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dc.contributor.authorNafea, Shaimaa M.
dc.contributor.authorSiewe, Francois
dc.contributor.authorHe, Ying
dc.date.accessioned2019-08-13T12:48:48Z
dc.date.available2019-08-13T12:48:48Z
dc.date.issued2019-09
dc.identifier.citationNafea, S.M., Siewe, F. and He, Y. (2019) A novel algorithm for dynamic student profile adaptation based on learning styles. Intelligent Systems Conference 2019, 5-6 September 2019, London, UKen
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/18332
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractE-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.en
dc.language.isoenen
dc.publisherIEEE Pressen
dc.subjectrecommender systemen
dc.subjectdynamic student profileen
dc.subjectstudent modellingen
dc.subjectadaptationen
dc.subjectalgorithmsen
dc.subjectlearning styleen
dc.subjectbehaviour patternsen
dc.subjectFSLSM modelen
dc.titleA novel algorithm for dynamic student profile adaptation based on learning stylesen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderNo external funderen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019-06-20
dc.researchinstituteCyber Technology Institute (CTI)en


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