Personalised learning path generation mechanism based on RL and knowledge graph

This article proposes a personalised learning path generation mechanism that combines reinforcement learning and knowledge graphs.It constructs a knowledge graph that includes knowledge points, student status, and historical behaviour.Using this image and learning trajectory, it constructed a student model.Then, the RL algorithm optimises the learning path based on real-time feedback.An experiment targeting 300 college students compared the proposed method with traditional methods.The results showed that reinforcement learning based methods improved learning outcomes by 12.5%, increased learning satisfaction by 23.7%, accelerated knowledge acquisition by 15%, and shortened average learning time by 8%.These findings confirm the effectiveness of this mechanism in improving learning outcomes and meeting individual student needs.