Title: Research on adaptive recommendation of network learning resources for personalised mobile learning platform

Authors: Xiujing Ju

Addresses: School of Marxism, Changchun College of Electronic Technology, Changchun, 130000, China

Abstract: In order to overcome the problems of long recommendation time and low recommendation accuracy in traditional learning resource recommendation methods, this paper proposes a new adaptive recommendation method for online learning resources of personalised mobile learning platform. Firstly, analyse the structure of personalised mobile learning platform and the process of mobile learning. Secondly, calculate the value of learners' preference attribute for online learning resources, and conduct classification mining of personalised data. Finally, according to the prediction score, the similarity between learners' cognitive ability and the difficulty of learning resources, and the similarity between learners' scenarios and the types of online learning resources, the adaptive recommendation degree is calculated, and Top-N is selected from the recommendation results to recommend to learners. The experimental results show that the proposed method can shorten the adaptive recommendation accuracy and reduce the recommendation time, and the recommendation accuracy is always about 96%.

Keywords: personalised mobile learning platform; E-learning resources; adaptive recommendation; personalised data; learners' cognitive ability; long recommendation time.

DOI: 10.1504/IJISD.2025.149098

International Journal of Innovation and Sustainable Development, 2025 Vol.19 No.5/6, pp.560 - 571

Received: 04 Jan 2023
Accepted: 21 Apr 2023
Published online: 14 Oct 2025 *

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