ArticlePDF Available

Scientist Spotlight Homework Assignments Shift Students’ Stereotypes of Scientists and Enhance Science Identity in a Diverse Introductory Science Class


Abstract and Figures

Research into science identity, stereotype threat, and possible selves suggests a lack of diverse representations of scientists could impede traditionally underserved students from persisting and succeeding in science. We evaluated a series of metacognitive homework assignments (" Scientist Spotlights ") that featured counterstereotypical examples of scientists in an introductory biology class at a diverse community college. Scientist Spotlights additionally served as tools for content coverage, as scientists were selected to match topics covered each week. We analyzed beginning-and end-of-course essays completed by students during each of five courses with Scientist Spotlights and two courses with equivalent homework assignments that lacked connections to the stories of diverse scientists. Students completing Scientist Spotlights shifted toward counterstereotypical descriptions of scientists and conveyed an enhanced ability to personally relate to scientists following the intervention. Longitudinal data suggested these shifts were maintained 6 months after the completion of the course. Analyses further uncovered correlations between these shifts, interest in science, and course grades. As Scientist Spotlights require very little class time and complement existing curricula, they represent a promising tool for enhancing science identity, shifting stereotypes, and connecting content to issues of equity and diversity in a broad range of STEM classrooms.
Content may be subject to copyright.
CBE—Life Sciences Education • 15:ar47, 1–18, Fall 2016 15:ar47, 1
Research into science identity, stereotype threat, and possible selves suggests a lack of
diverse representations of scientists could impede traditionally underserved students from
persisting and succeeding in science. We evaluated a series of metacognitive homework
assignments (“Scientist Spotlights”) that featured counterstereotypical examples of scien-
tists in an introductory biology class at a diverse community college. Scientist Spotlights
additionally served as tools for content coverage, as scientists were selected to match top-
ics covered each week. We analyzed beginning- and end-of-course essays completed by
students during each of five courses with Scientist Spotlights and two courses with equiv-
alent homework assignments that lacked connections to the stories of diverse scientists.
Students completing Scientist Spotlights shifted toward counterstereotypical descriptions
of scientists and conveyed an enhanced ability to personally relate to scientists following
the intervention. Longitudinal data suggested these shifts were maintained 6 months af-
ter the completion of the course. Analyses further uncovered correlations between these
shifts, interest in science, and course grades. As Scientist Spotlights require very little class
time and complement existing curricula, they represent a promising tool for enhancing sci-
ence identity, shifting stereotypes, and connecting content to issues of equity and diversity
in a broad range of STEM classrooms.
Whether or not we consciously register the impacts of this messaging, we are regularly
bombarded with information regarding the types of people who work in science, tech-
nology, engineering, and mathematics (STEM). From television shows and movies to
websites, news articles, and advertisements, the media recurrently conveys images of
who does science, more often than not showcasing a relatively narrow view of science
and scientists. Setting the media aside, perhaps we need look no further than our own
classrooms to understand the ways scientists are portrayed. Many students are likely
to get their earliest and most direct experiences with “real” scientists when attending
college STEM classes—classes taught by a mostly white, mostly male faculty nation-
wide (National Science Foundation, 2013). Our textbooks, in the very rare instances
they connect content to discussions of specific scientists, can tend to focus the most
attention on individuals matching common scientist stereotypes (e.g., Darwin and
Mendel in Reece et al., 2014). Even our classrooms themselves may, through their
physical layouts and decorations, convey messages regarding who can participate in
STEM (Cheryan et al., 2009). We might wonder, then, what are the impacts of these
recurrent messages on students enrolled in postsecondary STEM classes, particularly
in the increasingly diverse classroom environments of the United States? And what, if
anything, might faculty do in response to this messaging?
Jerey N. Schinske,* Heather Perkins, Amanda Snyder, and Mary Wyer
Biology Department, De Anza College, Cupertino, CA 95014; Psychology Department, North
Carolina State University, Raleigh, NC 27695
Scientist Spotlight Homework
Assignments Shift Students’ Stereotypes
of Scientists and Enhance Science Identity
in a Diverse Introductory Science Class
Pat Marsteller, Monitoring Editor
Submitted January 15, 2016; Revised June 11,
2016; Accepted June 14, 2016
*Address correspondence to: Jerey N. Schinske
© 2016 J. N. Schinske et al. CBE—Life Sciences
Education © 2016 The American Society for Cell
Biology. This article is distributed by The
American Society for Cell Biology under license
from the author(s). It is available to the public
under an Attribution–Noncommercial–Share
Alike 3.0 Unported Creative Commons License
“ASCB®” and “The American Society for Cell
Biology®” are registered trademarks of The
American Society for Cell Biology.
CBE Life Sci Educ September 1, 2016 15:ar47
by guest on September 1, 2016 from
Supplemental Material can be found at:
15:ar47, 2 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
Scientist Stereotypes Impact Persistence and Success in
STEM by Influencing Science Identity, Sense of Belonging,
and Stereotype Threat
The messages we convey to students, either intentionally or
unintentionally, regarding who does science can influence stu-
dents’ stereotypes of scientists. Many lines of evidence point to
the importance of these stereotypes in shaping students’ sense
of belonging in STEM, with implications for persistence and
success in STEM programs. For example, stereotypical represen-
tations of scientists in the media (Tanner, 2009; Cheryan et al.,
2013; DeWitt et al., 2013; Martin, 2015) and in classroom dec-
orations (Cheryan et al., 2009) have the potential to reduce
interest in STEM fields among women and people of color. On
the other hand, a variety of studies suggest students are more
likely to pursue majors and careers in STEM if they agree with
certain “positive” stereotypes of scientists (Beardslee and
O’Dowd, 1961; Wyer, 2003; Schneider, 2010). Our own work
further suggests that holding counterstereotypical images of
scientists might be an important factor in predicting success in
science classes (Schinske et al., 2015).
These findings illustrate the importance of science identity,
a sense of belonging, and stereotype threat in determining per-
sistence and success in STEM classes. Identity refers to the
extent to which we view ourselves as a particular “kind of per-
son” (Gee, 2000), with science identity more specifically refer-
ring to whether we see ourselves as scientists. If students hold
stereotypes that portray scientists as a different “kind of person”
than themselves, those students might conclude they are not
“science people.” This mismatch between a student’s personal
sense of identity and a science identity can hamper persistence
in STEM (Seymour and Hewitt, 1997; Brickhouse et al., 2000).
Harboring views of scientists that differ from students’ percep-
tions of themselves could also cause students to feel as though
they do not belong in science. The extent to which students feel
a sense of belonging similarly correlates with levels of achieve-
ment and motivation in school settings (Goodenow, 1993;
Roeser et al., 1996).
Feeling that one differs from stereotypical descriptions of
people in a particular field of study can additionally hinder
achievement in that field due to stereotype threat. Under ste-
reotype threat, students harbor an often subconscious fear of
confirming a negative stereotype about their groups (Steele,
1997). For example, students of color, women, and first-gener-
ation college students might fear confirming a stereotype that
their groups are not good at science due to a perception that
scientists are white men from privileged, highly educated back-
grounds. This threat can undermine engagement and perfor-
mance, even among students who are otherwise well qualified
academically (Steele, 1997). Even subtle cues involving a lack
of women or people of color visually represented in an aca-
demic environment or on a flyer can trigger dramatic reduc-
tions in interest and performance due to stereotype threat
(Inzlicht and Ben-Zeev, 2000; Purdie-Vaughns et al., 2008).
More specific to science contexts, stereotype threat has been
described as a significant factor in predicting interest, per-
sistence, and success in STEM majors, especially for women
and students of color (Hill et al., 2010, chap. 3; Beasley and
Fischer, 2012). Interventions that remove the conditions that
trigger stereotype threat can reduce or even entirely eliminate
achievement gaps between women and men or between stu-
dents of color and white students in test scores and course
grades (e.g., Steele and Aronson, 1995; Good et al., 2003;
Cohen et al., 2006).
What Can Faculty Do in STEM Classes to Broaden
the Image of the Scientist?
Given the evidence suggesting that stereotypes of scientists
impact persistence and success in STEM, efforts to feature
counterstereotypical images of scientists have the potential to
narrow equity gaps and broaden participation in STEM. Stereo-
types of scientists are malleable (Cheryan et al., 2015), and pre-
vious work suggests that providing counterstereotypical mes-
saging could enhance interest and success in STEM among
underserved populations of students (McIntyre et al., 2004;
Steinke et al., 2009; Cheryan et al., 2013).
One common strategy for introducing counterstereotypical
images of scientists to students is to increase the prevalence and
visibility of diverse STEM “role models”—individuals who stu-
dents may choose to emulate. Marx and Roman (2002) describe
how role models are chosen through “selective, social compari-
son whereby certain attributes are copied and others are
excluded.” Because comparisons of social similarity may involve
the visible personal characteristics of potential role models,
many studies have focused on the potential benefits of gender-
or race/ethnic-matched role models. For example, the presence
of female role models has served to mitigate stereotype threat
and boost math performance among female students (Marx
and Roman, 2002; Marx and Ko, 2012). In terms of race/eth-
nicity, both white and nonwhite students tend to select race/
ethnic-matched career role models (Karunanayake and Nauta,
2004), and having a race/ethnic-matched instructor role model
has been shown to correlate with student success (Dee, 2004;
Fairlie et al., 2011).
While these results would suggest placing a priority on seek-
ing out gender/race/ethnic-matched role models for STEM stu-
dents, other studies have failed to find distinct benefits of role
models who match students’ own races/ethnicities and genders
(Ehrenberg et al., 1995; Maylor, 2009; Phelan, 2010). Perhaps
explaining these discrepancies, Marx and Roman (2002) point
out that the attributes important to seek in a role model will ulti-
mately be those attributes of importance to the individual choos-
ing the role model (e.g., the attributes considered important by
students). Because social identities are informed by many differ-
ent factors, and individuals have multiple identities that resonate
in different contexts (Gee, 2000), it might be difficult to predict
which role model attributes will be most important in encourag-
ing students to form a science identity. Buck et al. (2008) provide
guidance in this area in finding that students needed to identify
someone “who cared about them and shared common interest/
experiences” in order for role models to be effective. This work
implies that faculty interested in enhancing students’ science
identity and sense of belonging in STEM should, in addition to
identifying diverse role models in terms of gender/race/ethnic-
ity, place a priority on featuring individuals to whom students
might personally relate, based on interests and experiences.
Moving from Identifying Role Models to Showcasing
Possible Selves
The concept of “possible selves” might represent a more useful
and precise way to think of counterstereotypical examples than
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 3
Intervention to Enhance Science Identity
does the concept of “role modeling.” Possible selves refer to
everything that each of us “is tempted to call by the name of
me” (James, 2005) or the set of “individually significant hopes,
fears, and fantasies” that define oneself (Markus and Nurius,
1986). Individuals can reflect upon their own possible selves,
and these possible selves are understood to influence motiva-
tion and future behavior (Markus and Nurius, 1986). Students
weigh their possible selves in constructing school identities, and
these interactions between possible selves and academic identi-
ties mediate the potency of stereotype threat (Steele, 1997;
Oyserman et al., 2006). Possible selves more specifically play an
important role in the development of a science identity (Hunter,
2010), and students’ “possible science selves” might help
explain career choices in STEM (Steinke et al., 2009; Mills,
2014). Taken together, this implies students’ science identities
and resistance to stereotype threat might be enhanced if they
see their own their own possible selves reflected in STEM. This
highlights a subtle but important difference between the con-
cepts of role models and possible selves. Compared with featur-
ing scientist role models that represent people students are
expected to become more like, seeing one’s possible self in a sci-
entist would involve seeing someone in science you already are
Goals and Scope of This Study
Given the evidence that counterstereotypical perceptions of sci-
entists are important in diverse science classrooms (Schinske
et al., 2015) and that viewing one’s possible selves in science
might enhance science identity (Hunter, 2010; Mills, 2014) and
mitigate stereotype threat (Oyserman et al., 2006), we devel-
oped and evaluated a classroom intervention to introduce stu-
dents to counterstereotypical examples of scientists. In evaluat-
ing the intervention, which we call “Scientist Spotlights” (see
Methods), we sought to explore the following four hypotheses.
Hypothesis 1: Scientist Spotlights will shift students’ descrip-
tions of scientists toward nonstereotypical descriptions.
Hypothesis 2: Scientist Spotlights will enhance students’
ability to see their possible selves in science by enhancing
their ability to relate to scientists.
Hypothesis 3: Shifts in scientist stereotypes and relatability
of scientists will correlate with students’ interest in science.
Hypothesis 4: Shifts in scientist stereotypes and relatability
of scientists will correlate with course grades.
Below we review the development of the Scientist Spotlight
intervention, the study context, and our mixed-methods analy-
sis of student essays and quantitative surveys to evaluate the
Development of Scientist Spotlights in a Diverse
Community College Biology Classroom
We developed Scientist Spotlights as regular, out-of-class assign-
ments both to introduce counterstereotypical examples of sci-
entists and to assist in the coverage of course content while
requiring little class/grading time. Featured scientists were
selected to 1) present diverse perspectives on who scientists are
and how science is done and 2) match the content areas being
covered at the time of each assignment. In each Scientist
Spotlight, students reviewed a resource regarding the scientist’s
research (e.g., a journal article or popular science article) and a
resource regarding the scientist’s personal history (e.g., an
interview, Story Collider podcast, or TED Talk). Because these
assignments included the review of materials that introduced
course content to students, they replaced weekly textbook read-
ings. One of the Scientist Spotlights assigned to students read as
Ben Barres is a Stanford professor of neurobiology. He studies
diseases related to signaling in the nervous system, and in par-
ticular the roles of supporting cells around neurons. Dr. Barres
is also a leader in science equity and the effort to address gen-
der gaps. He is uniquely positioned to address these issues,
since he has presented both as a female and a male scientist at
different times in his career.
