Department of Industrial and Computer Engineering, Birjand University of Technology, Birjand, Iran.
10.22052/scj.2024.253154.1161
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.
Niksirat, M., Tayyebi, J., & Izadkhah, M. M. (2023). Comparing the performance of artificial intelligence methods in predicting students' academic progress. Soft Computing Journal, (), -. doi: 10.22052/scj.2024.253154.1161
MLA
Malihe Niksirat; Javad Tayyebi; Mohammad Mahdi Izadkhah. "Comparing the performance of artificial intelligence methods in predicting students' academic progress". Soft Computing Journal, , , 2023, -. doi: 10.22052/scj.2024.253154.1161
HARVARD
Niksirat, M., Tayyebi, J., Izadkhah, M. M. (2023). 'Comparing the performance of artificial intelligence methods in predicting students' academic progress', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2024.253154.1161
VANCOUVER
Niksirat, M., Tayyebi, J., Izadkhah, M. M. Comparing the performance of artificial intelligence methods in predicting students' academic progress. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2024.253154.1161
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