Title: An intelligent statistical method of real-time status of English teaching assistance resources from the perspective of MOOCs

Authors: Lizhen Shi

Addresses: Zhoukou Vocational and Technical College, Zhoukou, 466000, China

Abstract: This study takes improving the clustering statistics quality of resources as the expected goal and designs an intelligent real-time statistics method of English teaching auxiliary resources from the perspective of MOOCs. Firstly, the data of English teaching auxiliary resources are screened, and the relationship between the English teaching assistance resource data is determined according to the association rules, and the cosine similarity between the data is judged by the sparse matrix, so as to effectively reduce the calculation error of data status similarity through data preprocessing. Then, the English teaching assistance resource status matrix is constructed to make statistics on the teaching assistance resource data status. According to the text, the calculation error of the proposed method is less than 4%, and the ARI index is always higher than 0.9. The above results also fully prove the validity of the method.

Keywords: MOOC teaching; support vector machine; SVM; sparse matrix; cluster processing; smarter statistics.

DOI: 10.1504/IJCEELL.2024.135267

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.1, pp.100 - 110

Received: 05 Aug 2021
Accepted: 11 Feb 2022
Published online: 03 Dec 2023 *

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