A Machine Learning Based Hybrid Recommendation System for Smart Learning Environments

Abstract

The rapid growth of digital education platforms has increased the need for intelligent systems that can provide personalized learning experiences. Traditional recommendation systems often rely on a single recommendation technique, which may not effectively address the diverse learning preferences and requirements of students. This project presents a Machine Learning Based Hybrid Recommendation System for Smart Learning Environments that combines collaborative filtering, content-based filtering, and machine learning algorithms to deliver accurate and personalized learning recommendations. The system analyzes learner profiles, academic performance, learning history, interests, and behavioral patterns to recommend relevant courses, study materials, video lectures, quizzes, and learning activities. The hybrid approach helps overcome challenges such as data sparsity, cold-start problems, and limited recommendation accuracy. By continuously learning from user interactions and feedback, the system adapts to changing learner preferences and improves recommendation quality over time. The proposed system enhances learner engagement, supports adaptive learning, and contributes to better academic outcomes by providing learners with suitable educational resources tailored to their individual needs.