1. View the Wall Street Journal article about Ben Barres by
clicking here (Begley, 2006).
2. Then, review Dr. Barres’ article in the journal Nature by
clicking here (Allen and Barres, 2009)
(If you are interested in hearing more from Ben Barres, you
can search for him on YouTube. He has some videos on his
research and also on his experiences as a transgender
After reviewing these resources, write a 350 word or more
reflection with your responses to what you saw. You might
wish to discuss:
1. What was most interesting or most confusing about the
articles you read about Dr. Barres?
2. What can you learn about neuron signaling (action poten-
tials, synapses, supporting cells) from these articles?
3. What do these articles tell you about the types of people
that do science?
4. What new questions do you have after reviewing these
The above example was assigned before a unit on neuron
signaling and therefore assisted in the introduction of content
in that area. The writing prompts were aimed at creating oppor-
tunities for metacognition (Tanner, 2012). Prompts changed
slightly from one assignment to the next, but the third prompt
about the “types of people that do science” was always included.
A photograph of the featured scientist was also included with
each assignment. Students submitted responses to Scientist
Spotlights through an online course-management system (Moo-
dle), and submissions were scored only for timeliness and word
Study Design
We used a quasi-experimental, nonequivalent-groups design
(Shadish et al., 2002; Trochim, 2006) to evaluate Scientist
Spotlights in a Human Biology course at a diverse community
college during the Fall 2013–Fall 2015 academic terms. Human
Biology is a one-quarter lecture/lab general education course
open to any student, but targeting transfer students and those
with interests in human health careers. Students in five sections
of Human Biology during that time period completed Scientist
Spotlights on a weekly basis (hereafter “Scientist Spotlight
by guest on September 1, 2016 from
15:ar47, 4 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
Homework” students). Each Scientist Spotlight was worth 10
points, so the assignments (n = 10) contributed a total of 100
points to the final course grade (865 points in the whole
course). Efforts were made to attend to multiple axes of diver-
sity when selecting scientists to feature, with special attention
to the racial/ethnic diversity of students in these classes. Half of
the weeks featured female scientists and seven out of 10 weeks
featured at least one nonwhite scientist. Occasionally, more
than one scientist was featured during a Scientist Spotlight
assignment. Selected scientists represented diverse socioeco-
nomic backgrounds, gender identities, interests outside science,
paths to careers in science, temperaments, ages, sexual orienta-
tions, and countries of origin. Supplemental Material, part A,
lists the names of individuals featured in Scientist Spotlights
during this study. The full set of 10 Scientist Spotlight assign-
ments, including readings and resources, is available by request
to the corresponding author.
During the same time period, students in two sections of
Human Biology did not perform Scientist Spotlights. Instead,
those students completed comparable metacognitive online
assignments (example in Supplemental Material, part B) based
on popular science articles and journal articles compiled in a
course reader (hereafter “Course Reader Homework” students).
Although no explicit instruction regarding scientist stereotypes
took place in these classes, three scientists were briefly dis-
cussed during lecture presentations. An African-American
female scientist (Jewel Plummer Cobb), a white male scientist
(Neil Shubin), and a Japanese male scientist (Masayasu Kojima)
were all mentioned during class while highlighting certain
research findings related to course content. Students saw pho-
tographs of all three scientists and watched brief videos featur-
ing Dr. Cobb and Dr. Shubin but did not perform any individ-
ual/group work or metacognitive activities surrounding these
Quasi-experimental approaches, by definition, lack random-
ization in assigning participants to groups (Shadish et al., 2002;
Trochim, 2006). As such, students self-selected into Human
Biology course sections and the instructor (J.N.S.) selected sec-
tions in which to implement Scientist Spotlight versus Course
Reader Homework. While nonrandom assignment to groups
can limit researchers’ ability to infer causal connections between
interventions and outcomes, quasi-experimental approaches
can still provide robust and valuable insights and offer advan-
tages over randomized experiments in certain contexts (Shad-
ish et al., 2002). We attempted to ensure as much equivalence
as possible between groups in that all classes adhered to the
same curricular expectations, were taught at similar times of
the day in similarly arranged classrooms, and used the same
types of in-class activities. The same faculty member (J.N.S.)
served as instructor for all of the course sections involved in this
study, though one Course Reader Homework section was
cotaught by another faculty member. We controlled for various
student-level differences between groups during statistical
analyses and used these “weighted means” in evaluating our
hypotheses (see Methods and Supplemental Material, part E). It
should be noted that, in the analyses that follow, we consider
students as the experimental units. This was considered most
appropriate in this instance, because Scientist Spotlights were
designed to interact with individual students in different ways,
raising interest in students as individual observations. We do,
however, control for course section in analyses to account for
trends based on grouping at the class level.
Student Population
This work was conducted at a large (22,000 students) Califor-
nia community college that is a designated Asian American and
Native American Pacific Islander–Serving Institution
(AANAPISI). The majority (59%) of students come from low-so-
cioeconomic status (low-SES) families and the majority (66.2%)
indicate the educational goal of transferring to a 4-year institu-
tion. Approximately 20% of Human Biology students state the
intention of majoring in biology. Forty-six percent of students
report that Human Biology is the first college science class they
have taken, and 13% of students report that Human Biology is
the first science class they have ever taken at any level.
A total of 364 students initially enrolled in the five sections
of Human Biology that completed Scientist Spotlight Home-
work (x = 73 students per class). One hundred thirty-nine stu-
dents initially enrolled in the Course Reader Homework sec-
tions (x = 70 students per class). However, 26 students from
Scientist Spotlight Homework classes and 13 students from
Course Reader Homework classes dropped the course within
the first 2 weeks of class, leaving 338 students as the final
enrollment for Scientist Spotlight Homework sections and 126
students in Course Reader Homework sections.
The table in the Supplemental Material, part C, compares
the demographic characteristics of students in these classes. We
defined “underserved” racial/ethnic groups as those groups
that have persistently entered STEM majors at lower rates com-
pared with their prevalence on campus and experienced com-
paratively lower success rates in STEM classes. This included
students identifying as Latino/a, Black, Native American, Fili-
pino/a, Pacific Islander, and Southeast Asian (e.g., Vietnamese,
Laotian, Cambodian, Indonesian). The majority of Scientist
Spotlight and Course Reader Homework students identify as
members of underserved groups (Supplemental Material, part
C). Students in these Human Biology classes identified 25
different first languages spoken, with English, Spanish, and
Vietnamese representing the most common first languages
Assessment of Scientist Stereotypes and Possible Science
Selves through Short-Essay Surveys
In evaluating Scientist Spotlights, we used a mixed-methods
approach in which we reviewed short-essay responses from stu-
dents for context and themes and then coded student responses
into categories for quantitative analysis. Two essay prompts
were used. The first prompt was designed to address hypothesis
1 by eliciting students’ stereotypes of scientists. This prompt
read, “Based on what you know now, describe the types of peo-
ple that do science. If possible, refer to specific scientists and
what they tell you about the types of people that do science”
(hereafter “stereotypes prompt”). This prompt was described
and its validity was explored by Schinske et al. (2015). The
second prompt was developed as an exploratory method for
assessing students’ possible selves in science. That is, assessing
whether students perceived scientists as reflecting their possi-
ble selves, and if so, what aspects of themselves they saw
reflected in scientists (hypothesis 2). We chose to approach
this topic by surveying the extent to which students could
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 5
Intervention to Enhance Science Identity
“personally relate” to scientists. The prompt consisted of the
challenge statement: “I know of one or more important scientist
to whom I can personally relate,” followed by a Likert scale
including “agree,” “somewhat agree,” “somewhat disagree,”
“disagree,” and “I don’t know.” Following the Likert scale, stu-
dents were instructed: “Please explain your opinion of the state-
ment” (hereafter “relatability prompt”). This prompt was devel-
oped and face validity was established through multiple
quarters of testing in class and informal talk-aloud trials with
students. Even though an “I don’t know” response was essen-
tially the same as “disagree” when students responded whether
they knew of one or more relatable scientists (see also Results),
we found it important to include an “I don’t know” option.
Some students were more comfortable circling “I don’t know”
than “disagree,” which sounded like a “wrong” answer to them.
These two prompts were printed on one side of a sheet of
paper, so students had approximately half a sheet to respond
to each prompt. J.N.S. provided the surveys to students on the
first and last days of each Human Biology course, telling stu-
dents, “I am very interested in students’ ideas about science
and scientists, so I appreciate you taking 5–10 min to respond
to these prompts. There are absolutely no right or wrong
answers and there’s nothing I would like more than to see
many different thoughts on the topic. Your responses will not
be graded and will not be reviewed in connection with your
name.” Though responses were not graded, students received
five points (out of 865 course points) for participating and
completing surveys. When looking for shifts in attitudes about
scientists in these surveys, only papers from students who
submitted both beginning- and end-of-course responses were
considered. As preliminary results suggested students in Sci-
entist Spotlight Homework classes were adopting new atti-
tudes regarding scientist stereotypes and the relatability of
scientists, we were interested in whether those shifts would
be maintained over time. To assess these shifts longitudinally,
J.N.S. sent an online survey that included the stereotypes and
relatability prompts to Scientist Spotlight Homework students
approximately 6 months after the end of class.
Analysis of Students’ Descriptions of Scientists
We anonymized and randomized student papers and followed
the procedures of Schinske et al. (2015) to categorize responses
to the stereotypes prompt. While reviewing student responses,
we recorded the words, phrases, and names students used to
describe scientists, and tallied the frequencies of those descrip-
tions among the papers. Exemplar quotes were selected to rep-
resent the most common themes and provide context. Pseud-
onyms were used in place of student names to protect
anonymity. Students’ descriptions of scientists were then coded
as Stereotypes, Nonstereotypes, or Fields of Science. Following
our previous work (Schinske et al., 2015), we defined Stereo-
types as any wid ely represented des criptions of sci ent ists match-
ing stereotypes uncovered by Mead and Metraux (1957). Non-
stereotypes included less commonly used descriptions of
scientists not reported in that previous work. Fields of Science
included names of science fields or career types (e.g., biologist).
We previously demonstrated that independent reviewers reli-
ably code descriptions as Stereotypes (0.86 interrater correla-
tion) and Nonstereotypes (0.89 interrater correlation; Schinske
et al., 2015). We recorded the number of descriptions from
each category for each student, then converted those numbers
into percentages out of total comments (e.g., percent of Stereo-
types out of all comments) to partly control for differences in
the lengths of responses between students.
Changes in the proportions of Stereotypes and Nonstereo-
types were analyzed using repeated-measure analysis of covari-
ance (RM-ANCOVA). Proportions of Stereotypes/Nonstereotypes
acted as dependent variables, with time (beginning vs. end of
course) and treatment (Scientist Spotlight Homework vs.
Course Reader Homework) input as between-subjects factors.
Gender, race/ethnicity (categorized as traditionally under-
served vs. traditionally well served), and course section were
used as covariates.
Analysis of Students’ Ability to Personally Relate
to Scientists
We reviewed short-essay responses to the relatability prompt
and transcribed each of students’ statements (e.g., “Don’t know
any scientists,” “Relate to musician scientist,” “Relate to Rosa-
lind Franklin”) into the top of a spreadsheet. As those state-
ments reappeared in subsequent papers, we tallied the appear-
ance of the statements in the spreadsheet. Exemplar quotes
were selected to represent the most common themes and pro-
vide context for why students could or could not personally
relate to scientists.
Changes in students’ relatability Likert-scale selections from
the beginning to the end of the course, were analyzed using
RM-ANCOVAs. Relatability Likert scores acted as the depen-
dent variables, with time and treatment input as between-sub-
jects factors. Gender, race/ethnicity, and course section were
used as covariates.
Analysis of Student Interest in Science and Collection of
Demographic Information
The exploration of hypothesis 3 required comparing shifts in
students’ stereotypes of scientists and ability to relate to scien-
tists to shifts in science interest. To monitor student interest,
during the first and the last weeks of class, students completed
an online survey (Supplemental Material, part D). The survey
included eight quantitative items adapted from the Student
Assessment of their Learning Gains Survey (Seymour et al.,
2000), which were reshaped into the “Science Interest” scale.
Students responded to prompts such as “Presently I am enthu-
siastic about this subject” on a five-point Likert scale, ranging
from “not at all” to “a great deal.” Supplemental Material, parts
G and H, provide details regarding how the Science Interest
scale was derived from these items. In separate questions, stu-
dents indicated whether they were majoring in biology or
another STEM field and whether they had taken previous sci-
ence classes (Supplemental Material, part D). As we also wished
to look for interactions involving student demographics, the
final page of the surveys asked students to identify their gender
and racial/ethnic identities and first spoken language. Students
received five participation points (out of 865 course points) for
completing these quantitative surveys.
Prior work suggested broader student outcomes, like grades
and interest in science, relate to holding nonstereotypical
views of scientists (Schinske et al., 2015) and developing pos-
sible science selves (Steinke et al., 2009; Mills, 2014). We
therefore created categorical variables to distinguish students
by guest on September 1, 2016 from
15:ar47, 6 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
who exhibited these characteristics. Specifically, we compared
end-of-course with beginning-of-course values to categorize
students as either decreasing versus not decreasing in their
proportion of Stereotypes, increasing versus not increasing in
their proportion of Nonstereotypes, and increasing versus not
increasing in relatability. The relationships between each of
these categorical variables and Science Interest were tested in
a 2 × 2 × 2 (categorical variable × stereotype change × time)
RM-ANCOVA controlling for gender, race/ethnicity, course
section, and past science class experience.
