Predicting Student Performance Using Educational Data Mining

Nehal Eleyan; Mariam Al Akasheh; Esraa Faisal Malik; Omar Hujran

doi:10.1109/SNAMS58071.2022.10062500

Predicting Student Performance Using Educational Data Mining

Nehal Eleyan, Mariam Al Akasheh, Esraa Faisal Malik, Omar Hujran

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Data mining methods have been employed successfully in several industries, including education, where they are known as educational data mining methods. Educational data mining aims to extract in-depth knowledge from raw data to build automated systems that could be used in the educational sector. With the advancement of data mining technologies, it is now possible to mine educational data to enhance educational practices. This study, therefore, uses educational data mining techniques to predict the final grades of secondary school students. This study has employed several Machine Learning (ML) algorithms, such as classification trees, regression trees, logistic Regression, and Multiple Regression. In addition, the R programming language was used to develop the prediction models. The dataset used in this study was obtained from two secondary schools in Portugal. According to the findings, classification trees and logistic Regression fared better than regression trees and multiple Regression.

Original language	English
Title of host publication	2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022
Editors	Paolo Ceravolo, Christian Guetl, Yaser Jararweh, Elhadj Benkhelifa
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320480
DOIs	https://doi.org/10.1109/SNAMS58071.2022.10062500
Publication status	Published - 2022
Externally published	Yes
Event	9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022 - Milan, Italy Duration: Nov 28 2022 → Dec 1 2022

Publication series

Name	2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

Conference

Conference	9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022
Country/Territory	Italy
City	Milan
Period	11/28/22 → 12/1/22

Keywords

Educational data mining
classification tree
logistic Regression
multiple Regression
prediction
regression tree
student performance

ASJC Scopus subject areas

Media Technology
Computer Networks and Communications
Information Systems and Management
Safety, Risk, Reliability and Quality
Education
Communication

Access to Document

10.1109/SNAMS58071.2022.10062500

Cite this

Eleyan, N., Al Akasheh, M., Malik, E. F., & Hujran, O. (2022). Predicting Student Performance Using Educational Data Mining. In P. Ceravolo, C. Guetl, Y. Jararweh, & E. Benkhelifa (Eds.), 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022 (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SNAMS58071.2022.10062500

Predicting Student Performance Using Educational Data Mining. / Eleyan, Nehal; Al Akasheh, Mariam; Malik, Esraa Faisal et al.
2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. ed. / Paolo Ceravolo; Christian Guetl; Yaser Jararweh; Elhadj Benkhelifa. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Eleyan, N, Al Akasheh, M, Malik, EF & Hujran, O 2022, Predicting Student Performance Using Educational Data Mining. in P Ceravolo, C Guetl, Y Jararweh & E Benkhelifa (eds), 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022, Institute of Electrical and Electronics Engineers Inc., 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022, Milan, Italy, 11/28/22. https://doi.org/10.1109/SNAMS58071.2022.10062500

Eleyan N, Al Akasheh M, Malik EF, Hujran O. Predicting Student Performance Using Educational Data Mining. In Ceravolo P, Guetl C, Jararweh Y, Benkhelifa E, editors, 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022). doi: 10.1109/SNAMS58071.2022.10062500

Eleyan, Nehal ; Al Akasheh, Mariam ; Malik, Esraa Faisal et al. / Predicting Student Performance Using Educational Data Mining. 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. editor / Paolo Ceravolo ; Christian Guetl ; Yaser Jararweh ; Elhadj Benkhelifa. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022).

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keywords = "Educational data mining, classification tree, logistic Regression, multiple Regression, prediction, regression tree, student performance",

author = "Nehal Eleyan and {Al Akasheh}, Mariam and Malik, {Esraa Faisal} and Omar Hujran",

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AB - Data mining methods have been employed successfully in several industries, including education, where they are known as educational data mining methods. Educational data mining aims to extract in-depth knowledge from raw data to build automated systems that could be used in the educational sector. With the advancement of data mining technologies, it is now possible to mine educational data to enhance educational practices. This study, therefore, uses educational data mining techniques to predict the final grades of secondary school students. This study has employed several Machine Learning (ML) algorithms, such as classification trees, regression trees, logistic Regression, and Multiple Regression. In addition, the R programming language was used to develop the prediction models. The dataset used in this study was obtained from two secondary schools in Portugal. According to the findings, classification trees and logistic Regression fared better than regression trees and multiple Regression.

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Predicting Student Performance Using Educational Data Mining

Abstract

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Keywords

ASJC Scopus subject areas

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