{"title":"The application of deep learning in the innovation of intelligent English teaching mode","authors":"Yafang Chen","doi":"10.3233/jcm-237054","DOIUrl":null,"url":null,"abstract":"With the rapid development of deep learning technology, its application in various fields is increasingly extensive. Especially in the field of education, the application of deep learning technology has brought great challenges and changes to the traditional teaching mode. This research is aimed at the application of deep learning in intelligent English teaching mode. Firstly, the theory of deep learning is studied in depth, and the application cases of deep learning in other fields are discussed. Secondly, the research designs and implements an intelligent English teaching model based on deep learning, and carries out a lot of experiments and tests. The experimental results show that this new teaching mode can effectively improve the efficiency and effect of students’ English learning. However, it is also found that the model has some problems, such as model training needs a lot of computing resources, has certain requirements for hardware equipment, and some students have poor adaptability to the new learning mode. To solve these problems, a series of solutions are proposed. In general, although there are still some challenges in the application of deep learning in intelligent English teaching mode, its potential is huge and it has a profound impact on improving the quality of teaching.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Methods in Sciences and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jcm-237054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid development of deep learning technology, its application in various fields is increasingly extensive. Especially in the field of education, the application of deep learning technology has brought great challenges and changes to the traditional teaching mode. This research is aimed at the application of deep learning in intelligent English teaching mode. Firstly, the theory of deep learning is studied in depth, and the application cases of deep learning in other fields are discussed. Secondly, the research designs and implements an intelligent English teaching model based on deep learning, and carries out a lot of experiments and tests. The experimental results show that this new teaching mode can effectively improve the efficiency and effect of students’ English learning. However, it is also found that the model has some problems, such as model training needs a lot of computing resources, has certain requirements for hardware equipment, and some students have poor adaptability to the new learning mode. To solve these problems, a series of solutions are proposed. In general, although there are still some challenges in the application of deep learning in intelligent English teaching mode, its potential is huge and it has a profound impact on improving the quality of teaching.
期刊介绍:
The major goal of the Journal of Computational Methods in Sciences and Engineering (JCMSE) is the publication of new research results on computational methods in sciences and engineering. Common experience had taught us that computational methods originally developed in a given basic science, e.g. physics, can be of paramount importance to other neighboring sciences, e.g. chemistry, as well as to engineering or technology and, in turn, to society as a whole. This undoubtedly beneficial practice of interdisciplinary interactions will be continuously and systematically encouraged by the JCMSE.