Session Information
09 SES 15 B, Unraveling Mathematics and Science Reasoning Using TIMSS and PISA
Paper Session
Contribution
International large-scale assessments repeatedly show that students’ achievement is systematically patterned by both home-based resources and learning supports and school-based institutional conditions, with important implications for equity and opportunity (Schütz et al., 2008; Werfhorst & Mijs, 2010)(Schütz et al., 2008; Werfhorst & Mijs, 2010). TIMSS-based research similarly demonstrates that contextual factors relate not only to overall achievement but also to performance across content and cognitive domains, including the knowing–applying–reasoning structure (Balfaqeeh et al., 2022; Mullis & Martin, 2017; Zhang & Li, 2019). Yet less is known about how such home and school context patterns align with cross-cycle changes in cognitive-domain performance.
Against this background, this study investigates how the extraordinary performance increase observed in Türkiye between TIMSS 2019 and TIMSS 2023 co-occurs with patterns in home and school context indicators. The analytical focus is on reasoning achievement in mathematics and science. This focus is justified because reasoning represents higher-order cognitive processes within the TIMSS cognitive domains (Mullis & Martin, 2017), and domain-level outcomes may be sensitive to item characteristics and cognitive-domain features that warrant explicit consideration (Liou & Bulut, 2020; Nixon & Barth, 2014). Descriptive results indicate that the mean increase in mathematics reasoning is larger (Δ=+40.13) and that the relative share of reasoning within total mathematics achievement also increases (Δ=+2.27), while relative changes for knowing and applying are small and negative; a similar pattern is observed in science.
The study is structured around the following research questions:
- In TIMSS 2019 and 2023, how are home context indicators—home educational resources, parental education, and the home learning environment—associated with fourth-grade students’ absolute reasoning achievement in mathematics and science ?
- How are these home context indicators associated with the relative share of reasoning within total achievement in mathematics and science ?
- To what extent do school context indicators (e.g., academic emphasis, disciplinary climate, readiness, and resource shortages) provide additional explanatory power for reasoning outcomes after controlling for home context?
- How do the direction and magnitude of these associations change between TIMSS 2019 and TIMSS 2023?
The conceptual and theoretical framework adopts an opportunity-structure perspective, viewing achievement as the outcome of knowledge-acquisition processes that are shaped by both family-based resources and institutional conditions that structure learning opportunities (Baumert et al., 2009; Schütz et al., 2008; Werfhorst & Mijs, 2010). Accordingly, the incremental contribution of school context beyond home context is treated as central to equity-relevant interpretations. The European/international dimension is established through TIMSS’s standardised measurement framework and international database conventions, which enable comparable analyses across cycles and systems (Fishbein et al., 2021, 2025; Mullis & Martin, 2017; von Davier et al., 2024).
Method
This study draws on Türkiye’s Grade 4 data from TIMSS 2019 and TIMSS 2023 conducted by the IEA. The data include mathematics and science achievement assessments and standardised indicators derived from student, home, and school questionnaires (Mullis & Martin, 2017; Fishbein et al., 2021, 2025). The analytical sample comprises students who participated in the mathematics and science assessments in the respective cycles. All analyses apply student weights (TOTWGT) to account for TIMSS’s stratified, multistage sampling design (Fishbein et al., 2021, 2025). Data preparation and analysis followed TIMSS international database conventions. The IEA IDB Analyzer was used to merge the relevant files and generate an analysis-ready dataset, after which SPSS was used for variable preparation, descriptive statistics, and regression modelling; Excel supported data checks and validation (von Davier et al., 2024). Given the use of international large-scale assessment data, analytic decisions and reporting were guided by recommended practices for secondary analyses (Rutkowski et al., 2010). Reasoning outcomes in mathematics and science were operationalised in two complementary ways. First, absolute reasoning achievement was measured using TIMSS plausible values and summarised as M_REAS (mathematics) and S_REAS (science), consistent with plausible-value methodology (Wu, 2005). Second, a relative reasoning share indicator captured the proportion of reasoning within overall cognitive-domain performance (knowing, applying, and reasoning) and was operationalised as M_PCT_REAS and S_PCT_REAS, aligned with TIMSS’s cognitive domain structure (Mullis & Martin, 2017). Home context was represented by composite indices capturing home educational resources (HERI), parental education (PEI), and the home learning environment (HLEI). School context was represented by indicators such as academic emphasis, disciplinary climate, student readiness, and resource shortages (Fishbein et al., 2021, 2025). Analytically, the study estimated multiple and block (hierarchical) regression models separately for mathematics and science in each TIMSS cycle. Home context indicators were entered in the first block, and school context indicators were added in the second block to quantify schools’ incremental explanatory contribution (ΔR²) beyond home context and to compare the magnitude and pattern of associations between TIMSS 2019 and TIMSS 2023 (Rutkowski et al., 2010).
