Conference Agenda

International large-scale assessments (ILSAs) such as TIMSS, ICCS, PIRLS, ICILS, PISA, and TALIS provide critical insights into educational outcomes and inequalities across diverse national contexts. They are widely used to monitor trends, enable international comparisons, and inform educational policy and practice (Johansson, 2016). At the same time, ILSAs function as laboratories for methodological innovation, supporting research that links individual outcomes to institutional and systemic conditions (Strietholt & Scherer, 2018). This symposium brings together recent research drawing on multiple ILSAs and is organized into five interrelated thematic sessions addressing digital transformation, civic education, educational inequality, student engagement and well-being, and methodological challenges in international assessment research.

The first thematic area focuses on digital competencies and emerging technologies in education. Drawing on ICILS, TALIS, and TIMSS, papers examine cross-national variation in technology and AI integration, the role of teacher characteristics and professional learning, and how digital provision and pedagogical use relate to achievement and inequality, including disparities linked to socioeconomic background and early tracking.

The second thematic area addresses civic attitudes, values, and engagement among young people. Using ICCS data, contributions explore students’ intentions to participate politically, their attitudes toward minorities, immigrants, and gender equality, and how school experiences relate to tolerant dispositions and engagement with social and environmental issues.

The third thematic area examines structural inequalities and contextual effects in education systems. Papers investigate how school composition, segregation, and socioeconomic context shape achievement and well-being across countries, including intersectional patterns by gender, immigrant background, and socioeconomic status. The session also addresses systemic resource inequalities through evidence on mathematics and science teacher shortages and their unequal distribution across schools.

The fourth thematic area focuses on students’ attitudes, motivation, and psychosocial factors. Contributions examine long-term gender gaps in reading behavior and achievement, the relationship between teacher gender and student outcomes, and trends in test-taking motivation relevant for interpreting achievement changes.

The fifth thematic area is explicitly methodological, with papers analyzing item-level features of achievement scales, participation and sampling standards, and the measurement of socioeconomic status, highlighting how technical choices shape conclusions about trends and inequality.

Overall, the symposium fosters dialogue among researchers, policymakers, and practitioners and advances rigorous, policy-relevant use of ILSAs to support more equitable and effective education systems.

Digital technologies have become an integral component of contemporary education systems, yet evidence remains inconclusive regarding their actual contribution to student learning outcomes. While many schools have expanded their digital infrastructure, it is still unclear how the availability, quality, and pedagogical use of digital tools influence students’ competences in mathematics and science. This study examines the relationship between digital technology provision and student achievement by analysing data from TIMSS 2019 and TIMSS 2023, including student, teacher, and school questionnaires as well as performance results. Background and Purpose: The study addresses a central problem: although digital technologies are increasingly embedded in learning processes, little is known about how frequently and effectively they are used, and whether such use leads to improved student performance. The research explores digital provision, historical developments in school digitalisation, the impact of national strategies and COVID 19, and teachers’ digital competences. Additionally, it investigates the role of school leadership in supporting digital transformation, strategic decision making, and policy alignment. Research Questions: (1) How has the availability of digital technologies changed between 2019 and 2023? (2) To what extent does better digital provision correlate with higher achievement in mathematics and science? (3) What differences exist between urban and rural schools? Methods: The empirical analysis uses TIMSS student achievement data alongside school level and teacher level indicators. Data processing includes identifying correlations, comparing technological access and use across contexts, and analysing changes in digital environments over the two cycles. The analysis will primarily focus on countries that demonstrate high achievement levels in both mathematics and science, as well as countries characterised by advanced digital technology availability in schools and frequent pedagogical use of digital tools during lessons. Particular emphasis will be placed on Latvia, the home country of the authors, to provide a deeper contextual understanding of national developments and comparative positioning. Variables include students’ outcomes in mathematics and science, frequency of digital tool use, and digital infrastructure indicators for grade 4 settings. Expected Outcomes: The study is expected to clarify whether improvements in digital provision reflect meaningful pedagogical integration and whether such integration supports higher student performance. It will identify contextual disparities, highlight barriers to effective digital use, and assess trends in digital transformation across the two TIMSS cycles. Relevance: Based on the findings, recommendations will be developed for policymakers, school leaders, teachers, and families to enhance digital learning environments and strengthen students’ competencies in mathematics and science.