Analysis of Student Grades
Students’ course grades, expressed numerically (“A” = 4, “B” =
3, etc.), were included in analyses to explore correlations
between Stereotypes, Nonstereotypes, relatability, and in-class
achievement. As in tests for correlations involving interest in
science, we used the categorical variables we generated for
changes in Stereotypes, Nonstereotypes, and relatability in
ANCOVAs to explore connections between those variables and
course grades. These analyses controlled for gender, race/eth-
nicity, course section, and past science class experience.
All statistical analyses were performed in SPSS (SPSS for
Windows, 19.0.0, IBM, Armonk, NY). To enhance clarity and
readability, we present descriptive statistics and ANCOVA
tables from our analyses in the Supplemental Material, parts E
and F, rather than in the body of the article.
Hypothesis 1 Results: Scientist Spotlights Will Shift
Students’ Descriptions of Scientists toward Nonstereotypes
Students’ weekly Scientist Spotlight responses suggested the
assignments encouraged students to reflect on counterstereo-
typical examples of scientists while engaging with course con-
tent. Fernanda commented on her previous stereotypical
ideas about scientists and discussed how Charles Limb coun-
teracted those stereotypes by showing an interest in music
and a life outside of science could contribute to a scientific
I was able to see scientists in a different perspective … I used
to think scientists were mere geniuses who asked infinite, even
unpredictable questions nobody had the time to research. I
used to even think they were mere robots who ate, researched,
and slept on a daily basis. Yet, they have a life of their own …
I can tell Dr. Limb is a good musician whose love for the music
stretched to his eagerness to learn about the brain.—Fernanda,
a Latina student responding to the Scientist Spotlight on Charles
Melissa noted that Raymond Dubois’s “humble beginnings”
in an economically disadvantaged farming community repre-
sented a nontraditional path to science:
Dr. Dubois is such a unique person. He was born and raised to
be a farmer, and didn’t have very much money or aspiration …
He found science completely by accident and fell in love, and
from such humble beginnings he became one of the country’s
foremost experts in his field. It’s very impressive to see some-
one come from so traditionally unlikely a background and
become so well-known for his work.—-Melissa, a white female
student responding to the Scientist Spotlight on Raymond Dubois
Shifts toward counterstereotypical views of scientists were
also apparent in beginning- and end-of-course surveys. Two
hundred forty-five Scientist Spotlight Homework students and
84 Course Reader Homework students submitted both begin-
ning- and end-of-course responses to the stereotypes prompt.
This prompt stated, “Based on what you know now, describe
the types of people that do science. If possible, refer to specific
scientists and what they tell you about the types of people that
do science.” Table 1 shows the most prevalent themes found in
students’ responses at the beginning and end of the course for
both Scientist Spotlight Homework and Course Reader Home-
work sections. Beginning-of-course responses consisted mostly
of “positive” stereotypes of scientists (Mead and Metraux,
1957). For example, Cynthia and Theresa voiced the common
beginning-of-course opinion that scientists are highly intelli-
gent/knowledgeable individuals:
People who are … very intelligent and can think outside the
box [do science].—Cynthia, a white female Scientist Spotlight
Homework student
Intelligent people also do science. People [who] are good at
science and excel in math tend to be scientists, like Albert Ein-
stein.—Theresa, a white female Course Reader Homework
Matthew described scientists as innately curious:
I believe the types of people that do science are curious and
doubtful. Scientists are innately curious and they question
everything.—Matthew, a Vietnamese male Scientist Spotlight
Homework student
Mei added a love of science as a possible inherent character-
istic of scientists:
[Scientists] love science, at least the aspects that they work on
… They know a lot in their field but they are still eager to learn
more.—Mei, a Chinese female Course Reader Homework
It appears that, at the beginning of the course, students
largely identified scientists as having stereotypical, innate qual-
ities, such as intelligence, proficiency in math, curiosity, and
interest in their fields of study. Pamela similarly commented on
scientists’ intelligence but also described one of the most com-
mon noninnate characteristics of scientists from the beginning
of class. That is, scientists are people who do experiments or
apply the scientific method:
[Scientists are] smart people that are crazy/confused. [They]
study/research specific topics over long periods of time … cre-
ate experiments and do labs.—-Pamela, a Black/Latina Scien-
tist Spotlight Homework student
The stereotypes prompt asked students to name specific sci-
entists to illustrate the types of people who do science. How-
ever, many students explicitly expressed a lack of familiarity
with specific scientists at the beginning of the course. Albert
Einstein was the most common specific scientist discussed by
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 7
Intervention to Enhance Science Identity
students, as exemplified by Theresa’s response presented ear-
lier. Many students resorted to describing scientists simply as
those individuals who participate in certain, named scientific
fields or professions. For example,
The types of people who do science are teachers, professors,
NASA workers, nurses, doctors, etc. NASA scientists use sci-
ence to study space and the earth … Doctors use science to
study the human body.—Carlos, a Latino Course Reader Home-
work student
By the end of the course, most students from Scientist Spot-
light classes used Nonstereotypes to describe scientists
(Table 1A). Tania reflected on the ways her views of scientists
changed and stated that many scientists defy stereotypes of
individuals in their fields. Rather, scientists are “normal people”
like her:
Before I learned about scientists in this class, I thought scien-
tists were like “nerds” or what they show in movies. The char-
acters would be very geeky, had glasses, spoke monotone, and
thought they were above everyone. However, through all the
research I’ve done in this class, scientists are just normal peo-
ple like myself. They love to learn new things, they have a life
outside the laboratory, they are funMy opinion of people
that do science has completely changed thanks to this
class.—Tania, a Filipina Scientist Spotlight Homework student
Felipe reported that people from diverse countries and
socioeconomic backgrounds are scientists and that scientists
did not all have an innate interest in the field from an early age:
The types of people that do science are all kinds of people.
What I have learned through out this course is that it is possi-
ble to be a scientist under any circumstances, from poverty to
TABL E 1. Most common student descriptions of scientists from Scientist Spotlight Homework (A) and Course Reader Homework
(B) students at the beginning and end of the course
Beginning of Scientist Spotlight courses End of Scientist Spotlight courses
Most common descriptions of scientists Prevalence Most common descriptions of scientists Prevalence
People that do experiments (s) 24% All types of people (n) 49%
Curious (s) 20% Passionate (s) 24%
Biologists (f) 18% Cheerleaders (n) 22%
Especially intelligent (s) 17% Darlene Cavalier (n) 18%
Albert Einstein (s) 17% People that do experiments (s) 17%
Doctors (f) 15% People from outside the United States (n) 16%
People that look for “truths” (s) 13% Curious (s) 15%
Chemists (f) 13% Creative (s) 13%
Discover things (s) 12% Dedicated (s) 13%
People that investigate natural world (s) 11% Interested in science (s) 13%
Make the world better (s) 11% Go against stereotypes (n) 12%
Enjoy learning (s) 11% Discover things (s) 12%
Question things (s) 10% Rosalind Franklin (n) 11%
Psychologists (f) 10% Make the world better (s) 11%
Physicists (f) 10% Not just one type of person (n) 11%
Beginning of Course Reader Homework course End of Course Reader Homework course
Most common descriptions of scientists Prevalence Most common descriptions of scientists Prevalence
Curious (s) 32% Curious (s) 24%
Especially Intelligent (s) 18% Especially intelligent (s) 15%
People that do experiments (s) 14% People that do experiments (s) 14%
Discover things (s) 13% Discover things (s) 14%
Interested in science (s) 13% All types of people (n) 13%
Enjoy learning (s) 12% Chemists (f) 12%
Albert Einstein (s) 10% Make the world better (s) 12%
Chemists (f) 10% Doctors (f) 11%
Doctors (f) 10% Biologists (f) 11%
All types of people (n) 8% Albert Einstein (s) 8%
People that investigate natural world (s) 8% Enjoy learning (s) 8%
Open-minded (s) 8% People that investigate natural world (s) 8%
Biologists (f) 7% Passionate (s) 8%
Make the world better (s) 7% Creative (s) 7%
Astronomers (f) 7% Geneticists (f) 7%
Shading and letters in parentheses denote categories of descriptions per Schinske et al., 2015: s/turquoise = Stereotype; n/light green = Nonstereotype; f/gray = Field of
by guest on September 1, 2016 from
15:ar47, 8 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
being from a different country to having a stereotypical
assumption about a person, for example a cheerleader. Any-
one can be a scientist if they want to. One thing all scientists
we learned about had in common was that they weren’t inter-
ested in science until something sparked their interest.—Fe-
lipe, a Latino Scientist Spotlight Homework student
Matthew agreed that scientists need not be initially inter-
ested in science, citing the example of Carl Djerassi:
The types of people that do science vary greatly. One scientist,
Djerassi, in an interview said he had no interest in science as a
kid, but he eventually grew up to be the scientist that created
contraceptive pills for women.—Matthew, a Vietnamese male
Scientist Spotlight Homework student
Maria more specifically called attention to the fact that race
and sex are not determinants of an ability to be a scientist:
All types of people can do science … What I learned was that
your background/sex/race doesn’t determine if you will
become a scientist or not. It is all about the passion and love
for knowledge that human beings have.—Maria, a Latina
Scientist Spotlight Homework student
Cynthia, as well as Tania (noted earlier), pointed out that
interests outside of science can be as important to scientists as
an interest in science:
[Scientists] take their passion and often combine it with sci-
ence. For example, the scientist that was looking at musician’s
[sic] brains as they improvised music.—Cynthia, a white female
Scientist Spotlight Homework student
The above responses made the argument that many differ-
ent types of people, and perhaps all types of people, are sci-
entists. Indeed, at the end of the course, the majority of stu-
dents (55%) included descriptions of scientists fitting into at
least one of the following categories: all types of people, not
just one type of person, or go against stereotypes. The quota-
tions from Cynthia and Matthew further demonstrated that,
at the end of the course, many students had specific, counter-
stereotypical individuals in mind to inform their descriptions
of scientists.
Matthew and Felipe pointed out that many scientists did not
have an innate or early interest in science, and we no longer see
references to scientists as especially intelligent in these exem-
plars. Given that we believe all of the scientists featured in Sci-
entist Spotlights are very intelligent, we found it striking that
“intelligent” and “smart” largely disappeared as ways to
describe scientists (Table 1A). It appears that, while the fea-
tured scientists may still have been impressively smart, “intelli-
gent” was no longer a significant defining feature of scientists in
students’ minds. Rather, scientists were considered regular/
normal people who happened to find their way to careers in
science (responses of Matthew, Felipe, and Tania).
In contrast to the above findings from Scientist Spotlight
students, Course Reader Homework students largely contin-
ued to use stereotypes and generalities to describe scientists at
the end of the course (Table 1B). For example, Laila and Mei
continued to describe scientists in terms of their special intel-
People who work in science fields have absolutely incredible
intelligence.—Laila, Indonesian female Course Reader Home-
work student
Scientists have to be up-to-date about research, medicine, dis-
eases.—Mei, a Chinese female Course Reader Homework
Carlos, like many other students in Course Reader Home-
work classes, continued to define scientists in nebulous terms
through their fields/professions:
The types of people that do science are people that do astro-
physics, astronomy, chemistry, biology, physics, and geophysi-
cal science. There are NASA scientists that study space. Also
there are scientists that study humans and their environ-
ment.—Carlos, a Latino Course Reader Homework student
Theresa reiterated the importance of curiosity from her
beginning-of-course response:
All kinds of people do science, especially those who are really
curious about a certain scientific topic. Men can be scientists as
well as women … Albert Einstein is a very famous scien-
tist.—Theresa, a white female Course Reader Homework
Theresa and some other Course Reader Homework stu-
dents did mention at the end of the course that all types of
people do science, causing that description to increase in prev-
alence (Table 1B). It is interesting to note, however, that the
remainder of Theresa’s end-of-course response was nearly
identical to her beginning-of-course response—emphasizing
curiosity and raising the same example of Albert Einstein. In
other words, while a small number of Course Reader Home-
work students appear by the end of the course to be describing
a more inclusive version of who does science, those students’
responses still lacked the specific examples and expanded
descriptions of scientists we observed from Scientist Spotlight
In quantitatively analyzing these trends, an RM-ANCOVA
revealed significant interactions between treatment and the use
of Stereotypes, F(1,311) = 13.39, p < 0.001, η2 = 0.04, and
Nonstereotypes, F(1,311) = 16.51, p < 0.001, η2 = 0.05. When
looking solely at raw means, we observed all students using
fewer Stereotypes at posttest, but Scientist Spotlight Home-
work students showed a sharper decrease, suggesting that the
treatment produced a stronger decrease in Stereotype use.
However, an analysis of weighted means to isolate the variabil-
ity introduced by treatment condition from the variability intro-
duced by race/ethnicity, gender, and course section, showed no
significant differences in the decrease across groups. In terms of
Nonstereotypes, both raw and weighted means show a signifi-
cant increase among Scientist Spotlight students when com-
pared with Course Reader Homework students (Figure 1 and
Supplemental Material, parts E and F). Therefore, when con-
trolling for unequal group sizes and nonrandom assignment,
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 9
Intervention to Enhance Science Identity
our results suggested the completion of Scientist Spotlights was
associated with increases in the use of Nonstereotypes in des cri b-
ing scientists.
Hypothesis 2 Results: Scientist Spotlights Will Enhance
Students’ Ability to Personally Relate to Scientists
Scientist Spotlight Homework submissions provided evidence
of students encountering scientists to whom they could relate
on a personal level. For example, Binh could relate to Flossie
Wong-Staal and Juan Perilla because, like him, they were orig-
inally from outside the United States, albeit from countries dif-
ferent from his:
Another thing is scientists who are successful in the U.S. are
not necessary [sic] born in the U.S. These scientists are both
from another country but they’re really successful. It makes
me more confident in becoming a scientist because no one in
my family is a scientist and I’m not a U.S. citizen.—Binh, a
Vietnamese male student responding to the Scientist Spotlight on
Flossie Wong-Staal and Juan Perilla
On the other hand, Emily could relate to Charles Limb due
to shared interests outside science:
I found this Ted Talk with Charles Limb incredibly interesting
mostly because I am a musician myself who has been trained
both classically and in jazz.—Emily, a white female student
responding to the Scientist Spotlight on Charles Limb
Anthony found Agnes Day relatable due to their shared
racial/ethnic identities and because of what she represents to
people like him:
For my whole life I … wasn’t exposed to any scientist who
was of African American descent. That, as a fellow African
American, brought me joy as it shows that African Americans
are no longer abiding to the negative stigma we have. She’s
representing a powerful position for us and people have
noticed her work. It gave me incentive to push for my own
dreams and to succeed.—Anthony, a Black male student
responding to the Scientist Spotlight on Agnes Day
Some of the resources students reviewed during Scientist
Spotlights demonstrated that scientists experienced barriers,
inequities, and marginalization or that science itself can include
the study of social inequities (e.g., health disparities). These
themes spurred many students, like Anthony, to connect with
scientists through the lens of social justice. After learning about
Ben Barres’s personal story and path in science, Maria discussed
her views on gender equity in science and how that relates to
her experience at her community college. She further compares
what she learned about the biology content in this assignment
(glial cells) with the plight of women in science:
The fact that there are considerably less women in science
than men, is more of a socio-cultural problem, than a genetic
or gender problem. Personally, I feel optimistic, yes we are the
minority in science, and are paid less then men, and are dis-
criminated against, but when I look around my community
college I see many women succeeding, and unafraid to give
the best of them[selves] … In a way glia cells are a little bit
like the “women” of the nervous system; extremely important
for the survival of the cells, form the majority of the nerve cells
population, and are underestimated and perceived only as a
“supporter” cell.—Maria, a Latina student responding to the
Scientist Spotlight on Ben Barres
Gina responded to Agnes Day’s scientific work by proposing
that the type of science that gets done might depend largely on
the type of people doing the science. As a result, diversity in the
sciences might be required in order to understand the impor-
tance of, and go on to pursue, certain research areas:
Dr. Day is one of the first to complete a study in cancer con-
cerning the differences in race. If she was not African Ameri-
can I do not think that Dr. Day would understand the signifi-
cance of her research … As a strong Black woman representing
women and people of color in a White male driven field Dr.
Day defies what I believed about people who do science. I
wonder if the questions of science require diversity, collabora-
tion and personal passions in order to be answered.—Gina,
a Black/Native American female student responding to the
Scientist Spotlight on Agnes Day
Beginning- and end-of-course responses to the relatability
prompt additionally demonstrated distinct shifts in an ability to
personally relate to scientists. Two hundred eight Scientist Spot-
light Homework students and 86 Course Reader Homework stu-
dents submitted both beginning- and end-of-course responses to
the relatability prompt. The sample size for this prompt was
smaller than that for the stereotypes prompt, since it took longer
to develop and establish face validity for this prompt. As a result,
it was only presented at both time points to four of the five sec-
tions of Scientist Spotlight students. The final relatability prompt
stated: “I know of one or more important scientist to whom I
can personally relate,” which was followed by a Likert scale and
a space for qualitatively explaining the opinion selected. An “I
don’t know” option was included in the Likert scale and was
FIGURE 1. Average percent of Nonstereotypes among descriptions
of scientists at the beginning vs. end of the course for Course
Reader Homework and Scientist Spotlight Homework classes.
Graphs depict weighted means to control for unequal group sizes
and nonrandom assignment of students to treatment. Error bars
represent SE.
by guest on September 1, 2016 from
15:ar47, 10 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
coded as “Disagree” based on the qualitative explanations
provided by students selecting “I don’t know” (e.g., “I honestly
only know of one [scientist] and I’m nothing like him”).
Only 35% of students in Scientist Spotlight Homework
classes and 36% in the Course Reader Homework classes either
agreed or somewhat agreed with the relatability prompt at the
start of the course, indicating that students did not generally
feel they could relate to scientists. Students’ beginning-of-
course responses regarding their ability to relate to scientists
fell into two main categories. First, as exemplified by the
responses of Jesus and Evelyn, many students explicitly affirmed
that they were unable to relate to scientists:
I Don’t Know. I truly am terrible at relating to people that are
involved with science or math.—Jesus, a Latino Scientist Spot-
light Homework student
Disagree. I don’t personally relate to any scientist as most of
my friends and family members are not scientists.—Evelyn, a
Chinese female Course Reader Homework student
Ademar and Beth clarified that this was often because stu-
dents lacked familiarity with any actual scientists:
Disagree. I personally don’t know any scientist, and sometimes
I cannot see myself having the personal qualities of a scien-
tist.—Ademar, a Latino Course Reader Homework student
I Don’t Know. I’m not very familiar with scientists or their
names and studies.—Beth, a Black/Latina female Course Reader
Homework student
Second, among the few students who indicated at the begin-
ning of the course they could personally relate to scientists,
many, like Yvette, explained this was simply because they
appreciated the types of work scientists did:
Somewhat Agree. I am knowledgeable of various scientists but
I don’t feel personally relatable to them. I appreciate their
work and what it has done to better inform us as a soci-
ety.—Yvette, a Latina Scientist Spotlight Homework student
At end of the course, 79% of Scientist Spotlight Homework
students agreed or somewhat agreed that they could personally
relate to an important scientist. These students’ end-of-course
explanations differed markedly from their beginning-of-course
responses and included many details as evidence for relating to
(or not relating to) scientists. Two main themes arose as reasons
students related to scientists at the end of the course. First, many
students found they could relate to scientists due to shared inter-
ests or personal qualities. Lauren described how she could relate
to Charles Limb due to common interests surrounding music:
Agree. I relate the most with the neurologist/musician from the
first scientist spotlight … because I am also a musician.—Lauren,
a white female Scientist Spotlight Homework student
Jesus, on the other hand, related to Lawrence David due to
a shared sense of humor, an interest in making others laugh,
and a similar work ethic:
Somewhat Agree. I can relate to that one scientist who inter-
acted with poop. I loved his sense of humor and drive to com-
plete an experiment … I know that I can relate to him because
I love being funny to make people smile and also am deter-
mined to work on things until I finish.—Jesus, a Latino Scien-
tist Spotlight Homework student
Second, some students found scientists relatable if the scien-
tists did not originally expect to enter a career in science. Yvette
found she could relate to many of the scientists for this reason
and further explains that she is similarly reconsidering her
interest in studying science:
Somewhat Agree. In some of the spotlights some scientists felt
that they didn’t always want to pursue a career in science and
that it just happens. I’m starting to feel the same way. I’m not
originally a science major but I feel that I could have a future
in it if I find the right field.—Yvette, a Latina Scientist Spotlight
Homework student
While a less common theme, seeing scientists with matching
genders or races/ethnicities was important in making them
relatable for some students, like Rachel:
Somewhat Agree. Although I might not be that interested in
pursuing a career in science, being exposed to a wide variety
of diverse scientists, I feel like I could go into this field if I
wanted to. Many of the scientists we learned about were
women and many were a race other than White. These are
both characteristics I would use to describe myself.—Rachel, a
Filipina Scientist Spotlight Homework student
Others, like Tammy, indicated that it made scientists more
relatable to see they have encountered similar struggles or
injustices in life:
Agree. I can relate the most to Ben Barres because of the obvi-
ous discrimination he received as a woman. Being the older
sister of a very bright brother, I am often compared to him and
overlooked for my intelligence. Unless it comes from him, my
opinion is just that of a woman.—Tammy, a Black/Native
American female Scientist Spotlight Homework student
As seen in earlier quotes, many students at the end of the
course were able to name or describe specific scientists in their
responses, suggesting greater familiarity. Of course, this famil-
iarity did not always result in relatability. Amit simply could not
envision himself having the same passion for science:
Disagree. In our scientist spotlights, all the scientists came
from very different backgrounds. However, they all liked sci-
ence very much. I can’t relate to that. I don’t have any particu-
lar disdain for science, but I don’t enjoy it. I do think it is very
important, however.—Amit, an Asian Indian male Scientist
Spotlight Homework student
This presented a barrier to finding scientists relatable, even
when recognizing the featured scientists were very diverse. On
the other hand, notable shifts in qualitative responses toward
an increased ability to relate to scientists were sometimes
observed even among students whose Likert-scale relatability
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 11
Intervention to Enhance Science Identity
selections did not change (e.g., Yvette, who selected “some-
what agree” at both the beginning and end of the course).
Only 43% of Course Reader Homework students agreed or
somewhat agreed with the relatability prompt at the end of the
course. End-of-course qualitative responses from these students
were strikingly similar to their beginning-of-course responses,
with many students, like Evelyn and Beth, using language iden-
tical to what they had written at the beginning of the course:
I Don’t Know. None of my friends or family members are sci-
entists.—Evelyn, a Chinese female Course Reader Homework
Somewhat Disagree. I am not very familiar with scien-
tists.—Beth, a Black/Latina female Course Reader Homework
Responses reiterated beginning-of-course themes that most
students could not relate to, and did not even know of, any
scientists. This was in spite of the fact that some scientists were
introduced as part of certain lectures during Course Reader
Homework classes (see Methods).
Following an RM-ANCOVA, we observed an interaction
between treatment × time for relatability Likert-scale ratings on
the relatability prompt, F(1,276) = 8.49, p = 0.004, η2 = 0.03.
Course Reader Homework students’ end-of-course relatability
Likert scores did not differ significantly from their beginning-of-
course scores, while Scientist Spotlight students’ end-of-course
relatability scores were significantly higher than both their own
beginning-of-course scores and Course Reader Homework par-
ticipants’ end-of-course scores (Figure 2 and Supplemental
Material, parts E and F). Quantitative results therefore support
the hypothesis that Scientist Spotlights increase students’ sense
of relating to scientists.
Evidence Regarding Longitudinal Impacts of Scientist
Spotlights on Stereotypes and Relatability
Fifty-seven Scientist Spotlight Homework students submitted a
response to the stereotypes prompt 6 months after the end of
their courses (17% response rate). Of those, 47 had submitted
responses to the stereotypes prompt at all three time points
(beginning of term, end of term, 6 months after class). Fifty-two
students submitted a response to the relatability prompt
6 months after the end of their courses (15% response rate). Of
those, 27 had submitted responses to the relatability prompt at
all three time points. As the community college student popula-
tion is in constant flux, with students transferring to 4-year
schools or professional programs, moving between colleges, and
entering and exiting school at various times due to work and
family obligations, we were not surprised by the modest response
rate to a survey 6 months after the end of class. In spite of these
lower sample sizes, however, this 6-month follow-up subsample
appeared to match the larger sample in terms of demographics.
Three independent t tests for gender, race/ethnicity (tradition-
ally underserved vs. traditionally well served), and condition
demonstrated that gender, t(279) = 0.655, p = 0.513, and r ace/
ethnicity, t(69.87) = 0.908, p = 0.367, were similar between the
6-month follow-up sample and the larger, original sample.
Six months after the end of class, students appear to have
maintained the largely nonstereotypical ideas about scientists
they displayed at the end of the course. Table 2 shows the most
prevalent themes found in responses to the stereotypes prompt
from students who submitted essays at all three time points. We
additionally created word clouds to visually convey the full
range of scientist descriptions at each time point (Supplemental
Material, part I). Descriptions of scientists as representing
many/all types of people remained the most common theme in
the 6-month postclass responses. Students additionally contin-
ued to describe scientists as individuals who defy stereotypes,
and the idea that scientists have “special intelligence” contin-
ued to be relatively rare. Fifty-seven percent of students
included descriptions of scientists fitting into at least one of the
following categories 6 months after the course: all types of peo-
ple, not just one type of person, and go against stereotypes.
Three-way RM-ANCOVAs controlling for gender and race/
ethnicity (Supplemental Material, parts E and F) showed that
stereotypical descriptions dropped significantly at the end of
the course and remained low 6 months later, F(2,78) = 4.36,
p = 0.016, η2 = 0.10 (Figure 3a). Nonstereotypical descriptions
increased significantly at the end of the course and remained
high 6 months later, F(2,80) = 5 .97, p = 0.004, η2 = 0.13 (Figure
3b). Relatability similarly increased at the end of the course and
remained high 6 months later, though in this case the initial
increase was detected at a p value of 0.083, F(2,46) = 2.63,
p = 0.083, η2 = 0.10 (Figure 3c). This was likely because of the
smaller sample size available for the relatability prompt.
Hypothesis 3: Shifts in Scientist Stereotypes and
Relatability of Scientists Will Correlate with Students’
Interest in Science
We calculated both beginning- and end-of-course Science Inter-
est scores (Supplemental Material, parts G and H) for each stu-
dent. To test the relationship between shifts in Science Interest
and shifts toward majoring in STEM fields, we conducted a 2 × 2
(Science Interest × STEM major interest) RM-ANCOVA con-
trolling for gender, race/ethnicity, course section, and prior sci-
ence class experience. Values for STEM major interest came from
the online survey item “I am majoring or plan on majoring in
another Science or Math field” (Supplemental Material, part D).
FIGURE 2. Average relatability Likert-scale selections by students
at the beginning vs. end of the course for Scientist Spotlight
Homework and Course Reader Homework classes. Graphs depict
weighted means to control for unequal group sizes and nonran-
dom assignment of students to treatment. Error bars represent SE.
by guest on September 1, 2016 from
15:ar47, 12 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
treatment. One-way ANCOVAs suggested there was not a signif-
icant effect for the use of Stereotypes on grades, F(1,211) = 3.00,
p = 0.085, η2 = 0.01, bu t there was a significant effec t of Nonste-
reotypes, F(1,211) = 6.68, p = 0.010, η2 = 0.03. Students whose
use of Nonstereotypes increased earned significantly higher
course grades than those whose use of Nonstereotypes held
steady or decreased (Figure 5b and Supplemental Material,
parts E and F). To test the relationship between relatability and
course grade, we compared students whose relatability ratings
increased, those whose relatability ratings decreased, and those
whose ratings held steady. A one-way ANCOVA controlling for
race/ethnicity, gender, course section, and science experience,
suggested the grades of students whose ratings decreased
(x = 2.59, SE = 0.24) were lower than students whose ratings
held steady (x = 2.79, SE = 0.15) or increased (x = 3.01, SE =
0.10). However, the difference between groups was not signifi-
cant, F(1,171) = 1.65, p = 0.195, η2 = 0.02. The finding of a
correlation between an increase in Nonstereotypes and course
grades therefore provided partial support for hypothesis 4.
