Improving EFL Students' Reading Skills Using Graph Attention Networks and Intelligence-Based Tasks

Reading is an engaging practice that helps language learners understand texts. Develop and prioritize basic skills like reading strategies to improve performance and learn new languages. Reading and reading methods will be defined, distinct reading techniques reviewed, successful reading models established, and essential reading principles investigated. The suggested approach involves preprocessing, feature extraction, and model training. PREP techniques such stop word reduction and Porter stemming address data sparsity. Latent Dirichlet Allocation (LDA) selects features, and Hierarchical Generative Adversarial Network trains models. When compared to BERT and GAN, the recommended model wins. The model's 95.39% accuracy is impressive. This proposed shows that the suggested technique improves reading comprehension and text processing precision. Superior preprocessing, feature selection, and hierarchical GAN-based training make it superior to state-of-the-art methods.