Research Professorship of Learning Analytics in Higher Education

Ada Pellert and Ioana Jivet Photo: CATALPA
Research Professor Dr. Ioana Jivet at her appointment day with President Prof. Dr. Ada Pellert.

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.

  • We are currently expanding our team. Please find our vacancies here.

  • 2024


    • 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.


    • 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.



    • 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.
    • 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.


    • 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 Practicable learning analytics (pp. 177–192). Springer.



    • Alvarez, R. P., Jivet, I., Pérez-Sanagustin, M., Scheffel, M., & Verbert, K. (2022). Tools designed to support self-regulated learning in online learning environments: A systematic review. IEEE Transactions on Learning Technologies, 15(4), 508–522.
    • Kube, D., Weidlich, J., Jivet, I., Kreijns, K., & Drachsler, H. (2022). “Gendered differences versus doing gender”: A systematic review on the role of gender in CSCL. Unterrichtswissenschaft, 50(4), 661–688.


    • Alzahrani, A., Tsai, Y.-S., Kovanović, V., Moreno-Marcos, P. M., Jivet, I., Aljohani, N., & Gašević, D. (2022). Success-enablers of learning analytics adoption in higher education: A quantitative ethnographic study. Advances in Quantitative Ethnography: Third International Conference, ICQE 2021, Virtual Event, November 6–11, 2021, Proceedings 3, 395–409.
    • Jivet, I., Viberg, O., & Scheffel, M. (2022). Culturally aware learning analytics. Companion Proceedings of the 12th International Conference on Learning Analytics and Knowledge.
    • Karademir, O., Ahmad, A., Schneider, J., Di Mitri, D., Jivet, I., & Drachsler, H. (2022). Designing the learning analytics cockpit-a dashboard that enables interventions. Methodologies and Intelligent Systems for Technology Enhanced Learning, 11th International Conference 11, 95–104.



    • Kollom, K., Tammets, K., Scheffel, M., Tsai, Y.-S., Jivet, I., Muñoz-Merino, P. J., Moreno-Marcos, P. M., Whitelock-Wainwright, A., Calleja, A. R., Gasevic, D., et al. (2021). A four-country cross-case analysis of academic staff expectations about learning analytics in higher education. The Internet and Higher Education, 49, 100788.


    • Jivet, I., & Saunders-Smits, G. (2021). The effect of the covid-19 pandemic on a mooc in aerospace structures and materials. SEFI 49th Annual Conference, 258–267.
    • Jivet, I., Wong, J., Scheffel, M., Valle Torre, M., Specht, M., & Drachsler, H. (2021). Quantum of choice: How learners’ feedback monitoring decisions, goals and self-regulated learning skills are related. LAK21: 11th International Conference on Learning Analytics and Knowledge, 416–427.



    • Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education, 47, 100758.
    • Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., & Gašević, D. (2020). Learning analytics in european higher education—trends and barriers. Computers & Education, 155, 103933.



    • Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 41–50.
    • Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 31–40.



    • Davis, D., Jivet, I., Kizilcec, R. F., Chen, G., Hauff, C., & Houben, G.-J. (2017). Follow the successful crowd: Raising MOOC completion rates through social comparison at scale. Proceedings of the 7th International Conference on Learning Analytics and Knowledge, 454–463.
    • Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings 12, 82–96.



    • Davis, D., Chen, G., Jivet, I., Hauff, C., & Houben, G.-J. (2016). Encouraging metacognition and self-regulation in MOOCs through increased learner feedback. LAK Workshop on Learning Analytics for Learners 2016, 17–22.
Christina Lüdeke | 21.05.2024