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Learning environment impact on women undergraduate engineering students
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Content
Learning Environment Impact on Women Undergraduate Engineering Students
by
Trina L. Gregory
Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education
December 2024
© Copyright by Trina L. Gregory 2024
All Rights Reserved
The Committee for Trina L. Gregory certifies the approval of this Dissertation
Brandi Jones
Anthony Maddox
Monique Datta, Chair
Rossier School of Education
University of Southern California
2024
iv
Abstract
Women remain underrepresented in postsecondary engineering programs and engineering
careers in the United States. The fields of engineering and technology continue to grow at a rapid
pace, and job opportunities are only increasing. If the problem of gender parity in computer
science and engineering is not addressed, men will continue to dominate these fields. The goal of
the study was to identify those aspects of the undergraduate learning environment that have the
highest potential to impact women students to earn engineering degrees and pursue associated
careers. The data for this mixed-method study was collected through a survey and semistructured interviews administered to women engineering students. Engineering identity can be
improved by experiencing gender representation, participating in real-world projects, and
viewing failures as learning opportunities. Having an anonymous questions forum and
connections with course instructors and support staff will improve the likelihood of women
engineering students completing their degrees. To increase the likelihood of these students
pursuing careers, students need to reinforce self-efficacy and participate in societal impact
projects. These findings indicate the need for interactive teaching techniques, communitybuilding opportunities, gender representation in course staff, and training to combat
perfectionism.
Keywords: Women in STEM, computer science, engineering, higher ed, social cognitive
theory, learning environment, engineering identity, degree completion, gender representation,
societal impact projects, perfectionism
v
Dedication
I dedicate this dissertation to my daughter Quinn and son Evan who inspire and challenge
me to be their best mom. I am so proud of you two!
I also write this dissertation in loving memory of my mom Joyce Gwen Osterhus and my
dear friend Jennifer Coleman, whom both passed from cancer. Mom, I wish you were here to
witness the accomplishments of your daughters. Jenny, I strive to live by your favorite quote
written by Dr. Seuss, “To the world, you may be one person, but to one person you may be the
world.”
vi
Acknowledgments
I am blessed to be fully supported by my two biggest cheerleaders, my spouse Jason
Gregory and my twin sister Tracy Lee Provins. Jason, with you I am home. Thank you for
growing with me and continuing to support me in all I do. You make me a better person, and I
love our life together. Tracy, you are the best sister ever, and I love you more!
I am grateful to the rest of my family, friends, colleagues, and classmates for their
continual support even when this whole process took longer than expected.
I am inspired by the students at USC and others across the globe whom I have had the
privilege to engage with. I especially want to thank the students who filled out the survey and
participated in interviews. You all give me hope for a brighter future!
I am eternally grateful for the advice, patience, and wisdom shared with me by Dr.
Monique Datta, Dr. Brandi Jones, Dr. Anthony Maddox, and Dr. Marc Pritchard. You all inspire
me, and I am a better educator because of you.
vii
Table of Contents
Abstract.......................................................................................................................................... iv
Dedication....................................................................................................................................... v
Acknowledgments.......................................................................................................................... vi
List of Tables .................................................................................................................................. x
List of Figures................................................................................................................................ xi
List of Abbreviations .................................................................................................................... xii
Chapter One: Introduction to the Study.......................................................................................... 1
Context and Background of the Problem............................................................................ 2
Purpose of the Project and Research Questions.................................................................. 4
Importance of the Study...................................................................................................... 4
Overview of Theoretical Framework and Methodology .................................................... 5
Definition of Terms............................................................................................................. 6
Organization of the Study ................................................................................................... 8
Chapter Two: Review of the Literature .......................................................................................... 9
Current State of Women in STEM...................................................................................... 9
Factors Contributing to the Underrepresentation of Women in STEM............................ 11
Needed Supports for Women in Engineering Programs................................................... 28
Conceptual Framework..................................................................................................... 29
Summary........................................................................................................................... 30
Chapter Three: Methodology........................................................................................................ 33
Research Questions........................................................................................................... 33
Overview of Design .......................................................................................................... 33
viii
Research Setting................................................................................................................ 34
The Researcher.................................................................................................................. 35
Data Sources ..................................................................................................................... 36
Participants........................................................................................................................ 38
Instrumentation ................................................................................................................. 39
Data Collection Procedures............................................................................................... 41
Data Analysis.................................................................................................................... 42
Validity and Reliability..................................................................................................... 43
Ethics ............................................................................................................................... 44
Chapter Four: Results and Findings.............................................................................................. 46
Participants........................................................................................................................ 47
Results and Findings......................................................................................................... 52
Research Question One..................................................................................................... 58
Research Question Two .................................................................................................... 62
Research Question Three .................................................................................................. 68
Summary of Results.......................................................................................................... 73
Chapter Five: Recommendations.................................................................................................. 75
Recommendations for Practice ......................................................................................... 75
Limitations and Delimitations........................................................................................... 86
Future Research ................................................................................................................ 87
Conclusion ........................................................................................................................ 88
References..................................................................................................................................... 90
Appendix A: Survey Protocol....................................................................................................... 97
ix
Appendix B: Interview Protocol ................................................................................................. 104
x
List of Tables
Table 1: Data Sources ................................................................................................................... 34
Table 2: Demographic Categories for Survey............................................................................... 48
Table 3: Majors of Interview Participants..................................................................................... 50
Table 4: Interview Participants ..................................................................................................... 51
Table 5: Learning Environment Aspects Experienced.................................................................. 54
Table 6: Comfort Levels within the Learning Environment......................................................... 55
Table 7: Engineering Identity by Category................................................................................... 56
Table 8: Desired Behaviors........................................................................................................... 58
Table 9: Impact of Learning Environment on Engineering Identity............................................. 59
Table 10: Impact of Learning Environment on Completing Degree ............................................ 63
Table 11: Impact of Learning Environment on Grasp of Course Content .................................... 64
Table 12: Pursue a Career by Category......................................................................................... 69
Table 13: Impact of Learning Environment on Pursuing Career.................................................. 70
Table A1: Survey Instrument ........................................................................................................ 98
Table B1: Interview Instrument .................................................................................................. 105
xi
List of Figures
Figure 1: Conceptual Framework ................................................................................................. 30
Figure 2: Recommendations Based on Findings .......................................................................... 77
xii
List of Abbreviations
DEI Diversity, equity, and inclusion
EVT Expectancy-value theory
IRB Institutional review board
M-CS Major: Computer science
M-E Major: Engineering
RI-NW Racial identity: Non-white
RI-W Racial identity: White
SCCT Social cognitive career theory
SCT Social cognitive theory
STEM Science, technology, engineering, and mathematics
UES University engineering school
T-N Transfer: No
T-Y Transfer: Yes
UL-L Undergraduate level lower division
UL-U Undergraduate level upper division
1
Chapter One: Introduction to the Study
Women are underrepresented in postsecondary engineering programs in the United
States. The National Center for Education Statistics (NCES) annual report on the condition of
education stated that women earned 23% of the bachelor’s degrees in engineering for the 2018-
2019 academic year, while women earned 57% of the all bachelor’s degrees conferred (Irwin et
al., 2021). Roy (2019) reported for the Association of Engineering Education (ASEE) that
women earned 21.9% of the bachelor’s degrees in engineering for the 2017-2018 academic year.
The report showed that percentage of women enrolled in engineering bachelor’s degree programs
was 26.3% for the fall of 2018. According to the National Center for Women and Information
Technology (NCWIT), 57% of the 2016 bachelor’s degree recipients, across all disciplines, were
women while women earned only 19% of the 2016 computer and information sciences
bachelor’s degrees (2019). Even though the percentage of women pursuing and earning
engineering degrees has increased over the past decade, the percentage of women graduating
with engineering degrees is only halfway to gender parity.
The low number of women earning science, technology, engineering, and mathematics
(STEM) degrees leads to fewer women in these professional fields (NCWIT, 2019). Women are
underrepresented in STEM jobs, comprising 24% of these workers even though women comprise
47% of all workers in the United States (Noonan, 2017b). According to the Office of the Chief
Economist (OCE), employment in STEM jobs grew 24.4% from 2007 to 2017, while non-STEM
jobs only grew 4% in the U.S. (Noonan, 2017a). The demand in STEM occupations will
continue to grow, and women are needed to be equally represented when filling these positions.
The study will focus on the experiences of undergraduate engineering students who identify as
2
women to learn the barriers, supports, and other factors that influence them while pursuing their
desired degrees.
Context and Background of the Problem
Engineering fields have been historically dominated by men since postsecondary
institutions began offering engineering degrees. Before World War II, young women interested
in pursuing engineering degrees were discouraged and deterred by university professors,
administrators, and fellow male students (Bix, 2013). The women who did succeed were thought
of as oddities and invaders into masculine territory. In the 1940s, World War II caused a greater
demand for engineers, thus collegiate institutions partnered with the U.S. government and private
companies to provide wartime programs geared specifically for women. Approximately two
thousand women were trained for engineering positions through these programs, although most
of the women did not make lifetime careers out of engineering (Bix, 2013). Even though the
number of women pursing engineering degrees continued to increase in the 1950s and 1960s,
they still were less than one percent of the students enrolled and some of America’s most
prestigious engineering schools remained male-only. Caltech, one of those institutions, finally
allowed undergraduate women in 1970. Over the past 50 years, various efforts by organizations,
such as the Society of Women Engineers, have encouraged girls and young women to pursue
engineering degrees and careers including programs geared for students from elementary school
through high school.
Even with the positive efforts to increase the number of women in engineering, they were
not well represented in higher education programs. By the 1990s, academics and policymakers
noticed this disparity and created studies to determine the factors contributing to it (Bix, 2013).
3
Published in the first issue of the Journal of Women and Minorities in Science and Engineering,
Anderson (1994) conducted a qualitative study of forty women engineering students and found
the need to improve the advisement, curriculum, and educational environment. At the end of the
1990s, the biggest problem relating to the gender gap in engineering was the general public’s
lack of knowledge of engineering (Tietjen & Reynolds, 1999). Since many secondary students
and their parents did not understand the jobs of engineers, young women reported not being
encouraged to pursue engineering majors. Other main issues described in the Tietjen and
Reynolds’ article included the lack of role models, the lack of mentors, networking, and support
for women in engineering.
Women face various barriers when pursuing STEM majors. According to Dasgupta and
Stout (2014), the main ones are women’s lack of fit, women being outnumbered by men, and the
lack of role models and mentors. The lack of fit, also known as a sense of belonging, is mainly
caused by stereotype threat. Stereotype threat is a situation when an individual is affected due a
negative stereotype about their group (Steele, 2010). One such stereotype is that math,
technology, and engineering are fields for men, not women (Nosek et al., 2002). This stereotype
threat can be a barrier to women completing degrees in engineering (Cadaret et al., 2016). The
scarcity of women role models in engineering is detrimental to students pursuing engineering
majors.
Exposing girls to women role models in the STEM fields is important in increasing their
interest and ability to see themselves as being successful in those fields (Reinking & Martin,
2018). Having women role models and supportive faculty are ways to support women students
(Waychal & Henderson, 2018). Women comprise only 17% of the overall faculty in 315
engineering programs in higher education universities (Yoder, 2017). In all of academia, the
4
percentage of women who are full professors is 33%, while the percentage of women who are
full professors in computer science is only 15% (DuBow & Gonzalez, 2020). Undergraduate
students benefit from being taught by women faculty and having them as role models.
Purpose of the Project and Research Questions
The purpose of the study is to evaluate how the learning environments in an engineering
school influence women students in regard to their sense of engineering identity, the likelihood
that they will complete their engineering degrees, and the likelihood that they will pursue
engineering careers after graduation. The study is guided by the following research questions:
1. What aspects of the learning environment positively impact the sense of engineering
identity in women engineering students?
2. How do the aspects of the learning environment influence the likelihood of women
engineering students completing their engineering degrees?
3. How can aspects of the learning environment improve the likelihood that women
engineering students will pursue engineering careers after graduation?
Importance of the Study
Even though the numbers are increasing, women have not reached gender parity in
engineering programs in higher education. If this problem is not addressed, then men will
continue to dominate the engineering and computer science fields. Women hold 57% of the
professional occupations, but only 26% of the professional computing occupations (NCWIT,
2019). Degrees in computer science can lead to careers in software engineering, but only 19% of
software developers are women (U. S. Bureau of Labor Statistics, 2019). The National Center for
Science and Engineering Statistics (NCSES) surveyed scientists and engineers and reported that
men were more likely to work in science and engineering occupations while women were more
5
likely to work in a related occupation (NCSES, 2019). Thus a higher percentage of men are
getting engineering and computer sciences degrees and entering those fields while fewer women
are doing the same.
The job opportunities in the engineering and technology fields continue to grow. The
U.S. Bureau of Labor Statistics expects an increase of approximately 140,00 jobs for engineers
from 2016 to 2026 and an increase of over 255,000 jobs for software developers (Torpey, 2018;
U. S. Bureau of Labor Statistics, 2017). To meet the industry demands, women need to be part of
the solution.
Overview of Theoretical Framework and Methodology
The study was guided by Bandura’s social cognitive theory (SCT), which focuses on the
individual and how the modeling of others and the environment influence the individual’s
behaviors (Bandura, 2005). The cognition of an individual, their behaviors, and their
environment can have a reciprocal effect on each other. A basic building block of SCT is selfefficacy, which is the belief that an individual has the capability to achieve their goals (Bandura,
1977). The concept of engineering identity was also used in the study as part of the cognition of
an individual. Engineering identity is how a person is interested in engineering, sees themself as
an engineer, and becomes an engineer (Pierrakos et al., 2016). Environmental factors such as the
barriers and supports that women engineering students encounter may impact them to persist in
their chosen fields of study and to continue to pursue STEM careers.
The design of this study is mixed methods, which incorporates elements of quantitative
and qualitative approaches (Creswell & Creswell, 2018). Using the explanatory sequential
design, the study gathered quantitative data using a survey administered to undergraduate
engineering students who identify as women. The collection and analysis of the quantitative data
6
will potentially influence the questions for the interviews where participants were selected by
using purposeful sampling. The qualitative data provides more insight into the experiences of the
women students pursuing engineering and computer science degrees.
Definition of Terms
This section contains definitions used throughout this dissertation to provide a common
understanding of frequently used terms. These definitions express the key concepts related to this
study.
Engineering Identity
Engineering identity is a concept covering an individual’s beliefs about their competence
and interest in engineering as well as the recognition from others (Goodwin, 2016).
Female
In scientific terms, females have been defined as those that can reproduce. This paper
uses a broader definition of females which incorporates girls, women, and transgender women.
Using female as an adjective refers to an individual whose gender identity is a girl or woman.
Gender Identity
Gender identity is defined as an individual’s concept of self and refers to how an
individual perceives themselves (Jenkins, 2018). This definition does not need to be the same as
their assigned sex at birth.
Gender Representation
Gender representation refers to the breakdown of the male and female genders
represented within a given population. Since this paper focuses on the underrepresentation of
women in engineering, the various populations analyzed are the undergraduate population, the
instructors, and the support staff for courses in the engineering school.
7
Learning Environment
The learning environment incorporates various aspects of the environment in which
students learn including physical locations such as classrooms and offices as well as situations
such as attending office hours, asking questions virtually, and working on group projects.
Outcome Expectations
Outcome expectations are defined as the beliefs that intentional actions will lead to
desired outcomes (Bandura, 2001). For this study, the outcome expectations refer to the beliefs
about the STEM-related career outcomes achieved through education.
Self-Efficacy
Self-efficacy is the belief that an individual has the capability to achieve their goals
(Bandura, 1977).
Social Impact
The term social impact is defined as having a mission to meet societal needs and serve
the greater good (Anderson, 2015). In this paper, social or societal impact refers to having a
positive impact on society as a motivator for choosing a desired degree and career.
STEM
The acronym STEM—science, technology, engineering, and mathematics—includes core
occupations in the hard sciences, engineering, and mathematics. Whether to include positions
such as educators, health-care professionals, technicians, and social scientists is debatable and
changes depending on the source (Noonan, 2017b). The definition used in this paper includes
professional and technical support occupations in the fields of computer science and
mathematics, engineering, and life and physical sciences.
8
Women
The traditional definition of a woman is an adult female human being. This paper uses the
term women to refer to humans who were born female as well as any individual that identifies as
a woman such as transgender women. This broader definition is used in this paper since men
dominate the engineering and computer science fields (Noonan, 2017b).
Organization of the Study
This dissertation is comprised of five chapters. Chapter One explains the problem of
gender disparity in U.S. post-secondary schools in engineering programs where women represent
approximately 20% of the student population. The purpose and plan for the study are explained
as well the importance of researching engineering students in higher education. Chapter Two
contains a literature review providing a background of past research studying gender issues in
STEM education. Chapter Three gives details on the methodology for the study and how the data
will be collected. Chapter Four will provide details of the data collection and analysis. Chapter
Five will contain recommendations to increase the number of women entering engineering
schools, supports needed to improve the graduation rates as well as areas for future research.
