Conference Agenda

In educational science, surveys are commonly used to investigate students’ perspectives on a range of issues. Over the past decade, however, participation rates have declined substantially, a trend that appears to be global. While attrition has long been a challenge in longitudinal research, the combination of increasing non-participation at baseline and subsequent attrition presents a more serious methodological problem that warrants closer attention.

The Educational Longitudinal Database Evaluation Through Follow-up (UGU) collects data from approximately 10% of the total population of students born in a given year, corresponding to about 10,000 students assessed in Grades 6 (age 13), 9 (age 16), and 12 (age 19). The UGU database includes measures of multiple constructs, such as self-concept, interest, motivation, well-being, and cognitive ability.

Within the ongoing project The impact of personal, environmental, and biopsychosocial factors in and out of school on students’ mental health and well-being: A longitudinal cohort study, we examine students’ well-being and mental health from a holistic perspective using the biopsychosocial framework (Porter, 2020). A growing body of research over the past decade indicates a decline in well-being and mental health among young people (Klapp et al., 2023; Högberg et al., 2021; National Agency for Public Health, 2025). In a recent study, we found that students born in 2004 reported lower levels of social, psychological, and cognitive well-being compared with students born in 1998 (Klapp et al., 2023). We also showed that students’ cognitive well-being defined as understanding the teacher and beliefs about one’s intellectual ability to learn was a stronger predictor of final grade point average (GPA) than measured cognitive ability based on cognitive tests.

When conducting longitudinal analyses using more recent birth cohorts, it is therefore necessary to apply weighting procedures to address increasing levels of missing data. This raises an important methodological question: Are the weights and missing-data handling methods currently applied, such as full information maximum likelihood (FIML) estimation in Mplus sufficient to adequately address the growing challenges posed by non-participation and attrition?

To address missing data in the survey components of the Evaluation Through Follow-Up (UGU) research database, Statistics Sweden (SCB) has developed survey weights for each wave of data collection. These weights are primarily designed for cross-sectional analyses. However, given that UGU is a longitudinal database, there is a need for methods that more adequately handle missingness and attrition across multiple waves.

Although the UGU survey data are affected by both non-response and attrition, the database also contains complete register data covering the full population, with no missing values. This provides a unique opportunity to explore alternative approaches to handling longitudinal missingness. In this study, the wave-specific SCB weights are used as reference points while implementing and comparing three different imputation-based approaches that combine register data with incomplete survey data. Specifically, we generate imputed datasets using (1) full information maximum likelihood (FIML) as implemented in Mplus, (2) multiple imputation by chained equations (MICE) in R, and (3) random forest–based imputation in R.

To evaluate which approach most closely aligns with the cross-sectional weighting strategy while remaining suitable for longitudinal analyses, we compare results from cross-sectional regression models across the original datasets, the weighted datasets, and the imputed datasets generated using each method.

The analyses are based on nine items measuring cognitive, psychological, and social well-being, as well as one measure of cognitive ability. The study is conducted within the FEEL project, which has received ethical approval.

As a quality control mechanism for scientific knowledge production, peer review has been regarded since Merton's era as an ‘institutionalised early warning system’ enabling academic communities to achieve self-regulation. Peer reviewers serve as the primary evaluators and have long been considered the ‘academic gatekeepers’ maintaining procedural correctness and outcome validity in scientific research. They play a vital role in safeguarding the autonomy of academic communities and preserving the efficacy of scholarly exchange. Within China's doctoral training framework, anonymous peer review is established as a pivotal stage in the degree conferral process, with expert opinions directly determining the acceptance of doctoral dissertations. This creates potential conflicts of interest for doctoral candidates and their supervisory groups, while also shrouding peer reviewers and their decision-making processes in mystery – an aspect that has received insufficient theoretical and empirical attention. Moreover, the reliability and necessity of peer review within the global academic knowledge production process remain subject to persistent scrutiny. A significant reason lies in the longstanding non-public nature of peer review data, which has led to insufficient understanding, inadequate oversight, and even diminished trust in the peer review process among relevant stakeholders. This explains why, in much related research, peer review is often simplistically conceptualised as a binary “accept/reject” symbol for manuscripts, while the substantive evaluative information embedded in expert opinions and the process-related information—such as written exchanges between participants—has long been overlooked. As academic gatekeepers, peer reviewers must rigorously examine their focus points in scholarly evaluation while critically assessing the practical utility of the written reports shaped by expert opinions. This study employs text classification models to analyse doctoral dissertation review reports, revealing reviewers' primary focal points and evaluative intentions. This prompts the academic community to engage in deeper reflection and critical analysis regarding the function of review comments and the role of peer reviewers. The research aims to address the following questions: (1) What aspects of the thesis do reviewers primarily focus on? (2) What evaluative intentions do reviewers seek to convey through their written comments? (3) How do these focal points and intentions shape the role of reviewers and the function of peer review?

Research data management has become increasingly important in educational research, driven by the Open Science movement and the widespread adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al., 2016). Despite growing recognition of the importance of open data sharing, significant barriers persist in accessing FAIR data in the educational field (Bayer et al., 2023). One fundamental challenge concerns the technical interoperability of data formats across different statistical software platforms.

