Students’ Performance Prediction Using Multi-Channel Decision Fusion

  • H. Moradi
  • S. Abbas Moradi
  • L. Kashani
Part of the Studies in Computational Intelligence book series (SCI, volume 524)


A teacher or an artificial instructor, embedded in an intelligent tutoring system, is interested in predicting the performance of his/her students to better adjust the educational materials and strategies throughout the learning process. In this chapter, a multi-channel decision fusion approach, based on using the performance in “assignment categories”, such as homework assignments, is introduced to determine the overall performance of a student. In the proposed approach, the data gathered are used to determine four classes of “expert”, “good”, “average”, and “weak” performance levels. This classification is conducted on both overall performance and the performance in assignment categories. Then, a mapping from the performances in “assignment categories” is learned, and is used to predict the overall performance. The main advantage of the proposed approach is in its capability to estimate students’ performance after a few assignments. Consequently, it can help the instructors better manage their class and adjust educational materials to prevent underachievement.


Learning analytics Student performance prediction Educational data mining Decision fusion Assignment categories 



Bayesian knowledge tracing


Classification and regression tree


Chi-square automatic interaction detection


Educational data mining


Intelligent tutoring systems


Learning management system



The authors like to thank Babak Araabi and Maryam Mirian for their feedback on this chapter. Furthermore, the authors like to thank the ITS group at the Advanced Robotics and Intelligent Systems Laboratory for their help throughout this research.


  1. 1.
    Singell, L.D., Waddell, G.R.: Modeling retention at a large public university: can at-risk students be identified early enough to treat? Res. High. Educ. 51(6), 546–572 (2010)CrossRefGoogle Scholar
  2. 2.
    Tinto, V.: From theory to action: exploring the institutional conditions for student retention. In: Smart, J.C. (ed.) Higher Education: Handbook of Theory and Research, vol. 25, pp. 51–89. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Donnelly, J.: Use of web-based academic alert system for identification of underachieving students at an urban research institution. Coll. Univ. 85(4), 39–42 (2010)Google Scholar
  4. 4.
    Wass, R., Harland, T., Mercer, A.: Scaffolding critical thinking in the zone of proximal development. High. Educ. Res. Dev. 30(1), 317–328 (2011)CrossRefGoogle Scholar
  5. 5.
    Torabi, R., Moradi, P., Khantaimoori, A.R.: Predict student scores using bayesian networks. Procedia Soc. Behav. Sci. 46, 4476–4480 (2012)CrossRefGoogle Scholar
  6. 6.
    Ramaswami, M., Bhaskaran, R.: A CHAID based performance prediction model in educational data mining. Int. J. Comput. Sci. Issues 7(1), 10–18 (2010)Google Scholar
  7. 7.
    Kotsiantis, S.B.: Use of machine learning techniques for educational purposes: a decision support system for forecasting students’ grades. Artif. Intell. Rev. 37(4), 331–344 (2012)CrossRefGoogle Scholar
  8. 8.
    Baker, R., Pardos, Z., Gowda, S., Nooraei, B., Heffernan, N.: Ensembling Predictions of student knowledge within intelligent tutoring systems. In: Konstan, J., Conejo, R., Marzo, J., Oliver, N. (eds.) User Modeling, Adaption and Personalization. LNCS, vol. 6787, pp. 13–24. Springer, Heidelberg (2011)Google Scholar
  9. 9.
    Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)CrossRefGoogle Scholar
  10. 10.
    Ghazarian, A., Noorhosseini, S.M.: Automatic detection of users’ skill levels using high-frequency user interface events. J. User Model. User-Adap. Inter. 20(2), 109–146 (2010)CrossRefGoogle Scholar
  11. 11.
    Thai-Nghe, N., Drumond, L., Horvath, T., Krohn-Grimberghe, A., Nanopoulos, A., Schmidt-Thieme., L.: Factorization techniques for predicting student performance. In: Santos O. C., Boticario J. G. (eds.) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 129–153. IGI Global, Hershey (2012)Google Scholar
  12. 12.
    Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715–4729 (2013)CrossRefGoogle Scholar
  13. 13.
    Millán, E., Loboda, T., Pérez-de-la-Cruz, J.L.: Bayesian networks for student model engineering. Comput. Educ. 55(4), 1663–1683 (2010)CrossRefGoogle Scholar
  14. 14.
    Desmarais, M.C., d Baker, R.S.J.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Model. User Adapt. Inter. 22(1–2), 9–38 (2012)CrossRefGoogle Scholar
  15. 15.
    Black, P.: Pedagogy in theory and in practice: formative and summative assessments in classrooms and in systems. In: Corrigan, D., Gunstone, R., Jones, A. (eds.) Valuing Assessment in Science Education: Pedagogy, Curriculum, Policy, pp. 207–229. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Baker, R. S. J. d.: Data mining for education. In: McGaw, B., Peterson, P., Baker, E. (eds.) International Encyclopedia of Education (3rd edn.), vol. 7, pp. 112–118. Elsevier, Oxford (2010)Google Scholar
  18. 18.
    Chieu, V.M., Luengo, V., Vadcard, L., Tonetti, J.: Student modeling in orthopedic surgery training: exploiting symbiosis between temporal bayesian networks and fine-grained didactic. analysis. J. Artif. Intell. Educ. 20(3), 269–301 (2010)Google Scholar
  19. 19.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Oxford (2008)Google Scholar
  20. 20.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Oxford (2011)Google Scholar
  21. 21.
    Soman, K.P., Diwakar, S., Ajay, V.: Insight into Data Mining: Theory and Practice. Prentice Hall, India (2006)Google Scholar
  22. 22.
    Jia, B., Zhong, S., Zheng, T., Liu, Z.: The study and design of adaptive learning system based on fuzzy set theory. In: Cheok, Z.A.D., Müller, W., Zhang, X., Wong, K., (eds.) Transactions on Edutainment IV. LNCS, vol. 6250, pp. 1–11(2010)Google Scholar
  23. 23.
    Chrysafiadi, K., Virvou, M.: PeRSIVA: an empirical evaluation method of a student model of an intelligent e-learning environment for computer programming. Comput. Educ. 68, 322–333 (2013)CrossRefGoogle Scholar
  24. 24.
    Pardos, Z.A., Baker, R.S.J.d., Gowda, S.M., Heffernan, N.T.: The sum is greater than the parts: ensembling models of student knowledge in educational software. SIGKDD Explor. 13(2), 37–44 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of ECEUniversity of TehranTehranIran
  2. 2.Department of Psychology and Special EducationUniversity of TehranTehranIran

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