Comparing the performance of artificial intelligence methods in predicting students' academic progress

Articles in Press

Document Type : Original Article

Authors

Department of Industrial and Computer Engineering, Birjand University of Technology, Birjand, Iran.

Abstract

Todays, artificial intelligence methods can extract the knowledge hidden in the educational data sets by discovering the relationship between different features. This knowledge can help educational systems in making better decisions and having more advanced plans to improve the academic performance of students. The aim of this study was to identify the factors affecting the academic progress of students and to use a technique that can predict the academic progress of students with the highest percentage of accuracy. Accordingly, artificial intelligence methods including Support vector machine, K-nearest neighbor, Decision trees and Random forest have been applied. Finally, the models was evaluated using Accuracy, Precision, F-measure, Sensitivity, Specificity and Classification error. The results showed that the support vector machine had the best performance in predicting the academic progress of students.

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Main Subjects

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 27 November 2023

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History

  • Receive Date: 26 June 2023
  • Revise Date: 24 October 2023
  • Accept Date: 24 November 2023

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