An evaluation of English distance information teaching quality based on decision tree classification algorithm

被引：0

作者：

Liu X. ^{[1
]}

机构：

[1] Department of Basic Courses, Huaibei Vocational and Technical College, Anhui, Huaibei

来源：

International Journal of Information Technology and Management | 2024年 / 23卷 / 3-4期

关键词：

decision tree classification; index weight; information gain theory; membership matrix; rough set theory;

D O I：

10.1504/IJITM.2024.139586

中图分类号：

学科分类号：

摘要：

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds. Copyright © 2024 Inderscience Enterprises Ltd.

引用

页码：357 / 371

页数：14

共 15 条

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