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Pasternak (Ed.), Informatik für alle, Gesellschaft für Informatik, Bonn, 2019, p. 385.","chicago":"Schlichtig, Michael, Simone Opel, Carsten Schulte, Rolf Biehler, Daniel Frischemeier, Susanne Podworny, and Thomas Wassong. “Maschinelles Lernen im Unterricht mit Jupyter Notebook.” In <i>Informatik für alle</i>, edited by Arno Pasternak, 385. Bonn: Gesellschaft für Informatik, 2019.","apa":"Schlichtig, M., Opel, S., Schulte, C., Biehler, R., Frischemeier, D., Podworny, S., &#38; Wassong, T. (2019). Maschinelles Lernen im Unterricht mit Jupyter Notebook. In A. Pasternak (Ed.), <i>Informatik für alle</i> (p. 385). Gesellschaft für Informatik.","ieee":"M. Schlichtig <i>et al.</i>, “Maschinelles Lernen im Unterricht mit Jupyter Notebook,” in <i>Informatik für alle</i>, Dortmund, Germany, 2019, p. 385."},"place":"Bonn","title":"Maschinelles Lernen im Unterricht mit Jupyter Notebook","year":"2019","author":[{"id":"32312","full_name":"Schlichtig, Michael","orcid":"0000-0001-6600-6171","first_name":"Michael","last_name":"Schlichtig"},{"last_name":"Opel","first_name":"Simone","full_name":"Opel, Simone"},{"full_name":"Schulte, Carsten","first_name":"Carsten","last_name":"Schulte"},{"full_name":"Biehler, Rolf","last_name":"Biehler","first_name":"Rolf"},{"full_name":"Frischemeier, Daniel","last_name":"Frischemeier","first_name":"Daniel"},{"last_name":"Podworny","first_name":"Susanne","full_name":"Podworny, Susanne"},{"id":"21241","full_name":"Wassong, Thomas","first_name":"Thomas","last_name":"Wassong"}],"publication_identifier":{"isbn":["978-3-88579-682-4"]},"publication_status":"published","date_updated":"2025-05-25T20:01:30Z","main_file_link":[{"url":"https://dl.gi.de/handle/20.500.12116/28964"}],"language":[{"iso":"ger"}],"publication":"Informatik für alle","abstract":[{"text":"Data Science und Big Data durchdringt in ihren diversen Facetten unser tägliches Leben– kaum ein Tag, an dem nicht verschiedene Meldungen über technische Innovationen, Einsatzmöglichkeiten von Künstlicher Intelligenz (KI) und Maschinelles Lernen (ML) und ihre ethischen sowie gesellschaftlichen Implikationen in den unterschiedlichen Medien diskutiert werden. Aus diesem Grund erscheint es uns immens wichtig, diese Fragestellungen und Technologien auch in den Unterricht der Sekundarstufe II zu integrieren. Um diesem Anspruch gerecht zu werden, entwickelten wir im Rahmen eines Forschungsprojekts ein Curriculum, welches wir als konkretes Unterrichtskonzept innerhalb eines Projektkurses erprobt, evaluiert weiterentwickelt wird. Bei der Implementierung entschieden wir uns, zur aktiven Umsetzung von Konzepten von ML als Plattform Jupyter Notebook mit Python zu verwenden, da diese Umgebung durch die Verbindung von Code und Hypertext zur Dokumentation und Erklärung Medienbrüche im Lernprozess verringern kann. Zudem ist Python zur Implementierung der Methoden von ML sehr gut geeignet. Im Themenfeld des ML als Teilgebiet der KI legen wir den Fokus auf zwei unterschiedliche Lernverfahren um verschieden Aspekte von ML, u.A. wie Nachvollziehbarkeit unter gesellschaftlichen Gesichtspunkten zu vermitteln. Diese sind Künstliche Neuronale Netze (bei denen die Berechnung und Bedeutung der Kantengewichte zwischen den Neuronen für den Menschen insbesondere bei komplexeren Netzen kaum nachvollziehbar erschienen) und Entscheidungsbäume (strukturierte und gerichtete Bäume zur Darstellung von Entscheidungsregeln, welche auch für Schülerinnen und Schüler meist gut nachvollziehbares und verständliches KI-Modell darstellen). In diesem Workshop stellen wir konkrete Umsetzungsbeispiele inklusive der Programmierung für beide Verfahren mit Jupyter Notebook und Python als Teil einer Unterrichtssequenz vor und diskutieren diese.","lang":"ger"}],"date_created":"2019-11-07T14:08:13Z","type":"conference","department":[{"_id":"67"},{"_id":"97"}]}]