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Auflistung nach Autor:in "Kohlhase, Michael"

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  • Zeitschriftenartikel
    (Deep) FAIR mathematics
    (it - Information Technology: Vol. 62, No. 1, 2020) Berčič, Katja; Kohlhase, Michael; Rabe, Florian
    In this article, we analyze the state of research data in mathematics. We find that while the mathematical community embraces the notion of open data, the FAIR principles are not yet sufficiently realized. Indeed, we claim that the case of mathematical data is special, since the objects of interest are abstract (all properties can be known) and complex (they have a rich inner structure that must be represented). We present a novel classification of mathematical data and derive an extended set of FAIR requirements, which accomodate the special needs of math datasets. We summarize these as deep FAIR . Finally, we show a prototypical system infrastructure, which can realize deep FAIRness for one category (tabular data) of mathematical datasets.
  • Zeitschriftenartikel
    Editorial
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Cimiano, Philipp; Heyer, Gerhard; Kohlhase, Michael; Stein, Benno; Ziegler, Jürgen; Härder, Theo
  • Zeitschriftenartikel
    Erratum zu: Editorial
    (Datenbank-Spektrum: Vol. 21, No. 2, 2021) Cimiano, Philipp; Heyer, Gerhard; Kohlhase, Michael; Stein, Benno; Ziegler, Jürgen; Härder, Theo
  • Konferenzbeitrag
    Learning with ALeA: Tailored experiences through annotated course material
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Kruse, Theresa; Berges, Marc; Betzendahl, Jonas; Kohlhase, Michael; Lohr, Dominic; Müller, Dennis
    We present ALeA, an adaptive learning assistant for university courses. The intelligent tutoring system (ITS) provides services for both learners and instructors, based on semantically annotated course material. The system can generate learning tools like flashcards, guided tours, and quizzes, which can be tailored to individual learning progress. Currently, the system is only available for courses from the field of computer science, like artificial intelligence. We plan to expand it for further courses from different fields.
  • Conference paper
    Term Extraction for Domain Modeling
    (Proceedings of DELFI 2024, 2024) Kruse, Theresa; Lohr, Dominic; Berges, Marc; Kohlhase, Michael; Moghbeli, Halimeh; Schütz, Marcel
    Adaptive learning systems need to use domain and learner models to provide meaningful support for learners. Building fine-grained domain models by hand is very time-consuming, so the demand for partial automation is high. This paper investigates how term extraction tools can support constructing a domain model. Therefore, we study if different automatic term extraction tools give comparable results to a human annotator. Our results show that the current extraction tools support the process, but their results are not directly usable and still need human adjustments.