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.

Schools at risk: mapping learning poverty in Italian schools

Rossi, Lidia;Soncin, Mara;Lema, Melisa Lucia Diaz;Agasisti, Tommaso
2025-01-01

Abstract

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.
2025
Educational inequalities
Learning poverty
Regional disparities
Resilient schools
Schools at risk
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1311194
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