AfriLearn Lens: Explainable Behavioural Engagement Detection for Students in Low-Resource African Learning Environments
AfriLearn Lens: Explainable Behavioural Engagement Detection for Students in Low-Resource African Learning Environments
Predicting and understanding student engagement in online learning environments remains a critical challenge, particularly in African contexts where low-resource infrastructure limits the depth of available data. This paper introduces AfriLearn Lens, a machine learning pipeline that applies XGBoost classification and SHAP-based explainability to predict three-tier student engagement levels (Low, Medium, High) from Virtual Learning Environment (VLE) behavioural signals, using the Open University Learning Analytics Dataset (OULAD). The model achieves 80.78% accuracy on a held-out test set of 6,519 students. SHAP analysis reveals that temporal behavioural features — specifically active_days and unique_resources — are substantially stronger predictors of engagement than demographic variables. All code and results are available at https://github.com/Olameta/afrilearn-lens
learning analytics, educational data mining, SHAP, Africa, Machine learning, student engagement, Supervised Machine Learning, XGBoost, explainable AI, OULAD
learning analytics, educational data mining, SHAP, Africa, Machine learning, student engagement, Supervised Machine Learning, XGBoost, explainable AI, OULAD
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