Amanda Glazer
“You’re a girl, you overthink the problem too much.”
Introduction
For several decades now, the gender gap in mathematics degree attainment has remained static while the gender gap in college degree conferment has disappeared. According to the National Center for Education Statistics (U.S. Department of Education), women earned 57%, 60% and 52% of all Bachelor’s, Master’s and Doctoral degrees respectively in the U.S. in 2013-14. However, women earned only 43%, 41% and 29% of the Bachelor’s, Master’s and Doctoral degrees respectively in mathematics and statistics in the U.S. in the same year. Further, according to the National Science Foundation, women earned 42% of the Doctoral degrees in Life, Physical, Earth, Math, Computer and Social Sciences, Engineering, and Psychology, but women earned only 25% of the Doctoral Degrees in Mathematics and Computer Sciences in 2015 (National Science Foundation). Additionally, while women constitute 46.8% of the work force, they comprise 25.5% of “Computer and Mathematical Occupations” (U.S. Department of Labor). Prestigious, private universities are of particular interest, because the gender gap in mathematics appears to be worse at these universities.
Table 1:Gender Breakdown of Mathematics Departments at Five Group I Private Institutions
| Number | Percentage Women | |||||
|---|---|---|---|---|---|---|
| Bachelor’s | PhD | Senior Faculty | Bachelor’s | PhD | Senior Faculty | |
| Harvard | 245 | 58 | 25 | 20% | 12% | 4% |
| MIT | 663 | 139 | 51 | 28% | 20% | 8% |
| Yale | 176 | 31 | 17 | 26% | 16% | 6% |
| Princeton | 209 | 85 | 41 | 15% | 13% | 7% |
| Brown | 113 | 42 | 24 | 27% | 21% | 8% |
The above table reports the number of bachelor’s and doctoral degrees conferred in mathematics between AY09-10 and AY14-15 and the number of senior faculty members at institutions surveyed in the National Mathematics Survey. It also reports the percentage of these degree recipients and faculty members that are women. Degree data comes from the Integrated Postsecondary Education Data System (IPEDS); a collection of annual surveys administered by the U.S. Department of Education’s National Center for Education Statistics. Faculty data comes from each institution’s mathematics department website as of November 2016. Senior faculty members include Full Professors, Professors Emeriti and anyone listed under Senior Faculty on the department website (note: at Harvard this also includes Professors of the Practice).
Available data from five Group I Private universities gives further evidence of the gender gap in mathematics at the undergraduate, graduate and faculty level. As Table 1 shows, over a recent 6-year period, none of the above elite mathematics schools matched the national average percentage of mathematics Bachelor’s degrees awarded to women (44% in 2013-14 for “mathematics, general”). Low proportions of women graduates persist in the faculty at Group I Private universities; each of the five selected institutions have less than 10% female senior faculty members. This is unsurprising given that senior faculty composition both reflects the BA and PhD pipeline of prior years, and also influences the gender composition of new graduates.
Gender-based differences in mathematics achievement may reflect a number of factors. One major factor is a country’s level of gender equality and the prevalent societal gender stereotypes. In general, countries that are considered to be more gender equal (as measured by political opportunities and empowerment, education, etc.), have a smaller gender gap in mathematics at the secondary school level. In fact, the gender gap in mathematics amongst adolescents disappears in countries that are judged as completely gender equal (Nosek, 2009; Guiso, 2008; Marks, 2008; Hyde and Martz, 2009).
Within countries with greater gender inequality, one of the potential causes of the gender gap in mathematics is “stereotype threat.” Stereotype threat is when an individual feels at risk of confirming the negative stereotypes about their group. Stereotype threat can lead women to conform to gender stereotypes and perform worse on assessments of performance. In the U.S., stereotype threat has been linked among women at large public universities to worse performance on mathematics tasks (Spencer, Steele and Quinn, 1999). Stereotype threat has also been linked to decreased interest and persistence in Science, Technology, Engineering and Mathematics (STEM) fields (Woodcock, 2012; Shapiro and Williams, 2012).
Furthermore, negative stereotypes may lead to unconscious bias in faculty members. Studies have found that faculty – both men and women – tend to favor men in STEM fields by, for example, being more likely to select them for research positions (Moss-Racusin, 2012). Science faculty have also been found to use different language to describe female versus male students in letters of recommendation, focusing more on ability and using more effective language for male than for female students (Schmader, Whitehead and Wysocki, 2007). A number of other manifestations of unconscious bias in academia have been well documented, such as a tendency to hold women to higher standards and judge them more harshly for opportunities for advancement (Easterly and Ricard, 2011).
