Research Article
Highly Effective Digital Model Design and Practice for Deep Teaching Evaluation
@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
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.
