Authors: Stepfanie M. Aguillon, Gregor-Fausto Siegmund, Renee H. Petipas, Abby Grace Drake, Sehoya Cotner, Cissy J. Ballen
First Author’s Institution: Cornell University
Journal: CBE Life Science Education 19:ar12 (2020)
Many apps have been created to provide real-time feedback on how inequitable meetings and conversations are (for example, arementalkingtoomuch.com and Time To Talk). Yet, we haven’t focused as much attention on the classroom, specifically active learning environments.
In one study in a traditional lecture setting, researchers found that even though 60% of the students identified as women in a biology class, only 40% of the students who participated in class identified as women. However, there is some evidence that smaller class sizes and diverse teaching strategies can lead to more equitable participation.
Would it be the same for active learning classes which emphasize active engagement with course material?
To find out, the author’s of today’s paper observed 40 lectures of an introductory evolutionary biology and biodiversity course at a northeastern U.S. institution over two years.
The course consisted of 3 weekly 50 minute lecture meetings and 1 weekly 50 minute discussion section. These sections used a variety of active learning techniques such as prelecture assignments and quizzes, in-class group work, and iClicker questions. In addition, these active learning components contributed significantly to each student’s final grade.
To understand what was happening in the classroom, the researchers categorized each of the nearly 400 classroom interactions they observed. They also recorded the gender of the student in the interaction.
For example, if a student raised their hand and asked a question, the researchers called this an unprompted interaction while if the instructor asked students to discuss something in groups before randomly picking some groups to share, the researchers called this a group random call interaction. The full list of interaction types and examples are shown in table 1.
To start their analysis, the researchers first looked at the distributions of the type and number of interactions they observed. As shown in figure 1, the type and number of interactions by session observed showed much variation.
Across the courses, asking groups or individual students to report out (group random call and post-discussion) and a member of a group asking the instructor a question (group work) were the most common interactions the researchers observed.
Unsurprisingly, men participated more than expected for nearly all of the interaction types. Because the class was 40% male, participating as expected would have meant that 40% of the interactions were by male students. As shown in figure 2 though, not all of the differences were statistically significant (indicated by the ‘*’ above the bars).
Perhaps some of this could be explained by the gender of the instructor. Maybe women were more likely to participate when the instructor was also a woman.
Yet, that wasn’t the case. In fact, men still participated more than expected in group work, group random call, and unprompted interactions when the instructor was a woman.
If the gender of the instructor couldn’t explain the results, maybe there were some underlying differences in men and women in the course. For example, student’s sense of belonging and their self-efficacy (whether they believe they can do a task) can influence whether a student participates in class as can their competency with the material.
However, when the researchers looked at the end of semester survey data they had collected, they only found differences in some of these. For example, men only reported higher self-efficacy during the second year of the study but not the first.
When it came to grades, men did slightly better than women on exams. For the in class components however, men and women performed equally.
Taken together, differences in student’s self-efficacy or their grades couldn’t explain the differences in rates of participation. This finding suggests that there is something about the learning environment that is causing the differences in participation.
Overall, this study finds that men participated more in this active learning course than would have been expected based on the gender distribution in the course and having a female instructor teach the course didn’t change that. So while active learning may help alleviate performance gaps, there is still work to do in order to close the participation gaps and ensure that our courses foster equitable participation.
Figures used under CC BY-NC-SA 3.0. Header image courtesy of Allison Shelley/The Verbatim Agency for American Education: Images of Teachers and Students in Action . Used under CC BY-NC 2.0.
I am a physics and computational mathematics, science, and engineering PhD student at Michigan State University and the founder of PERbites. I’m interested in applying machine learning to analyze educational datasets and am currently studying the physics graduate school admissions process.