Title: The method of online classroom teaching quality evaluation based on deep data mining

Authors: Jing Hou

Addresses: Publicity Department, Yellow River Conservancy Technical Institute, Kaifeng 475004, China

Abstract: In order to overcome the problems of traditional online classroom teaching quality evaluation methods, such as low accuracy of quality evaluation and poor effect of classroom teaching quality improvement, this paper proposes an online classroom teaching quality evaluation method based on deep data mining. Fuzzy comprehensive evaluation method is used to quantify the evaluation index of online classroom teaching quality. The evaluation matrix is constructed to calculate the weight of classroom teaching quality evaluation index. The online classroom teaching quality evaluation indicators are classified by naive Bayes classification algorithm. With the help of deep data mining algorithm, this paper evaluates the post classification evaluation index, constructs the online classroom teaching quality evaluation model, and completes the online classroom teaching quality evaluation. The experimental results show that the accuracy of the proposed method is about 0.9, and it can effectively improve the quality of online classroom teaching.

Keywords: deep data mining; classifier; naive Bayes classification algorithm; evaluation matrix.

DOI: 10.1504/IJCEELL.2023.132388

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.433 - 444

Received: 10 May 2021
Accepted: 09 Aug 2021
Published online: 19 Jul 2023 *

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