Many reports have documented the shortfall in students gradu-
ating with STEM degrees in the United States and the urgent
need to recruit a more diverse STEM workforce (National
Academy of Sciences, 2007, 2011). Interventions with the
potential to enhance students’ science identities and reduce ste-
reotype threat could prove valuable in promoting interest and
success in STEM (Seymour and Hewitt, 1997; Brickhouse et al.,
2000; Hill et al., 2010, chap. 3; Beasley and Fischer, 2012). We
developed and tested an intervention in the form of weekly
homework assignments that were aimed at allowing students
to see their possible selves in science and promoting counterst-
ereotypical examples of who does science. In the following sec-
tions, we discuss the utility of Scientist Spotlights in light of our
A significant interaction for Science Interest was found, F(1,216)
= 10.39, p = 0.001, η2 = 0.05, in which students whose Science
Interest decreased or held steady showed a significant decrease
in STEM major interest from pretest (x = 3.70, SE = 0.16) to
posttest (x = 3.43, SE = 0.18), while students whose Science
Interest increased reported more STEM major interest at posttest
(x = 3.34, SE = 0.16) than at pretest (x = 3.74, SE = 0.18).
RM-ANCOVAs using the Science Interest scale (Supplemen-
tal Material, parts E and F) revealed that a decrease in the use
of Stereotypes correlated with higher Science Interest at the end
of the course, F(1,182) = 4.46, p = 0.036, η2 = 0.02 (Figure 4a).
We found a similar relationship between an increase in the use
of Nonstereotypes and Science Interest that approached signifi-
cance, F(1,182) = 3.32, p = 0.070, η2 = 0.02 (Figure 4b). Sci-
ence Interest additionally appeared to increase from beginning
of course (x = 3.287, SE = 0.076) to end of course (x = 3.568,
SE = 0.061) for students whose ability to relate to scientists
increased, but this finding did not achieve statistical signifi-
cance, F(1,184) = 2.10, p = 0.149, η2 = 0.01. In total, these
results provide partial support for the hypothesized relationship
between shifts in scientist stereotypes/relatability and an inter-
est in science/STEM majors.
Hypothesis 4: Shifts in Scientist Stereotypes and
Relatability of Scientists Will Correlate with Course Grades
As a first step, we tested whether the treatment had an effect on
course grades. A one-way ANCOVA, controlling for gender,
race/ethnicity, course section, and previous science class expe-
rience, revealed that Scientist Spotlight Homework students
earned significantly higher grades than Course Reader Home-
work students, F(1,279) = 6.68, p = 0.018, η2 = 0.02 (Figure 5a
and Supplemental Material, parts E and F).
Additional analyses were limited to Scientist Spotlight
Homework students to prevent confounds introduced by the
TABL E 2. Most common student descriptions of scientists from the beginning of the course, the end of the course, and 6 months after the
end of the course
Beginning of Scientist Spotlight courses End of Scientist Spotlight courses Six months after Scientist Spotlight courses
Most common descriptions
of scientists Prevalence
Most common descriptions
of scientists Prevalence
Most common descriptions
of scientists Prevalence
Curious (s) 27% All types of people (n) 41% All types of people (n) 47%
Albert Einstein (s) 24% Not just one type of person (n) 29% Not just one type of person (n) 20%
Especially intelligent (s) 22% Interested in science (s) 24% Curious (s) 18%
People that do experiments (s) 18% Passionate (s) 24% Make the world better (s) 18%
Discover things (s) 18% Cheerleaders (n) 22% Discover things (s) 18%
Interested in science (s) 14% Darlene Cavalier (n) 18% Passionate (s) 16%
Make the world better (s) 14% Curious (s) 16% Go against stereotypes (n) 16%
Enjoy learning (s) 12% People that do experiments (s) 16% Dedicated (s) 14%
Question things (s) 12% Discover things (s) 16% Rosalind Franklin (n) 12%
People that investigate natural
world (s)
10% People from outside the United
States (n)
14% Interested in science (s) 10%
Chemists (f) 10% Rosalind Franklin (n) 12% People that do experiments (s) 8%
Psychologists (f) 10% James Watson (s) 12% Struggled financially (n) 8%
Doctors (f) 10% Go against stereotypes (n) 12% Creative (s) 6%
People that look for “truths” (s) 8% Creative (s) 10% Enjoy learning (s) 6%
Biologists (f) 8% Not always interested in
science (n)
10% People that investigate natural
world (s)
Shading and letters in parentheses denote categories of descriptions per Schinske et al., 2015: s/turquoise = Stereotype; n/light green = Nonstereotype; f/gray = Field of
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 13
Intervention to Enhance Science Identity
FIGURE 3. Average percent of Stereotypes (a), percent of
Nonstereo types (b), and relatability Likert-scale selections (c) in
Scientist Spotlight students’ responses at the beginning of the
course, end of the course, and 6 months following the end of the
course. Error bars represent SE.
FIGURE 4. Relationships between changes in Stereotypes (a) and
Nonstereotypes (b) to changes in Science Interest from the
beginning of the course to the end of the course.
findings, factors that may influence the effectiveness of Scien-
tist Spotlights, and our anticipated future directions in explor-
ing Scientist Spotlights.
Scientist Spotlights Generated Shifts in Students’
Stereotypes of Scientists and Scientist Relatability
We used the stereotypes prompt to evaluate the impact of Sci-
entist Spotlights on students’ stereotypes of scientists. When
compared with a class performing a similar activity that lacked
connections with diverse scientists, students who completed
Scientist Spotlights adopted more nonstereotypical views of sci-
entists (Figure 1). These changes appeared to be sustained 6
months after the courses ended (Figure 3) and were associated
with higher course grades (Figure 5). Reductions in stereotypi-
cal descriptions of scientists further correlated with increases in
Science Interest (Figure 4a) and an enhanced interest in STEM
We piloted the relatability prompt as a tool for examining
students’ possible selves in a science context, making the case
that explicitly asking students about their ability to personally
relate to scientists would draw out descriptions of students’ pos-
sible selves in relation to scientists. While only 43% of Course
Reader Homework students found scientists relatable at the
end of the course, the vast majority (79%) of Scientist Spotlight
students did (Figures 2 and 4c). These students discussed
shared personalities and interests outside science as reasons for
being able to relate to scientists, with some students also com-
menting on certain scientists’ nontraditional paths to gaining an
interest in science. Many students used specific language such
by guest on September 1, 2016 from
15:ar47, 14 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
as “like me” or “I am also …” when describing why common
interests or personal qualities caused them to relate to scientists
after Scientist Spotlights. This suggested the relatability prompt
might have functioned as intended in creating opportunities for
students to reflect on their possible science selves.
These findings suggest Scientist Spotlights hold promise as a
tool for enhancing students’ possible science selves and disrupt-
ing stereotypes of scientists in diverse classroom settings. Prior
studies point to the importance of these shifts in forming a sci-
ence identity, mitigating stereotype threat, and enhancing stu-
dent interest and success (Steele, 1997; Oyserman et al., 2006;
Steinke et al., 2009; Hill et al., 2010, chap. 3; Hunter, 2010;
Beasley and Fischer, 2012; Mills, 2014).
Scientist Spotlights Represent a Simple Means for Raising
Issues of Diversity in STEM Classrooms
Faculty might feel particularly wary of adopting new activities
that overtly approach issues related to race and diversity due to a
lack of training in how to facilitate discussions in those areas (Sue
et al., 2009). STEM faculty commonly cite course content expec-
tations and concerns regarding time as barriers to implementing
innovative teaching strategies (Henderson and Dancy, 2007;
Austin, 2011). Scientist Spotlights offer faculty an approach for
openly addressing diversity in STEM classes while supporting
content goals and requiring little grading or class time.
Because Scientist Spotlights are assigned as homework and
are graded based on timeliness and word count, the activities
consume only a negligible amount of instructor time during and
outside of class. This is perhaps particularly the case when they
are assigned through an online course management system that
automatically displays word counts. After an initial investment
of time to identify scientists to feature and compose assignment
prompts, Scientist Spotlights become an easily sustainable class
Additionally, by connecting diversity themes to course con-
tent through Scientist Spotlights, faculty are able to structure
some of students’ content learning outside class. In this way,
Scientist Spotlights assist faculty in meeting their content
expectations, rather than taking time away from addressing
content. This follows the best practices discussed by Chamany
et al. (2008), who recommend “strategically embedding social
context into those topics that are traditionally reviewed in
biology courses.” Highlighting the struggles and inequities
experienced by scientists like Ben Barres also opened up oppor-
tunities for students to engage with issues of social justice in
science. Infusing course content with themes of equity and
social justice has been promoted as a particularly impactful way
to engage traditionally underserved and underprivileged popu-
lations of students in STEM (Chamany, 2006; Chamany et al.,
2008). At the same time, these themes of equity and diversity
were clearly contextualized within instructors’ comfort zone of
course content, which might allay instructor reservations about
raising such themes as part of a STEM class.
We predict that the strongest case for faculty adoption of
Scientist Spotlights, and eventually adoption of more extensive
diversity-related activities, might come from students them-
selves once faculty pilot Scientist Spotlights. Students in our
sample responded so immediately and effusively to Scientist
Spotlights, it appeared there was a great, unmet demand
among students to approach science content through this lens.
We predict that, if faculty see responses from their own stu-
dents similar to those shown here, they will feel energized and
empowered to become more deeply involved in addressing
diversity. Scientist Spotlights might therefore represent an
excellent introductory tool that could inspire further work on
equity and diversity in STEM by science faculty.
Suggestions for Implementation
While Scientist Spotlights are relatively simple activities, suc-
cessfully implementing them in a course likely depends in part
on how an instructor chooses scientists to feature, writes the
assignment prompts, introduces the assignments to the class,
and reports back on students’ submissions. In the following sec-
tions, we elucidate some of the factors we feel assisted in
achieving positive outcomes and reducing the potential for stu-
dent resistance.
Possible Selves as a Framework for Selecting Scientists to
Feature in Spotlights
We found the concept of possible selves to be helpful in identify-
ing scientists to feature. Rather than looking for scientists to
serve as role models that students should emulate, we sought
out scientists with whom students might already have similari-
ties; that is, scientists in whom students might see their possible
selves. While gender/race/ethnic matching was important for
FIGURE 5. Average course grades (0 = “F,” 4 = “A”) for Scientist
Spotlight Homework students vs. Course Reader Homework
students (a) and for students whose proportion of Nonstereotype
descriptions of scientists increased vs. did not increase (b). Error
bars represent SE.
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 15
Intervention to Enhance Science Identity
some students, students more often cited shared personal quali-
ties and outside interests as ways in which they saw themselves
in scientists. Given that Human Biology primarily serves non–
biology majors, it is not surprising that students also appreciated
that not all scientists aspired to a science career at a young age
and sometimes found science later in life. In consideration of the
above, it is important to identify scientists for whom some sort
of engaging biographical resource exists. It was in those
biographical resources that students most directly encountered
counterstereotypical information about scientists and found
information that reminded students of themselves. We opti-
mally hoped to find TED Talks, interviews, or podcasts featuring
scientists telling their own stories in their own voices. However,
we sometimes used printed interviews and biographical infor-
mation, as in the example regarding Ben Barres (see Methods).
The Story Collider ( proved a
particularly rich resource for identifying biographical informa-
tion regarding counterstereotypical scientists. The Story Collider
website includes hundreds of 10- to 20-min-long, often funny or
emotionally stirring autobiographical stories told by diverse sci-
entists. The podcast descriptions can be searched for certain key
terms through the website, which can be helpful in identifying
scientists working in areas connected with course content.
Metacognition as a Design Feature of Scientist Spotlight
In terms of the assignment prompt itself and the regularity of
the assignments, our work suggests that performing Scientist
Spotlights regularly and including a metacognitive question
about who does science assisted in achieving the outcomes we
observed. Course Reader Homework classes included three ref-
erences to scientists working in the fields being studied in class
(see Methods). Two of those scientists identified as people of
color and all three had counterstereotypical qualities. Students
were introduced to those scientists during class, saw pictures of
the scientists, and watched short videos featuring two of the
scientists. However, students did not engage in any individual
or group activities regarding the scientists and were not asked
to reflect on whether those segments of class impacted their
views of scientists. Our results suggested these students did not
substantially change their views of scientists. This suggests that
going beyond simply mentioning/showing diverse scientists in
class and moving to require regular work including metacogni-
tion about who does science might be key for stimulating larger
changes in the ways students view scientists. Science faculty are
increasingly aware that metacognition is necessary to drive last-
ing changes in students’ ideas and behaviors (Tanner, 2012).
We therefore propose that the prompt reading, “What do these
resources tell you about the types of people that do science?,”
might be important to include in every Scientist Spotlight
assignment, even if the other writing prompts vary from one
assignment to the next.
