Title: Research on online evaluation method of MOOC teaching quality based on decision tree-based big data classification

Authors: Jiefeng Wang; Humin Yang

Addresses: College of Education, Fuyang Normal University, Fuyang 236037, China ' College of Education, Fuyang Normal University, Fuyang 236037, China

Abstract: In order to improve the online evaluation ability to massive open online course (MOOC) teaching quality, an online evaluation method of MOOC teaching quality based on decision tree-based big data classification is proposed. First, a big data statistical analysis model is built to identify fuzzy degree parameters. Then, the quality index system is obtained to realise big data fusion and cluster analysis. It is concluded that this method has high accuracy in online evaluation. In this paper, the method shows that the accuracy as high as 0.996 when the number of iterations reaches 500. Its innovation lies in the analysis of the global optimal solution of online evaluation of distance MOOC teaching quality by using the big data decision tree model, which improves the information management of MOOC online evaluation.

Keywords: big data statistical analysis; decision tree-based classification; MOOC teaching quality; online evaluation method; information fusion and clustering.

DOI: 10.1504/IJCEELL.2023.127848

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.1, pp.10 - 22

Received: 14 Oct 2020
Accepted: 19 Dec 2020
Published online: 20 Dec 2022 *