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
相关论文
共 50 条
  • [31] Fault feature extraction and diagnosis method based on deep belief network
    Zhao, Guangquan
    Ge, Qiangqiang
    Liu, Xiaoyong
    Peng, Xiyuan
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2016, 37 (09): : 1946 - 1953
  • [32] Method of Vegetation Extraction Based on Deep Belief Network and Optimal Scale
    Liu Zujin
    Yang Ling
    Liu Zuhan
    Duan Linlin
    Qiao Xianxian
    Gong Jiaojiao
    LASER & OPTOELECTRONICS PROGRESS, 2018, 55 (02)
  • [33] A new node selection method of Deep Belief Network based on similarity
    Yan, Leifang
    Jiang, Mingyan
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING (AMCCE 2017), 2017, 118 : 645 - 649
  • [34] Multifocus Image Fusion Method Based on Convolutional Deep Belief Network
    Zhai, Hao
    Zhuang, Yi
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2021, 16 (01) : 85 - 97
  • [35] Fault diagnosis method of rolling bearing based on deep belief network
    Shang, Zhiwu
    Liao, Xiangxiang
    Geng, Rui
    Gao, Maosheng
    Liu, Xia
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2018, 32 (11) : 5139 - 5145
  • [36] Nonlinear system identification method based on improved deep belief network
    Yang Man
    Liu Ding
    Zhao Xiaoguo
    2018 CHINESE AUTOMATION CONGRESS (CAC), 2018, : 2379 - 2383
  • [37] Durability Prediction Method of Concrete Soil Based on Deep Belief Network
    Tian, Xiao
    Zhu, Niankun
    ADVANCES IN CIVIL ENGINEERING, 2022, 2022
  • [38] Coupled-deep belief network based method for image recognition
    Ma M.
    Xu X.
    Wu J.
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2018, 45 (05): : 102 - 107
  • [39] Fault diagnosis method of rolling bearing based on deep belief network
    Zhiwu Shang
    Xiangxiang Liao
    Rui Geng
    Maosheng Gao
    Xia Liu
    Journal of Mechanical Science and Technology, 2018, 32 : 5139 - 5145
  • [40] A fault detection method for wind turbines based on deep belief network
    El Bakri, Ayoub
    Sefriti, Selma
    Boumhidi, Ismail
    2020 FOURTH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS), 2020,
12345