sis 24(5):

Research Article

Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions

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  • @ARTICLE{10.4108/eetsis.5867,
        author={Meijuan Huang},
        title={Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={5},
        keywords={teaching evaluation in higher education institutions, random forest, gray wolf optimization algorithm, principal component analysis approach},
        doi={10.4108/eetsis.5867}
    }
    
  • Meijuan Huang
    Year: 2024
    Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions
    SIS
    EAI
    DOI: 10.4108/eetsis.5867
Meijuan Huang1,*
  • 1: Anhui Business and Technology College
*Contact email: 15395039166@163.com

Abstract

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities. METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges.