Abstract Women who start college in one of the natural or physical sciences leave in greater proportions than their male peers. The reasons for this difference are complex, and one possible contributing factor is the social environment women experience in the classroom. Using social network analysis, we explore how gender influences the confidence that college-level biology students have in each other’s mastery of biology. Results reveal that males are more likely than females to be named by peers as being knowledgeable about the course content. This effect increases as the term progresses, and persists even after controlling for class performance and outspokenness. The bias in nominations is specifically due to males over-nominating their male peers relative to their performance. The over-nomination of male peers is commensurate with an overestimation of male grades by 0.57 points on a 4 point grade scale, indicating a strong male bias among males when assessing their classmates. Females, in contrast, nominated equitably based on student performance rather than gender, suggesting they lacked gender biases in filling out these surveys. These trends persist across eleven surveys taken in three different iterations of the same Biology course. In every class, the most renowned students are always male. This favoring of males by peers could influence student self-confidence, and thus persistence in this STEM discipline.

Citation: Grunspan DZ, Eddy SL, Brownell SE, Wiggins BL, Crowe AJ, Goodreau SM (2016) Males Under-Estimate Academic Performance of Their Female Peers in Undergraduate Biology Classrooms. PLoS ONE 11(2): e0148405. https://doi.org/10.1371/journal.pone.0148405 Editor: Cheryl S. Rosenfeld, University of Missouri, UNITED STATES Received: June 6, 2015; Accepted: January 14, 2016; Published: February 10, 2016 Copyright: © 2016 Grunspan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Underlying data are subject to ethical restrictions as the data contain identifiable information, including gender and ethnicity, combined with sensitive information such as college grades. The limited, de-identified data can be obtained by contacting the corresponding author at grunny@uw.edu, pending ethical approval from the Institutional Review Board of the University of Washington. Funding: This research was supported in part by NSF DUE grant 1244847 (AJC). SMG was supported by R01-HD068395. Competing interests: The authors have declared that no competing interests exist.

Introduction Male faculty members outnumber female faculty members in every science, technology, engineering, and math (STEM) discipline [1]. The attrition of female STEM students from their disciplines can be seen in early stages of the progression to STEM careers including the transition into college and graduate school [2]. The experiences of women in STEM that may lead to this attrition can be subtle. It is generally no longer considered a matter of outright discrimination, but rather the accumulation of smaller experiences that determine whether a female student identifies with and persists in a scientific field [3–6]. One factor linked to persistence in STEM fields is self-confidence. This factor is heavily influenced by social interactions, particularly for women in historically male-dominated fields [7–9]. For example, a student whose abilities are endorsed by an influential person may experience increased performance and confidence; conversely, a student not receiving this affirmation experiences a decrease in performance and confidence [10,11]. While formative experiences like these can occur throughout one’s life, certain periods are more influential than others. Individuals are particularly attuned to cues confirming or discrediting their ability to succeed in a field during major transition periods [8,12]. Student experiences at one of those key transition points, introductory STEM courses, thus seem likely to influence current and future decisions to persist in STEM disciplines. STEM faculty members provide some of the first professional feedback and interactions that students receive in their disciplines. Unfortunately, both male and female faculty members behave in ways that subtly favor males in STEM disciplines: (a) they are more likely to spend time mentoring males [13], (b) they are more likely to respond to emails from males [14], and (c) they are more likely to call on males in class [15]. These subtle yet consistent biases appear to cause at least some female STEM students to experience a lower sense of belonging or confidence in their discipline, resulting in an increased tendency to leave science [16]. In addition to interactions with faculty members, interactions with other students could impact a student’s sense of belonging and confidence in her discipline. In contrast to the work on gender biases among faculty, only limited research has been performed on the disposition of current college-age students (the “millennial” generation) towards women in STEM and how this disposition may impact their female peers (but see [17,18]). Such research would not only help us to measure one force that may be acting to decrease undergraduate females’ sense of belonging in STEM fields; it would also help us predict whether we can expect these implicit biases to persist in future STEM faculty. In this paper we focus on the formative experience of nascent STEM professionals during an introductory college science course, a key transition period for the development of a STEM identity [19]. We explore this question in a biology classroom. We chose this field because females and males enroll equally in this discipline at the undergraduate level [20] and thus should represent a conservative case of the biases women in introductory STEM courses experience. We explore the impact of gender on how students perceive their peers, as well as how students are perceived by their peers. It is important to note that the gender data used in this study come from the school registrar, and are thus defined by information given during student enrollment. The registrar constrains choice for gender identification to ‘male’ or ‘female’ choices. Given these complications, we choose to refer to student genders, but recognize that in some cases the data may not accurately reflect the true gender identity of each student. To investigate how gender impacts peer perception, undergraduate students were asked to anonymously list class peers who they felt were “strong in their understanding of classroom material” at multiple time points throughout three iterations of a large introductory biology class. We employ longitudinal social network analyses of these data to (1) describe the distribution of nominations received between males and females, and (2) identify the factors that predict who a student will nominate as having mastered the content in their field. Finally, (3) we examine the characteristics of students receiving the most nominations in each class (to whom we refer to as “celebrities”). We focus on these students given our assumption that their ability to draw widespread acknowledgment of their excellence makes them among the most likely in the class to continue in the field beyond the undergraduate level.

