Conference Agenda

National educational databases play an increasingly important role in educational research, policy analysis, and system monitoring. Across many countries, Education Management Information Systems (EMIS) now contain detailed longitudinal information on students, teachers, schools, and educational trajectories. While these systems were primarily developed for administrative and policy purposes, they are increasingly used for research. At the same time, substantial differences exist across countries with respect to data structure, accessibility, legal frameworks, and possibilities for data linkage. This session brings together contributions from several national contexts (Denmark, Finland, Italy, Poland, Portugal, Sweden) to examine how education data infrastructures are used for research, and under which conditions they enable robust and policy-relevant analyses. The papers cover a range of educational systems, including national education registers, assessment databases, and links to international large-scale assessments, and illustrate both opportunities and constraints in working with large-scale education data.

A central theme of the session concerns data access and governance. Several contributions describe national arrangements for researcher access, including secure remote access environments, licensing procedures, and legal requirements related to data protection and privacy. Differences between countries highlight how institutional and regulatory frameworks shape what kinds of research can be conducted, and by whom. The papers also illustrate how anonymisation and pseudonymisation procedures affect analytical possibilities and raise methodological trade-offs between data protection and research quality.

A second theme is data linkage and methodological challenges. Many contributions demonstrate the analytical value of linking education data with other sources, such as national examinations, higher education registers, labor market data, or international large-scale assessments. These linkages enable the study of educational trajectories, persistence and dropout, inequalities, and policy effects over time. At the same time, the papers discuss challenges related to longitudinal consistency, missing data, hierarchical data structures, and the validity of administrative indicators, as well as the need for appropriate statistical models.

A third theme concerns the use of national education data for policy evaluation and system improvement. Several papers illustrate how administrative and assessment data are used to study educational inequalities, school effectiveness, student transitions, and dropout risk. The use of rich register data allows complex conceptual processes to be refined and examined empirically. Methodologically, the contributions range from descriptive statistical analyses to longitudinal modelling and quasi-experimental approaches addressing policy-relevant research questions.

Taken together, the session highlights both the research potential and the limitations of national education data systems. While these data offer unique opportunities for large-scale and longitudinal research, their use requires careful attention to data quality, governance, and methodological choices. By presenting experiences from different national contexts, the session contributes to a more informed discussion on how educational databases can be used responsibly and effectively for research and policymaking.

Increasingly, policymakers and scholars are interested in the role of schools in fostering students’ democratic competencies. At the same time, the expansion of private schooling through market-oriented reforms has intensified debates about whether private schools support or undermine democratic development. Although a substantial body of research has examined associations between private schooling and democratic competencies and civic participation, existing evidence is limited by three recurring features: a reliance on research designs that do not allow for causal inference, an absence of evidence on knowledge and skills, and a concentration on the United States (Shakeel et al., 2024). As a result, there remains limited robust evidence on the extent to which private schools shape students’ democratic competencies, particularly in European contexts. This paper addresses these gaps by examining the effects of private school attendance on students’ democratic competencies in Sweden. These democratic competencies include civic knowledge/skills, political interest, civic self-efficacy, and students’ beliefs about threats to democracy. The Swedish context offers a particularly informative setting, which allows publicly funded private schools to operate nationally alongside public schools within a common regulatory and curricular framework. We draw on a novel linked dataset that combines data from the 2022 International Civic and Citizenship Education Study (ICCS) with Swedish register data. First, to understand potential mechanisms, we present descriptive differences in civic learning opportunities and relevant school and classroom characteristics between private and public schools, drawing on student, teacher, and principal questionnaire data from ICCS. Next, to estimate treatment effects, we address non-random selection into private schools by employing entropy balancing (Hainmueller, 2012) and leveraging pre-treatment course grades in several subjects, as well as detailed demographic characteristics, which aligns with existing standards for quasi-experimental design (What Works Clearinghouse, 2022). We test the robustness of our estimates using an approach to sensitivity analysis (Frank et al., 2013). Preliminary results suggest positive effects of private schools on some democratic competencies and not others. Additional analyses, including tests of heterogeneity and sensitivity, are ongoing. By providing credibly causal evidence from a non-U.S. context using linked international assessment and register data, this study contributes to ongoing debates about the democratic implications of private schooling. That said, any efforts toward potential reforms that promote the expansion of private school access through policies such as vouchers should very carefully consider the implications and any potential for unintended consequences (see Baker et al., 2025).

