Design of an Intelligent Recommendation System for Personalized Educational Content Based on Students' Learning Styles Using Machine Learning Techniques: A Literature Review

Abstract

This study presents a comprehensive literature review on intelligent recommendation systems designed for personalizing educational content according to students' learning styles using machine learning techniques. As a review paper, it synthesizes findings and conclusions from existing empirical studies and theoretical frameworks rather than reporting new experimental data. Emphasizing the importance of personalized learning in enhancing student engagement and academic outcomes, the review examines key methodologies such as decision trees, support vector machines, and adaptive feedback loops for their effectiveness in accurately classifying learning styles and tailoring content recommendations. The review highlights the improvements achieved by hybrid recommendation models in precision, recall, and learner engagement compared to traditional systems. Furthermore, the study discusses the potential applicability of these systems within the Libyan educational context, addressing specific challenges and opportunities for localized implementation. The findings underscore the potential of machine learning-driven recommendation systems to create adaptive, learner-centered educational environments. This literature-based approach lays the groundwork for future empirical research, including case studies within Libya, and informs real-world deployment strategies.