Predicting Grades Based on Students’ Online Course Activities

  • Aleš Černezel
  • Sašo Karakatič
  • Boštjan Brumen
  • Vili Podgorelec
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 185)


We researched the possibility of predicting the final grades of university students with the help of online course management systems. By using the activity logs from the system we identify those variables that could be used during predictions. We experimentally narrowed-down the selection to two variables that would be useful for constructing linear regression models for grade prediction. The identified variables were the number of specific activities and the intermediate grades of the students. An experiment was conducted in order to evaluate the selection regarding five courses, which would show whether these two variables could help build a prediction model with accuracy of up to 91.7 % for a given course.


Data mining Knowledge discovery E-learning Online course Web-based education system Grade prediction 


  1. 1.
    Zaïane, O.R., Xin, M., Han, J.: Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In: Proceedings of the Advances in Digital Libraries Conference, Washington, DC, USA, pp. 19–29 (1998)Google Scholar
  2. 2.
    Minaei-Bidgoli, B., Tan, P.-N., Punch, W.F.: Mining interesting contrast rules for a web-based educational system. In: Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004, pp. 320–327 (2004)Google Scholar
  3. 3.
    Donnellan, D., Pahl, C.: Data mining technology for the evaluation of web-based teaching and learning systems. In: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, vol. 2002 pp. 747–752 (2002)Google Scholar
  4. 4.
    Nilakant, K., Mitrovic, A.: Applications of data mining in constraint-based intelligent tutoring systems. In: Proceedings of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning Through Intelligent and Socially Informed Technology, Amsterdam, The Netherlands, pp. 896–898 (2005)Google Scholar
  5. 5.
    Grob, H.L., Bensberg, F., Kaderali, F.: Controlling open source intermediaries - a web log mining approach. In: 26th International Conference on Information Technology Interfaces, 2004, vol. 1, pp. 233–242 (2004)Google Scholar
  6. 6.
    Arroyo, I., Murray, T., Woolf, B.P., Beal, C.: Inferring unobservable learning variables from students’ help seeking behavior. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) Intelligent Tutoring Systems, pp. 782–784. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Pritchard, D., Warnakulasooriya, R., Pritchard, D., Warnakulasooriya, R.: Data from a web-based homework tutor can predict student’s final exam score. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, vol. 2005 pp. 2523–2529 (2005)Google Scholar
  8. 8.
    Feng, M., Heffernan, N.T., Koedinger, K.R.: Looking for sources of error in predicting student’s knowledge. In: American Association for Artificial Intelligence 2005 Workshop on Educational Datamining (2005)Google Scholar
  9. 9.
    Chen, G.-D., Liu, C.-C., Ou, K.-L., Liu, B.-J.: Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. J. Educ. Comput. Res. 23(3), 305–332 (2000)CrossRefGoogle Scholar
  10. 10.
    Minaei-Bidgoli, B., Punch, W.F.: Using genetic algorithms for data mining optimization in an educational web-based system. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2724. Springer, Heidelberg (2003)Google Scholar
  11. 11.
    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
  12. 12.
    Hämäläinen, W., Vinni, M.: Comparison of machine learning methods for intelligent tutoring systems. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 525–534. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Moodle - Open-source learning platform.
  14. 14.
    Field, A.P.: Discovering Statistics Using SPSS, 3rd edn. Sage, London (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aleš Černezel
    • 1
  • Sašo Karakatič
    • 1
  • Boštjan Brumen
    • 1
  • Vili Podgorelec
    • 1
  1. 1.Faculty of Electrical Engineering, Computer and Information ScienceInstitute of Informatics, University of MariborMariborSlovenia

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