Improving Online Learning via VARK Learning Styles and Machine Learning-Driven Personalisation

Understanding student engagement and academic performance is crucial in online learning environments. However, many learning management systems (LMS) lack mechanisms to adapt to diverse learning styles and support meaningful collaboration. This study addresses these challenges by proposing a Personalised and Collaborative Learning Experience (PCLE) framework that integrates the Visual, Auditory, Reading/Writing, and Kinaesthetic (VARK) learning style model with collaborative filtering techniques. Unlike existing approaches that rely only on rating data, PCLE incorporates personalised learning styles into the recommendation process to create learner-centred outcomes. To overcome the lack of publicly available datasets containing personalised learning style data, a self-collected dataset was developed to reflect authentic learner preferences. Benchmark datasets from Coursera and Udemy were also used to validate baseline collaborative filtering performance. Three machine learning models—K-Nearest Neighbours (KNN), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF)—were applied and evaluated using Mean Absolute Error (MAE), Hit Rate (HR), and Average Reciproca