Analyzing Students and Subjects Interactions Through Social Network Analysis: A Predictive Analysis on Students’ Retention

Education is a complex connection of subjects, prerequisites, and students enrolled in a subject. It is necessary to examine and analyze interaction networks among students and subjects, as network structures are important in determining students' survivability and timely completion of their course. In this context, it is necessary to examine the interaction between students and subjects, as network structures are important in terms of determining the most enrolled subjects and the cause of over-connected and under-connected subjects. This study utilized Social Network Analysis (SNA) to map networks of subjects and students. Nodes are subjects, and relationships are the frequency of students enrolled in the subjects. The datasets of student records from 2019 to 2023 are composed of 652 datasets, which are composed of 54 subjects and 601 students. Information Management 1 (Fund. Of Database), Multimedia Technologies, and System Analysis and Design obtained the highest Eigenvalue centrality in-degree values. All the subjects involved were offered in the 2nd year for the 2nd semester. These subjects were the most central entities in the BSIT curriculum, or this indicates these subjects were the most enrolled subjects among all students. It also observed that subjects without prerequisites and minor subjects obtained the same number of EC and in-degree connections. However, subjects with prerequisites and major subjects obtained lower Eigenvector centrality (EC) and in-degree connections. Major subjects with high EC and degree connections were subjects enrolled by students who failed prerequisite subjects once or twice and then passed those subjects and enrolled in the subject together with the regular students.