A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
arXiv:2510.22266v3 Announce Type: replace-cross 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
The increasing availability of large educational microdata sets and advancements in machine learning techniques are enabling more sophisticated analyses of socio-educational factors.
This study demonstrates how AI can be used to improve public policy formulation in critical sectors like education, directly impacting human capital development and national competitiveness.
The application of multi-level machine learning models offers a more granular understanding of student performance drivers, allowing for more targeted and evidence-based educational interventions.
- · Brazilian government
- · Educational policymakers
- · Data scientists in education
- · Students in Brazil
- · Ineffective educational programs
- · Traditional, less data-driven policy approaches
More effective allocation of resources in the Brazilian basic education system through data-driven insights.
Improved educational outcomes could boost long-term economic productivity and reduce social inequality in Brazil.
Successful implementation could inspire other nations to adopt similar AI-driven approaches to public policy in education and other sectors.
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Read at arXiv cs.AI