There are various teaching methods developed in order to attainsuccessful 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 involvingthird year batchthat consistedof 76 female and 24 male students. A friendship network was drawn based on the information obtained at the end of semester 5 and it wasinvestigated based on two metrics–centralitymeasures and Girvan-Newman algorithm. At the end of semester 5, grades were added asthe 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 studentsweregiven 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/quizzesin their respective groups. The final grades indicatedthat the performance of the students of Group 1 wasmuch superior and there wasclear 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 incorporatiniginstructor’s minimal intervention, the performance of the students can be improved by creating a social contagion effect through group assignment clustering.