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<p>School attendance is a fundamental prerequisite for children’s and adolescents’ academic, social, and personal development. Yet, despite decades of research and policy efforts, rates of problematic school absenteeism remain persistently high. This study investigates the predictive potential of school-level factors to identify schools at secondary level in Brazil at risk of elevated absenteeism. Using data from 22,476 schools, we consolidate three main categories of predictors: school infrastructure, human resources, and regional characteristics, combined with attendance records and demographic information. Five machine learning models were compared, and Random Forest was selected due to its balance of predictive performance, interpretability, and statistical confirmation through the DeLong test. Building on this, we apply interpretable machine learning techniques, particularly SHapley Additive exPlanations (SHAP), to reveal how the importance of predictors varies across different school complexity levels, defined as the structural and organizational conditions under which schools operate. Furthermore, we conduct counterfactual “what-if” scenarios, systematically perturbing key features to explore how policy-relevant adjustments could shift schools from high to low absenteeism risk. Our results highlight how structural, staff-related, and contextual features interact differently depending on school complexity, offering actionable insights to inform more equitable educational interventions and policy decisions.</p>

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Interpretable machine learning for predicting school absenteeism at-scale in Brazil: evidence from 22,000 schools

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

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  • Abílio Nogueira Barros,
  • Felipe Vieira Roque,
  • Tiago Paulino,
  • Augusto Schmidt,
  • Flavia Galvani,
  • Rafael Oliveira,
  • Leonardo Brandão Marques,
  • Diego Dermeval,
  • Anita Gea Martinez Stefani,
  • Marisa de Santana da Costa,
  • Emanuel Marques Queiroga,
  • Elthon Oliveira,
  • Cristian Cechinel,
  • Thales Vieira

摘要

School attendance is a fundamental prerequisite for children’s and adolescents’ academic, social, and personal development. Yet, despite decades of research and policy efforts, rates of problematic school absenteeism remain persistently high. This study investigates the predictive potential of school-level factors to identify schools at secondary level in Brazil at risk of elevated absenteeism. Using data from 22,476 schools, we consolidate three main categories of predictors: school infrastructure, human resources, and regional characteristics, combined with attendance records and demographic information. Five machine learning models were compared, and Random Forest was selected due to its balance of predictive performance, interpretability, and statistical confirmation through the DeLong test. Building on this, we apply interpretable machine learning techniques, particularly SHapley Additive exPlanations (SHAP), to reveal how the importance of predictors varies across different school complexity levels, defined as the structural and organizational conditions under which schools operate. Furthermore, we conduct counterfactual “what-if” scenarios, systematically perturbing key features to explore how policy-relevant adjustments could shift schools from high to low absenteeism risk. Our results highlight how structural, staff-related, and contextual features interact differently depending on school complexity, offering actionable insights to inform more equitable educational interventions and policy decisions.