In recent decades, research on teaching quality has increasingly emphasized inquiry-based
instructional practices that engage students in higher-order thinking and reasoning processes
(e.g., Kelley & Knowles, 2016). Among these approaches, cognitive activation,
i.e., teaching practices that challenge students intellectually, stimulate reflection, and require
them to apply knowledge in novel situations, has gained particular attention (e.g., Förtsch et al., 2016; Klieme et al., 2006). Empirical evidence has consistently shown that cognitively activating instruction is positively associated with student achievement, motivation, and long-term learning outcomes (e.g., Baumert et al., 2010; Klieme et al., 2009).

A previous study by Atlay, Tieben, Hillmert, and Fauth (2019), found that while cognitively activating teaching, as reported by students, was generally associated with higher student achievement, its benefits were greatest for students from higher socioeconomic status (SES) backgrounds. In science education, the relevance of cognitive activation is particularly strong. Scientific inquiry requires students to reason with evidence, interpret data, draw conclusions, and develop systematic strategies to investigate scientific questions, which aligns closely with cognitively activating instruction (Lederman, 2019). However, evidence from previous TIMSS cycles suggests that simply measuring the frequency of inquiry-based activities may not effectively capture their impact, since the relationship between inquiry frequency and achievement does not appear to be linear (Mullis, Martin, & von Davier, 2021).

This study empirically examines these issues in the Swedish context, investigating
whether teachers’ generic and subject-specific cognitive activation practices contribute to
student science achievement and whether these effects differ by student socioeconomic
background. Using data from the Trends in International Mathematics and Science Study
(TIMSS), which links eighth-grade students directly to their science teachers, the study
employs a within-student between-subjects (WSBS) design. This design
enables robust estimation of how teaching practices predict student learning outcomes.

Based on previous findings suggesting that cognitively activating teaching may offer greater advantages to students from higher SES backgrounds, we hypothesize that the effects of cognitive activation on science achievement will differ by SES, potentially offering greater
advantages to high-SES students, thereby widening rather than reducing existing achievement
disparities. Accordingly, this study addresses the following research questions:

1. To what extent do teachers’ generic and subject-specific cognitive activation practices
   impact student achievement in science?
2. Does the effectiveness of teachers’ generic and subject-specific cognitive activation
   practices on student achievement vary depending on students’ socioeconomic
   background?

This study adopts the Three Basic Dimensions (TBD) framework (Klieme et al., 2009)
to conceptualize teaching quality, which distinguishes three core dimensions: Cognitive
Activation, Supportive Climate, and Classroom Management. While these dimensions
jointly characterize teaching quality, the present study focuses specifically on the Cognitive
Activation dimension and examines its association with student achievement in science.

Despite strong theoretical support, empirical findings on inquiry-based teaching and
cognitively activating practices are mixed. Other studies show positive relations
when inquiry involves hands-on or application-oriented activities such as experimentation,
modeling, or drawing conclusions from investigations (Grabau & Ma, 2017). Although cognitively activating instruction is assumed to support all learners, evidence suggests that its benefits may not be distributed evenly. Using PISA 2012 data, Caro et al. (2016) found that cognitive activation was generally associated with higher achievement but yielded stronger benefits for students from higher socioeconomic backgrounds. By contrast, longitudinal studies, such as Baumert et al. (2010) found no evidence of SES-based differences in the effects of cognitive activation. Overall, the existing evidence on how cognitive activation interacts with student socioeconomic background remains inconclusive, indicating a need for more robust research designs capable of isolating within-student variation, such as the WSBS approach used in the present study.