Anwar, C., Aznem, A., Payung, L. T., & Hidayanto, N. (2025). Empowering Education through Digital Leadership: The Evolving Role of School Principals. Formichella, M. M., Alderete, M. V., & Di Meglio, G. (2020). New technologies in households: Is there an educational payoff? Evidence from Argentina. Education in the Knowledge Society, 21, 181–1814. https://doi.org/10.14201/eks.23553 Gizatullina, O. (2023). MODERN COMPETENCIES OF A TEACHER FOR EFFECTIVE WORK IN A DIGITAL EDUCATIONAL ENVIRONMENT. https://doi.org/10.5281/ZENODO.7630210 Jeong, D. W., Moon, H., Jeong, S. M., & Moon, C. J. (2024). Digital capital accumulation in schools, teachers, and students and academic achievement: Cross-country evidence from the PISA 2018. International Journal of Educational Development, 107, 103024. https://doi.org/10.1016/j.ijedudev.2024.103024 Karlsson, L. (2022). Computers in education: The association between computer use and test scores in primary school. Education Inquiry, 13(1), 56–85. https://doi.org/10.1080/20004508.2020.1831288

This paper investigates cross-national patterns of artificial intelligence (AI) use among secondary school teachers using data from the Teaching and Learning International Survey (TALIS) 2024. The study is theoretically grounded in established models of technology adoption, particularly the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) and the Technological Pedagogical Content Knowledge framework (TPACK; Mishra & Koehler, 2006). These perspectives emphasize that technology integration is shaped by teachers’ digital competencies, perceived usefulness, ease of use, pedagogical beliefs, and contextual support (Davis, 1989; Scherer et al., 2021). Building on research on digital media use, which has shown substantial heterogeneity among teachers (Pozas et al., 2022), this study examines how AI-related practices vary across countries. Applying multilevel latent class analysis (MLCA; Henry & Muthén, 2010), the study identifies distinct teacher-level AI usage profiles and explores how their prevalence and predictors differ internationally. Latent class approaches have proven effective for analyzing technology use patterns (Graves & Bowers, 2018), yet systematic cross-national analyses focusing on AI remain scarce. The analysis distinguishes between individual characteristics (Level 1) and system-level influences (Level 2), including digital infrastructure, leadership support, and professional development opportunities (Buabeng-Andoh, 2012; OECD, 2020). Three research questions guide the study. First, it examines whether similar AI usage profiles emerge across countries or whether some profiles are context-specific (Eickelmann & Vennemann, 2017). Second, it analyzes how the probability of belonging to specific profiles varies internationally. Third, it investigates whether profile membership is mainly explained by teacher-level factors—such as self-efficacy, attitudes toward AI, and training—or by broader structural conditions (Ifenthaler & Schweinbenz, 2016; UNESCO, 2021). System-level effects are captured through aggregated indicators from teacher and school leader reports, including AI-related policies, training accessibility, and governance frameworks. Additional national indicators such as innovation capacity and education expenditure are considered. This multilevel approach allows a comprehensive analysis of contextual determinants of AI integration. Methodologically, MLCA identifies latent subgroups based on reported AI practices such as lesson planning, assessment, adaptive learning, and support for students with special needs. Measurement invariance tests assess cross-national comparability, and model selection is guided by statistical fit indices and interpretability. Overall, the study provides internationally comparable evidence on the diversity of AI adoption among teachers and highlights the importance of targeted support strategies tailored to specific national contexts. The findings contribute to a differentiated understanding of AI integration in education and inform policy and professional development initiatives.

Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of ICT into teaching. International Journal of Education and Development using ICT, 8(1), 136–155. Davis, F. D. (1989). Perceived usefulness and perceived ease of use of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Eickelmann, B., & Vennemann, M. (2017). Teachers’ attitudes regarding ICT in European countries. European Educational Research Journal, 16(6), 733–761. https://doi.org/10.1177/1474904117725899 Graves, S., & Bowers, C. (2018). Exploring teacher and student use of technology. Education and Information Technologies, 23(6), 2445–2464. Henry, K. L., & Muthén, B. (2010). Multilevel latent class analysis. Structural Equation Modeling, 17(2), 193–215. Ifenthaler, D., & Schweinbenz, V. (2016). Teachers’ perspectives on learning analytics. Teaching and Teacher Education, 56, 157–167. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge. Teachers College Record, 108(6), 1017–1054. OECD. (2020). TALIS 2018 results. OECD Publishing. Pozas, M., Letzel, V., & Schneider, C. (2022). Teaching and technology use: Patterns and predictors. British Journal of Educational Technology, 53(4), 842–860. Scherer, R., Siddiq, F., & Tondeur, J. (2021). The technology acceptance model: A meta-analysis. Computers & Education, 128, 13–35. UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO et al. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425–478.

Computational thinking (CT) skills are increasingly important as digital technologies transform education and the labour market, and they also support broader outcomes such as critical thinking and academic achievement. (Lei et al., 2020; Kumar & Mohd, 2024). Schools, and especially teachers, play a central role in developing CT, yet little is known about how teacher characteristics vary between high- and low-performing schools. Evidence suggests that high-performing schools are more likely to have teachers who are motivated, collaborate frequently, and participate actively in professional development, all of which support effective teaching and improved student outcomes (Lorenz et al., 2022). Highly qualified teachers tend to work in schools with academically stronger students, while less experienced teachers often teach in schools with higher concentrations of disadvantaged students (Pop-Eleches & Urquiola, 2013; OECD, 2019). Socioeconomic context also influences teacher characteristics and instructional practices. Schools with higher-SES students generally have better resources, more experienced staff, and greater instructional stability, whereas lower-achieving schools face limited resources, higher teacher turnover, and fewer opportunities for professional growth (Yang Hansen et al., 2016; Sirin, 2005). Teachers in high-achieving schools are more likely to employ cognitively demanding methods, such as inquiry-based learning, formative assessment, structured feedback, and problem-solving tasks, which support CT, critical reasoning, and data analysis (Hattie, 2009). This study examines information technology, computer studies (or similar) teacher characteristics in the highest- and lowest-achieving schools in countries where students’ average in International Computer and Information Literacy Study (ICILS) 2023 CT achievement exceeds the overall study mean: Belgium (Flemish), Chinese Taipei, the Czech Republic, Denmark, Finland, France, Latvia, the Republic of Korea, and the Slovak Republic. A particular focus is placed on the topics most frequently taught in top-performing schools and how these relate to what students actually learn. Using ICILS 2023 information technology, computer studies (or similar) teacher and student questionnaires, the analysis identifies which CT-related activities are emphasized. While the specific emphasis on topics and activities is expected to vary across countries, clustering countries with similar patterns allows for targeted recommendations to help teachers strengthen students’ CT skills.

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. Kumar, I., & Mohd, N. (2024). Ways of using computational thinking to improve students’ ability to think critically. https://doi.org/10.4018/979-8-3693-0782-3.ch015 Lei, H., Chiu, M., Li, F., Wang, X., & Geng, Y. (2020). Computational thinking and academic achievement. Children and Youth Services Review. https://doi.org/10.1016/j.childyouth.2020.105439 Lorenz, R., Endberg, M., & Bos, W. (2022). Teaching with technology: A large-scale, international, and multilevel study of the roles of teacher and school characteristics. Computers & Education, 179, 104408. https://doi.org/10.1016/j.compedu.2021.104408 OECD. (2019). TALIS 2018 results (Volume I): Teachers and school leaders as lifelong learners. OECD Publishing. https://dx.doi.org/10.1787/1d0bc92a-en Pop-Eleches, C., & Urquiola, M. (2013). Going to a better school: Effects and behavioral responses. The American Economic Review, 103(4), 1289–1324. https://doi.org/10.1257/aer.103.4.1289 Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417 Yang Hansen, K., Gustafsson, J. E., & Rosén, M. (2016). Context factors and student achievement in the IEA studies: Evidence from TIMSS. Large-Scale Assessments in Education, 4(1), Article 12. https://doi.org/10.1186/s40536-016-0020-6

Digital competences are widely regarded as a key prerequisite for educational success, labour market participation, and social inclusion (Fraillon, 2025; Hoe, 2025). International large-scale assessments (ILSAs) such as the International Computer and Information Literacy Study (ICILS) play a central role in producing policy-relevant knowledge on these competences and in shaping educational discourse. International evidence from ICILS shows substantial cross-national variation in students’ digital competences, which—similar to traditional competence domains—is closely associated with socioeconomic background (Fraillon, 2025). However, less is known about how institutional features of education systems structure inequalities in digital competences, and how such inequalities emerge within data-driven, internationally comparative research contexts. In particular, early between-school tracking constitutes a core institutional design feature that systematically differentiates learning environments at an early stage, yet its role in shaping digital competence inequalities has received little empirical attention. This paper addresses this gap by examining early tracking as a structural moderator of socioeconomic inequalities in digital competences. We ask: (1) To what extent does the association between socioeconomic status (SES) and digital competences vary across countries? (2) Does early tracking affect average levels of digital competences? (3) Does early tracking moderate the relationship between SES and digital competences? Theoretically, the paper brings together insights from the digital divide literature, which conceptualises inequalities in digital access, use, and competences as socially structured (Dijk, 2020), and theories of educational inequality that emphasise the role of cultural and economic capital in shaping learning opportunities (Bourdieu, 1983). Early tracking is understood as an institutional mechanism that allocates students to differentiated learning environments (Terrin & Triventi, 2023), thereby structuring access to pedagogical resources, digital infrastructures, and forms of knowledge valued within education systems. Empirically, the analyses draw on pooled data from ICILS 2013, 2018, and 2023, covering 44 education systems, including 12 characterised by early tracking. Digital competences are measured using the Computer and Information Literacy (CIL) and Computational Thinking (CT) scales. Multilevel regression models with students nested in schools and countries are estimated, including cross-level interactions between SES and early tracking. Preliminary results based on ICILS 2023 indicate strong socioeconomic gradients in digital competences across systems. While early tracking shows no significant effect on average competence levels, inequalities by SES are significantly steeper in early-tracking systems. These findings suggest that early tracking primarily structures the distribution—rather than the overall level—of digital competences.

Bourdieu, P. (1983). Ökonomisches Kapital, kulturelles Kapital, soziales Kapital. In R. Kreckel (Ed.), Soziale Ungleichheiten (pp. 183–198). Schwartz. Dijk, J. van. (2020). The digital divide. Polity. Fraillon, J. (2025). ICILS 2023 International Report: An International Perspective on Digital Literacy. International Association for the Evaluation of Educational Achievement (IEA). https://www.iea.nl/sites/default/files/2025-03/ICILS_2023_International_Report.pdf Hoe, S. L. (2025). Digital transformation and the future of work: Closing the digital skills gap. Development and Learning in Organizations: An International Journal, 39(3), 14–17. https://doi.org/10.1108/DLO-06-2024-0167 Terrin, É., & Triventi, M. (2023). The Effect of School Tracking on Student Achievement and Inequality: A Meta-Analysis. Review of Educational Research, 93(2), 236–274. https://doi.org/10.3102/00346543221100850