9
Chapter Two: Review of the Literature
This literature review examines the research concerning the underrepresentation of
women in STEM. The research regarding the current state of women in STEM careers is
presented to provide context for the next sections focusing on the various factors contributing to
the underrepresentation of women in STEM and the most prevalent barriers faced by women
throughout their education and career development. This literature review also explores some of
the supports needed by women to help them persist in the pursuit of a STEM education and
career. These supports include mentorship opportunities, the importance of positive role models,
and institutional supports such as cooperative education programs, clubs, and organizations at the
high school and college levels. Social cognitive theory is the theoretical framework guiding the
study and the conceptual framework.
Current State of Women in STEM
Despite notable improvements over the past few decades, women remain significantly
underrepresented in STEM careers worldwide. This underrepresentation is most pronounced in
math-heavy STEM fields such as computer science, engineering, and the physical sciences. In
1972, only 1% of graduates in engineering in the United States were women, but by 1999 this
percentage had increased to roughly 20% women. Heilbronner (2011) observed a decline in
STEM academic achievement in the United States, relative to foreign nations. In 2018, women
comprised one-half the U.S. workforce but remained underrepresented in STEM fields (NaphanKingery & Ellito, 2018). Raelin et al. (2014) reported that women earn approximately only 20%
of all undergraduate degrees in engineering. Nosek et al. (2002) noted that while no significant
gender gap existed in high-school participation in science and math (National Science
Foundation [NSF], 1996), women did not proportionally go on to establish careers in math-
10
intensive fields. Engineering had the lowest percentage of women at 16%, while women only
comprised 34% of the careers in the physical sciences and 35% of the careers in math and
computer science (Nosek et al., 2002).
The report entitled “Highlights from the NCWIT Scorecard” presents a synopsis of the
current state of affairs (DuBow & Gonzalez, 2020): Overall, the percentage of women pursuing
and completing degrees in STEM fields has been slowly but steadily increasing, which is a trend
toward gender parity. However, many barriers remain to attain parity between men and women
in STEM. For example, while the number of girls taking computer science courses in high school
has been rising, many schools do not offer rigorous programs in computer science, leading to
unequal access to these academic pathways across ethnic and socioeconomic axes (DuBow &
Gonzalez, 2020). Over the past decade, the percentage of women earning bachelor's degrees in
computer and information sciences has improved from roughly 17% in the 2008-2009 academic
year to just over 20% in the 2018-2019 academic year (DuBow & Gonzalez, 2020). However,
the percentage of women in math-heavy STEM fields such as computer science, engineering,
and the physical sciences is still much lower than that of women in other STEM-related fields
such as psychology, life science, and the social sciences (Ceci et al., 2014; Nosek et al., 2002).
Racial stereotypes compound the effects of gender bias against women in STEM.
Simmons and Lord (2019) reported that structural barriers rooted in racism and racial
segregation have contributed historically to the difficulties facing women pursuing STEM
educations and careers, and these barriers continue to exist today. Despite working hard to
disprove these stereotypes over the past few decades, African American students still experience
resistance when pursuing STEM fields. Racial stereotypes and gender-based stereotypes are
11
interconnected and should be considered together when attempting to improve the acceptance of
women in engineering and computer science.
Since the early 1970s, the representation of women graduating with STEM degrees and
pursuing careers in STEM has increased significantly. While some STEM fields are nearing
gender parity, current research demonstrates that the representation of women in engineering,
physics and computer science fields in particular remains below parity. The following sections
explore the reasons for this gender disparity in STEM, starting with the factors that contribute to
the underrepresentation of women in these fields, and then discussing the supports which can
help to reduce this gender disparity.
Factors Contributing to the Underrepresentation of Women in STEM
The literature has identified various barriers and other factors contributing to the
underrepresentation of women in STEM. Gender bias, stereotype threat, and microaggressions
contribute to an environment that is non-conducive to women’s pursuit of education and careers
in STEM. Girls who begin pursuing an education in STEM sometimes shift into other subject
areas prior to attaining a career in STEM, leading to leakage in the STEM pipeline. Engineering
identity and self-efficacy also have significant impacts on women’s propensity for an education
and future career in engineering and computer science. Learning environments play a key role in
determining whether or not women will persist in STEM-related studies throughout the duration
of their education. In addition, the experiences of girls and young women prior to and during
college impact their persistence in STEM.
Gender Bias
STEM careers are still perceived by young women as male-dominated, and this
perception leads to a tendency for women not to pursue careers in these fields. Males have
12
historically been assumed to possess more aptitude and more interest in math, science, and
technology than women (National Science Foundation [NSF], 1996; Nosek et al., 2002). Hyde
and Linn’s (2006) review of meta-analyses of research on psychological gender differences
showed negligible variance on math and science standardized tests between boys and girls. Since
students have similar psychological traits and cognitive abilities across genders, this male-centric
perception of STEM is unjustified.
Green and Sanderson (2018) explored the likelihood of students remaining in STEM
fields over the long term and observed a significant bias against women in STEM. The authors
conducted a regression analysis of data from the “Beginning Postsecondary Students
Longitudinal Study” (BPS) of 2003-2009 from the National Center of Education Statistics. They
observed that women’s attainment and persistence are positively impacted by ability, selfefficacy, high-quality mathematics education in high school, and attending smaller colleges. The
gender bias against women had a significant negative correlation with the attainment of and
persistence in STEM.
Markarova et al. (2019) discovered that students perceive STEM subjects such as math,
physics, and chemistry as masculine, although with considerable differences in the strength of
the association depending on the specific sub-discipline: Math had the strongest male-centric
association, while chemistry had the least. Such predominantly masculine images of STEM tend
to result in a decreased likelihood of pursuing STEM education and careers among women
(Markarova et al., 2019).
How engineering is taught in college is also geared towards male audiences. Course
materials and curricula tend to be dry, analytical, and isolated from the potential positive societal
impacts of technology and engineering (Anderson, 1994). However, women tend to respond
13
more positively and effectively when course materials are taught in a connected, intuitive manner
that emphasizes context, creativity, and humanistic aspects of the subject (Anderson, 1994).
Gender bias has a real and observable impact on hiring practices as shown in the
following two studies: Moss-Racusin et al. (2012) performed a study in which engineering
faculty members were asked to evaluate and provide feedback on a job application with half
given a female name and the other half given a male name. The results indicated that the
participating faculty members, both women and men, consistently favored the male applicant
when asked whether or not they would hire and mentor the applicant, and the male applicant
received a consistently higher initial salary offer than the female applicant. The study concluded
that a gender-based bias in the perceived competence of others is prevalent amongst both male
and female engineering faculty, across a wide range of STEM specializations. Reuben et al.
(2014) conducted a study in which participants were asked to perform an arithmetic task, after
which mock hiring decisions were made by some of the participants based on various attributes
of the other participants. The study looked at how often “bad” hiring decisions were made,
defined as erroneously hiring the candidate who had done worse on the arithmetic task. When
only the appearance of a mock job applicant was considered in the hiring decision, “bad” hiring
decisions showed a strong bias in favor of male candidates. The male-favoring bias was less
pronounced when the hiring decision was made based on the candidates’ self-evaluation of their
performance on the arithmetic task, and the bias was less pronounced still when hiring decisions
were made based on the actual results of the arithmetic task. However, the male-centric hiring
bias never disappeared entirely. The authors concluded that implicit gender bias has a real and
observable impact on hiring practices (Reuben et al., 2014).
14
The perception that men naturally possess more aptitude in STEM fields has led to strong
gender biases within this subject area. However, numerous studies demonstrate that this
perception does not correlate to the reality of women’s aptitude in STEM. These biases pose
significant barriers to women entering STEM studies, to their persistence with a STEM
education, and to their likelihood of being hired into STEM jobs. These gender biases largely
stem from an external misperception of women’s efficacy in technology and math. In the next
section, another kind of barrier is explored, this one based on an internalized misperception of
the need to conform to stereotypes.
Stereotype Threat
Another factor that impedes women’s progress in STEM fields is stereotype threat.
Stereotype threat is a psychological phenomenon in which an individual feels a strong need to
conform to the societal stereotypes associated with a group or groups with which they identify.
This is typically coupled with a strong desire not to confirm negative stereotypes about such
groups.
Cheryan et al. (2015) ascertained that cultural stereotypes effectively serve as gatekeepers
for girls’ willingness to enter or not enter STEM fields of study. These stereotypes cover various
aspects of girls’ perception of STEM, including the kinds of people who are involved in STEM
(socially isolated people who are focused on technology at the exclusion of all else), the values
of those involved in STEM, and the kind of work done by people in the field (isolated rather than
collaborative). When male-centric stereotypes are allowed to persist, women tend to be
dissuaded from pursuing a career in STEM. But when the stereotypes are altered to be more
inclusive and more accurate, women’s interest in STEM increases (Cheryan et al., 2015).
15
Racial stereotypes have a compounding negative effect on women’s willingness to pursue
engineering as a field of study and career goal. The Black women studied by Allen et al. (2022)
frequently encountered negative messaging from those around them, including assumptions of
incompetence and implications that they did not belong in engineering. This messaging often
came from within their own racial communities and even from their own family members.
Cadaret et al. (2016) studied stereotype threat as a barrier to women’s attainment and
persistence in STEM, using the eight-item Stereotype Vulnerability Scale (Spencer, 1993) as a
measurement yardstick. They determined that while stereotype vulnerability was itself not a
major contributor, stigma consciousness (an awareness of negative societal perceptions of
women) did negatively impact women’s confidence and sense of self-efficacy in pursuing a
degree in STEM.
Research has demonstrated that stereotype threat and stigma consciousness have real and
measurable impacts on women’s perception of self-efficacy. Some studies have also provided an
indication that these effects may be causally related to their likelihood of pursuing and persisting
in STEM fields of study. The next section explores another factor which can erode women’s
sense of confidence in their ability to succeed in STEM.
Microaggressions
Women encounter microaggressions in STEM settings, which is detrimental to their
experiences and decreases their interest in pursuing careers in those fields. The term
microaggression refers to a subtle everyday communication, either verbal or nonverbal, which
has the effect of denigrating an individual because of their membership in a particular societal
group. Research indicates that microaggressions play a significant role in decreasing women’s
interest in STEM as an educational and career goal.
16
Naphan-Kingery and Ellito (2018) performed a study of 404 female undergraduate
students to determine how gender-focused microaggressions affected their propensity to persist
in STEM fields. They found that women who experience more microaggressions are less likely
to have positive engineering self-efficacy and less likely to persist in a STEM-focused education
or to attain a career in STEM. True-Funk et al. (2021) performed an intersectional study of the
impacts of microaggressions on students. They interviewed 42 engineering undergraduates who
fell into seven intersectional identities, across both predominantly White and historically Black
educational institutions. Results of the study showed that microaggressions have five macroeffects, including reduced self-efficacy, a stronger sense of otherness, stereotype threat, a feeling
of isolation based on race and/or gender, as well as a more-empowered sense of self.
Microaggressions can also occur due to racial stereotypes. Stitt and Happel-Parkins
(2019) reported incidents of Black women being refused help by their peers, and being told that
they were expected to conform to traditional societal roles rather than engage in STEM fields.
These kinds of microaggressions were committed by people both within and outside of
academia. Allen et al. (2022) reported similar microaggressions towards women belonging to
racial minority groups, resulting in many of the women questioning their own suitability for
STEM fields. The study also noted that these microaggressions are often ignored, rather than
being acknowledged and addressed appropriately.
Women of color experience a combination of both racially-motivated microaggressions
and gender-based microaggressions, making it even more difficult for these women to pursue
educations and careers in computer science and engineering. Charleston et al. (2014) reported
that African American women believed that the computer science field is unwelcoming to
women in general, and especially so to women of color. Allen et al. (2022) recommended that
17
students be provided with ample opportunities to form strong bonds with peers, instructors,
mentors, and support staff as a means of combatting gender- and racially-based stereotypes and
microaggressions.
Research suggests that gender bias, stereotype threat, and microaggressions have negative
impacts on women’s attainment of and persistence in STEM fields. However, this is not the
whole story. Ceci et al. (2014) noted that research into the STEM gender gap at times seems
contradictory, but that these contradictions arise primarily when historical results are conflated
with current findings. Gender discrimination has seen a significant reduction over the past few
decades, but it is still relied upon too heavily as the sole explanation for the gender gap in STEM.
As such, Ceci et al. (2014) recommended re-focusing on other factors that contribute to gender
disparity, such as negative pre-college experiences of girls.
The remainder of this literature review explores some of the other factors that contribute
to gender disparity. Young women without a clear understanding of what a career in STEM
entails are less likely to pursue STEM degrees. Women who lack a strong sense of engineering
identity, and women lacking a strong sense of self-efficacy in STEM-related skillsets, are also
less likely to pursue a STEM education or a STEM career. Women’s experiences prior to
embarking on a STEM career pathway also appear to play a significant role in their likelihood of
entering and persisting in the field. Leakage in the STEM pipeline occurs when people who
begin a STEM-oriented education do not complete their degrees, or do not seek a career in
STEM after graduation; reducing the number of women who leak out of the pipeline is one way
to improve gender disparity in STEM.
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Knowledge of Engineering as a Field of Study
Students’ lack of knowledge about engineering, science, and technology careers is one of
the greatest factors contributing to low enrollment in related majors (Tietjen & Reynolds, 1999).
This knowledge includes an understanding of what a career in STEM entails, the kinds of work
they would find themselves doing in such a career, and the impacts they can have on society by
developing STEM expertise. The Harris Poll (1998) ascertained that Americans tend to
understand what a scientist does quite well, but they are much less well-informed about the kinds
of things engineers do in their careers. Tietjen and Reynolds (1999) claimed that nearly 80% of
women in the United States are not well informed about the engineering profession, and as a
result, are less likely to encourage their daughters to pursue engineering careers. In a qualitative
study of undergraduate women, Anderson (1994) reported that most participants had only a
rudimentary understanding of engineering and its possibilities as a career. Moreover, students
who were chosen for STEM based purely on their academic success did not fare as well as those
who had self-selected for engineering; this self-selection was correlated positively with better
knowledge of engineering prior to university (Anderson, 1994). Anderson revealed that
educational institutions provided insufficient resources to help students answer the questions of
whether or not to pursue engineering and which sub-discipline to focus on.
Students are more likely to choose engineering as a career if they realize that engineering
can have a positive impact on the world (Godwin, 2016). Canney and Bielefeldt (2015)
demonstrated that a desire to have a positive impact on society was a motivating factor for
women to pursue a career in engineering. This effect extends into the classroom. Anderson
(1994) observed that women reported more satisfaction when their courses were geared toward
demonstrating the impact of their work on society. However, ensuring that young women have
19
an accurate understanding of the potential societal benefits of engineering can be challenging.
Diekman et al. (2010) reported that STEM fields are commonly regarded as antithetical to
communal goals or at best that they have little impact on society at large.
Providing female students and those who support them with more-accurate and morecomplete knowledge of engineering, and how it can positively impact the world around them,
could help to encourage more women to pursue degrees and careers in various engineering
fields. This knowledge also enables women to form a connection between the kinds of things
professional engineers and scientists do and their own sense of identity. And when young women
form an early sense of engineering identity, they are more likely to pursue STEM studies and
STEM careers.
Engineering Identity
More women will be recruited and retained in engineering if they can identify themselves
as engineers. During their educational careers, students develop a sense of identity based on the
subjects they study, the activities they undertake, and their interests. A strong sense of
engineering identity has been shown to positively impact students’ persistence in STEM studies
and attainment of careers in engineering (Godwin, 2016; Tallman et al., 2019). Godwin (2016)
developed tools to quantitatively measure engineering identity, based on three constructs: interest
(a curiosity about engineering or a desire to pursue engineering studies), self-efficacy (a belief in
one’s ability to understand and practice engineering), and recognition (whether others perceive
oneself to be an engineer). These tools were developed and refined based on previous tools
designed to measure physics identity developed by Harzari et al. (2010).
A female student’s perception of engineering can influence their own sense of identity as
a woman and an engineer. Nosek et al. (2002) performed two studies that indicate a perception
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amongst college-level women of STEM as being a male-oriented field of study, combined with
their own sense of identity as women, made it difficult for these students to achieve a sense of
engineering identity for themselves. The Nosek et al.’s (2002) studies employed principles of
cognitive consistency theory (Greenwald et al., 2002) in their theoretical framework.
Tallman et al. (2019) studied undergraduate engineering students and identified various
factors associated with developing a strong engineering identity. Students with a focus on
technical knowledge, for example, tend to develop a strong sense of engineering identity. A
student’s engineering identity was also found to be augmented by the student’s surprise at just
how wide-ranging engineering studies are, and how impactful engineering can be on improving
society (Tallman et al., 2019).
Women are more likely to pursue STEM educations and careers when they have built a
strong sense of engineering identity. But in order to incorporate engineering into their identity,
women must attain confidence that they can succeed in their studies and in their careers. This
sense of competency and one’s ability to succeed is known as self-efficacy, and it is a key factor
contributing to women’s desire to pursue and persist in a STEM education.
