Mitigating Cognitive Biases in Predicting Student Dropout: Global and Local Explainability with Explainable Boosting Machine

This study explores the application of Explainable Artificial Intelligence (XAI) techniques to mitigate cognitive biases in predicting student dropout. Focusing on the Explainable Boosting Machine (EBM), we compare its performance and explainability with Logistic Regression and XGBoost models. While EBM and Logistic Regression have inherent explainability, we employ SHAP and Morris Sensitivity Analysis for XGBoost to provide both local and global explanations. Our findings indicate that the inherently interpretable nature of EBM supports clear and actionable decision-making in educational settings. When integrated with additional XAI methods for comparative analysis with models like Logistic Regression and XGBoost, the approach can further enhance the understanding of key factors contributing to student dropout.