Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm - Ashraf, Erum (2023)
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.