Research on Adaptive Education Path Dynamic Programming Algorithm Based on Reinforcement Learning and Cognitive Graphs

The rapid evolution of Adaptive Education highlights the necessity of personalized learning paths that cater to the unique cognitive styles, preferences, and capabilities of each student. Traditional educational methods often fail to accommodate the diverse needs of learners, resulting in inefficient learning experiences. Current systems rely heavily on static models that do not dynamically adjust to the learner’s evolving needs. This research addresses these limitations by proposing an innovative approach that integrates reinforcement learning and cognitive graphs to optimize learning paths in real-time. The proposed method combines dynamic learner models, real-time performance tracking, and predictive analytics, allowing for a highly adaptive educational system. By employing reinforcement learning algorithms, the system continuously refines its model based on learner feedback and past interactions. The introduction of cognitive graphs enhances the system’s ability to represent complex relationships between concepts and learning materials, enabling more effective content sequencing and personalized learning. Experimental results show that the adaptive education system significantly improves learner performance compared to traditional approaches, demonstrating its efficacy in real-world educational. This work offers a promising solution for overcoming the challenges faced by conventional educational systems, providing a flexible, personalized, and data-driven learning environment that better supports diverse learners’ needs.