Title: Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis

Authors: Shijuan Shen; Qingqing Shi; Xiaojing Bai

Addresses: Institute for Design and Art, Hebei Academy of Fine Arts, ShiJiazhuang 050700, China ' Institute for Design and Art, Hebei Academy of Fine Arts, ShiJiazhuang 050700, China ' Institute for Design and Art, Hebei Academy of Fine Arts, ShiJiazhuang 050700, China

Abstract: In order to improve the accuracy of teaching quality evaluation and reduce the evaluation time, an online and offline mixed teaching quality evaluation algorithm based on multivariate statistical analysis is designed. Association rules are used to set the influencing factors to determine the rule conditions, and to determine the influencing factors of teaching quality. The weight of influencing factors is calculated, and Lagrange multiplier is introduced to determine the influencing factors. The influencing factors are grouped by factor analysis method, and the determined influencing factors with high correlation are classified by cluster analysis. The discriminant function criterion is constructed by discriminant analysis, and the discrimination and evaluation of different influencing factors are realised. The experimental results show that the highest evaluation accuracy of the method in this paper reaches 97%, indicating that it effectively improves the accuracy of the evaluation and reduces the evaluation time.

Keywords: multivariate statistical analysis; online and offline mixed teaching; quality assessment; Lagrange multiplier; discriminant function.

DOI: 10.1504/IJCEELL.2024.140721

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.5, pp.477 - 488

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

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