sis 24(2):

Research Article

Application Integrated Deep Learning Networks Evaluation Methods of College English Teaching

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  • @ARTICLE{10.4108/eetsis.4494,
        author={Jie Guo},
        title={Application Integrated Deep Learning Networks Evaluation Methods of College English Teaching},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={11},
        keywords={English teaching evaluation in higher education, deep learning network, self-encoder, integrated learning},
        doi={10.4108/eetsis.4494}
    }
    
  • Jie Guo
    Year: 2023
    Application Integrated Deep Learning Networks Evaluation Methods of College English Teaching
    SIS
    EAI
    DOI: 10.4108/eetsis.4494
Jie Guo1,*
  • 1: Hefei Preschool Education College, Hefei 230001, Anhui, China
*Contact email: gloriaguoguo2023@163.com

Abstract

INTRODUCTION: The construction of English evaluation methods in colleges and universities, as the essential part of English teaching in colleges and universities, is conducive to the improvement of the quality of English teaching in colleges and universities, which makes the existing English teaching more objective and reasonable, and makes the means of English teaching rich in science. OBJECTIVES: Aiming at the current wisdom teaching evaluation design methods exist evaluation indexes exist objectivity is not strong, accuracy is poor, single method and other problems. METHODS:Proposes a college English teaching evaluation method based on a deep learning network. First, the evaluation index system of English in colleges and universities is constructed by analyzing the principle of selecting evaluation indexes of English in colleges and universities; then, the deep learning network is improved through self-coder and integrated learning methods to construct the evaluation model of English teaching in colleges and universities; finally, the effectiveness and efficiency of the proposed method is verified through simulation experiment analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solved the problems of low evaluation accuracy and non-objective system indexes of English teaching evaluation methods in colleges and universities.