Boosting Students’ Performance with the Aid of Social Network Analysis
DOI:
https://doi.org/10.46754/umtjur.v1i3.76Keywords:
social networks, students’ performance, centrality measures, Girvan-NewmanAbstract
There are various teaching methods developed in order to attain successful delivery of a subject without prior knowledge of the interaction among the students in a class. Social network analysis (SNA) can be used to identify individual, intermediate and group measures of interaction in a classroom. The idea is on identifying ways to boost the students’ performance by means of lecturer’s intervention based on their interaction. The case study was conducted involving third year batch that consisted of 76 female and 24 male students. A friendship network was drawn based on the information obtained at the end of semester 5 and it was investigated based on two metrics – centrality measures and Girvan-Newman algorithm. At the end of semester 5, grades were added as the attributes of the network. 12 clusters were found in this batch and a distinct pattern was identified between performing and poor achieving students. At the beginning of the 6th semester, the students were given the option to choose between 2 groups. Group 1 was unperturbed without any lecturer’s intervention whereas the performing students’ clusters in Group 1 were preserved but the students in poor performing clusters were distributed among performing clusters. The students were then asked to carry out assignments/quizzes in their respective groups. The final grades indicated that the performance of the students of Group 1 was much superior and there was clear evidence that those poor performing students in the 5th semester performed much better in semester 6. This shows that by understanding the students’ interaction and incorporating instructor’s minimal intervention, the performance of the students can be improved by creating a social contagion effect through group assignment clustering.
References
Almaatouq A, Radaelli L, Pentland A, Shmueli E (2016). Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change. PLoS ONE 11(3): e0151588. doi.org/10.1371/journal.pone.0151588
Batagelj, V. and Mrvar A. (2006). Pajek: analysis and visualization of large networks. Retrieved from http://mrvar.fdv.uni-lj.si/pajek/, Nov. 10 2018.
Borgatti, S.P., Everett M.G., and Freeman L.C. (1999): Ucinet 5 for Windows: Software for Social Network Analysis. Natic: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. Thousand Oaks, CA.
Burt, R.S. and Minor M.J. (1983): Applied Network Analysis. Beverly Hills: Sage.
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12): 7821-7826.
R Development Core Team (2006). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org. Dec.12, 2006.
Vaquera E, Kao G. Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Social Science Research. 2008; 37(1):55–72.
Dame, R., & Libes, S. (1993). Oyster reefs and nutrient retention tidal creeks. Journal of Experimental Marine Biology and Ecology, 171(6), 251-258.
Kernis, M. H., Cornell, D. P., Sun, C. R., Berry, A., Harlow, T., & Bach, J. S. (1993). There's more to self-esteem than whether it is high or low: The importance of stability of self-esteem. Journal of Personality and Social Psychology, 65, 1190-1204.
