In this study we utilise data from multiple cycles of Trends in Mathematics and Science Study (TIMSS) to investigate changes in the relationship between opportunity to learn (OTL), socioeconomic status (SES), and achievement in mathematics and science over time and across countries. The two core concepts in this study are SES and OTL. Our first concept, SES, has long been established as a key predictor of academic outcomes (White, 1982). Our second concept is OTL. At its heart, OTL focuses on the notion that students will test better on topics and content that they have been taught than those they have not (Eggen, Pelgrum, & Plomp, 1987). The main analytical model is a mediation model where SES and OTL have direct effects on achievement. Furthermore, SES has an indirect effect on achievement which is mediated through OTL, which tests for different educational opportunity. Studies examining OTL on the individual level imply that the concept is a promising predicator of academic achievement, with meta-analyses of school effectiveness variables demonstrating OTL to be a consistent predictor of achievement, with average effect size (d-coefficient) of .44 (Scheerens, 2017). OTL has been demonstrated to meditate the relationship between SES and achievement in mathematics in studies using PISA data (see Schmidt, Burroughs, Zoido, & Houang, 2015), while in studies of TIMSS data, students from more advantaged backgrounds have been shown to receive greater exposure to higher mathematics content (Carnoy, Khavenson, Loyalka, Schmidt & Zakharov, 2016). We examine the fit of various operationalisations of the OTL construct (e.g. content coverage alone or in conjunction with preparedness to teach) in both mathematics and science. The study includes data from some 25 countries, representing diverse global perspectives. We use path analysis in our study to estimate OTL as a mediator for SES on achievement (see Schmidt et al., 2015). Preliminary analyses suggest differences in the strength of the OTL achievement relationship between countries (e.g. Yang Hansen & Strietholt, 2018), and between subjects, with the predictive power of OTL hypothesised to be stronger in mathematics than science, in line with prior studies of OTL in TIMSS (e.g. Luyten, 2017).

Carnoy, M., Khavenson, T., Loyalka, P., Schmidt, W., & Zakharov, A. (2016). Revisiting the Relationship Between International Assessment Outcomes and Educational Production: Evidence From a Longitudinal PISA-TIMSS Sample. American Educational Research Journal, 53(4), 1054-1085. Eggen, T. J. H. M., Pelgrum, W. J., & Plomp, T. (1987). The implemented and attained mathematics curriculum: Some results of the second international mathematics study in The Netherlands. Studies in Educational Evaluation, 13(1), 119-135. Luyten, H. (2017). Predictive Power of OTL Measures in TIMSS and PISA. In J. Scheerens (Ed.), Opportunity to Learn, Curriculum Alignment and Test Preparation: A Research Review (pp. 103-120): Springer International Publishing. Scheerens, J. (2017). Meta-Analyses and Descriptions of Illustrative Studies. In J. Scheerens (Ed.), Opportunity to Learn, Curriculum Alignment and Test Preparation: A Research Review (pp. 23-54): Springer International Publishing. Schmidt et al. (2015). The Role of Schooling in Perpetuating Educational Inequality: An International Perspective. Educational Researcher, 44(7), 371-386. White, K. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461-481. Yang Hansen, K., & Strietholt, R. (2018). Does schooling actually perpetuate educational inequality in mathematics performance? A validity question on the measures of opportunity to learn in PISA. ZDM, 50(4), 643-658.