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<p>Early identification of schools with a high percentage of students at risk of learning poverty is crucial for effective and targeted interventions. This study investigates the use of an innovative combination of large-scale administrative datasets and advanced statistical techniques to predict schools at risk of learning poverty in Italy in the 2018–2019 academic year. The aim is to identify school-level factors associated with learning poverty, with a specific focus on socioeconomically deprived and resilient schools. In addition to school demographic characteristics, the findings highlight the importance of factors related to teachers, principals, funding, and parental involvement. The results provide insights for policy-makers to design targeted interventions and allocate resources more efficiently to reduce learning poverty and prevent dropout.</p>

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Schools at risk: mapping learning poverty in Italian schools

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月1日

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  • Lidia Rossi,
  • Mara Soncin,
  • Melisa Lucia Diaz Lema,
  • Tommaso Agasisti

摘要

Early identification of schools with a high percentage of students at risk of learning poverty is crucial for effective and targeted interventions. This study investigates the use of an innovative combination of large-scale administrative datasets and advanced statistical techniques to predict schools at risk of learning poverty in Italy in the 2018–2019 academic year. The aim is to identify school-level factors associated with learning poverty, with a specific focus on socioeconomically deprived and resilient schools. In addition to school demographic characteristics, the findings highlight the importance of factors related to teachers, principals, funding, and parental involvement. The results provide insights for policy-makers to design targeted interventions and allocate resources more efficiently to reduce learning poverty and prevent dropout.