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

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Datum

2022

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Gesellschaft für Informatik e.V.

Zusammenfassung

Adaptive 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.

Beschreibung

Selmanagić, André; Simbeck, Katharina (2022): Designing Granular Competency Frameworks for Adaptive Learning on the Example of Naïve Bayes Classifiers. Proceedings of DELFI Workshops 2022. DOI: 10.18420/delfi2022-ws-31. Bonn: Gesellschaft für Informatik e.V.. pp. 137-147. DELFI: Workshop. Karlsruhe. 12.-14. September 2022

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