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The interplay between rich and big data in programming education research

dc.contributor.authorKeuning, Hieke
dc.contributor.editorSchulz, Sandra
dc.contributor.editorKiesler, Natalie
dc.date.accessioned2024-09-03T16:26:17Z
dc.date.available2024-09-03T16:26:17Z
dc.date.issued2024
dc.description.abstractTo conduct solid research on how students learn programming, we need both ‘rich data’ and ‘big data’. In the past decades, researchers have been collecting both types of data, such as large datasets of programs written by students, containing numerous mistakes, but also more fine-grained data, such as verbalizations of what students were thinking when solving a challenging programming problem. While there is an interplay between these two types of data, they are typically used to answer different questions. There are also several existing datasets available for conducting programming education research, however, these are more often ‘big’ rather than ‘rich’, and it is not trivial to find and use them. In this talk I will show several examples of my research, in which I have (re)used datasets to study aspects of how students learn to program, discussing the need for collecting, analyzing and sharing big as well as rich data.en
dc.identifier.doi10.18420/delfi2024_01
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44490
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofProceedings of DELFI 2024
dc.relation.ispartofseriesDELFI
dc.subjectComputing education research
dc.subjectprogramming datasets
dc.titleThe interplay between rich and big data in programming education researchen
dc.typeText/Keynote abstract
mci.conference.date09.-11. September 2024
mci.conference.locationFulda
mci.conference.sessiontitleKeynote I
mci.document.qualitydigidoc
mci.reference.pages19-21

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