Dr. Jakub Kuzilek

English

Dr. Jakub Kuzilek Foto: Privat

Dr. Jakub Kuzilek

Wissenschaftlicher Mitarbeiter in der Forschungsprofessur „Learning Sciences in Higher Education“

E-Mail: jakub.kuzilek

Universitätsstraße 27 – PRG / Gebäude 5
58097 Hagen


Was ist meine Rolle in CATALPA?

Ich bin Forscher mit Schwerpunkt auf Bildungsdaten und deren Nutzung zur Verbesserung von Lernprozessen und Lernerfahrungen. Damit tue ich das, was ich am besten kann – ich arbeite mit Daten, verbessere Datenflüsse und liefere KI-gestützte Lösungen, um die Lernfortschritte und den Lernerfolg zu verbessern und nützliche Erkenntnisse über die Lernprozesse zu gewinnen.

Warum CATALPA?

Meiner Meinung nach ist CATALPA in einer einzigartigen Position, um umfassende datengestützte Lösungen zur Verbesserung der Lehre und des Lernens an der FernUniversität in Hagen anzubieten. Ich glaube, dass die Daten die Art und Weise verändern können, wie wir Studierende unterrichten, insbesondere im Online- und Fernunterricht. Mit rund 80.000 Studierenden gibt es eine Fülle von Informationen und Möglichkeiten, nicht nur das Lernen an der FernUniversität, sondern auch im gesamten Hochschulbereich in Deutschland zu verändern. Und dabei möchte ich helfen.

    • Doktorand in KI und Biokybernetik (CTU in Prag; 2009–2013)

    • Wissenschaftlicher Mitarbeiter im Bereich Lernanalytik und Bildungsdatenauswertung (KMi, OU, Milton Keynes; 2013–2017)

    • Wissenschaftlicher Mitarbeiter und Forscher im Bereich KI in der Bildung (CTU in Prag, 2010–2020)

    • Forscher im Bereich Lernanalytik und Bildungsdatenauswertung (CSES, HU Berlin, 2020–2025)

    • Educational Data Mining

    • Explainable AI

    • Machine Learning & Signal Processing

    • Learning Analytics

    • Learning Analytics infrastructures

    • Educational Technology

  • Ich bin derzeit dem Projekt LEAD:FUH angeschlossen.

  • 2025

    Konferenzbeiträge

    • Seidel, N., Burchart, M., Haake, J. M., Schumacher, C., & Kuzilek, J. (2025). Detecting interaction patterns in educational collaborative writing. In Proceedings of the 28th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).
    • Swamy, V., Kuzilek, J., Pinto, J., Paquette, L., Kaser, T., Liu, Q. S., & Cohausz, L. (2025). The 2nd Human-Centric eXplainable AI in Education (HEXED) Workshop. In Proceedings of the 18th International Conference on Educational Data Mining. Symposium conducted at the meeting of International Educational Data Mining Society.

    2024

    Zeitschriftenartikel

    • Çavuş, M., & Kuzilek, J. (2024). An Effect Analysis of the Balancing Techniques on the Counterfactual Explanations of Student Success Prediction Models. Journal of Measurement and Evaluation in Education and Psychology(15 (Special Issue)), 302–317. https://doi.org/10.21031/epod.1526704Çavuş, M., & Kuzilek, J. (2024). An Effect Analysis of the Balancing Techniques on the Counterfactual Explanations of Student Success Prediction Models. Journal of Measurement and Evaluation in Education and Psychology(15 (Special Issue)), 302–317. https://doi.org/10.21031/epod.1526704

    • Konferenzbeiträge

    • Çavuş, M., & Kuzilek, J. (July 2024). The Actionable Explanations for Student Success Prediction Models: A Benchmark Study on the Quality of Counterfactual Methods. In 17th Educational Data Mining Conference 2024, Atlanta, Georgia, USA. https://ceur-ws.org/Vol-3840/HEXED24_paper1.pdf

      Palomino, A., Fischer, A., Kuzilek, J., Nitsch, J., Pinkwart, N., & Paassen, B. (2024). EdTec-QBuilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in German Language. In K.-W. Chang, A. Lee, & N. Rajani (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations) (pp. 26–35). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-demo.3

      Rüdian, S., Schumacher, C., Hanses, M., Kuzilek, J., & Pinkwart, N. (2024). Rule-based and prediction-based computer-generated Feedback in Online Courses. In IEEE International Conference on Advanced Learning Technologies (ICALT). http://dx.doi.org/10.1109/ICALT61570.2024.00089

    • 2022

    • Zeitschriftenartikel

    • Kuzilek, J., Zdrahal, Z., Vaclavek, J., Fuglik, V., Skocilas, J., & Wolff, A. (2022). First-Year Engineering Students’ Strategies for Taking Exams. International Journal of Artificial Intelligence in Education, 1–26.

