Title: Recommended methods for teaching resources in public English MOOC based on data chunking

Authors: Zhenhua Wei

Addresses: Yongcheng Vocational College, Yongcheng 476600, China

Abstract: In order to overcome the problems of time-consuming and high recommendation error in traditional public English MOOC teaching resource recommendation methods, this paper proposes a new public English MOOC teaching resource recommendation method based on data partition. The data of public English MOOC teaching resources are collected, and hierarchical clustering algorithm is used to preprocess public English MOOC teaching resources to support the recommendation demand of mobile MOOC teaching resources. According to the preprocessing results, the data block algorithm is used to divide the resource data iteratively. Finally, we calculate the similarity of users' resource use and preference, and construct the public English MOOC teaching resource recommendation model based on the index weight results. Comparative validation results show, in the conventional method, the proposed method recommended consuming less and less precision compared to the recommended.

Keywords: data partition; public English; MOOC teaching resources; block partition.

DOI: 10.1504/IJCEELL.2023.129213

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.2/3, pp.192 - 202

Received: 24 Feb 2021
Accepted: 13 May 2021
Published online: 01 Mar 2023 *

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