Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering
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Statistics > Applications
Title:Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering
Abstract: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.
| Comments: | 15 pages |
| Subjects: | Applications (stat.AP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.14124 [stat.AP] |
| (or arXiv:2607.14124v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2607.14124
arXiv-issued DOI via DataCite
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