Self-Efficacy
Self-efficacy can be a strong influence on students selecting college majors and persisting
in those majors, and the lack of self-efficacy inhibits women from pursuing engineering degrees.
Self-efficacy refers to an individual’s belief in their ability to achieve specific goals. A pervasive
finding throughout the literature is that self-efficacy is positively correlated to students’
attainment and persistence in STEM career pathways (Green & Sanderson, 2018). Higher selfconfidence and self-efficacy have been linked to a higher resilience among women to the
deleterious effects of negative gender stereotypes (Hausmann, 2013). Women who experience a
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progressive degradation of self-esteem during engineering university programs show reluctance
to speak about the problem with their advisors, in part because they assume the problem is
internal, not systemic in nature (Anderson, 1994).
Woodcock and Bairaktarova (2015) studied correlations between first-year engineering
students’ actual performance on an engineering task versus the self-evaluations of their
performance on the same task. While no significant gender differences in real performance were
observed, the women in the study tended to greatly underestimate their own performance,
whereas the men tended to more accurately assess how well they had done. Attrition rates in the
first two years of college-level engineering studies were observed to be much higher amongst
women than men, and one driver of this gender gap was revealed to be the tendency of women to
underestimate their own aptitude. Chronic underestimation of their own aptitude eroded the
ability of these women to master new material, exacerbating higher attrition rates (Woodcock &
Bairaktarova, 2015).
Many studies have observed a correlation between self-efficacy and the key variables of
the social cognitive career theory (SCCT) model—math-intensive interests, goals, persistence,
and performance. A longitudinal study by Lent et al. (2008b) showed that not only are these
variables correlated, but there exists a causal relationship; self-efficacy predicts the other
variables. The findings of Lent et al. (2008b) did not, however, support the SCCT hypothesis that
outcome expectations drive students’ interests and goals, nor regarding interest in a subject being
a prerequisite for forming educational goals in that subject. Evans et al. (2020) also demonstrated
that math self-efficacy is a predictor of students choosing a STEM major. In a study of data
obtained from the National Education Longitudinal Study (ELS), 2002, high self-efficacy in
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math at the high school level was found to be a predictor of students choosing a STEM major in
college.
In a study of 579 sophomore engineering students, Inda et al. (2013) noted that the selfefficacy of both women and men helped to determine their interest in STEM-related studies.
Self-efficacy predicted the students’ outcome expectations. Moreover, self-efficacy was shown
to be directly linked to the students’ persistence in their engineering studies. Both women and
men reported that their perception of contextual supports and barriers directly influenced their
sense of self-efficacy, but the female participants had less confidence in their ability to complete
their engineering programs (Inda et al., 2013).
Heilbronner (2011) also determined that belief in one’s own ability to succeed in STEM
was a key predictive variable in determining whether an individual would ultimately select a
STEM major at the university level. Heilbronner (2011) proposed that this linkage between selfefficacy and persistence in STEM is related to the individual’s perception of how malleable their
skills are, and the way they interpret failure. Individuals who saw their abilities as being fixed or
innate were less likely to tolerate early failures, whereas those who viewed their abilities as
changeable, and viewed failures as learning opportunities, were more likely to persist in their
STEM studies.
Self-efficacy in engineering has been shown to be positively associated with both the
likelihood of graduation and the likelihood of seeking an engineering career (Naphan-Kingery &
Ellito, 2018). They observed and explored two strategies employed by students when attempting
to reconcile their gender identity with their engineering identity: In one approach, female
students tended to de-emphasize their gender and attempt to blend in with their male counterparts
(engineering identity centrality). In another approach, female students would embrace their
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gender identity and take positive steps to attempt to improve men’s perceptions of women in
STEM (gender identity centrality). Of the two approaches, Naphan-Kingery and Ellito (2018)
observed that only gender identity centrality resulted in an increased likelihood of persistence via
self-efficacy.
Not all studies have observed a tendency toward lower STEM self-efficacy in women.
Wilson et al. (2015) reported that while women did demonstrate lower self-efficacy than men in
general, the self-efficacy gender gap mostly disappeared within STEM disciplines. However, the
literature makes it clear that a positive correlation exists between the strength of an individual’s
sense of self-efficacy in STEM and that individual’s propensity to pursue an education and a
career in STEM. A college applicant’s sense of STEM self-efficacy is formed during her precollege years, and the strength of her self-efficacy can be either reinforced or eroded during her
undergraduate education. In the next section, the impacts of early life experiences on women’s
STEM persistence will be explored.
Experiences Prior to Embarking on a Career in STEM
A woman’s experiences during her formative years, from elementary school to postsecondary education, can have a significant positive or negative impact on the likelihood that she
will persist in a STEM education and ultimately attain a career in a STEM field. Ceci et al.
(2014) conducted detailed life-course analyses, comparing men and women in math-intensive
STEM fields to counterparts in less math-heavy fields. They determined that pre-college
experiences, starting as early as kindergarten, can directly affect a student’s likelihood of
pursuing STEM majors in university. The authors recommend a stronger focus on pre-college
factors when combating the STEM degree gender gap. Heilbronner (2011) showed that receiving
high-quality science instruction in high school is one key predictor of success in STEM at the
24
university level; developing a strong sense of self-efficacy prior to college was another
significant predictor. A similar effect was observed by Evans et al. (2020). High self-efficacy in
math, developed at the high school level, is a predictor of choosing a STEM major in college.
Dasgupta and Stout (2014) analyzed gender disparities in STEM from childhood to midadulthood. Barriers to women’s pursuit of a STEM education and career occurred during
childhood and adolescence, during emerging adulthood, and also in women’s professional lives.
Pre-college, male-centric gender roles, and stereotypes had a negative impact on women’s
pursuit of STEM. Parental expectations, lack of parental support, and pressure from peers also
had detrimental effects. Another factor during this phase of a woman’s life was a sense that their
personal values did not align with the values inherent in a STEM career. In higher education,
women experience additional barriers such as a sense that they do not fit into the engineering
culture, being outnumbered by male STEM students, and a lack of role models and mentors.
Finally, in their professional lives, women in STEM experienced gender bias in hiring and
promotion, problems maintaining a healthy work and life balance, and difficulties returning to a
STEM career after a period away from the field.
Anderson (1994) noted that the engineering learning environment was typically
inhospitable to women and that the way in which engineering was taught had a significant
impact on female students’ persistence in engineering. He observed that when an individual
earns low grades early on in college, this situation leads to a loss of self-esteem which results in
the individual being less likely to persist in a STEM field. This effect was especially pronounced
in women, and the author posits that the effect may arise because women tend to be more likely
than men to give up when a self-assessment of their own performance is not close to perfect
(Anderson, 1994). DuBow and Gonzalez (2020) reported that the percentage of women faculty
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in computer science was lower than the academic average. Carrell et al.’s (2010) study indicated
that female students who were instructed by female professors in their introductory STEM
classes were more likely to pursue a STEM major than female students who took similar classes
taught by male professors. This result suggests that increasing the number of female instructors
may make the learning environment more hospitable to women, which in turn could improve
women’s selection of and persistence in STEM fields.
Ro and Knight (2016) explored how teaching methods at the collegiate level, the
structure of the university curriculum, and the level of participation in co-curricular activities
during college affected learning outcomes amongst women in STEM programs. They revealed
that a greater curricular emphasis on professional skills, and a greater use of student-centric
teaching methods, both resulted in higher self-reported design skills amongst female students.
Women who were more active in non-STEM related clubs and similar extracurricular clubs and
activities reported higher communication skills, and higher fundamental design skills, than their
counterparts who had not participated in such programs.
Smith and Gayles (2017) studied women transitioning from college into careers in
STEM, and how their college experiences had impacted their decision-making process. They
reported that the study participants’ decisions were shaped primarily by three factors during
college: their experiences in the field, gender dynamics (low female-to-male student ratio, a
gender-hostile environment, and worries about gender bias), and the degree to which an
individual’s self-knowledge aligns with their occupational knowledge.
Most of the literature suggests that positive educational experiences are a driver of the
future success of women in STEM. But some research indicates that the experiences of women,
at the collegiate level in particular, have little impact on those women’s likelihood of persisting
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and earning a STEM degree (Green & Sanderson, 2018). However, this study showed that
women’s perceptions of their ability (self-efficacy) had a positive impact on their STEM
performance. It also showed that high school math preparation and attendance at small colleges
increased the likelihood that a student would switch into a STEM field.
The experiences of girls and women throughout their childhood and into adulthood can
influence their decisions to pursue STEM degrees and careers. Creating positive learning
environments appears to be an effective means of increasing the number of women in
engineering careers. Helping women to build self-efficacy and engineering identity can both
increase women’s interest in STEM fields and reduce the number of women who switch away
from STEM after having embarked on a STEM-related education. The loss of STEM students to
other fields during their educational years and early careers is known as “leakage” in the STEM
pipeline. In the next section, the factors that drive STEM pipeline leakage are explored.
Leakage in the STEM Pipeline
The STEM pipeline is a metaphor used in STEM research to describe the flow of students
through the entire education system and ending with a career in STEM (Allen-Ramdial &
Campbell, 2014). The term leaky pipeline is often used to describe the high attrition rate of
female and minority students in the STEM pipeline. Reducing STEM pipeline leakage will lead
to more STEM students graduating and having careers in those fields.
Both girls and boys who show talent in STEM sometimes choose not to pursue a college
education in the field (Heilbronner, 2011). A study of 360 students who scored well on the SATVerbal and SAT-Math explored the factors which led to them pursuing, or failing to pursue,
college-level degrees in STEM. The four factors identified were interest, ability, self-efficacy,
and the students’ experiences prior to college. Of these, self-efficacy emerged as having the
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strongest correlation to the students’ persistence in STEM. Students’ sense of self-efficacy
started out high, but as the difficulty of the subject matter increases, self-efficacy tends to
become eroded. Individuals who view their abilities as innate and unchangeable could not
tolerate early failures in their studies and tended to self-divert into non-STEM fields. On the
other hand, individuals who view skills as malleable and treat failures as learning opportunities
were less likely to drop out of STEM (Heilbronner, 2011).
Ball et al. (2017) employed expectancy-value theory (EVT) as a framework when
evaluating the STEM attitudes of elementary students. The study revealed that by increasing
students’ academic-related intrinsic values, the students more readily tended to form an affinity
with STEM subject matter. An increase in students’ academic utility values also appeared to
improve their attitude toward the importance of STEM. Previous research had suggested that
improvements in EVT-related variables could help to mitigate early STEM pipeline leaks (Ball
et al., 2016). Thus Ball et al. (2017) concluded that by improving students’ STEM attitudes along
the axes of intrinsic value and academic utility, pipeline leakage can be reduced.
Naphan-Kingery and Ellito (2018) reported that women who graduate with degrees in
STEM move into STEM careers at a lower rate than their male counterparts. Amongst students
who graduated between 2010 and 2013, 75% of the men were observed to be working in STEM
careers or enrolled as full-time graduate students by 2015. By contrast, only 62% of the women
were working in STEM or pursuing STEM graduate degrees by 2015. DuBow and Gonzalez
(2020) noted that more women than men report feeling “stalled” in their technology careers and
tend to leave the tech workforce.
The goal of the STEM pipeline is to increase the number of people in STEM careers, but
more women than men are leaving at this final stage. The literature offers many clues as to the
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reasons for leakage from the pipeline. These include underestimation of perceived ability (low
self-efficacy) in STEM, seeing one’s own abilities as static and unchangeable, a deficiency in
students’ academic-related intrinsic and utility values, and negative experiences during women’s
educational years. In the next section, various supports that can help to mitigate the factors that
lead to STEM pipeline leakage are discussed.
Needed Supports for Women in Engineering Programs
Research has identified various supports that have a positive impact on women pursuing
engineering and computer science degrees. Student-centered pedagogical methods can be
employed by involving more interactions with faculty and peers (Waychal & Henderson, 2018).
Cooperative learning environments can encourage female students to participate during class,
thus increasing their knowledge and confidence. Educators could show the real-world
applications of engineering and science through student-oriented discussions and demonstrations
(Godwin et al., 2016). Engaging female students in their own education will help to keep them in
their respective programs.
Another way to support female engineering and computer science students is to provide
them with female role models and mentors (Waychal & Henderson, 2018). These role models
could be faculty members, higher-level students, or women from industry. One way to provide
good role models for female students is to increase the number of female faculty members in
STEM (Yoder, 2017). According to Dasgupta and Stout, “Role models also serve as mentors
who guide professional development, champion students’ work, and broaden their professional
network” (2014, p. 24). They suggest that academic departments should provide opportunities
for senior women in STEM fields to meet and mentor students.
29
Female students need an environment in which they feel represented and supported. One
way to accomplish this is to provide formal structures such as clubs and organizations involving
women in engineering and computing (Waychal & Henderson, 2018). Examples of these include
non-profit and professional organizations such as the Association for Women in Computing,
Girls in Tech, Society for Women Engineers, and Women in Science and Engineering. Local
chapters of these organizations can be created on college campuses. Women need to be
supported in their pursuit of degrees in engineering and computer science.
Conceptual Framework
The conceptual framework for the study is based on social cognitive theory (SCT)
developed by Bandura. SCT provides a lens through which to evaluate the factors involved in
women pursuing and completing bachelor's degrees in engineering and computer science. The
theory focuses on the individual and how the modeling of others (behaviors and consequences)
and the environment influence the individual’s actions. Self-efficacy, or the belief that an
individual has the capability to achieve their goals, can also influence the individual (Bandura,
1977). Engineering identity is how a person sees themself as an engineer (Pierrakos et al., 2016)
and has been incorporated into the conceptual framework. Self-efficacy and engineering identity
are grouped in the cognitive component. For the conceptual framework, the environment is
comprised of aspects of the learning environment that may affect an individual student. Those
aspects include gender representation in the classroom and by instructors and course support
staff, a gender-neutral curriculum, and an anonymous way to ask questions. The conceptual
framework aims to determine how these key concepts influence women in pursuing and
persisting in undergraduate engineering programs in higher education.
30
Figure 1
Conceptual Framework
Summary
Women’s representation within STEM fields has improved steadily over the past few
decades. Despite these gains, women remain underrepresented in STEM careers worldwide. The
lack of gender parity is most pronounced in math-centric fields such as engineering, computer
science and the physical sciences. Research indicates that this gender gap widens as students
progress through the STEM pipeline, being least pronounced in high school and most
pronounced with respect to women’s rates of graduation and career attainment in STEM. Despite
a clear trend toward improved gender parity in STEM graduation rates and STEM careers, key
barriers to women’s success in STEM remain.
Various factors contribute to the lack of gender parity in STEM fields. External gender
bias (the perception that STEM fields are inherently masculine in nature) is one key component.
Internalized stereotype threats (women’s perceptions of STEM as being isolated, non-
31
collaborative, and/or lacking positive societal impacts) also plays a significant role. The
persistence of subtle microaggressions against women’s pursuit of a STEM education and career
continue to exacerbate the problem. Barriers to entry into the STEM pipeline include a lack of
knowledge about what a STEM career entails, and insufficient positive pre-college experiences
with STEM subject matter. Not all women who enter the STEM pipeline complete their degrees
and go on to pursue a career in the field; this form of attrition is known as pipeline leakage.
The literature has established engineering identity and self-efficacy as key factors
enabling women to persist in STEM. A woman’s engineering identity stems in part from her
interest in the subject matter; it is reinforced by positive experiences within the learning
environment, academic successes, and the receipt of recognition from others. A key component
of a women’s sense of engineering identity is STEM self-efficacy (a belief in one’s ability to
succeed in science, engineering and mathematics). One reason for the disproportionate leakage
of women from the STEM pipeline is that women tend to hold themselves to impossibly high
standards; early academic failures tend to result in these women giving up on STEM. However,
women who recognize that their abilities are malleable and who view failures as opportunities
for learning and growth fare better and are more likely to persist in STEM than those who view
their skills as innate and immutable.
The literature indicates that a number of key supports are required in order to improve
gender parity in STEM education and STEM careers. Women who are exposed to the positive
societal impacts of engineering, science and mathematics tend to pursue education and careers in
STEM. Educational methods that focus on collaboration, human interaction, mentorship and the
provision of strong female role models produce more-favorable results than those that lack such
32
a focus. Clubs and other organizations that highlight and support women’s involvement in STEM
are also important supports.
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Chapter Three: Methodology
This chapter contains the research questions, design, and other aspects related to the
research study. The purpose of this study was to evaluate how the learning environments in an
engineering school influence women students in regard to their sense of engineering identity, the
likelihood that they will complete their engineering degrees, and the likelihood that they will
pursue engineering careers after graduation. After listing the research questions, the following
topics will be covered: research design and setting; researcher’s positionality, data sources,
collection, and analysis; participants; validity and reliability; limitations and delimitations; and
ethics.
