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Toward “Inclusifying” the Underrepresented Minority in STEM Education Research

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Abstract

Research in undergraduate STEM education often requires the collection of student demographic data to assess outcomes related to diversity, equity, and inclusion. Unfortunately, this collection of demographic data continues to be constrained by socially constructed categories of race and ethnicity, leading to problematic panethnic groupings such as “Asian” and “Latinx.” Furthermore, these all-encompassing categories of race and ethnicity exasperate the problematic “underrepresented minority” (URM) label when only specific races and ethnicities are categorized as URMs.
Toward Inclusifyingthe Underrepresented Minority in STEM
Education Research
Haider Ali Bhatti
a
a
SESAMEGraduate Group in Science and Mathematics Education, University of California, Berkeley, Berkeley,
California, USA
Research in undergraduate STEM education often requires the collection of student demographic data to
assess outcomes related to diversity, equity, and inclusion. Unfortunately, this collection of demographic
data continues to be constrained by socially constructed categories of race and ethnicity, leading to problem-
atic panethnic groupings such as Asianand Latinx.Furthermore, these all-encompassing categories of
race and ethnicity exasperate the problematic underrepresented minority(URM) label when only specic
races and ethnicities are categorized as URMs. We have long seen calls for improved outcomes related to
URMs in undergraduate STEM education, but seldom have we seen our own understanding of what it means
to be a URM go beyond socially constructed categories of race and ethnicity. If we aim to not only improve
diversity outcomes but also make undergraduate STEM education more equitable and inclusive, we must
reevaluate our use of the term URMand its implications for demographic data collection. The classica-
tions of underrepresentedand minorityare more nuanced than simple racial categories. Though there
has been development of alternative terms to URM, each with their own affordances, the main goal of this
article is not to advocate for one term over another but rather to spark a much-needed dialogue on how we
can inclusifyour collection of racial and ethnic demographic data, particularly through data disaggregation
and expanding our denition of what it means to be both underrepresentedand a minoritywithin
STEM.
KEYWORDS underrepresented minority, URM, disaggregate, race, ethnicity, diversity, equity, inclusion
PERSPECTIVE
Not in recorded history has there been a nation so
demographically complex. So it falls to us, the American
citizens of the 21
st
century, to fashion from this diversity,
historysrst world nation.(Former U.S. census director
Kenneth Prewitt [1])
There is a pressing need for the increasing diversity of
the United States to be more proportionally represented
within the science and engineering (S&E) workforce of the
future (2). Admittedly, this association of diversitywith
racial heterogeneity is rather one-dimensional,as described
by Lehman (3) and Tienda (4) (for a more inclusive denition
of diversity, see the U.S. Ofce of Personnel Management
report titled Guidance for Agency-Specic Diversity and
Inclusion Strategic Plans[5]). Nonetheless, in our efforts to
meet this need, we must enhance our perspective when it
comes to collecting student demographic data. Such enhance-
ment involves an equity-oriented disaggregation of student de-
mographic data, as discussed by two especially relevant publica-
tions that greatly expand on this topicFrom Equity Talk to
Equity Walk: Expanding Practitioner Knowledge for Racial Justice in
Higher Education (6) and Measuring Race: Why Disaggregating Data
Matters for Addressing Educational Inequality (7). Unfortunately, our
collection of student demographic data, particularly race and eth-
nicity, remains relatively constrained to panethnic groups, such as
Latinxand AsianAmerican. By doing this, we greatly diminish
the ethnic heterogeneity within these diverse groups that span
across intersectional identities (7). For example, in the Asian
American panethnic group, underrepresented subpopulations
such as Hmong Americans, Cambodian Americans, and Laotian
Americans are indiscernibly co-categorized with more overrepre-
sented counterparts. In essence, what we may consider the suc-
cessof the Asian American demographic as a whole, often al-
ready perpetuated by the model minority stereotype (8, 9),
may very well be due to the disproportionate success of specic
subgroups, while the struggles of other subgroups remain
consistently concealed.
Citation Bhatti HA. 2021. Toward inclusifyingthe
underrepresented minority in STEM education research. J
Microbiol Biol Educ 22:e00202-21. https://doi.org/10.1128/
jmbe.00202-21.
Address correspondence to SESAMEGraduate Group in Science
and Mathematics Education, University of California, Berkeley,
Berkeley, California, USA. E-mail: haideralibhatti@berkeley.edu.
Received: 14 July 2021, Accepted: 23 August 2021,
Published: 30 September 2021
Copyright © 2021 Bhatti. https://creativecommons.org/licenses/by-nc-nd/4 .0/Thisis an o pen-accessart icle distributed under the terms of the CreativeCommons Attribution-NonCommercial-NoDerivatives 4.0 International license.
