Title: Study on quality evaluation of online and offline mixed teaching reform based on big data mining

Authors: Guoxia Hu; Suntai Sun; Zhongxiao Sun

Addresses: College of Marxism, Guangxi Minzu Normal University, Chongzuo, 532200, China ' College of Art, Guangxi Minzu Normal University, Chongzuo, 532200, China ' Gansu Zhongzhixin Engineering Project Management Co., Ltd., Dingxi, 743000, China

Abstract: In order to improve the accuracy of the reform quality research and shorten the overall research time, the reform quality research is carried out based on the big data mining technology. First, the local density information of the data is calculated and the required samples are mined. Secondly, the probabilistic undirected graph model is used to remove the noise in the mining samples and improve the accuracy of the sample data. Finally, the PCA algorithm in big data is used to calculate the contribution rate of the sample data, and the reform evaluation model is constructed. The test results of different indicators show that the accuracy rate of the research method is 92.6%, and the evaluation time is only 12.7 s, which can effectively improve the evaluation accuracy and shorten the evaluation time.

Keywords: big data mining; online and offline mixed teaching; PCA algorithm; reform in education; quality assessment.

DOI: 10.1504/IJCEELL.2024.140713

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.5, pp.453 - 463

Received: 26 Aug 2022
Accepted: 04 Nov 2022
Published online: 02 Sep 2024 *

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