Who and what gets recognized in peer recognition

Previous work has identified that recognition from others is an important predictor of students’ participation, persistence, and career intentions in physics. However, research has also found a gender bias in peer recognition in which student nominations of strong peers in their physics course disproportionately favor men over women. In this study, we draw on methods from social network analysis and find a consistent gender bias in which men disproportionately undernominate women as strong in their physics course in two offerings of both a lecture course (for science and engineering, but not physics, majors) and a distinct lab course (for science, engineering, and physics majors). We also find in one offering of the lecture course that women disproportionately undernominate men, contrary to what previous research would predict. We expand on prior work by also probing two data sources related to who and what gets recognized in peer recognition: students’ interactions with their peers (who gets recognized) and students’ written explanations of their nominations of strong peers (what gets recognized). Results suggest that the nature of the observed gender bias in peer recognition varies between the instructional contexts of lecture and lab. In the lecture course, the gender bias is related to who gets recognized: both men and women disproportionately overnominate their interaction ties to students of their same gender as strong in the course. In the lab course, the gender bias is also related to what gets recognized: men nominate men more than women because of skills related to interactions, such as being helpful. These findings illuminate the different ways in which students form perceptions of their peers and add nuance to our understanding of the nature of gender bias in peer recognition.

Figure 1

Online survey prompts analyzed in this study. Students typed their responses into the text boxes and could enter up to 15 peers’ names for each prompt. Students were given access to the course roster.

Figure 2

Flowchart depicting our stages of data analysis.

Figure 3

Toy interaction and recognition networks to exemplify how we calculated percent overlap and fraction of interaction network edges kept in the recognition network. Black edges indicate edges that appear in the interaction network but not the recognition network, blue edges indicate edges that appear in both the interaction and recognition networks (edges kept), and orange edges indicate edges that appear in the recognition network but not the interaction network. In this case, the percent overlap is $\frac{1}{2}$ because three out of six recognition network edges also appear in the interaction network. The fraction of interaction network edges kept in the recognition network is $\frac{1}{3}$ because three out of nine interaction network edges also appear in the recognition network.

Figure 4

Recognition networks for all analyzed courses. Nodes are colored by gender and sized proportional to indegree (number of received nominations as strong in the course). Edges point from the nominator to the nominee.

Figure 5

Coefficient estimates, represented as log-odds, for the gender variables of the exponential random graph models (values shown in Table 5 in Appendix pp2). The base term (i.e., coefficient estimate of zero) is nominations from man to man. Error bars represent standard errors and asterisks indicate statistical significance.

Figure 6

Fraction of edges in each interaction network that also appear in the corresponding recognition network for each combination of nominator and nominee gender. Error bars represent standard errors of the proportions.

Figure 7

Fraction of nominations falling under each (a) category of our coding scheme within each course and (b) code of our coding scheme for the lab course only, split by gender of the nominator and nominee. Results are aggregated across the fall and spring offerings of each course. We coded 300 (205 in lab and 95 in lecture), 106 (79 in lab and 27 in lecture), 179 (127 in lab and 52 in lecture), and 274 (168 in lab and 106 in lecture) explanations from man to man, man to woman, woman to man, and woman to woman, respectively. Fractions do not necessarily sum to one because each explanation could receive multiple codes. Nominations made by men and women received comparable numbers of codes: men’s nominations received an average of 1.4 codes and women’s nominations received an average of 1.3 codes. Error bars represent standard errors of the proportions.

Figure 8

ERGM goodness-of-fit diagnostics for the recognition network of the fall offering of the lecture course. The horizontal axis represents a network measure (outdegree, indegree, or edgewise shared partners—a measure of transitivity) and the vertical axis represents either the proportion of nodes or proportion of edges in the network. Plots compare the distribution of each network measure for the observed data (thick black line) to that for 10 network simulations generated using the estimated model coefficients (box plots).

Figure 9

Interaction networks for all analyzed courses. Nodes are colored by gender and sized proportional to indegree (number of received nominations). Edges point from the nominator to the nominee.

Figure 10

Fraction of nominations that are interaction-based and observation-based falling under each (a) category and (b) code of our coding scheme, split by course. Fractions do not necessarily sum to one because each explanation could receive multiple codes. Error bars represent standard errors of the proportions.