A Hybrid Machine Learning Approach for the Prediction of Grades in Computer Engineering Students

  • Diego Buenaño-FernandezEmail author
  • Sergio Luján-Mora
  • David Gil
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


The growing application of information and communication technologies (ICTs) in teaching and learning processes has generated an overload of valuable information for all those involved in education field. Historical information from students’ academic records has become a valuable source of data that has been used for different purposes. Unfortunately, a high percentage of research has been developed from the perspective and the need of teachers and educational administrators. This perspective has left the student in the background. This paper proposes the application of a hybrid machine learning approach, with the aim of laying the groundwork for a future implementation of a recommendation system that allows students to make decisions related to their learning process. The work has been executed on the historical academic information of students of computer engineering degree. The results obtained in this article show the effectiveness of applying a hybrid machine learning approach. This architecture is composed of, on the one hand, techniques of supervised learning applied with the objective of classifying the data in clusters, and on the other hand, having this initial classification, unsupervised learning techniques applied with the objective of carrying out a predictive analysis of students’ historical grade records.


  1. 1.
    Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8, 1230 (2018)Google Scholar
  2. 2.
    Park, K., Ali, A., Kim, D., An, Y., Kim, M., Shin, H.: Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 26, 2194–2205 (2013)CrossRefGoogle Scholar
  3. 3.
    Gil, D., Fernández-Alemán, J., Trujillo, J., García-Mateos, G., Luján-Mora, S., Toval, A.: The effect of green software: a study of impact factors on the correctness of software. Sustainability 10, 3471 (2018)CrossRefGoogle Scholar
  4. 4.
    Sweeney, M., Lester, J., Rangwala, H.: Next-term student grade prediction. In: Proceedings—IEEE International Conference on Big Data, pp. 970–975 (2015)Google Scholar
  5. 5.
    Rechkoski, L., Ajanovski, V. V., Mihova, M.: Evaluation of grade prediction using model-based collaborative filtering methods. In: IEEE Global Engineering Education Conference, pp. 1096–1103. EDUCON (2018)Google Scholar
  6. 6.
    Hwang, C.S., Su, Y.C.: Unified clustering locality preserving matrix factorization for student performance prediction. IAENG Int. J. Comput. Science. 42, 1–9 (2015)Google Scholar
  7. 7.
    Hershkovitz, A., Nachmias, R.: Learning about online learning processes and students’ motivation through web usage mining. Interdiscip. J. E-Learn. Learn. Objects. 5, 197–214 (2009)Google Scholar
  8. 8.
    Mashiloane, L., Mchunu, M.: Mining for marks: a comparison of classification algorithms when predicting academic performance to identify “students at risk.” In: Proceedings of: International Conference, Mining Intelligence and Knowledge Exploration, pp. 541–552. MIKE (2013)Google Scholar
  9. 9.
    Huete, J.F., Fernández-luna, J.M., Campos, L.M.De, Rueda-morales, M.A.: Using past-prediction accuracy in recommender systems. Inf. Sci. 199, 78–92 (2012)CrossRefGoogle Scholar
  10. 10.
    Bydžovská, H.: Are collaborative filtering methods suitable for student performance prediction? In: Proceedings of 17th Portuguese Conference on Artificial Intelligence, pp. 425–430. EPIA (2015)Google Scholar
  11. 11.
    Georgios, K., Sotiris, K., Panagiotis, P.: Predicting student performance in distance higher education using semi-supervised techniques. In: Proceedings of 5th International Conference, pp. 259–270. MEDI (2015)Google Scholar
  12. 12.
    Polyzou, A., Karypis, G.: Grade prediction with models specific to students and courses. Int. J. Data Sci. Analytics. 2, 159–171 (2016)CrossRefGoogle Scholar
  13. 13.
    Rendle, S.: Factorization machines. In: Proceedings—IEEE International Conference on Data Mining, pp. 995–1000. ICDM (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad de Las AméricasQuitoEcuador
  2. 2.Universidad de AlicanteAlicanteSpain

Personalised recommendations