Title: Classification method of English micro-course resources on MOOC platform based on improved decision tree algorithm

Authors: Juan Wang

Addresses: Wuhan Technical College of Communications, Wuhan, Hubei, China

Abstract: To solve the problem of inaccurate classification results of current educational resource classification methods, a MOOC micro-class resource classification method based on improved decision tree algorithm is proposed. Firstly, adaptive reconstruction technology and feature sequence detection technology are used to build a storage structure model for optimising unbalance data of cloud resource distribution space on MOOC platform. Then, the information gain rate of each attribute is determined by using the information entropy of each attribute and the decision tree is constructed. Finally, the improved information entropy is used to optimise the decision tree to realise the classification of English micro-lesson resources. The experimental results show that the AUC value of the classification results of the proposed method is higher than 0.85, and the precision rate, recall rate and F-scale value are higher than 0.7, which indicates a high accuracy of resource classification.

Keywords: decision tree algorithm; MOOC platform; English micro-course resources; classification method; information entropy; information gain rate.

DOI: 10.1504/IJCAT.2024.141358

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.34 - 43

Received: 27 Oct 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

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