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

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-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 languageEnglish
Title of host publicationSoft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings
EditorsBee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry
PublisherSpringer-Verlag
Pages246-258
Number of pages13
ISBN (Print)9789811334405
DOIs
Publication statusPublished - 11 Dec 2018
Event4th International Conference on Soft Computing in Data Science, SCDS 2018 - Bangkok, Thailand
Duration: 15 Aug 201816 Aug 2018

Publication series

NameCommunications in Computer and Information Science
Volume937
ISSN (Print)1865-0929

Conference

Conference4th International Conference on Soft Computing in Data Science, SCDS 2018
Country/TerritoryThailand
CityBangkok
Period15/08/1816/08/18

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

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