发表机构
Labcin - Nature Inspired Computing Lab, Federal University of Esp\'irito Santo, Vit\'oria, Brazil PPGI - Graduate Program in Computer Science, Federal University of Esp\'irito Santo, Vit\'oria, Brazil
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用巴西学校普查和基础教育评估系统数据，经预处理、归一化及层次聚类识别学校类型，再用因果分析技术探究学校特征与教育成果的潜在因果关系，揭示不同学校概况及平均表现差异，助于理解影响教育绩效的因素。
AI 中文摘要
大规模教育数据集可用性增加，促使定量方法用于研究学校表现，但学校机构异质性和教育数据结构复杂性给传统统计建模带来挑战。本研究基于结构、教学和人口特征调查学校类型，利用巴西学校普查和基础教育评估系统（Saeb）数据，经预处理、归一化及层次聚类识别相似结构特征的学校组，再用因果分析技术探究学校特征与教育成果潜在因果关系。结果揭示不同学校概况及平均表现的显著差异，因果分析有助于理解影响教育绩效的因素。
英文摘要
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.
Comments15 pages
