Predicting E-Learning Efficacy and Gap Closure in Post-Pandemic Higher Education Using Machine Learning

The COVID-19 pandemic shifted higher education from traditional in-person classrooms toward flexible digital delivery. While emergency remote teaching preserved instructional continuity during lockdowns, it also widened academic disparities. This study examines whether online education has since closed the learning gap it helped create. We analyzed 509 survey responses collected across the eight administrative divisions of Bangladesh, yielding 284 unique response profiles after duplicate removal. Using a quantitative supervised machine learning framework, we evaluated seven socio-technical predictors: administrative division, internet access quality, device availability, teacher online class hours, student engagement, community readiness, and institutional support. Random Forest, K-Nearest Neighbors, and Support Vector Machine classifiers were tuned by grid search with 5-fold cross-validation on the training partition and assessed on a held-out test set. Random Forest achieved the strongest holdout performance, with a test accuracy of 84.21% and a weighted F1-score of 0.83, while SVM attained the highest cross-validation accuracy at 83.29%. Feature importance analysis identified Institutional Support as the dominant contributor at 0.54, followed by regional context at 0.12. Across the evaluated classifiers, the surveyed cohort predominantly reflected a perception that the educational gap was only partially closed. These findings indicate that although online platforms have improved flexibility, access, and instructional continuity, virtual tools alone cannot replicate the collaborative presence of physical classrooms. Digital learning therefore works best as a complementary component of a balanced hybrid model rather than a full replacement for traditional instruction.