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12 SES 10 A: Open Research Data, Discourse, and Practice in Education
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12. Open Research in Education
Paper Using Open Research Data for Remembrance Education: Game Development with Holocaust Studies University Koblenz, Germany Presenting Author:In contemporary knowledge societies, access to scientific knowledge and research data is becoming increasingly significant (Stehr, 2023). Science is therefore not only challenged to generate new knowledge, but also to make it accessible, usable and socially meaningful. In this context, the Open Science movement (UNESCO, 2021) promotes transparency, accessibility and participation, principles that are also firmly anchored in European research policy, particularly within Horizon Europe. Open access to publications and research data is understood as a key prerequisite for democratic knowledge production, following the principle “as open as possible, as closed as necessary” (European Commission, 2026). Educational science plays a central role in this debate, as it is fundamentally concerned with the pedagogical mediation, contextualisation and transfer of scientific knowledge (Diederichs & Desoye, 2023). Against this background, the paper explores the potential of games as a form of science communication and knowledge transfer. Games are increasingly discussed as innovative tools for communicating complex scientific issues to broader audiences (Ouariachi et al., 2017; Asplund, 2020). Serious games, in particular, aim to combine learning objectives with playful elements (Schrader, 2022), enabling users to engage cognitively and emotionally with complex topics. However, the use of games becomes especially challenging in the field of Holocaust education. While games may offer new forms of access and engagement, their use in remembrance culture is highly contested (Tillmann, 2023). Critics warn against trivialisation, inappropriate representations and ethically problematic forms of immersion (Frasca, 2000). Research emphasises that serious games addressing the Holocaust must be grounded in scientifically validated sources, clearly distinguish between fiction and historical reality, and follow explicit ethical and educational guidelines (Schwarz, 2023; Zimmermann, 2023). This paper addresses these debates by examining an exploratory teaching project situated at the intersection of Open Science, science communication and adult education. The guiding research questions are: By focusing on adult education, the paper highlights a learning context that is often overlooked in discussions of Holocaust education and serious games, despite its relevance for lifelong learning and democratic remembrance cultures. Methodology, Methods, Research Instruments or Sources Used The empirical basis of the paper is a teaching project conducted with fourth-semester Bachelor students in Education at the University of Koblenz. Within two workshop-based seminars, students developed serious game concepts for remembrance work based on open research data from Holocaust-related projects. In the first workshop (2025), students worked with anonymised quantitative data from the citizen science project BEFEM – Citizen Science Research into Family Histories and National Socialism, funded by the Rhineland-Palatinate state parliament. The dataset provided insights into knowledge levels, attitudes and family references to National Socialism within a regional context. The project was introduced comprehensively to ensure transparency regarding methodology, scope and limitations of the data (Lenzgen, Jansen & Engel 2023). A second workshop (planned for 2026) will complement this approach by using qualitative audiovisual material from the oral history project The Effect of Reparations – A Jewish-German History of Experience, hosted on the Oral-History.Digital platform (Oral-History.Digital 2024). In line with FAIR principles, Oral-History.Digital makes the interviews searchable, accessible, linkable and reusable as audiovisual research data. FAIR stands for: Findable, Accessible, Interoperable and Reusable. It ensures that research data is easily discovered, accessed, interpreted and used by humans and machines alike (Wilkinson et al. 2016. In both workshops, students developed game concepts using the Triadic Game Design framework (Harteveld, 2011), which balances the dimensions of reality, meaning and play. The project follows an exploratory qualitative approach, drawing on observations, student artefacts (game prototypes) and reflective questionnaires. Conclusions, Expected Outcomes or Findings The first workshop resulted in four different game prototypes, including a board game, a role-playing game, an escape game and a story-based game. The findings indicate that working with research data in game development posed significant challenges. Some groups largely