Although faculty of all genders can exhibit bias, professor gender can have an important offsetting effect. Female professors and role models in STEM fields can have a positive effect on female students’ success (Carrell, Page, and West, 2010; Bettinger and Long, 2005). Furthermore, the success of peers, both close friends and classmates, often leads to more advanced course taking in adolescence. This influence is particularly evident amongst female students in regards to female peers (Riegle-Crumb, Farkas and Muller; 2006). This suggests the importance of female instructors, role models and successful female peers in order to provide an opposition to gender stereotypes. The current makeup of most mathematics departments in Group I Private schools, therefore, has the potential to exaggerate these stereotypes, as they are predominately composed of male faculty.
Despite the abundance of research on the gender gap in STEM fields and its potential causes, much of the research focuses on students in secondary and elementary school. The research I have come across that does focus on undergraduates tends to focus broadly on STEM fields and does not always focus on mathematics in particular. Although the gender gap in mathematics begins at a very young age, it persists and worsens throughout college (Griffith, 2010; Brainard and Carlin, 1997). Therefore, it is imperative to conduct studies to understand the gender gap at the undergraduate level. Furthermore, there is evidence that the prestige of the school correlates with a larger gender gap in mathematics (Weeden, Thébaud and Gelbgiser, 2017; Espinosa, 2011). This indication serves as motivation to further study elite universities to understand the larger gender gap at these institutions.
This study focuses on mathematics undergraduate students at Group I Private mathematics universities in order to understand in particular the gender gap in mathematics at prestigious universities. In order to investigate the climate in these departments, I conducted a survey of undergraduate mathematics students at the five elite universities identified in Table 1 in Spring 2016. The primary goal of the survey was to assess the climate of various prominent mathematics departments nationwide, in particular with regards to gender. The study also included the collection of in-depth information about Harvard, which will form the basis for a case study. This paper will be a mixed methods paper presenting some of the key results of this survey using both quantitative and qualitative data. My analysis presents additional evidence of the gender gap in mathematics and bias within the field at the undergraduate level at Group I Private universities.
Methods
Data for the National Mathematics Survey was collected in February and March of 2016 at five prestigious, private schools (Harvard, MIT, Princeton, Yale, and Brown). While any student at any university could fill out the survey, these five schools were primarily targeted for responses. Each of these five schools has an acceptance rate of less than 10% and is one of the top 15 mathematics schools in the U.S. based on their graduate program in mathematics (US News & World Report). There were 2,668 total respondents. The survey was sent out through the help of undergraduate contacts at the targeted universities and the Mathematics and Statistics department at many of these schools. For example, at Harvard, the math, statistics, applied mathematics and computer science departments sent out the survey to all concentrators. All mathematics concentrators at Harvard were also sent a personal email with the survey link.
In order to understand the climate of mathematics departments and make-up of mathematics students at Group I Private, mathematics universities, I analyze the data from these five, targeted universities. I focus on mathematics majors/concentrators as well as students who have taken mathematics at the given university. The mathematics major/concentrator respondent breakdown for the five, targeted elite universities by gender is as follows:
Table 2: Total Number of Respondents and Estimated Response Rate
| Math Respondents | Estimated Overall Response Rate | Estimated Female Response Rate | |||
|---|---|---|---|---|---|
| Total | Male | Female | |||
| Harvard | 89 | 27 | 58 | 54% | 83% |
| MIT | 84 | 50 | 33 | 19% | 27% |
| Yale | 44 | 34 | 10 | 38% | 33% |
| Princeton | 44 | 29 | 14 | 32% | 67% |
| Brown | 33 | 20 | 10 | 44% | 49% |
The above table gives the number of undergraduate mathematics majors/concentrators that filled out the National Mathematics Survey to some degree of completion, the estimated response rate and female response rate at the listed universities.
The estimated response rate was calculated by averaging the total number of mathematics Bachelor’s degrees conferred (2010-2015) at a given university each year to estimate mathematics degrees conferred in an average year at that university and then multiplied by four to get the approximate number of mathematics undergraduates over four years (freshmen through seniors). The total number of survey respondents, over all four years, from a given university that were mathematics majors/concentrators was divided by this number to estimate the response rate. Female mathematics major/concentrator response rate was estimated in a similar way.
The 10-minute survey was delivered via Qualtrics, an online platform. Questions covered a wide range of topics including college academics, advising/research, study habits, student mathematics organizations, mathematics department community, family background, mentorship, high school academics and other demographics. Respondents could opt to not answer any question.
