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Classical knowledge representation methods traditionally work with established relations such as synonymy, hierarchy and unspecified associations. Recent developments like
ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships. In a
summarizing overview we show which relations are currently utilized in elaborated knowledge representation methods and which may be inherently hidden in folksonomies and ontologies.
This paper presents a work in progress whose
purpose is to model the handled, acquired, correct and
erroneous knowledge of individual learners engaged in
learning activities through virtual learning environments.
This knowledge is represented according to a cognitivecomputational
model which also serves to represent the
domain knowledge via an authoring tool. The latter
generates structures that allow the tutor to provide an
effective feedback to improve significantly the cognitive
level of the learner.
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D. Schlör, J. Pfister, и A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), стр. 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
S. Straka, M. Koch, A. Carolus, M. Latoschik, и C. Wienrich. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, Association for Computing Machinery, (2023)
A. Dockhorn. IEEE Computational Intelligence Magazine, 17 (4):
52-53(ноября 2022)This is an immersive article. Therefore, extended interactive ressources are provided at the publisher webpage. A pre-print can be found at: https://aiexplained.github.io/.