An Optimised Hybrid Framework for Predicting Learner Dropout in Massive Open Online Courses Using Machine Learning and Behavioural Analytics

Abstract

This research addresses the persistent challenge of high dropout rates in Massive Open Online Courses (MOOCs) by developing and evaluating an enhanced predictive model capable of identifying learners at risk of disengagement. The study introduces the Integrated Stacked Ensemble Learning Dropout Prediction (ISELDP) model, which combines machine learning and meta-heuristic optimisation to improve predictive performance and interpretability. Employing an explanatory, sequential quantitative methodology, the research was conducted in two phases: (1) developing and testing the proposed model using a secondary in-session dataset, and (2) validating the model with primary behavioural and demographic data collected from 466 MOOC participants through a structured survey. The proposed ISELDP integrates multiple base learners (random forest, AdaBoost, gradient boosting, and XGBoost) with a multilayer perceptron (MLP) meta-learner, supported by an optimised feature selection framework based on a hybrid genetic algorithm–correlation feature selection (GA–CFS) technique. The evaluation across balanced and unbalanced datasets demonstrates that the enhanced ISELDP significantly outperforms benchmark models, achieving an average accuracy of 91% and an F1-score of 88%. The hybrid optimisation process effectively reduces feature redundancy, mitigates overfitting, and enhances the model’s generalisability across heterogeneous data. Complementary statistical analyses of the survey data identified motivation and engagement as the most influential predictors of MOOC retention, mediated by self-management and social influence factors, and indirectly affected by course design. These findings emphasise the centrality of motivational and behavioural dimensions in sustaining learner persistence, offering a broader understanding of dropout triggers beyond in-session activity metrics. The research contributes theoretically by defining the specifications of effective dropout prediction models that integrate deep learning and meta-heuristic optimisation. Methodologically, it advances predictive learning analytics through a validated hybrid framework that balances accuracy, interpretability, and adaptability in dynamic learning environments. Practically, it provides a data-driven foundation for designing proactive retention strategies and personalised interventions in MOOCs. Despite limitations related to data scope, computational complexity, and temporal coverage, the findings establish a replicable and extensible model for future cross-platform and longitudinal studies in predictive educational analytics. Compared to state-of-the-art approaches, this study proposes a hybrid GA–CFS feature selection method embedded in a stacked ensemble framework. The proposed approach yields a more compact feature set while enhancing prediction accuracy and robustness compared to conventional single-method techniques.