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JPTest - Grading Data Science Exercises in Jupyter Made Short, Fast and Scalable

dc.contributor.authorTröbs, Eric
dc.contributor.authorHagedorn, Stefan
dc.contributor.authorSattler, Kai-Uwe
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T13:59:57Z
dc.date.available2023-02-23T13:59:57Z
dc.date.issued2023
dc.description.abstractJupyter 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.en
dc.identifier.doi10.18420/BTW2023-37
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40343
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectJupyter
dc.subjectTeaching
dc.subjectExercising
dc.subjectUnit-Testing
dc.subjectAutomation
dc.titleJPTest - Grading Data Science Exercises in Jupyter Made Short, Fast and Scalableen
dc.typeText/Conference Paper
gi.citation.endPage679
gi.citation.publisherPlaceBonn
gi.citation.startPage673
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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