Title: An online teaching quality evaluation method based on deep belief network

Authors: Chun Liang; Hai-lin Peng

Addresses: College of Management, Hunan City University, Yiyang, 413000, China ' Faculty of Teacher Education, West Yunnan University, Lincang, 677000, China

Abstract: In order to improve the evaluation effect and accuracy of online teaching quality, an online teaching quality evaluation method based on deep belief network is proposed. We establish the evaluation index system of online teaching quality, collect the data related to teaching quality, teaching attitude, teaching content, teaching methods and teaching influence by using crawler technology, and extract the data characteristics of online teaching quality evaluation index. Combined with the data characteristics, the online teaching quality evaluation model is constructed by using the deep belief network, and the evaluation index data is input into the evaluation model to obtain the online teaching quality score. The experimental results show that the error rate of the proposed method is only 4.9%, and the average accuracy rate of online teaching quality evaluation is as high as 97.2%, which has the characteristics that the accuracy of online teaching quality evaluation is higher than the effect.

Keywords: online teaching; deep belief network; DBN; teaching quality evaluation; restricted Boltzmann machine; RBM; evaluation index system.

DOI: 10.1504/IJCEELL.2024.139935

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.4, pp.354 - 367

Received: 13 May 2022
Accepted: 30 Aug 2022
Published online: 12 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article