Applications of machine learning techniques for software engineering learning and early prediction of students’ performance

Mohamed Alloghani; Dhiya Al-Jumeily; Thar Baker; Abir Hussain; Jamila Mustafina; Ahmed J. Aljaaf

doi:10.1007/978-981-13-3441-2_19

Applications of machine learning techniques for software engineering learning and early prediction of students’ performance

Mohamed Alloghani, Dhiya Al-Jumeily, Thar Baker, Abir Hussain, Jamila Mustafina, Ahmed J. Aljaaf

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising student performance in learning software is obscure. Furthermore, some of the studies that have explored the use of machine learning in predicting student performance in software learning have only used Random Forest, and as such, this study used the same dataset to implement 7 other algorithms and establish the most efficient. The study used two different sets of data and established that Neural Network was the most efficient with regards to the first dataset although Random Forest was the most efficient with regards to the second dataset. Both the NN graphics and RF tree diagram are presented, and the predictions from the two models also compared.

Original language	English
Title of host publication	Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings
Editors	Bee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry
Publisher	Springer-Verlag
Pages	246-258
Number of pages	13
ISBN (Print)	9789811334405
DOIs	https://doi.org/10.1007/978-981-13-3441-2_19
Publication status	Published - 11 Dec 2018
Event	4th International Conference on Soft Computing in Data Science, SCDS 2018 - Bangkok, Thailand Duration: 15 Aug 2018 → 16 Aug 2018

Publication series

Name	Communications in Computer and Information Science
Volume	937
ISSN (Print)	1865-0929

Conference

Conference	4th International Conference on Soft Computing in Data Science, SCDS 2018
Country/Territory	Thailand
City	Bangkok
Period	15/08/18 → 16/08/18

Bibliographical note

Funding Information:
We are grateful to the entire SETAP project team and we appreciate Professor D. Petkovic of San Francisco State University, Prof. Rainer Todtenhoefer of Fulda University, and Professor Shihong Huang of Florida Atlantic University for their role in the project and for sharing the data with UCI Machine Learning Repository.

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.

Keywords

Data mining
Machine learning
Performance prediction
Random Forest
Software engineering

Access to Document

10.1007/978-981-13-3441-2_19

Cite this

Alloghani, M., Al-Jumeily, D., Baker, T., Hussain, A., Mustafina, J., & Aljaaf, A. J. (2018). Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. In B. W. Yap, A. H. Mohamed, & M. W. Berry (Eds.), Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings (pp. 246-258). (Communications in Computer and Information Science; Vol. 937). Springer-Verlag. https://doi.org/10.1007/978-981-13-3441-2_19

Alloghani, Mohamed ; Al-Jumeily, Dhiya ; Baker, Thar et al. / Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings. editor / Bee Wah Yap ; Azlinah Hj Mohamed ; Michael W. Berry. Springer-Verlag, 2018. pp. 246-258 (Communications in Computer and Information Science).

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abstract = "Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising student performance in learning software is obscure. Furthermore, some of the studies that have explored the use of machine learning in predicting student performance in software learning have only used Random Forest, and as such, this study used the same dataset to implement 7 other algorithms and establish the most efficient. The study used two different sets of data and established that Neural Network was the most efficient with regards to the first dataset although Random Forest was the most efficient with regards to the second dataset. Both the NN graphics and RF tree diagram are presented, and the predictions from the two models also compared.",

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Alloghani, M, Al-Jumeily, D, Baker, T, Hussain, A, Mustafina, J & Aljaaf, AJ 2018, Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. in BW Yap, AH Mohamed & MW Berry (eds), Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings. Communications in Computer and Information Science, vol. 937, Springer-Verlag, pp. 246-258, 4th International Conference on Soft Computing in Data Science, SCDS 2018, Bangkok, Thailand, 15/08/18. https://doi.org/10.1007/978-981-13-3441-2_19

Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. / Alloghani, Mohamed; Al-Jumeily, Dhiya; Baker, Thar et al.
Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings. ed. / Bee Wah Yap; Azlinah Hj Mohamed; Michael W. Berry. Springer-Verlag, 2018. p. 246-258 (Communications in Computer and Information Science; Vol. 937).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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AU - Alloghani, Mohamed

AU - Al-Jumeily, Dhiya

AU - Baker, Thar

AU - Hussain, Abir

AU - Mustafina, Jamila

AU - Aljaaf, Ahmed J.

N1 - Funding Information: We are grateful to the entire SETAP project team and we appreciate Professor D. Petkovic of San Francisco State University, Prof. Rainer Todtenhoefer of Fulda University, and Professor Shihong Huang of Florida Atlantic University for their role in the project and for sharing the data with UCI Machine Learning Repository. Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2018/12/11

Y1 - 2018/12/11

N2 - Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising student performance in learning software is obscure. Furthermore, some of the studies that have explored the use of machine learning in predicting student performance in software learning have only used Random Forest, and as such, this study used the same dataset to implement 7 other algorithms and establish the most efficient. The study used two different sets of data and established that Neural Network was the most efficient with regards to the first dataset although Random Forest was the most efficient with regards to the second dataset. Both the NN graphics and RF tree diagram are presented, and the predictions from the two models also compared.

AB - Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising student performance in learning software is obscure. Furthermore, some of the studies that have explored the use of machine learning in predicting student performance in software learning have only used Random Forest, and as such, this study used the same dataset to implement 7 other algorithms and establish the most efficient. The study used two different sets of data and established that Neural Network was the most efficient with regards to the first dataset although Random Forest was the most efficient with regards to the second dataset. Both the NN graphics and RF tree diagram are presented, and the predictions from the two models also compared.

KW - Data mining

KW - Machine learning

KW - Performance prediction

KW - Random Forest

KW - Software engineering

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M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85059061666

SN - 9789811334405

T3 - Communications in Computer and Information Science

SP - 246

EP - 258

BT - Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings

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A2 - Mohamed, Azlinah Hj

A2 - Berry, Michael W.

PB - Springer-Verlag

T2 - 4th International Conference on Soft Computing in Data Science, SCDS 2018

Y2 - 15 August 2018 through 16 August 2018

ER -

Alloghani M, Al-Jumeily D, Baker T, Hussain A, Mustafina J, Aljaaf AJ. Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. In Yap BW, Mohamed AH, Berry MW, editors, Soft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings. Springer-Verlag. 2018. p. 246-258. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-3441-2_19

Applications of machine learning techniques for software engineering learning and early prediction of students’ performance

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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