Applications of Machine Learning Techniques for Software Engineering Learning and Early Prediction of Students’ Performance

  • Mohamed AlloghaniEmail author
  • Dhiya Al-Jumeily
  • Thar Baker
  • Abir Hussain
  • Jamila Mustafina
  • Ahmed J. Aljaaf
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)


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.


Data mining Random Forest Performance prediction Software engineering Machine learning 



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.


  1. 1.
    Reddy, L., et al.: A modern approach student performance prediction using multi-agent data mining technique. i-Manager’s J. Softw. Eng. 10(1), 14–20 (2015)CrossRefGoogle Scholar
  2. 2.
    Asif, R., Merceron, A., Pathan, M.: Predicting student academic performance at degree level: a case study. Int. J. Intell. Syst. Appl. 7(1), 49–61 (2014)Google Scholar
  3. 3.
    Mueen, A., Zafar, B., Manzoor, U.: Modeling and predicting students’ academic performance using data mining techniques. Int. J. Mod. Educ. Comput. Sci. 8(11), 36–42 (2016)CrossRefGoogle Scholar
  4. 4.
    Devasia, T., Vinushree, T., Hegde, V.: Prediction of students’ performance using educational data mining. In: International Conference on Data Mining and Advanced Computing (SAPIENCE) (2016)Google Scholar
  5. 5.
    Petkovic, D., et al.: Using the random forest classifier to assess and predict student learning of software engineering teamwork. In: IEEE Frontiers in Education Conference (FIE) (2016)Google Scholar
  6. 6.
    Petkovic, D.: Work in progress: a machine learning approach for assessment and prediction of teamwork effectiveness in software engineering education. In: Frontiers in Education Conference Proceedings (2012)Google Scholar
  7. 7.
    Petkovic, D., et al.: Software engineering teamwork assessment and prediction using machine learning. In: Frontiers in Education Conference (IEEE), pp. 1–8 (2014)Google Scholar
  8. 8.
    Louppe, G.: Understanding random forests: from theory to practice. arXiv preprint (2014)Google Scholar
  9. 9.
    Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class AdaBoost. Ann Arbor 1001, 1612 (2006)zbMATHGoogle Scholar
  10. 10.
    Witten, I.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mohamed Alloghani
    • 1
    • 2
    Email author
  • Dhiya Al-Jumeily
    • 1
  • Thar Baker
    • 1
  • Abir Hussain
    • 1
  • Jamila Mustafina
    • 3
  • Ahmed J. Aljaaf
    • 1
    • 4
  1. 1.Liverpool John Moores UniversityLiverpoolUK
  2. 2.Abu Dhabi Health Services Company (SEHA)Abu DhabiUAE
  3. 3.Kazan Federal UniversityKazanRussia
  4. 4.Centre of ComputerUniversity of AnbarRamadiIraq

Personalised recommendations