I am currently designing my upper-level undergraduate class I will teach next fall. The proposed course description* begins with:
Computational Systems Biology
A survey of network models used to gain a systems-level understanding of biological processes. Topics include computational models of gene regulation, signal transduction pathways, protein-protein interactions, and metabolic pathways…
As a result, I’ve been keeping my eye out for networks (or, mathematically-speaking, graphs) in biology. I found a fascinating network in this recently-published paper:
Males Under-Estimate Academic Performance of Their Female Peers in Undergraduate Biology Classrooms
Grunspan DZ, Eddy SL, Brownell SE, Wiggins BL, Crowe AJ, et al. (2016) Males Under-Estimate Academic Performance of Their Female Peers in Undergraduate Biology Classrooms. PLoS ONE 11(2): e0148405. doi: 10.1371/journal.pone.0148405
I often see reports on gender bias in computer science, but I somehow thought that biology would be the least gender biased of the STEM disciplines. I was surprised that this type of bias has been uncovered in biology, and in classes with more female students than male students. The paper has already been highlighted on sources such as Science Daily, The Atlantic, and the Huffington Post, among others. The wealth of information in the paper — from the experimental design to the study setting to the final results — warrants an important, broad discussion.
In this post, however, I’ll focus on the networks.
The authors conducted multiple surveys where students nominated the “best performers” in their introductory biology courses at a large American university. These surveys were given at different parts of the course, and they were conducted across three different iterations of the same undergraduate biology class. Figure 1 of the paper shows two networks displaying two surveys from the same class, six weeks apart.
These networks show the students (represented as nodes in the graph) in a particular class, and “votes” as directed edges from nominators to nominees. Male students are shown in green, and female students are shown in orange. The size of nodes indicates the number of nominations received by each student. The structure of these networks is striking. There are many students who do not nominate anyone and are not nominated by anyone, resulting in “singleton” nodes. In both networks, there is a general cohort of students that receive nominations; however the distribution of these nominations are much more skewed in the second survey.
The intuitive trend that we see in these graphs is that “the green nodes tend to get bigger” corresponding to a larger proportion of nominations go to male students. However we see female students also receive more nominations in the second survey compared to the first. The authors quantify these aspects using exponential-family random graph models (ERGMs) to assign coefficients on model statistics relating to gender, outspokenness, and grade. They found a specific gender bias, that male students tend to nominate other male students, after controlling for grade and outspokenness. Female students, on the other hand, do not exhibit a gender bias toward nominating males (or females for that matter), after controlling for these factors.
There are many, many other factors that may contribute to these observations, and some are noted in the paper. The courses were taught (and in some cases co-taught) by four male instructors and only one female instructor, the classes ranged in size from 196 to 760 students, one class employed “random call” lists rather than calling on raised hands. Besides outspokenness, interactions in lab sections and outside class would undoubtedly affect students’ perceptions. This paper opens a tremendously important conversation about implicit gender bias in the classroom, even in majors with more female students than male students. As the paper concludes,
This gender biased pattern in celebrity was experienced by over 1,500 students in our analyses. This number is striking, but less worrisome than the millions of students who attend college STEM classes that may perpetuate the same biases described here.
Grunspan et al., PLOS ONE 2016.
* Pending approval of various college committees – it may change