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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References
Alspaugh, C.: Identification of some components of computer programming aptitude. Journal for Research in Mathematics Education, 89–98 (1972)
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)
Evans, G., Simkin, M.: What best predicts computer proficiency? Communications of the ACM 32(11), 1322–1327 (1989)
Deckro, R., Woundenberg, H.: Mba admission criteria and academic success. Decision Sciences 8(4), 765–769 (1977)
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)
Butcher, D., Muth, W.: Predicting performance in an introductory computer science course. Communications of the ACM 28(3), 263–268 (1985)
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)
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)
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)
Fowler, G., Glorfeld, L.: Predicting aptitude in introductory computing: A classification model. AEDS Journal 14(2), 96–109 (1981)
Petersen, C., Howe, T.: Predicting academic success in introduction to computers. AEDS Journal 12(4), 182–191 (1979)
Konvalina, J., et al.: Identifying factors influencing computer science aptitude and achievement. AEDS Journal 16(2), 106–112 (1983)
Hostetler, T.: Predicting student success in an introductory programming course. ACM SIGCSE Bulletin 15(3), 40–43 (1983)
Campbell, P., McCabe, G.: Predicting the success of freshmen in a computer science major. Communications of the ACM 27(11), 1108–1113 (1984)
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)
Mazlack, L.: Identifying potential to acquire programming skill. Communications of the ACM 23(1), 14–17 (1980)
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)
Chamillard, A.: Using student performance predictions in a computer science curriculum. ACM SIGCSE Bulletin 38(3), 260–264 (2006)
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)
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)
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)
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)
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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
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