ARCHIVES

Personalized e-learning systems aim to adapt learning content according to learner behavior and knowledge progression. This work proposes an enhanced adaptive learning framework integrating Deep Reinforcement Learning and Knowledge Tracing for intelligent learning path generation. Learner interactions including quiz performance, activity duration, and engagement behavior are analyzed continuously. The Knowledge Tracing model estimates learner mastery while the Deep Q-Network determines optimal learning actions. Experimental simulation using 5000 learners and 500 modules demonstrates improved cumulative reward, learner retention, and course completion compared with traditional systems.