An Implementation of Q-Learning Framework for an effective recommendation of ideological and political course resources

Deep reinforcement learning provides promising innovations in improving educational resource suggestions through dynamically adjusting to user interests and learning patterns. Current ideological and political course suggestion frameworks usually are based on static collaborative filtering or content-based approaches, which cannot reflect users' changing interests and situational learning demands. Conventional methods, such as matrix factorization and rule-based filtering, have weak personalization and poor responsiveness to real-time feedback. The suggested model utilizes a Deep Q-Network (DQN)-informed reinforcement learning architecture that optimizes resource choices in real time based on reward signals drawn from student interactions, engagement levels, and retention. With the incorporation of a state-action-reward strategy, the model systematically searches for optimal recommendation policies, promoting more relevant and pedagogically sound content presentation. Comparative analysis verifies the deep reinforcement learning (DQN) accomplishes a considerable performance gain with 99.3% accuracy and 98.7% F1-score, surpassing traditional collaborative filtering and hybrid models. The adaptability and contextual awareness of the superior system verify its efficacy in educational personalization.