Academic Resilience Among Socioeconomically Disadvantaged Students: A Data Analysis Based on PISA 2022 Data

This study investigates academic resilience—the success of disadvantaged students despite adversity—using PISA 2022 data. We examine predictors of mathematical resilience in socioeconomically disadvantaged students (bottom 25% ESCS), where resilience is defined as achieving top-quartile math performance within their country. The analysis uses fixed-effects regression, multiple imputation for missing data, and principal component analysis (PCA) on a multidimensional set of predictors. The models account for PISA's complex survey design and measurement error.Key findings show that a positive mathematics learning disposition (e.g., self-efficacy) is the strongest predictor of resilience. Conversely, adverse home learning environments, grade repetition, and paid work are significant negative predictors. We also find that female students show lower resilience, with significant variation across countries. Sensitivity analyses confirmed the robustness of these findings.The study provides evidence on protective and risk factors to inform targeted educational interventions and highlights the importance of fostering a resilient mindset.