Analyzing University Dropout Rates Using Bayesian Methods: A Case Study in University Level in Mexico

In Mexico's higher education, student dropout issue is critical, influenced by multiple factors. The Instituto Tecnológico Superior de Comalcalco (ITSC) recorded 3,499 students across eight engineering and two bachelor's programs in 2018, experiencing a significant dropout rate of 9%. This article presents a web platform using Bayesian methods to analyze and interpret dropout data from ITSC. Specifically, the Computer Systems Engineering program, most impacted by dropouts, was analyzed to identify underlying patterns and factors. Utilizing Naive Bayes models, the platform categorizes students and assesses dropout probabilities, offering insights for data-driven strategies to mitigate this challenge in higher education.