High School English Performance Analysis Using Interpretable Machine Learning Approach

DOI

10.2991/978-94-6463-044-2_33How to use a DOI?

Keywords

Machine learning; Interpretable; XGBoost algorithm; English teaching

Abstract

Currently, English learning is being taken to a new level for Chinese students. The transformation of English language teaching and learning is in full swing across the education sector in China. However, few studies have discussed the relationship between high school students’ English language learning performance and other courses. To address these issues and gain a practical understanding of the relationship between students’ English learning performance and other courses, this paper first collects learning data from 532 students in 10 courses at a high school in China. Second, this paper uses an integrated learning algorithm called XGBoost to predict the students’ English learning performance. Specifically, the dataset is divided into a training set and a test set, and we train the model on the training set and test it on the test set. The test results show that the method in this paper can predict students’ English performance well (MAE < 0.03, MSE < 0.07, RMSE < 0.04). Moreover, Chinese and mathematics scores were highly correlated with students’ English scores. Based on the above findings, this paper further proposes relevant teaching suggestions. The results of this paper provide a practical reference for teaching on each campus.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Shufang Qu
AU  - Hun Lee Koay
PY  - 2022
DA  - 2022/12/27
TI  - High School English Performance Analysis Using Interpretable Machine Learning Approach
BT  - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022)
PB  - Atlantis Press
SP  - 246
EP  - 254
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-044-2_33
DO  - 10.2991/978-94-6463-044-2_33
ID  - Qu2022
ER  -