Open AccessA Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning
Author(s) -
Dongjun Ge,
Xiaoyue Wang,
Jingting Liu
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i03.20471
Subject(s) - computer science , artificial intelligence , fuzzy logic , mathematics education , china , evaluation methods , boosting (machine learning) , quality (philosophy) , deep learning , machine learning , psychology , engineering , philosophy , epistemology , political science , law , reliability engineering
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.