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

JUCS - Journal of Universal Computer Science 31(10): 1017-1041, doi: 10.3897/jucs.131773

Rodrigo Costa Camargos^‡, Ismar Frango Silveira^‡

‡ Universidade Presbiteriana Mackenzie, São Paulo, Brazil

Corresponding author: Rodrigo Costa Camargos ( rodrigocamargos@hotmail.com )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Citation: Camargos RC, Silveira IF (2025) Mitigating Cognitive Biases in Predicting Student Dropout: Global and Local Explainability with Explainable Boosting Machine. JUCS - Journal of Universal Computer Science 31(10): 1017-1041. https://doi.org/10.3897/jucs.131773

Abstract

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.

Keywords

Learning Analytics, Explainable Artificial Intelligence, Distance Education