An online teaching quality evaluation method based on deep belief network

被引：0
作者：
Liang, Chun [1 ]
Peng, Hai-lin [2 ]
机构：
[1] Hunan City Univ, Coll Management, Yiyang 413000, Peoples R China
[2] West Yunnan Univ, Fac Teacher Educ, Lincang 677000, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING | 2024年 / 34卷 / 04期
关键词：
online teaching; deep belief network; DBN; teaching quality evaluation; restricted Boltzmann machine; RBM; evaluation index system; NEURON;
D O I：
10.1504/IJCEELL.2024.139935
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
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.
引用
收藏
页码：354 / 367
页数：15
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