Publication:
Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm

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dc.contributor.authorAshraf, Erum
dc.date.accessioned2025-10-08T07:00:54Z
dc.date.available2025-10-08T07:00:54Z
dc.date.issued2023-12
dc.description.abstractE-learning's popularity surges due to technology, flooding Massive Open Online Course (MOOC) platforms with courses, causing information overload. Recommender systems filter courses but struggle with learning styles due to lack of standardized datasets and measurement approaches, hindering data collection in resource-constrained educational institutions. This research streamlines course selection by matching it with learners' styles. It involves identifying potential courses learning style, validated through genetic and surrogate meta-heuristics optimization algorithms, and employing Felder-Silverman model for learning style identification. The proposed scheme supported the personalized course recommendations to students suitable with student learning style.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/22738
dc.language.isoen
dc.subjectAlgorithms
dc.subjectWeb-based instruction
dc.titleApproach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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