Quantitative Data
Background
In general mathematics majors/concentrators at the various universities were very well prepared upon entering college. Respondents were asked to report the courses they had taken prior to enrolling in their current undergraduate institution. Almost all surveyed mathematics majors/concentrators at the five universities had completed calculus and often more likely than not multivariable calculus before enrolling in college. Many mathematics students had also completed several other advanced mathematics courses. For example, at Harvard, 47% of mathematics concentrators had taken Linear Algebra and 27% Differential Equations prior to enrolling at Harvard. In general, a larger proportion of men than women have a background in advanced mathematics upon entering college.
Table 3: Advanced Courses Taken Before Entering University
| Calculus | Multivariable Calculus | Linear Algebra | |||||
|---|---|---|---|---|---|---|---|
| Male | Female | Male | Female | Male | Female | ||
| Harvard | 97% | 96% | 72% | 44% | 52% | 41% | |
| MIT | 96% | 94% | 66% | 64% | 60% | 24% | |
| Yale | 91% | 100% | 79% | 60% | 50% | 50% | |
| Princeton | 90% | 93% | 66% | 71% | 52% | 43% | |
| Brown | 100% | 100% | 50% | 30% | 30% | 60% | |
The above table gives the percentage of undergraduate mathematics majors/concentrators that took calculus, multivariable and linear algebra before attending the given university.
Mathematics majors/concentrators also seemed to come from a rather supportive background in terms of parental support. Mathematics majors/concentrators were asked: “On a scale of 1 to 5, how much would you say your parents/guardians emphasized STEM education in your upbringing? 1 corresponds to did not emphasize STEM and 5 to highly emphasized STEM.” At the five institutions, female mathematics respondents had an average above 3.5 indicating parental STEM emphasis of moderate to high degree. This level of emphasis indicates that female mathematics majors/concentrators had at least a medium amount of parental STEM emphasis in their upbringing. At each of these universities except Princeton, female mathematics majors/concentrators had a higher degree of parental encouragement (and lower standard deviation in responses) than their male counterparts.
Table 4: Parental STEM Encouragement
| Male | Female | |
|---|---|---|
| Harvard | 3.72 (1.22) | 4.63 (.86) |
| MIT | 3.77 (1.22) | 4.12 (1.09) |
| Yale | 3.48 (1.19) | 3.8 (.98) |
| Princeton | 3.73 (.94) | 3.64 (.81) |
| Brown | 2.94 (1.2) | 4 (.89) |
The above table gives the mean and standard deviation (in parentheses) of response to the question “On a scale of 1 to 5, how much would you say your parents/guardians emphasized STEM education in your upbringing? 1 corresponds to did not emphasize STEM and 5 to highly emphasized STEM.” Responses are broken down by female mathematics majors/concentrators and male mathematics majors/concentrators at each university.
Furthermore, at each of these five institutions, the majority of female mathematics majors/concentrators and over 45% of male mathematics majors/concentrators has at least one parent with a STEM degree. At all of these schools, female mathematics majors/concentrators are as or more likely to have a parent with a STEM degree than male mathematics students. At each of these universities, except Princeton, female math students are more likely to have a parent with a math degree then their male counterparts. At least 10% of surveyed female math students at each of these universities have a parent with a math degree.
Table 5: Parent with STEM/Mathematics Degree
| Parent with STEM Degree | Parent with Mathematics Degree | |||
|---|---|---|---|---|
| Male | Female | Male | Female | |
| Harvard | 74% | 74% | 14% | 15% |
| MIT | 46% | 76% | 4% | 12% |
| Yale | 47% | 50% | 9% | 30% |
| Princeton | 57% | 64% | 24% | 14% |
| Brown | 60% | 80% | 0% | 40% |
The above table gives the percentage of undergraduate mathematics majors/concentrators at a given university that have at least one parent with a STEM degree or, in particular a mathematics degree.
In terms of mentorship, a mentor being defined in the questionnaire as a parent, relative, family friend, teacher or other adult who especially encouraged the respondent to study math, the majority of mathematics undergraduates had at least one mathematics mentor. Very few mathematics undergraduates never had a mathematics mentor, although it was far more common amongst men than women. At most of the five universities, female mathematics undergraduates were more likely to have 4 or more mentors and less likely to have never had a mathematics mentor. There is more variation in the number of mentors male math students had, although it is more likely that men had four or more mentors than no mentor, except at Brown.
