Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–26, 2023, Zhengzhou, China

Research Article

Research on English Teaching Assessment based on Analytic Hierarchy Model

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343663,
        author={Yiwen  Hu},
        title={Research on English Teaching Assessment based on Analytic Hierarchy Model},
        proceedings={Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24--26, 2023, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2024},
        month={4},
        keywords={english teaching assessment; analytic hierarchy model; eigenvalues; multiple levels},
        doi={10.4108/eai.24-11-2023.2343663}
    }
    
  • Yiwen Hu
    Year: 2024
    Research on English Teaching Assessment based on Analytic Hierarchy Model
    ITEI
    EAI
    DOI: 10.4108/eai.24-11-2023.2343663
Yiwen Hu1,*
  • 1: Nanjing Normal University
*Contact email: rebeccahu06@126.com

Abstract

At present, English teaching assessment is extremely important for development of education and teaching management, which can improve the education quality, guarantee the fairness of English education. However, existing assessment relies on the feedback from students and ignore the reasonable utilization of mathematical models. In this work, we utilize the analytic hierarchy method for English teaching evaluation. Initially, a hierarchical model was established to decompose the evaluation of English teaching into multiple levels and factors including the top level is the overall teaching quality goal, the middle layer contains the sub-goals of teaching methods, performance, feedback. Finally, the bottom layer is the specific indicators under these sub-goals. Further, we adopted eigenvalues to calculate the weights of each factor and performed consistency tests to ensure the reasonableness and accuracy of the evaluation. From our real-word data simulation results, we can observe that our model outperforms traditional evaluation methods and learning models.