ENEM under a Socioeconomic Perspective: Analysis and Evaluation Through Dimensionality Reduction - Mendieta, Cristiano C.; Vignatti, André L. (Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), 2026)
This study investigates the relationship between socioeconomic factors and student academic performance in the 2022 ENEM, applying dimensionality reduction techniques to the microdata set provided by INEP. This dataset includes information collected from the exam, such as test scores, answer keys, evaluated items, participant scores, and responses to the socioeconomic questionnaire. The research compares linear methods, such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Independent Component Analysis (ICA), with non-linear methods, such as Autoencoders and Pairwise Controlled Manifold Approximation Projection (PaCMAP), in binary and multiclass classification scenarios. The results indicate that linear methods provide a good balance between accuracy and computational efficiency, especially in binary classification scenarios. However, non-linear methods are more suitable for capturing complex structures in multiclass classifications, despite their higher computational cost. The Feature Selection technique using XGBoost proved effective in identifying key variables that differentiate students based on socioeconomic characteristics and academic performance. This study provides a comprehensive analysis of large educational datasets, generating results that can guide the formulation of public policies aimed at promoting equity within the Brazilian educational system.