sis 24(2):

Research Article

Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System

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  • @ARTICLE{10.4108/eetsis.4491,
        author={Yanan Li and Fang Nan and Hao Zhang},
        title={Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={11},
        keywords={competent teaching quality evaluation, multi-dimensional, deep limit learning machine, intelligent optimization algorithm},
        doi={10.4108/eetsis.4491}
    }
    
  • Yanan Li
    Fang Nan
    Hao Zhang
    Year: 2023
    Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System
    SIS
    EAI
    DOI: 10.4108/eetsis.4491
Yanan Li1,*, Fang Nan1,*, Hao Zhang1,*
  • 1: Qinggong College North China University Of Science And Technology, Tangshan 063000, Hebei, China
*Contact email: luokaiwei88@163.com, luokaiwei88@163.com, luokaiwei88@163.com

Abstract

INTRODUCTION: The construction of the wisdom teaching evaluation system, as the essential part of the institution's teaching reform, is conducive to developing the institution's disciplines, making the existing teaching more standardized, and making the means of teaching diversified, intelligent, and convenient. OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are evaluation indexes that need to be more comprehensive, a single method, and system standard limitations. METHODS: Proposes an intelligent optimization algorithm for a multi-dimensional innovative teaching quality evaluation method. First of all, the multi-dimensional wisdom teaching evaluation system is constructed by analyzing the influencing factors of teaching quality evaluation; then, the parameters of the depth limit learning machine are optimized by the bird foraging search algorithm, and the multi-dimensional wisdom teaching evaluation model is constructed; finally, the validity and stability of the proposed method are verified by the analysis of simulation experiments. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problem of low evaluation accuracy and incomplete system of teaching quality evaluation methods.