A Context-Aware Content Recommendation Engine for Personalized Learning using Hybrid Reinforcement Learning Technique

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In the evolving landscape of e-learning, delivering personalized content that aligns with learners' needs and preferences is crucial. This study proposes a Context-Aware Content Recommendation Engine (CACRE) that utilizes a Hybrid Reinforcement Learning (HRL) technique to optimize personalized learning experiences. The engine incorporates learners' contextual data, such as learning pace, preferences, and performance, to deliver tailored recommendations. The proposed HRL model combines Deep Q-Learning for dynamic content selection and Policy Gradient Methods to adapt to individual learning trajectories. Experimental results demonstrate significant improvements in learner engagement, content relevance, and knowledge retention. This approach underscores the potential of context-aware recommendation systems to revolutionize personalized education by fostering adaptive and interactive learning environments.