Materials and Methods Ethics statement We obtained human subjects approval from the University of Washington Institutional Review Board (#44438). Because students were not asked to do anything outside of the normal class curriculum, an altered consent process was approved for use in this study. Subjects were informed that a research study was taking place and that their data would be analyzed as part of this study. Students were informed that they could opt out of the study at any time by filling out a form in a centralized office. Classroom and classroom data Data come from three different iterations of the same large introductory undergraduate biology class (n = 196, 759, and 760, corresponding to class A, B, and C, respectively) at a large American university that engages in very high research activity (an R1 university). The class of interest is the second in a series of three introductory Biology classes, where the first class in the series served as a pre-requisite. Because this was the second class, many students enter the class already knowing many of their peers. Student demographic information, including gender, was collected from the Office of the University Registrar and course grades from the Department of Biology. All three iterations of this course included a lab section with a maximum capacity of 24 students that met once a week for several hours. Classes A, B, and C contained 9, 33, and 33 lab sections, respectively. The gender distribution within lab sections is approximately normal and mirrors that of the overall class (Mean = 57.4% female, SD = 0.11). The lecture portion of the course met for 50 minutes a day four days out of the week, and employed active learning techniques in all three iterations of the course. In all three cases, lectures were split into two sections with approximately 100 students in each for class A, and approximately 375 in each for Classes B and C; the instructor stayed consistent between lectures each class day to assure minimal differences between the two sections. Classes A and B were both taught by a male instructor, while Class C had three total instructors: two male instructors teaching 75% of class days and one female instructor teaching 25% of class days. All three iterations of the class included three exams spaced throughout the quarter, and a non-cumulative final exam that took place one week after the end of the quarter. Grades were not publically posted in any of the three classes. A measurement of student outspokenness was collected by polling the course instructor of record immediately after the end of each course, and thus represents active participation as perceived by the instructor who was blind to the hypotheses being tested. Thus, a student who frequently offers an incorrect answer in class is considered equally outspoken as students who frequently offer the correct answers. Because measurements come from instructors, the list may be subject to each instructor’s own implicit biases. All three classes consisted primarily of white and Asian students (40.5% and 29.9% of entire population across the three classes, respectively). Student ethnicity is not included in these analyses for two reasons. First, the diversity in each classroom is such that statistical power to understand the perception of minority students is lacking. Second, this issue is substantial enough to warrant its own separate analysis. Student networks All network surveys were administered via a confidential online survey. For Class A, students were given a class roster after the first and second exams and were asked to mark students they felt were particularly strong with class material. In Class B students were asked at the beginning of the class to list students by name who they felt would do particularly well in the course. After the first, second, and third exams, they were asked to list students they felt were particularly strong with class material. The same collection method was performed in Class C as Class B, but in addition students were surveyed again before the final exam of the course. Surveys in Class C distinguished between students who responded and didn’t know anyone they felt were knowledgeable and students who didn’t list anybody due to a non-response to the survey. Thus, Class C offers the most accurate means to calculate response rates. An average of 81.4% (SD = 0.02) of students responded across the five surveys in this class, with 82.8% of female students responding (SD = 0.02) and 79.9% of males responding (SD = 0.01). We have no reason to believe that Classes A or B differed in response rates, or that response rates were skewed by gender in any manner. Analysis of nominations To assess the hypotheses about nomination structure, we used exponential-family random graph models (ERGMs). This approach can be thought of as a kind of generalization of logistic regression to social networks–with the log-odds of a tie (here, a nomination) between two actors being dependent on a set of predictors of interest [21]. Those predictors may include characteristics of either or both nodes (e.g. their gender, class performance or outspokenness). However, it can also include structural factors involving the other ties in the network–e.g. the tendency for ties in a directed network to be mutual, or to form a triangle. When such structural terms are present, ties become conditionally dependent and estimation becomes more difficult, with Markov chain Monte Carlo-based methods the current state of the art for estimation [22]. Nevertheless, the coefficients may still be interpreted in terms of their contribution to the conditional log-odds of a tie, given all of the other ties in the network. We specify two models, both of the general form: where Y ij represents the value of the tie from i to j, which equals 1 if i nominates j and 0 if they did not (we discuss missing data for these values in the SI). The quantity represents the complement of y ij, i.e. the state of all of the ties in the network other than y ij. The δ vector represents the amount by which the model statistics change when y ij is toggled from 0 to 1, and the θ vector represents the coefficients on these statistics. The first model contains seven model statistics (δ 1 through δ 7 ) and the second model contains nine (δ 1 through δ 9 ): δ 1 = 1 for all dyads [the main effect or intercept]; δ 2 = 1 if j nominates i, and 0 otherwise [mutuality]; δ 3 = 1 if i is female and 0 otherwise [female nominator]; δ 4 = 1 if both i and j are female and 0 otherwise [female-female bias]; δ 5 = 1 if both i and j are male and 0 otherwise [male-male bias]; δ 6 = 1 if i and j are in the same lab section [lab homophily]; δ 7 = -1 if j has no nominations other than that from i [0-indegree]; δ 8 = j’s final grade in the class [grade of nominee]; δ 9 = 1 if j is outspoken, and 0 otherwise [outspokenness of nominee]; We use the R package network to process and store the data, and the R package ergm to estimate the θ coefficients for our two models for each survey wave [22,23]. The terms involving gender, grade, or outspokenness represent our core theoretical measures. We include mutuality since it is a basic phenomenon in directed networks (those where the relationship from i to j does not necessarily equal that from j to i), and we include lab homophily given that labs are a major structural element of the course. We include a unique propensity for individuals to have no nominations (called 0-indegree in network terminology) since this dramatically improved the fit of the model to the observed in-degree distribution, which is a condition for the statistical inference we later conduct (see S1 appendix for more information). Moreover, it is reasonable to expect that measures of renown such as that here would have more variation than expected by chance–that is, with more students who have either no or many nominations than otherwise expected. The δ on this term is negative given the unique condition that adding a tie reduces the statistic of interest (nodes without ties).

