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Mitigating the low enrollment rates for women in engineering and computer science
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LOW ENROLLMENT FOR WOMEN
Mitigating the Low Enrollment Rates for Women in Engineering and Computer Science
by
Cheryl K. DeMatteis
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
May 2018
Copyright 2018 Cheryl K. DeMatteis
LOW ENROLLMENT FOR WOMEN
ii
DEDICATION
To my family, you make everything possible.
To my nephew Wiley, for being the young man that you are. On the toughest days, I was
motivated to continue – to be the example that you deserve.
To my wife Lani, for your unconditional love, unwavering support, tolerance, patience, and for
being willing to carry the load during this deployment.
To my mother, for everything – you would be so proud.
LOW ENROLLMENT FOR WOMEN
iii
ACKNOWLEDGEMENTS
To my dissertation committee, this would not have been possible without your guidance –
I am extremely grateful. Dr. Cathy Krop, for reading every revision and providing me prompts
that helped me make this dissertation representative of the effort I put into this program. Dean
Emily Allen, for trusting me with your college and your staff – I am touched by your
commitment to those you serve. Dr. Lawrence Picus, my dissertation chair, for providing the
flexibility to get comfortable with how to approach this dissertation. Having confidence in you
gave me the freedom to evolve how I wanted to tell this story.
To the professors of the OCL program, thank you for your commitment to my success.
Dr. Monique Datta, you helped me form the foundation from which all of this is built upon. Dr.
Douglas Lynch, you challenged me to figure it out! Dr. Holly Ferguson, I love your approach to
teaching! I am so grateful for the time spent talking through my Chapter 4; that conversation
was the basis for my defense and it could not have gone any better. Dr. Cathy Krop, your
support was invaluable as I explored changes to my study, and your feedback was always exactly
what I needed to take my work to the next level. Dr. Esther Kim, you gave freely of your time to
share your experiences and to have a rich discussion of how to approach my data collection and
methodology.
The Aerospace Corporation, I am tremendously grateful for the financial support, and for
the latitude I was provided in the pursuit of this degree. Dr. Wanda Austin, Dr. David Gorney,
Dr. Wayne Goodman, and Scott Gustafson thank you for your letters of support.
To Cohort 3, I could not have asked for a more talented and supportive group of
professionals to be associated with. #NoDoctorsDown #FightOn
LOW ENROLLMENT FOR WOMEN
iv
TABLE OF CONTENTS
DEDICATION ....................................................................................................................................................................... ii
ACKNOWLEDGEMENTS ............................................................................................................................................... iii
TABLE OF CONTENTS ................................................................................................................................................... iv
List of Tables ..................................................................................................................................................................xi
List of Figures ............................................................................................................................................................. xiii
Abstract ......................................................................................................................................................................... xiv
CHAPTER 1: INTRODUCTION ..................................................................................................................................15
Introduction of the Problem of Practice ......................................................................................................... 15
Organizational Context and Mission ................................................................................................................ 15
Organizational Performance Status ................................................................................................................. 18
Related Literature ..................................................................................................................................................... 19
Importance of Addressing the Problem ........................................................................................................... 20
Organizational Performance Goal ..................................................................................................................... 21
Description of Stakeholder Groups .................................................................................................................... 22
Stakeholder’s Performance Goals ...................................................................................................................... 23
Stakeholder Group for the Study ........................................................................................................................ 23
Purpose of the Project and Questions ............................................................................................................... 24
Conceptual and Methodological Framework ............................................................................................... 24
Organization of the Study ...................................................................................................................................... 25
CHAPTER 2: LITERATURE REVIEW .....................................................................................................................26
Review of the Literature ......................................................................................................................................... 26
Underrepresentation of Women in STEM....................................................................................................... 26
LOW ENROLLMENT FOR WOMEN
v
Low Enrollment for Women in STEM .......................................................................................................27
Low Representation of Women in Scientific and Engineering Careers ..................................28
Underlying Causes of Underrepresentation .................................................................................................. 29
Self-efficacy ............................................................................................................................................................29
Gender Bias ............................................................................................................................................................31
Attrition ....................................................................................................................................................................33
Stereotype threat .............................................................................................................................................. 33
GPA and exposure to AP courses ................................................................................................................. 34
Classroom Environment and Pedagogy...................................................................................................34
Family Responsibilities and Culture .........................................................................................................35
The Double Bind: Sexism and Racism ......................................................................................................36
Factors Determining Career Choice .................................................................................................................. 37
Self-efficacy ............................................................................................................................................................37
Influence of Family .............................................................................................................................................39
Interventions ................................................................................................................................................................ 39
Mentoring ................................................................................................................................................................40
Community Based Programs.........................................................................................................................40
Holistic University Programs ........................................................................................................................41
