A dynamic evaluation of MOOC online English teaching based on decision tree algorithm

被引：1

作者：

Qian, Lu ^{[1
]}

机构：

[1] Jiangxi Police Coll, Dept Publ Secur Management, Nanchang 330199, Jiangxi, Peoples R China

来源：

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING | 2024年 / 34卷 / 2-3期

关键词：

Chi-square statistical method; relevance; decision tree algorithm; MOOC online English teaching; dynamic evaluation; suppress noise;

D O I：

10.1504/IJCEELL.2024.137113

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Aiming at the problems of low evaluation accuracy, complex evaluation process and time-consuming evaluation, this paper designed a dynamic evaluation method of MOOC online English teaching based on decision tree algorithm. Firstly, the data type is determined, and the correlation and correlation degree of the data are determined by Chi-square statistics method and mutual information method. Then, the redundant data and noise are removed by calculating the data centroid distance in the data set. Finally, the attribute value of evaluation decision tree is determined by decision tree, the evaluation model is constructed, and the evaluation error is corrected by decision tree pruning method to achieve dynamic evaluation of MOOC online English teaching. Experimental results show that the proposed method has the highest accuracy of 97% and takes 1.6 s, which effectively improves the evaluation efficiency.

引用

页码：167 / 178

页数：13