Dr. Jakub Kuzilek

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Dr. Jakub Kuzilek Photo: Private

Dr. Jakub Kuzilek

Researcher at research professorship "Learning Science in Higher Education"

Email: jakub.kuzilek

Universitätsstraße 27 – PRG / Building 5
58097 Hagen

What is my role within CATALPA?

I am researcher focused on the educational data and their use for improving the learning processes and study experience. Thus, I am doing what I am best at – working with data, improving data flows and delivering AI driven solutions to improve retention, learning gain and to provide the useful insights on the learning processes.

Why CATALPA?

By my opinion, CATALPA is in unique position to deliver full-scale data-driven solutions to improve the education and learning at FernUniversität in Hagen. I believe that the data can change the way how we are teaching student, especially in online and distance learning settings. With 80.000-ish students there will be lot of information and space for the changing not just the learning at FernUniversität but also in the higher education sector in Germany. And I want to help with that.

    • PhD student in AI & Biocybernetics (CTU in Prague; 2009 – 2013)
    • Research associate in Learning Analytics & Educational Data Mining (KMi, OU, Milton Keynes; 2013 – 2017)
    • Research assistant & Researcher in AI in Education (CTU in Prague, 2010 – 2020)
    • Researcher in Learning Analytics & Educational Data Mining (CSES, HU Berlin, 2020 – 2025)

    • Educational Data Mining

    • Explainable AI

    • Machine Learning & Signal Processing

    • Learning Analytics

    • Learning Analytics infrastructures

    • Educational Technology

  • My current affiliation is with the project LEAD:FUH.

  • 2025

    Conferences

    • 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

    Journals

    • 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

    • Conferences

    • 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

    • Journals

    • 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.

    • Conferences

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

    • 2021

    • Conferences

    • 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

  • I am a SoLAR member.

CATALPA | 23.01.2026