Summer Bridge Programs ..............................................................................................................................42
The Clark and Estes (2008) Gap Analytic Conceptual Framework .................................................... 43
Stakeholder Knowledge, Motivation and Organizational Influences ................................................ 44
Knowledge ..............................................................................................................................................................44
Knowledge influences...................................................................................................................................... 44
Motivation ...............................................................................................................................................................47
Self-efficacy theory ........................................................................................................................................... 48
LOW ENROLLMENT FOR WOMEN
vi
Student self-efficacy ......................................................................................................................................... 49
Expectancy value theory ................................................................................................................................. 50
Attainment value ............................................................................................................................................... 50
Organizational Influences ...............................................................................................................................52
Cultural models and cultural settings ........................................................................................................ 53
Conclusion ..................................................................................................................................................................... 56
CHAPTER 3: METHODOLOGY .................................................................................................................................57
Purpose of the Project.............................................................................................................................................. 57
Research Questions ................................................................................................................................................... 57
Conceptual Framework .......................................................................................................................................... 58
Participating Stakeholders and Sample Selection ..................................................................................... 60
Data Collection ......................................................................................................................................................60
Surveys .....................................................................................................................................................................62
Observation ............................................................................................................................................................65
Documents and Artifacts .................................................................................................................................65
Data Analysis ............................................................................................................................................................... 66
Credibility and Trustworthiness ......................................................................................................................... 66
Validity and Reliability ............................................................................................................................................ 68
Ethics ............................................................................................................................................................................... 69
Limitations and Delimitations ............................................................................................................................. 71
Limitations ..............................................................................................................................................................71
Delimitations .........................................................................................................................................................72
CHAPTER 4: RESULTS AND FINDINGS ...............................................................................................................73
Participating Stakeholders ................................................................................................................................... 74
LOW ENROLLMENT FOR WOMEN
vii
Survey Administration .....................................................................................................................................76
Data Analysis .........................................................................................................................................................77
Results ............................................................................................................................................................................. 78
Knowledge Results .............................................................................................................................................81
Students need to know the benefits of a STEM-related career.......................................................... 82
Students need to know what they will be able to achieve with STEM majors ............................. 83
Students need to know about their own gender stereotypes ............................................................ 85
Summary of knowledge results .................................................................................................................... 86
Motivation Results ..............................................................................................................................................86
Students need to believe that they can succeed in math and science ............................................. 87
Students need to connect STEM careers with being able to make the world a better place ... 90
Summary of motivation influence results................................................................................................. 93
Organizational Results .....................................................................................................................................94
Students’ learning environment in math and science is not gender-inclusive ............................ 95
Students’ learning environment in math and science does not reflect diversity ........................ 96
Summary of organization influence results ............................................................................................. 97
Summary of Knowledge, Motivation, and Organization Results ................................................97
LaunchPad Results .............................................................................................................................................99
College and engineering intent ..................................................................................................................... 99
Knowledge .........................................................................................................................................................104
Motivation .........................................................................................................................................................105
Organization .....................................................................................................................................................108
Summary of LaunchPad Results ............................................................................................................... 109
Findings ....................................................................................................................................................................... 109
Research Question #1 Findings ........................................................................................................................ 110
Knowledge ........................................................................................................................................................... 110
LOW ENROLLMENT FOR WOMEN
viii
Motivation ............................................................................................................................................................ 110
Organization ....................................................................................................................................................... 111