Researchers in the social and educational sciences use various software for statistical analysis, including SPSS, Stata, R, SAS, and Python. Each software has proprietary data formats that are only partially compatible, with other solutions. This non-interoperability undermines FAIR principles and creates obstacles to replication studies and data reuse. To meet diverse user needs, data producers must offer multiple formats through redundant work that is error-prone and costly. Moreover, researchers frequently struggle to find suitable data for secondary analysis, with many reporting difficulties related to data compatibility and accessibility (Bayer et al., 2022).

The Open Data Format (ODF) represents a promising solution to these interoperability challenges (Han et al, 2024). Developed through the KonsortSWD consortium within Germany's National Research Data Infrastructure (NFDI), ODF is a non-proprietary, multilingual, metadata-enriched, and zip-compressed data format explicitly designed to meet FAIR principles. The format consists of a CSV file containing the raw data, an XML metadata file structured according to the DDI Codebook standard (Vardigan et al., 2008), and a version file, all packaged in a standardized archive. Import and export packages are currently available for R, Python, and Stata, enabling seamless data exchange across these platforms.

This contribution examines the practical implementation of ODF in the context of national education monitoring in Germany. As part of the ShaReD (Sharing and Reusing Data) cooperation at DIPF | Leibniz Institute for Research and Information in Education, data used in national education reporting is prepared for publication by the end of 2026. These data, drawn from various sources including survey data, administrative data from educational institutions, and official documents, form the empirical foundation for system monitoring and educational governance. The decision to publish these monitoring data in ODF format provides an opportunity to reflect on the format's advantages and limitations in real-world application.

Longitudinal research data are fundamental to advancing educational research, yet their structure, accessibility, and reusability vary widely across countries and institutional settings, with profound implications for researchers’ capacity to conduct longitudinal and comparative studies (Pavolini et al., 2025). The Horizon Europe project LINEup (Longitudinal Data for INequalities in Education) addresses this challenge by providing the first systematic mapping of national and regional longitudinal datasets on student learning outcomes in primary and secondary education (ISCED 1–3) across Europe. The mapping identifies what longitudinal data exist, where they are located, how they are documented, and to what extent they can be reused for research on educational inequalities.

Existing literature shows that educational inequalities emerge early and persist across key transitions, shaped by institutional arrangements and contextual conditions (Sirin, 2005; Van de Werfhorst & Mijs, 2010; Jackson & Jonsson, 2013; Chmielewski, 2019) - a pattern confirmed by recent reviews of longitudinal studies in Europe (Campos et al., 2025; Kampylis et al., 2024; Symonds et al., 2025). Understanding these dynamics requires evidence capable of tracing how gaps develop and evolve over time (Gangl, 2010; Skopek & Passaretta, 2021). Yet longitudinal data in Europe remain unevenly available, inconsistently documented, and difficult to access.

The study adopts a conceptual understanding of data as socio-technical objects: their analytical value depends not only on content but also on infrastructures, documentation practices, governance models, and access conditions (Leonelli, 2020). Drawing on Open Science principles, the project examines how metadata quality, management structures, and transparency shape research possibilities. Despite growing demand for stronger longitudinal evidence, researchers still face barriers related to insufficient documentation, heterogeneous variable definitions, lack of harmonisation, opaque access procedures, and uneven integration of administrative data - constraints that hinder the analysis of how inequalities in education arise, widen, or narrow over time.

Recent scholarship on open research data underscores the need for transparent documentation, interoperable metadata standards, and governance frameworks that enable data reuse (Borgman, 2015; Leonelli, 2020; Wilkinson et al., 2016). Yet in education, progress remains uneven. The LINEup project contributes to this debate by offering an empirically grounded model for structuring metadata to enhance comparability, support harmonisation, and enable researchers to assess datasets’ analytical potential.

We developed a shared identification and documentation protocol applied across 32 European countries (EU Member States, EEA associated countries, Switzerland, and the United Kingdom), resulting in a systematic analysis of 103 longitudinal datasets meeting strict inclusion criteria. The mapping reveals substantial variation in dataset quality, periodicity, sample size, measurement comparability, socio-demographic coverage, linkage possibilities, and access openness. The findings depict a fragmented landscape: some countries maintain well-established infrastructures, while others display discontinuities, limited documentation, or restricted access.

Building on this empirical work, the paper introduces the LINEup Education Data Explorer, an open, user-oriented digital infrastructure designed to enhance the findability, transparency, and reusability of longitudinal educational datasets. The Explorer consolidates country- and dataset-level metadata, documentation links, classification variables, and access procedures into a searchable platform grounded in FAIR principles. It aims to lower entry barriers for researchers and educational stakeholders, increase the visibility of existing data sources, and support cross-country research. By transforming a static mapping into a dynamic infrastructure, the Explorer provides a replicable model for data documentation and reuse in educational research. It enables comparative insights into European data ecosystems, supports reflections on harmonisation feasibility, and offers a practical tool for planning future longitudinal data collections.