Educational commitment and social networking: The power of informal networks

The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students’ persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the “middle of the pack,” the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike’s information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence—these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness—a measure of one’s immersion in the network—helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students’ persistence in STEM majors.

Figure 1

Model of persistence. We expand the model of persistence proposed in Ref. [7] to include frequency of the interactions and out-of-class networks. The categories of factors that may affect students’ persistence are individual attributes, classroom context, as well as in- and out-of-class community.

Figure 2

Changes in out-of-class network involvement for each section as a function of time. (a) Student involvement in the network versus week (for each of the sections). Student involvement is taken as the percentage of students enrolled in the course who either listed other students or were listed by peers on the out-of-class part of the SNA survey. In three out of four sections, over 90 % of students are in the network by week 8 (for the Fall 2015_A section, the values are consistently lower by about 20 %). Note that the third collection was administered in week 8 in three out of the four groups (i.e., Fall 2015_A, Fall 2015_B, Fall 2016_A) and at the beginning of week 9 in one group (i.e., Fall 2016_C). (b) When normalized by the size of the network at week 13, it becomes clear that the network evolution is comparable between sections and that the equilibrium is basically reached by midsemester (week 8, the third collection). (c) An example of the out of class network at week 2 and 8 for section Fall 2015_B (see Fig. S1 in the Supplemental Material [20] for the other sections). At week 2, only a few connections are present. By week 8, however, a strong network of students exists, with only a handful of students not embedded within the network. The size of the nodes in both networks corresponds to closeness at week 8 and the color indicates whether a node is a student (magenta) or a person that was not enrolled in the course (gray).