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There are various teaching methods developed in order to attain successfuldelivery of a subject without prior knowledge of the interaction among thestudents in a class. Social network analysis can be used to identifyindividual, intermediate and group measures of interaction in a classroom. Theidea is on identifying ways to boost the students performance by means oflecturers intervention based on their interaction. The case study was conductedinvolving third year batch that consisted of 76 female and 24 male students. Afriendship network was drawn based on the information obtained at the end ofsemester 5 and it was investigated based on two metrics namely centralitymeasures and Girvan Newman algorithm. At the end of semester 5, grades wereadded as the attributes of the network. 12 clusters were found in this batchand a distinct pattern was identified between performing and poor achievingstudents. At the beginning of the 6th semester, the students were given theoption to choose between 2 groups. Group 1 was unperturbed without anylecturers intervention whereas the performing students clusters in Group 1 werepreserved but the students in poor performing clusters were distributed amongperforming clusters. The students were then asked to carry out assignments andquizzes in their respective groups. The final grades indicated that theperformance of the students of Group 1 was much superior and there was clearevidence that those poor performing students in the 5th semester performed muchbetter in semester 6. This shows that by understanding the students interactionand incorporating instructors minimal intervention, the performance of thestudents can be improved by creating a social contagion effect through groupassignment clustering.

Boosting Students' Performance With The Aid Of Social Network Analysis