Machine learning model identifies socioeconomic level as key predictor of student performance

Researchers have developed a multi-level machine learning model to analyze student performance using microdata from Brazil's System of Assessment of Basic Education (SAEB). The study integrated data on student socioeconomic status, teacher profiles, school indicators, and principal management. A Random Forest model achieved 90.2% accuracy and an AUC of 96.7%, outperforming other ensemble algorithms. Explainable AI (XAI) techniques revealed that the school's average socioeconomic level is the most significant predictor of student performance, highlighting the systemic nature of academic achievement. AI

IMPACT Provides an interpretable tool for policymakers to address educational disparities by identifying systemic factors influencing student performance.

RANK_REASON Academic paper detailing a machine learning approach to analyzing student performance data. [lever_c_demoted from research: ic=1 ai=1.0]

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