Recommendation of adaptive learning paths for English MOOCs based on reinforcement learning

Abstract

A reinforcement learning framework is developed for adaptive learning path recommendation in large-scale English MOOCs, leveraging a Deep Q-Network (DQN) architecture integrated with unsupervised clustering. K-means++ initializes and optimizes strategies by extracting consistent user groups, and unsupervised clustering analysis addresses data sparsity and cold start scenarios. The recommendation strategy is updated in real-time thru a composite reward function, which is sensitive to completion rates, engagement metrics, and evaluation gains. Comprehensive experiments using the OULAD and proprietary MOOC datasets indicate that, compared to traditional recommendation baselines, the proposed method has achieved significant improvements in accuracy, recall, and F1-score. Robustness tests confirmed the model's resilience to noise and incomplete data. This indicates that the model maintains adaptability in diverse learning pathways. These findings demonstrate the effectiveness and scalability of combining DQN-based reinforcement learning with advanced student profiling in a MOOC environment.