Effective College English Teaching Based on Teacher-student Interactive Model

Author:

Fang Hui1,Shi Hongmei2,Zhang Jiuzhou1ORCID,Karuppiah Marimuthu3

Affiliation:

1. College of Teachers' Education, Dali University, Dali, Yunnan, China

2. College of Foreign Languages, Dali University, Dali, Yunnan, China

3. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India

Abstract

English has become an utterly crucial device to take part in global verbal exchange and competition. It is essential to enhance English teaching's flexibility to meet the desires to improve the market economy. Therefore, powerful coaching strategies and language identification are considered challenging factors in existing methods. The proposed model includes hypothesized relationships among college students' conception of learning English, their perceptions of the study room environment, and their approaches to learning. They are examined using the Pre-trained Teacher–Student Fixed Interactive Model (PTSFIM). This model proposes a new way to develop the teaching process providing the baseline of record excellence towards a strategic performance control framework for an institute. The traditional strategies emphasize the benefits of the interactive approach and accentuate their effectiveness through Structural Multivariate Equation (SME) analysis in enhancing students' innovative thinking, research, and reasoning abilities. The reciprocal instructional analysis optimizes students' models to memorize for a longer duration. The evaluation of the study's outcomes suggests that interactive learning can assist college students that predict different results in participating inside the speech system and gain the best knowledge. The simulation analysis is performed based on accuracy, performance, and efficiency proves the reliability of the proposed framework.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. English Teaching Ability Evaluation Algorithm based on K-Means Data Algorithm;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

2. Empowering language learning through IoT and big data: an innovative English translation approach;Soft Computing;2023-06-29

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