Learning Models for Student Performance Prediction

  • Rafael Cavazos
  • Sara Elena GarzaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10633)


Predicting student performance supports educational decision-making by allowing directives and teachers to detect students in special situations (e.g. students at risk of failing a course or dropping out of school) and manage these in a timely manner. The problem we address consists of grade prediction for the courses of a given academic period. We propose to learn a predictive model for each course. Two cases can be distinguished: historical grades are unavailable for prediction (first semester) and historical grades are available. For the first case, features that include selection test scores, socioeconomic information, and middle school the student comes from are proposed. For the second case, features that include past grades from similar courses are proposed. To test our approach, we gathered data from a Mexican public high school (three generations, 2,000 students, four semesters, and 24 courses). Our results indicate that features such as numerical ability, family, motivation, and social sciences are relevant for prediction without historical grades, while grades from the immediate previous semester are relevant for prediction with historical grades. Additionally, support vector machines and linear regression are suitable techniques for tackling grade prediction.


Student performance Machine learning Educational data mining 


  1. 1.
    Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011)CrossRefGoogle Scholar
  2. 2.
    Baker, R.S., Yacef, K.: The state of educational data mining in 2009: a review and future visions. JEDM-J. Educ. Data Min. 1(1), 3–17 (2009)Google Scholar
  3. 3.
    Siemens, G., Baker, R.S.J.d.: Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. LAK 2012, pp. 252–254. ACM, New York (2012)Google Scholar
  4. 4.
    Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 601–618 (2010)CrossRefGoogle Scholar
  5. 5.
    Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) Learning Analytics, pp. 61–75. Springer, New York (2014). Scholar
  6. 6.
    Dutt, A., Ismail, M.A., Herawan, T.: A systematic review on educational data mining. IEEE Access PP, 1 (2017)Google Scholar
  7. 7.
    Koedinger, K.R., D’Mello, S., McLaughlin, E.A., Pardos, Z.A., Rosé, C.P.: Data mining and education. Wiley Interdisc. Rev.: Cognit. Sci. 6(4), 333–353 (2015)CrossRefGoogle Scholar
  8. 8.
    Kotsiantis, S.B., Pierrakeas, C.J., Pintelas, P.E.: Preventing student dropout in distance learning using machine learning techniques. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003 Part II. LNCS (LNAI), vol. 2774, pp. 267–274. Springer, Heidelberg (2003). Scholar
  9. 9.
    Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Predicting students’ performance in distance learning using machine learning techniques. Appl. Artif. Intell. 18(5), 411–426 (2004)CrossRefGoogle Scholar
  10. 10.
    Kotsiantis, S., Patriarcheas, K., Xenos, M.: A combinational incremental ensemble of classifiers as a technique for predicting students performance in distance education. Knowl.-Based Syst. 23(6), 529–535 (2010)CrossRefGoogle Scholar
  11. 11.
    Márquez-Vera, C., Cano, A., Romero, C., Noaman, A.Y.M., Mousa Fardoun, H., Ventura, S.: Early dropout prediction using data mining: a case study with high school students. Expert Syst. 33(1), 107–124 (2016)CrossRefGoogle Scholar
  12. 12.
    Xing, W., Guo, R., Petakovic, E., Goggins, S.: Participation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)CrossRefGoogle Scholar
  13. 13.
    Desai, A., Shah, N., Dhodi, M.: Student profiling to improve teaching and learning: a data mining approach. In: 2016 International Conference on Data Science and Engineering (ICDSE), pp. 1–6, August 2016Google Scholar
  14. 14.
    Ramaswami, M., Bhaskaran, R.: A CHAID based performance prediction model in educational data mining. IJCSI Int. J. Comput. Sci. Issues 7, 10–18 (2010)Google Scholar
  15. 15.
    Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 29, 119–127 (1980)CrossRefGoogle Scholar
  16. 16.
    Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance. Technical report, University of Minho (2008)Google Scholar
  17. 17.
    Mishra, T., Kumar, D., Gupta, S.: Mining students’ data for prediction performance. In: 2014 Fourth International Conference on Advanced Computing Communication Technologies, pp. 255–262, February 2014Google Scholar
  18. 18.
    Ahmed, A.B.E.D., Elaraby, I.S.: Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)Google Scholar
  19. 19.
    Daud, A., Aljohani, N.R., Abbasi, R.A., Lytras, M.D., Abbas, F., Alowibdi, J.S.: Predicting student performance using advanced learning analytics. In: Proceedings of the 26th International Conference on World Wide Web. WWW 2017 Companion, Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, pp. 415–421 (2017)Google Scholar
  20. 20.
    Kelchen, R.: The landscape of competency-based education: enrollments, demographics, and affordability, pp. 1–20. American Enterprise Institute (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Facultad de Ingeniería Mecánica y EléctricaUANLSan Nicolás de los GarzaMexico

Personalised recommendations