Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 2023, Wuhan, China

Research Article

Highly Effective Digital Model Design and Practice for Deep Teaching Evaluation

Download122 downloads
  • @INPROCEEDINGS{10.4108/eai.8-9-2023.2340093,
        author={Yong  Wei and Yue  Sun and Hui  Wang},
        title={Highly Effective Digital Model Design and Practice for Deep Teaching Evaluation},
        proceedings={Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8--10, 2023, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICMEIM},
        year={2023},
        month={11},
        keywords={correlation analysis digital campus evaluation system statistical analysis teach-ing effectiveness teaching evaluation},
        doi={10.4108/eai.8-9-2023.2340093}
    }
    
  • Yong Wei
    Yue Sun
    Hui Wang
    Year: 2023
    Highly Effective Digital Model Design and Practice for Deep Teaching Evaluation
    ICMEIM
    EAI
    DOI: 10.4108/eai.8-9-2023.2340093
Yong Wei1,*, Yue Sun2, Hui Wang1
  • 1: Shenzhen Institute of Information Technology
  • 2: Shanghai International Studies University
*Contact email: tsh-xyz@163.com

Abstract

Education instills ideals and aids in the overall growth of society. It allows people to shape themselves into more fiscally responsible contributors to society. Student eval-uation of teaching is an important part of the management process of the digital cam-pus, which is of great significance to the improvement of teaching quality and the im-plementation of teaching supervision and management. Conventional teaching evalua-tion mechanisms are based on students' feedback, which is time consuming and not accurate. Hence, this research work proposes a 5-level teaching evaluation system that utilizes information technology to efficiently and accurately establish a deep teaching evaluation system centered on objective data that can be further analyzed and pro-cessed. This teaching evaluation model dynamically analyzes and mines teaching evaluation data, reflecting teaching level more truly and objectively and improving teaching effectiveness. Experimental results show that the proposed model based on a deep evaluation system is statistically significant and is used to assess student quality quickly and accurately.