Baker, Bruce D., Black, Derek, Cowen, Joshua, Green, Preston, & Jennings, Jennifer L. (2025). A framework for evaluating and reforming school vouchers. (EdWorkingPaper: 25 -1142). https://doi.org/10.26300/CX43-TR11 Frank, K. A., Maroulis, S. J., Duong, M. Q., & Kelcey, B. M. (2013). What would it take to change an inference? Using Rubin’s Causal Model to interpret the robustness of causal inferences. Educational Evaluation and Policy Analysis, 35(4), 437–460. https://doi.org/10.3102/0162373713493129 Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25–46. https://doi.org/10.1093/pan/mpr025 Shakeel, M. D., Wolf, P. J., Johnson, A. H., Harris, M. A., & Morris, S. R. (2024). The public purposes of private education: A civic outcomes meta-analysis. Educational Psychology Review, 36(2), 40. https://doi.org/10.1007/s10648-024-09874-1 What Works Clearinghouse. (2022). What Works Clearinghouse procedures and standards handbook: Version 5.0. U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. https://ies.ed.gov/ncee/wwc/Docs/referenceresources/Final_WWC-HandbookVer5.0-0-508.pdf

Student dropout from programmes in higher education is often framed as inherently problematic: public and private resources are spent on studies that do not result in a degree, study places are occupied that could have gone to someone else, and individuals forgo earnings while studying (e.g., Yorke, 1998; Hovdhaugen, 2009). At the same time, dropping out of a study programme is not always problematic for the individual, for example when students transfer to a preferred programme, move and continue their studies elsewhere, or leave because of an attractive job offer. This paper synthesises the dropout/retention/attrition literature and develops an operationalised typology of student dropout using high-quality, full-population Swedish register data that capture individuals’ life trajectories as sequences of states (e.g. enrolment states in the education system and labour market positions). Although the literature on student dropout is united in its references to Tinto’s theory of student departure (1975; 1993), the scholarly and administrative field remains fragmented in terminology and measurement. As Kehm et al. notes, the research field needs “to establish a clearer concept of dropout and distinguish between different types of dropout behavior” (2019, p. 158). Common distinctions, such as voluntary versus involuntary dropout, transfers to other programmes versus exits from higher education, temporary versus permanent dropout, and early versus late dropout (Tinto, 1975; 1993; Kehm et al., 2019), are not mutually exclusive and rarely have standardised operationalisations. As a result, dropout estimates are difficult to compare across studies and contexts, and very different student trajectories risk being treated as the same phenomenon. For example, as Tinto (1975) noted, a failure to distinguish exits from transfers may lead to gross overestimations of dropout numbers on a system level. The purpose of the study is to estimate the prevalence of different dropout patterns in higher education and to identify which student groups are most exposed to specific forms of dropout. Building on existing classifications and measures, and leveraging register data on individual students’ trajectories into their higher education study programme (e.g. prior studies/credentials and admission), through their programme (e.g. progression and academic performance), and out of their programme (e.g. into employment, unemployment, or further studies), the paper proposes a coherent typology of dropout trajectories with explicit operational definitions. By proposing operational definitions, it also aims to facilitate cross-study comparability across institutional or national contexts and enable more focused discussions of when, how, and for whom student dropout constitutes a problem.

Hovdhaugen, E. (2009). Transfer and dropout: Different forms of student departure in Norway. Studies in Higher Education, 34(1), 1–17. https://doi.org/10.1080/03075070802457009 Kehm, B. M., Larsen, M. R., & Sommersel, H. B. (2019). Student dropout from universities in Europe: A review of empirical literature. Hungarian Educational Research Journal, 9(2), 147–164. https://doi.org/10.1556/063.9.2019.1.18 Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089 Tinto, V. (1993). Leaving College: Rethinking the Causes and Cures of Student Attrition. University of Chicago Press. https://doi.org/10.7208/chicago/9780226922461.001.0001 Yorke, M. (1998). Non-completion of full-time and sandwich students in English higher education: Costs to the public purse, and some implications. Higher Education, 36(2), 181–194. https://doi.org/10.1023/A:1003217602541