The present study uses Swedish data from TIMSS 2015, 2019, and 2023. In addition to student achievement scores, contextual information is collected from students, teachers, and school principals. From the student questionnaire, the scale related to students’ socioeconomic background is selected. In addition, from the teacher questionnaire, variables related to teachers’ generic and subject-specific cognitive activation practices are selected. The choice of variables is justified by previous literature indicating their influence on student achievement. Given the hierarchical sampling structure of TIMSS studies, clustering is accounted for in the present study by adjusting standard errors at the classroom level. An ideal way to study the effect of teaching practices on student achievement would be to randomly assign students to different instructional approaches. In reality, however, the allocation of students to teaching practices is not random (Bietenbeck, 2014). To overcome these issues, earlier research has relied on student fixed-effects models (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018). Estimating such models requires data with repeated observations of students across different subjects. TIMSS makes this possible by providing multiple science subject teachers (biology, chemistry, and physics) for each student within the same cycle. Building on this feature, a student fixed-effects approach is applied to estimate the impact of teachers’ generic and subject-specific cognitive activation practices on science achievement, while eliminating unobservable characteristics that remain constant across biology, chemistry, and physics teaching contexts. TIMSS grade 8 sample is used, since the students are usually taught science by at least two subject-specialist teachers in biology, chemistry, and physics. This structure allows a focus on practices of both generic cognitive activation (GCA) and subject-specific cognitive activation (SCA) within science classrooms.The WSBS identification relies on within-student differences in exposure to cognitive activation reflecting differences between teachers rather than systematic differences between science subjects. We therefore assume that teachers’ subject-specific cognitive activation (SCA) is functionally comparable across biology, chemistry, and physics for a given student. This assumption is plausible because the SCA indicators used in TIMSS represent cross-disciplinary scientific practices that can be used in all three science subjects. While the content necessarily differs by domain, the items target the same underlying pedagogical dimension aiming to provide students with opportunities to engage in inquiry-oriented scientific activity. The within-country student weight is applied. Data preparation is conducted in SPSS, while RStudio and Stata are used for factor score estimation, sensitivity analyses, and model estimation.

The effects of teachers’ generic and subject-specific cognitive activation practices are estimated using within-student, between-subjects analyses with student and subject fixed effects (biology, chemistry, and physics). Models are developed stepwise, estimating linear associations between GCA/SCA and achievement, adding interactions with SES, and extending to quadratic terms (GCA², SCA²) to test for non-linearity. Across all specifications, the estimated effects of GCA and SCA are not statistically significant, the SES interactions are small and statistically non-significant, and teacher qualifications included as controls are not statistically significant predictors of student achievement in science lessons. In the Swedish TIMSS science context examined here, cognitively activating practices neither widened nor reduced SES-based achievement differences, indicating that cognitive activation did not function as an equity-relevant mechanism in this setting. Sensitivity analysis and robustness tests support the reliability of these findings, and the absence of significant effects under this more robust specification suggests that previously reported positive associations may partly reflect residual confounding rather than the independent influence of cognitive activation. One limitation is that only about 50% of students showed within-student variation in GCA and SCA, which reduces identifying variation in the WSBS design and may help explain the null effects. Another limitation is that the study relies on teacher-reported measures of instructional practice, which may not fully capture implementation quality of cognitively activating instruction.

Atlay, C., Tieben, N., Hillmert, S., & Fauth, B. (2019). Instructional quality and achievement inequality: How effective is teaching in closing the social achievement gap?. Learning and Instruction, 63, 101211. Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., ... & Tsai, Y. M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American educational research journal, 47(1), 133-180. Bietenbeck, J. (2014). Teaching practices and cognitive skills. Labour Economics, 30, 143- 153. Bietenbeck, J., Piopiunik, M., & Wiederhold, S. (2018). Africa’s skill tragedy: Does teachers’ lack of knowledge lead to low student performance?. Journal of human resources, 53(3), 553-578. Caro, D. H., Lenkeit, J., & Kyriakides, L. (2016). Teaching strategies and differential effectiveness across learning contexts: Evidence from PISA 2012. Studies in educational evaluation, 49, 30-41. Förtsch, C., Werner, S., von Kotzebue, L., & Neuhaus, B. J. (2016). Effects of biology teachers’ professional knowledge and cognitive activation on students’ achievement. International Journal of Science Education, 38(17), 2642-2666. Grabau, L. J., & Ma, X. (2017). Science engagement and science achievement in the context of science instruction: A multilevel analysis of us students and schools. International Journal of Science Education, 39(8), 1045-1068. Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM education, 3(1), 11. Klieme, E., Lipowsky, F., Rakoczy, K., & Ratzka, N. (2006). Qualit.tsdimensionen und Wirksamkeit von Mathematikunterricht [Quality dimensions and effectiveness of mathematics teaching]. Untersuchungen zur Bildungsqualität von Schule, 127-146. Klieme, E., Pauli, C., & Reusser, K. (2009). The pythagoras study: Investigating effects of teaching and learning in Swiss and German mathematics classrooms. In T. Janik & T. Seidel (Eds.), The power of video studies in investigating teaching and learning in the classroom (pp. 137–160). Waxmann Publicing Co. Lederman, N. G. (2019). Contextualizing the relationship between nature of scientific knowledge and scientific inquiry: Implications for curriculum and classroom practice. Science & Education, 28(3), 249-267. Mullis, I. V., Martin, M. O., & von Davier, M. (2021). TIMSS 2023 Assessment Frameworks. International Association for the Evaluation of Educational Achievement.