E-learning has become an indispensable teaching method that offers users the flexibility to overcome time and location constraints. However, despite the development of numerous learning management systems, most have similar limitations in content delivery, as they offer the same courses and services to all users, without considering their varied profiles. This paper proposes a model to personalize educational content by creating an individualized learning path in MoodleCloud. The system aims to improve learning outcomes and optimize time efficiency by tailoring educational content to individual student profiles. We detect learner characteristics by combining a collaborative approach based on the VARK questionnaire and an automatic classification approach using a decision tree based on user interactions. Then, we apply a dual content-based filtering of academic resources based on learners’ learning styles and knowledge levels. The effectiveness of the strategy is evaluated through a comparative study involving experimental and control groups. The results show that the students in the personalized learning group achieved an average post-test score of 15.35, compared to 13.95 in the control group. In addition, the average duration of learning was reduced by 4 days. These findings confirm that personalized learning paths significantly improve learning efficiency and knowledge acquisition.