ignored the data, while others adhered too closely to it. Quantitative data, in particular, proved difficult to integrate meaningfully into game mechanics, as it was context-specific and not suitable for direct factual representation. At the same time, students were required to integrate ethical guidelines inspired by Adorno’s (2012) Erziehung nach Auschwitz, which ruled out purely informational or quiz-based approaches. Designing games for adult education added another layer of complexity, as learning in this field often takes place in non-institutional, voluntary and self-directed contexts. Student feedback highlights ethical considerations as the central challenge. The tension between serious engagement and playful design was perceived as constant, alongside concerns about trivialisation and inappropriate representations. Nevertheless, games were widely regarded as a promising medium for addressing complex and emotionally charged topics of remembrance, provided that ethical boundaries and educational intentions are clearly defined. Overall, the project demonstrates both the potential and the limits of using open research data for serious game development in Holocaust education. It contributes to current debates on Open Science by showing that openness alone does not guarantee pedagogical usability, but requires careful didactic translation, especially in ethically sensitive fields. The presentation is planned to include both results from the first workshop (2025) with quantitative data and the as yet unavailable results with qualitative data (2026) from the second workshop. The workshop is scheduled to take place in spring 2026. References Adorno, T.W. (2012). Erziehung nach Auschwitz. In: Bauer, U., Bittlingmayer, U.H., Scherr, A. (eds) Handbuch Bildungs- und Erziehungssoziologie. Bildung und Gesellschaft. VS Verlag für Sozialwissenschaften, Wiesbaden. https://doi.org/10.1007/978-3-531-18944-4_7 Asplund, T. (2020). Credibility aspects of research-based gaming in science communication: The case of The Maladaptation Game. Journal of Science Communication, 19(1), A01.https://doi.org/10.22323/2.19010201 Diederichs, T., & Desoye, A. (2023). Transfer in Erziehungswissenschaft und Pädagogik. In T. Diederichs & A. Desoye (Eds.), Transfer in Pädagogik und Erziehungswissenschaft: Zwischen Wissenschaft und Praxis. Juventa. European Commission. (2026). Open science in Horizon Europe.https://rea.ec.europa.eu/open-science_en Frasca, G. (2000). Ephemeral games: Is it barbaric to design videogames after Auschwitz? In M. Eskelinen & R. Koskimaa (Eds.), CyberText Yearbook 2000 (pp. 172–180). University of Jyväskylä. Gertrudis-Casado, M. C., Gálvez-de-la-Cuesta, M. C., Romero-Luis, J., & Gertrudix, M. (2022). Serious games as an efficient strategy for science communication in the COVID-19 pandemic. Revista Latina de Comunicación Social, 80, 40–62.https://doi.org/10.4185/RLCS-2022-1788 Glouftsis, T. (2022). Implicated gaming: Choice and complicity in ludic Holocaust memory. History and Theory, 61(4), 134–151.https://doi.org/10.1111/hith.12277 Hammer, J., & Turkington, M. (2021). Designing role-playing games that address the Holocaust. International Journal of Designs for Learning, 12(1), 42–53.https://doi.org/10.14434/ijdl.v12i1.31265 Harteveld, C. (2011). Triadic game design: Balancing reality, meaning and play. Springer. Lenzgen, H., Jansen, P.-E., & Engel, I. (2023). Zwischenergebnisse Projekt BEFEM: Bürgerwissenschaftliche Erforschung der Familiengeschichte von Einheimischen und Migrant:innen und ihr Verhältnis zur NS-Geschichte. Landtag Rheinland-Pfalz.https://landtag-rlp.de/files/pdf1/zwischenergebnis_froschungsprojekt-befem.pdf Majkowski, T. Z., & Suszkiewicz, K. (2021). Cardboard genocide: Board game design as a tool in Holocaust education. GAME: The Italian Journal of Game Studies, 9, 71–90.https://www.gamejournal.it/wp-content/uploads/2021/01/I9_GAME_05_MAJKOWSKISUSZKIEWICZ.pdf Pfister, E., & Zimmermann, F. (2021). “No one is ever ready for something like this”: On the dialectic of the Holocaust in first-person shooters as exemplified by Wolfenstein: The New Order. International Public History, 4(1), 35–46.https://doi.org/10.1515/iph-2021-2020 Oral-History.Digital. (2024). Interviews zur Wirkung der Wiedergutmachung.https://www.oral-history.digital/news/2024-11-22-touro.html Ouariachi, T., Olvera-Lobo, M. D., & Gutiérrez-Pérez, J. (2017). Analyzing climate change communication through online games: Development and application of validated criteria. Science Communication, 39(1), 10–44.https://doi.org/10.1177/1075547016687998 Schrader, C. (2022). Serious games and game-based learning. In O. Zawacki-Richter & I. Jung (Eds.), Handbook of open, distance and digital education (pp. 1–14). Springer. Schwarz, A. (2023). Digitale Spiele aus Perspektive der Geschichtswissenschaft. In Ç. Uzunoğlu, C. Huberts, & M. Brandt (Eds.), Handbuch Erinnern mit Games: Digitale Spiele als Chance für die Erinnerungskultur (pp. 32–35). Stiftung Digitale Spielekultur. Stehr, N. (2023). Understanding society and knowledge. Edward Elgar. Wilkinson, M., Dumontier, M., Aalbersberg, I.et al. (2026)The FAIR Guiding Principles for scientific data management and stewardship.Sci Data3, 160018. https://doi.org/10.1038/sdata.2016.18 12. Open Research in Education
