Predicting Student Exam’s Scores by Analyzing Social Network Data

  • Michael Fire
  • Gilad Katz
  • Yuval Elovici
  • Bracha Shapira
  • Lior Rokach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7669)


In this paper, we propose a novel method for the prediction of a person’s success in an academic course. By extracting log data from the course’s website and applying network analysis methods, we were able to model and visualize the social interactions among the students in a course. For our analysis, we extracted a variety of features by using both graph theory and social networks analysis. Finally, we successfully used several regression and machine learning techniques to predict the success of student in a course. An interesting fact uncovered by this research is that the proposed model has a shown a high correlation between the grade of a student and that of his “best” friend.


Social Network Analysis Data Mining Score Prediction Machine Learning Web Log Analysis Multi Graph 


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  1. 1.
    Alspaugh, C.: Identification of some components of computer programming aptitude. Journal for Research in Mathematics Education, 89–98 (1972)Google Scholar
  2. 2.
    Cottam, J., Menzel, S., Greenblatt, J.: Tutoring for retention. In: Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, pp. 213–218. ACM (2011)Google Scholar
  3. 3.
    Evans, G., Simkin, M.: What best predicts computer proficiency? Communications of the ACM 32(11), 1322–1327 (1989)CrossRefGoogle Scholar
  4. 4.
    Deckro, R., Woundenberg, H.: Mba admission criteria and academic success. Decision Sciences 8(4), 765–769 (1977)CrossRefGoogle Scholar
  5. 5.
    Cronan, T., Embry, P., White, S.: Identifying factors that influence performance of non-computing majors in the business computer information systems course. Journal of Research on Computing in Education 21(4), 431–446 (1989)Google Scholar
  6. 6.
    Butcher, D., Muth, W.: Predicting performance in an introductory computer science course. Communications of the ACM 28(3), 263–268 (1985)CrossRefGoogle Scholar
  7. 7.
    Ting, S., Robinson, T.: First-year academic success: A prediction combining cognitive and psychosocial variables for caucasian and african american students. Journal of College Student Development (1998)Google Scholar
  8. 8.
    Bennedsen, J., Caspersen, M.: Optimists have more fun, but do they learn better? on the influence of emotional and social factors on learning introductory computer science. Computer Science Education 18(1), 1–16 (2008)CrossRefGoogle Scholar
  9. 9.
    Keen, K., Etzkorn, L.: Predicting students’ grades in computer science courses based on complexity measures of teacher’s lecture notes. Journal of Computing Sciences in Colleges 24(5), 44–48 (2009)Google Scholar
  10. 10.
    Fowler, G., Glorfeld, L.: Predicting aptitude in introductory computing: A classification model. AEDS Journal 14(2), 96–109 (1981)Google Scholar
  11. 11.
    Petersen, C., Howe, T.: Predicting academic success in introduction to computers. AEDS Journal 12(4), 182–191 (1979)Google Scholar
  12. 12.
    Konvalina, J., et al.: Identifying factors influencing computer science aptitude and achievement. AEDS Journal 16(2), 106–112 (1983)Google Scholar
  13. 13.
    Hostetler, T.: Predicting student success in an introductory programming course. ACM SIGCSE Bulletin 15(3), 40–43 (1983)CrossRefGoogle Scholar
  14. 14.
    Campbell, P., McCabe, G.: Predicting the success of freshmen in a computer science major. Communications of the ACM 27(11), 1108–1113 (1984)CrossRefGoogle Scholar
  15. 15.
    Rountree, N., Rountree, J., Robins, A., Hannah, R.: Interacting factors that predict success and failure in a cs1 course. ACM SIGCSE Bulletin 36(4), 101–104 (2004)CrossRefGoogle Scholar
  16. 16.
    Mazlack, L.: Identifying potential to acquire programming skill. Communications of the ACM 23(1), 14–17 (1980)CrossRefGoogle Scholar
  17. 17.
    Allinson, C., Hayes, J.: The cognitive style index: A measure of intuition-analysis for organizational research. Journal of Management Studies 33(1), 119–135 (1996)CrossRefGoogle Scholar
  18. 18.
    Chamillard, A.: Using student performance predictions in a computer science curriculum. ACM SIGCSE Bulletin 38(3), 260–264 (2006)CrossRefGoogle Scholar
  19. 19.
    Christakis, N., Fowler, J.: The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357(4), 370–379 (2007)CrossRefGoogle Scholar
  20. 20.
    Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., Pentland, A.: Incremental learning with accuracy prediction of social and individual properties from mobile-phone data. In: First International Workshop on Wide Spectrum Social Signal Processing (WS3P), Netherlands, Amsterdam (2012)Google Scholar
  21. 21.
    Eckersley, P.: How Unique Is Your Web Browser? In: Atallah, M.J., Hopper, N.J. (eds.) PETS 2010. LNCS, vol. 6205, pp. 1–18. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael Fire
    • 1
  • Gilad Katz
    • 1
  • Yuval Elovici
    • 1
  • Bracha Shapira
    • 1
  • Lior Rokach
    • 1
  1. 1.Telekom Innovation Laboratories and Information Systems Engineering DepartmentBen-Gurion University of the NegevBeer-ShevaIsrael

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