Prediction of Students’ Performance in English Using Machine Learning Algorithms

Author:

Jun-on Nipa,Intaros Pimpaka,Suwannaut Sarawut

Abstract

In this work, a new machine learning-based model is proposed to predict undergraduate students' reading scores using their listening scores as the primary data. The performance of several machine learning techniques, including neural networks, gaussian process regression, and random forests, was calculated and compared in order to predict the reading test results of the students. The dataset included the listening and reading test results of 1145 students who took the English proficiency exam at Lampang Rajabhat University's language center in Lampang, Thailand. According to the results, the suggested model has a classification accuracy range of 64–75%. Only three different types of parameters—listening scores, departmental data, and faculty data—were used to make the predictions.

Publisher

Canadian Center of Science and Education

Subject

Linguistics and Language,Language and Linguistics,Education

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Grades Prediction Through Use of The BP Nerual Network Model;2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE);2024-03-01

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