Instructor Talk as a Strategy for Securing Student Buy-In
Alongside content expectations and time limitations, fear of stu-
dent resistance represents another of the main barriers to the
adoption of new teaching strategies by faculty (Henderson and
Dancy, 2007; Seidel and Tanner, 2013). We encountered very
little evidence of student resistance to completing Scientist
Spotlights in these classes. Students completed Scientist Spot-
lights at very high rates, earned high scores, and seemed to find
the assignments engaging and helpful. Students’ acceptance of
Scientist Spotlights might partially relate to the flexibility stu-
dents had to engage with either the course content part of the
activity or the scientist biography part of the activity. Students
were allowed to independently determine how much of their
submissions focused on the “types of people that do science”
prompt compared with the course contentrelated prompts. In
this way, students could settle into their own comfort zones of
discussing issues of content versus issues of diversity and scien-
tist stereotypes.
The non–content language instructors use to frame new
activities and debrief completed activities (“instructor talk”)
might additionally play a large role in reducing student resis-
tance and creating effective environments for applying innova-
tive strategies (Seidel et al., 2015). While Scientist Spotlights
are largely out-of-class activities, J.N.S. spent a small amount of
class time at the start of the course establishing a classroom
culture conducive to performing Scientist Spotlights and
explaining his pedagogical decision to use these assignments.
Specifically, he made clear his reasons for incorporating Scien-
tist Spotlights into the course and his goals for the assignments,
expressed that there were no “right” or “wrong” ways to
respond, and noted that students could write about whatever
parts of the assignments resonated most strongly with them
each week. They need not strictly respond to each assignment
prompt in equal amounts or in the order shown.
Following the first and second Spotlights, J.N.S. spent
5 minutes in class sharing anonymous student quotes to
demonstrate how different students engaged with course con-
tent and reflected on their notions of scientists through the
assignments. J.N.S. especially looked for quotes similar to
Gina’s (discussed earlier) demonstrating the importance of the
types of people who do science to the types of scientific ques-
tions that get pursued. This showed students in their own words
that diversity is necessary to ensure diverse scientific questions
are addressed and that it is important to understand who does
science when considering what currently is and is not known
about the topics studied in class.
While quasi-experimental studies can represent a robust means
of addressing education research questions, it is critical to explore
alternate explanations for outcomes that might stem from the
lack of random assignment to quasi-experimental groups
(Shadish et al., 2002). Though the course sections we studied
were equivalent in many respects, they differed slightly in stu-
dent demographics, timing during the year, and lecture location.
It is possible, for example, that differences observed between Sci-
entist Spotlight Homework and Course Reader Homework
groups were influenced by slight variations in student racial/
ethnic or gender identities between those groups. This would
confound our ability to attribute differences to our intervention.
Similar scenarios could be proposed for differences in lecture
locations or timing during the year. However, all lecture rooms
were similarly appointed and neither treatment group was iso-
lated to a single part of the year. The five Scientist Spotlight
courses took place throughout the year (three Fall classes, one
Winter class, one Spring class), while one Course Reader Home-
work class took place in the Fall and the other in the Spring.
by guest on September 1, 2016 from
15:ar47, 16 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
Though differences between the courses appeared relatively
subtle, we used statistical corrections to partition out variance
introduced by demographics, course section differences, and
the unequal sizes of quasi-experimental groups (i.e., lower
number of Course Reader Homework students). The resulting
“weighted means” were used in evaluating our hypotheses.
These weighted means often differed substantially from means
observed in our raw data (Supplemental Material, part E). This
provided us more assurance that the differences we observed
were due to the Scientist Spotlights but at the cost of variability
that may have demonstrated a more robust effect. As a result, it
might be argued that our results provide only conservative esti-
mates of the impacts of Scientist Spotlights due to overly
aggressive statistical corrections. That said, some researchers
argue that statistical corrections are still insufficient to account
for a lack of randomization, and issues with unequal group
characteristics could confound the ability to make strong infer-
ences (Shadish et al., 2002).
Other differences between our quasi-experimental groups
included drop/fail/withdrawal (DFW) rates and the fact that
one Course Reader Homework group was cotaught with a sec-
ond instructor. From our results, it is apparent that 72% of Sci-
entist Spotlight Homework students submitted both a begin-
ning- and end-of-course stereotypes prompt essay, but only
67% of Course Reader Homework students did so. This might
partially relate to differences in DFW rates between Scientist
Spotlight and Course Reader Homework classes, effectively
resulting in higher attrition in Course Reader Homework
classes. Scientist Spotlight Homework classes had a 20% DFW
rate compared with a 23% DFW rate in Course Reader Home-
work classes (for reference, the average DFW rate across all
Human Biology classes at this college is 29%). It is also possible
that Course Reader Homework students were less engaged in
class, causing more of them to miss one of the days when a
survey was scheduled. In either case, if the lower response rate
among Course Reader Homework classes occurred dispropor-
tionately among students who shifted toward higher levels of
Nonstereotypes/relatability, then attrition in those classes could
partly account for differences observed between quasi-experi-
mental groups. This scenario seems unlikely, however, given
that our findings suggest students conveying higher levels of
Nonstereotypes and relatability have increased success in class
(Schinske et al., 2015; current study). It seems more likely that
attrition could have masked larger differences between our
groups by eliminating additional data points for Course Reader
Homework students who did not shift in these variables.
It is also possible that the addition of a coteacher for one
Course Reader Homework section influenced these differences
between groups as well as our results. However, J.N.S. main-
tained control over relevant course assignments in all sections,
and the cotaught section was equivalent to the others in terms
of its curriculum expectations and types of class activities. Fur-
ther, we included course section as a covariate in analyses to
control for course-level differences. While we observed signifi-
cant variation in dependent variables among students, we did
not observe such variation between course section groups.
With regard to descriptions of scientists reported from stu-
dent essays, our study did not seek to establish certain descrip-
tions as “good” and others as “bad” in relation to enhancing suc-
cess or interest in biology. While some studies have categorized
certain scientist stereotypes as “positive” and “negative” (Mead
and Metraux, 1957), we did not explore students’ cultural eval-
uations of specific stereotypes and cannot conclude whether
individual students view such associations positively or nega-
tively. Further surveys and interviews would be necessary to
evaluate the deeper meanings and relative importance of various
descriptions within the Stereotypes and Nonstereotypes catego-
ries. It should additionally be noted that our results do not pro-
vide specific insights regarding the mechanism(s) behind the
outcomes observed surrounding Scientist Spotlights. Future
work could explore the roles of metacognition, stereotype threat
reduction, identification of possible selves, and other factors as
mechanisms underlying these results.
Other possible limitations involve our proposed assessment
of students’ possible science selves and the nature of our survey
activities more generally. We used the concept of “relatability”
as a means of capturing possible selves, making the case that
the prompt explicitly asked students about whether they could
relate to a scientist they knew. This was an exploratory narra-
tive approach, and whether it fully captures a student’s sense of
their own potential talents and abilities as scientists is a ques-
tion for further exploration. Our measure was also limited in its
ability to capture how students thought of themselves in terms
of the characteristics of scientists they named. A more precise
measure of students’ sense of self-as-scientist could be helpful
to expand upon and clarify the present findings.
Finally, results presented in this paper might not be broadly
generalizable to all school settings. Qualitative studies have the
strength of more deeply exploring student ideas but can lack
the generalizability of some quantitative studies (Johnson and
Christensen, 2008, pp. 441–442). We conducted our study in
the unique environment of a large, diverse community college
in the San Francisco Bay Area. One might anticipate different
results or student reactions in less diverse settings in different
parts of the United States. The types of exemplar quotes we
report and the frequencies of themes we observed in students’
essays, therefore, might be specific to our student population
and teaching context.
Future Directions
We envision multiple opportunities to extend this work in the
future, ranging from further explorations of the present find-
ings in Human Biology classes to dissemination of the interven-
tion across new institutions and teaching contexts. In light of
the limitations discussed in the previous section, pursuing study
designs that match students to quasi-experimental groups or
randomize participants could reveal further significant trends
and more fully illuminate the impacts of the intervention.
Assessing Scientist Spotlights in additional class contexts would
assist in exploring the generalizability of our findings. We also
believe further explorations of the relatability prompt and other
measures that might evaluate students’ possible science selves
could yield valuable insights into broadening participation in
STEM. For example, while we observed intriguing trends con-
necting shifts in relatability to broader student outcomes, such
as higher Science Interest and course grades, these trends did
not achieve statistical significance. Further studies of relatabil-
ity would assist in more fully illuminating its connections to
these broader outcomes and clarifying its relationship to the
broader concept of possible science selves.
by guest on September 1, 2016 from
CBE—Life Sciences Education • 15:ar47, Fall 2016 15:ar47, 17
Intervention to Enhance Science Identity
Future studies might additionally more directly explore the
impacts of Scientist Spotlights on stereotype threat or class-
room equity gaps. That certain shifts related to Scientist Spot-
lights correlated with increased Science Interest and higher
course grades is encouraging and raises interesting questions
about how students of different genders and races/ethnicities
experienced these outcomes. However, our unequal group sizes
and the nonrandom distribution of students among conditions
prevented us from drawing conclusions along these lines. Fur-
ther, the trends we observed in Science Interest were in relation
to shifts in stereotypes/relatability, not treatment effects.
Observing treatment effects related to Science Interest might
require more robust controls and might be assisted by studies
exploring students’ sense of themselves as scientists in relation
to Science Interest. Additional longitudinal data would also
assist in understanding the enduring impacts of Scientist Spot-
lights. Longer-term follow-up data from both Scientist Spotlight
students and control students would allow us to investigate
how sustained shifts in stereotypes and relatability correlate
with motivation and behavior in the future, specifically as they
relate to pursuing and persisting in STEM majors.
Perhaps the most exciting extension of this work involves
engaging additional faculty in the creation and deployment of
Scientist Spotlights in new institutional and classroom contexts.
Through our workshops and presentations at conferences, a
wide array of faculty from diverse STEM (and non-STEM) fields
have expressed interest in using Spotlights in class. The only
somewhat time-consuming step in using Scientist Spotlights is
the work done before the start of a course to select scientists,
gather appropriate scientific and biographical resources regard-
ing the scientists, and compose the assignment prompts. It
might therefore be useful to nucleate a community of STEM
faculty to build Scientist Spotlight modules for many different
curricular areas. This could result in a database of ready-to-use
assignments matching a wide range of content areas and could
additionally build a strong community of STEM educators
focused on issues of equity and diversity.
We extend our appreciation to Kimberly Tanner, Jennifer
Myhre, the monitoring editor, and three anonymous reviewers
for providing valuable feedback with regard to this article and
to Jahana Kaliangara and Monica Cardenas for assisting in pro-
cessing and presenting preliminary data leading up to this
study. J.N.S. thanks Sonya Dreizler, Veronica Neal, Mallory
Newell, IMPACT AAPI, and the Equity Action Council at De
Anza College for their support. The organizers of the Confer-
ence on Understanding Interventions That Broaden Participa-
tion in Science Careers kindly provided travel funding to sup-
port our presentation of preliminary findings from this work in
a lunchtime plenary in 2015. IMPACT AAPI and the Office of
Staff and Organizational Development at De Anza College have
generously provided J.N.S. and A.S. with travel funds to pres-
ent on Scientist Spotlights at national meetings.
webpage/dbasse_072578.pdf (accessed 9 December 2015).
Beardslee DC, O’Dowd DD (1961). The college-student image of the scien-
tist: scientists are seen as intelligent and hard-working but also as uncul-
tured and not interested in people. Science 133, 997–1001.
Beasley MA, Fischer MJ (2012). Why they leave: the impact of stereotype
threat on the attrition of women and minorities from science, math and
engineering majors. Soc Psychol Educ 15, 427–448.
Begley S (2006, July 13). He, once a she, oers own view on science spat. Wall
Street Journal. (accessed
9 December 2015).
Brickhouse NW, Lowery P, Schultz K (2000). What kind of a girl does science?
The construction of school science identities. J Res Sci Teach 37, 441–458.
Buck GA, Clark VLP, Leslie-Pelecky D, Lu Y, Cerda-Lizarraga P (2008). Exam-
ining the cognitive processes used by adolescent girls and women scien-
tists in identifying science role models: a feminist approach. Sci Educ 92,
Chamany K (2006). Science and social justice. J Coll Sci Teach 36, 54.
Chamany K, Allen D, Tanner K (2008). Making biology learning relevant to
students: integrating people, history, and context into college biology
teaching. CBE Life Sci Educ 7, 267–278.
Cheryan S, Master A, Meltzo AN (2015). Cultural stereotypes as gatekeep-
ers: increasing girls’ interest in computer science and engineering by di-
versifying stereotypes. Front Psychol 6, 49.
Cheryan S, Plaut VC, Davies PG, Steele CM (2009). Ambient belonging: how
stereotypical cues impact gender participation in computer science.
J Pers Soc Psychol 97, 1045.
Cheryan S, Plaut VC, Handron C, Hudson L (2013). The stereotypical com-
puter scientist: gendered media representations as a barrier to inclusion
for women. Sex Roles 69, 58–71.
Cohen GL, Garcia J, Apfel N, Master A (2006). Reducing the racial achieve-
ment gap: a social-psychological intervention. Science 313, 1307–
Dee TS (2004). Teachers, race, and student achievement in a randomized
experiment. Rev Econ Stat 86, 195–210.
DeWitt J, Archer L, Osborne J (2013). Nerdy, brainy and normal: children’s
and parents’ constructions of those who are highly engaged with sci-
ence. Res Sci Educ 43, 1455–1476.
Ehrenberg RG, Goldhaber DD, Brewer DJ (1995). Do teachers’ race, gender,
and ethnicity matter? Evidence from the national educational longitudi-
nal study of 1988. Ind Labor Relat Rev 48, 547–561.
Fairlie RW, Homann F, Oreopoulos P (2011). A Community College Instruc-
tor Like Me: Race and Ethnicity Interactions in the Classroom (No.
w17381), Cambridge, MA: National Bureau of Economic Research.
Gee JP (2000). Identity as an analytic lens for research in education. Rev Res
Educ 25, 99–125.
