Reinforcement Learning for Personalized Education in Adaptive Learning Systems
Abstract
Personalized education is transforming traditional learning paradigms by tailoring content to individual needs. This paper proposes a reinforcement learning-based adaptive learning system that dynamically adjusts educational content and strategies based on learner performance and engagement. The system uses a reward mechanism to optimize learning paths, ensuring that each learner achieves their goals efficiently. Experiments on e-learning platforms demonstrate significant improvements in knowledge retention and user satisfaction compared to traditional static systems. The study highlights the potential of reinforcement learning to enhance educational outcomes and make learning experiences more engaging and effective.
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