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Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 41

A VARK learning style-based Recommendation system for Adaptive E-learning

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DOI: http://dx.doi.org/10.15439/2024F5253

Citation: Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 41, pages 1–8 ()

Full text

Abstract. Adaptive e-learning provides the best recommendations of learning resources according to the needs of the student, including learning style, knowledge level, personality, and the time they can spend on learning materials. Despite technological advancements, current e-learning platforms often fail to consider individual learning styles and knowledge gaps, leading to less effective learning experiences. This research evaluates the effectiveness of creating an adaptive e-learning system that uses the VARK learning model and a recommendation system to identify learning styles and provide personalized learning experiences to students based on their knowledge gap and learning preference in particular topics. The system first administers a VARK e-questionnaire to determine the student's learning style, followed by a pre-test to assess their knowledge level. Based on these assessments, the system assigns a personalized e-learning path aligned with the student's dominant learning style and addresses knowledge gaps in specific topics. The proposed system is expected to enhance learning experiences by providing personalized educational content that aligns with individual learning style and addresses specific knowledge deficiencies. This approach has the potential to substantially enhance educational outcomes and effectiveness of learning by delivering customized educational experiences that cater to the unique requirements of every student.

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