Volume 22, Number 3 Journal of Microbiology & Biology Education 1
Perspective
Moreover, Asian Americans are certainly not alone
in this inadvertent homogenization of socially constructed
races and ethnicities. Other subgroups, such as the Black
descendants of enslaved African Americans are indistin-
guishably categorized with the children of African immi-
grants, leading to mismatches between diversity initiatives
and the intended beneciaries (7). If we truly want STEM
education to be equitable and inclusive for all, our use of de-
mographic measures like race and ethnicity must shift from
panethnic and monolithic categories to more disaggregated
categories that break down race and ethnicity into appro-
priate subpopulations (7). Additionally, we must also solicit
other indicators of studentscultural backgrounds that are
known to impact the academic opportunity gaps we aim to
tackle, such as parental education and parent/student nativity
(10). The need to disaggregate is not new, as others have
mentioned or supported this same idea, often in the context of
enhancing empirical research results (1116). Presented here is a
further call to reevaluation and actionin essence, going from
equity talkto equity walk,as McNair et al. (6) suggest.
THE CASE OF THE UNDERREPRESENTED MINORITY
The National Science Foundation (17) denes blacks or
African Americans, Hispanics or Latinos, and American
Indians or Alaska Nativesas underrepresented minority
(URM) groups because their representation in S&E education
and S&E employment is smaller than their representation in
the U.S. population. The use of the strictly race-/ethnicity-
based URM label is widely present throughout STEM educa-
tion literature, along with a multitude of variations that differ
based on the degree of demographic aggregation (Table 1).
Some of these labels are further aggregated beyond race (i.e.,
they include low-income or low socioeconomic status [SES]
students), while others are presented as more inclusive alter-
natives to the canonical URM label. In either case, it is impor-
tant to recognize that aggregation of demographic data is
usually done to enhance statistical power (e.g., reference 24),
and the lack of disaggregation may be due to constraining
factors like the need to ensure concise analyses within manu-
scripts or the response cost of additional analyses on subpo-
pulations (25). Demographic data aggregation may also be
necessary to ensure the anonymity of study participants, par-
ticularly when sample sizes are small and only a few study
participants identify as members of already underrepresented
racial and ethnic groups.
My purpose here is not to endorse any particular one
of the labels in Table 1, though I do believe that we should
engage in a discussion on what (if any) label, whether al-
ready existing or something new, best exemplies our goal
of equity and inclusion for all students. Instead, my purpose
here is to at least begin a wider and explicit acknowledg-
ment of the inherent limitations of whatever label we use in
our scholarly work. If, for example, we are discussing his-
torically underrepresentedstudents, we should make clear
our denition of this population, the diversity within the
subpopulations, and the limitations of aggregated analyses
based on this label. This recommendation is supported by
efforts such as the Racial Heterogeneity Project, which have
shown that the seemingly innocuous aggregation of racial
and ethnic groups actually undermines the expansive within-
group diversity inherent to each of the individual groups
(26). Furthermore, it is important to recognize that even the
canonical URM label has been critiqued as a tool of oppres-
sion(20) and even regarded as degrading and dehumaniz-
ing(27). Clearly, there is an opportunity for improvement
here, not only because of the problematic nature of aggre-
gate labels like URM, but also because there are pedagogic
benetsand subsequent research benetsto the collec-
tion and analysis of more nuanced, disaggregated student de-
mographic data.
USING DATA DISAGGREGATION TO INCLUSIFYURM
In the ongoing effort to promote diversity, equity, and
inclusion (DEI) in STEM education, an add-diversity-and-stir
approach is not enough. Simply increasing the raw numbers of
diversestudents will not sufce, though it is an important
step in the overall process of improving DEI outcomes in
STEM education. We must consider students as more than
just diverse and instead consider how students come from a
range of intersectional identities, especially those who have
been historically or are presently excluded. With this more
expansive framing, we can better ensure that we are not only
diversifyingSTEM education, but also inclusifyingSTEM
education. Diversity and inclusion are not the same (4), and
without inclusion, we may unintentionally compromise our
efforts to promote diversity within STEM education (28).
Specically, though the URM label may be useful in our efforts
to diversify STEM education, we can further inclusify this label
by enhancing it through a more disaggregated lens. For exam-
ple, Mukherji et al. (10) propose a redenition of URM that
includes disaggregated demographics such as the country of
birth of the student, the country of origin of the parent, and
parental educational achievement. They argue that these addi-
tional data points, along with disaggregated racial and ethnic
subgroups, can increase the social sensitivity in identifying
factors that will close the academic achievement gap and pro-
mote educational equality for all the diverse groups(10).
