A dynamic reinforcement feedback network-based intelligent feedback mechanism in online learning platforms

Online learning platforms’ intelligent feedback mechanisms suffer from static strategies and inadequate content adaptability, failing to meet learners’ dynamic needs. This paper proposes the Dynamic Reinforcement Feedback Network (DRF-Net), integrating Dynamic State Perception, PPO decision-making, and LLaMA 3 generation modules. Experiments on the KDD Cup and OpenEdX datasets show that DRF-Net achieves a learning effect improvement rate of 0.35 ± 0.05 (34.6% higher than traditional models) and a cumulative reward of 56.8 ± 4.2 (33.6% higher than single reinforcement learning models). Ablation experiments confirm the necessity of each module — removing the Dynamic State Perception module reduces the learning effect improvement rate by 22.9%. Future work will expand datasets, optimize adaptability to extreme states, and promote the model’s application in real scenarios.