Innovation of College English Teaching Methods Based on Deep Learning

Authors

  • Gangtao Chen North Beijing Vocational and Technical College, Beijing 101400, China
  • Qi Zhang Shaanxi Railway Institute, Weinan 714000, Shaanxi, China
  • Yuqian Han Chang Sha Normal University, Changsha 410100, China

Abstract

To provide innovative strategies to ensure the richness of teaching content, flexibility of teaching methods and efficiency of teaching process, this study uses an empirical experiment to explore the effectiveness of a college English teaching model based on deep learning. Two groups of 150 college students were selected as experimental subjects. One group received a personalized teaching mode based on deep learning, and the control group were taught by means of traditional pedagogy. The study compared the differences between the two groups in terms of English proficiency, learning attitudes and habits, learning efficiency, affective cognition and collaborative teamwork. The results show that the experimental group was significantly
better than the control group according to all the evaluation indicators, indicating that the teaching model based on deep learning has significant advantages in improving the effectiveness of students’ learning, improving learning attitudes and habits, improving learning efficiency, enhancing emotional cognitive stability and strengthening collaborative and cooperative team skills. These findings provide strong evidence for the improvement and optimization of college English teaching methods, and prove that the teaching model based on deep learning has great potential in promoting college students’ English skills in the reading, writing and speaking of English.

Keywords: deep learning; college English; English teaching; innovative method

Cite As

G. Chen, Q. Zhang, Y. Han, " Innovation of College English Teaching Methods Based on Deep Learning",
Engineering Intelligent Systems, vol. 33 no. 4, pp. 355-364, 2025.

 

 

 

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Published

2025-07-01

Issue

Vol. 33 No. 4 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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