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        Learning Analytics, Artificial Intelligence und Data Mining in der Hochschulbildung

        Beiträge zur Learning AID 2024

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        Contributor(s)
        Queckenberg, Robert (editor)
        Leschke, Jonas (editor)
        Persike, Malte (editor)
        Language
        German
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        Abstract
        Wie kann Künstliche Intelligenz (KI) die Hochschulbildung unterstützen? Welche Rolle spielen Learning Analytics? Und welche Chancen und Risiken birgt ihr Einsatz? Die Beiträger*innen bieten einen Querschnitt über aktuelle Fragen zu didaktischen, rechtlichen, technischen sowie hochschulstrategischen Aspekten rund um den Einsatz von KI in der Hochschule. Sie präsentieren u.a. Praxisbeispiele zum Einsatz generativer KI in Lehrveranstaltungen oder zur Unterstützung von KI-Kompetenzen, aber auch technische Lösungen zur Bereitstellung generativer KI an Hochschulen und berichten über konkrete Ausgestaltungen von Learning Analytics-Systemen, die als Beispiele für die Praxis dienen können.
        URI
        https://library.oapen.org/handle/20.500.12657/105820
        Keywords
        Bildung; Education; Digitalisierung; Digitalization; Science; Wissenschaft; Educational Research; Bildungsforschung; Digital Media; Digitale Medien; Computer Sciences; Informatik; University; Hochschule
        DOI
        10.14361/9783839475836
        ISBN
        9783839475836, 9783839475836, 9783837675832
        Publisher
        transcript Verlag
        Publisher website
        https://www.transcript-verlag.de/
        Publication date and place
        2025
        Imprint
        transcript
        Series
        Zukunft der Hochschule, 8
        Classification
        Society and culture: general
        Political economy
        Media studies: internet, digital media and society
        Pages
        240
        Rights
        https://creativecommons.org/licenses/by-sa/4.0
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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