Summary of Findings for Research Question #1 ............................................................................. 111
Research Question #2 Findings ........................................................................................................................ 113
Knowledge ........................................................................................................................................................... 113
Motivation ............................................................................................................................................................ 113
Organization ....................................................................................................................................................... 114
Summary of Findings for Research Question #2 ............................................................................. 114
Summary ..................................................................................................................................................................... 116
CHAPTER 5: RECOMMENDATIONS ................................................................................................................... 119
Introduction .............................................................................................................................................................. 119
Recommendations for Practice to Address KMO Influences ............................................................... 121
Knowledge Recommendations ................................................................................................................. 121
Introduction ......................................................................................................................................................121
Conceptual knowledge solution .................................................................................................................122
Motivation Recommendations .................................................................................................................. 123
Introduction ......................................................................................................................................................123
Self-efficacy .......................................................................................................................................................124
Attainment value .............................................................................................................................................125
Organization Recommendations .............................................................................................................. 126
Introduction ......................................................................................................................................................126
Cultural model ..................................................................................................................................................127
Cultural setting ................................................................................................................................................128
Integrated Implementation and Evaluation Plan ................................................................................... 129
Implementation and Evaluation Framework .................................................................................... 129
LOW ENROLLMENT FOR WOMEN
ix
Organizational Purpose, Need and Expectations............................................................................. 131
Level 4: Results and Leading Indicators............................................................................................... 132
Level 3: Behavior .............................................................................................................................................. 134
Critical behaviors ............................................................................................................................................134
Required drivers .............................................................................................................................................136
Organizational support .................................................................................................................................136
Level 2: Learning .............................................................................................................................................. 137
Learning goals ..................................................................................................................................................137
Program ..............................................................................................................................................................138
Components of learning ................................................................................................................................139
Level 1: Reaction .............................................................................................................................................. 140
Evaluation Tools ............................................................................................................................................... 141
Immediately following the program implementation ........................................................................141
Delayed for a period after the program implementation ..................................................................144
Data Analysis and Reporting ...................................................................................................................... 145
Strengths and Weaknesses of the Approach .............................................................................................. 147
Limitations and Delimitations .......................................................................................................................... 148
Limitations ........................................................................................................................................................... 148
Delimitations ...................................................................................................................................................... 149
Future Research ...................................................................................................................................................... 150
Conclusion .................................................................................................................................................................. 151
References ....................................................................................................................................................................... 154
APPENDIX A ................................................................................................................................................................... 175
LaunchPad Survey 1 of 3 ..................................................................................................................................... 176
LaunchPad Survey 2 of 3 ..................................................................................................................................... 192
LOW ENROLLMENT FOR WOMEN
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LaunchPad Survey 3 of 3 ...................................................................................................................................... 204
APPENDIX B ................................................................................................................................................................... 218
Observation Checklist ........................................................................................................................................... 218
Peer Observation Form ........................................................................................................................................ 219
APPENDIX C ................................................................................................................................................................... 224
Informed Consent and Information Sheet ................................................................................................... 224
APPENDIX D ................................................................................................................................................................... 228
LaunchPad Schedule and Agenda ................................................................................................................... 228
