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

Authors: Houssam Eddine Aouarib; Fatima Zohra Laallam; Salah Eddine Henouda; Mohamed Fouzi Djouhri

Addresses: Artificial Intelligence and Information Technology Laboratory (LINATI), Department of Computer Science and Information Technologies, Faculty of New Technologies of Information and Communication, Kasdi Merbah University, Ouargla, Algeria ' Artificial Intelligence and Information Technology Laboratory (LINATI), Department of Computer Science and Information Technologies, Faculty of New Technologies of Information and Communication, Kasdi Merbah University, Ouargla, Algeria ' Artificial Intelligence and Information Technology Laboratory (LINATI), Department of Computer Science and Information Technologies, Faculty of New Technologies of Information and Communication, Kasdi Merbah University, Ouargla, Algeria ' Artificial Intelligence and Information Technology Laboratory (LINATI), Department of Computer Science and Information Technologies, Faculty of New Technologies of Information and Communication, Kasdi Merbah University, Ouargla, Algeria

Abstract: 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%.

Keywords: student dropout prediction; massive open online course; MOOC; TabNet; genetic algorithm; big data; machine learning; ML; deep learning; DL.

DOI: 10.1504/IJBIDM.2026.152475

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.2/3, pp.120 - 151

Received: 12 Dec 2024
Accepted: 11 Aug 2025
Published online: 23 Mar 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article