Title: Data mining method of English autonomous learning behaviour based on decision tree

Authors: Juan Zhang; Pingyang Li; Xiaoli Sun

Addresses: Changchun College of Electronic Technology, Changchun 130000, China ' Changchun College of Electronic Technology, Changchun 130000, China ' Changchun College of Electronic Technology, Changchun 130000, China

Abstract: Because the traditional data mining methods of English autonomous learning behaviour have the problems of low accuracy and long mining time, a decision tree-based data mining method of English autonomous learning behaviour is proposed. Firstly, the students' learning behaviour data is collected, and then the collected behaviour data is classified by the decision tree method, and the data is divided into different types. Finally, according to the data classification results, the cart decision tree method is used to obtain the optimal split point of the decision tree through tree building and pruning operations, and the optimal binary tree is generated to realise the data mining of English autonomous learning behaviour. The experimental results show that the highest comprehensive coefficient of data mining of the research method in this paper is increased by 0.08 and 0.04 respectively, and the accuracy and efficiency of data mining are improved.

Keywords: decision tree; learning behaviour; data mining; data acquisition platform; information gain; collaborative filtering; cart decision tree; prune.

DOI: 10.1504/IJCEELL.2024.140719

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.5, pp.516 - 526

Received: 30 Aug 2022
Accepted: 04 Nov 2022
Published online: 02 Sep 2024 *

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