Research Questions
Using SCT as the theoretical framework, the following research questions guided this
study:
1. How do aspects of the learning environment influence the sense of engineering identity in
women engineering students?
2. What impact does the learning environment have on the likelihood of women engineering
students completing their engineering degrees?
3. What aspects of the learning environment improve the likelihood that women engineering
students will pursue engineering careers after graduation?
Overview of Design
The overall design of the research was a mixed methods approach, which incorporated
elements of quantitative and qualitative approaches (Creswell & Creswell, 2018). This
methodology originated around the late 1980s and early 1900s, based on work from a range of
research fields including education, management, sociology, and evaluation. Over the years,
34
various strategies have been identified under the mixed method approach. This study used the
explanatory sequential design, which is a two-phase approach. First, the study gathered
quantitative data using a survey administered to undergraduate engineering students who identify
as women. The collection and analysis of the quantitative data then influenced the questions for
the interviews where participants were selected using purposeful sampling. The qualitative data
provided more insight into the lived experiences of women pursuing engineering and computer
science bachelor’s degrees. The explanatory sequential design was chosen to have a more
complete understanding of the changes needed for women to pursue and earn engineering
degrees through a combination of quantitative and qualitative data.
Table 1
Data Sources
Research Questions Survey Interviews
RQ1: How do aspects of the learning environment
influence the sense of engineering identity in women
engineering students?
X X
RQ2: What impact does the learning environment have on
the likelihood of women engineering students completing
their engineering degrees?
X X
RQ3: What aspects of the learning environment improve
the likelihood that women engineering students will
pursue engineering careers after graduation?
X X
Research Setting
The study was conducted at a large private research university in the western United
States that offers degrees in a broad range of majors including arts, humanities, social sciences,
natural sciences, and engineering. The organization of focus is an engineering school within this
35
university. To protect anonymity, University Engineering School (UES) is used in this study to
represent the engineering school. UES offers bachelor’s, master’s, and Ph.D. degrees in
computer science and various engineering fields including, but not limited to, biomedical,
chemical, computer, electrical, and mechanical. The mission of the organization is to be an
excellent school of engineering that educates students and conducts new research. The student
population at the engineering school at the time of the study was over 8,000 students with over
2,500 undergraduates and approximately 1,100 women undergrad engineering students. This
university was an appropriate place to conduct this research due to the large number of students
and the researcher’s access to these students.
The Researcher
As a woman who was an engineering student and software engineer, I closely identified
with the participants of the study. I was passionate about conducting this research to help close
the gender gap in the engineering and computer science fields. My positionality and identity
supported the analytical approach to quantitative research. The survey was an adaptation used in
previous research done by Lent et al. (2008). Adapting an existing instrument is one of the
strategies that was used to maximize validity (Creswell & Creswell, 2018). The survey questions
were pilot-tested and peer-reviewed, which addresses the issue of construct validity by
generating feedback on item content and format (Creswell & Creswell, 2018). The data collected
from the survey was anonymous. There was a separate link to a survey that allowed those
interested in participating in an interview to enter their information. Thus, all data collected from
the survey was not associated with any individual.
Interviews were conducted for the qualitative part of the study. The semi-structured
questionnaire had also been pilot tested. Data was gathered from the survey and interviews as
36
another strategy to reduce biases and assumptions. As a teaching faculty member at the
engineering school where the study was conducted, I had insight into the support structures that
existed for women engineering students. I also hired engineering students as teaching assistants
for the courses and programs I managed. This situation could have created a negative power
dynamic where the participants may not want to be honest and open. To counteract this, all
undergraduate women engineering students were invited to participate in the survey, and the
responses were collected anonymously. The participants for the interviews were purposefully
selected to ensure that I did not interview any students whom I supervised. My positionality and
identity allowed me to relate to the participants and made them feel comfortable during the
interviews. To mitigate assumptions and bias, I used reflective memoing after each interview
(Merriam & Tisdell, 2016). Interviewees were able to review their own transcripts and correct
any misstatements. I also solicited feedback on emerging findings, known as respondent
validation (Merriam & Tisdell, 2016). To protect the identities of the participants, all data
collected including audio and video recordings has been kept securely and will be destroyed after
they are no longer needed.
Data Sources
Following the explanatory sequential design, quantitative data was first collected through
a survey. This survey approach produces a numerical description of the attitudes, trends, and
opinions of a population (Creswell & Creswell, 2018). After analysis and purposeful sampling,
interviews were conducted. These narratives from the interviews provided insight into the lived
experiences of the participants (Merriam & Tisdell, 2016).
37
Survey
This study used a self-administered survey to provide quantitative data about the trends,
attitudes, and opinions of a population (Creswell & Creswell, 2018). The survey was created
using Qualtrics, an experience management software tool. The survey was available online
through a unique link allowing participants to use their own computing devices such as laptop
computers and mobile phones. After asking demographic questions, participants answered
questions adapted from an assessment tool by Godwin (2016) to measure engineering identity.
The questions to determine self-efficacy were adapted from an SCCT assessment used in a study
to evaluate the social cognitive predictors of adjustment to engineering majors (Lent et al.,
2013). The language in the questions was modified to account for computer science majors since
the computer science department is an academic unit within UES. Some questions were
eliminated to reduce the length of the survey and to increase the response rate (Creswell &
Creswell, 2018).
Interviews
For qualitative data gathering, semi-structured interviews were conducted with 11
individuals. The interviews were held online using the Zoom platform during the spring semester
of 2022. After taking the survey, 18 women submitted their names and contact information as
potential interview participants. After contacting all of them using emails and phone calls, 12
students agreed to be interviewed. During the month I was conducting interviews, one student
became ill and decided not to be interviewed. After interviewing these women, I reached a point
of saturation based on the key concepts.
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Participants
The purpose of the study focused on the lived experiences of undergraduate engineering
students in higher education who identify as women. Thus, the participants needed to be selected
from the target population of undergraduate women at an engineering school, specifically UES.
In order to collect as much data as possible, the survey was available to all undergraduate women
who identify as women utilizing a census approach. Emails were sent to all undergraduate
engineering students requesting those that identified as women to complete the survey. At the
particular engineering school where this study was administered, there were over 2,500
undergraduates and approximately 1,100 women undergrad engineering students. In order to
recruit participants, the researcher coordinated with the office that supports undergraduate
students and the academic departments at the engineering school to distribute a link to the
Qualtrics survey. With a population size of 1,100, a margin of error of 5%, and a confidence
level of 90%, according to the Raosoft sample size calculator
(http://www.raosoft.com/samplesize.html), the recommended sample size was 218.
For qualitative interviews, the participants were individuals that meet the criteria of the
purpose of the study (Creswell & Creswell, 2018). Thus, the participants for the study needed to
be selected from the target population of undergraduate women at an engineering school,
specifically UES. The target sample size was 10 – 12 participants, who were selected from the
survey participants that opted to share their names and contact information in a separate survey
from the quantitative survey instrument. If there are not enough volunteers, then more
participants would have been recruited through the academic departments at UES. The ideal
interviewees were pursuing degrees from various departments at the engineering school
including electrical and computer engineering, mechanical engineering, biomedical engineering,
39
and computer science. To enable this, purposely selection was used to capture the experiences of
students from various engineering majors.
Instrumentation
The survey protocol used for this study is included in Appendix A. A key step in
designing the survey is to first identify the purpose of the survey research (Creswell & Creswell,
2018). Since the purpose is to evaluate the lived experiences of women engineering students, the
survey contains descriptive questions. The overall approach is modifying an SCCT assessment
used in a study to evaluate the social cognitive predictors of adjustment to engineering majors
(Lent et al., 2013). After contacting Lent, the originator of the survey instrument, via email, I
received a document with the instrument and an article assessing social cognitive constructs
(Lent & Brown, 2006). A majority of the questions were closed questions allowing the
participants to select an answer from a list of choices. Some of the questions allowed each
participant to select the ‘Other’ option and enter their own choice. For the level of measurement
for these questions, the first three of the four types were used: nominal, ordinal, interval, and
ratio (Salkind, 2014). The last two questions were open allowing a multi-line free response from
the participants.
The first two questions asked about the participants’ gender and racial/ethnic identity for
demographic information, which used a nominal level of measurement. The next block of
questions inquired about the grade level, transfer status, major, and intended graduation year to
verifying each respondent met the criteria for participation. The following block of four
questions to evaluate engineering identity used the slider question type to allow the user to select
a whole number representing a scale from strongly disagree to strongly agree, which used an
interval level of measurement. The next group of questions evaluating self-efficacy allowed the
40
participant to select a number using a 1 – 5 scale to identify how they agree/disagree with four
statements where 1 is strongly disagree and 5 is strongly agree. These groups evaluated
engineering identity and self-efficacy, which refers to the first research question. The following
five groups of questions focused on determining the participants’ perceived impact of the various
aspects of their learning environment on their identity as engineers, understanding course
content, completing their engineering degrees, and pursuing a career. The last set of questions
used a 1 – 5 scale to have to participants respond to their comfort level with getting help from
instructors and support staff based on gender, working in groups, and having an anonymous way
to get course help.
The interview protocol used for this study is included in Appendix B. The overall
approach was a semi-structured questionnaire with 12 main questions. The semi-structured
interview contained questions that guided the researcher toward key concepts and allowed the
researcher flexibility in changing the ordering and wording of the questions depending on
responses from the participants (Merriam & Tisdell, 2016). Many of the questions had probes to
help direct the participants toward the key concepts in the conceptual framework. Qualitative
interview questions can be described using six categories: experience and behavior, opinion and
values, feeling, knowledge, sensory, and background/demographic (Patton, 2015). The
instruction used for this study mainly contained experience and behavior, opinion and values,
and demographic questions. There is one knowledge question and some of the probes were
feeling questions.
The first three questions aimed to collect demographic data regarding the college majors
of the participants, which helped determine their interests, outcome expectations, and goals. Six
questions focus on the experiences and behaviors of the participants in their engineering
41
education to help determine the supports and barriers they experience. A knowledge question
was also included to help determine supports. The protocol included two opinion and values
questions to acquire data regarding choice goals and outcome expectations.
Data Collection Procedures
The survey was created online using Qualtrics allowing participants to use their own
computing devices such as laptop computers and mobile phones. After being reviewed by the
dissertation chair, the researcher, and fellow doctoral students, the Qualtrics survey was opened
on March 4, 2021. On March 7, the student services office at UES sent an email to all women
undergraduate students that contained a link to the survey. The researcher’s goal was to reach
over 200 responses, thus the response rate was closely monitored on the Qualtrics website.
Having reached almost 100 responses, the researcher requested the student services’ office to
send another email with the survey link on March 16. The Qualtrics website showed 140
responses on March 22, thus another email was sent by the student services office. After
reaching over 200 responses, the survey was closed on March 31.
In order to conduct the interviews, the researcher requested an hour time slot with each
participant. After communicating and settling on a date for each interview that accommodated
each participant’s schedule, the researcher created an online meeting using Zoom, which enabled
individuals to use audio and video. Due to the global pandemic caused by COVID-19,
conducting in-person interviews was not permitted by the university. The researcher and the
participants were located in their own living spaces. To capture data, the Zoom platform allowed
the host to record the session containing the audio and video of whomever was talking
throughout the meeting. This platform also created a transcript of the audio noting the name of
the person talking and a timestamp of when they started and stopped. The transcript was powered
42
by otter.ai, and the transcript file that was generated was downloaded separately. The researcher
chose to use this method of recording and transcribing interview data since it ensured that all
information was preserved and could be used for analysis (Merriam & Tisdell, 2016). The
researcher was prepared to also record the audio using the Voice Memos app on her phone.
Data Analysis
Due to the explanatory sequential design of this study, the data collected from the survey
was analyzed first before administering and collecting data from the interviews. Then the
qualitative data was analyzed.
Survey
Survey data was collected using Qualtrics, and first analyzed using their available tools.
The number of respondents who started the survey as well as the ones who fully completed it
was recorded and analyzed. The groups of questions categorize the theoretical constructs that
will be evaluated such as engineering identity, self-efficacy, and outcome expectations. More
analysis needed to be done outside of Qualtrics, thus the data was downloaded and evaluated
using JMP.
Interviews
To evaluate the data collected from the interviews, the data analysis was inductive and
comparative (Merriam & Tisdell, 2016). From the transcriptions, I looked for patterns based on
the key concepts from the conceptual framework. The patterns or themes generated was used to
develop a thematic analysis helping to answer the research questions (Johnson & Christenson,
2017).
43
Validity and Reliability
In quantitative research, validity refers to drawing meaningful and useful conjectures
from the instruments. The three traditional forms of validity are content validity, predictive or
concurrent validity, and construct validity (Creswell & Creswell, 2018). Adapting an existing
instrument and conducting pilot tests of a survey are strategies to maximize the validity.
Construct validity refers to measuring hypothetical concepts in a meaningful way. The researcher
endeavored to measure hypothetical concepts such as engineering identity, self-efficacy, and
outcome expectations by adapting an existing tool used in previous studies.
Reliability in quantitative research refers to the consistency or repeatability of an
instrument (Creswell & Creswell, 2018). The main types of reliability for instruments are testretest, parallel forms, internal consistency, and interrater (Salkind, 2014). The researcher
administered one survey during a single semester of a school year, thus test-retest and parallel
forms were not used. The researcher focused on internal consistency by computing Cronbach’s
alpha which correlated the score for each item with the total score for each individual and then
compared that to the variability for all individual item scores. This was done on groups of
questions that were asking about a specific construct. The survey was publicized through the
engineering school’s office that supports undergraduates and the various departments. During the
time period when the survey was available, the response rates were continually monitored.
In a qualitative study, validity is based on the accuracy of the results from the standpoint
of the researcher, participant, or readers of the study. The terms trustworthiness, authenticity, and
credibility are used in qualitative studies to address validity (Creswell & Creswell, 2018). To
ensure validity, various procedures can be used such as triangulation, member checking, rich
descriptions of the findings, bias clarification, presentation of discrepant information, peer
44
debriefing, and external auditor (Creswell & Creswell, 2018; Merriam & Tisdell, 2016). For this
study, I used member checking by having the participants read and verify the transcripts of their
interviews. Peer debriefing was also utilized to verify that the study and its results resonated with
readers other than the researcher. For reliability, the researcher documented the procedures used
in the study including the interview protocol and how the data was transcribed and analyzed. The
researcher documented the process of coding and continually compared data with their meanings
to prevent a drift in the definition of codes.
Ethics
Protecting the individuals participating in this research was the highest priority. The
research plan was submitted to Institutional Review Board (IRB) at the University of Southern
California (USC) as an exempt study with human participants. The survey was distributed after
getting IRB approval. Participation in this research study was voluntary. No students at UES
were forced to participate. Before starting the survey, participants were presented with the
overall goals of the research, my name and contact information, and a time estimation for taking
the survey. Each participant clicked on a link to start the anonymous survey, and participants
were not asked to enter their names or student identification numbers. All data from the survey
was stored securely and will be destroyed after the dissertation has been accepted. At the end of
the survey, participants were given the opportunity to participate in an interview by clicking on a
separate survey that gathered their names and contact information. The responses from the
survey were not associated with their names.
Due to the nature of the interviews, I knew who I was interviewing for the second phase
of the research study. Each participant selected a pseudonym that I used when coding the data
and when referring to them in any form of writing. Transcripts and videos of these interviews
45
were saved securely and will be destroyed when they are no longer needed. I asked for
permission from each participant before recording the interviews. Each participant had the option
to show their video, which allowed them the option to only have their audio recorded. All
participants allowed me to record and take written notes of the interviews. These notes and any
artifacts created during this research were stored securely and will be destroyed after the
dissertation has been accepted.
46
Chapter Four: Results and Findings
The purpose of the study was to evaluate how the learning environments in an
engineering school influence women students’ sense of engineering identity, the likelihood that
they will complete their engineering degrees, and the likelihood that they will pursue engineering
careers after graduation. This study focuses on the following research questions, all focused on
assessing the impacts of the learning environment on their cognitive and desired behaviors:
1. How do aspects of the learning environment influence the sense of engineering
identity in women engineering students?
2. What impact does the learning environment have on the likelihood of women
engineering students completing their engineering degrees?
3. What aspects of the learning environment improve the likelihood that women
engineering students will pursue engineering careers after graduation?
Five aspects of the learning environment were explored: The degree to which students
had access to women instructors; the degree to which students had access to women support
staff; the use of gender-neutral learning materials in the classroom; the students’ ability to ask
questions anonymously; and the degree to which students had access to projects with a clear
societal impact. Participants were then asked to evaluate the impact of each of these five aspects
of the learning environment on their engineering identity, their grasp of the subject matter, their
likelihood of graduating with a degree in engineering, and their likelihood of pursuing a career in
engineering or computer science. Participants were also asked to rate their comfort level with
asking questions of men and women professors, their comfort asking questions of men and
women support staff, their comfort working on group projects, and their comfort level with
asking questions anonymously.
