Two Studies in Social Networks and Education: Empirical Investigations
03 Jul 2019 Wednesday, 01:30 PM to 03:00 PM
COM2 Level 4
Executive Classroom, COM2-04-02
Social networking is prevalent and vital in education as learning is a social process achieved through seamless communication, knowledge sharing, and collaborations. Social network analysis has attracted great interest in educational research, and yielded insightful explanations for many educational phenomena, such as student interactions and diffusion of social influence. While early studies have primarily focused on the static networks, recent studies suggest that the nature of student networks is dynamic, i.e., students create and deactivate social ties, thereby altering the structure of the networks in which they participate over time. Nowadays, the transition from traditional social networking to IT-enabled social networking has not only expanded the opportunities for students to communicate and interact, but also has provided several opportunities for researchers to examine and characterize the social network dynamics. Drawing on these opportunities, I conduct two studies in my dissertation to 1) assess the impact of social influence on student academic performance, and 2) characterize the interdependencies between student online and offline social networks, i.e., how student online interactions affect their offline relations, and vice versa.
In my first study, I propose a three-stage Monte Carlo Markov Chain (MCMC)-based co-evolution model to empirically assess the effects of peer influence on academic performance by controlling for the homophily effects: 1) in course selection, and 2) in personal interactions within a course. Using granular and de-identified student-level data over four years from a major public university, my empirical analyses provide support for the existence of both peer influence and homophily in the study process of course selection and personal interaction. In addition, I find evidence of non-linear peer influence, i.e., influence is more significantly positive among higher achieving students, than lower achieving ones. The results from the first study shed light on the possible underlying mechanisms of how in-course interpersonal networks evolve, and how students' performances are subsequently affected by their peers' behaviors and decisions. Findings of this study offer useful and prescriptive insights about the prevalence and role of social influence in education and labor markets.
The second study in my dissertation incorporates multiplex network theories into a multi-media framework to empirically investigate the relationships between online and offline communications in college students. Relying on a longitudinal dataset, I model the joint dynamics of online and offline networks. My preliminary results show that the effect of online networks on offline networks is different from the effect of the offline network on online networks. More specifically, while the online ties complement offline interactions through positive associations, the offline ties displace online ties through negative associations. In addition, I examine the reciprocity effect and find that individuals are more likely to reciprocate within one network, and less likely to reciprocate across different networks. The second study seeks to contribute to IS media communication literature and social network studies by distinguishing the direction of effects and offering empirical evidence for multiplex network dynamics.