Hou-Shi Yu

Cross-Fitted Multi-View Neural Risk Augmentation for Interpretable University Dropout Prediction

Early dropout prediction requires models that integrate heterogeneous educational records without information leakage. We propose multi-view neural risk augmentation (MVNRA), a cross-fitted framework that separates early-warning predictors into academic, contextual, and digital-engagement views. View-specific encoders...