Abstract
Increasing student retention (successfully finishing a particular course) and persistence (continuing through a sequence of courses or 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 network analysis as an 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 -- an introductory physics course that strongly emphasizes interactive learning -- predicts 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 predicting individual student's persistence based on centrality measures are fairly high -- up to $75\%$, making the centrality a good predictor of persistence. These findings suggest that increasing student social integration may help in improving persistence in science, technology, engineering, and mathematics fields.
Highlights
AND MOTIVATIONIncreasing the retention of students in a particular course and their persistence in continuing through a sequence of courses or their major area of study has always been a big challenge for universities
We looked at the networks without the instructional staff as we were interested mainly in the peer-to-peer interactions [25]
Following Tinto, he suggested that social network analysis (SNA) sheds new light on understanding student integration through individual’s social ties, i.e., “a dimension that previous operationalizations of integration have missed” [21]
Summary
Increasing the retention of students in a particular course and their persistence in continuing through a sequence of courses or their major area of study has always been a big challenge for universities. Almost half of first-time students who leave their initial institution by the end of the first year never come back to college [2]. One way to approach this problem is to examine student academic and social integration using the tools of social network analysis (SNA). The basic premise linking networks to persistence is that students’ communities and interactions likely influence whether they remain in a particular class, major or in school overall.
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