Chart 1: Number of Mathematics Mentors
Additionally, mathematics undergraduates generally found mathematics mentors at a young age. Women mathematics majors/concentrators tend to find their first mathematics mentor at a younger age then men. Mathematics majors/concentrators, in general, tend to find their first mathematics mentor at a young age, but there is more variance in this age amongst men. With the exception of male math students at Brown, it is more likely that math students found their first math mentor when they were younger than 10 than when they were older than 18.
Chart 2: Age First Mentor Found
Math 55 (Harvard)
When studying the gender gap in mathematics undergraduates, it is interesting to investigate the experience in first year college mathematics courses. This experience can be influential in determining whether or not a student pursues a mathematics degree. Of particular interest in the Harvard mathematics department is the course Math 55. Math 25 (Honors Linear Algebra and Real Analysis) and Math 55 (Honors Abstract Algebra/Real and Complex Analysis) are the most advanced first year course offerings at Harvard. Math 55 prides itself in being regarded as one of the most difficult undergraduate mathematics courses in the world. 63% of surveyed Harvard mathematics concentrators’ first mathematics course at Harvard was Math 25 or Math 55. Only 5% of surveyed mathematics concentrators began in a single variable calculus course (Math M or 1).
A large percentage of female mathematics concentrators, 26%, reported feeling dissuaded from taking a mathematics course due to the gender imbalance in a mathematics course. Math 55 exemplifies this gender imbalance. Five of the last ten semesters (50%) have seen no women enrolled in Math 55. Over the past 5 years, less than 7% (11/163) of the students enrolled in Math 55 have been women.
Table 6: Math 55 Enrollment
| Year | Semester | Total Enrollment | Percent Female |
|---|---|---|---|
| 2012-2013 | Fall | 14 | 0% |
| Spring | 13 | 0% | |
| 2013-2014 | Fall | 17 | 12% |
| Spring | 15 | 13% | |
| 2014-2015 | Fall | 17 | 0% |
| Spring | 15 | 7% | |
| 2015-2016 | Fall | 11 | 0% |
| Spring | 12 | 0% | |
| 2016-2017 | Fall | 25 | 19% |
| Spring | 24 | 9% |
The above table gives the total enrollment and the percentage of those enrolled that are women for the past ten semesters in Harvard’s Math 55.
The Harvard Mathematics department website advertises this course as “probably the most difficult undergraduate math class in the country” and that “problem sets can take anywhere from 24 to 60 hours to complete.” The department says that “a thorough knowledge of multivariable calculus and linear algebra is almost absolutely required” and that “Students who benefit the most from this class have taken substantial amounts of advanced mathematics and are fairly fluent in the writing of proofs.” They describe the demographics of this class as “often contains former members of the International Math Olympiad teams, and in any event, it is designed for people with some years of university level mathematical experience.”
As shown in the “Background” section of this paper, there is a substantial proportion of sampled female concentrators that have taken calculus, multivariable calculus and linear algebra, however none of them opted to take Math 55.
Department and Faculty Involvement
Involvement in the mathematics department community is important, because it illuminates how welcome and comfortable students feel in the department. Interaction and comfort with faculty similarly indicate these aspects of the department. In general, faculty involvement with students at the five universities was reported to be low. Most mathematics undergraduates reported asking mathematics faculty members for advice rather infrequently (once a semester or less). Female mathematics students tend to ask for advice less frequently than male mathematics students.
Chart 3: Frequency of Faculty Advice
Not only do mathematics undergraduates ask for advice from faculty rather infrequently, but high proportions (over a third at Princeton and Brown and over half at Harvard, MIT and Yale) of mathematics undergraduates also felt that they had at most one mathematics faculty member they could ask to write them a letter of recommendation. With the exception of Brown, surveyed women feel they have fewer mathematics faculty members they can ask for letters of recommendation then men.
Chart 4: Letters of Recommendation
Beyond faculty advice and letters of recommendation, feelings of involvement in the mathematics department were not ranked very high for most mathematics undergraduates. The mean was under 3.5 for every school indicating a moderate to low degree of perceived involvement. The mean for female mathematics students was lower than for male mathematics students at each of the five universities.
Table 7: Perceived Department Involvement
| Dept. Involvement: | Male | Female |
|---|---|---|
| Harvard | 2.64 (.87) | 2.3 (1.27) |
| MIT | 2.49 (.77) | 2.38 (.72) |
| Yale | 2.88 (.96) | 1.9 (.7) |
| Princeton | 3.36 (.93) | 3.17 (.99) |
| Brown | 3.19 (1.01) | 3.13 (1.05) |
The above table gives the mean and standard deviation (in parentheses) of response to the question “On a scale of 1 to 5, how involved would you say you are in the mathematics department? 1 corresponds to not involved at all and 5 to very involved.” Responses are broken down by female mathematics majors/concentrators and male mathematics majors/concentrators at each university.