APPENDIX E ................................................................................................................................................................... 229
Traceability Matrix ................................................................................................................................................ 229
LOW ENROLLMENT FOR WOMEN
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List of Tables
Table 1 SU Demographic Data from 2016 ............................................................................... 16
Table 2 Organizational Mission, Global Goal, and Stakeholder Performance Goals ............... 23
Table 3 Gender Differences in Workforce Representation ....................................................... 29
Table 4 Assumed Knowledge Influences ................................................................................... 47
Table 5 Assumed Motivational Influences ................................................................................ 52
Table 6 Assumed Organizational Influences............................................................................. 55
Table 7 Mapping of Assumed Influences to Data Collection Method ........................................ 62
Table 8 Description of Survey Instruments ............................................................................... 64
Table 9 LaunchPad Student Population from the Local Area High Schools.............................. 75
Table 10 Ethnicity of Participants ............................................................................................ 75
Table 11 Highest Level of Education for Participants’ Parents ................................................ 76
Table 12 Daily Attendance Percentages ................................................................................... 76
Table 13 Assumed Influences ................................................................................................... 80
Table 14 Knowledge Influences ............................................................................................... 81
Table 15 Validation Status for Knowledge Influences .............................................................. 86
Table 16 Motivation Influences ................................................................................................ 87
Table 17 Qualitative Results for the Motivation Influence of Attainment Value ........................ 91
Table 18 Validation Status for Motivation Influences ............................................................... 94
Table 19 Organizational Influences ......................................................................................... 95
Table 20 Validation Status for Organizational Influences ........................................................ 97
Table 21 Summary of Assumed Influences and Validation Status ............................................. 98
Table 22 Statistical Results for LaunchPad Responses ........................................................... 103
LOW ENROLLMENT FOR WOMEN
xii
Table 23 Coding Results Related to Motivation ...................................................................... 108
Table 24 Validated Influences ................................................................................................ 112
Table 25 Description of the Validated Influences ................................................................... 120
Table 26 Summary of Validated Knowledge Influences and Recommendations ...................... 122
Table 27 Summary of Validated Motivation Influences and Recommendations ....................... 124
Table 28 Summary of Validated Organization Influences and Recommendations ................... 127
Table 29 The New World Kirkpatrick Model Four Levels of Evaluation ................................. 130
Table 30 Outcomes, Metrics, and Methods for External and Internal Outcomes .................... 133
Table 31 Critical Behaviors, Metrics, Methods, and Timing for Students ............................... 135
Table 32 Required Drivers to Support Students’ Critical Behaviors ....................................... 136
Table 33 Components of Learning for the Program ............................................................... 140
Table 34 Components to Measure Reactions to the Program ................................................. 141
Table 35 Representative Questions from the LaunchPad End of Program Survey .................. 143
Table 36 Sample Questions for a Post-LaunchPad Survey ..................................................... 144
Table 37 Legend for Traceability Matrix................................................................................ 229
LOW ENROLLMENT FOR WOMEN
xiii
List of Figures
Figure 1. State University undergraduate enrollment ................................................................ 18
Figure 2. Undergraduate enrollment ......................................................................................... 19
Figure 3. Conceptual Framework.............................................................................................. 59
Figure 4. Survey responses for Knowledge influence................................................................ 82
Figure 5. Sampling of quantitative results for conceptual knowledge ........................................ 84
Figure 6. Strong indicators of math, engineering, and science self-efficacy............................... 89
Figure 7. Lower self-efficacy.................................................................................................... 90
Figure 8. Career importance ..................................................................................................... 92
Figure 9. A lack of confidence in an engineering career............................................................ 93
Figure 10. Gender bias ............................................................................................................. 95
Figure 11. A significant number of students did not have access to a role model or a mentor. ... 96
Figure 12. College intention: .................................................................................................. 100
Figure 13. Pre-LaunchPad and Post-LaunchPad responses ..................................................... 101
Figure 14. Student intent - comparative results. ...................................................................... 102
Figure 15. Post-LaunchPad survey results exploring ............................................................... 104
Figure 16. Survey results exploring the knowledge impact of the LaunchPad program ........... 105
Figure 17. Post-LaunchPad survey results exploring the motivation impact ............................ 106
Figure 18. Survey results exploring the motivation impact of the LaunchPad program ........... 107
Figure 19. Increased engineering or computer science intention. ............................................ 115
Figure 20. Fall 2018 LaunchPad participants’ State University application data. .................... 116
Figure 21. The New World Kirkpatrick Model ....................................................................... 131
Figure 22. Measures of Career and College Intent .................................................................. 146
LOW ENROLLMENT FOR WOMEN
xiv
Abstract
Women are underrepresented in in engineering and computer science fields of study, and in the
workforce (National Science Foundation, 2016). Furthermore, although women enroll in college
at higher rates than men, they declare engineering as a major 10% less than men. The purpose of
this study was to evaluate the State University, College of Engineering, Computer Science, and
Technology LaunchPad program, and to perform a Clark and Estes (2008) gap analysis of the
assumed knowledge, motivation, and organizational factors that affect the lower enrollment rates
for women in engineering and computer science. The assumed influences were developed by a
thorough review of the literature and validated using a transformative mixed methods study.
Surveys, observations, document review, and artifact collection were used to validate the
assumed influences. The study participants were female high school students between their
junior and senior year. Findings from this study concluded that the participants demonstrated
lower self-efficacy and attainment value; they experienced gender bias and did not have access to
female role models; and they did not understand what they could achieve with a STEM degree or
career. Recommendations are provided in Chapter 5 to improve the performance of the
LaunchPad program and to help the ECST meet an organizational goal of increased enrollment
by women. These recommendations are based on the validated influences and were developed
using the New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016).
LOW ENROLLMENT FOR WOMEN
15
CHAPTER 1: INTRODUCTION
Introduction of the Problem of Practice
Women have low self-efficacy in science, technology, engineering, and math (STEM).
