Exploring Multi-Level Predictors of Science Achievement in Korea: A Hierarchical Linear Modeling Approach

This study investigates the multilevel factors influencing science achievement among Korean secondary school students using data from the PISA 2022 assessment. Employing hierarchical linear modeling (HLM), the analysis incorpo- rates predictors at both student and school level to capture the nested structure of educational data. The results indicate that socio-economic status (SES) and science-related attitudes are the key determinants of science achievement, with student-level factors exerting a more substantial influence than school-level characteristics. While school size shows a small but significant positive effect, student-teacher ratio and school type exhibit minimal impact. These findings underscore the importance of fos- tering positive attitudes towards science and ensuring equitable learning opportunities to reduce educational disparities. This study provides valuable insights for policymakers and educators aiming to enhance science education through targeted, student-centered interventions.