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.

翻译：识别基础教育阶段学生成绩的影响因素是巴西制定有效公共政策的核心挑战。本研究提出一种多层次机器学习方法，利用巴西基础教育评估系统（SAEB）的微观数据对九年级和高中学生的学业水平进行分类。该模型创新性地整合了四类数据源：学生社会经济特征、教师专业背景、学校指标及校长管理概况。对四种集成算法的对比分析证实了随机森林模型的优越性，该模型达到90.2%的准确率与96.7%的曲线下面积（AUC）。为超越预测层面，本研究采用基于SHAP的可解释人工智能（XAI）方法，揭示学校平均社会经济水平是最显著的预测因子，证明系统因素对学业表现的影响远大于孤立个体特征。核心结论表明：学业成绩是与学校生态系统深度关联的系统性现象。本研究为促进教育公平的政策制定提供了数据驱动、可解释的工具，旨在消除学校间的系统性差距。