Title: A classification method of College Mathematics MOOC teaching resources based on machine learning

Authors: Xuemei Shen

Addresses: Academic Affairs Office, Xinyang Vocational and Technical College, Xinyang, 464000, China

Abstract: In order to improve the classification effect and efficiency of College Mathematics MOOC teaching resources, a classification method of College Mathematics MOOC teaching resources based on machine learning is proposed. Firstly, the mean method is used to clean the sample data of College Mathematics MOOC teaching resources, and the maximum and minimum standardisation method is used to sample the data. Then the adaptive sliding window mutual information method is used to extract the characteristics of College Mathematics MOOC teaching resources. Finally, based on the two-step clustering algorithm, the MOOC teaching resources of College Mathematics are classified. The experimental results show that this method has a good classification effect on College Mathematics MOOC teaching resources. The classification recall rate remains above 93%, the classification accuracy remains above 92%, and the classification time is only 18.6 s. It can effectively improve the classification efficiency of College Mathematics MOOC teaching resources, and has good practical application performance.

Keywords: College Mathematics; machine learning; MOOC teaching resources; two-step clustering algorithm; teaching resource classification.

DOI: 10.1504/IJCEELL.2024.139944

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.4, pp.314 - 326

Received: 25 Apr 2022
Accepted: 30 Aug 2022
Published online: 12 Jul 2024 *

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