While involvement in the mathematics department was not particularly high for either gender, a non-trivial proportion of both male and female mathematics undergraduates expressed a desire to be more involved in the mathematics department. With the exception of female mathematics concentrators at Brown, over 40% of mathematics concentrators/majors expressed a desire to be more involved in their mathematics department than they are currently.
Table 8: Want More Involvement
| Dept. Involvement: | Male | Female |
|---|---|---|
| Harvard | 48% | 59% |
| MIT | 48% | 45% |
| Yale | 47% | 60% |
| Princeton | 47% | 43% |
| Brown | 41% | 30% |
The above table gives the percentage of female and male mathematics majors/concentrators at each university that reported wanting to be more involved in the mathematics department than they currently are.
In sum, mathematics undergraduates do not frequently ask for advice from mathematics faculty nor do they feel like they have many mathematics faculty members they can ask for a letter of recommendation. Mathematics undergraduates also feel a low level of involvement in their mathematics department, but a desire to increase their level of involvement.
Gender Related Questions
Large proportions of female mathematics undergraduates at the surveyed universities have felt dissuaded from taking a mathematics course due to the gender imbalance in the mathematics course:
Table 9: Dissuaded from Mathematics Class Due to Gender Imbalance
| Dissuaded Math Class | Female Students | Female Concentrators/Majors |
|---|---|---|
| Harvard | 28% | 26% |
| MIT | 23% | 39% |
| Yale | 36% | 60% |
| Princeton | 18% | 14% |
| Brown | 31% | 22% |
The above table gives the percentage of surveyed female students who have taken a mathematics class and female mathematics concentrators/majors at each university that have felt dissuaded from taking a mathematics course due to the gender imbalance.
Not only are sizable proportions of women mathematics concentrators feeling dissuaded from taking mathematics courses due to the gender imbalance, they are also facing comments in regards to their gender and mathematics ability. For example, at Harvard, 66% of surveyed female mathematics concentrators have faced gendered comments from other students. These comments and examples of such comments are discussed in the next section.
Qualitative Data
The survey provided opportunities for respondents to give examples of comments that they had faced or elaborate on their experiences in mathematics. The relevant questions from the survey questionnaire are:
- “In the environment(s) in which you practice mathematics (e.g. mathematics class, office hours, the mathematics lounge), has another student ever made a comment concerning your gender in regards to your mathematics skills?”
- “In the environment(s) in which you practice mathematics (e.g. mathematics class, office hours, the mathematics lounge), has a mathematics faculty member ever made a comment concerning your gender in regards to your mathematics skills?”
Respondents were also asked to rate how negatively or positively these comments affected them and elaborate on these comments (if applicable). In order to further preserve anonymity all school names have been eliminated.
Comments from Other Students
Amongst the 5 schools reported in the previous sections, 50% of female students (from all majors, have taken at least one college mathematics course) reported comments from other students in regards to their gender and mathematics ability of which 91% were reported to have had a neutral or negative effect. Respondents were asked to give examples or elaborate on the types of comments that they had received from other students in regards to their gender and mathematics ability. The following are some the most frequent categories of comments with representative examples and explanations from surveyed female students for each category.
Gender Stereotypes
These comments reinforced negative stereotypes about women and women in mathematics. Typically they reinforce the negative stereotype that women are not good at mathematics.
- “You don’t count as a girl because you’re good at math”
- “You’re the only girl I know who is good at math”
- “”She does math AND is hot, it’s crazy” perpetuates the stereotype that to do maths (as a girl) you can’t take care of your appearance (otherwise you must be dumb).”
- “Assertions that women are not capable of the same brilliance as men.”
- “You’re a girl, you overthink the problem too much.”
- “There has been a lot of surprise that I am good at math, and recently, someone even asked me if I had the right room for a class.”
- “Girls shouldn’t be able to do math”
Disrespect and Disregard for Intelligence
These comments illuminate a lack of respect for female intelligence in mathematics. Women’s ideas and intelligence are often disregarded. If regarded, their success is attributed solely to their gender and not their intelligence in this type of comment.
- “Sometimes people don’t want to work with me because I’m a girl – the most common experience I have is explaining something to a male classmate, only to have him ask another male classmate to validate what I’ve said. UGH.”
