Title: Collaborative recommendation model of MOOC online learning resources based on scoring matrix

Authors: Jun Yao

Addresses: School of Sports Health Technology, Jilin Sport University, Changchun 130022, China

Abstract: In this paper, a MOOC online learning resource collaborative recommendation model based on scoring matrix is proposed. The features of online learning resources and learners' learning level are determined, and the differences between them are processed by kernel function to extract the features of online learning resources. The data recommendation matrix is constructed through the score matrix, and the missing characteristic data values in the score matrix are expressed. The data weight of online learning resources is calculated, and the MOOC online learning resources collaborative recommendation model is designed. The experimental results show that the accuracy of MOOC online learning resources recommended by the proposed model is always higher than 90%.

Keywords: online learning resources; collaborative recommendation model; Jaccard coefficient; scoring matrix.

DOI: 10.1504/IJCEELL.2023.132416

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.445 - 455

Received: 10 May 2021
Accepted: 09 Aug 2021
Published online: 19 Jul 2023 *

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