Design and Application of English Learning System Based on Web Technology and Hybrid Deep Attention-Based Recurrent Neural Network

The rapid integration of web technology in education has created new opportunities for improving English learning through interactive, accessible, and scalable systems. Existing systems, however, tend to suffer from limitations such as limited personalization, stale content presentation, and poor performance assessment, which discourage learners and hinder their progress measurement. Conventional approaches have used rule-based learning management systems or rudimentary multimedia integration, without adaptive feedback and realtime analytics. The model suggested integrates a Hybrid Deep Attention-Based Recurrent Neural Network (HDARNN) architecture using a blend of attention mechanisms and bidirectional GRU layers to facilitate personalized learning paths, context-sensitive recommendations, and smart assessment feedback. The model was developed and evaluated on big learner datasets with an accuracy of 99.8 % and an F1score of 98.9 %, surpassing typical baselines by considerable margins. Comparative analysis proves better adaptability, more engagement, and lower error rates. Through the use of webbased interactive modules, adaptive learning analytics, and smart content sequencing, the proposed system guarantees better comprehension, vocabulary acquisition, and speaking skill for different learner profiles, thus creating a more efficient, adaptive, and data-driven English learning environment.