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dc.contributor.authorNafea, Shaimaaen
dc.contributor.authorSiewe, Francoisen
dc.contributor.authorHe, Yingen
dc.date.accessioned2017-10-26T14:13:35Z
dc.date.available2017-10-26T14:13:35Z
dc.date.issued2017-09
dc.identifier.citationNafea, S., Siewe, F. and He, Y. (2017) An Adaptive Learning Ontological Framework Based on Learning Styles and Teaching Strategies. Proceedings of 85th ISERD International Conference, Cairo, Egypt, 11th-12th September 2017en
dc.identifier.urihttp://hdl.handle.net/2086/14720
dc.description.abstractOntology are increasingly being used in a variety of applications, and particularly in adaptive e-learning. They have the potential to enable developers to create adaptive course content for specified domains. E-learning applications are thus able to use technology and educational content in order to generate content that matches the student's capabilities and knowledge. This personalises learning, rather than assuming that "one-size-fits-all" and providing all learners with the same content, which is what the majority of e-learning systems do. This study introduces a new approach that takes into account the fact that each learner has an individual learning style and needs. The approach enables to adapt the course content, teaching strategy and learning objects so that they correspond to each student’s learning styles. This is achieved with the use of artificial intelligent in the form of an ontology and rule-based reasoning. The proposed system takes some of the key design aspects such as extensibility, reusability, and maintainability into consideration in order to enhance performance of adaptive course content recommendation.en
dc.language.isoenen
dc.publisherInternational Conference on Education and E-learningen
dc.subjectE-learningen
dc.subjectOntologyen
dc.subjectSemantic weben
dc.subjectAdaptive Student Profileen
dc.subjectlearning styleen
dc.subjectteaching strategyen
dc.titleAn Adaptive Learning Ontological Framework Based on Learning Styles and Teaching Strategiesen
dc.typeConferenceen
dc.researchgroupSoftware Technology Research Laboratory (STRL)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-08-09en
dc.researchinstituteCyber Technology Institute (CTI)en


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