Personalized learning support system for special education: a real-time feedback mechanism based on deep reinforcement learning

The development of personalized learning support systems for special education is crucial to address the limitations of traditional one-size-fits-all approaches in meeting diverse learner needs. Existing systems struggle with effectively processing multidimensional behavioral data, adapting instructional strategies dynamically, and maintaining interpretability in real-world educational settings. This study proposes a three-module hierarchical reinforcement learning framework comprising: (1) a Behavioral Feature Extractor (BFE) combining dilated convolutions and attention mechanisms for temporal pattern recognition, (2) an Adaptive Policy Selector (APS) using hierarchical DQN to map features to pedagogical strategies, and (3) a feedback optimization module with pedagogical importance sampling. Experimental results on the ECLS-K dataset demonstrate significant improvements, including 89% overall strategy accuracy (vs. 78% for flat DQN), 85% appropriateness for special education cases (22% higher than ablated versions), and 5.7x better rare event coverage compared to standard experience replay. The framework successfully addresses key challenges in adaptive learning technologies while maintaining 87% strategy diversity and 3.4x sample efficiency over non-adaptive baselines, establishing a new standard for interpretable, data-driven personalized education systems.