A Study of Prediction Accuracy of English Test Performance Using Data Mining and Analysis

Abstract

This paper focused on the effect of data mining in predicting students' English test scores. With the progress of data mining analysis, there are more applications in teaching, and data mining to achieve the prediction of students’ test scores is important to support the educational work. In this paper, the C4.5 decision tree algorithm was improved by combining Taylor's series, and then the data of students' English tests in 2019-2020 were collected for experiments. The results showed that the scores of “Comprehensive English” and “Specialized English” had a great influence on the score of CET-4, and the improved C4.5 algorithm was more efficient than the original one, maintained a fast computation speed even when the data volume was large, and had an accuracy of more than 85%. The results demonstrate the accuracy of the improved C4.5 algorithm for predicting students’ English test scores. The improved C4.5 algorithm can be extended and used in reality.