Unveiling Patterns in Maranhão's 2023 ENEM Results Through Unsupervised Machine Learning

Abstract

This investigation examines the performance of students from Maranhão, Brazil, in the 2023 edition of the National High School Exam (ENEM) using unsupervised machine learning to uncover latent patterns in large-scale educational data. Leveraging the CRISP-DM framework on microdata from millions of ENEM participants, we applied Recursive Feature Elimination (RFE) with a Random Forest classifier to select key socioeconomic variables, followed by association rule mining using the FP-Growth algorithm across multiple experimental configurations. The results reveal strong associations between low academic performance and factors such as parental education and occupation, lack of household technology (e.g., computers and washing machines), and gender. These findings demonstrate the utility of unsupervised learning for descriptive educational analytics, providing practical insights for targeted policy-making, resource allocation, and regional equity monitoring. This research addresses the underrepresentation of Maranhão in the educational data mining literature and proposes a scalable analytical framework applicable to other developing regions. Despite limitations in data completeness, the approach offers a replicable model for using artificial intelligence to inform public education strategies in socially vulnerable areas.