Title: A review of hybrid collaborative filtering algorithms for ELT resources under cognitive diagnosis price

Authors: Xianghong Tang

Addresses: School of Intelligent Manufacturing and Architectural Engineering, Yongzhou Vocational Technical College, Yongzhou, 425000, China

Abstract: The study takes English exercises in English teaching resources as the starting point, and combines cognitive diagnosis theory to assess students' knowledge and ability levels. On the basis of integrating the traditional collaborative filtering algorithm, the sorting learning method is introduced, and the combination of the two becomes a hybrid collaborative filtering algorithm. The results show that the accuracy of the proposed hybrid collaborative filtering algorithm under cognitive diagnosis is as high as 98%, with stable performance in accuracy, F1 value and recall rate. The results outperformed the collaborative filtering algorithm, providing learners with English teaching resources that are more in line with their cognitive ability. The English exercises recommended by the algorithm have better learning effects than those recommended by the collaborative filtering algorithm, effectively providing learners with personalised English teaching resources. In practice, the English exercises recommended by the algorithm were more effective than those recommended by the collaborative filtering algorithm.

Keywords: cognitive diagnosis; sequencing learning methods; hybrid collaborative filtering algorithms; English language teaching.

DOI: 10.1504/IJCSYSE.2024.137447

International Journal of Computational Systems Engineering, 2024 Vol.8 No.1/2, pp.56 - 65

Received: 08 Aug 2022
Accepted: 13 Feb 2023
Published online: 19 Mar 2024 *

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