Multi-Level Explainable AI for Predicting Student Depression Risk: Global, Subgroup, and Individual Insights

Depression among university students is a critical global problem that affects academic performance, well-being, and dropout rates. Although machine learning models offer strong predictive power, their opacity limits their adoption in educational and clinical contexts. Most existing approaches empha...

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Autors principals:	Leonard Chukwualuka Nnadi, Chukwuemeka Paul Isiwu, Dake Ding, Daniel M. Muepu, Yutaka Watanobe
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2026-01-01
Col·lecció:	IEEE Access
Matèries:	Explainable artificial intelligence (XAI) student depression prediction SHAP hierarchical LIME (H-LIME) counterfactual explanations educational data mining
Accés en línia:	https://ieeexplore.ieee.org/document/11345125/
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