Academic resilience in the Latin America region post COVID-19 pandemic -- an explainable machine learning analysis of its determinants and heterogeneity using alternative definitions

Abstract

The learning crisis in the Latin American region (i.e., higher rates of students not reaching basic competencies at secondary level) is worrying, particularly post-pandemic given the stronger role of inequality behind achievement. Within this scenario, the concept of student academic resilience (SAR), students who despite coming from disadvantaged backgrounds reach good performance levels, and an analysis of its determinants, are policy relevant. In this paper, using advancements on explainable machine learning methods (the SHAP method) and relying on PISA 2022 data for 9 countries from the region, we identify leading factors behind SAR using diverse indicators. We find that household inputs (books and digital devices), gender, homework, repetition and work intensity are leading factors for one indicator of academic resilience, whereas for other indicator leading drives fall into the school domain: school size, the ratio of PC connected to the internet, STR and teaching quality proxied by certified teachers and professional development rates and school type. Also, we find negative associations of SAR with the length of school closures and barriers for remote learning during the pandemic. The paper's findings adds to the scare regional literature contributing to future policy designs where key features behind SAR can be used to lift disadvantaged students from lower achievement groups towards being academic resilient.