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Auflistung nach Autor:in "Blanc, Berit"

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  • Konferenzbeitrag
    Critical reflection of AI applications for persons with disabilities in vocational rehabilitation
    (Proceedings of DELFI Workshops 2020, 2020) Beudt, Susan; Blanc, Berit; Feichtenbeiner, Rolf; Kähler, Marco
    Applications of artificial intelligence are increasingly being used to support work and learning in the workplace. Adaptivity and recommender systems, as key features of such innovative technologies, allow for enhanced personalization. Most notably, persons with disabilities may benefit from such technologies at work and during on-the-job training. Adapting such systems to very heterogeneous target groups, however, is not easily done. Implementing AI-based assistive systems in various educational settings in vocational training, especially in vocational rehabilitation, can also be challenging. This position paper looks at existing AI-based applications to analyze their potential for more inclusive workplaces and qualification processes. Furthermore, those technologies are discussed in the context of current ethical discourses to identify to what extent normative requirements are being reflected in existing AI-based applications.
  • Konferenzbeitrag
    Recommender Systems for Vocational Training and Education: Insights from Germany’s ”Innovationswettbewerb INVITE“Prog
    (Proceedings of DELFI Workshops 2024, 2024) Rashid, Sheikh Faisal; Reichow, Insa; Blanc, Berit
    The integration of recommender systems (RS) into digital vocational education and training (VET) programs holds significant potential for personalized learning and skill development. While most scientific studies have traditionally focused on the application of RS in higher education, this paper shifts the focus to the VET sector. It provides an overview of the development and application of RS within the context of the German funding program “INVITE”. INVITE supports 35 multi-stakeholder projects fostering innovation in digital learning platforms for VET. Out of the 35 projects, 22 develop RS tailored to different target groups and domains. The RS primarily aim to enhance learner support by recommending adaptive learning paths, personalized learning content, and further training opportunities. Based on the available documentation, this paper provides a structured analysis of the developed RS across diverse VET application domains within the INVITE program.
  • Konferenzbeitrag
    Was wirkt? Eine Literaturstudie zur Wirksamkeit von Systemeigenschaften in Mathematik-Lernumgebungen
    (21. Fachtagung Bildungstechnologien (DELFI), 2023) Blanc, Berit; Reichow, Insa; Paaßen, Benjamin
    Diese Literaturstudie untersucht die Lernwirksamkeit typischer Systemeigenschaften digitaler Mathematik-Lernumgebungen wie bettermarks, nämlich: (A) Vollständigkeit der Aufgaben und Inhalte, (B) Intelligente Interaktionswerkzeuge, (C) Mikro-Adaptivität, (D) Makro-Adaptivität und als Rahmenbedingung (E) Einsatz im Klassenverbund. Die Auswertung ergab besonders starke Evidenz für elaboriertes und adaptives Feedback bei der Bearbeitung von Aufgaben (C), reichhaltige Interaktionswerkzeuge (B), eine feine Auflösung von Aufgabenschritten und Feedback (A) sowie die Einbindung der Lehrkräfte (E). Eine Forschungslücke besteht hinsichtlich der Wirksamkeit makro-adaptiver Strategien (D).