Ashraf, Erum (2023) Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm. PhD thesis, Universiti Sains Malaysia.
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Abstract
E-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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | T Technology > T Technology (General) > T1-995 Technology(General) |
| Divisions: | Pusat IPv6 Termaju Negara (National Advanced IPv6 Centre of Excellence NAv6) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 08 Oct 2025 07:05 |
| Last Modified: | 08 Oct 2025 07:05 |
| URI: | http://eprints.usm.my/id/eprint/62921 |
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