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

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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

  • September 2021– August 2024

  • 2023

    Conferences

    • Kasakowskij, R., Haake, J. M., & Seidel, N. (2023). Self-Assessment Task Processing Behavior of Students in Higher Education. Proceedings of the 16th International Conference on Educational Data Mining, 334–341. https://doi.org/10.5281/zenodo.8115707
    • Seidel, N., Dürhager, R., Goldammer, M., Henze, A., Langenbrink, F., Otto, J., & Stirling, V. (2023). Shared listening experience for hyperaudio textbooks. DELFI 2023 – Die 212 Fachtagung Bildungstec Hnologien Der Gesellschaft für Informatik e.v., in print.

    2022

    Conferences

    • Menze, D., & Seidel, N. (2022). Support for Reading Comprehension in Digital Course Texts. In M. Mandausch & P. A. Henning (Eds.), Workshop proceedings DELFI 2022 (pp. 105–116). https://doi.org/10.18420/delfi2022-ws-21
    • Menze, D., Seidel, Ni., & 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. (pp. 27–38). Gesellschaft für Informatik. https://doi.org/10.18420/delfi2022-011
    • 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. (pp. 69–74). Gesellschaft für Informatik. https://doi.org/10.18420/delfi2022-015
    • Seidel, N., & Menze, D. (2022). Interactions of reading and assessment activities. In S. Sosnovsky, P. Brusilovsky, & A. Lan (Eds.), 4th workshop on intelligent textbooks, 2022 (pp. 64–76). CEUR-WS. http://ceur-ws.org/Vol-3192/
    • Seidel, N., & Schumacher, C. (2022). Workshop Learning Analytics - Intertweening Learning Analytics and Adaptive Learning. In M. Mandausch & P. A. Henning (Eds.), Proceedings of the DELFI workshops 2022 (pp. 99–103). Gesellschaft für Informatik e.V. https://doi.org/10.18420/delfi2022-ws-20

    Data Sets

    2021

    Journals

    • 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

    Conferences

    • Haake, J. M., Seidel, N., Burchart, M., Karolyi, H., & Kasakowskij, R. (2021). Accuracy of self-assessments in higher education. DELFI 2021 – Die 19. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 97–108. https://dl.gi.de/handle/20.500.12116/36995
    • Schumacher, C., Seidel, N., & Rzepka, N. (2021). Workshop Learning Analytics - Considering student diversity with regard to assessment data and discrimination. In A. Lingnau (Ed.), Proceedings of the DELFI workshops (pp. 113–119). https://repositorium.hs-ruhrwest.de/frontdoor/deliver/index/docId/733/file/DELFI_2021_WS.pdf
    • Seidel, N., Karolyi, H., Burchart, M., & Witt, C. de. (2021). Approaching Adaptive Support for Self-regulated Learning. In D. Guralnick, M. E. Auer, & A. Poce (Eds.), Innovations in learning and technology for the workplace and higher education (pp. 409–424). Springer International Publishing.

    Chapters in Books

    • Goram, M., & Veiel, D. (2021a). Ethical Behavior and Legal Regulations in Artificial Intelligence (Part One): Supporting Sovereignty of Users While Using Complex and Intelligent Systems. In S. J. Thompson (Ed.), Machine law, ethics, and morality in the age of artificial intelligence (pp. 12–26). IGI Global.
    • Goram, M., & Veiel, D. (2021b). Ethical Behavior and Legal Regulations in Artificial Intelligence (Part Two): Representation of Law and Ethics in Intelligent Systems. In S. J. Thompson (Ed.), Machine law, ethics, and morality in the age of artificial intelligence (pp. 27–46). IGI Global.

    Data Sets

    2020

    Journals

    • Goram, M., & Veiel, D. (2020). A Context Model for Intelligible Explanations in Adaptive Personalized Learning Environments. International Journal of Information and Education Technology (IJIET), 10(5), 351–355.

    Conferences

    • Goram, M., & Veiel, D. (2020a). Linking Legal and Domain-specific Requirements in a Context-based Adaptive Personalized Learning Environment. 10th International Symposium on Frontiers in Ambient and Mobile Systems, ANT 2020, 170, 995–1002.
    • Goram, M., & Veiel, D. (2020b). Supporting different Roles and Responsibilities in Developing and Using Context-based Adaptive Personalized Collaboration Environments Compliant to the Law. International Conference on Human-Computer Interaction.
    • Goram, M., & Veiel, D. (2020c). Towards Traceability of Decision-making in Intelligent Context-based Adaptive System Environments. In S. Rudolph & G. Marreiros (Eds.), Proceedings of the 9th european starting AI researchers’ symposium 2020 co-located with 24th european conference on artificial intelligence (ECAI 2020), santiago compostela, spain, august, 2020 (Vol. 2655). CEUR-WS.org.
    • 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. (pp. 145–150). Gesellschaft für Informatik (GI e.V.). https://dl.gi.de/handle/20.500.12116/34152
    • Seidel, N. (2020). Video Segmentation as an Example for Elaborating Design Patterns through Empirical Studies. Proceedings of the European Conference on Pattern Languages of Programs 2020, 15. https://doi.org/10.1145/3424771.3424778
    • Seidel, N., Rieger, M. C., & Walle, A. (2020). Semantic Textual Similarity of Course Materials at a Distance-Learning University. In Thomas W. Price, Peter Brusilovsky, Sharon I-Han Hsiao, Ken Koedinger, & Y. Shi (Eds.), Proceedings of 4th educational data mining in computer science education (CSEDM)workshop co-located with the 13th educational data mining conference (EDM2020), virtual event, july 10, 2020. CEUR-WS.org. http://ceur-ws.org/Vol-2734/paper6.pdf

    Software

    2019

    Conferences

    • 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) (pp. 33–44). Gesellschaft für Informatik.
    • Seidel, N. (2019). Democratic power structures in virtual communities. Proceedings of the 24st European Conference on Pattern Languages of Programs, Article No. 31, 1—–8. https://doi.org/https://doi.org/10.1145/3361149.3361181

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