- “being a female in math is difficult. I will never be as respected as my male peers. Many an occasion occurs when collaborating where my ideas are completely disregarded as the only female in the room until they have exhausted all their ideas and then finally consider it (maybe)”
- “one of my problem set partners mentioned that I should have an easy time finding a summer internship because I’m a girl.”
- “Expressions of surprise at my capability to do certain things”
- “If you are a woman and you struggle with math, inevitably someone who thinks he’s too clever to be sexist will say something about men’s superior spatial reasoning or complain that professors and departments cut female students extra slack to improve their gender ratio… It’s bitter and annoying, but at this point in my education it makes me more determined than discouraged to hear that sort of thing.”
- “It’s not like girls will ever need to use math, the only stem they can do is regurgitating biology”
- “You only got this opportunity because you’re a girl”
“For a girl”
The “for a girl” comments measure a mathematician’s ability against their gender. Women are not deemed as being good at mathematics generally but rather out of the pool of other women.
- “You’re good at math for a girl”
- “These comments, over time, have had a negative effect– because other people encouraged me to compete in the arena of “women in math” and imply that it’s good enough– I need not compare myself to “people in math” because I’m “good at math for a girl”.”
Pressure to Represent
These elaborations discuss a pressure some female mathematics students feel to represent their gender well, since there are very few women in the mathematics program. They also touch on a sense of competition felt amongst female math students to do well.
- “A mathematics course professor was once discussing the exam results and was showing the results via demographics. The professor went out of his way to mention that “for some reason, women tended to much worse than men.” Although the data showed this was a true statement, stating it out loud for the 90% male and 10% female class to hear really put a weird shame and pressure on female students, who already struggle socially and stigmatically in the department.”
- “I feel alone, increasing the pressure to do well to represent my gender”
- “if a female student asked a stupid question my friend used to say to me “that’s one less girl we have to compete with””
Comments from Mathematics Faculty
Respondents were asked to give examples or elaborate on the types of comments that they had received from faculty in regards to their gender and mathematics ability. Among the 5 schools previously discussed, 15% of surveyed female students have received a comment from a mathematics faculty member in regards to their gender and mathematics ability, of which 63% of such comments were reported to have a neutral or negative effect. Examples and elaborations of such comments are as follows:
- “Grad student indicated that he felt that there was no need to be concerned about the number of women in math or to try to encourage more women to take math. He also stated that he would not mentor a female student in the same way he would mentor a male student because she might falsely charge him with sexual harassment. When I confided to a professor that I was having difficulty in his class because I didn’t have a study group to work with in an almost exclusively male environment, his response was to tell me that if I wasn’t able to keep up with the class, perhaps I should request a tutor or drop the course in favor of an easier one. Professor stated that women simply came in with less math preparation in high school, and that there was nothing the university could do to fix that. Professor insinuated that I might not be good enough at math to do a senior thesis, and that I should instead opt for a senior seminar. Interestingly, the only other people I have found who heard this from him seem to be women.”
- “There are a couple professors who will actively encourage female undergraduates to try to fight the gender imbalance in the department, which tends to have slightly positive effect. Every once in a while other professors will slip and say something that’s a little bit misogynistic, but this is mostly much older professors, so I tend to just ignore it.”
- “A math faculty member with whom I’d been interacting with for several months confusing me on several occasions for one of the only other women of my ethnicity in the department. On another occasion, hearing secondhand about a fellow student who was dissuaded from doing research by a male faculty member specifically on account of her gender.”
- “”I didn’t finish that pset last week.” “Of course, look who you were working with” (a group of girls)”
- “A visiting professor walked through at office hours for my math class and commented how much more attractive girls in math were now than in his day.”
- “I’ve had a Math professor looking me up and down with surprise before talking to me and asking me if I’m sure I can handle maths research.”
- “They said that they were glad to see a girl who was interested in math, which was reassuring in my math skills, yet made me concerned as to the assumption that most girls aren’t interested”
- “Not explicitly referencing my gender as the rationale, but having me and another girl my age work with a younger and/or less experienced group of students.”
