Date
Publisher
arXiv
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 principal 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.
Study at a glance
- Question: Which individual, academic, socioeconomic, and institutional factors best predict Brazilian basic education student performance?
- Setting and data: Brazil, 2023 SAEB assessment microdata covering 6,482,168 valid 9th-grade and high school student records across 70,151 schools.
- Method: Supervised machine learning classification comparing four ensemble models with Boruta feature selection and explainable AI (SHAP TreeExplainer).
Why it matters
Identifying the determinants of academic success is a central challenge for formulating effective educational policies in Brazil, where student performance spans multiple systemic and individual analytical levels.
Key findings
- A Random Forest model achieved 90.2% accuracy and an AUC of 96.7% in classifying students as performing above or below average.
- A school's average socioeconomic level was the single most dominant predictor of student performance, showing systemic context has a greater impact than isolated individual attributes.
- Higher parental education, home computer access, and higher percentages of adequately trained teachers were strongly associated with above-average student performance.
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