Self-efficacy, as defined by Bandura (1977), is the belief that a person can achieve what he or
she is attempting. Research on gender differences in academic self-efficacy has shown that the
self-efficacy gap between genders begins to grow at age 14, and the gap almost doubles by the
age of 23 (Huang, 2013). Subsequently, university enrollment rates for women in STEM were
discovered to be 39% lower than for men (Allum & Okahana, 2015). Furthermore, the National
Center for Science and Engineering Statistics confirmed that women are earning fewer STEM
degrees than men (NSF, 2013). This study will explore the factors that influence STEM self-
efficacy, career choices, and STEM enrollment rates for women, and will offer recommendations
to mitigate these effects.
Organizational Context and Mission
State University (SU) is one of 23 campuses within the California State University
system. State University was founded in the 1940s and is in a large urban city in California;
there are more than 27,000 students who reflect the rich ethnic diversity of the area enrolled in
SU. State University is a federally recognized Hispanic-serving, Asian American and Native
American Pacific Islander-serving, and Minority-serving institution. As illustrated in Table 1,
the largest population of students are seniors, with freshmen the next largest population. The
largest ethnic population is Hispanic, and there are more women enrolled than men; the college
of engineering has the lowest percentage enrollment of all majors.
LOW ENROLLMENT FOR WOMEN
16
Table 1
SU Demographic Data from 2016
1
Student Population - 27,827
Freshman 23.1%
Asian 14.2%
Sophomore 9.4%
Pacific Islander 0.1%
1st time Transfer 13.2%
Hispanic 60.9%
Junior 10.5%
Black 4.1%
Senior 29.0%
White 7.8%
Post Bac 2.3%
International 7.9%
Graduates 11.3% Two Races 2.0%
Unknown 2.9%
American Indian 0.1%
Health and Human Services 23.4%
Total Undergraduates 88.7%
Natural and Social Sciences 18.7%
Total Graduate Students 11.3%
Business and Economics 17.0%
Arts and Letters 14.9%
Male 41.9%
Education 11.1%
Female 58.1%
Engineering, Computer Science, and
Technology
7.2%
Undecided 7.7%
Note. The decline in population is likely sophomore attrition, and the increased population of
seniors is the ‘pile-up’ of seniors based on the higher rate of graduates at 5-6 years.
The Department of Engineering was founded in 1953, and expanded into the college of
Engineering, Computer Science, and Technology (ECST) in 2001. As of fall 2016, ECST
enrolled over 3,000 undergraduate and 640 graduate students. There are accredited Bachelor of
Science (BS) degree programs in Civil, Electrical, and Mechanical Engineering, Computer
Science, Industrial Technology, and BS programs in Aviation Administration, Fire Protection
1
Source: State University Office of Institutional Research
LOW ENROLLMENT FOR WOMEN
17
Administration and Technology, and Graphic Communication (Engineering Computer Science,
and Technology Strategic Plan 2015 – 2020, 2015). In addition, the College offers Master of
Science (MS) degrees in Civil, Electrical, and Mechanical Engineering, and Computer Science,
as well as a Master of Arts (MA) in Industrial Arts. The vision of the ECST is “to be known for
distinctive, student-centered programs that develop innovative professionals ready to solve the
complex technical problems of our time.” The mission is “to successfully prepare the next
generation of engineering, computer science, and technology professionals for Urban City and
beyond,” and their motto is to “Commit to Excellence. Engage in Community” (ECST, 2015, p.
5). There are three strategic focus areas in the ECST Strategic Plan: Student Success, Faculty
and Staff Excellence, and Community Engagement. A key initiative within the Student Success
Strategic Focus is to “Enhance and expand Summer Bridge programs, starting with high schools
and continuing through the Freshman-Sophomore transition” (ECST, 2015, p. 15).
The ECST has established a range of programs to support students as they begin the
pursuit of their degree, these include:
• Mathematics, Engineering, Science Achievement (MESA), a middle and high
school STEM program supporting teachers and students. State University is a
MESA schools program center.
• STEP, a summer program for freshman transition to ECST programs.
• FYrE@ECST, provides freshmen STEM skills and support
• BOOST, transitions students into their second year with a community-based
summer engineering project
• LaunchPad, summer STEM recruitment program for young women between their
junior and senior year in high school.