Increasing segregation in Swedish compulsory schools has been frequently reported over the past decades (e.g., Yang Hansen, Patsis & Gustafsson, 2025) in relation to the decentralization, deregulation, privatization, and marketization reforms. However, little is known about the reforms’ long-term consequences for municipal differences in educational outcomes, educational inequality, and school segregation. The reforms of quasi-markets, institutional differentiation and school choice are often expected to increase openness and efficiency in education provision, they may also risk greater inequality and segregation across local contexts (e.g, Böhlmark, Holmlund & Lindahl, 2016). However, municipality-level longitudinal evidence remains limited for recent decades. Existing studies at the municipal level (e.g., Boman, 2022), rely typically on a small number of time points, which limits their ability to capture longer-term trends and to provide credible evidence. Using Swedish register data from 1998 to 2008, Gustafsson and Yang Hansen (2013) found increasing differences in academic outcomes between municipalities, particularly in metropolitan areas, with most variation occurring between schools within municipalities and between public and independent schools. Since 2008, Sweden has introduced further reforms to grading and curricula and experienced substantial societal changes, including rising income inequality and increased refugee settlement concentrated in certain municipalities. These developments may have affected patterns of segregation in compulsory education. This study therefore examines recent developments in school outcomes in Swedish compulsory schools, with particular attention to municipal variation and changes in educational equality over time. Using Swedish register data of all students between 2014 and 2024, we examine complete cohorts of students leaving compulsory school during the decade since 2014. The data include GPA at Grade 9 and detailed sociodemographic information, e.g., parental educational level, migration background. These data were aggregated to school and municipality levels. Additional municipality demographic factors, such as municipality types, percentage of inhabitants with migration background, teacher and school resources. To capture both initial differences and change over time, we apply two-level latent growth modeling techniques (e.g., Kaplan, 2009; McCormick, 2021), which allow for the analysis of heterogeneous developmental trajectories across municipalities while accounting for compositional differences in forms of time-varying covariates and time-invariant covariates. The expected findings may suggest that the consequences of market-oriented school reforms in combination of grading system curriculum and societal changes are neither uniform nor static but develop over time and interact with local conditions. The study contributes to the literature by providing longitudinal, municipality-level evidence on educational segregation.

Boman, B. (2022). Regional differences in educational achievement: a replication study of municipality data. Frontier in Education, 7, 854342, https://doi.org/10.3389/feduc.2022.854342 Böhlmark, A., Holmlund, H. & Lindahl, M. (2016). Parental choice, neighbourhood segregation or cream skimming? An analysis of school segregation after a generalized choice reform. J Popul Econ 29, 1155–1190. https://doi.org/10.1007/s00148-016-0595-y Gustafsson, J.-E., & Yang Hanssen, K. (2013). Förändringar i kommunskillnader i grundskoleresultat mellan 1998 och 2008. Pedagogisk Forskning I Sverige, 16(3), 161. Hämtad från https://publicera.kb.se/pfs/article/view/53009 Kaplan, D. (2009). Latent growth curve modeling. In Latent growth curve modeling (2nd ed., Vol. 10, pp. 155-180). SAGE Publications, Inc., https://doi.org/10.4135/9781452226576 McCormick, E. M. (2021). Multi-Level Multi-Growth Models: New opportunities for addressing developmental theory using advanced longitudinal designs with planned missingness. Developmental Cognitive Neuroscience, 51, 101001. https://doi.org/https://doi.org/10.1016/j.dcn.2021.101001 Yang Hansen, K., Patsis, P., & Gustafsson, J. E. (2025). How does school composition mitigate socioeconomic and ethnic gaps in students’ achievement in Sweden: A long-term trend between 1988 and 2020. Educational Review, 1–26. https://doi.org/10.1080/00131911.2025.2599761

Every year INVALSI (National Institute for the Evaluation of the Education and Training System) administers national learning assessments from primary to upper secondary school. In doing so, it contributes to the development of the education system in Italy. Infact, on a national scale, it provides the Ministry of Education and Merit (MIM) with information on students’ competency levels. At school level, INVALSI works closely with headteachers, teachers, and assessment coordinators to support the improvement of students’ competencies. The richness of INVALSI data lies in their large-scale coverage and detailed information at both student and school levels, enabling in-depth analyses of educational outcomes and inequalities. Among our research activities, one example concerns the development of a methodology capable of representing complex phenomena, such as school fragility, thus allowing spatial and temporal comparisons among Italian schools. A school-level composite indicator has been designed to highlight potential weaknesses within the education system and to provide a practical tool for planning interventions, due to its immediate and clear interpretability. Multiple data sources are integrated, including INVALSI and MIM data for the family context, school context and the test results dimensions. For the territorial context dimension, the Revenue Agency and ISTAT data are used. Another one involves the application of spatial statistical models for better representation of territorial disparities and to identify groups of schools in which socio-economic conditions have a greater impact on student assessments. The use of a spatial approach enabled not only the mapping of critical areas but also the identification of institutions that may be more vulnerable. This method could prove to be a relevant tool for a more efficient planning of educational interventions by adapting to the specificity of local contexts thus contributing to a deeper understanding of educational inequalities in Italy. Finally, machine learning models offer powerful predictive tools for large-scale educational data and policy interventions. A study focuses on a cohort of upper secondary school graduates enrolled in university, coming from a dataset built on the combination of different data sources, to apply different classification models for preventing dropout risk. These methods effectively identify students at risk, which could support guidance activities and policy planning. These studies demonstrate the potential of data for education not only for academic research but also for practical applications in territorial planning and policy development.

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