用因果模型和聚类分析巴西学校表现差异，发现不同学校类型影响教育成果。
Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering
- 通过聚类识别出具有相似结构、教学与人口特征的学校类型。
- 不同学校类型间平均成绩存在显著差异，部分因素有因果关联。
- 为理解学校效能机制提供数据支持，适合教育政策研究者参考。
大规模教育数据的可用性推动了定量方法在学校表现研究中的应用。然而，学校间的异质性及教育数据的结构复杂性给传统统计建模带来挑战。本研究基于巴西基础教育评估系统（Saeb）数据，结合巴西学校普查数据，通过数据预处理与归一化后进行层次聚类，识别出具有相似结构、教学与人口特征的学校类型。随后采用因果分析技术探究学校特征与教育成果之间的潜在因果关系。结果表明存在明显不同的学校类型，且各类别间平均表现有统计学显著差异。因果分析揭示了可能影响教育表现的结构性与情境性因素，有助于深化对学校有效性机制的理解。
原文摘要 · Abstract (English)
The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance. However, institutional heterogeneity among schools and the structural complexity of educational data pose substantial challenges to traditional statistical modeling approaches. This study investigates the existence of school typologies based on structural, pedagogical, and demographic characteristics, and examines how these typologies relate to performance in the Brazilian Basic Education Assessment System (Saeb). Using data from the Brazilian School Census and Saeb, data preprocessing and normalization procedures are applied followed by hierarchical clustering to identify groups of schools with similar structural profiles. After the identification of these typologies, causal analysis techniques are employed to investigate potential causal relationships between school characteristics and educational outcomes. The results reveal the presence of distinct school profiles and statistically significant differences in average performance among them. The causal analysis provides insights into the structural and contextual factors that may influence educational performance, contributing to a better understanding of the mechanisms associated with school effectiveness.
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。