Adaptive Personalized Learning Environment (APLE II) to Support Self-Regulation and Domain Competencies in (Distance) Higher Education

APLE II is a CATALPA project.

Knowing where one's own weaknesses lie, where learning gaps exist, and thus raising awareness of one's own learning process - APLE (I and II) investigates how a digital platform can best support students in their learning success. For example, students receive recommendations for their next learning steps and can reflect on their learning behavior through visualizations.


Project goals and research questions

The project APLE II builds on the results of the predecessor project APLE (see end of page). The main goal of the project is to explore the design and use of an adaptive personalized learning environment (APLE) in (distance) higher education to improve both students’ personal educational success in relation to domain competency as well as their personal self-regulation competencies.

Current technologies such as learning analytics, data mining, and computational linguistics are used to analyze learning behavior and implement course-related adaptations. Within this adaptation architecture, learners receive recommendations on relevant next learning steps and can reflect on their learning behavior through visualizations and personalized prompts. The tools developed in APLE II are going to be evaluated in field studies in order to support learners in planning learning tasks, reading extensive course texts and performing adaptive (self-)assessments.

  • Dr. Niels Seidel

  • Regina Kasakowskij (research assistant)
    Dennis Menze (research assistant)
    Slavisa Radovic (research assistant)
    Chiara Sandführ (student assistant)

  • September 2021– August 2024

  • Refereed publication

    • Menze, D., Seidel, N., & Kasakowskij, R. (2022). Interaction of reading and assessment behavior. 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.
    • 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.
    • Seidel, N. (2022). Mapping course text to hyperaudio. 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.
    • Kasakowskij, R. (2022, in press). Auswahl und Generierung von passenden Feedbacks auf Basis eines Feedback Rating System Frameworks. MedienPädagogik: Zeitschrift für Theorie Und Praxis Der Medienbildung.
    • Seidel, N., Karolyi, H., Burchart, M., de Witt, C. (2022). Approaching Adaptive Support for Self-regulated Learning. In: Guralnick, D., Auer, M.E., Poce, A. (eds) Innovations in Learning and Technology for the Workplace and Higher Education. TLIC 2021. Lecture Notes in Networks and Systems, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-90677-1_39
    • Haake, J. M., Seidel, N., Burchart, M., Karolyi, H., & Kasakowskij, R. (2021). Accuracy of self-assessments in higher education. In DELFI 2021 – Die 19. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (p. 97–108). Bonn. Abgerufen von https://dl.gi.de/handle/20.500.12116/36995
    • Seidel, N., Haake, J. M., & Burchart, M. (2021). From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment. eleed, 14(1). http://nbn-resolving.de/urn:nbn:de:0009-5-52421
    • Seidel, N., Rieger, M. C., & Walle, A. (2020). Semantic Textual Similarity of Course Materials at a Distance-Learning University. In Educational Data Mining in Computer Science. http://ceur-ws.org/Vol-2734/paper6.pdf
    • Haake, J. M., Seidel, N., Karolyi, H., & Ma, L. (2020). Self-Assessment mit High-Information Feedback. In R. Zender, D. Ifenthaler, T. Leonhardt, & C. Schumacher (Eds.), DELFI 2020 – Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (p. 145–150). Bonn: Gesellschaft für Informatik. https://dl.gi.de/handle/20.500.12116/34152
    • Seidel, N. (2020). Video segmentation as an example for elaborating design patterns through empirical studies. In Proceedings of the Online European Conference on Pattern Languages of Programs (p. 1–15). Irsee: ACM. https://dl.acm.org/doi/10.1145/3424771.3424778
    • Rieger, M. C., & Seidel, N. (2019). Semantic Textual Similarity von textuellen Lernmaterialien. In N. Pinkwart & J. Konert (Eds.), Die 17. Fachtagung Bildungstechnologien, Lecture Notes in Informatics (LNI) (p. 33–44). Bonn: Gesellschaft für Informatik. https://dl.gi.de/handle/20.500.12116/24417
    • de Witt, C. & Karolyi, H. (2019). Adaptivität im Hochschulstudium. Konzeptionelle Überlegungen zur digitalisierten Unterstützung von selbstreguliertem Lernen. In MEDIENPRODUKTION – Online Zeitschrift für Wissenschaft und Praxis, (13), p. 2–9. http://www5.tu-ilmenau.de/zeitschrift-medienproduktion/index.php/category/ausgabe-13/

    Other publications

    • Menze, D., Seidel, N. (2022). Reading and scroll behavior in a distance learning course. https://anonymous.4open.science/r/A0F145/
    • Steinkohl, K., Burchart, M., Seidel, N., Kasakowskij, R., Haake, J. M. (2021). SelfAssess - A Moodle-Plugin for self assessment and self evaluation. https://github.com/D2L2/qtype_selfassess
    • Haake, J. M., Ma, L., & Seidel, N. (2021). Self-Assessment Questions - Operating Systems and Computer Networks. https://doi.org/10.5281/zenodo.5021350, 2021
    • 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.
    • 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.
    • Datasets

      Haake, J. M., Ma, L., & Seidel, N. (2021). Self-Assessment Questions - Operating Systems and Computer Networks. https://doi.org/10.5281/zenodo.5021350, 2021

    Software

APLE 09.2018 - 08.2021

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Adaptive Personalized Learning Environment (APLE) to Support Self-Regulation and Domain Competencies in (Distance) Higher Education

The project was part of the research center CATALPA „Center of Advanced Technology Assisted Learning and Predictive Analytics“ at the FernUniversität in Hagen and jointly carried out by Prof. Dr.-Ing. Jörg M. Haake and Prof. Dr. Claudia de Witt (Project lead Haake, Deputy lead: de Witt)

Project duration: September 2018 – August 2021

The main goal of the project is to explore the design and use of an adaptive personalized learning environment (APLE) in (distance) higher education to improve both students’ personal educational success in relation to domain competency as well as their personal self-regulation competencies. Current technologies such as learning analytics and data mining will be used to generate recommendations for relevant next steps and learning materials, to encourage students to reflect on and be conscious of their own learning processes, to support social learning, and to raise awareness of individual learning behavior and address possible learning difficulties. This will be accomplished primarily by presenting visualizations of immediate individual feedback via a Learning Analytics Dashboard (LAD) and adaptive integrated prompts. Through further reflective support, students will achieve a consciousness of their own learning progress, development, and processes as well as potential courses of action. Personalized prompts will support the implementation of learning plans. The project cooperates for this purpose with the FernUniversität’s Center for Media and IT (ZMI).

Research Team Members

Technical Director