The Prediction of English Online Network Performance Based on the XGBoost Algorithm

Abstract

The demand of C language informatization for English learning has increased greatly, and strengthening the application of informatization in teaching has become a current trend, while deep learning algorithms have been applied in various tasks due to their obvious advantages. In this paper, an English score prediction method based on the XGBoost algorithm is proposed. In order to verify the effectiveness of the model in English score prediction, the principle of the XGBoost algorithm is firstly analyzed as a basis. The English test scores of a university for 2019–2021 were used as the basic data source, and the output probabilities of the proposed model were used to compare the results under different conditions. The experimental results show that the predicted scores are basically consistent with the actual scores. From a scientific point of view, the ability to predict unknown data with low error suggests that it enables students and teachers to identify the underlying factors that make it difficult for students to answer questions. Understanding these causes is useful for designing high-quality courses and lesson plans.