Discussion
From the quantitative data, it is apparent that an extremely mathematically well-prepared group of students go on to concentrate in mathematics. Mathematics concentrators typically come into their undergraduate university with strong backgrounds in regards to course work and parental support. More often than not, they have already completed a course in calculus and multivariable calculus and have at least one parent with a STEM degree. At Harvard, mathematics concentrators typically begin with the most challenging courses, i.e. most concentrators begin in Math 25 or 55. Female mathematics concentrators tend to come from even stronger family backgrounds (e.g. more parental STEM emphasis). At first thought, this all seems very positive: top schools are attracting an extremely talented group of mathematics students. The flip side to this, however, is that there are very few people concentrating in mathematics that do not come from this particular background. This enforces the stereotypes of what a mathematician looks like and potentially closes off the field to a wide group of people by making the barrier to entry too high. Female mathematics students came from equally strong and well-supported backgrounds, if not more so than males, in regards to family background and mentorship. This indicates that the barrier to entry is even higher for women.
At the five Group I Private, surveyed universities, there is a consistent trend in substantial proportions of female mathematics undergraduates feeling dissuaded from taking mathematics courses due to the gender imbalance and facing comments in regards to their gender and mathematics ability. The qualitative data further illustrates this problem. There are numerous examples of comments from both other students and faculty that enforce mathematics stereotypes and insinuate that women are less capable at mathematics. One of the most frequently cited type of comment was the “for a girl” comment, where students or faculty expressed surprised at a female mathematics students capability due to her gender or always complemented the student not amongst all students but amongst female students specifically. This further highlights that students and faculty members believe in the stereotype of who can be a mathematician (i.e. men).
While there is variation amongst surveyed universities, the basic trends remain the same. Universities attract well-prepared and supported mathematics students, but large proportions of female students face a negative mathematics climate. The national statistics on the proportion of women receiving degrees in mathematics is already an alarming indicator of the gender gap. The National Mathematics Survey goes further in showing the negative consequences at elite universities. Female mathematics students feel dissuaded from taking mathematics courses, often feel more disrespected in mathematics environments and face subtly and overtly sexist comments. In order to create a diverse and inclusive environment that allows everyone the equal opportunity to thrive as a mathematician, serious changes must be made.
Recommendations
National statistics and results from the National Mathematics Survey indicate a clear gender gap in mathematics and a variety of issues resulting from this gender gap. The natural question then is: what can be done to improve the climate of university mathematics departments and work to eliminate the gender gap in mathematics? I conclude with a list of recommendations. These are concrete suggestions of actions that I believe mathematics departments can take to improve the gender gap and overall climate.
- Hire more female faculty members
This is the most important item as the faculty members are how the department presents itself. There is a clear lack of female faculty members at the surveyed universities. A number of studies (Carrell, Page, and West, 2010; Bettinger and Long, 2005) have found that female professors have a positive effect on the performance of female undergraduates. The extremely low proportion of female faculty members perpetuates the gender gap in mathematics. Female faculty members can serve as role models and mentor figures to women in mathematics. Mathematics departments should be extending several offers to female mathematicians and be actively working to recruit them. Enough offers should be extended to result in an increase in female mathematics faculty members.
Note: this also extends to all positions that the department has control over. The department should actively work to recruit female mathematicians as junior faculty as well. The department should also think about this in terms of graduate students and postgraduate students, as, for example, the most recent class of mathematics graduate students at Harvard includes no women.
- Mandatory Unconscious Bias Training
Require all faculty members and mathematics course assistants (CAs) to complete an unconscious bias training. A variety of literature exists on unconscious bias and its harmful effects on women in STEM fields (Hill, Corbett and St Rose, 2010; Easterly and Ricard, 2011). In order to mitigate its effects, require all faculty members and CAs to participate in an unconscious bias training. This will also help in reducing comments in regards to gender and mathematics ability by making people more aware of the effects of their actions and comments.
- Invite female mathematicians to speak
Invite prominent female mathematicians, at least once a semester, to speak. This highlights the accomplishments of female mathematicians and provides female role models to mathematics students. This also helps to negate the stereotypes surrounding who can be a mathematician. It will counter the “for a girl” comments that were prevalent in the comments section of the survey, by enforcing that women can be and are successful at mathematics not just “for a girl”.
- Mentorship Program
Create a mentorship program to provide undergraduates with a diverse set of mentors – perhaps pairing graduate students, postdocs or other faculty members with undergraduates. This allows a diverse set of undergraduates to receive a diverse set of advice and mentorship. This will bring more concentrators in by providing more support and a more welcoming environment, as they will have someone to turn to for advice.
- Encourage a collaborative environment
Make sure the structure is in place for the mathematics department to be as inclusive and collaborative as possible. For example, at Harvard, a mathematics night was created once a week in a house dining hall. At mathematics night, students can meet other students in their classes, work together and get help from their teachers.
