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.
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Fire, M., Katz, G., Elovici, Y., Shapira, B., Rokach, L. (2012). Predicting Student Exam’s Scores by Analyzing Social Network Data. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_59
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DOI: https://doi.org/10.1007/978-3-642-35236-2_59
Publisher Name: Springer, Berlin, Heidelberg
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