Good C, Aronson J, Inzlicht M (2003). Improving adolescents’ standardized
test performance: an intervention to reduce the eects of stereotype
threat. J Appl Dev Psychol 24, 645–662.
Goodenow C (1993). Classroom belonging among early adolescent stu-
dents: relationships to motivation and achievement. J Early Adolesc 13,
Henderson C, Dancy MH (2007). Barriers to the use of research-based in-
structional strategies: the influence of both individual and situational
characteristics. Phys Rev Spec Top Phys Educ Res 3, 020102.
Hill C, Corbett C, St Rose A (2010). Why So Few? Women in Science, Tech-
nology, Engineering, and Mathematics, Washington, DC: American Asso-
ciation of University Women.
Hunter CW (2010). Identifying barriers and bridges in developing a
science identity. Doctoral Dissertation, Olympia, WA: Evergreen State
Inzlicht M, Ben-Zeev T (2000). A threatening intellectual environment: why
females are susceptible to experiencing problem-solving deficits in the
presence of males. Psychol Sci 11, 365–371.
James W (2005). The Principles of Psychology, vol. 1, South Australia:
eBooks@Adelaide, University of Adelaide.
sandbox/great-books-redux/corpus/html/principles.html (accessed 20
December 2015; original work published 1890).
Allen NJ, Barres BA (2009). Neuroscience: glia—more than just brain glue.
Nature 457, 675–677.
Austin AE (2011). Promoting evidence-based change in undergrad-
uate science education: a paper commissioned by the National
Academies National Research Council Board on Science Education.
by guest on September 1, 2016 from
15:ar47, 18 CBE—Life Sciences Education • 15:ar47, Fall 2016
J. N. Schinske et al.
Johnson B, Christensen L (2008). Educational Research: Quantitative, Qual-
itative, and Mixed Approaches, Thousand Oaks, CA: Sage.
Karunanayake D, Nauta MM (2004). The relationship between race and stu-
dents’ identified career role models and perceived role model influence.
Career Dev Q 52, 225–234.
Markus H, Nurius P (1986). Possible selves. Am Psychol 41, 954.
Martin D (2015). Women in science: are portrayals on primetime television
negative, and what are eects of exposure to such content? Doctoral
Dissertation, University of Missouri–Columbia.
Marx DM, Ko SJ (2012). Superstars “like” me: the eect of role model similar-
ity on performance under threat. Eur J Soc Psychol 42, 807–812.
Marx DM, Roman JS (2002). Female role models: protecting women’s math
test performance. Pers Soc Psychol Bull 28, 1183–1193.
Maylor U (2009). “They do not relate to Black people like us”: Black teachers
as role models for Black pupils. J Educ Policy 24, 1–21.
McIntyre RB, Lord CG, Gresky DM, Frye GDJ, Bond CF Jr (2004). A social
impact trend in the eects of role models on alleviating women’s math-
ematics stereotype threat. Curr Res Soc Psychol 10, 116–136.
Mead M, Metraux R (1957). Image of the scientist among high-school stu-
dents. Science 126, 384–390.
Mills LA (2014). Possible science selves: informal learning and the career in-
terest development process. In: 11th International Conference on Cog-
nition and Exploratory Learning in Digital Age (CELDA) Proceedings, ed.
DG Sampson, JM Spector, and P Isaias, Lisbon: International Association
for Development of the Information Society, 275–279.
National Academy of Sciences (2007). Rising above the Gathering Storm:
Energizing and Employing America for a Brighter Economic Future,
Washington, DC: National Academies Press.
National Academy of Sciences (2011). Expanding Underrepresented Minority
Participation: America’s Science and Technology Talent at the Cross-
roads, Washington, DC: National Academies Press.
National Science Foundation (2013). Women, Minorities, and Persons with
Disabilities in Science and Engineering: 2013 (Special Report NSF 13-304),
Arlington, VA: National Center for Science and Engineering Statistics. (accessed 9 December 2015).
Oyserman D, Bybee D, Terry K (2006). Possible selves and academic outcomes:
how and when possible selves impel action. J Pers Soc Psychol 91, 188.
Phelan JE (2010). Increasing women’s aspirations and achievement in sci-
ence: the eect of role models on implicit cognitions. Doctoral Disserta-
tion, New Brunswick, NJ: Rutgers University Graduate School.
Purdie-Vaughns V, Steele CM, Davies PG, Ditlmann R, Crosby JR (2008).
Social identity contingencies: how diversity cues signal threat or safety for
African Americans in mainstream institutions. J Pers Soc Psychol 94, 615.
Reece JB, Urry LA, Cain ML, Wasserman SA, Minorsky PV, Jackson RB (2014).
Campbell Biology, 10th ed., San Francisco: Pearson.
Roeser RW, Midgley C, Urdan TC (1996). Perceptions of the school psycho-
logical environment and early adolescents’ psychological and behavioral
functioning in school: the mediating role of goals and belonging. J Educ
Psychol 88, 408–422.
Schinske J, Cardenas M, Kaliangara J (2015). Uncovering scientist stereo-
types and their relationships with student race and student success in a
diverse, community college setting. CBE Life Sci Educ 14, ar35.
Schneider JS (2010). Impact of undergraduates’ stereotypes of scientists on
their intentions to pursue a career in science. Doctoral Dissertation,
Raleigh: North Carolina State University.
handle/1840.16/6184 (accessed 9 December 2015).
Seidel SB, Reggi AL, Schinske JN, Burrus LW, Tanner KD (2015). Beyond the
biology: a systematic investigation of noncontent instructor talk in an
introductory biology course. CBE Life Sci Educ 14, ar43.
Seidel SB, Tanner KD (2013). “What if students revolt?” Considering student
resistance: origins, options, and opportunities for investigation. CBE Life
Sci Educ 12, 586–595.
Seymour E, Hewitt NM (1997). Talking about Leaving: Why Undergraduates
Leave the Sciences, Boulder, CO: Westview.
Seymour E, Wiese D, Hunter A, Danrud SM (2000, March). Creating a bet-
ter mousetrap: on-line student assessment of their learning gains.
Paper originally presented to the National Meeting of the American
Chemical Society, San Francisco, March 27, 2000.
docs/SALGPaperPresentationAtACS.pdf (accessed 20 December 2015) .
Shadish WR, Cook TD, Campbell DT (2002). Experimental and Quasi-exper-
imental Designs for Generalized Causal Inference, Boston: Wadsworth/
Cengage Learning. (accessed 13
December 2015).
Steele CM (1997). A threat in the air: how stereotypes shape intellectual iden-
tity and performance. Am Psychol 52, 613.
Steele CM, Aronson J (1995). Stereotype threat and the intellectual test
performance of African Americans. J Pers Soc Psychol 69, 797.
Steinke J, Lapinski M, Long M, Van Der Maas C, Ryan L, Applegate B (2009).
Seeing oneself as a scientist: media influences and adolescent girls’ sci-
ence career-possible selves. J Women Minor Sci Eng 15, 279–301.
Sue DW, Torino GC, Capodilupo CM, Rivera DP, Lin AI (2009). How white
faculty perceive and react to dicult dialogues on race implications for
education and training. Couns Psychol 37, 1090–1115.
Tanner KD (2009). Learning to see inequity in science. CBE Life Sci Educ 8,
Tanner KD (2012). Promoting student metacognition. CBE Life Sci Educ 11,
Trochim MK (2006). Quasi-experimental design. www.socialresearchmeth- (accessed 5 April 2016).
Wyer M (2003). Intending to stay: images of scientists, attitudes toward wom-
en, and gender as influences on persistence among science and engi-
neering majors. J Women Minor Sci Eng 9(1).
.html (accessed 9 December 2015).
by guest on September 1, 2016 from

Supplementary resource (1)

... We believe Coordinated extends to creating environments and opportunities for diverse groups of people to actively work together. Geoscience departments can address these issues by actively engaging in campus communities and recruiting students (Ormand et al., 2021), showing diverse examples of geoscientists in academic environments (Schinske et al., 2016), engaging students in environmental justice and place-based learning (Urgeoscience, 2020), and providing professional development for faculty tackling implicit bias, stereotype threat, and solo status (i.e., being the only member of a group) (CRLT, 2021;Sekaquaptewa & Thompson, 2002;Steele, 2010). Departments must also address historical inequities without solely relying on institutional policies. ...
Full-text available
Practitioners and researchers in geoscience education embrace collaboration applying ICON (Integrated, Coordinated, Open science, and Networked) principles and approaches which have been used to create and share large collections of educational resources, to move forward collective priorities, and to foster peer‐learning among educators. These strategies can also support the advancement of coproduction between geoscientists and diverse communities. For this reason, many authors from the geoscience education community have co‐created three commentaries on the use and future of ICON in geoscience education. We envision that sharing our expertise with ICON practice will be useful to other geoscience communities seeking to strengthen collaboration. Geoscience education brings substantial expertise in social science research and its application to building individual and collective capacity to address earth sustainability and equity issues at local to global scales The geoscience education community has expanded its own ICON capacity through access to and use of shared resources and research findings, enhancing data sharing and publication, and leadership development. We prioritize continued use of ICON principles to develop effective and inclusive communities that increase equity in geoscience education and beyond, support leadership and full participation of systemically non‐dominant groups and enable global discussions and collaborations.
... Part of this deficiency could reasonably be attributed to a lack of interest and/or a lack of self-efficacy in science leading to low uptake of higher science courses in both high school and undergraduate courses as well as low interest in compulsory high-school science classrooms. Declining interest in science in schools has been documented numerous times over the past 20 years [3,[7][8][9][10] with many studies looking for strategies to counteract this declining interest [11][12][13][14]. Studies on motivation [15][16][17][18], attitude [10,18,19], emotional impact [20], social settings [21,22], classroom environments [23][24][25][26], and other factors are important as they form the basis for the development of one's self-efficacy in a given context [27]. ...
Full-text available
This paper presents a new astronomy self efficacy instrument, composed of two factors, one relating to learning astronomy content, which we call astronomy personal self efficacy, and the other relating to the use of astronomical instrumentation, specifically the use of remote robotic telescopes for data collection. The latter is referred to as the astronomy instrumental self efficacy factor. The instrument has been tested for reliability and construct validity. Reliability testing showed that factor 1 had a Cronbachs alpha of 0.901 and factor 2 had a Cronbachs alpha of 0.937. Construct validity was established by computing one way analyses of variances, with the p value suitably protected, using independent variables peripherally related to the constructs. These analyses demonstrate that both scales possess high construct validity. The development of this astronomy specific instrument is an important step in evaluating self efficacy as a precursor to investigating the construct of science identity in the field of astronomy.
... It is especially important for URM students to develop science identities, gain a sense of belonging, and increase their self-efficacy in order to persist in STEM careers (Trujillo and Tanner, 2014). To facilitate this, it is important to use examples that display the diversity of scientists and research in organismal biology so students can relate to these examples and gain a sense of belonging (Schinske et al., 2016). ...
Full-text available
Organismal courses are inherently integrative, incorporating concepts from genetics, physiology, ecology and other disciplines linked through a comparative and phylogenetic framework. In a comprehensive organismal course, the organisms themselves are a lens through which students view and learn major concepts in evolutionary biology. Here, we present the learning goals of five core concepts (phylogenetics, biogeography, biodiversity, evo-devo, and key traits) we are using to transform organismal courses. We argue that by focusing on organismal knowledge and authentic examples, students learn foundational concepts and investigate biological hypotheses through the content that is unique to individual organismal groups. By using active learning strategies to teach core concepts, instructors can promote an inclusive classroom designed to engage students from diverse backgrounds and facilitate mastery and retention to test understanding of core biological concepts. This paper provides justification for why organismal biology needs to be kept as part of the biology curriculum, outlines the framework we are using to transform organismal courses, and provides examples of different ways instructors can incorporate active learning strategies and in-class activities in organismal courses in ways that enable their application to further investigation of both foundational and translational sciences for students.
... Initial attempts to address some of these inadequacies in the classroom have focused on diversity and inclusion in one-time events, sporadic addition of activities and content, or extracurricular mentoring that may or may not have been meaningfully integrated into the curriculum (11)(12)(13). Several successful strategies include more diverse examples of scientists and the impact of science (14,15), practices that promote student engagement and classroom equity. Techniques such as allowing students ample time to write and respond, asking students to write a values affirmation (16), and linking inclusive pedagogies to universal course design principles also enhance classroom inclusiveness (17,18). ...
Full-text available
ABSTRACT Gateway college science courses continue to exclude students from science, disproportionately discriminating against students of color. As the higher education system strives to reduce discrimination, we need a deliberate, iterative process to modify, supplement, or replace current modalities. By incorporating antiracist, just, equitable, diverse, and inclusive (AJEDI) principles throughout course design, instructors create learning environments that provide an antidote to historically oppressive systems. In this paper, we describe how a community of microbiology instructors who all teach Tiny Earth, a course-based undergraduate research experience, created and rapidly integrated antiracist content and pivoted to an online format in response to the social unrest and pandemic of 2020. The effort strengthened an existing teaching community of practice and produced collective change in classrooms across the nation. We provide a perspective on how instructor communities of practice can be leveraged to design and disseminate AJEDI curriculum.
This paper describes a randomized and controlled efficacy study conducted in high school biology classrooms across the USA. In this study, teachers implemented the use of Data Nuggets, activities designed to bring real research and data into the classroom. These materials can be embedded within the existing instructional modality of any given curriculum, thus infusing these curricula with science stories and associated datasets. Our design had teachers incorporate Data Nuggets into one of their class sections, while teaching a second class section in a business-as-usual manner. Although students in both conditions improved similarly in quantitative reasoning over the course of the study semester, we saw several key differences for students taught using the intervention as compared to those taught using only standard instruction. Students in classrooms that utilized Data Nuggets spent more time engaged in the practices of science and had greater improvement in their ability to construct scientific explanations. In addition, students using the intervention activities showed increases in both their self-efficacy in data-related tasks and their interest in STEM careers. Finally, the effects of teacher implementation on student outcomes when using Data Nuggets were assessed.
