeated three times. Results DKBNE-AlexNet achieved an MAE of 11.424 (95% group-bootstrap CI 10.389-12.560), RMSE of 14.843, and R 2 of 0.117. Random Forest and LightGBM achieved slightly lower MAEs of 10.916 and 10.918, respectively, but the presentation-level difference between DKBNE-AlexNet and Random Forest was not statistically significant (Wilcoxon p = 0.388; Holm-adjusted p = 1.000). Distinct learning materials accessed and active learning days were among the strongest predictive contributors. Discussion DKBNE-AlexNet provides a competitive and stable framework for early learner-outcome prediction, while strong tree-based models remain important baselines. The framework is best interpreted as an early-warning learning-analytics tool rather than a direct measure of teacher effectiveness.