How reading competence and age at arrival shape educational outcomes: An analysis of academic success among immigrant students in Germany using data from PISA 2012-2022

Background Germany has one of the largest performance gaps between immigrant and non-immigrant students across all PISA competencies, with disparities persisting even after adjusting for socioeconomic factors. Current large-scale assessment studies often treat immigrant students as a homogeneous group, potentially obscuring critical heterogeneity in educational experiences and masking opportunities for targeted intervention. This homogenization particularly affects policy responses to the increasing diversity of immigrant populations following recent migration waves. Purpose This study investigates how migration-related biographical factors—particularly age at arrival and reading competence—predict academic achievement among 1st generation immigrant students in Germany. Using PISA data from four cycles spanning a decade of educational and demographic change, we examine temporal stability of these relationships and their implications for assessment methodology and educational equity. Methods We analyzed 1st generation immigrant students' performance across four PISA cycles (2012–2022) using multilevel modeling where students are nested within assessment years. Predictors included data-driven age at arrival categories, teacher-rated reading comprehension, home language environment, and socioeconomic indicators. Results Teacher-rated reading comprehension demonstrated the strongest predictive power across all assessment outcomes. Our models best explained reading performance, followed by science and mathematics at a considerable distance. Socioeconomic factors proved irrelevant in all models except for mathematics performance. No model supported the assumption that reading comprehension changed between assessment years. Conclusions Current PISA reporting practices that aggregate immigrant students' obscure meaningful heterogeneity and may perpetuate educational inequities. Teacher-based assessments showed higher predictive validity, suggesting standardized tests may systematically underestimate immigrant students’ capabilities by conflating language proficiency with content knowledge. These findings support implementing differentiated reporting frameworks that acknowledge migration timing and language development trajectories.