University Student Dropout: Exploring the Explainability of the Predictive Model

Flores V.; Heras S.; Julian V. · Communications in Computer and Information Science (2025) · DOI: 10.1007/978-3-031-73058-0_11

Resumen

University dropout is a persistent challenge that affects students, institutions and the education system in general. To address it, it is necessary to understand the causes and how they are integrated into Artificial Intelligence (AI) models. The proposal goes beyond predicting dropout, it is now required to complement it with a global and local perspective of the model using AI explainability techniques (XAI). Model transparency is crucial to support informed decision making. This study describes an approach to interpret AI models in a Latin American university, using techniques such as Shapley Additive exPlanations (SHAP), and to serve as a tool to design and deploy local and global intervention actions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Instituciones: Universidad Nacional de Moquegua