A Novel Optimised Feature Selection Method for In-Session Dropout Prediction Using Hybrid Meta-Heuristics and Multi-Level Stacked Ensemble Learning

High dropout rates on in-session learning platforms pose a significant challenge to student retention and the overall success of educational programmes. This study proposes a novel framework that integrates multi-level stacked ensemble learning with optimised feature selection using a hybrid approach combining Genetic Algorithm (GA) with Correlation-Based Feature Selection (CFS). The model employs a Multi-Layer Perceptron (MLP) as a meta-learner, aggregating predictions from multiple ensemble-based base classifiers to enhance predictive accuracy. To improve generalisation and reduce noise, the proposed approach applies GA-CFS-driven feature optimisation in conjunction with data balancing techniques. Experimental results demonstrate that the proposed model outperforms benchmark approaches, achieving improvements of up to 22% in prediction accuracy and 12% in F1-score over standard stacked ensemble methods. These results highlight the effectiveness of combining meta-heuristic optimisation with ensemble learning to advance dropout prediction in online learning environments.