Teaching Quality Reduces Socioeconomic Inequalities in Student Achievement - Evidence from PISA 2022

• In Event: Beyond Mere Performance: Educational Equity and Effectiveness in International Large-Scale Assessment Research

Abstract

Purposes and Theoretical framework: Teaching quality is widely recognized as key determinant of students’ academic achievement and is commonly conceptualized through three dimensions: cognitive activation, teacher support, and classroom management (Klieme et al., 2009). While the positive relationship between high-quality instruction and student achievement is well-established (Hattie, 2009), its potential to foster educational equity has received comparatively less empirical attention. Existing studies, often based on data from single-country contexts, have produced mixed findings regarding whether teaching quality can mitigate socioeconomic disparities in learning outcomes (Atlay et al., 2019). This gap is particularly relevant given the pivotal role schools play in reducing educational inequalities (González-Betancor et al., 2021). High-quality instruction has the potential not only to improve overall achievement but also to provide equitable learning conditions that support all students, regardless of socioeconomic background, in accessing and engaging with academic content.
Method, Data and Materials: Data from the 2022 cycle of the Programme for International Student Assessment (PISA) was analyzed to explore this question. The dataset encompasses responses from N = 607,635 15-year-old students across 79 countries. Educational achievement was defined in terms of mathematical competence, evaluated through standardized competence tests. Teaching quality was assessed based on students’ responses in a background questionnaire, focusing on their perceptions of their mathematics teacher’s classroom management, cognitive activation, and support. Socioeconomic status was determined using the ESCS composite index (OECD, 2024b). A meta-analytic approach was employed (Brunner et al., 2024), treating each participating country as a separate study to estimate a summary effect of the relationship between ESCS and mathematical competence. Subsequently, moderation analyses were conducted to investigate whether specific aspects of teaching quality influence the association between socioeconomic status and academic achievement.
Results: The results revealed a significant overall effect, with a summary regression coefficient of ß = .35 (SE = .01), 95% CI [.33, .37]. This finding indicates that a higher socioeconomic status is significantly associated with higher mathematical competence scores. Further, we examined whether aspects of teaching quality moderate the positive relationship between socioeconomic status and mathematical competence identified. The results revealed significant negative moderating effects were found for Cognitive activation (ß = -.14, SE = .03) and Teacher Support (ß = -11, SE = 0.03). These findings suggest that both teaching quality dimensions significantly weaken the relationship between socioeconomic status and mathematical competence, potentially reducing socioeconomic inequalities in student achievement. On the contrary, no significant moderating effect was found for Classroom Management (ß = -.04, SE = .04)
Scientific significance: The findings indicate that aspects of teaching quality, particularly cognitive activation and teacher support, are associated with a reduced link between students’ socioeconomic backgrounds and academic achievement. This aligns with theoretical perspectives regarding the equity potential of teaching quality (Nilsen et al., 2020) and addresses a significant gap in the existing literature (Atlay et al., 2019) by utilizing robust, large-scale international assessment data. These results carry crucial implications for educational research and policy, as they underscore the importance of teaching quality in fostering equity in educational outcomes.

Authors

• Tamara Kastorff, Technical University of Munich

• Pia Todtenhöfer, Technical University of Munich

• Samuel Greiff, University of Luxembourg