Discussion The underrepresentation of women in STEM is a complex and daunting problem. Increasing gender equity requires tackling both inequalities in students’ initial interest in STEM and the retention of women who have expressed that interest. While there is strong evidence that precollege factors influence a student’s initial decision to major in a STEM field [24], the causes of attrition after students initially declare a STEM major are less commonly explored. Studies on attrition of STEM-oriented women have found sense of belonging [16], decisions to start families [24], and confidence that one can succeed in one’s chosen profession (11) all influence a woman’s decision to leave STEM. In particular, professional confidence is lower for women in STEM than for men [12]. This confidence is influenced by many inputs, but one of the major ones, especially for women in STEM fields, is receiving verbal messages and encouragement from individuals with influence, such as teachers and peers [9]. Unfortunately, current faculty hold a gender bias that impacts the experiences of women in STEM [13] and could result in less support from faculty. Here we demonstrate that the peers of female students in introductory biology classes can also exhibit gender biases, adding to the list of subtle experiences that can lead to the attrition of females from STEM careers. In three iterations of an undergraduate biology class, we found that even after controlling for actual course performance and outspokenness, male peers still disproportionately nominate males as being knowledgeable about biology while females nominate males and females equally. This indicates that males hold a bias against their female peers’ competence in biology. Our finding of peers as a second source of differential treatment by gender, beyond known biases of faculty, contributes to a more complete picture of the experiences of undergraduate women in STEM fields. The coalescence of subtle messages about their STEM abilities from both faculty and peers may undermine the self-confidence females have to persist in STEM fields beyond their undergraduate education [25]. The finding that a gender bias impacts the perception of millennial students may at first seem surprising, but is supported by work on implicit biases. Implicit biases are unconscious associations that people hold related to certain groups. Across many cultures, STEM is associated with males and not females [26]. Interestingly, male STEM majors in the US hold the strongest associations between maleness and science, while female STEM majors show some of the weakest implicit biases between gender and science [27]. These differences in the gender-STEM stereotypes held may explain why male undergraduate STEM majors nominate more males, but females do not demonstrate this bias. It also helps explain why male faculty demonstrate biases in hiring and mentoring, but many female STEM faculty do not [28]. One potential analytical concern for the current study is multiple comparisons. This occurs when statistical analyses involves multiple outcome measures, testing for an effect of multiple independent variables on a single outcome measure, or when the research design is repeated across several populations. In each case, the chance of finding a false positive is increased by adding another test. Because we repeated our study design three times and include multiple independent variables in our models, we are performing multiple tests, and thus have increased chances of a false positive. However, the repeated significance of our main result (that males over-nominate their male peers) across every survey gives us no reason to suspect that they are spurious due to multiple comparisons. It appears that males consistently hold a bias against their female peers’ competence in biology. The classroom environment can influence student perceptions of their peers Our work suggests that processes in the classroom may either be reinforcing pre-existing implicit biases over the quarter, or at least facilitating behaviors based on these biases. The end of every class term shows a stronger male bias than the beginning. This pattern is mediated by two class-related factors: 1) whether or not a student is outspoken in class and 2) level of achievement in the class. These factors, which seem to influence the opinions of both male and female peers, have previously been found to differ by gender in biology: males are more likely to be heard speaking in class and males slightly, but systematically, outperform women [15]. Instructors may be able to interrupt this process by equalizing the rate at which students of all genders speak up in class, closing the achievement gaps in their classrooms, or using more student-centered instruction in ways that do not rely primarily on whole class discussions (e.g. small group-work only). We propose that the specific classroom environment can influence the effect size of the male bias, with some support for this hypothesis from Class C. In this term males did not behave differently than in previous years. Females, however, developed a stronger bias towards nominating other females than in the other two classes. Though this bias was not significant, it effectively lessens the overall magnitude of bias towards male students. Although we cannot specifically pinpoint why this was the case, this class differed from the other two in two critical ways. First, one of the three instructors in this course was female, whereas all instructors were male in the other two classes. Female instructors, when they are considered role models, have been shown to reduce the science-gender biases of female students, and this may have impacted the latter’s nomination patterns [29]. Second, during classroom discussions, all three instructors in Class C employed ‘random call,’ in which the instructor selects students to speak based on a randomized class list rather than by choice, more extensively than in the two other classes. Random call has been shown to eliminate the gender gap in class volunteers, leading to more females speaking up in class [15] and limiting the opportunity for one student to dominate classroom conversations and the instructors attention [30]. These differences in Class C seem to indicate that other factors in the classroom environment could mitigate the extent to which gender and renown are correlated. It is important to keep in mind that this mitigation seems to come from a larger female-female bias. This counteracting gender bias is likely undesirable compared to eliminating the male-male bias and achieving complete gender equity. Further research is needed to understand how to best achieve this equity in peer perception. Biology is a conservative case; patterns may be more extreme across STEM The context of this research on peer perceptions was an introductory biology classroom. We can only speculate on the peer biases present in other STEM fields, but we predict that the male bias observed in this study may be conservative relative to other STEM fields for three reasons. First, biology is thought to be the STEM field with the most gender equity: undergraduate enrollment is nearly equal in terms of males and females [31] and slightly more women than men earn degrees in the biological sciences. Thus females in biology do not have to contend with the biases associated with being the sole representative of their gender in a STEM classroom [32]. Second, there is also a perception that biology lacks a strong math basis, and does not invoke the math-gender stereotype as strongly [33]. Thus, stereotypes about women’s math ability may not be undermining how their peers perceive them to the same extent it might in more explicitly math-based fields like physics or computer science. Finally, biology is a field that people believe does not require “brilliance”, unlike other STEM fields [34]. This perception means that stereotypes that males are more intelligent may not impact peer perceptions as strongly as it does in fields that are considered to require brilliance, like physics and math. For these reasons, we argue that the gender inequities in peer perception in the classrooms presented in this paper are likely conservative compared to classrooms in other STEM fields. Further, this dynamic may exist beyond STEM fields. However, explicit tests are required to confirm these hypotheses.

