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Designing Granular Competency Frameworks for Adaptive Learning on the Example of Naïve Bayes Classifiers

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dc.contributor.authorSelmanagić, André
dc.contributor.authorSimbeck, Katharina
dc.contributor.editorMandausch, Martin
dc.contributor.editorHenning, Peter A.
dc.date.accessioned2023-01-13T13:11:49Z
dc.date.available2023-01-13T13:11:49Z
dc.date.issued2022
dc.description.abstractAdaptive learning environments that follow a competency-based learning approach require granular, domain-specific competency frameworks (models) for the continuous assessment of a learner’s knowledge and skills as well as for the subsequent personalization of instruction. This case-study describes the iterative creation process for a competency framework in the domain of Naïve Bayes classifiers, including the design principles that led to the framework and the tools used for making it publishable as linked, open data.en
dc.identifier.doi10.18420/delfi2022-ws-31
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39920
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofProceedings of DELFI Workshops 2022
dc.relation.ispartofseriesDELFI
dc.subjectcompetency frameworks
dc.subjectcompetency modelling
dc.subjectadaptive learning
dc.subjectlinked data
dc.subjectopen data
dc.subjectsemantic web
dc.titleDesigning Granular Competency Frameworks for Adaptive Learning on the Example of Naïve Bayes Classifiersen
dc.typeText/Conference Paper
gi.citation.endPage147
gi.citation.publisherPlaceBonn
gi.citation.startPage137
gi.conference.date12.-14. September 2022
gi.conference.locationKarlsruhe
gi.conference.sessiontitleDELFI: Workshop
gi.document.qualitydigidoc

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