Paper Balancing Privacy and Utility in the Reuse of Administrative Educational Data: Challenges for Open Science and Equity 1: Universidade da Beira Interior, Portugal; 2: Instituto de Telecomunicações; 3: ISEG Research; 4: Universidade Lusófona; 5: INTREPID Lab Presenting Author:Open research has become a central paradigm in contemporary educational research, driven by growing demands for transparency, reproducibility, and societal relevance. These practices include open access to publications, preregistration, open methodologies, and the sharing of data and analytical code throughout the research lifecycle. In education, they support cumulative knowledge building, enable replication and secondary analysis, and strengthen the empirical basis of policy and practice. The adoption of open research principles in education is closely aligned with the FAIR data principles (Wilkinson, 2016), which emphasize that research outputs should be findable, accessible, interoperable, and reusable. In this context, research data—particularly administrative records generated by Educational Management Information Systems (EMIS)—have become a cornerstone for advancing scientific knowledge and informing public policy. EMIS data provide comprehensive population coverage, high temporal granularity, and relatively low costs for secondary research, making them especially valuable for large-scale and longitudinal educational studies. However, the reuse of administrative educational data is often limited by tensions between the public interest in data openness and the ethical and legal obligations imposed by data protection frameworks such as the General Data Protection Regulation (GDPR) (EU, 2016). Educational research relies heavily on personal and sensitive data, including demographic information, academic records, behavioral traces, and institutional data. These risks are heightened by the involvement of minors and vulnerable populations and by the high-dimensional, linkable nature of contemporary educational datasets (Fazendeiro et al., 2025; Ferrão et al., 2022, 2023). Consequently, unregulated data sharing may lead to re-identification risks and potential harm. Data anonymization plays a critical role in enabling open research in education while mitigating these risks. Anonymization refers to a set of technical and organizational measures designed to irreversibly prevent the identification of data subjects under reasonably foreseeable conditions (Sweeney et al., 2018; Prasser et al. 2020). Unlike pseudonymization, anonymization aims to eliminate both direct and indirect identification risks, and thus constitutes not only a technical safeguard but also a methodological and ethical requirement for responsible data sharing within Open Science frameworks. This article reflects on the practical challenges of preparing, anonymizing, and documenting sensitive educational data for reuse in accordance with Open Science and FAIR principles. Drawing on microdata from the Brazilian National Student Performance Exam (ENADE) and the RAIDES database, provided by the Portuguese Directorate-General for Education and Science Statistics (DGEEC), the study explores the trade-offs between data anonymization and analytical utility in real-world administrative datasets. Special emphasis is placed on the impact of anonymization decisions on the interpretability and inclusiveness of educational data. The findings indicate that widely used anonymization techniques, including advanced methods such as (ε, δ)-Differential Privacy (Dwork & Roth, 2014; Bild et al., 2018) may introduce subtle yet significant biases. Although the overall statistical structure of the data may appear to be preserved, these techniques can disproportionately suppress or distort minority groups and rare categories, particularly across racial, socio-economic, or institutional dimensions. Such distortions pose substantial risks for equity-oriented research, as the omission or misrepresentation of underrepresented populations in anonymized datasets can result in misleading analyses and compromised evidence-based policy decisions. These results indicate that the transition toward FAIR-compliant educational data infrastructures cannot rely solely on technical solutions. Rather, it requires sustained and structured dialogue among researchers, data infrastructure providers, and domain experts to ensure that anonymization practices uphold both scientific validity and social inclusivity. We contend that the active involvement of domain experts in the interpretation of