47
The participants were asked to rate their sense of engineering identity, in terms of how
they are perceived by family, how they are perceived by their instructors, how they are perceived
by their peers, and how they perceive themselves. Finally, participants were asked to rate the
likelihood that they will remain in their engineering program next semester, whether or not they
felt earning a degree in engineering is realistic, the likelihood that they will graduate with a
degree in engineering, and the likelihood that they will pursue a career in engineering postgraduation.
Using this quantitative data, the study attempted to identify correlations between aspects
of the learning environment and the factors that measure the participants’ likelihood of future
success in the engineering field. Implicit correlations were explored by directly asking
participants to rate their sense of how each aspect of the learning environment would impact
their plans and likelihood of future success in STEM. Responses from the qualitative interviews
were then analyzed to support and further illuminate the conclusions drawn from the quantitative
study.
Participants
The study gathered quantitative data through a survey and qualitative data from semistructured interviews. The survey was sent to all undergraduates at UES whose gender was
marked as female in the school system. The first question of the survey had the respondent select
their gender identity from the following list: man, non-binary, woman, and prefer not to say. Of
the 223 students that started the survey, 213 selected the woman option, nine participants
selected non-binary, one participant answered prefer not to say, and 10 did not answer. Only
those who identified as a woman were allowed to continue the survey.
48
After participants completed the survey, they were invited to be potential interviewees by
entering their contact information in another survey. Those students who responded to the second
survey were contacted, and ultimately 11 students were interviewed. All those interviewed were
undergraduate engineering students who identified as women.
Survey Participants
Of the participants who selected the woman option for their gender identity, 211
answered the question regarding racial or ethnic identification. The results were 71 Asian or
Asian American; 65 White or European American; 27 Chicana/o/x, Hispanic, or Latina/o/x; nine
Middle Eastern, North African, Arab, or Arab American; seven African, African American, or
Black; one Native Hawaiian or Pacific Islander; one American Indian, Indigenous, or Alaska
Native; 29 identified as two or more; and one preferred not to say. The non-White options were
grouped into one category in order to analyze survey data based on being White and non-White.
The demographic categories and their abbreviations are displayed in Table 2.
Table 2
Demographic Categories for Survey
Category Subcategory Abbreviation n
Racial identity Non-white
White
RI-NW
RI-W
145
65
Undergraduate level Lower division
Upper division
UL-L
UL-U
110
98
Transfer No
Yes
T-N
T-Y
179
29
Major Computer science
Engineering
M-CS
M-E
50
157
A total of 208 participants answered the question regarding their current grade level.
With 63 first-year students responding, they had the highest percentage at 30% of the total
49
responses. The percentages for the other three levels were 23% with 47 second-year students,
26% with 55 third-year students, and 21% with 43 fourth-year or higher students. In case
graduate students (master’s or Ph.D.) accessed the survey, the option of graduate student was
available. If any participant selected that option, the survey ended without letting the participant
answer any more questions. First- and second-year students are considered lower-division
undergraduate students, while those in the other levels are known as upper-division
undergraduate students. The survey data was analyzed using these two categories.
The survey contained another demographic question asking if the participant was a
transfer student into the engineering school with two options: yes and no. If a student answered
no, then they started at the engineering school as a first-year student. At 86%, a majority of the
responses did not transfer with 179 non-transfer students. With 14%, only 29 students transferred
from another college or university.
The final demographic question asked for the major of each participant. The survey
included the 14 majors offered at UES as well as the option of non-engineering to capture any
participants who changed their major to a non-engineering one. A total of 208 participants
responded to this question. The 14 options were grouped into the eight majors displayed in Table
3. One student selected the non-engineering option which then ended the survey for that
participant. The students that selected one of the three computer science majors were grouped in
the M-CS category while the rest of the engineering majors including computer engineering were
grouped together with in the M-E category as shown in Table 2.
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Table 3
Majors of Interview Participants
Major n %
Aerospace and astronautical engineering 15 7.0
Biomedical engineering 22 11.0
Chemical engineering 22 11.0
Civil and environmental engineering 21 10.0
Computer engineering and computer science 67 32.0
Industrial and systems engineering 19 9.0
Mechanical engineering 24 11.5
Non-engineering 1 0.5
Those participants who completed the survey were asked to provide their names and
contact information if they were interested in being interviewed. Following the mixed-method
methodology, those interested students were contacted and asked to be interviewed. The next
section describes the participants that were interviewed for the qualitative part of the study.
Interview Participants
The interview participants are undergraduate engineering students at UES who identify as
women. Initially, 12 students agreed to be interviewed. During the process, one of them became
ill and decided to withdraw from the study. After interviewing 11 students, a consensus was
achieved, eliminating the need to replace the student that withdrew. Each interview participant
selected a pseudonym inspired by a real or fictional woman in STEM with examples given such
as Ada Lovelace, Grace Murray Hopper, and Margaret Hamilton. The selected pseudonyms with
the racial identities and majors of the participants are listed in Table 4 and are used throughout
the chapter.
51
Table 4
Interview Participants
Pseudonym Racial/ethnic identity Major
Ada Hispanic Computer engineering and computer science
Emmy Asian Chemical engineering
Grace Asian Computer engineering and computer science
Gwynne White Aerospace engineering
Jade White Computer science
Katherine White Computer science
Lillian Hispanic Industrial and systems engineering
Mae Asian Biomedical engineering
Margaret Asian Computer science
Parisa Middle Eastern Computer science
Peggy Asian Mechanical engineering
Of the 11 interview participants, four students were pursuing degrees in computer ccience
through the Computer Science department and two students were computer engineering and
computer science majors through the Electrical and Computer Engineering department. The
other five participants were majoring in the following five disciplines: aerospace engineering,
biomedical engineering, chemical engineering, industrial and systems engineering, and
mechanical engineering. The two departments from UES not represented were Astronautical
Engineering and Civil and Environmental Engineering. The interview protocol contained semistructured questions pertaining to the participants’ experiences as undergraduate engineering
students without questions specific to a particular major. Having representation from all
departments or majors was not a requirement of the study.
52
The participants were interviewed during their first three years as undergraduates. Two of
them were first-year students, also known as freshmen. Five students were second-years or
sophomores, and four participants were third-years or juniors. Probably due to the hectic
schedule of the last semester of their undergraduate studies, no four-year or graduating seniors
filled out the survey to be potentially interviewed. Of the 11 students, only two of them were
transfer students, which means that they did not begin their undergraduate education at UES.
Having detailed the participants from the survey and interviews, the following sections
report the results and findings from the mixed-methods study. Within each research question, the
sections follow the explanatory sequential design by first starting with the quantitative data
collection and analysis, which is the survey results. The qualitative findings are presented to
better explain the survey results and tell the stories of the participants’ experiences as
undergraduate women studying engineering and computer science.
Results and Findings
The quantitative data was gathered through Qualtrics and analyzed using Qualtrics and
JMP Pro 17. The Likert Scale was employed for the interval questions and used a scale from 1 to
5. When participants were asked how often they experience the five aspects of the learning
environment, the responses and their corresponding numbers were used: Never (1), Rarely (2),
Sometimes (3), Mostly (4), and Always (5). Most of the other interval questions asked the
participants on how they disagree or agree with statements, and the following responses and their
corresponding numbers were used: Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4),
Strongly Agree (5). When participants were asked about the level of comfort with specific
aspects, the responses and their corresponding numbers were used: Strongly Uncomfortable (1),
Uncomfortable (2), Neutral (3), Comfortable (4), and Strongly Comfortable (5). The Cronbach’s
53
alpha coefficient was calculated to measure the internal consistency of the four questions relating
to engineering identity. The resulting value was .84 on a 0 to 1 scale, which shows a high degree
of reliability. Comparison of the means based on category used the sample t-test to highlight
statistically significant differences utilizing the 95% confidence interval (CI).
The qualitative data was gathered through detailed note-taking, transcripts provided by
Zoom, and analysis done in the ATLAS.ti tool. Guided by the three research questions, data was
coded and grouped by themes. Quotes from participants were verified from the audio transcripts
and video recordings.
Quantitative Results Overview
After answering demographic questions, the survey participants responded to questions
regarding how often they experience specific aspects of the learning environment, their comfort
with specific activities, and their engineering identity. The aspects of the learning environment
evaluated were gender representation in the classroom with the instructors and during office
hours with course support staff. For this study, support staff referred to teaching assistants,
graders, course producers, learning assistants, and other people paid to support the learning
experience of students. The participants were also asked about the learning environment having
gender-neutral language in course materials, an anonymous way to ask questions, and projects
with a social impact. The survey inquired about their comfort with asking questions to professors
and course staff who are women in comparison to those who are men, asking questions
anonymously, and working on group projects. The descriptive statistics are displayed in the
following subsections.
54
Learning Environment Aspects Experienced
The five learning environment aspects evaluated were not experienced at the same rates
across the participants. Survey participants responded to questions about how often they
experienced women instructors and women course support staff in their engineering courses;
how often gender-neutral language was used in course materials; how often the participants were
given access to a forum for asking questions anonymously; and how often participants had the
opportunity to work on projects with a perceived societal impact. These results are summarized
in Table 4.
Table 5
Learning Environment Aspects Experienced
Aspect n M SD
Women instructors 186 2.10 0.72
Women support staff 186 2.96 0.79
Gender-neutral language 184 3.65 0.95
Anonymous questions forum 186 3.59 1.16
Societal impact projects 185 2.61 0.89
Students were not regularly being taught by women instructors and 35 students had never
had a women instructor. The mean response for this question was 2.10, indicating that the
participants rated their exposure to women instructors at a level of Sometimes (3) or worse on
average. The results improved for the question regarding women support staff in their courses for
help and during office hours. With only five students never experiencing women staff, the mean
of 2.96 indicated most students had opportunities to receive course support from women. The
literature shows that having women as instructors and course support staff allows students to see
women as engineers, which is helpful to all genders. These instructors and support staff may also
55
become mentors and role models to the students which has the potential to help students identify
as engineers.
Participants were asked to rate their experience with gender-neutral language in learning
materials and an anonymous way to ask questions. The means of 3.65 for gender-neutral
language and 3.59 for anonymous questions both indicated a relatively high level of exposure to
these two environmental factors amongst the respondents. Only four students had never
experienced gender-neutral language, while nine had never had an anonymous way to ask
questions. Societal impact projects were experienced less often, with 18 of the 185 respondents
selecting Never (1). The mean of 2.61 on this question indicates that fewer than half of the
respondents rated their experience with societal impact projects at a level of Sometimes (3) or
better.
Comfort Levels
Participants were more comfortable asking questions to women than men and were
comfortable asking questions anonymously. Survey participants were asked to select the level of
comfort regarding aspects within the learning environment including the first four aspects from
Table 4. Three added aspects were men instructors, men support staff, and group projects. By
calculating the mean of the means for asking questions to instructors and support staff who are
men versus those who are women, the participants reported that they were more comfortable
overall learning from women than from men. Table 5 summarizes these results.
Table 6
Comfort Levels within the Learning Environment
Aspect n M SD
Men instructors 175 3.20 1.06
56
Aspect n M SD
Men support staff 175 3.41 1.01
Women instructors 175 3.96 0.89
Women support staff 175 4.21 0.72
Anonymous questions forum 175 4.31 0.73
Group projects 175 3.52 0.98
Engineering Identity
The engineering identity of the participants was evaluated using four questions assessing
how they are perceived by others as engineers, and how they perceive themselves as engineers.
These results demonstrate that there is reliability within the four questions for engineering
identity. The data was evaluated based on the demographics detailed in Table 2. The descriptive
statistics showing the engineering identity based on demographics are shown in Table 6.
Table 7
Engineering Identity by Category
Comparison n M SD df t F p*
All 190 3.88 0.81
RI-NW
RI-W
132
57
3.91
3.81
0.83
0.76 1 –0.79 0.59 .003
UL-L
UL-U
101
89
3.86
3.90
0.89
0.71 1 0.37 0.13 .001
T-N
T-Y
164
26
3.91
3.68
0.82
0.70 1 –1.52 1.83 .010
M-CS
M-E
48
141
3.96
3.86
0.76
0.82 1 –0.76 0.55 .003
Note. 95% CI utilized for calculating p values.
The data in Table 6 shows a significant difference between the engineering identity of
transfer students and non-transfer students. Those students who started their undergraduate
57
education in the engineering school had a mean of 3.91, while those that transferred had a mean
of 3.68. Another interesting result was that the non-White students had a higher score (3.91) than
the White students (3.81). The calculated mean for the computer science students was 3.96
compared to 3.86 for those pursuing other engineering degrees.
Overall, the results show that many students have a good sense of engineering identity.
Since all the participants are engineering students, the goal is for all of them to have a very
strong sense of engineering identity. When students see themselves as engineers, they will
become them. Ultimately, the mean should be 5 or close to it. This is an area that needs to be
improved to have more students completing their degrees and pursuing careers in STEM fields.
Desired Behaviors
The overall aim of this study was to identify aspects of the learning environment that can
positively influence women students’ behaviors with respect to persisting in their engineering
studies in the short term, graduating with an engineering degree, and ultimately pursuing a career
in engineering post-graduation. To establish a baseline from which improvements can be made,
participants were asked to assess their current likelihood of persisting in engineering with respect
to each of these three desirable outcomes. They were also asked to assess whether earning a
bachelor’s degree in engineering or computer science is a realistic goal for themselves. Table 7
summarizes these results.
Across all four aspects of engineering persistence queried in the study, the results were
encouraging: The participants overwhelmingly responded that they intended to continue their
engineering studies next semester, with a mean of 4.65. Regarding their likelihood to earn a
degree, and their sense of whether such a degree is a reasonable goal, results were only slightly
lower, with means of 4.58 and 4.60 respectively. Participants were somewhat less certain about
58
whether or not they would pursue a career after graduation. However, the mean of 4.22 on this
question still indicates a strong tendency for women engineering students to want to earn their
living working as an engineer after college.
Table 8
Desired Behaviors
Aspect n M SD
Continue next semester 186 4.65 0.71
Engineering is a realistic goal 186 4.58 0.65
Will graduate with degree 186 4.60 0.67
Pursue engineering career 186 4.22 0.94
The participants were queried on their perceptions of the impact of the learning
environment aspects on engineering identity, understanding course content, completing their
engineering degrees, and pursuing engineering careers. These results are described in the
following sections as they relate to the research questions posed by this study.
Research Question One
The first research question asked: “How do aspects of the learning environment influence
the sense of engineering identity in women engineering students?” The survey from the study
collected quantitative results identifying the learning environment aspects that the participants
perceive would improve their engineering identity. The interviews garnered qualitative findings
focusing on why some of participants did not have a strong sense of engineering identity.
Quantitative Results
The quantitative data analysis demonstrated that three aspects of the learning
environment most strongly impacted participants’ sense of engineering identity: First, having
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women educators; second, having women support staff; and third, having an opportunity to
participate in projects with societal impact. These aspects had a mean of 3.84 or greater. Of the
two other aspects studied, experiencing gender-neutral language was also shown to have a
positive effect on participants’ sense of engineering identity. But with a mean of 3.50, this effect
was less pronounced than that of women instructors and support staff and that of societal impact
projects.
With a mean of only 3.23, the aspect of the learning environment with the least impact on
engineering identity was the ability to ask questions anonymously. This result indicates that
while being able to ask questions anonymously likely has a mildly positive impact on
engineering identity, this aspect of the learning environment is not as critical as having strong
gender representation and an opportunity to work on projects with clearly perceived societal
impacts. The means and standard deviations for the impact on engineering identity across all five
aspects of the learning environment are summarized in Table 8.
Table 9
Impact of Learning Environment on Engineering Identity
Impact n M SD
Women instructors 183 4.09 0.83
Women support staff 179 3.84 0.82
Gender-neutral language 177 3.50 0.98
Anonymous questions forum 175 3.23 0.96
Societal impact projects 175 3.89 0.92
Qualitative Findings
Regarding the learning environment and its impact on engineering identity, the interview
protocol contained questions on how the participants described an engineer and how they see
60
themselves as engineers. The first main theme that emerged from the interviews was their sense
of engineering identity. The second theme focused on their underestimation of their abilities
which affected their sense of engineering identity.
Engineering Identity
Of the 11 students surveyed, seven reported having a strong sense of engineering identity.
Two respondents cast themselves as actively working towards achieving a full sense of
engineering identity: Emmy described herself as an “engineering student” while Mae reported
that she was an “engineer in training.” Only two respondents reported a weak sense of
engineering identity.
Parisa saw herself as an engineer and expressed that she felt this way despite her parents
wanting her to become a medical doctor. Her self-confidence and perseverance were notable.
She had only one female engineering professor and said that she would like to see more women
as instructors, as well as more professors and support staff that share her ethnic background.
Margaret reported a strong sense of engineering identity. She attributed this in part to
having worked on practical, hands-on projects. “Since I've started actually making things on my
own, working on real projects and I’ve started getting confidence in myself and that way, I
definitely would consider myself a computer scientist.” Peggy stressed the importance of
working on hands-on engineering projects as a collaborative endeavor: “I was very aware of the
fact that build teams are very important part of an engineering career.”
