Back to Search Start Over

Research on the Construction of English Autonomous Learning Model Based on Computer Network-Assisted Instruction.

Authors :
Tian, Mijuan
Fu, Rong
Tang, Qianjun
Source :
Computational Intelligence & Neuroscience. 6/15/2022, p1-9. 9p.
Publication Year :
2022

Abstract

As a supplement to traditional teaching methods, computer-assisted teaching methods can reflect modern educational concepts, such as creating student-led, teacher-led environments. The goal of college English education is to enable them to communicate effectively in English in their future academic, work, and social interactions, while also developing students' self-learning skills. Chinese society improves overall cultural competence and adapts to the needs of international communication. Self-directed learning is not static and will increase or decrease with time, discipline, and conditions and is an evolving process. Understanding learning, taking responsibility for one's own learning, and learning how to learn are all beneficial. Students abound in school life and even throughout their lives. In this paper, we try to propose a computer-based method for constructing an independent English learning model based on a practical study of computer network technology for the development of self-learning ability of non-English majors in a university. This paper uses comparative analysis techniques to compare traditional paper-and-pencil examinations and computer-based online evaluations and analyzes the effects of each. The survey showed that 81% of the students preferred the computer-based assessment. Therefore, the focus of this research is to strengthen the oral English training in college and create an authentic English learning environment for students to really feel the standard English pronunciation, intonation, and knowledge of grammar, listening, reading, writing, and translation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16875265
Database :
Academic Search Index
Journal :
Computational Intelligence & Neuroscience
Publication Type :
Academic Journal
Accession number :
157455990
Full Text :
https://doi.org/10.1155/2022/8646463