Cognitive and behavioral neurology approaches to personalized Japanese language learning path recommendations

This paper proposes a personalized Japanese language learning path recommendation framework grounded in cognitive and behavioral neurology.

The framework integrates the Cognitive Behavioral Neurological Pathway Model (CBNPM) and the Adaptive Cognitive Behavioral Pathway Optimization (ACBPO) strategy to represent learner cognitive states, behavioral patterns, and learning progress, thereby enabling dynamic adjustment of individualized learning trajectories. Hierarchical reinforcement learning, variational inference, and audio visual correspondence are incorporated to optimize learning paths through reward and cognitive cost functions.

Experimental results across four datasets demonstrate that the proposed method consistently outperforms baseline models. It achieves an accuracy of 89.12 and an AUC of 88.85 on the Cognitive Patterns dataset, 90.34 and 90.11 on the Behavioral Metrics dataset, and accuracies of 89.34 and 90.12 on the Neurological Responses and Personalized Learning Path datasets. These results indicate that the proposed framework can more effectively model cognitive behavioral interactions and improve personalized learning path recommendations for Japanese language acquisition.