Underestimation of Abilities
The qualitative findings of research question one also highlighted another aspect of
engineering identity and self-efficacy that has been identified in the literature—the tendency for
women engineering students to underestimate their abilities. Jade said that she had to unlearn
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perfectionism. Grace reported, “The way I perceive myself can sometimes be harsher than how
they're perceiving me because, generally, a lot of the people here are very kind.”
Jade reported that she is finally overcoming her perfectionism in her senior year. She
wished her first-year professors had taught her how to deal with this issue. Grace recognized that
developing a strong sense of engineering identity is a multifaceted endeavor: “You have to build
not only like physical things, but your own character. Becoming an engineer is a lot about selfresilience because a lot of people want you to quit. But I think once I sort of solidified my
mindset I was like, ‘No, this is what I want to do: I want to be stubborn. I don't want to quit.’”
Research has shown that women engineering students who view their abilities as
malleable and think of failures as opportunities for learning fare better than those who see their
skillset as innate and immutable. One of Lillian’s responses underscored this idea: “Definitely
there has been a change, I guess, in the last two years of like having this like growth mindset
instead. … I've been more like flexible with knowing it's okay to not have the same expertise as
everybody else. My focus on being an engineer might be different from others. That is what has
kept me going.”
Summary: Research Question One
All study participants either reported having a strong preexisting sense of engineering
identity or described themselves as making good progress towards that goal during their
university studies. Qualitative findings supported the findings of the quantitative analysis for the
first research question: Respondents wanted to see improvements in gender representation
amongst instructors and reported that working on real-world projects helped them to develop
their sense of engineering identity. Additionally, the results supported the observation from the
literature that women who view failures as learning opportunities fare better than those who hold
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themselves to impossibly high standards. Engineering identity is a key element of success and
persistence in undergraduate engineering education. The second research question builds on
these results by exploring ways in which the learning environment can help women engineering
students to complete their degrees.
Research Question Two
The second question focused on the impact of the learning environment on women
engineering students completing their engineering degrees. The quantitative results from the
survey are reported in the next section followed by the qualitative findings from the interviews.
The interviews confirmed and expanded on the findings from the survey.
Quantitative Results
In the survey, participants were first asked to rate their agreement with the statement: “I
am fully committed to getting my college degree in engineering or computer science.” Of the
187 participants who responded to this question, 94% agreed or strongly agreed with this
statement, while 5% were undecided and 1% disagreed or strongly disagreed. These results
provide a baseline, upon which improvements can potentially be made by improving aspects of
the learning environment.
After establishing this baseline, the second research question was analyzed in two ways:
First, participants were asked directly how impactful each of the five aspects of the learning
environment would be on their likelihood of graduating with an engineering degree. Second,
participants were asked to assess how each of these five aspects of learning environment
impacted their grasp of the materials being taught. The latter question was designed to reinforce
the first, by providing an indirect assessment of students’ propensity to graduate, based on the
premise that a solid grasp of course material is necessary in order to successfully earn a degree.
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Results of the first (direct) question indicated that being able to work on projects that
have a societal impact would have the strongest positive influence on women engineering
students successfully completing their degrees. Participants also expressed that having women
instructors and women course support staff would also have a positive effect on completing their
degrees. The ability to ask anonymous questions was less impactful, while gender-neutral
language had the weakest impact on students’ likelihood of completing their engineering
degrees. These results are presented in Table 9.
Table 10
Impact of Learning Environment on Completing Degree
Impact n M SD
Women instructors 183 3.61 0.98
Women support staff 179 3.60 0.93
Gender-neutral language 177 3.18 0.92
Anonymous questions forum 175 3.45 0.97
Societal impact projects 175 3.78 0.99
Results of the second (indirect) question showed that students’ grasp of course materials
was most strongly impacted in a positive way by having an anonymous forum in which to ask
questions. Societal impact projects also showed a strong correlation to the students’ grasp of the
materials. With means at or above 3.60, having women support staff and women instructors also
correlated somewhat to a better grasp of course materials, whereas gender-neutral language was
not significantly correlated to grasp of the subject matter. These results are summarized in Table
10.
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Table 11
Impact of Learning Environment on Grasp of Course Content
Impact n M SD
Women instructors 183 3.46 0.89
Women support staff 179 3.69 0.94
Gender-neutral language 177 3.07 0.90
Anonymous questions forum 175 3.98 0.97
Societal impact projects 175 3.75 0.94
Qualitative Findings
In the pursuit of determining how aspects of the learning environment impact
accomplishing their degree, the interview protocol contained questions about gender-neutral
course curriculum, communication strategies for getting help, helpful teaching techniques, and
experiences attending office hours. The first theme that surfaced was the overall use of genderneutral language in the course content and during lectures. The next theme was the preference
and use of an anonymous questions forum that enabled students to get help without having to
raise their hands and speak during lectures. The last theme was gender representation (i.e., seeing
women) amongst their professors and course support staff such as teaching assistants.
Gender-Neutral Language
All eleven interview participants expressed that gender-neutral language was used in
syllabi for their courses. None of the respondents directly linked the use of gender-neutral
language to their likelihood of completing their degree, nor to their grasp of the materials.
However, there was a general sense amongst the participants that gender-neutral language and
the inclusion of women in course materials was appreciated and valuable. On this topic Grace
offered, “I was actually pretty happy to see Tina Trojan on one of my exams… instead of like
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Tommy all the time. It's like Tina Trojan telling Billy Bruin that he, like, messed up. It's like
something small that's just nice to see it mentioned.” Parisa echoed a similar sentiment: “I would
say it's pretty gender neutral, for the most part and honestly I kind of like it that way because, at
least what we're doing, it doesn't need to lean one way or the other. I would say that I would
love to hear more about women in computer science.” Ada said that her course materials in
university seemed more gender-neutral than those used in high school: “I have noticed in our
assignments they always use both he and she pronouns which is nice to see. Whenever the
language that my professors have used is inclusive of everyone, which is big change from high
school because in high school I felt like professors always made engineers ‘he’ or doctors ‘he did
this,’ and the moment you think of a doctor or something, a man comes to mind.”
Anonymous Questions Forums
Ten out of the 11 students interviewed said that they benefited from having an
anonymous way to ask questions regarding course content. Gwynne did not feel comfortable
asking questions to male instructors or support staff. “I feel like a lot of times you don't ask
questions to them, either because if you ask us, you know get like a 20-minute explanation on
the question that you need answered. ... if I had a woman, I could ask my question and get an
answer for sure.” Margaret also expressed having anxiety when asking questions in class and
online. “My first CS class, I think, is where I felt the most anxiety about asking questions, as
there were only three girls in the class.” Katherine’s response underscored the importance of
anonymous questions forums: “It has the anonymous feature, and I always ask anonymously. I
would definitely never use it if it wasn't anonymous.”
Parisa was the one respondent who did not see value in the anonymous questions forum.
Her opinion of it was shaped by one negative experience; she said the response she received on
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the forum was highly condescending. As such, she said that she will not use the forum again,
regardless of anonymity. However, she found that certain instructors and learning assistants were
much more approachable than others; once she feels comfortable, she prefers to just ask her
questions in person. “If I have questions, I usually go to office hours and just talk to them.
Sometimes I just go for career advice and stuff like that.”
Gender Representation Amongst Instructors and Staff
A majority of the participants agreed that having women instructors and support staff
improved their sense of acceptance within their engineering programs and motivated them to
engage more deeply. Mae noted, “I do think having women faculty or even just TAs is really
helpful in making the environment more comfortable.” Peggy had a positive experience with a
woman professor and decided to take an elective course taught by the same professor: “And I’m
totally going to look again to see what other courses or any other electives just because I love
how she teaches. She's also one of the most understanding professor ever.” Grace stated:
I would like to see some more women as my professors, but I understand that it is a maledominated field, so this is to be expected. It's not something that's shocking to me, but I
would still love to see more women leading my classes.
Margaret had only had one female professor, about whom she said, “that [professor] was
cool, but all my other programming professors have been White men.” Margaret added that she
has experienced greater gender diversity amongst the course support staff than amongst her
professors. Lillian experienced mostly male teaching assistants and said that she would like to
see more women and more teaching assistants of any gender who share her ethnic background
(Latinx).
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Grace mentioned that gender parity doesn’t apply just to instructors and staff. She
reported that having other women as classmates improved her comfort level in university. She
said, “In CS, at least, I’ve been really lucky to meet a lot of women and it's been great and
makes the experience a lot easier. It makes it easier to talk about concepts, to approach them,
and to ask them questions.”
Jade had a negative experience with a woman engineering professor. “She was very
unfeeling, very robotic. It was hard to progress.” Jade went on to say that one of her more
memorable positive experiences in university was actually with a male professor: “Having a
good professor made a huge difference because I was constantly asking questions they never
made me feel bad.” Jade’s overall takeaway was that connecting with an instructor is more
important than the gender of the instructor. Grace made a similar observation, noting that one of
her male professors knew her name and seemed to really care about her and her education. Grace
said that she wanted more professors who cared about their students, and with whom she could
make a personal connection.
Summary: Research Question Two
This study has shown that women engineering students are already quite likely to persist
until graduation. However, further improvements can potentially be made by tweaking certain
aspects of the learning environment. In particular, improving access to projects with a perceived
societal impact would likely have the strongest positive influence on students’ chances of
completing their engineering degrees, while providing anonymous question forums would likely
have the strongest positive impact on the students’ grasp of course materials. There was strong
agreement amongst the study participants that developing a connection with their instructors and
support staff, and having women serve in these roles, are key drivers of their educational success.
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The third research question extends this analysis further, by asking which aspects of the learning
environment could potentially be adjusted in order to improve the likelihood that women
engineering students will go on to pursue a career in engineering post-graduation.
Research Question Three
The third research question asked, “What aspects of the learning environment improve
the likelihood that women engineering students will pursue engineering careers after
graduation?” One of the desired behaviors of these students is to pursue careers in the
engineering and computer science fields. The quantitative results report the percentage of
students planning on pursuing a career that matched their degree as well as the learning
environment aspects that would impact this behavior. The qualitative findings describe the
concerns of the participants and potential improvements to the learning environment regarding
engineering careers.
Quantitative Results
In the quantitative survey, participants were first asked their agreement with the
statement: “I plan on pursuing a career in engineering or computer science after graduation.” Of
the 187 participants who responded to this question, 79% agreed or strongly agreed with this
statement while 16% were undecided and 5% disagreed or strongly disagreed. A total of 187
students answered the question and the mean was 4.23 with a standard deviation of 0.94. The
statistics showing the intent to pursue engineering careers based on the categories from Table 2
are shown in Table 11.
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Table 12
Pursue a Career by Category
Comparison n M SD df t F p*
All 187 4.23 0.94
RI-NW
RI-W
130
56
4.25
4.16
0.92
0.99 1 -0.60 0.39 .536
UL-L
UL-U
100
87
4.25
4.21
0.89
0.99 1 -0.31 0.10 .755
T-N
T-Y
162
25
4.22
4.28
0.93
0.98 1 0.28 0.08 .775
M-CS
M-E
47
140
4.40
4.17
0.77
0.98 1 -1.67 2.19 .141
Note. 95% CI utilized for calculating p values.
The analysis from Table 11 shows that the likelihood of pursuing a career in engineering
differs across students belonging to various categories. Although there is no statistically
significant difference, the analysis showed that computer science students are more likely to
pursue careers pertaining to their degrees (M = 4.40, SD = 0.77) than their non-computer-science
peers (M = 4.17, SD = 0.98). The computer science students at this engineering school also had a
higher sense of engineering identity (M = 3.96, SD = 0.76) and are more likely to earn
engineering degrees. Earning engineering degrees and having engineering careers are the desired
behaviors for this study.
These results serve to establish a baseline, upon which various aspects of the learning
environment might be tweaked to improve desired behaviors and outcomes with respect to
women students’ pursuit of engineering careers after graduation. The study asked students to
directly assess how each of the five aspects of the learning environment under study might
impact their likelihood of pursuing an engineering career. Of these five aspects, the one with the
strongest potential impact on students’ career choice was the availability of societal impact
projects, with 73% of respondents agreeing or strongly agreeing with the assertion. Gender
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representation amongst both instructors and, to a lesser degree, support staff were also observed
to have a positive impact on engineering career pursuit, with 72% and 70% of respondents
respectively agreeing or strongly agreeing with the assertion. The use of gender-neutral language
in course materials and the availability of anonymous questions forums were found to be the
least impactful on students’ career choice. These results are summarized in Table 12.
Table 13
Impact of Learning Environment on Pursuing Career
Impact n M SD
Women instructors 183 3.91 0.94
Women support staff 179 3.73 0.90
Gender-neutral language 177 3.25 0.93
Anonymous questions forum 175 3.27 0.94
Societal impact projects 175 4.02 0.93
Qualitative Findings
During the interviews, the participants answered questions to determine the aspects of the
learning environment that would improve the likelihood of them pursuing engineering careers.
The questions included sharing influential experiences with professors and experiences working
on group projects. The findings are grouped into three themes: social impact problems,
instructors as career counselors, and diversity hire concerns.
Societal Impact Projects
The importance of hands-on, real-world projects with a clearly-perceived and positive
impact on society was a common theme amongst the participants of this study. Margaret felt that
social impact was a key component of her education and eventual pursuit of a career: “I have a
lot of questions about making my career socially meaningful.” Margaret went on to say that she
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would like to have a mentor who could assist her in understanding the positive impacts she could
have as an engineer. She stressed that she chose UES in part because of its focus on projects with
societal impact. Grace also described projects with real-world applications as a key factor in both
her enjoyment of her engineering studies, and her choice of university. Grace stated:
Every time I do a project, I feel super good about myself, even though it was the hardest
thing I’ve probably ever done up until this point. … I think UES in general does such a
good job with really emphasizing projects and in the actual application of our work, so I
really like that part about UES.
Peggy stressed the importance of understanding the connection between theoretical
subject matter and its concrete applications: “[I] love my fluid dynamics professor because he
made every single problem super relevant. Obviously he had to super simplify it to be an
undergrad-level problem, but he used real-world examples and used his experience to make it
relevant.”
Lillian expressed a desire to solve some of society’s most challenging problems in her
engineering career: “I also like to talk about climate change, and all these differences and things
we could do to better the world and, be more eco-friendly and be on the same scale like sharing
humanity with nature and vice versa.” She went on to express an interest in melding her
engineering knowledge with soft skills, in order to reach beyond engineering in her career. “To
be an engineer, but also be like a community activist and be involved in the community, it
doesn't always have to be all the super tech work.” She asked of herself, “How do I create
methods and systems to help the community to find solutions to problems?” and concluded, “I'm
like, oh my god yes, this is the way it is; this is what an engineer is meant to do.”
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Instructors as Career Counselors
Some respondents noted that their instructors played an important role in helping them to
forge a career path. Kristen reported that her most influential professor not only helped her with
coursework, but also gave her career advice. Gwynne also reported that her professors gave good
career advice.
Diversity Hire Concerns
Five interview participants expressed concerns about being a diversity hire when
pursuing their STEM careers. These women experience hearing comments from their male peers.
Margaret expressed her concerns this way:
One thing I was going to talk about as far as being like a woman in tech that I get a lot is
the idea that you're just here because of some like diversity program, the idea of just
being a diversity hire, being under-qualified to be there. I've talked to even some of my
male friends who are racial minorities, about being here. There's definitely a fear of
being perceived in that way, and so I think that's been pretty influential on my
experience as a woman in technology because I feel like I need to be proving myself all
the time. I know that's what people see. I've even got comments straight to my face
where people are like, ‘I bet it's going to be easier for you [because you’re a woman].’
Parisa spoke of a double standard for women pursuing engineering as a career: “I think it's like
an unwritten rule in engineering overall that as a woman, if you want the same job or the same
position as a male, you need to be 10 times better.”
Summary: Research Question Three
This study established an encouraging baseline, showing that women engineering
students are already quite likely to pursue careers in engineering after graduation, with transfer
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students being slightly less like to do so than their non-transfer counterparts. Self-efficacy
amongst women engineering students appears to increase the longer these students persist in
their university studies, suggesting that prior successes serve to build and reinforce self-efficacy.
Engineering identity and self-efficacy also appear to be somewhat higher overall for computer
science students, when compared with their non-computer-science counterparts. Research
question three showed that of the five studied aspects of the learning environment, the
availability of societal impact projects had the strongest potential to positively impact students’
likelihood of pursuing an engineering career post university. Gender representation was also
shown to be important to students’ desire to pursue engineering as a career.
Summary of Results
This dissertation studied five aspects of the learning environment, with the goal of
understanding which of these aspects have the greatest potential to produce positive impacts on
three desirable behaviors and cognitive outcomes amongst women engineering students. The
study indicated that baseline levels of engineering identity and persistence in engineering
amongst the women engineering students studied is already reasonably strong. However, there is
still room for improvement especially with regards to long-term persistence in engineering
(degree attainment and career pursuit).
