用机器学习分析巴西学生表现，发现学校整体水平比个人因素影响更大。
A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
- 融合学生、教师、学校和校长四类数据，构建多层级预测模型
- 随机森林模型准确率达90.2%，AUC达96.7%，优于其他三种集成算法
- 通过可解释AI揭示：学校平均社会经济水平是影响成绩的最关键因素
识别影响巴西基础教育阶段学生学业表现的因素，是制定有效公共政策的核心挑战。本研究采用多层级机器学习方法，基于教育评估体系（SAEB）的微观数据，对九年级及高中学生学业水平进行分类。模型整合了学生社会经济特征、教师专业背景、学校指标与校长管理特征四类数据。四种集成算法对比分析表明，随机森林模型表现最优，准确率达90.2%，受试者工作特征曲线下面积（AUC）为96.7%。为进一步超越预测，引入可解释人工智能（XAI）技术中的SHAP方法，结果显示学校平均社会经济水平是最关键的预测因子，表明系统性因素对学业表现的影响远超个体特征。研究结论强调，学业表现是一种深层嵌入学校生态系统的系统性现象。该研究提供了一个数据驱动且可解释的工具，可支持以缩小校际差距为目标的教育公平政策制定。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。