Anal Acharya

Explainable Machine Learning for Student Outcome Prediction using Boosting and Additive Models

This study proposes an explainability-driven framework for student outcome prediction by comparing a black-box high-performance model with an inherently interpretable model. While machine learning models are increasingly used for academic earlywarning systems, their lack of transparency limits real-world deployment...