anonymized data is essential to prevent privacy-preserving technologies from inadvertently compromising the educational equity and social justice goals they are intended to advance. Methodology, Methods, Research Instruments or Sources Used The anonymization process began with a structured audit of the dataset to identify direct identifiers, quasi-identifiers, and sensitive attributes. Direct identifiers, including names, unique identification numbers, and contact information, were removed prior to analysis. Quasi-identifiers—such as age, grade level, gender, geographic indicators, and institutional characteristics—were identified based on their potential to enable re-identification when combined. Sensitive attributes, including academic achievement measures and behavioral indicators, were retained subject to appropriate transformation. A combination of anonymization techniques was applied to address different sources of disclosure risk (Mendes & Vilela, 2017; Santos et al., 2020). Generalization and suppression were used to reduce the granularity of quasi-identifiers, for example by grouping continuous variables into ranges and aggregating geographic information. These transformations were guided by k-anonymity principles (Sweeney, 2002), ensuring that each record was indistinguishable from at least k−1 other records with respect to selected quasi-identifiers. Differential Privacy (DP), a probabilistic framework that provides formal, mathematical guarantees, offers a distinct approach to privacy protection. A mechanism satisfies DP if the inclusion or exclusion of a single individual’s data does not substantially influence the output, with the level of protection governed by the privacy parameters ε (and δ). In this study, the (ε, δ)-DP model is applied to the dataset using the SafPub algorithm (Bild et al., 2018). Although most DP algorithms are designed for query-based settings in which the data custodian retains control over the data and does not release it directly (Jain et al., 2018), SafPub enables the application of DP in contexts where the goal is to publish an anonymized version of the dataset. An iterative risk–utility assessment was conducted to balance privacy protection with analytical validity. Disclosure risk was evaluated by examining equivalence class sizes, the presence of outliers, and the plausibility of linkage attacks using external data sources. Data utility was assessed by comparing key descriptive and inferential statistics between the original and anonymized datasets, including means, variances, and correlations relevant to the research questions. Anonymization parameters were adjusted iteratively until an acceptable balance between risk reduction and information loss was achieved. Finally, all anonymization decisions and transformations were systematically documented to support transparency, reproducibility, and responsible reuse. Documentation includes descriptions of removed variables, applied transformations, residual risks, and known limitations of the anonymized dataset. Conclusions, Expected Outcomes or Findings This article has examined data anonymization as a foundational methodological element of open research in education. While open research practices offer substantial benefits for transparency, reproducibility, and cumulative knowledge generation, their implementation in educational contexts is constrained by the sensitive nature of educational data. Anonymization therefore serves as a critical enabling mechanism that allows data to be shared responsibly without compromising the privacy and rights of research participants. The methods described demonstrate that effective anonymization is not a one-time technical step but an iterative and context-dependent process. By combining identifier removal, generalization, suppression, and sampling-based techniques within a structured risk–utility framework, it is possible to substantially reduce re-identification risk while preserving analytical usefulness. Transparent documentation of anonymization procedures further supports reproducibility and responsible secondary use. However the results shown in (Fazendeiro et al., 2025; Ferrão et al., 2022) suggest that while the risk of reidentification is low in anonymized data, such data may be quite useless for some educational research purposes. Regardless of whether k-anonymity or differential privacy is used, anonymization of low-dimensional datasets can compromise data analysis, inferences, and research conclusions. Even in high-dimensional data, the loss or underrepresentation of minority groups may introduce bias. Therefore, it is imperative to conduct a systematic and comprehensive assessment of the limitations that the use of anonymized data imposes on educational research, and to design and implement public policies and intervention programmes aimed at mitigating these constraints, thereby strengthening quantitative educational research. Regulations governing the accreditation of researchers and access to microdata for research in the public interest should carefully balance data protection requirements with the need to advance scientific knowledge. As educational research increasingly relies on large-scale, longitudinal, and high-dimensional data, traditional de-identification methods may be insufficient, highlighting the need to explore advanced privacy-preserving techniques and secure data access infrastructures. References Bild, R., Kuhn, K.A., Prasser, F. ,SafePub: a truthful data anonymization algorithm with strong privacy guarantees. Proc Priv Enhancing Techno. 