A Deep Learning-Based Blended Teaching Model for Enhancing English Proficiency in English Education

This research introduces an advanced blended instructional model for college-level English as second language education, integrating traditional in-person teaching with cutting-edge online learning components powered by Conditional Random Field (CRF) techniques within a deep learning framework. As a hybrid paradigm, blended learning has gained prominence in higher education due to its ability to enhance student engagement and learning outcomes. The CRF-based model optimizes pedagogical strategies through dynamic, adaptive, and personalized learning experiences, addressing diverse cognitive profiles and preferences. It integrates various instructional modalities—classroom interactions, digital resources, and interactive activities—into a cohesive framework, with CRF algorithms modeling sequential dependencies crucial for language acquisition tasks such as syntactic parsing and part-of-speech tagging. These foundational tasks enable students to internalize linguistic structures, fostering proficiency in English. By leveraging advanced deep learning architectures like recurrent neural networks (RNNs) and transformer models alongside large-scale linguistic datasets, the model achieves significant gains in accuracy, generalization, and responsiveness to individual learner needs. Empirical results demonstrate a 16% improvement in overall English proficiency and a 27% enhancement in reading comprehension compared to traditional methods, underscoring the transformative potential of AI-driven methodologies in language education. This study not only advances theoretical insights into instructional design but also establishes a robust framework for optimizing higher education through the integration of deep learning techniques.