Title: A construction of online teaching quality evaluation model based on big data mining

Authors: Weijuan Li

Addresses: Dean's Office, Yellow River Conservancy Technical Institute, Kaifeng 475004, China

Abstract: This paper designs an online teaching quality evaluation model based on big data mining. Firstly, the online teaching big data is preprocessed to improve data retrieval accuracy and save evaluation time. Then, a self-coding network is established to effectively represent data features and complete data reconstruction through data coding/decoding, so as to effectively mine teaching quality data. Finally, six first-level indicators and 16 second-level indicators are designed to complete the construction of online teaching quality evaluation model by setting the weight of each indicator. According to the simulation experiment, the evaluation time of model of this paper is 25 s-29 s, the retrieval accuracy of online teaching data is closer to 1, and the comprehensive evaluation accuracy is between 94% and 96%, indicating that the model has higher evaluation efficiency and reliability, and better application effect.

Keywords: big data mining; quality of teaching; index weight; self-coding network; quality evaluation.

DOI: 10.1504/IJCEELL.2024.135271

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.1, pp.1 - 12

Received: 30 Sep 2021
Accepted: 12 Jan 2022
Published online: 03 Dec 2023 *

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