2018; 2018(1):67–87. https://doi.org/10.1515/popets-2018-0004. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042. EU Regulation 2016/679 of the European Parliament and of the Council of 27 April on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) in Official Journal of the European Union, L 119, pp. 1–88, 2016. Fazendeiro, P., Prata, P., & Ferrão, M. E. (2025). Subtle biases introduced in equity studies through data anonymization. PLoS ONE, October 8. https://doi.org/https://doi.org/10.1371/journal.pone.0332441. Ferrão, M. E., Prata, P., & Fazendeiro, P. (2022). Utility-driven assessment of anonymized data via clustering. Scientific Data, 9(1), 456. https://doi.org/10.1038/s41597-022-01561-6. Ferrão, M. E., Prata, P., & Fazendeiro, P. (2023). Anonymized data assessment via analysis of variance: an application to higher education evaluation. In Lecture Notes in Computer Science (pp. 130–141). https://doi.org/10.1007/978-3-031-37108-0_9. Jain, P., Gyanchandani, M. & Khare, N. Differential privacy: its technological prescriptive using big data. J Big Data 5, 15 (2018). https://doi.org/10.1186/s40537-018-0124-9. Mendes, R., Vilela, J.P., Privacy-Preserving Data Mining: Methods, Metrics, and Applications, in IEEE Access, vol. 5, pp. 10562-10582, 2017, doi: 10.1109/ACCESS.2017.2706947. Prasser, F., Eicher, J., Spengler, H., Bild, R., & Kuhn, K. A. (2020). Flexible data anonymization using ARX—Current status and challenges ahead. Softw Pract Exper. 50, 1277–1304. https://doi.org/10.1002/spe.2812. Santos W., Sousa G., Prata P., Ferrao M.E. Data Anonymization: K-anonymity Sensitivity Analysis. In: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). Sevilla, Spain: IEEE; 2020. pp. 1–6. Available from: https://ieeexplore.ieee.org/document/9141044/. Sweeney, L., Loewenfeldt, M. V., Perry, M., “Saying it’s Anonymous Doesn't Make It So: Re-identifications of “anonymized” law school data,” Technology Science. 2018111301. November 13, 2018. https://techscience.org/a/2018111301. Sweeney L. k-Anonymity: a model for protecting privacy. Int J Unc Fuzz Knowl Based Syst. 2002;10(05):557–570. https://dataprivacylab.org/projects/kanonymity/kanonymity.pdf. Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18. 12. Open Research in Education
Paper EduTopics – New Functions, Developments and the Future of the App DIPF | Leibniz Institute for Research and Information in Education Presenting Author:Since ECER 2024 in Nicosia, the interactive webapp EduTopics: ECER provides interested users access to information on the contributions to ECER since 1998 (Christ et al., 2025a). In 2025, the webapp EduTopics was updated with additional functions based on feedback from the audience in Nicosia, general users and the national representatives and link convenors of the EERA networks. In 2025, EduTopics provided the basis for several contributions at ECER 2025 in Belgrade, was presented at EARLI 2025 in Graz and was used for various publications as well, we were able to gather additional feedback on its design and functionality. Meanwhile, the presentations beyond ECER with other (inter-)national organizations opened up new collaborations regarding the inclusion of data of other conferences on educational research in EduTopics. The resulting transformation from EduTopics: ECER to a cross-national, cross-conference webapp simply called EduTopics, its new design and functions are the focus of this presentation. EduTopics contains data and manipulable visualisations on the authors, countries of affiliation, EERA-networks and themes for n > 30,000 contributions from ECER (exact number of contributions from other sources are to be determined). The themes of the contributions were identified by applying the computer linguistic machine learning approach topic modelling – more specifically latent Dirichlet allocation (LDA, Blei et al., 2003) – which identifies word and document clusters in large literature corpora (Blei & Lafferty, 2009; Vayansky & Kumar, 2020). A hierarchical approach to topic modelling enables the identification both of broader, overarching supertopics and more detailed smaller subtopics similar to super- and subcategories in qualitative content analysis. By aggregating the resulting (sub- and super-)document-topic-weights in