- Restructure introductory courses
If necessary, restructure introductory courses to be more welcoming of students from a diverse set of mathematical backgrounds. Make sure all classes (e.g. Calculus I) count for concentration/major credit so students coming in with less course background are not at a disadvantage in completing the concentration. Make sure there are tutors available for students in first year mathematics courses and that students know how to utilize the support resources available to them.
- Provide funding to organizations working to create diversity in the department
At Harvard, Gender Inclusivity in Mathematics is an organization that works to reduce the gender gap in mathematics and create a more inclusive environment in the mathematics department. Providing funding for events sponsored by groups of this nature, including community dinners, speaker series and discussion groups, supports these goals. If a group of this sort does not exist, start one. Create an Association for Women in Mathematics (AWM) student chapter in order to create a more inclusive community.
References
Brainard, Suzanne G., and Linda Carlin. “A longitudinal study of undergraduate women in engineering and science.” Frontiers in Education Conference, 1997. 27th Annual Conference. Teaching and Learning in an Era of Change. Proceedings.. Vol. 1. IEEE, 1997.
“Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2016-17 Edition, “Labor Force Statistics from the Current Population Survey,” on https://www.bls.gov/cps/cpsaat11.htm
Carrell, Scott E., Marianne E. Page, and James E. West. “Sex and science: How professor gender perpetuates the gender gap.” The Quarterly Journal of Economics 125.3 (2010): 1101-1144.
Easterly, Debra M., and Cynthia S. Ricard. “Conscious Efforts to End Unconscious Bias: Why Women Leave Academic Research.” Journal of Research Administration 42.1 (2011): 61-73.
Espinosa, Lorelle. “Pipelines and pathways: Women of color in undergraduate STEM majors and the college experiences that contribute to persistence.” Harvard Educational Review 81.2 (2011): 209-241.
Griffith, Amanda L. “Persistence of women and minorities in STEM field majors: Is it the school that matters?.” Economics of Education Review 29.6 (2010): 911-922.
Guiso, Luigi, et al. “Culture, gender, and math.” SCIENCE-NEW YORK THEN WASHINGTON- 320.5880 (2008): 1164.
Hill, Catherine, Christianne Corbett, and Andresse St Rose. Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women. 1111 Sixteenth Street NW, Washington, DC 20036, 2010.
Hyde, Janet S., and Janet E. Mertz. “Gender, culture, and mathematics performance.” Proceedings of the National Academy of Sciences 106.22 (2009): 8801-8807.
Marks, Gary N. “Accounting for the gender gaps in student performance in reading and mathematics: evidence from 31 countries.” Oxford Review of Education 34.1 (2008): 89-109.
Moss-Racusin, Corinne A., et al. “Science faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109.41 (2012): 16474-16479.
National Science Foundation, National Center for Science and Engineering Statistics. 2016. Doctorate Recipients from U.S. Universities: 2015. Special Report NSF 17-306. Arlington, VA.
Nosek, Brian A., et al. “National differences in gender–science stereotypes predict national sex differences in science and mathematics achievement.” Proceedings of the National Academy of Sciences 106.26 (2009): 10593-10597.
Riegle-Crumb, Catherine, George Farkas, and Chandra Muller. “The role of gender and friendship in advanced course taking.” Sociology of Education 79.3 (2006): 206-228.
Schmader, Toni, Jessica Whitehead, and Vicki H. Wysocki. “A linguistic comparison of letters of recommendation for male and female chemistry and biochemistry job applicants.” Sex Roles 57.7-8 (2007): 509-514.
Shapiro, Jenessa R., and Amy M. Williams. “The role of stereotype threats in undermining girls’ and women’s performance and interest in STEM fields.” Sex Roles 66.3-4 (2012): 175-183.
Spencer, Steven J., Claude M. Steele, and Diane M. Quinn. “Stereotype threat and women’s mathematics performance.” Journal of experimental social psychology 35.1 (1999): 4-28.
U.S. Department of Education. Institute of Education Sciences, National Center for Education Statistics.
Bettinger, Eric P., and Bridget Terry Long. “Do faculty serve as role models? The impact of instructor gender on female students.” The American Economic Review 95.2 (2005): 152-157.
Weeden, Kim A., Sarah Thébaud, and Dafna Gelbgiser. “Degrees of Difference: Gender Segregation of US Doctorates by Field and Program Prestige.” Sociological Science 4 (2017): 123-150.
Woodcock, Anna, et al. “The consequences of chronic stereotype threat: domain disidentification and abandonment.” Journal of personality and social psychology 103.4 (2012): 635.