Instead of classifying URM based on a singular, panethnic de-
mographic data point, we can use multiple demographic data
points (such as those available through the university registrar
or undergraduate admissions department) to form a more
representative compositeURM label, similar to an individual
diversity index (29). Importantly, we must remain cognizant of
the incredible complexity within studentsbackgrounds and life
experiencesa complexity that can never be fully encapsulated
through any type of label, however composite it may be.
To add more context to this call for inclusifying the
URM label, the Association of American Medical Colleges
BHATTI: TOWARD INCLUSIFYINGURM IN STEM EDUCATION
2Journal of Microbiology & Biology Education Volume 22, Number 3
(AAMC) provides a model of change through their own
redenition of URM (18). In 2004, based on the Grutter v.
Bollinger Supreme Court decision, the AAMC redened
URM as racial and ethnic populations that are underrepre-
sented in the medical profession relative to their numbers
in the general population.Monolithic racial or ethnic
groups were no longer part of the denition. According to
the AAMC, this change accomplished three objectives:
i. A shift in focus from a xed aggregation of four
racial and ethnic groups (Blacks, Mexican-
Americans, Native Americans, and mainland
Puerto Ricans) to a continually evolving underly-
ing reality. The denition accommodates includ-
ing and removing underrepresented groups on
the basis of changing demographics of society
and the profession.
ii. A shift in focus from a national perspective to a re-
gional or local perspective on underrepresentation.
iii. Stimulation of data collection and reporting on the
broad range of racial and ethnic self-descriptions.
Each of these objectives can also inform how we in STEM
education (re)dene URM. Interestingly, after the AAMC
implemented their redenition, an analysis of URM denitions
used by diversity programs across U.S. academic health cen-
ters showed that there can still be considerable variation in
dening URM. However, the majority of programs used de-
nitions that were not strictly conned to specic races and
ethnicities (30). Just as Page et al. (30) recommended, a 2016
report by the AAMC (31) also concluded that the future of
TABLE 1
Variations of the URM label
Label
a
Description
African Americans/Blacks, Hispanic/Latino(a), and Native Americans/
Alaskan Natives (AHN) (19)
First letters of African Americans/Blacks,”“Hispanic/Latino(a),
and Native Americans/Alaskan Natives; directly embraces and
references racial and ethnic identities.
b
Black, Indigenous, and people of color (BIPOC) First letters of Black,”“Indigenous,and people of color;
directly references racial and ethnic identities.
Excluded identity (EI) (20)
Suggested replacement for URM; foregrounds the education
system as the active agent of exclusion. Recognizes that identities
are multidimensional (some privileged, some not) and that
individuals may experience intersecting and compounding forms
of marginalization or exclusion.
First-generation and underrepresented ethnic minority (FG-URM)
(21)
First-generation African American, Latino/a, and Native American
students for whom neither parent obtained a 4-yr college degree.
Historically underrepresented, underserved, minoritized,
marginalized
Usage is somewhat interchangeable, primarily based on racial/
ethnic categories; may include women in STEM, may include low-
income or low socioeconomic status (SES) students.
Minoritized groups in STEM (MGS) (16) Low-income or URM students
Nondominant (22)
This label better accounts for key issues of power and power
relations than do other existing labels and conceptions (e.g.,
minority,’‘underrepresented,’‘underserved). Non-dominant
also challenges normative notions of members of cultural
communities, while simultaneously addressing the legacy of
inequality for such communities.
Person excluded because of their ethnicity or race (PEER) (23)
In U.S. science, persons who identify as Black or African
American, Latinx or Hispanic, and peoples indigenous to the
spaces comprising the United States and its territories.
Underrepresented (in medicine) (18)
Racial and ethnic populations that are underrepresented in the
medical profession relative to their numbers in the general
population.
Underrepresented minority (URM) (e.g., reference 17)
Blacks or African Americans, Hispanics or Latinos, and American
Indians or Alaska Natives who are underrepresented in S&E. That
is, their representation in S&E education and S&E employment is
smaller than their representation in the U.S. population.
a
The labels are alphabetically arranged. Labels were chosen based on a nonexhaustive review of representative literature. From that
literature, representative examples were chosen based on their prevalence, level of aggregation, or if they were explicitly suggested as
alternatives to other labels.
b
Williams also emphasizes that, the right to rename a group lies within the hands of its members.