Results of the study also provided answers to the three research questions: First, results
showed that having women instructors, women support staff, and access to projects with
perceived societal impact are most likely to positively impact engineering identity. Second,
results indicate that access to societal impact projects has the strongest direct positive correlation
to degree attainment. The opportunity for students to learn from women was shown to also have
a positive impact on students’ likelihood of graduating in engineering. Results also suggest that
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grasp of engineering course materials is most strongly impacted by students’ access to
anonymous questions forums; societal impact projects also appear to have a positive impact on
students’ understanding of engineering concepts. Third, this study’s results indicate that societal
impact projects have the strongest link to career pursuit amongst women engineering students.
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Chapter Five: Recommendations
Despite strong gains over the past few decades, women remain underrepresented in mathintensive STEM fields such as engineering and computer science. The goal of this study was to
identify ways in which the learning environment can be modified to improve gender parity in
computer science and engineering, by reducing pipeline leakage and by improving women’s
sense of engineering identity. Chapters One, Two and Three outlined the problem of practice and
the theoretical framework which guided the study, surveyed the extensive literature on the topic
of gender parity within the STEM pipeline, and outlined the mixed-methods approach utilized to
collect data. Chapter Four detailed the results of the study along both the quantitative (N = 225)
and qualitative (N = 11) dimensions. Chapter Five recommends six specific changes to the
learning environment. Based on the literature and confirmed by the results of this study, these six
changes appear to be most likely to produce positive improvements in women students’
engineering identity and to reduce leakage of women from the STEM pipeline. Chapter Five
concludes by analyzing the limitations and delimitations of the present study and offers
suggestions for future research.
Recommendations for Practice
Social cognitive theory (SCT) was the theoretical framework for both the study and the
recommendations presented in this dissertation. This framework asserts that there exist
bidirectional links between an individual’s cognitive state, the environment in which she finds
herself, and her behaviors. In this study, five aspects of the learning environment were analyzed
to determine their impacts on women students’ sense of engineering identity (a desired cognitive
outcome) and also their impacts on women’s likelihood of both completing their degrees in
engineering and going on to secure a career in engineering post-graduation (desired behavioral
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outcomes). The five aspects of the learning environment studied were: Students’ access to
women instructors, students’ access to women support staff, the use of gender-neutral language
in course materials, the availability of anonymous questions forums, and access to hands-on
projects with perceived positive societal impact.
The results of the quantitative study showed that women’s sense of engineering identity
was most positively influenced by the presence of women instructors and support staff within the
learning environment. The twin goals of women completing their degrees and pursuing careers in
engineering and computer science (reduction of STEM pipeline leakage) were most positively
impacted by women’s access to societal impact projects. Women engineering students’
persistence and career pursuit were also positively impacted by improved gender parity within
the learning environment (women instructors and staff). Grasp of materials was shown to be
positively impacted by anonymous questions forums.
The qualitative study expanded greatly on these results. In their one-on-one interviews,
the study participants shared valuable insights and rich details about the aspects of the learning
environment which had the most positive or negative impacts on their desire to pursue an
education in STEM in the first place, the development of their sense of self-efficacy, their
persistence with their engineering undergraduate studies, and their propensity to purse
engineering as a career. Based on these results and the concrete suggestions provided by the
interviewees themselves, the following six recommendations were developed as shown in Figure
2.
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Figure 2
Recommendations Based on Findings
Recommendation 1: Teaching Techniques
A recurring theme amongst the responses obtained from the study’s participants was the
importance of presenting information in a personal, interactive, and meaningful manner. Grace
suggested that her retention and grasp of lecture materials is improved by applying those
concepts to hands-on projects. Emmy also noted the value of hands-on experience in the learning
environment. Parisa and Ada both expressed a preference for visual demonstrations over verbal
explanations. Ada added that she values live interactive coding sessions over seeing code
snippets presented in written lecture materials. Mae echoed similar sentiments, expressing a
desire for visual demos and group activities to reinforce and enhance the learning experience.
Traditional teaching methods in engineering subjects tend to be dry, analytical, and often
lack societal context. These methods tend to appeal more to male audiences (Anderson, 1994).
Based on the feedback obtained in the current study, women engineering students clearly value
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an interactive and personal approach to presenting course material that stresses the meaning and
practical applications of the material being taught.
Two additional ideas for women-focused improvements to the learning environment
arose out of the qualitative study. Margaret expressed an interest in professors providing
recordings of their live lectures to students after the lecture, as a matter of routine practice. Doing
so would allow students like Margaret to review the materials post-facto in a non-linear fashion.
Lillian suggested an increased use of in-class polling software. Such software encourages realtime communication between professors, teaching assistants, and students. Some of the students
surveyed expressed discomfort with speaking up in class but said they would be more likely to
respond to a live online poll. Allowing students to view recordings and respond to in-class polls
is an effective alternative to asking questions during class for those who are uncomfortable doing
so.
Recommendation 2: Societal Impact Projects
In the preceding section, a recommendation was made to present course materials in a
hands-on manner that stresses how the theoretical concepts are applied in the real world. A key
dimension of this topic is an exploration of the societal impacts of technology. One of the
strongest signals obtained from the quantitative study was that women engineering students
appear less likely to leak from the STEM pipeline when they have a firm sense of how their work
as an engineer will positively impact society. The opportunity to work on projects with societal
impacts was the aspect of the learning environment most strongly correlated with women
engineering students completing their degrees, and 73% of respondents said that working on
projects with a clear positive societal impact also improved their likelihood of pursuing a career
in engineering. Another strong quantitative signal was that explanations of the societal impacts
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of engineering aided women students’ grasp of the materials. As such, it is recommended that
undergraduate engineering programs increase their focus both on providing projects with societal
impact and on stressing the societal implications of engineering in course lectures and course
materials.
The study’s qualitative findings reinforced this recommendation. Margaret, Grace, Peggy
and Lillian all mentioned the importance of working on hands-on projects with a clear positive
societal impact. These students all said that societal impact projects improved both the quality of
the women’s education and their desire to pursue a career in engineering.
This conclusion is also well-supported by the literature: Women are more likely to
choose engineering as a career goal when they understand the potential for engineers to have a
positive impact on society (Canney & Bielefeldt, 2015; Godwin, 2016). Women engineering
students reported more satisfaction with their educational experience when societal impacts are
stressed (Anderson, 1994). Engineering identity is also correlated positively with a student’s
understanding of the positive societal impacts of engineering (Ro & Knight, 2016; Tallman et al.,
2019). Engineering teaching methods that stress applications, context, and societal impacts
should appeal more to women, and thereby help improve their persistence in STEM. If applied
consistently over an extended time period, this approach has the potential to combat the general
societal perception that engineering either has little impact on the wellbeing of society or is
actively harmful to societal goals (Diekman et al., 2010).
Recommendation 3: Community Building
Recommendations 1 and 2 arise from the observation that women engineering students
respond best to theoretical materials when they are clearly linked to their real-world applications,
and in particular to their positive societal impacts. A central idea here is that women engineering
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students seem to favor and benefit from a focus on the human aspects of technology. A related
idea is that when the educational experience itself has an increased focus on human interactions
and connections, women will be more likely to thrive and to build a strong sense of engineering
identity.
Variations on this theme came through repeatedly in this study’s qualitative findings.
Some respondents expressed a strong desire to become involved in workshops and clubs. Grace
wanted to see a workspace for women that was more than just physical. In community college,
she created a Discord server for all of the women in the computer science program; she would
like to see this kind of collaborative culture continue at this engineering school.
Recommendations also appear in the literature regarding the formation of clubs and other
community-building organizations to support women in STEM educational environments
(Waychal & Henderson, 2018). The literature also cites the need to acknowledge and address the
role of racism as it intersects sexism, especially at the university level (Allen et al., 2022).
Simmons and Lord (2019) also recommended adopting an intersectional approach to supporting
women in STEM, taking into account not only gender but also race and ethnicity. Ensuring that
undergraduate engineering students are aware of support organizations such as the National
Society of Black Engineers (NSBE), the Society of Hispanic Professional Engineers (SHPE), and
the American Indian Science and Engineering Society (AISES) should help to provide these
much-needed supports.
Mentorship was another form of human connection mentioned by multiple respondents.
Lillian said she wants to connect with senior undergraduate student as peer mentors. Margaret
expressed a desire to find a mentor with whom she can be honest and wanted to build more than
just a superficial relationship. She also suggested matching mentors to students based on their
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career goals. Peggy said that her peer and industry mentors in high school were very helpful; she
now mentors high school students herself. Peggy stressed that mentors help students to see the
possibilities of pursuing a STEM education. Both Kristen and Gwynne echoed the value of
mentors, both in terms of understanding the material and in terms of providing career guidance.
Respondents also repeatedly described a strong desire to make personal connections with
their peers and their instructors. Katherine said she was looking for ways to meet other
engineering students. Both Jade and Grace told stories in which they had made a strong human
connection with an instructor, and both reported the value of knowing that their instructors cared
about the success and well-being of their students. This idea also appears in the literature.
Waychal and Henderson (2018) recommended educational approaches that involve building
increased connections between faculty and students.
The literature also recommends an increased focus on cooperative learning environments
that encourage class participation. Godwin et al. (2016) suggested that real-world applications of
technology should be taught via live demonstrations and discussions that stress student
involvement. Ro and Knight (2016) reported that increased use of student-centric teaching
methods resulted in higher reported self-efficacy amongst women engineering students.
Recommendation 4: Women Instructors and Staff
Another result of this study is that women engineering students tend to benefit in multiple
ways from the presence of other women in the educational environment. An increased number of
women instructors and support staff was shown to be positively correlated with improved
engineering identity and an improved sense of belonging amongst women engineering students
(Research Question One). Gender parity amongst instructors and support staff were correlated
well with women students’ likelihood of completing their degree, and with their grasp of course
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materials (Research Question Two). Career pursuit was also correlated to the prevalence of
women professors and staff (Research Question Three).
Aiming for greater gender parity in the learning environment makes sense. SCT shows
that learning often comes from observation, imitation, and modeling. As such, having more
women role models ought to have a positive influence on student outcomes. Women professors
and staff can also serve as mentors, offering advice and conferring wisdom. Gender parity in the
learning environment also helps to bring efficacy expectations into alignment with outcome
expectations through vicarious experience: Seeing other women achieve degrees and careers in
engineering helps women students to recognize their own potential to achieve the same
outcomes. When more of their peers are women, students can more easily build an improved
sense of belonging, resulting in stronger self-efficacy and engineering identity. Improvements in
gender parity amongst instructors and support staff can also have a self-reinforcing effect: As
more women earn degrees and pursue careers in engineering, these women can go on to serve as
role models either by becoming instructors themselves or by providing more examples of women
succeeding in the engineering industry. This idea is reinforced by the literature. Waychal and
Henderson (2018) described the importance of female role models and mentors as a means of
supporting women engineering students. Reinking and Martin (2018) reported that exposing girls
to women role models in STEM improves their interest levels and sense of self-efficacy within
these fields.
These same principles can be applied to the presence of instructors and staff whose racial
identity more closely matches that of the student body (Simmons & Lord, 2019). Women of
color are more likely to face barriers when pursuing an education and career in engineering,
relative to their white counterparts, due to the compounding effects of racial discrimination (Kim
83
& O’Brien, 2018). Allen et al. (2022) recommended that racially marginalized students be given
support in forming strong bonds with their peers, mentors, and instructors. Colleges and
universities should make the presence of people of color in mentorship, advisor, and faculty roles
an explicit goal (Stitt & Happel-Parkins, 2019).
Another aspect of gender parity is the use of gender-neutral course materials. This aspect
of the learning environment was less-strongly correlated to the desired outcomes of improved
engineering identity and persistence in the STEM pipeline in the qualitative study. However, the
participants in the qualitative study repeatedly mentioned the positive impacts of seeing genderneutral language and examples in their course materials, lectures, and projects. Grace mentioned
how pleased she was to see the character of Tina Trojan appearing in her course materials as
opposed to Tommy Trojan. Multiple respondents noted that the course materials were more
gender-neutral than in high school and expressed strong support for this trend.
Recommendation 5: DEI Training
Results of the qualitative segment of this study reinforced the notion that gender bias,
microaggressions, stereotype threat, and stigma consciousness pose significant barriers to
women’s success in engineering. Gender bias and microaggressions are external barriers: Gender
bias occurs when people with whom a woman engineering student interacts believe (falsely) that
engineering is inherently a male-centric field of endeavor, thereby discouraging the student from
pursuing education or careers in the field (Green & Sanderson, 2018) or preventing her from
being accepted into an educational program or job (Moss-Racusin et al., 2012). Gender bias can
also manifest as teaching methods that favor men, or as course content depicting engineering as
being practiced primarily by men (Anderson, 1994). Stereotype threat and stigma consciousness
84
are internal barriers: Women sense that they must conform to male-centric behavioral patterns
and male-centric communication styles to “fit in” as an engineer (Cheryan et al., 2015).
All interview participants expressed frustration with having experienced gender bias,
microaggressions, and stereotype threat. Gwynne reported having to match the behavior of her
male colleagues, such as their level of confidence and aggression, to gain their respect. Parisa
experienced male students talking over her, especially in group projects. She said that male
group members often didn’t listen to her ideas and assumed she was stupid. Ada said of the male
course support staff, “They were just very condescending and you can tell, they were like ‘tech
bros.’” While studying in the library with a fellow female student, Mae experienced
microaggressions and sexist comments from a male student.
One way to reduce the external barriers of gender bias and microaggressions is with
diversity, equity, and inclusion (DEI) training. Professors and staff should be encouraged to
acknowledge that sexism and racism both still exist, and to take deliberate steps to prevent them.
Educators, particularly men, should be provided training in how to adjust their communication
style when working with women in order to reduce the impression of condescension and
competitiveness, to reduce the intimidation factor, and to encourage collaborative approaches to
learning. Instructors, mentors, and advisors should likewise be trained to recognize, acknowledge
and address the additional challenges facing racial minorities within the student body (Allen et
al., 2022; Simmons & Lord, 2019). Students should also be provided a similar form of DEI
training.
Internal barriers such as stereotype threat and stigma consciousness can be overcome in
various ways. Gender-neutral course materials and an increase in the number of women
instructors and staff can help women to see that they have a place in the engineering field. Clubs
85
and organizations on campus that foster camaraderie and a sense of belonging for women
engineering students are another means of combating internal barriers. A reduction in external
factors such as gender bias and microaggressions can also have a synergistic effect on women’s
ability to overcome internal barriers.
Recommendation 6: Training to Counteract Perfectionism
A key barrier described in the literature and reinforced by the results of this study was the
dual problem of perfectionism and unjustifiably low self-efficacy amongst women engineering
students. Woodcock and Bairaktarova (2015) found that women tended to greatly underestimate
their ability to perform engineering tasks. Women also seem to have a stronger tendency to hold
themselves to impossibly high standards (Anderson, 1994). Pipeline leakage results in part from
a tendency of women to quit in the early years of their engineering education after having
experienced initial failures (Ball et al., 2017; Heilbronner, 2011). Research has consistently
demonstrated that improvements in self-efficacy are strongly correlated to increases in
persistence, rates of graduation, and the pursuit of careers in engineering amongst women
engineering students (Evans et al., 2020; Green & Sanderson, 2018; Heilbronner, 2011).
These results were reinforced by the current study. Grace reported a tendency to be too
hard on herself and recognized that success in engineering comes not only from developing
technical ability but also from building character and personal resilience. Jade explained that she
had suffered from the detrimental effects of perfectionism throughout university and said that she
wished she had received training or guidance explicitly targeted towards combating
perfectionism. Lilian also recognized the dangers of holding oneself to unrealistically high
standards; in recent years she has been focused on developing a growth mindset.
86
Heilbronner (2011) reported that women engineering students who viewed their skillset
as malleable were more likely to earn their degree in engineering than their counterparts who
viewed their skills as innate and immutable. Women who viewed their failures as learning
opportunities were more resilient and persistent than those who interpreted failures as an
indication that they lacked the necessary talent to succeed in a STEM field. This result points to a
potentially effective means of reducing STEM pipeline leakage: First-year women engineering
students should be provided with training and/or workshops specifically targeted at reinforcing
an incremental view of intelligence and skillsets. An open discussion forum would likely yield
positive results by providing a supportive environment in which young women can learn from
one another. First-year women engineering students should be given the opportunity to hear
success stories from more-senior women students and graduates.
Limitations and Delimitations
The results of this study provide a sense of direction and focus as we pursue
improvements in the desired outcomes: Women students attaining a stronger sense of
engineering identity, more women graduating with a degree in engineering, and more women
pursuing a career in engineering after graduation. However, the scope of this study was
necessarily limited, due to time constraints and IRB restrictions. All of the participants in this
study were current students within the University Engineering School (UES), and the sample size
was quite small (only 211 women started the survey; 11 students were interviewed). The
computer science department and its associated degrees are contained within the UES thus my
study focused on students pursuing engineering and computer science degrees.
