Title: Study on personalised adaptive learning teaching course recommendation method based on GP-DINA

Authors: Jie Luo

Addresses: Changsha Environmental Protection Vocational College, Changsha, 410004, Hunan, China

Abstract: In order to improve the accuracy and recall of learning and teaching course recommendations, this paper proposes a personalised adaptive learning and teaching course recommendation method based on GP-DINA. Firstly, the learning styles of learners are categorised based on their learning behaviour information and personalise the clustering of learning styles. Then, a GP-DINA model is constructed to analyse the similarity parameters of learners; and based on this model the similarity of learning behaviour and preference parameters of course content are estimated. Next, the weights of user behaviour preferences and course prerequisite preferences are determined. Finally, the total recommended weight is obtained through the information entropy weight allocation method, and a personalised adaptive learning teaching course recommendation list is generated. The results show that the recommendation accuracy of this method is 98.1%; the recall rate can reach 99.6%, and the cumulative revenue can reach 68%, indicating that the ranking recommendation index is highly effective.

Keywords: GP-DINA model; XMMC information extraction model; preference weight; information entropy; weight allocation.

DOI: 10.1504/IJITM.2025.151552

International Journal of Information Technology and Management, 2025 Vol.24 No.3/4, pp.191 - 203

Received: 05 Sep 2024
Accepted: 07 Feb 2025
Published online: 05 Feb 2026 *

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