2025

Conférence

L’étude vise à prédire les abandons dans les MOOCs en comparant des modèles d’apprentissage automatique (ML) et d’apprentissage profond (DL), tout en intégrant des techniques d’explicabilité (XAI) pour comprendre les comportements des apprenants.

Massive Open Online Courses (MOOCs) face high
dropout rates, often exceeding 80%, undermining their educational
potential. This study presents a comparative evaluation
of Machine Learning (ML) and Deep Learning (DL) models for
early dropout prediction using the KDDCup2015 dataset, with a
dual focus on predictive performance and model interpretability
through eXplainable AI (XAI) techniques. Among traditional
ML models, the Decision Tree (DT) achieves the highest
performance (90.18% AUC-PR by Week 4), outperforming
Logistic Regression (LR) and Support Vector Machine (SVM).
In the ensemble category, AdaBoost leads with 90.35% AUCPR.
The hybrid CNN-LSTM outperforms standalone CNN and
LSTM models, reaching up to 93,76% AUC-PR. XAI analysis
reveals that frequent platform access, navigation patterns and
problem solving activities are key predictors of dropout. These
insights support early interpretable interventions to improve
learner retention while maintaining model transparency.

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