CLASSIFICATION OF STUDENTS' ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION

Derya Çınar; Sevcan Yılmaz Gündüz

doi:10.18038/estubtda.1394885

EN TR

CLASSIFICATION OF STUDENTS' ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION

Abstract

Students' success in high school plays an important role in shaping their lives, as it also affects their success in university placement. It is very important to be able to predict this situation so that in case of failure, precautions can be taken, and a solution can be produced. If success situations and failure can be predicted, success can be increased and stabilized with encouragement and support. In this study, students' academic performances were tried to be estimated with the datasets prepared with secondary school students in Portugal. The datasets include students' answers about the factors thought to affect their success-failure and their grades. The wide use and efficiency of machine learning algorithms have also affected studies on predicting student success. Different algorithms have been applied using different methods in the datasets and the correct prediction rate was tried to be maximized. Experiments were carried out using the 10-fold cross validation method. Deep learning, multilayer perceptrons, simple logistic regression, decision table, one rule, iterative classifier optimizer, logistic model tree and fuzzy unordered rule induction algorithm have been used to predict the student academic success. These algorithms have been tested with the classical and bagging methods. The experiments also tested the efficiency of the algorithms in predicting student success by selecting features and comparing the results.

Keywords

Machine learning, Classification, Students’ academic success

Supporting Institution

Eskişehir Technical University

Project Number

21LÖT098

CLASSIFICATION OF STUDENTS' ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION

Abstract

Students' success in high school plays an important role in shaping their lives, as it also affects their success in university placement. It is very important to be able to predict this situation so that in case of failure, precautions can be taken, and a solution can be produced. If success situations and failure can be predicted, success can be increased and stabilized with encouragement and support. In this study, students' academic performances were tried to be estimated with the datasets prepared with secondary school students in Portugal. The datasets include students' answers about the factors thought to affect their success-failure and their grades. The wide use and efficiency of machine learning algorithms have also affected studies on predicting student success. Different algorithms have been applied using different methods in the datasets and the correct prediction rate was tried to be maximized. Experiments were carried out using the 10-fold cross validation method. Deep learning, multilayer perceptrons, simple logistic regression, decision table, one rule, iterative classifier optimizer, logistic model tree and fuzzy unordered rule induction algorithm have been used to predict the student academic success. These algorithms have been tested with the classical and bagging methods. The experiments also tested the efficiency of the algorithms in predicting student success by selecting features and comparing the results.

Keywords

Machine learning, Classification, Students’ academic success

Project Number

21LÖT098

References

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Details

Primary Language

English

Subjects

Information Systems (Other)

Journal Section

Research Article

Authors

Derya Çınar
0009-0005-4729-2172
Türkiye

Sevcan Yılmaz Gündüz ^*
0000-0002-1736-9942
Türkiye

Publication Date

June 28, 2024

Submission Date

November 23, 2023

Acceptance Date

April 18, 2024

Published in Issue

Year 2024 Volume: 25 Number: 2

DOI

https://doi.org/10.18038/estubtda.1394885

IZ

https://izlik.org/JA88CR74PB

APA

Çınar, D., & Yılmaz Gündüz, S. (2024). CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 25(2), 262-277. https://doi.org/10.18038/estubtda.1394885

AMA

1.Çınar D, Yılmaz Gündüz S. CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION. Estuscience - Se. 2024;25(2):262-277. doi:10.18038/estubtda.1394885

Chicago

Çınar, Derya, and Sevcan Yılmaz Gündüz. 2024. “CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 (2): 262-77. https://doi.org/10.18038/estubtda.1394885.

EndNote

Çınar D, Yılmaz Gündüz S (June 1, 2024) CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 2 262–277.

IEEE

[1]D. Çınar and S. Yılmaz Gündüz, “CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION”, Estuscience - Se, vol. 25, no. 2, pp. 262–277, June 2024, doi: 10.18038/estubtda.1394885.

ISNAD

Çınar, Derya - Yılmaz Gündüz, Sevcan. “CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25/2 (June 1, 2024): 262-277. https://doi.org/10.18038/estubtda.1394885.

JAMA

1.Çınar D, Yılmaz Gündüz S. CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION. Estuscience - Se. 2024;25:262–277.

MLA

Çınar, Derya, and Sevcan Yılmaz Gündüz. “CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 25, no. 2, June 2024, pp. 262-77, doi:10.18038/estubtda.1394885.

Vancouver

1.Derya Çınar, Sevcan Yılmaz Gündüz. CLASSIFICATION OF STUDENTS’ ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION. Estuscience - Se. 2024 Jun. 1;25(2):262-77. doi:10.18038/estubtda.1394885

CLASSIFICATION OF STUDENTS' ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION

Abstract

Keywords

Supporting Institution

Project Number

CLASSIFICATION OF STUDENTS' ACADEMIC SUCCESS USING ENSEMBLE LEARNING AND ATTRIBUTE SELECTION

Abstract

Keywords

Project Number

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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