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Auflistung nach Schlagwort "Jupyter"

1 - 5 von 5
  • Workshopbeitrag
    Integration von Gamification und Learning Analytics in Jupyter
    (DELFI 2021, 2021) Brocker, Annabell; Judel, Sven; Schroeder, Ulrik
    Das Erlernen von Programmiersprachen und Programmierkonzepten ist für Anfängerinnen und Anfänger mit vielen Hürden verbunden, was zu hohen Abbruch- und Durchfallquoten führt. Als Gründe werden häufig fehlende Motivation oder zu wenig interaktive Materialien genannt. Die Nutzung von Jupyter Notebooks als interaktive Programmierumgebung kann als Lösungsansatz für das zweite Problem genutzt werden. Ein Ansatz zur Steigerung der Motivation stellt Gamification dar. In diesem Poster wird ein Ansatz zur Integration von Gamification in Jupyter Notebooks und deren übergeordneten Organisationseinheiten JupyterLab und JupyterHub präsentiert. Verschiedene Spielelemente und –Mechaniken werden in die Programmierumgebung integrieren um die Motivation der Lernenden zu steigern. Mittels Learning- und Gamification Analytics werden die Maßnahmen und deren Wirkung beobachtet und evaluiert.
  • Konferenzbeitrag
    Investigating Feedback Types in JupyterLab for Programming Novices
    (21. Fachtagung Bildungstechnologien (DELFI), 2023) Brocker, Annabell; Schroeder, Ulrik
    Providing valuable, actionable feedback, such as small-step hints and explanations of errors and misconceptions, is essential for guiding novice programmers towards solutions while fostering their code development. This paper presents a comprehensive review of feedback types, available within the interactive programming environment JupyterLab. We distinguish between lower-level, immediate feedback during programming and higher-level, follow-up feedback for reoccurring misconceptions and problems over time. We further discuss potential extensions to provide even more feedback in JupyterLab, such as valuation and recognition of correct activities.
  • Konferenzbeitrag
    JPTest - Grading Data Science Exercises in Jupyter Made Short, Fast and Scalable
    (BTW 2023, 2023) Tröbs, Eric; Hagedorn, Stefan; Sattler, Kai-Uwe
    Jupyter Notebook is not only a popular tool for publishing data science results, but canalso be used for the interactive explanation of teaching content as well as the supervised work onexercises. In order to give students feedback on their solutions, it is necessary to check and evaluatethe submitted work. To exploit the possibilities of remote learning as well as to reduce the workneeded to evaluate submissions, we present a flexible and efficient framework. It enables automatedchecking of notebooks for completeness and syntactic correctness as well as fine-grained evaluationof submitted tasks. The framework comes with a high level of parallelization, isolation and a shortand efficient API.
  • Conference Proceedings
    Künstliche Intelligenz und maschinelles Lernen im Informatikunterricht der Sek. I mit Jupyter Notebooks und Python am Beispiel von Entscheidungsbäumen und künstlichen neuronalen Netzen
    (INFOS 2021 – 19. GI-Fachtagung Informatik und Schule, 2021) Bovermann, Klaus; Fleischer, Yannik; Hüsing, Sven; Opitz, Christian
  • Konferenzbeitrag
    Notes on the Code Quality Culture on Jupyter (Notebooks)
    (Softwaretechnik-Trends Band 39, Heft 2, 2019) Speicher, Daniel; Dong, Tiansi; Cremers, Olaf; Bauckhage, Christian; Cremers, Armin B.
    While we argued in that code quality needs to take context into account, there is now software that demands a really different quality culture like we would be entering another planet. Jupyter, to be precise: A “Jupyter Notebook is [a] web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.” It consists of text and code cells. The content of code cells is sent on demand to a Python session, executed and the output inserted below the cell. We will approach the quality of notebooks from the perspective of communicative code and design patterns.