Predicting student exam's scores by analyzing social network data

  • Authors:

  • Michael Fire;Gilad Katz;Yuval Elovici;Bracha Shapira;Lior Rokach

  • Affiliations:

  • Telekom Innovation Laboratories and Information Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Telekom Innovation Laboratories and Information Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Telekom Innovation Laboratories and Information Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Telekom Innovation Laboratories and Information Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Telekom Innovation Laboratories and Information Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel

  • Venue:
  • AMT'12 Proceedings of the 8th international conference on Active Media Technology
  • Year:
  • 2012

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Abstract

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.