A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata

• URL: http://arxiv.org/abs/2510.22266v1
• Date: Sat, 25 Oct 2025 12:15:30 GMT
• Title: A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
• Authors: Rodrigo Tertulino, Ricardo Almeida,
• Abstract summary: This study introduces a multi-level machine learning approach to classify the proficiency of 9th-grade and high school students.<n>Our model integrates four data sources: student socioeconomic characteristics, teacher professional profiles, school indicators, and director management profiles.
• Score: 0.07161783472741748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying the factors that influence student performance in basic education is a central challenge for formulating effective public policies in Brazil. This study introduces a multi-level machine learning approach to classify the proficiency of 9th-grade and high school students using microdata from the System of Assessment of Basic Education (SAEB). Our model uniquely integrates four data sources: student socioeconomic characteristics, teacher professional profiles, school indicators, and director management profiles. A comparative analysis of four ensemble algorithms confirmed the superiority of a Random Forest model, which achieved 90.2% accuracy and an Area Under the Curve (AUC) of 96.7%. To move beyond prediction, we applied Explainable AI (XAI) using SHAP, which revealed that the school's average socioeconomic level is the most dominant predictor, demonstrating that systemic factors have a greater impact than individual characteristics in isolation. The primary conclusion is that academic performance is a systemic phenomenon deeply tied to the school's ecosystem. This study provides a data-driven, interpretable tool to inform policies aimed at promoting educational equity by addressing disparities between schools.

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