BHATTI: TOWARD INCLUSIFYINGURM IN STEM EDUCATION
Volume 22, Number 3 Journal of Microbiology & Biology Education 3
diversity and inclusion efforts in medicine must utilize disaggre-
gated race and ethnicity data: The disaggregation of racial and
ethnic minority subpopulations is pivotal to grasping a full view of
barriers and challenges in professional and graduate education.
The same can and should be said for STEM education.
Importantly, there is also a pedagogical basis for consid-
ering diversity beyond race and ethnicity (3), particularly
when it comes to understanding the role of culture. As
Gutiérrez and Larson (32) point out, Too often educators
equate culture with race and ethnicity and make assump-
tions about studentscultural practices based solely or pri-
marily on the studentsmembership in a particular racial or
ethnic group(32). Again, the monolithic view of race and
ethnicity proves to be problematic. Instead, culture should
be viewed as a verb, consisting of repertoires of practice,
rather than as a noun, or simply as belonging to a particular
racial or ethnic community (32, 33). Based on this concep-
tual shift, when we are interested in collecting information
about who our students are and where they are coming
from, we must be especially cognizant of the cultural prac-
tices they participate in and their history of participation in
those practicesnot just their racial and ethnic identities.
Though collecting this type of data may not be the typical
demographic data we are accustomed to analyzing, it can be
a jumping-off point into a variety of new research questions
and insights rooted in a cultural-historical analysis.
A SINGLE THOUGH IMPORTANT ELEMENT
To conclude this research-based call to action, I would
like to share a critical personal reection that inspired me
to delineate these ideas more formally through this letter.
For most of my life, it was easy enoughI simply checked
off Asianas my racial identity. But eventually, my level of
comfort with selecting this racial identity began to change.
As I started doing education research, I came to learn that
by checking off Asian,I was automatically grouped into an
overrepresented majoritycategory, specically in the context
of STEM education. However, throughout my life, and especially
in my STEM education, I have never felt overrepresented.As
a Muslim, Pakistan-born child of immigrant parents without col-
lege degrees, my time spent in STEM (both as a student and
researcher) has seldom been with those who share similar identi-
ties. Yet for some reason, this concealment of my cultural identity
by the monolithic racial identity of Asianhas long felt like an
unquestioned norm. This personal reection makes me think
about others who would fall into this same predicament, like the
Syrian refugee student who ends up selecting whiteor the
Rohingya refugee student who selects Asian”—both of whom
would be terribly miscategorized as both overrepresentedand
part of a majority.
We as researchers have an opportunity to better
respect the cultural identities of our students by ensuring
that our demographic data collection goes beyond the historically
normative yet limiting concepts of race and ethnicity. Of course,
those categories are still important, and they certainly matter
(23). I recognize that there is quite an entrenched (and needed)
relationship between funding efforts and the promotion of racial
diversity(e.g.,references34,35).Thisisgood,butitneedstoget
better. Just as STEM disciplinary cultures are not a monolith (36),
neither are our studentscultural backgrounds. By collecting and
analyzing more disaggregated demographic data, whether related
to race and ethnicity, nativity, parental education, cultural
practices, etc., not only are we opening up the possibility for
even more potential insights in our own research, we are also
empowering students with a more inclusive way to express who
they are and how they identify themselves. And perhaps most
importantly, we are also showing them that the monolithic cate-
gories of race and ethnicity are not all that matter. I am reminded
of Justice Powells opinion as he delivered the judgment of the
Supreme Court in the landmark case of Regents of Univ. of Cal. v.
Bakke (1978):
The diversity that furthers a compelling state interest
encompasses a far broader array of qualications and
characteristics of which racial and ethnic origin is but a
single though important element. (37)
Let us continue to recognize racial and ethnic origin as
an important element in the pursuit of necessary diversity
outcomes, while also recognizing that it is just one element
in our studentsexpansive repertoires of sociocultural prac-
tices and experiences.
ACKNOWLEDGMENTS
I was inspired to write this letter based on an insightfully
critical analysis by Tiffani L. Williams in her June 2020 article,
“‘Underrepresented MinorityConsidered Harmful, Racist
Language(19), along with several illuminating conversations on
the topic at the 2020 Society for the Advancement of Biology
Education Research (SABER) conference. I also thank John Yang Li
for discussions on the Asian identity in America.
I declare no sources of support for the work presented
in the article. I declare no potential conicts of interest.
