Konferenzbeitrag
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.