Adaptive Ensemble Meta-Learning for Enhanced Student Focus Prediction

Accurately predicting when students lose focus is essential to educational data mining systems. The system benefits from timely interventions and personalized learning strategies through this signal. Previous works, however, have not been able to achieve this goal since they only utilize simple ensemble strategies and have shallow feature representations. A meta-learning ensemble architecture with an adaptive mechanism for prediction of student focus is introduced in this paper. It combines residual correction XGBoost, multi-stage gradient boosting with adaptive learning rates, and a confidence-weighted dynamic meta-learner.Our method averts to the major three problems highlighted in the previous work: 1) Single-stage error correction is improved by two-stage residual learning mechanism for the first time; 2) By performance-aware and diversity-enhanced meta-learning, static ensemble weighting is replaced with dynamic ensemble weighting; 3) Four tailor-made behavioral interaction features for student engagement dynamics are used to substitute generic feature engineering. The large-scale dataset of 14,003 students was used for the evaluation of the model, and the results were outstanding: R2=0.9989 (99.89% variance explained) and RMSE=0.2016. The performance reported here is 17.49% better than the average of existing literatures (82.4%) and 1.89% better than the best single-model approach (98.0%). Moreover, ablation experiments verify that each proposed component leads to quantifiable performance increases. The cross-dataset evaluation on independent Kaggle data resulting in R2=0.9580 with a 0.41% train-test gap, whereas an 8.0% gap in previous work. SHAP explainability analysis shows that the custom engagement proxy feature is the most influential factor, followed by stress level and attendance.