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Issue title: Artificial Intelligence as a maturing and growing technology: An urgent need for intelligent systems
Guest editors: X. Yuan and M. Elhoseny
Article type: Research Article
Authors: Fang, Chuanxin; *
Affiliations: Yantai Nanshan University, Longkou, Shandong, China
Correspondence: [*] Corresponding author. Chuanxin Fang, Yantai Nanshan University, Longkou, Shandong, China. E-mail: fengchao1335695@163.com.
Abstract: English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.
Keywords: Support vector machine, decision tree, online teaching, teaching quality, feature recognition
DOI: 10.3233/JIFS-189313
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2709-2719, 2021
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