Students' network integration as a predictor of persistence in introductory physics courses

Abstract

Increasing student retention (successfully finishing a particular course) and persistence (continuing through the major area of study) is currently a major challenge for universities. While students' academic and social integration into an institution seems to be vital for student retention, research into the effect of interpersonal interactions is rare. We use the network analysis approach to investigate academic and social experiences of students in the classroom. In particular, centrality measures identify patterns of interaction that contribute to integration into the university. Using these measures, we analyze how position within a social network in a Modeling Instruction (MI) course -- a course that strongly emphasizes interactive learning -- impacts their persistence in taking a subsequent physics course. Students with higher centrality at the end of the first semester of MI are more likely to enroll in a second semester of MI. Moreover, we found that chances of successfully inferring the persistence based on centrality measures are fairly high -- up to \(75\%\), making the centrality a good predictor of persistence. These findings indicate that student social integration influences persistence and that it may help in designing retention strategies in STEM fields.