Beyond Grades: Multi-Target Deep Learning for Early Academic Risk Detection

Source abstract

Early identification of academic risk remains a major challenge in public higher education, where institutional data are often fragmented and underused. This study proposes a multi-output deep learning model to predict five formative indicators during the semester: assignment performance, missing assignments, procrastination rate, exam performance, and exam accuracy. The dataset included 4,230 student-course records and 26 features extracted from learning and assessment platforms. A multitask regression architecture with shared and task-specific layers was optimized through Bayesian search and regularization. The model achieved strong predictive performance (R² = 0.9803; MAE = 0.0239; RMSE = 0.0404), outperforming traditional machine learning baselines. SHAP analysis identified temporality and engagement variables as the most influential predictors. Additional ordinal and clustering analyses revealed engagement profiles useful for early feedback and support. Results highlight the value of multi-output learning analytics for timely and privacy-aware academic intervention.