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.
Publisher
International Association for Educators and Researchers (IAER)
Subject
Electrical and Electronic Engineering,General Computer Science
Reference23 articles.
1. Mustafa Abdalrassual Jassim, “Analysis of the Performance of the Main Algorithms for Educational Data Mining: A Review”, IOP Conference Series: Materials Science and Engineering, Print ISSN: 1757-8981, Online ISSN: 1757-899X, pp. 1-10, Vol. 1090, No. 1, March 2021, Published by IOP Publishing, DOI: 10.1088/1757-899X/1090/1/012084, Available: https://iopscience.iop.org/article/10.1088/1757-899X/1090/1/012084.
2. M. Besher Massri, Joao Pita Costa, Marko Grobelnik, Janez Brank, Luka Stopar et al., “A Global COVID-19 Observatory, Monitoring the Pandemics Through Text Mining and Visualization”, Informatica: An International Journal of Computing and Informatics, Print ISSN: 0350-5596, Online ISSN: 1854-3871, pp. 49-55, Vol. 46, No. 1, March 2022, Published by the Slovenian Society Informatika, DOI: 10.31449/inf.v46i1.3375, Available: https://www.informatica.si/index.php/informatica/article/view/3375/1741.
3. David Perez-Guaita, Guillermo Quintas, Zeineb Farhane, Roma Tauler and Hugh J. Byrne, “Corrigendum to "Data mining Raman microspectroscopic responses of cells to drugs in vitro using multivariate curve resolution-alternating least squares" [Talanta 208 (2020) 120386]“, Talanta: The International Journal of Pure and Applied Analytical Chemistry, ISSN: 0039-9140, pp. 1, Vol. 236, September 2022, DOI: 10.1016/j.talanta.2021.122682, Available: https://www.sciencedirect.com/science/article/pii/S0039914021006032?via%3Dihub.
4. A Andreasyan and A Balyakin, “Transformation of education through Big Data: digital twins case study“, Journal of Physics: Conference Series, Print ISSN: 1742-6588, Online ISSN: 1742-6596, pp. 1-6, Vol. 2210, No. 1, March 2022, Published by IOP Publishing Ltd, DOI: 10.1088/1742-6596/2210/1/012003, Available: https://iopscience.iop.org/article/10.1088/1742-6596/2210/1/012003/pdf.
5. Raya Mohammed Mahmood and Sefer Kurnaz, “Employing Data Mining to Predict Professional Identity”, Journal of Information Science and Engineering, ISSN: 1016-2364, pp. 193-203, Vol. 36, No. 2, 2020, Published by Institute of Information Science, Academia Sinica, Taiwan, DOI: 10.6688/JISE.202003_36(2).0001, Available: https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10162364-202003-202003050003-202003050003-193-203.