Title: Dynamic classification of English teaching resources based on frequent itemset mining

Authors: Xiuling Shi; Meiping Peng

Addresses: College of English, Zhejiang Yuexiu University, Shaoxing, Zhejiang, China ' School of Information Engineering, Shaoguan University, Shaoguan, Guangdong, China

Abstract: In order to solve the problems of poor resource classification accuracy and poor teaching resource classification performance in English teaching resource classification methods, this paper proposes a dynamic classification method for English teaching resources based on frequent itemset mining. Obtain English teaching resource data through frequent itemset mining methods, and perform word removal processing on the mined data. Design a TextRank keyword extraction model for English teaching resources based on network graph extraction method, and calculate the importance of keywords. By improving the TextRank keyword extraction method through frequent itemset mining, dynamic classification results of English teaching resources are obtained. The experimental results show that the English teaching resource classification method proposed in this paper takes only 15 s, with a resource classification accuracy of 99.6%, and an AUC value consistently changing around 1, indicating better performance in English teaching resource classification.

Keywords: frequent itemsets mining; teaching resources; resource classification; TextRank keyword extraction model; network diagram.

DOI: 10.1504/IJCAT.2023.138834

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.345 - 352

Received: 19 Oct 2023
Accepted: 11 Jan 2024
Published online: 31 May 2024 *

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