Strategies for Creating Equitable and Effective Classrooms: Lessons from Feminism
by Ellen Spertus, Judy Goldsmith, and Colleen Lewis
Feminism has often been criticized — rightly, in our opinion — for focusing on the struggles of educated white women. Intersectional feminism, on the other hand, is the belief that different forms of oppression, such as sexism, racism, ageism, and classism, are interconnected and cannot be fully understood, or countered, in isolation. We believe that intersectional feminism can be applied to become a more effective teacher of all students by (1) avoiding dichotomous thinking about students, (2) minimizing power differences, and (3) taking into account past and ongoing differences in students’ experiences. The three of us presented these ideas at a panel “Strategies for Creating Equitable and Effective Classrooms: Lessons from Feminism” at Grace Hopper 2015.
Ellen felt that, when she attended MIT, some professors viewed students as belonging to one of two groups: winners or losers. These professors devoted their resources to the first group and wasted as little as possible on the latter. Some professors openly said that they taught to the top 10%, perhaps because they identified with them or thought that only these students were destined for greatness. We see two problems with this philosophy. First, not every student has had the advantages of the top 10%: Due to poverty, racism, or sexism, for example, some students have not had the same educational opportunities as other students. Second, whether or not some students are “better” than others, all of them deserve the best education that the school can provide. Dividing students into first- and second-class citizens is just as pernicious as other forms of discrimination, even if some women of color, for example, make it into the top group and some well-off white men are in the bottom.
Ellen took an alternative approach when recently teaching an introductory computer science course. When most students bombed an early test, she could have congratulated herself on distinguishing the good students from the bad students or just gone on to the next topic. Instead, she apologized to the class for testing them prematurely and gave them another opportunity to learn the material and demonstrate their knowledge. While this did slow down the pace for the top students — perhaps ones who had already taken a programming class — it kept the majority of students from getting left behind and becoming unable to understand subsequent material. No students complained about being insufficiently challenged; if they had, they would have been offered supplemental work.
While most professors are loathe to admit mistakes, we think that all professors should do so, especially when teaching computer programming, a field in which it is impossible to work without making mistakes. The best way to teach students that making mistakes is okay is to cheerfully admit one’s own. For example, Ellen thanks students and gives them prizes when they catch her mistakes in lecture and, when pointing out bugs in student programs, lets them know she found the mistake so quickly because she has made it herself. This type of reassurance is especially important to students who might have absorbed the stereotype that computer science is not for people like them (of color, female, immigrant, old, disabled, of low socioeconomic status, etc.)
As an educator, it is important to understand that your students’ experiences differ from your experiences in ways that you can’t expect. Colleen privately asks her mentees to share experiences where they felt welcome and unwelcome to better understand her students’ experiences and how she might create an inclusive classroom. To make students feel more at ease about their differences, Ellen is open about her physical impairments (strabismus and an essential tremor) and Judy talks about her own learning disability. On the first day of class, Judy has students practice saying “I don’t understand” out loud. This gives them explicit permission to politely challenge what she has said or written; it also normalizes and shares the possibility of being confused. She also talks about the Myers-Briggs notion of introverts and extroverts as ones who think before speaking, vs. ones who think by speaking; she asks for cooperation from the extroverts to allow the introverts time to think before the extroverts jump in with answers. When class participation is graded, she includes the option of online discussion forums for those uncomfortable speaking on the spot in class.
We all work to make our classes safe places in which to discover and learn, but also to experience frustration and confusion. We talk in different ways about the discovery process, and how all of us experience setbacks and discover our own mistakes. We try to make clear that this is part of the process, not a mark of inadequacy or non-belonging. For example, when Ellen points out mistakes in student programs, she says that she found the mistake so quickly because she’s made it herself.
We all know that there are stereotypes about who participates in various fields and stereotypes about the abilities and behaviors of various groups of people. While we might like to think we don’t treat students differently based on these stereotypes, they are so prevalent and unconscious that no one is immune. We encourage readers to take an implicit association test to learn about their own unconscious biases. Ellen was chagrined to discover that she has an implicit bias against women in science, despite her years teaching at a women’s college.
It’s important to know about our own biases so we can counter them. Unchecked, our implicit assumptions about students affect the ways in which we:
- give students hints on a homework assignment — whether we tell them the answer or lead them to it, and our tone of voice, facial expression, and body language;
- describe their strengths and weaknesses in a letter of recommendation;
- counsel a student who received a bad exam grade;
- make recommendations of classes to take; and
- grade students, even in scientific fields.
In addition to being aware of the possible effect of stereotypes on us, we need to be aware of how they affect students. Numerous studies have demonstrated a phenomenon called stereotype threat, in which students’ performance suffers if they are reminded of stereotypes, either directly, such as being asked to check a box at the top of a test indicating their gender, or indirectly, such as being told that a test is a measure of intelligence, when they know that members of their group are thought to be less intelligent than other groups. Stereotype threat can be reduced by:
- not asking for demographic information at the beginning of a test;
- encouraging students to think of themselves as members of a positively regarded community, such as college students studying mathematics rather than as women studying mathematics;
- emphasizing that tests are a measure of current knowledge, not innate ability;
- mentioning reasons that students might do poorly on an exam (such as anxiety) that have nothing to do with competence; and
- providing diverse role models.
While true equal opportunity in the classroom cannot be achieved in an unjust society, there are strategies that can be effective at improving experiences for underrepresented and underserved students. We have found employing them to be rewarding for us and appreciated by the students, especially those who initially felt most ill at ease in the computer science classroom. We hope that, in addition to increasing the diversity of the computing profession, we help pave the way for more diverse faculties that fully represent available talent.
Bates, Laura (2014). Sexism, double discrimination and more than one kind of prejudice. The Guardian (Guardian Media Group).
Crenshaw, Kimberlé W. (2001). Demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics, in Mabokela, Reitumetse Obakeng, & Green, Anna L., Sisters of the academy: Emergent Black women scholars in higher education, Sterling, Virginia: Stylus Pub, pp. 57–80.
Easterly, Debra M., & Ricard, Cynthia S. (2011). Conscious efforts to end unconscious bias: Why women leave academic research, Journal of Research Administration, 42(1), pp. 61–73.
National Center for Women in Information Technology (2010). How can reducing unconscious bias increase women’s success in IT?
Pennington, C. R., Heim, D., Levy, A. R., & Larkin, D. T. (2016). Twenty years of stereotype threat research: A review of psychological mediators, PLoS One 11(1).
Valian, Virginia (2005). Beyond gender schemas: Improving the advancement of women in academia, Hypatia, 20(3), pp. 198–213.