regard to the aforementioned covariates (i.e. authors, countries, networks), the thematic foci and trends of authors, countries and networks can be identified, visualized, interpreted and compared. Additionally, users can upload own singular texts or entire corpora to determine the latent topics of the text(s) based on the topics of ECER, the EERA-network most relevant to the uploaded text and contributions with similar content. The general development process of the webapp is ongoing but each version is uploaded separately to ensure the replicability and reproducibility of the results of research papers utilizing the app (for 2024 see Christ et al., 2024; for 2025 see Christ et al., 2025b). New functions of the 2026 version of EduTopics (see Christ et al., 2026) include: • A new global settings menu to apply consistent filters to the entire conferences and their dataset. Options for filtering include for example years and data source (i.e. conference). Additionally, future developments are discussed such as the development of a community platform to enable a community-led approach to the qualitative aspect of topic labelling or the development of a data and modelling pipeline to include additional data sources. Methodology, Methods, Research Instruments or Sources Used The central method of EduTopics is LDA topic modelling. The themes in the corpus were identified via a hierarchical, nested topic modelling approach by simultaneously modelling (1) a model with a large number of topics (between k > 300) capturing smaller thematic clusters and (2) a broader model with a smaller number of topics (around k = 50) representing broader central themes within the corpus. The similarities between and within models are determined by aggregating various distance and similarity measures both on the document and word level (Aletras & Stevenson, 2014; Blair & Mulvenna, 2020). This similarity measure can then be utilized to determine the nesting of the subtopics (of model 1) within the supertopics (model 2) as well as the pairwise relationship of topics within one of the two models. The results of both topic models and the results of the frequency analyses of the covariates (i.e. author, country, EERA network, year) were aggregated via their categorical values/mean-topic-weights which in turn resulted in for example author-topic-weights, EERA-network-country distributions and various trend analyses. All results are available as interactive and manipulable figures and tables, allowing for example filtering of the data or downloading relevant documents in tabular formats. Exemplary results to be presented include differences in thematic foci and trends between the different data sources or the integrated mapping functions both for single texts and literature collections. Conclusions, Expected Outcomes or Findings This contribution provides in-depth information into the current and future development of the app EduTopics. In addition, it presents use-cases of the app specifically and for natural language processing and machine learning in general for educational research purposes. It also highlights how the app and the utilized methods in general open up new ways for researchers to access data, which may not have been possible before with traditional methods such as qualitative content analysis. The discussion will focus on potential future challenges for modelling multilingual literature corpora and for deploying the aforementioned community-platform and the data pipeline. The paper aims to elicit constructive feedback from participants to support the ongoing development and improvement of EduTopics. References Aletras, N., & Stevenson, M. (2014, April). Measuring the similarity between automatically generated topics. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers (pp. 22-27). Blair, S. J., Bi, Y., & Mulvenna, M. D. (2020). Aggregated topic models for increasing social media topic coherence. Applied intelligence, 50(1), 138-156. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022. Blei, D. M., & Lafferty, J. D. (2009). Topic models. In Text mining (pp. 101-124). Chapman and Hall/CRC. Christ, A., Röschlein, J., & Schindler, C. (2026). EduTopics: ECER. https://dipf-lis.shinyapps.io/EduTopics [placeholder url] Christ, A., Röschlein, J., & Schindler, C. (2025a). EduTopics: ECER: A webapp for interactive visualisation and exploration of ECER contributions since 1998. European Educational Research Journal, 14749041251405570. Christ, A., Röschlein, J., & Schindler, C. (2025b). EduTopics: ECER 2025. https://dipf-lis.shinyapps.io/EduTopicsECER Christ, A., Röschlein, J., & Schindler, C. (2024). EduTopics: ECER - An interactive app for visualising and exploring the contributions of ECER-conferences for 1998 to 2024. https://dipf-lis.shinyapps.io/EduTopicsECER2024 Vayansky, I., & Kumar, S. A. (2020). A review of topic modeling methods. Information Systems, 94, 101582. | ||