A decision tree-based method for detecting middle school students' behaviour characteristics in online English learning

Abstract

In order to solve the problem of low accuracy and long detection time caused by poor feature extraction effect of online English learning students' behavioural characteristics detection, this paper proposes a method of online English learning students' behaviour characteristics detection based on decision tree. Firstly, the concept and structure of decision tree are analysed, and the classification steps are designed. Secondly, weighted principal component analysis was used to extract the behaviour characteristics of students. Then, the characteristic data is standardised. Finally, the C4.5 decision tree algorithm is used to construct a student behaviour feature detection model to detect students' behaviour characteristics in online English learning. The experimental results show that the feature detection rate of the proposed method is as high as 99.5%, the accuracy is 96.2%, and the detection time is 19.8 s. Therefore, the feature detection effect of the proposed method is good, the accuracy is high, and the detection time is effectively shortened.