JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Comparison of machine learning classification models for predicting student academic performance
Abstract
In this study, various models were developed using machine learning algorithms to predict the academic performance of students in the Programming Course at Kırıkkale University. The data, collected through a survey from 170 associate degree students, consists of 9 input attributes, including demographic information, high school education information, academic status, and socio-economic characteristics, and one output attribute. The data were analysed using the WEKA software. According to the results, the J48 algorithm was identified as the most successful model with an accuracy rate of 96.11%. The Random Forest and k-NN algorithms also closely followed J48 with accuracy rates of 95.83% and showed successful results. The Naive Bayes algorithm exhibited the lowest performance with an accuracy rate of 78.06%. In terms of error rates, the Random Forest algorithm showed the best performance with the lowest MAE, RMSE, RAE, and RRSE values. This study demonstrates the applicability of machine learning techniques in education and proves that they can be used as a tool for early detection of at-risk students. In future studies, it is planned to improve the performance of the models and test their generalizability by using larger datasets and different machine learning algorithms.

Keywords : Machine learning, educational data mining, academic performance prediction, WEKA

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Article available in :
Volume 3, Issue 2, 2025
Page : 41-46
https://doi.org/10.51271/JCEEES-0027
Article Information
Received : 08 Tem 2025
Accepted : 27 Ağu 2025
Published : 03 Eki 2025
Corresponding Author :

Mahmut Ünver: Department of Computer Technologies, Kırıkkale Vocational School, Kırıkkale University, Kırıkkale, Turkiye

[email protected]
Citation : Ünver, M. (2025). Comparison of machine learning classification models for predicting student academic performance. J Comp Electr Electron Eng Sci, 3(2), 41-46.

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