LA DIVA is a CATALPA project.

Learners are individual, have different strengths, are in different stages of life, and therefore need different types of support, especially in digital distance learning. This is precisely where LA DIVA comes in and determines the potential of learning analytics.

Project goals and research questions

The goal of the project is to investigate the potential of Learning Analytics for supporting adaptive learning in distance education, with a particular focus on the diversity of the learners. This potential will be investigated on the basis of three central issues:

  1. Diversity-Inspired Adaptive Assessment: Possibilities for adapting the assessments and tasks offered in a concrete learning situation, with respect to three dimensions: selection of task types, parameterization/generation of tasks, and adaptive feedback and solution hints.
  2. Diversity-Inspired Adaptive Support for Collaborative Learning: Possibilities for supporting dynamic education and reconfiguration of learning groups as well as adaptive support for diverse learning groups on the basis of individual profiles, group profiles, and interaction data.
  3. Emergence of Student Profiles: Collection and analysis of student data as the basis for a long-term investigation into whether and how the diversity of the student body changes over time in relation to learning-relevant parameters.
  • Prof. Dr. Jörg Haake (FernUniversität) und Prof. Dr. Niels Pinkwart (HU, DFKI and Visiting Professor with CATALPA) - see also Cooperations.

    • Dr. Clara Schumacher (HU)
    • Dr. Jakub Kuzilek (HU)
    • Marc Burchart, M.Sc. (FeU)
    • Dr. Lihong Ma (FeU)
    • Dr. Niels Seidel (FeU)
  • 01 April 2020 through 31 March 2024

  • Refereed publication

    • Kasakowskij, R., Kasakowskij, T., & Seidel, N. (2022). Generation of Multiple True False Questions. In P. A. Henning, M. Striewe, & M. Wölfel (Eds.), DELFI 2022 – Die 21. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (p. in press). Gesellschaft für Informatik.
    • Burchart, M. (2022, in press). Auf dem Weg zur skalierbaren Unterstützung des kollaborativen Schreibens in hochdiversen Fernlerngruppen. In MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbildung (Bd. 48). Sektion Medienpädagogik der Deutschen Gesellschaft für Erziehungswissenschaft - DGfE
    • Schumacher, C. & Kuzilek, J. (2022). How do students perceive algorithmic grouping in higher education? 12th International Learning Analytics and Knowledge Conference, Virtual Conference.
    • Schumacher, C., & Kuzilek, J. (2021). Perfect match? Investigating students’ perceptions about algorithmic grouping in higher education. AECT 2021 Conference, Ohio.
    • 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. Apr. 2021
    • Seidel, N., Rieger, M. C., & Walle, A. (2020). Semantic Textual Similarity of Course Materials at a Distance-Learning University. In T. W. P. And, P. B. And, S. I.-}Han H. And, K. K. And, & Y. Shi (Hrsg.), Proceedings of 4th Educational Data Mining in Computer Science Education (CSEDM) Workshop co-located with the 13th Educational Data Mining Conference (EDM 2020), Virtual Event, 10-06-2020.
    • Weiher, B., Seidel, N., Burchart, M., & Veiel, D. (2021). Indicators of group learning in collaborative software development teams. In Andreas Lingnau (Eds.), Proceedings of DELFI Workshops 2021 (p. 164-175). Dortmund: Gesellschaft für Informatik.

    Other publications

    • Schumacher, C., Seidel, N., & Rzepka, N. (2021). Workshop Learning Analytics - Considering student diversity with regard to assessment data and discrimination. In A. Lingnau (Eds.) Proceedings of DELFI Workshops 2021 (p. 113–119). Dortmund.
    • Seidel, N., & Schumacher, C. (2022). Workshop Learning Analytics - Intertwining Learning Analytics and Adaptive Learning. In M. Mandausch (Ed.) Proceedings of DELFI Workshops 2022 (p. in press). Karlsruhe.