For a long time, I had been interested in why students pursued their majors and how they
were influenced by their internal thinking. I did not enter my postsecondary school as an
87
engineering major due to negative internal thinking. I was drawn to SCT since it provided a
framework with the key concepts I want to study such as engineering identity, self-efficacy, and
outcome expectations. Focusing on undergraduate students allowed me to research those students
first getting into the fields of engineering and computer science.
For the anonymous survey, I was not able to control the truthfulness of the respondents
and if they chose to complete the entire survey. The introduction to the survey included the
importance of being honest and thorough. The analytical analysis of the survey was limited by
the number of participants, although the survey was available to all undergraduate engineering
students who identified as women at UES. The global pandemic caused by COVID-19 was
affecting communities across the world. To reduce the risk of infections, all interviews were
conducted over an online platform. I relied on the internet connections of the participants.
Knowing the limitations and delimitations allowed me to create back-up plans and clearly
defined the focus of this research.
Future Research
The reliability of the results could be improved by repeating the study with a larger and
more varied sample of women engineering students, across multiple universities across the
United States (or even worldwide). Additionally, a future study should investigate a wider
variety of learning environment aspects, and each aspect could be explored in more depth. A
longitudinal study that evaluates students when their first year of their engineering education and
their final semester would be beneficial to capture those students who left engineering during
their undergraduate education.
One barrier described in the literature that also came through in this study was the
disproportionate tendency toward perfectionism amongst women engineering students relative to
88
their male counterparts. This is potentially a rich area of new research. Studies should be
performed to establish the strength of the correlation between a growth-oriented mindset and
women students’ likelihood of graduating and attaining a career in engineering. An exploration
of the factors that lead to disproportionate incidence of perfectionism amongst women would be
appropriate.
Further research could explore the effectiveness of various mitigation approaches. What
changes to the learning environment could help to combat perfectionism amongst women
engineering students? How can we educate young women to change their attitude towards
failures? The most promising strategy or strategies could be implemented in the form of a pilot
study at selected institutions. The end goal of such research would be to establish a standard
protocol for providing appropriate training and resources to women engineering students to
combat the detrimental effects of perfectionism and to foster higher levels of self-efficacy
amongst women engineering students.
Conclusion
Despite decades of research and efforts to improve the situation, women remain
underrepresented in postsecondary engineering programs and engineering careers in the United
States. While women earned a majority (57%) of the bachelor’s degrees conferred in 2019, only
23% of the engineering graduates in that year were women. The fields of engineering and
technology continue to grow at a rapid pace, and job opportunities are only increasing. However,
if the problem of gender disparity in computer science and engineering is not addressed, men
will continue to dominate these fields.
A multitude of factors contribute to the problem of gender disparity in engineering. The
focus of this dissertation was inspired in part by the article “Stop Telling Women They Have
89
Imposter Syndrome” (Tulshyan & Burey, 2021). Rather than study internal factors such as
imposter syndrome and low self-efficacy, this dissertation focused on the externalities that
impact women engineering students. In particular, the goal of the study was to identify those
aspects of the college learning environment that have the highest potential to reduce the number
of women who leak from the STEM pipeline and therefore never obtain a degree in technology
or go on to pursue a career in engineering or computer science.
The survey asked students to rate the impact of five aspects of their learning environment
on their identity as engineering students, their grasp of course content, their likelihood of
completing an engineering or computer science degree, and their likelihood of pursuing careers
in technology after graduation. The five learning environment factors under study were: the
opportunity to be instructed by women, access to women support staff, the use of gender-neutral
curriculum, the availability of anonymous forums for asking questions, and opportunities to work
on projects with a clear societal impact.
Women belong and are needed in engineering and computer science. Since the birth of
computer science as a field of study, women have played a pivotal role. Their unique
perspectives as engineers are crucial to solving the many grave problems facing our world today.
By improving the college learning environment for women students, gender representation in
these critical fields can be significantly improved.
90
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Appendix A
Survey Protocol
Research Questions
1. How do the outcome expectations of women students influence their decisions to pursue
engineering and computer science degrees?
2. How does the self-efficacy and interests of women engineering students impact them
during their postsecondary education?
3. What are the barriers and supports that women engineering students encounter and how
does this impact them?
Consent
We are conducting a research study focusing on the experiences of engineering undergraduate
students who identify as women and non-binary. This survey contains questions to evaluate
outcome expectations, goals, and interests, which are your personal beliefs concerning your
major. It also contains questions about supports and barriers you have encountered at this school.
The study should take you around 15 minutes to complete. Your participation in this research is
voluntary. You have the right to withdraw at any point during the study. To participate in this
study, you must be at least 18 years old and identify yourself as a woman or non-binary. The
survey will not ask for your name or contact information. Your responses will be kept
completely confidential.
98
The principal investigator of this study is Trina Gregory, and she can be contacted at
trinagre@usc.edu.
Please click the link below to begin the survey. Thank you!
Table A1
Survey Instrument
Survey Question Response Options RQ Concept
1. What is your gender
identity?
Man
Non-binary
Woman
Prefer not to say
Other (please specify) [with
text entry]
N/A Qualification
2. What is your racial or
ethnic identification?
American Indian,
Indigenous, or Alaska
Native
Asian
Black or African American
Chicana/o, Hispanic, or
Latina/o
Native Hawai’ian or Other
Pacific Islander
White
Two or more
Prefer not to say
Other (please specify) [with
text entry]
N/A Demographics
3. What is your current
grade level?
First-year Undergraduate
(Freshman)
Second-year Undergraduate
(Sophomore)
Third-year Undergraduate
(Junior)
Fourth-year or Fifth-year
Undergraduate (Senior)
Graduate Student
Other (please specify) [with
text entry]
N/A Qualification
99
4. Did you transfer into
this school from
another college such as
a community college?
Yes
No N/A Background
5. What is your current
major?
Aerospace Engineering
Applied Mechanics
Astronautical Engineering
Biomedical Engineering
Chemical Engineering
Civil Engineering
Computer Engineering and
Computer Science
Computer Science
Computer Science/Business
Administration
Computer Science (Games)
Electrical Engineering
Environmental Engineering
Industrial and Systems
Engineering
Mechanical Engineering
Physics/Computer Science
Other (please specify) [with
text entry]
N/A Qualification
6. What do you plan on
graduating?
Spring 2022
Spring 2023
Spring 2024
Spring 2025
Other (please specify) [with
text entry]
N/A Qualification
7. Indicate how you feel
about the following
statements regarding
being an engineer.
[Slider]
a. My family see me as
an engineer
.
b. My instructors see me
as an engineer
.
c. My peers see me as an
engineer
.
d
. I see myself as an
engineer
.
1. No, not at all
2. Seldom
3. Sometimes
4. Often
5. Yes, very much
1 Engineering
Identity
8. Indicate your level of
agreement with each
1. Strongly Disagree
2. Disagree
3. Undecided
2 Self-Efficacy
100
of the following
statements. [Slider]
a. I plan to remain
enrolled in an
engineering or
computer science
major over the next
semester
.
b. I think that earning a
bachelor’s degree in
engineering or
computer science is a
realistic goal for me
.
c. I am fully committed
to getting my college
degree in engineering
or computer science
.
d. I plan on pursuing a
career in engineering
or computer science
after graduation
.
4. Agree
5. Strongly Agree
9. How often have you
experiences the
following in your
engineering courses?
[Slider]
a. Women instructors
(e.g., lecturers,
professors)
b. Women course support
staff (e.g., TAs, CPs,
graders)
c. Gender
-neutral
language in course
materials (e.g., syllabi,
slides, assignments)
d. An anonymous way to
ask questions such as
an online forum (e.g.,
Piazza, edstem)
e
. Course projects that
have a societal impact
1. Never
2. Rarely
3. Sometimes
4. Mostly
5. Always
1, 2 Gender
Representation
10. To what extent do you
disagree or agree with
how impactful having
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
1, 2, 3
Gender
Representation,
Engineering
101
women instructors is
regarding...? [Slider]
a. My identity as an
engineering student
b. My grasp of the course
content
c. The likelihood of
completing my
engineering or
computer science
degree
d. The likelihood of
pursuing a career in
engineering or
computer science
5. Strongly Agree Identity, SelfEfficacy
11. To what extent do you
disagree or agree with
how impactful having
women support staff,
such as teaching
assistants, is
regarding...? [Slider]
a. My identity as an
engineering student
b. My grasp of the course
content
c. The likelihood of
completing my
engineering or
computer science
degree
d. The likelihood of
pursuing a career in
engineering or
computer science
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
5. Strongly Agree
1, 2, 3
Gender
Representation,
Engineering
Identity, SelfEfficacy
12. To what extent do you
disagree or agree with
how impactful having
gender
-neutral language
in course materials is
regarding...? [Slider]
a. My identity as an
engineering student
b. My grasp of the course
content
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
5. Strongly Agree
1, 2, 3
Course
Curriculum,
Engineering
Identity, SelfEfficacy
102
c. The likelihood of
completing my
engineering or computer
science degree
d. The likelihood of
pursuing a career in
engineering or computer
science
13. To what extent do you
disagree or agree with
how impactful having
an anonymous way to
ask course questions is
regarding...? [Slider]
a. My identity as an
engineering student
b. My grasp of the course
content
c. The likelihood of
completing my
engineering or computer
science degree
d. The likelihood of
pursuing a career in
engineering or computer
science
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
5. Strongly Agree
1, 2, 3
Support
Strategies,
Engineering
Identity, SelfEfficacy
14. To what extent do you
disagree or agree with
how impactful having
projects with a societal
impact is regarding...?
[Slider]
a. My identity as an
engineering student
b. My grasp of the course
content
c. The likelihood of
completing my
engineering or
computer science
degree
d. The likelihood of
pursuing a career in
engineering or
computer science
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
5. Strongly Agree
1, 2, 3
Course
Curriculum,
Engineering
Identity, SelfEfficacy
103
15. What level of comfort
do you have with the
following activities?
[Slider]
a. Asking questions
during class to
professors who are
men
b. Asking questions
during class to
professors who are
women
c. Asking questions
during office hours to
course staff that are
men
d. Asking questions
during office hours to
course staff that are
women
e. Asking questions
anonymously for
course support
f. Working on group
projects for my
engineering courses
1. Strongly Disagree
2. Disagree
3. Undecided
4. Agree
5. Strongly Agree
1, 2, 3
Gender
Representation,
Support
Strategies
16. If you have had any
influential experiences
being a woman
engineering student
and would like to
share, please do so.
Open 1, 2, 3
Conclusion
Thank you for participating in this survey. Your responses were submitted anonymously. As part
of the study, I will like to interview approximately 10 undergraduate women students to get more
insights into their experiences. If you are interested in participating in a 1-hour interview over
Zoom, please click the link below to a different survey that will gather your contact information.
104
Appendix B
Interview Protocol
Research Questions
1. How do the outcome expectations of women students influence their decisions to pursue
engineering and computer science degrees?
2. How does the self-efficacy and interests of women engineering students impact them
during their postsecondary education?
3. What are the barriers and supports that women engineering students encounter and how
does this impact them?
Introduction
Thank you for meeting with me today. I’d like to introduce myself. I’m Trina Gregory, a
doctoral student at USC. I’m conducting a study regarding the experience of undergraduate
women pursuing engineering and computer degrees in higher education. I would like to have a
conversation with you about your experience. Your perspective is really valuable to this study.
I’m going to take some notes while we are talking to record your responses and come back to
them if I need to. I would also like to record, if that is OK with you. The purpose of the recording
is to make sure I capture your perspectives accurately. I will be the only one viewing the
recording, and all data, including your names and names you mention, will be kept confidential.
If you are more comfortable, you are welcome to keep your video off. May I record our
interview? Thank you.
105
Table B1
Interview Instrument
Interview Questions Potential Probes RQ
Addressed
Key
Concept
Addressed
Q Type
(Patton)
1. First, I would like to
know what is your
current major?
When do you plan
on graduating? N/A Goal Demographic
Transition: Thank you.
2. What was the major
you declared when
you first entered
college?
If you were
undecided, which
major did you think
you would most
likely choose at the
time?
N/A Goal Demographic
Transition: That is
impressive.
3. With your current
major, what are your
plans after earning
your bachelor’s
degree.
What is your career
goal five years
from now?
1 Outcome
expectation
Demographic
Transition: What an
exciting path you are
on.
4. When you first
entered college, how
would you have
described an
engineer [may use a
specific field such as
biomedical
engineering or
computer scientist]?
What is the career
path of an
engineer?
How has your
definition changed?
1
Outcome
expectation,
goal
Opinions and
values
Transition: Your
response is
intriguing.
5. How or when did you
become interested in
your major?
What or whom
influenced your
decision to pursue
your major?
1, 3
Outcome
expectation,
interest,
goal, support
Experiences
and behaviors
Transition: That sounds
that had a strong
impact on you.
106
6. What keeps you
engaged or interested
in continuing to
pursue your major?
Are they any
specific
experiences or
individuals you
would like to
share?
1, 2, 3
Goal,
interest, selfefficacy,
supports
Experiences
and behaviors
Transition: I would like
to hear about your
experiences in your
engineering courses.
7. As a student, would
you share with me an
influential
experience you have
had with your
professors?
How do you feel
women students are
treated by their
professors?
3 Barriers and
supports
Experiences
and behaviors,
feeling
Transition: That is very
interesting.
8. How would you
describe your
interactions with
your classmates in
engineering.
Tell me how your
interactions differ,
if at all, between
men and women.
2, 3
Selfefficacy,
barriers and
supports
Experiences
and behaviors
Transition: Now I’d like
to hear about your
experiences inside
and outside of your
engineering courses.
9. Describe the support
structures that have
been most helpful to
you in being an
engineering student?
These could
include student
organizations,
university- or
school-led
programs, faculty,
courses, and your
peers.
3 Barriers and
supports
Experiences
and behaviors
Transition: That sounds
helpful.
10. What are the
activities or
organizations, if any,
that specifically help
women in
engineering that you
have participated in?
What are the ones
you know about? 3 Barriers and
supports Knowledge
107
Transition: On the other
hand,
11. Describe any
challenges that you
have encountered
during your
undergraduate
engineering
education.
How, if at all, are
they related to you
being a woman?
If you have any,
what are some
suggestions to
improve this?
3 Barriers and
supports
Experiences
and behaviors
Transition: Thank you. I
know that it can be
difficult to reflect on
the negative side.
12. How do you think
engineering can
make a positive
impact on society
including the
environment?
Do you think this is
true for all
engineering
majors? Please
explain.
1, 2
Outcome
expectations,
goal, interest
Opinions and
Values
Conclusion
We have covered a lot of territory talking about your experiences as women engineering student.
Is there anything else you would like to share with me?
I am going to stop the recording now. It's been a pleasure meeting with you today. May I reach
out to you in the future if I have any future questions or need clarification on your responses?
Thank you so much for your time.
Abstract (if available)
Abstract
Women remain underrepresented in postsecondary engineering programs and engineering careers in the United States. The fields of engineering and technology continue to grow at a rapid pace, and job opportunities are only increasing. If the problem of gender parity in computer science and engineering is not addressed, men will continue to dominate these fields. The goal of the study was to identify those aspects of the undergraduate learning environment that have the highest potential to impact women students to earn engineering degrees and pursue associated careers. The data for this mixed-method study was collected through a survey and semi-structured interviews administered to women engineering students. Engineering identity can be improved by experiencing gender representation, participating in real-world projects, and viewing failures as learning opportunities. Having an anonymous questions forum and connections with course instructors and support staff will improve the likelihood of women engineering students completing their degrees. To increase the likelihood of these students pursuing careers, students need to reinforce self-efficacy and participate in societal impact projects. These findings indicate the need for interactive teaching techniques, community-building opportunities, gender representation in course staff, and training to combat perfectionism.
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Asset Metadata
Creator
Gregory, Trina Lynn
(author)
Core Title
Learning environment impact on women undergraduate engineering students
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Organizational Change and Leadership (On Line)
Degree Conferral Date
2024-12
Publication Date
09/16/2024
Defense Date
08/08/2024
Publisher
Los Angeles, California
(original),
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
Computer Science,degree completion,engineering,engineering identity,gender representation,higher ed,learning environment,OAI-PMH Harvest,perfectionism,social cognitive theory,societal impact projects,women in STEM
Format
theses
(aat)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Datta, Monique Claire (
committee chair
), Jones, Brandi (
committee member
), Maddox, Anthony (
committee member
)
Creator Email
trinagre@usc.edu;gregorygeeks@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC11399ASZW
Unique identifier
UC11399ASZW
Identifier
etd-GregoryTri-13529.pdf (filename)
Legacy Identifier
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Document Type
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Format
theses (aat)
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Gregory, Trina Lynn
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texts
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(batch),
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
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Tags
degree completion
engineering identity
gender representation
higher ed
learning environment
perfectionism
social cognitive theory
societal impact projects
women in STEM