Expected Outcomes
Türkiye’s TIMSS Grade 4 results show a substantial increase in mathematics and science achievement between 2019 and 2023. Descriptive evidence suggests that the improvement is not confined to a single content domain: gains are observed across mathematics content areas (including Number, Geometry, and especially Data) and across science domains (including Physical Science and Earth Science), consistent with TIMSS reporting structures. The relative stability of standard deviation values across cycles is consistent with a broad upward shift in performance rather than change concentrated among a narrow high-achieving subgroup. The change is also not cognitively neutral. Within the TIMSS cognitive-domain framework, the relative share of Reasoning increases while the shares of Knowing and Applying decline in both mathematics and science. In Türkiye, the reasoning share rises by +2.27 percentage points in mathematics and +1.65 in science, indicating that overall performance gains are accompanied by a relative strengthening in higher-order reasoning. Regression results addressing the research questions indicate that home-based factors are consistently associated with absolute reasoning achievement in both subjects. In particular, parental education (PEI) and the home learning environment (HLEI) show robust positive associations with reasoning outcomes, whereas home indicators explain comparatively less variance in the relative reasoning share. School-based factors remain relevant after controlling for home context, providing an additional but more modest increment in explained variance for absolute reasoning outcomes. Overall, the findings suggest that Türkiye’s 2019–2023 achievement gains and the reasoning-oriented shift are more strongly patterned by home-based opportunity structures, while school context contributes incremental explanatory power beyond home, especially for absolute reasoning achievement—an equity-relevant pattern in international large-scale assessment contexts.
References
Balfaqeeh, A., Mansour, N., Forawi, S. (2022). Factors Influencing Students’ Achievements in the Content and Cognitive Domains in TIMSS 4th Grade Science and Mathematics in the United Arab Emirates. Education Sciences, 12(9). https://doi.org/10.3390/educsci12090618 Baumert, J., Lüdtke, O., Trautwein, U., & Brunner, M. (2009). Large-scale student assessment studies measure the results of processes of knowledge acquisition: Evidence in support of the distinction between intelligence and student achievement. Educational Research Review, 4(3), 165–176. https://doi.org/10.1016/j.edurev.2009.04.002 Fishbein, B., Foy, P., & Yin, L. (2021). TIMSS 2019 User Guide for the International Database (2nd ed.). (2021930389; 2nd Ed.). Boston College, TIMSS & PIRLS International Study Center. https://timssandpirls.bc.edu/timss2019/international-database/ Fishbein, B., Taneva, M., & Kowolik, K. (2025). TIMSS 2023 User Guide for International Database. Boston College, TIMSS & PIRLS International Study Center. https://timss2023.org/data Liou, P.-Y., & Bulut, O. (2020). The Effects of Item Format and Cognitive Domain on Students’ Science Performance in TIMSS 2011. Research in Science Education, 50(1), 99–121. https://doi.org/10.1007/s11165-017-9682-7 Mullis, I. V. S., & Martin, M. O. (2017). TIMSS 2019 Assessment Frameworks. Boston College, TIMSS & PIRLS International Study Center. http://timssandpirls.bc.edu/timss2019/frameworks/ Nixon, R. S., & Barth, K. N. (2014). A Comparison of TIMSS Items Using Cognitive Domains. School Science and Mathematics, 114(2), 65–75. https://doi.org/10.1111/ssm.12054 Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International Large-Scale Assessment Data: Issues in Secondary Analysis and Reporting. Educational Researcher, 39(2), 142–151. https://doi.org/10.3102/0013189X10363170 Schütz, G., Ursprung, H. W., & Wößmann, L. (2008). Education Policy and Equality of Opportunity. Kyklos, 61(2), 279–308. https://doi.org/10.1111/j.1467-6435.2008.00402.x von Davier, M., Fishbein, B., & Kennedy, A. (2024). TIMSS 2023 Technical Report (Methods and Procedures). Boston College, TIMSS & PIRLS International Study Center. https://timss2023.org/methods/ Werfhorst, H. G. V. de, & Mijs, J. J. B. (2010). Achievement Inequality and the Institutional Structure of Educational Systems: A Comparative Perspective. Annual Review of Sociology, 36(Volume 36, 2010), 407–428. https://doi.org/10.1146/annurev.soc.012809.102538 Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2), 114–128. https://doi.org/10.1016/j.stueduc.2005.05.005 Zhang, L., & Li, Z. (2019). How Does Inquiry-Based Scientific Investigation Relate to the Development of Students’ Science Knowledge, Knowing, Applying, and Reasoning? An Examination of TIMSS Data. Canadian Journal of Science, Mathematics and Technology Education, 19(3), 334–345. https://doi.org/10.1007/s42330-019-00055-9
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