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

Feng Hongli
Additional contact information
Feng Hongli: Foreign Language Teaching School, Ningxia Medical University, Ningxia, Yinchuan, China

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 3s, 6247-6266

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... sue-3s/6247-6266.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... n-english-education/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:8:y:2024:i:3s:p:6247-6266

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().