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

TL;DR

This study employs a multi-level machine learning approach, particularly Random Forest, combined with Explainable AI to identify systemic factors influencing student performance in Brazil's basic education, highlighting the importance of school-level socioeconomic context.

Contribution

It introduces an integrated multi-source data model and applies XAI to reveal systemic influences on student performance, advancing policy-relevant insights.

Findings

Random Forest achieved 90.2% accuracy and 96.7% AUC.

School socioeconomic level is the most influential predictor.

Systemic factors outweigh individual characteristics in affecting performance.

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 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.