LOW ENROLLMENT FOR WOMEN
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The LaunchPad program is the focus of this study; this was the pilot year for LaunchPad.
Organizational Performance Status
Although women enroll in State University at a higher rate than men, they enroll in the
ECST at significantly lower rates than men. Figure 1 illustrates the enrollment history for
undergraduates at State University based on gender; on average, women enroll at a rate 20%
higher than men. Figure 2 illustrates a reversal in gender majority for undergraduates in the
ECST. Specifically, men enroll at a rate five times higher than women.
2
Figure 1. State University undergraduate enrollment: average female enrollment is 60%, average
male enrollment is 40%.
2
Source: State University Office of Institutional Research. Enrollment data from Fall 2016 used.
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
State University
Male Female
LOW ENROLLMENT FOR WOMEN
19
Figure 2. Undergraduate enrollment for the College of Engineering, Computer Science, and
Technology: the average male enrollment is 85%, and the average female enrollment is 15%.
Related Literature
Many studies have concluded that women have lower math and science self-efficacy than
men (Cordero, Porter, Israel, & Brown, 2010; Lent, Lopez, & Bieschke, 1991). Furthermore,
low self-efficacy is a predictor of lower levels of enrollment for women in STEM programs
(Diekman, Brown, Johnston & Clark, 2010). In similar studies, Sawtelle, Brewe, and Kramer
(2012) and Beyer (2014) confirmed that low self-efficacy in women results in lower levels of
enrollment in specific STEM courses: physics and computer science. Combining race and
gender, Ong, Wright, Espinosa, and Orfield (2011) performed an extensive literature review to
examine the “double bind”, which is the challenge that women of color face: simultaneous
racism and sexism (Malcom, Hall, & Brown, 1976) as cited in Ong et al. (2011). In their review,
Ong et al. (2011) presented that women of all races are equally interested in STEM; however,
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
College of Engineering
Male Female
LOW ENROLLMENT FOR WOMEN
20
racism, sexism, and family expectations influence women of color away from STEM.
Alternately, Lent, Brown, and Larkin (1986) concluded that there were no differences in self-
efficacy between men and women; however, they did determine that there is a correlation
between higher self-efficacy, grades, and enrollment. Nonetheless, there is consensus that low
self-efficacy in women has a negative impact on their performance and interest in STEM.
Furthermore, women make career choices based on their STEM self-efficacy (Deemer, Thoman,
Chase, & Smith, 2013).
Importance of Addressing the Problem
The problem of low enrollment for women in STEM is important to solve for a variety of
reasons. National-level goals have been set forth to strengthen STEM education and to increase
the number of STEM graduates by one million within the next 10 years (Office of the Press
Secretary, 2014; President’s Council of Advisors on Science and Technology, 2012; White
House Office of Science and Technology Policy, 2014). The President’s Council of Advisors on
Science and Technology (PCAST) warned that if the United States is unable to meet this goal, its
role as a technological leader will be at risk. On a related topic, women and underrepresented
minorities (URMs) make up 70% of the collegiate population (PCAST, 2012), so increasing
STEM enrollment for women would have an immediate and positive affect on the number of
students in the STEM pipeline. As these students graduate, the number of STEM practitioners
will increase and contribute to meeting the national goal set forth by PCAST (2012).
Furthermore, increasing the number of women entering the STEM workforce will increase the
diversity of the workforce.
Workforce diversity has a positive impact on productivity for high-tech firms; researchers
cite improved innovation, stronger customer relationships, improved productivity, and increased
LOW ENROLLMENT FOR WOMEN
21
speed and agility as benefits to a diverse workforce (Garnero, Kampelmann, & Rycx, 2014;
Salomon & Schork, 2003). Furthermore, in a study of over 1,000 businesses, Herring (2009)
found that diversity has a positive influence on businesses: increased sales revenue, more
customers, greater market share, and greater relative profits. Increasing the number of STEM
graduates and increasing the diversity of those graduates not only helps achieve the national goal
of more STEM practitioners, it also is good for business. In addition to contributing to the
national goal and adding to the diversity of the STEM workforce, increasing the number of
women in STEM careers provides increased financial security and independence for women.
Women receive a 23% earning advantage when working in a STEM-related career as compared
to working in a non-STEM career (Olitsky, 2014).
