Title: A personalised recommendation method for English teaching resources on MOOC platform based on data mining

Authors: Yalei Yan; Liya Chen; Wenjing Wang

Addresses: Applied Foreign Language and International Education Department, Luohe Vocational Technology College, Luohe, 462000, China ' Applied Foreign Language and International Education Department, Luohe Vocational Technology College, Luohe, 462000, China ' Applied Foreign Language and International Education Department, Luohe Vocational Technology College, Luohe, 462000, China

Abstract: In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

Keywords: data mining; MOOC platform; English teaching resources; personalised recommendation; knowledge graph; neural network.

DOI: 10.1504/IJBIDM.2024.140883

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.292 - 305

Received: 17 Jul 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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