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6Journal of Microbiology & Biology Education Volume 22, Number 3
Article
Doubts about belonging in the classroom are often shouldered disproportionately by students from historically marginalized groups, which can lead to underperformance. Ecological-belonging interventions use a classroom-based activity to instill norms that adversity is normal, temporary, and surmountable. Building on prior studies, we sought to identify the conditions under which such interventions are effective. In a chemistry course (study 1), students from underrepresented ethnic backgrounds underperformed relative to their peers in the absence of the intervention. This performance gap was eliminated by the intervention. In an introductory biology course (study 2), there were no large performance gaps in the absence of the intervention, and the intervention had no effect. Study 2 also explored the role of the instructor that delivers the intervention. The intervention boosted scores in the classrooms of instructors with a fixed (versus growth-oriented) intelligence mindset. Our results suggest that ecological-belonging interventions are more effective in more threatening classroom contexts.
Article
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We tested the hypothesis that underrepresented students in active-learning classrooms experience narrower achievement gaps than underrepresented students in traditional lecturing classrooms, averaged across all science, technology, engineering, and mathematics (STEM) fields and courses. We conducted a comprehensive search for both published and unpublished studies that compared the performance of underrepresented students to their overrepresented classmates in active-learning and traditional-lecturing treatments. This search resulted in data on student examination scores from 15 studies (9,238 total students) and data on student failure rates from 26 studies (44,606 total students). Bayesian regression analyses showed that on average, active learning reduced achievement gaps in examination scores by 33% and narrowed gaps in passing rates by 45%. The reported proportion of time that students spend on in-class activities was important, as only classes that implemented high-intensity active learning narrowed achievement gaps. Sensitivity analyses showed that the conclusions are robust to sampling bias and other issues. To explain the extensive variation in efficacy observed among studies, we propose the heads-and-hearts hypothesis, which holds that meaningful reductions in achievement gaps only occur when course designs combine deliberate practice with inclusive teaching. Our results support calls to replace traditional lecturing with evidence-based, active-learning course designs across the STEM disciplines and suggest that innovations in instructional strategies can increase equity in higher education.
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Inspired by the social and economic benefits of diversity, we analyze over 9 million papers and 6 million scientists to study the relationship between research impact and five classes of diversity: ethnicity, discipline, gender, affiliation, and academic age. Using randomized baseline models, we establish the presence of homophily in ethnicity, gender and affiliation. We then study the effect of diversity on scientific impact, as reflected in citations. Remarkably, of the classes considered, ethnic diversity had the strongest correlation with scientific impact. To further isolate the effects of ethnic diversity, we used randomized baseline models and again found a clear link between diversity and impact. To further support these findings, we use coarsened exact matching to compare the scientific impact of ethnically diverse papers and scientists with closely-matched control groups. Here, we find that ethnic diversity resulted in an impact gain of 10.63% for papers, and 47.67% for scientists.
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Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self-efficacy, and sense of social belonging in a large (more than 250 students) introductory STEM course. A transition to active learning closed the gap in learning gains between non-URM and URM students and led to an increase in science self-efficacy for all students. Sense of social belonging also increased significantly with active learning, but only for non-URM students. Through structural equation modeling, we demonstrate that, for URM students, the increase in self-efficacy mediated the positive effect of active-learning pedagogy on two metrics of student performance. Our results add to a growing body of research that supports varied and inclusive teaching as one pathway to a diversified STEM workforce.
Book
The United States demography is changing rapidly. How are we capturing these shifts? Do the racial categories that exist accurately represent the individuals who fall into them? Have long-standing categories hindered our understanding of racial inequality? These questions are particularly significant in education, where a precise view of students—who achieves and who requires greater resources—is critical. This volume brings together the expertise of scholars from a range of disciplines to explore the current state of racial heterogeneity, data practice, and educational inequality. They offer recommendations to guide future research, practice, and policy with the goal of better understanding and meeting the needs of our diverse student population in the years to come.
Article
Despite their initial high interest in science, students who belong to excluded racial and ethnic groups leave science at unacceptably high rates. “Fixing the student” approaches are not sufficient at stemming the loss. It is time to change the culture of science by putting inclusive diversity at the center.
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Research suggests that science, technology, engineering, and mathematics (STEM) departments are a productive unit of focus for systemic change efforts. In particular, they are relatively coherent units of culture, and cultural changes are critical to creating sustainable improvements. However, the STEM disciplines are often treated as a monolith in change literature, and unique aspects of these different disciplinary cultures-and consequences for change efforts-remain somewhat underdeveloped. This exploratory study focuses on similarities and differences among STEM disciplinary cultures, drawing on data gathered from scholars in discipline-based education research who attended two sessions at the 2017 Transforming Research in Undergraduate STEM Education conference. Our analyses of these data help begin to characterize disciplinary cultures using the theoretical lens of four frames: structures, symbols, power, and people. We find preliminary evidence for both similarities and differences among the cultures of STEM disciplines. Implications for change efforts and future directions for research are discussed.