Conclusion Our findings have strong implications regarding the effectiveness of existing strategies to increase women in STEM fields. Without addressing social dynamics that perpetuate gender biases in the college classroom, simply increasing the number of young women entering STEM majors may not be enough. The patterns of uneven peer perceptions by gender shown in our student population suggest that future populations of academics may perpetuate the same gender stereotypes that have been illuminated among current faculty. This may not only be the case because the male students receiving high celebrity are reaffirmed in their abilities and are better able to advance through the STEM pipeline than women who do not receive this affirmation, but also because the existence of “celebrity” males and other individuals with distinction can impact and reaffirm the stereotypes held by others [35]. 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. In addition to current impacts on the peers in their classes, the students in these classes are potential future faculty members. Although we cannot directly compare the magnitude of gender bias between current faculty and millennial students, our work implies that the chilly environment for women may not be going away any time soon.

Acknowledgments We thank Mary Pat Wenderoth, Scott Freeman, Erin Shortlidge, and Jim Collins for their thoughtful comments on this manuscript. We thank the many students for their participation in this study. Lastly, we thank Arielle Desure, Carrie Sjogren, Katherine Cook, and Sarah Davis for their help compiling the network data used in these analyses. We also thank Nicholas Horrocks and two anonymous reviewers for their useful comments.

Author Contributions Conceived and designed the experiments: DZG BLW SLE AJC. Performed the experiments: DZG BLW SLE AJC. Analyzed the data: DZG SMG. Contributed reagents/materials/analysis tools: DZG SMG. Wrote the paper: DZG SLE BLW AJC SMG SEB.