Evaluation method of English online course teaching effect based on ResNet algorithm

Article type: Research Article

Authors: Yang, Juan^{; *}

Affiliations: School of Foreign Languages and Literature, Xiangsihu College of Guangxi University for Nationalities, Nanning, China

Correspondence: [*] Corresponding author. Juan Yang, School of Foreign Languages and Literature, Xiangsihu College of Guangxi University for Nationalities, Nanning 530008, China. E-mail: gsqtl84@163.com.

Abstract: In order to improve the accuracy of English online course teaching effect evaluation results, a paper proposed an English online course teaching effect evaluation method based on ResNet algorithm. The effect of College English online teaching was evaluated from five aspects: pre-class preparation, teaching content, basic skills, ability training, and teaching methods. Each evaluation item was set with seven levels of scoring standards. An evaluation model of the classroom teaching effect was constructed based on convolutional neural network according to the internal relationship between facial expression recognition and classroom teaching effect evaluation. The problem of network depth deepening affecting the accuracy of evaluation in convolutional neural network models was innovatively solved by utilizing the ResNet algorithm. The evaluation of the effectiveness of English online course teaching was achieved. The experimental results showed that this method could effectively improve the effect of English online course teaching evaluation and improve the teaching quality of English online courses.

Keywords: ResNet algorithm, English online teaching, teaching evaluation, face recognition, convolutional neural network

DOI: 10.3233/JIFS-230048

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4907-4916, 2023