    • Konferenzbeiträge

    • Schumacher, C., & Kuzilek, J. (2022). How do students perceive algorithmic grouping in higher education? In Companion Proceedings of the LAK2022.

    • 2021

    • Konferenzbeiträge

    • Schumacher, C., & Kuzilek, J. (2021). Perfect Match? Investigating Students’ Perceptions About Algorithmic Grouping in Higher Education. AECT International Convention.

      Schumacher, C., & Kuzilek, J. (2021, June 9). Student perspectives on automatic grouping in higher education. Presented at Junges Forum für Medien und Hochschulentwicklung, Virtual Conference.

      Schumacher, C., Reich-Stiebert, N., Kuzilek, J., Burchart, M., Raimann, J., Voltmer, J.‑B., & Stürmer, S. (2021). Group perceptions vs. group reality: Exploring the fit of self-report and log file data in the process of collaboration. In Companion proceedings of Conference on Learning Analytics and Knowledge 2021, Virtual Conference, 15-04-2021.

    • Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students' use of self‐assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268. https://doi.org/10.1111/jcal.12744
    • Kuzilek, J., Zdrahal, Z., Vaclavek, J., Fuglik, V., Skocilas, J., & Wolff, A. (2023). First-Year Engineering Students’ Strategies for Taking Exams. International Journal of Artificial Intelligence in Education, 33(3), 583–608. https://doi.org/10.1007/s40593-022-00303-4
    • Marmolejo-Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic, S., Kovanović, V., González, J., Kneib, T., Bühlmann, P., Kook, L., Briseño-Sánchez, G., & Ospina, R. (2023). Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 13(1), e1479. https://doi.org/10.1002/widm.1479
    • Schumacher, C., Ifenthaler, D., & Kuzilek, J. (2023, August 22). Using Students’ Interaction with Self-Assessments During the Semester for Predicting Course Success. EARLI 2023 - 20th Biennial EARLI Conference, Tessaloniki.
    • Kuzilek, J., Zdrahal, Z., & Fuglik, V. (2021). Student success prediction using student exam behaviour. Future Generation Computer Systems, 125, 661–671. https://doi.org/10.1016/j.future.2021.07.009
    • Kuzilek, J., Zdrahal, Z., Vaclavek, J., Fuglik, V., & Skocilas, J. (2020). Exploring exam strategies of successful first year engineering students. In C. Rensing, H. Drachsler, V. Kovanović, N. Pinkwart, M. Scheffel, & K. Verbert (Eds.), Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 124–128). ACM. https://doi.org/10.1145/3375462.3375469
    • Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4, 170171. https://doi.org/10.1038/sdata.2017.171
    • Wolff, A., Moore, J., Zdrahal, Z., Hlosta, M., & Kuzilek, J. (2016). Data literacy for learning analytics. In D. Gašević, G. Lynch, S. Dawson, H. Drachsler, & C. Penstein Rosé (Eds.), Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16 (pp. 500–501). ACM Press. https://doi.org/10.1145/2883851.2883864
    • Zdrahal, Z., Hlosta, M., & Kuzilek, J. (2016). Analysing Performance of First Year Engineering Students. In Learning Analytics and Knowledge: Data Literacy for Learning Analytics Workshop. https://oro.open.ac.uk/58597/
    • Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015). OU Analyse: Analysing at-Risk Students at the Open University. https://oro.open.ac.uk/42529/1/__userdata_documents4_ctb44_Desktop_analysing-at-risk-students-at-open-university.pdf
    • Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdrahal, Z., & Wolff, A. (2014). Modelling Student Online Behaviour in a Virtual Learning Environment. In Proceedings of the 4th International Conference on Learning Analytics and Knowledge, New York, US. http://ceur-ws.org/Vol-1137/LA_machinelearning_submission_4.pdf

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CATALPA | 23.01.2026