In an effort to increase community college (CC) biology education research (BER), an NSF-funded network called CC Bio INSITES (Community College Biology Instructor Network to Support Inquiry into Teaching and Education Scholarship; INSITES for short) was developed to provide intellectual, resource, and social support for CC faculty (CCF) to conduct BER. To investigate the efficacy of this network, we asked about the barriers and supports INSITES CCF have experienced when conducting BER and how specific INSITES supports have mitigated barriers and provided support for network members to engage in BER. We conducted interviews and focus groups with 17 network participants, representing 15 different CCs. Qualitative thematic analysis revealed six main barriers that INSITES CCF experience when conducting BER: time constraints, knowledge, incentives or rewards, administrative or peer support, infrastructure, and stigma or misconceptions associated with being CCF. Participants indicated how the supports provided by INSITES helped to mitigate each barrier. Social support was especially critical for CCF to develop a sense of belonging to the CC BER community, though that did not extend to the broader BER community. We describe how these supports function to support BER and recommend four actions for future support of CCF conducting BER.
Community colleges have an opportunity to promote achievement of more science, technology, engineering, and mathematics (STEM) students and meet larger goals of scientific advancement and educational equity. Understanding community college students' needs and backgrounds is key to increasing students' success in STEM fields and realizing this potential. The objective of this paper is to use data from the U.S. Department of Education's National Center for Education Statistics and other sources to characterize community college students and their academic achievement and to offer equity-based approaches to increase success, particularly in STEM. Here, I document that community college students, who constitute approximately one-third of U.S. undergraduates, are a unique population with greater proportions of underrepresented STEM minorities, parents, and students requiring developmental education. They are also more likely to be older, working, part-time, low-income, and first-generation students and more likely to differ demographically from faculty. I also found lower rates of academic achievement among community college students, including lower rates of retention and STEM degree attainment with evidence of even lower achievement for STEM underrepresented groups. The data point to the need for equity-based strategies to address achievement disparities for STEM community college students, including increasing community college faculty diversity and sensitivity to diverse students' needs and experiences; adopting inclusive, active-learning pedagogies; and reforming developmental education.
Sexual and gender minorities face considerable inequities in society, including in science. In biology, course content provides opportunities to challenge harmful preconceptions about what is "natural" while avoiding the notion that anything found in nature is inherently good (the appeal-to-nature fallacy). We provide six principles for instructors to teach sex- and gender-related topics in postsecondary biology in a more inclusive and accurate manner: highlighting biological diversity early, presenting the social and historical context of science, using inclusive language, teaching the iterative process of science, presenting students with a diversity of role models, and developing a classroom culture of respect and inclusion. To illustrate these six principles, we review the many definitions of sex and demonstrate applying the principles to three example topics: sexual reproduction, sex determination or differentiation, and sexual selection. These principles provide a tangible starting place to create more scientifically accurate, engaging, and inclusive classrooms.
Full-text available
Students who identify as Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) continue to report feelings of being unsafe at school due to their sexual orientation, gender expression, and gender identity. Access to an inclusive curriculum and supportive teachers can have a significant positive impact on LGBTQ students’ feelings of safety, academic performance, educational aspirations, and sense of school belonging and well-being. Unfortunately, these supports are often not included in science classrooms. One response is for science teacher education programs to ensure that pre-service science teachers (PSSTs) are prepared to enact gender and sexual diversity (GSD)-inclusive science teaching (GSDST) in their future science classrooms. GSD topics, perspectives, and themes are often excluded in teacher education programs, and research has broadly investigated pre-service teachers’ attitudes and beliefs about GSD-inclusive teaching. However, little is known about PSSTs’ attitudes and beliefs about GSDST, how they change over time, as well as how interventions should be designed to foster GSD-inclusive attitudes and beliefs. Addressing these gaps in the literature will inform best practices for science teacher education programs to design and implement professional development for PSSTs. Using a convergent mixed methods research design, this dissertation collected quantitative and qualitative data to explore how PSSTs’ attitudes and beliefs about GSDST changed after participating in a GSD-infused intervention designed around a conceptual framework of teaching advocacy for queer youth and to explore which design elements of the intervention contributed to changes in their attitudes and beliefs. Results showed that the PSSTs were mostly supportive of measures indicative of GSDST prior to the intervention, and there was an overall trend in favor of GSDST with small to medium effect sizes after the intervention. Qualitative findings indicated that although there was a general positive trend in PSSTs’ attitudes and beliefs about GSDST and that the participants reflected positively on the intervention, there were no deep changes in their attitudes and beliefs as evidenced through their focus on student safety and an “Add LGBT and Stir” approach prior to and after the intervention. Furthermore, the qualitative data revealed five design features of the intervention that contributed to the observed changes: group dialogue; coherence to Ambitious Science Teaching; GSD terminology; knowledge of intersex, hormones, and LGBTQ scientists; and relevant case studies. Results suggested that more research should be done on how to support deeper changes in PSSTs’ attitudes and beliefs about GSDST that equip them with skills necessary to disrupt the macro- level structures of science education that continue to marginalize queer students. These findings will contribute to a more nuanced understanding of how to prepare science teachers to address GSDST and better meet the needs of all their students consistent with recent calls for GSD equity in science education.
Science, technology, engineering, and math (STEM) workers need both motivation and interpersonal skills in STEM disciplines. The aims of the study were to identify clusters of adolescents who vary in math and science motivation and interpersonal skills and to explore what factors are related to membership in a high math and science motivation and interpersonal skills cluster. Participants included 467 adolescents (312 female; M age = 15.12 to SD = 1.71 year) recruited from out-of-school STEM programs in the US and UK. Findings from latent class analyses revealed four clusters, including a “High Math and Science Motivation and Interpersonal Skills” group, as well as groups that exhibited lower levels of either motivation or interpersonal skills. STEM program belonging, and STEM identity are related to membership in the high motivation and skills cluster. Findings provide insight into factors that may encourage motivation and interpersonal skills in adolescents, preparing them for STEM workforce entry.
Full-text available
Instructors create classroom environments that have the potential to impact learning by affecting student motivation, resistance, and self-efficacy. However, despite the critical importance of the learning environment in increasing conceptual understanding, little research has investigated what instructors say and do to create learning environments in college biology classrooms. We systematically investigated the language used by instructors that does not directly relate to course content and defined the construct of Instructor Talk. Transcripts were generated from a semester-long, cotaught introductory biology course (n = 270 students). Transcripts were analyzed using a grounded theory approach to identify emergent categories of Instructor Talk. The five emergent categories from analysis of more than 600 quotes were, in order of prevalence, 1) Building the Instructor/Student Relationship, 2) Establishing Classroom Culture, 3) Explaining Pedagogical Choices, 4) Sharing Personal Experiences, and 5) Unmasking Science. Instances of Instructor Talk were present in every class session analyzed and ranged from six to 68 quotes per session. The Instructor Talk framework is a novel research variable that could yield insights into instructor effectiveness, origins of student resistance, and methods for overcoming stereotype threat. Additionally, it holds promise in professional development settings to assist instructors in reflecting on the learning environments they create.
Full-text available
Despite having made significant inroads into many traditionally male-dominated fields (e.g., biology, chemistry), women continue to be underrepresented in computer science and engineering. We propose that students' stereotypes about the culture of these fields-including the kind of people, the work involved, and the values of the field-steer girls away from choosing to enter them. Computer science and engineering are stereotyped in modern American culture as male-oriented fields that involve social isolation, an intense focus on machinery, and inborn brilliance. These stereotypes are compatible with qualities that are typically more valued in men than women in American culture. As a result, when computer science and engineering stereotypes are salient, girls report less interest in these fields than their male peers. However, altering these stereotypes-by broadening the representation of the people who do this work, the work itself, and the environments in which it occurs-significantly increases girls' sense of belonging and interest in the field. Academic stereotypes thus serve as gatekeepers, driving girls away from certain fields and constraining their learning opportunities and career aspirations.
In order for the United States to maintain the global leadership and competitiveness in science and technology that are critical to achieving national goals, we must invest in research, encourage innovation, and grow a strong and talented science and technology workforce. Expanding Underrepresented Minority Participation explores the role of diversity in the science, technology, engineering and mathematics (STEM) workforce and its value in keeping America innovative and competitive. According to the book, the U.S. labor market is projected to grow faster in science and engineering than in any other sector in the coming years, making minority participation in STEM education at all levels a national priority. Expanding Underrepresented Minority Participation analyzes the rate of change and the challenges the nation currently faces in developing a strong and diverse workforce. Although minorities are the fastest growing segment of the population, they are underrepresented in the fields of science and engineering. Historically, there has been a strong connection between increasing educational attainment in the United States and the growth in and global leadership of the economy. Expanding Underrepresented Minority Participation suggests that the federal government, industry, and post-secondary institutions work collaboratively with K-12 schools and school systems to increase minority access to and demand for post-secondary STEM education and technical training. The book also identifies best practices and offers a comprehensive road map for increasing involvement of underrepresented minorities and improving the quality of their education. It offers recommendations that focus on academic and social support, institutional roles, teacher preparation, affordability and program development. © 2011 by the National Academy of Sciences. All rights reserved.
The present research examined the relationship between number of successful role models and alleviation of performance deficits that women suffer under mathematics stereotype threat. Men and women were reminded of the stereotype, read brief biographies of 0-4 successful women, and took a difficult math test. Women who read no biographies scored worse than men; women who read 4 biographies scored as well as men. Increases in women's performance across the number of role models were consistent with a power function trend predicted by social impact theory (Latane, 1981). This relationship with social impact theory suggests new directions in understanding how role models alleviate stereotype threat.
This research examines the relationship between career related self-concept and dimensions of informal learning of science. The overlapping dimensions of career interest development and informal learning suggest that self-directed informal learning of science can advance individual self-concept for possible scientific self. Possible selves and future scientific selves theories are presented as a perspective for understanding career related aspirations, goals, and fear. In this preliminary research the author seeks to examine the possible role that informal science learning may play in students' sense of scientific self by examining connections between wanting to work in science and dimensions of informal learning and career interest development. Findings from a pilot study of future science selves among n = 63 students in grade 8 are discussed. Additional research is planned among students groups who visit a laser interferometer gravitational wave observatory (LIGO) with hand-on science exploratorium in the Southwest region of the United States.
A number of studies have identified correlations between children's stereotypes of scientists, their science identities, and interest or persistence in science, technology, engineering, and mathematics. Yet relatively few studies have examined scientist stereotypes among college students, and the literature regarding these issues in predominantly nonwhite and 2-yr college settings is especially sparse. We piloted an easy-to-analyze qualitative survey of scientist stereotypes in a biology class at a diverse, 2-yr, Asian American and Native American Pacific Islander-Serving Institution. We examined the reliability and validity of the survey, and characterized students' comments with reference to previous research on stereotypes. Positive scientist stereotypes were relatively common in our sample, and negative stereotypes were rare. Negative stereotypes appeared to be concentrated within certain demographic groups. We found that students identifying nonstereotypical images of scientists at the start of class had higher rates of success in the course than their counterparts. Finally, evidence suggested many students lacked knowledge of actual scientists, such that they had few real-world reference points to inform their stereotypes of scientists. This study augments the scant literature regarding scientist stereotypes in diverse college settings and provides insights for future efforts to address stereotype threat and science identity.
Social and psychological influences restrict women's choice and pursuit of careers in science.
A general theory of domain identification is used to describe achievement barriers still faced by women in advanced quantitative areas and by African Americans in school. The theory assumes that sustained school success requires identification with school and its subdomains; that societal pressures on these groups (e.g., economic disadvantage, gender roles) can frustrate this identification; and that in school domains where these groups are negatively stereotyped, those who have become domain identified face the further barrier of stereotype threat, the threat that others' judgments or their own actions will negatively stereotype them in the domain. Research shows that this threat dramatically depresses the standardized test performance of women and African Americans who are in the academic vanguard of their groups (offering a new interpretation of group differences in standardized test performance), that it causes disidentification with school, and that practices that reduce this threat can reduce these negative effects.
Conference Paper
A solid body of research highlights pedagogical practices that impact undergraduate learning in STEM education in productive ways. At the same time, however, current reform efforts often meet resistance, and change toward evidence-based pedagogical practices among those teaching undergraduate students is often not easy or rapid. The presentation will draw on the research and literature on organizational change, academic organizations, and academic work to examine forces and factors that serve as barriers and opportunities for improving undergraduate STEM education. The presentation will take a systems approach, viewing the universities and colleges where undergraduates learn and faculty teach as complex organizations in which an array of factors are relevant to organizational change, and, more specifically, to the decisions faculty members make regarding their use of evidence-based approaches in their undergraduate teaching. Such a systems approach draws attention to: (1) individual faculty members’ values, backgrounds, abilities, and aspirations as they relate to their teaching decisions; (2) the various organizational contexts internal and external to higher education organizations that influence faculty members’ teaching decisions and practices, including the department, college, institution as a whole, and other organizations such as government bodies and accrediting agencies; and (3) the array of elements within the organizational context that can serve as “levers” or barriers to faculty members’ decisions about their teaching, including evaluation and reward systems, workload allocations, professional development opportunities, and leadership practices. Based on research findings, the presentation will suggest that efforts to foster reform in undergraduate science teaching need to take into account individual characteristics and differences among faculty members, and factors within organizational contexts that promote or inhibit change. Specifically, the presentation will highlight how professional development, reward systems, and leadership strategies can support reform efforts. The overall thesis will be that addressing barriers to reform requires examining and utilizing an array of factors embedded within the contexts in which faculty members work and STEM undergraduate education is offered.