Organizational Performance Goal
The ECST strategic plan was finalized in January 2015. The strategic focus areas in the
plan are student success, faculty and staff excellence, and community engagement. Each of the
focus areas contains three or four goals with five to seven key initiatives. The organizational
performance goal, which will be the focus of this work, is derived from a key initiative within
the student success focus area. This initiative is to “increase the number of female students
enrolling in ECST majors, and ensure that the college climate is inclusive for all students”
(ECST, 2015, p. 15). The specific organizational goal established by the Dean’s Advisory Board
is to increase the number of female students enrolling in ECST from 15% to 25% by 2020.
The average enrollment rate for women in STEM across all of the SUs is 15%, and the
national average for women enrolled in engineering is 21% (Yoder & Ph, 2011). The ECST
target of 25% will be challenging, but it will more closely align SU ECST with the national
average. Furthermore, achieving this goal will position SU ECST as a leader in enrollment rates
LOW ENROLLMENT FOR WOMEN
22
for women in engineering within the SU system. The ECST administrative staff will track
progress towards achieving this goal using SU enrollment data. Performance will also be
reported through the ECST participation in the annual American Society for Engineering
Education (ASEE) survey.
Description of Stakeholder Groups
The three stakeholder groups who will contribute to meeting the organizational goal are
the incoming students, the ECST faculty, and the Administration. Table 2 provides a description
of the goals for the organization and for each stakeholder. The students, specifically the female
students, are instrumental to being able to meet this performance goal. The university faculty
will be key contributors to meeting this goal by representing a gender-inclusive learning
environment to potential students, and through contributions to summer programs designed to
recruit female students. The ECST dean is responsible for supporting summer recruiting
programs, and for ensuring that female candidates are recruited for all faculty searches.
LOW ENROLLMENT FOR WOMEN
23
Stakeholder’s Performance Goals
Table 2
Organizational Mission, Global Goal, and Stakeholder Performance Goals
Organizational Mission
To successfully prepare the next generation of engineering, computer science, and technology
professionals for an urban city and beyond.
Organizational Performance Goal
By 2020, the enrollment rate for women in the State University College of
Engineering, Computer Science, and Technology will increase from 15% to 25%.
ECST Administrators ECST Faculty Students
By May 2018, the Dean of
the ECST will develop a
program to recruit, hire, and
retain more female faculty.
By May 2018, the faculty of
the ECST will have received
professional development on
how to create a gender-
inclusive learning
environment.
By Fall 2018, 50% of
students who participated in
the 2017 ECST LaunchPad
program will matriculate into
the ECST.
Stakeholder Group for the Study
To achieve an increase in female enrollment from 15% to 25%, all stakeholders will need
to meet their goals. If the dean does not sponsor a summer recruiting program and bring more
female faculty into the ECST, the female students will not be attracted to the SU ECST.
Similarly, if the ECST faculty do not create a gender-inclusive learning environment, prospective
female students will not see the ECST as a welcoming environment and will not enroll at an
increased rate. The students are a critical component of the organizational goal, and key to
achieving it. If the students do not elect to enroll in the ECST at SU, the dean and ECST
faculties’ contributions will be for naught. Therefore, the stakeholder of focus for this study will
be the female students. It will be important to understand the factors that influence the decisions
that the female students make regarding their field of study and their career.
LOW ENROLLMENT FOR WOMEN
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Purpose of the Project and Questions
The purpose of this project is to conduct a needs analysis in the areas of knowledge,
motivation, and organizational resources necessary to reach the organizational performance goal
of an increase from 15% to 25% enrollment rate for women in the ECST by the year 2020. The
analysis will begin by generating a list of possible needs and will then move to examining these
systematically to focus on actual or validated needs. While a complete needs analysis would
focus on all stakeholders, for practical purposes the stakeholder that will be the focus of this
analysis is the female students.
As such, the questions that guide this study are the following:
1. What are the knowledge, motivation, and organizational factors that contribute to the
STEM-related career decisions and field of study selections that female high school
students make?
2. What knowledge, motivation, and organizational factors does the LaunchPad program
influence such that the female high school student participants are more likely to enroll in
engineering or computer science, and/or pursue a career in engineering or computer
science?
