Title: A recommendation method for online learning resources of mathematics courses based on feature graph clustering

Authors: Zhixia Duan; Na Zhao

Addresses: Jiyuan Vocational and Technical College, JiYuan, 459000, China ' Jiyuan Vocational and Technical College, JiYuan, 459000, China

Abstract: Due to the low comprehensiveness of the traditional method to the analysis of learners' needs, the degree of fitting between the recommended learning resources and the actual needs of learners is low. To solve this problem, a recommendation method for online learning resources of mathematics courses based on feature graph clustering is proposed. Construct learner feature map from cognitive level and learning preference, and analyse their attribute characteristics, behaviour characteristics and learning characteristics. Then on the basis of clustering processing, resources with the same clustering characteristics are matched as the recommendation target. The test results show that the satisfaction of the recommendation results of this method is always stable at more than 90.0%, the maximum and minimum F1-score values are 0.52 and 0.46, respectively, and it has high stability, which is obviously better than the traditional method.

Keywords: online learning resources; cognitive level; learning preferences; characteristic atlas; feature similarity; clustering processing; fitting degree.

DOI: 10.1504/IJRIS.2023.136351

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.3/4, pp.189 - 195

Received: 12 Jul 2022
Accepted: 17 Aug 2022
Published online: 31 Jan 2024 *

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