A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning
Dongjun Ge, Xiaoyue Wang, Jingting Liu, Department of Preschool Education, Shijiazhuang Preschool Teachers College, Shijiazhuang 050228, China
iJET Volume 16, Number 3, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany
Developed countries regard preschool education as an important starting point and foundation for elite training. In recent years, preschool education has also attracted a growing attention in developing countries like China. Considering the significance of the teaching quality of preschool teachers to lifelong academic achievement, this paper designs a teaching quality evaluation model for preschool teachers based on deep learning. Firstly, a progressive system with a hierarchical structure was developed for the relevant evaluation indices. Then, the fuzzy comprehensive evaluation of each index layer and evaluation criterion was determined by the principle of fuzzy relationship synthesis. Finally, an evaluation prediction model was established based on extreme gradient boosting (XGBoost) algorithm and technology services’ ResNet (TS-ResNet), and proved effective and accurate through experiments. The research results provide a reference for the application of the proposed model in other evaluation prediction scenarios.
Ge, D., Wang, X. & Liu, J. (2021). A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(3), 127-143. Kassel, Germany: International Journal of Emerging Technology in Learning.