Research Professorship of Learning Analytics in Higher Education

Ioana Jivet, Kamila Misiejuk und Volkan Yücepur Photo: Hardy Welsch

Supporting student success with learner data

Learning at a university can be challenging for students due to limited feedback, difficulties in managing time, and adapting to self-directed learning. At the same time, teachers face the significant task of providing individualized support to each student, adding to their workload. Could the data breadcrumbs that students leave online as they interact with learning environments help us understand where the challenges lie and what kind of student support would be most effective?

Jun.-Prof. Dr. Ioana Jivet is dedicated to designing and implementing student-facing learning analytics feedback systems. This interdisciplinary effort, at the crossroads of computer and data science, artificial intelligence, learning sciences, and human-computer interaction, aims to develop innovative feedback systems. These systems will not only provide personalized student support but also offer actionable insights, thereby significantly enhancing the educational experience in higher education settings.

  • The group’s research focuses on three directions, all related to the life-cycle of developing student feedback systems.

    1. Information needs and data sources—Arguably, one of the most important aspects of feedback design is choosing relevant and meaningful information to provide as feedback. We investigate data sources and key indicators for student-facing learning analytics that are essential for effective learning and are perceived as valuable by students, ensuring a balance of pedagogical grounding, human-centered design, and technical feasibility.

    2. Delivery and sense-making—Computed relevant learning analytics indicators are nothing without an appropriate way of delivering this information to its users. It is crucial that students and teachers can easily unpack and make sense of the information provided. Here, we explore design features that support student sense-making, be it with LA dashboards or text generated with LLMs, but also how we can keep the inner workings of our systems transparent to the students.

    3. Reflection and action — Once a feedback system is in place, it is essential to understand how the system is being used by students, what insights they extract from the provided feedback, and who benefits the most from the system. With this information at hand, we explore effective reflection triggers and how support for reflection and action can be embedded into the student-facing learning analytics for the students who need it.

    In each of these three directions, we also aim to understand how student skills, goals, and cultural values influence expectations, needs, concerns, and the adoption of learning analytics, allowing us to personalize the systems.

  • Jun.-Prof. Dr. Ioana Jivet

    Ioana JivetPhoto: Hardy Welsch

    Maria Efstathiadou

    Assistence oft the research professorship Learning Analytics

    Email: learning.analytics

    Phone: +49 2331 987-4678

    PRG, Room B 113 (1st Floor)

    Dr. Kamila Misiejuk

    Kamila-misiejuk-hw 500x600Photo: Hardy Welsch

    Volkan Yücepur

    Yuecepur Volkan Web2Photo: Hardy Welsch
  • 2024

    Journals

    • Cardenas Hernandez, F. P., Schneider, J., Di Mitri, D., Jivet, I., & Drachsler, H. (2024). Beyond hard workout: A multimodal framework for personalised running training with immersive technologies. British Journal of Educational Technology.
    • Gombert, S., Fink, A., Giorgashvili, T., Jivet, I., Di Mitri, D., Yau, J., Frey, A., & Drachsler, H. (2024). From the automated assessment of student essay content to highly informative feedback: A case study. International Journal of Artificial Intelligence in Education, 1–39.
    • Karademir, O., Di Mitri, D., Schneider, J., Jivet, I., Allmang, J., Gombert, S., Kubsch, M., Neumann, K., & Drachsler, H. (2024). I don’t have time! But keep me in the loop: Co-designing requirements for a learning analytics cockpit with teachers. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12997
    • Viberg, O., Kizilcec, R. F., Jivet, I., Monés, A. M., Oh, A., Mutimukwe, C., Hrastinski, S., & Scheffel, M. (2024). Cultural differences in students’ privacy concerns in learning analytics across germany, south korea, spain, sweden, and the united states. Computers in Human Behavior Reports, 14, 100416. https://doi.org/10.1016/j.chbr.2024.100416
    • Weidlich, J., Fink, A., Jivet, I., Yau, J., Giorgashvili, T., Drachsler, H., & Frey, A. (2024). Emotional and motivational effects of automated and personalized formative feedback: The role of reference frames. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.13024

    Conferences

    • Menzel, L., Jivet, I., Gombert, S., Schmitz, M., Giorgashvili, T., & Drachsler, H. (2024). 2nd workshop on highly informative learning analytic (HILA). Companion Proceedings of the 14th International Conference on Learning Analytics and Knowledge.
    • Prieto, L. P., Viberg, O., Rodrı́guez-Triana, M. J., Jivet, I., Chen, B., & Scheffel, M. (2024). Culture and values in learning analytics: A human-centered design and research approach. Companion Proceedings of the 14th International Conference on Learning Analytics and Knowledge.

    2023

    Journals

    • Egetenmeier, A., & Jivet, I. (2023). Ten years of learning analytics in the german-speaking space: Success, failure and lessons learned. Workshopband Der 21. Fachtagung Bildungstechnologien (DELFI), 13–16.
    • Ferguson, R., Khosravi, H., Kovanović, V., Viberg, O., Aggarwal, A., Brinkhuis, M., Buckingham Shum, S., Chen, L. K., Drachsler, H., Guerrero, V. A., Hanses, M., Hayward, C., Hicks, B., Jivet, I., Kitto, K., Kizilcec, R., Lodge, J. M., Manly, C. A., Matz, R. L., … Yan, V. X. (2023). Aligning the goals of learning analytics with its research scholarship: An open peer commentary approach. Journal of Learning Analytics, 10(2), 14–50. https://doi.org/10.18608/jla.2023.8197
    • Kaliisa, R., Jivet, I., & Prinsloo, P. (2023). A checklist to guide the planning, designing, implementation, and evaluation of learning analytics dashboards. International Journal of Educational Technology in Higher Education, 20(1), 28.
    • Woitt, S., Weidlich, J., Jivet, I., Orhan Göksün, D., Drachsler, H., & Kalz, M. (2023). Students’ feedback literacy in higher education: An initial scale validation study. Teaching in Higher Education, 1–20.
    • Wollny, S., Di Mitri, D., Jivet, I., Muñoz-Merino, P., Scheffel, M., Schneider, J., Tsai, Y.-S., Whitelock-Wainwright, A., Gašević, D., & Drachsler, H. (2023). Students’ expectations of learning analytics across europe. Journal of Computer Assisted Learning, 39(4), 1325–1338.

    Conferences

    • Kizilcec, R. F., Viberg, O., Jivet, I., Martinez Mones, A., Oh, A., Hrastinski, S., Mutimukwe, C., & Scheffel, M. (2023). The role of gender in students’ privacy concerns about learning analytics: Evidence from five countries. LAK23: 13th International Conference on Learning Analytics and Knowledge, 545–551.

    Chapters in Edited Books

    • Viberg, O., Jivet, I., & Scheffel, M. (2023). Designing culturally aware learning analytics: A value sensitive perspective. In O. Viberg & Å. Grönlund (Eds.), Practicable learning analytics (pp. 177–192). Springer.
  • Maria Efstathiadou

    Assistence oft the research professorship Learning Analytics

    E-Mail: learning.analytics

    Telefon: +49 2331 987-4678

    PRG, Room B 113 (1st Floor)

Christina Lüdeke | 02.12.2024