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

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.

Figure 1

Graduation rates in 1996 and 2008 (a) by gender and (b) by ethnicity. The 4-year graduation rates increased by about 6%, compared to 4% increase of the 6-year graduation rates, for both males and females. By ethnicity, only the increase in 4-year graduation rate for Hispanics (25.3%) and Blacks (10.9%) was substantial [1].

Figure 2

Simplified Model Linking Classrooms, Learning and Persistence. The three categories of factors that may affect students’ persistence are individual attributes, classroom context, and in-class community.

Figure 3

An excerpt from the SNA survey that was given starting in the Spring 2015 semester.

Figure 4

In each of the networks, $X$ has higher centrality than $Y$ according to (a) indegree, (b) outdegree, (c) eigenvector, (d) closeness, and (e) betweenness.

Figure 5

The changes in network density for each section as a function of time. The $x$ axis is rescaled to account for the adjustment on the data collection schedule. The solid lines represent a line of best fit for each section ($R_{\mathrm{F}14}^2=0.65$, $R_{\mathrm{F}15_A}^2=0.76$, $R_{\mathrm{F}15_B}^2=0.83$).