Title: An English learning behaviour data mining based on improved ensemble learning algorithm

Authors: Lin Fan; Pengqi Cao; Yunxia Du

Addresses: School of International Education (International School of Engineering), Henan Polytechnic, Zhengzhou, 450000, China ' School of International Education (International School of Engineering), Henan Polytechnic, Zhengzhou, 450000, China ' Ministry of Public Infrastructure, Hebi City Electromechanical Information Engineering School, Hebi, 458030, China

Abstract: In order to enhance the learning effectiveness of English learners, this paper proposes an English learning behaviour data mining method based on improved ensemble learning algorithm. A web crawler is used to collect behavioural information of learners during the process of learning English, and learner profiles are constructed. The data is pre-processed, and collaborative filtering algorithms are employed to extract features of English learning behaviours. By treating English learning behaviour features as input vectors and data mining results as output vectors, an improved stacking ensemble learning model based on chain rules is constructed. This model is utilised to obtain data mining results for English learning behaviour. The experimental results show that the normalised difference accuracy of the proposed method is always above 90%, and the mAP value is always above 93%, indicating that the proposed method has high accuracy and good mining effect in English learning behaviour data mining.

Keywords: ensemble learning; English learning; learning behaviour; data mining; chain rules; stacking ensemble learning model.

DOI: 10.1504/IJBIDM.2025.143924

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.1/2, pp.32 - 45

Received: 22 Nov 2023
Accepted: 02 May 2024
Published online: 14 Jan 2025 *

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