GA-TabNet: a novel approach for early dropout prediction in MOOCs based on genetic algorithms and TabNet

Massive open online courses (MOOCs) represent one of the most effective educational methodologies due to their cost-effectiveness, flexibility, ubiquity, and their role in facilitating and improving education. MOOCs possess the capacity to revolutionise global education; nevertheless, the high dropout rates often undermine their effectiveness. The emergence of machine learning, deep learning techniques, and educational big data enables academics to address the student dropout problem through big data analytics. This study addresses the critical challenges of student dropout prediction by proposing GA-TabNet, an innovative model that combines a genetic algorithm with TabNet for early dropout prediction. The results of this study were validated using the Open University Learning Analytics dataset. The proposed model attained an average accuracy exceeding 92%. Furthermore, it outperformed traditional predictive models, including support vector machine, long short-term memory, logistic regression, multilayer perceptron, decision trees, and random forest models, by margins ranging from 0.79% to 4.79%.