Group roles in unstructured labs show inequitable gender divide

Instructional labs are being transformed to better reflect authentic scientific practice, often by removing aspects of pedagogical structure to support student agency and decision making. We explored how these changes impact men’s and women’s participation in group work associated with labs through clustering methods on the quantified behavior of students. We compared the group roles students take on in two different types of instructional settings: (i) highly structured traditional labs, and (ii) less structured inquiry-based labs. Students working in groups in the inquiry-based (less structured) labs assumed different roles within their groups, however men and women systematically took on different roles and men behaved differently when in single- versus mixed-gender groups. We found no such systematic differences in role division among male and female students in the traditional (highly structured) labs. Students in the inquiry-based labs were not overtly assigned these roles, indicating that the inequitable division of roles was not a result of explicit assignment. Our results highlight the importance of structuring equitable group dynamics in educational settings, as a gendered division of roles can emerge without active intervention. As the culture in physics evolves to remove systematic gender biases in the field, instructors in educational settings must not only remove explicitly biased aspects of curricula but also take active steps to ensure that potentially discriminatory aspects are not inadvertently reinforced.

Figure 1

Bar plot of code counts from two observers used to form the basis of a chi-squared test to validate the observation protocol used in this study. Two observers documented the same lab period, and the resulting contingency table (given by the raw counts displayed on the graph and shown in Table 3) was used to determine statistical validity of the method. Here, the two distributions are statistically indistinguishable indicating that the observers captured the same distribution of student actions.

Figure 2

Box plots of raw data revealing the highly non-Gaussian nature of the code distributions. Each faded point is the accumulated codes for a student in a lab period for a particular category (the horizontal spread of the points is just to visualize all the points), and so darker regions represent more total codes of that value (with the darkest regions near zero). Note that the median for all codes except other is less than or equal to one, reflecting the fact that over half of students were observed engaging in that behavior once or less than once. This, combined with the fact that there are a large number of outliers, is an indication that students either engage in a particular activity a lot or not at all.

Figure 3

Elbow plot used to determine the optimal number of clusters for the data. The average squared distance from each point to the center of its assigned cluster is plotted as a function of the number of clusters. There is a kink at five, indicating that the optimal number of clusters for the data is five. Our results were compared against 10 000 randomly generated student profiles. Note that the elbow is well below random, a sign that the data can be clustered. Superimposed on the graph is a two-dimensional visualization of the data and random points for qualitative comparison. The data show structure (brown points in lower left), whereas the random points form a blob (gray points in center right).

Figure 4

Two-dimensional visualization of behavior clusters and their centers. Each point represents a unique student profile, with profiles from the same group connected by a gray line (solid for less-structured inquiry labs, and dashed for highly structured traditional labs). Circles represent students in the traditional labs and stars in the inquiry labs, and black edges indicate women’s profiles. All points in the laptop cluster are stars, whereas all points in the paper cluster are circles, a reflection of the pedagogical differences in the labs (students in the traditional labs were filling out paper worksheets, whereas in the inquiry labs were filling out electronic notebooks). Clusters are characterized by their centers, and here the centers of the five clusters are given by large $Z$ scores for each of our codes.

Figure 5

Breakdown of codes by decomposing coarse behavior (e.g., “handling laptop”) into more fine-grained behavior (e.g., “analyzing data”). Ten videos were coded, resulting in 23 decomposed profiles from 17 different students (five students appeared in more than one video). (a) A breakdown of each code, showing the fraction of time students engaged in a particular task while coded as a particular behavior. Three of the five codes (desktop, equipment, and laptop) were directly decomposed into subcodes while analyzing videos, as shown in (b) illustrating a sample coded time series. Four additional “group states” were coded in the videos, representing large group behavior (discussing with a TA or UTA, conversing with other groups, whole class discussions and announcements, and using a whiteboard). We decomposed the other code by overlapping it with these larger group states. The paper code was purely represented by students filling out paper worksheets in the traditional labs.

Figure 6

Cluster compositions for each of the five clusters, broken down both by lab type, gender, and group composition. In all plots, the $y$ axis represents a fraction of student profiles and errors are calculated using the standard error on the fraction of a population shown [see Eq. (1) for additional details]. (a) Cluster distributions broken down by lab type. (b) Clusters further broken down by gender. We see that there are disproportionately more women in the laptop cluster than men, and disproportionately more men than women in the equipment cluster. (c) Cluster distributions were further broken down in the inquiry lab by group type (men and women in mixed-gender groups and single-gender groups). Upon inspection, we see that the laptop difference remained, while a difference emerged in other. Furthermore, far more men are high-equipment users when in single-gender groups. Because of insufficient statistics, no comparison can be made with women in single-gender groups, and the data are presented for completeness.

Figure 7

Fraction of groups with members in identical clusters (light ring) and different clusters (dark ring) illustrating role division in the different labs. Almost half of groups in the traditional labs had all members in the same cluster (primarily paper cluster), whereas the majority of groups in the inquiry labs had members in multiple clusters indicating an increase in task division.

Figure 8

Intragroup variances of the relative behaviors among students, signifying the amount of task division within groups. Each plot shows VAR($\mathrm{\Delta }N$) for all student profiles contained within the labeled lab and group types along with their Bayesian confidence intervals. (a) Comparing across lab types, the intragroup variances are remarkably larger in the inquiry lab groups than in the traditional lab groups for all codes besides paper, indicating a greater range of behaviors and an increase in task division. (b) Within the inquiry labs, the intragroup variances are comparable among groups of differing composition suggesting that similar degrees of task division were taking place. (Female single-gender groups not included due to insufficient statistics).

Figure 9

Histograms of intragroup deviations for men and women within the inquiry lab’s mixed-gender groups for the desktop, equipment, laptop, and other behavior codes, with the $y$ axis representing the number of student profiles. Each student deviates from their group’s average by $\mathrm{\Delta }N$ [defined in Eq. (a2) in Materials and Methods A]. A positive $\mathrm{\Delta }N$ denotes a student engaging in a behavior more often relative to their group members. We quote $p$ values calculated from the Mann-Whitney U test statistic on all plots and find significant differences between men and women for the laptop and other codes. We also find a borderline result of men handling the equipment more than their group members.