3. What are the recommended knowledge, motivation, and organizational solutions that will
increase the number of female students that choose a STEM-related career, resulting in an
increased enrollment of female students in the State University College of Engineering,
Computer Science, and Technology?
Conceptual and Methodological Framework
This study will use the Clark and Estes (2008) conceptual framework for completing a
gap analysis to determine the causes of the performance gaps illustrated above. The factors that
LOW ENROLLMENT FOR WOMEN
25
contribute to the performance gaps will be identified using personal experience, a review of
empirical research, and performing thought experiments (Maxwell, 2013). These factors will be
validated using three surveys, observations, document and artifact gathering, and an analysis of
demographic data. The recommended solutions will be based on the findings of this study and
will be supported by empirical evidence.
Organization of the Study
This study is organized in five chapters. Chapter 1, this chapter, provides the reader an
introduction into the challenges of low enrollment of women in STEM fields. It also introduced
the organization, its mission, goals, and stakeholders. The conceptual framework of the gap
analysis was also briefly introduced. Chapter 2 provides a literature review of topics
surrounding underrepresentation of women in STEM fields of study and careers. Topics of low
self-efficacy, attrition, influence on career choices, the K-12 experience, and summer bridge
programs will be explored. Chapter 3 will describe the assumed influences that are preventing
the organization from meeting its goal. The methodology for the study will also be described.
Chapter 4 will present the data, an analysis of the data, and the results of the study. Chapter 5
will focus on solutions to closing the perceived gaps in organizational performance. The
recommended solutions will be based on empirical evidence found in data and the literature. An
implementation and evaluation plan will also be provided.
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26
CHAPTER 2: LITERATURE REVIEW
Review of the Literature
This literature review will examine the underrepresentation of women in STEM. The
review begins with general research on the low enrollment rates for women pursuing STEM
degrees and the underrepresentation of women in the STEM workforce. This is followed by an
overview of the underlying causes of underrepresentation. The review then presents a discussion
of factors that influence the career choices women make as they consider STEM. Finally, the
general research concludes with a review of interventions focused on improving gender equity in
STEM. Following the general research literature, the focus turns to the Gap Analysis
Framework (Clark & Estes, 2008). Specifically, the assumed knowledge, motivation, and
organizational influences on students when they consider career choices and fields of study as
they matriculate into post-secondary education.
Underrepresentation of Women in STEM
Women are underrepresented in the pursuit of STEM degrees. (Allum & Okahana, 2015;
Deemer et al., 2013). According to the President’s Council of Advisors on Science and
Technology (PCAST, 2012), although women and underrepresented minorities comprise 70% of
the student population, they comprise only 45% of the STEM population. Furthermore, although
women make up more than half of the first-time graduate enrollment, they are underrepresented
in engineering fields. For example, the 2014 Council of Graduate Schools survey reported that
women constituted 57.9% of the master’s level enrollment, but only 25% of the engineering and
32.5% of the math and computer science population (Allum & Okahana, 2015). National goals
have been set forth to increase the number of STEM graduates by one million within the next 10
years (Office of the Press Secretary, 2014; White House Office of Science and Technology
LOW ENROLLMENT FOR WOMEN
27
Policy, 2014). In 2013, the number of science and engineering bachelor’s degrees awarded was
615,000 (NSF, 2016). The women’s contribution to engineering and computer science degrees
were approximately 19% and 20% respectively. Increasing the enrollment of women in STEM
will help achieve these goals.
Low Enrollment for Women in STEM
Although women enroll in college at a higher rate than men, they enroll in STEM at
disproportionately lower rates. Chen (2009) examined the demographics of students pursuing an
associate or bachelor’s degree who matriculated between 1995-6 and 2001; the percentage of
men enrolling across all STEM fields
3
was higher than the percentage of women enrolling. Men
enrolled at a rate of 33%, and women enrolled at a rate of 14%. Specific to engineering and
computer science, the largest gap of inequality was in engineering with 15.1% male enrollment
and 2.7% female enrollment (Chen, 2009). In a more recent report, the 2016 National Science
Board (NSB) Science and Engineering Indicators report described gender differences in students
declaring intentions in engineering; in 2007 women declared engineering as a major 10% less
than men, and the 2013 graduation rates were lower for women in engineering by 7% (NSF,
2016). A survey of 2014 enrollment concluded that the gender gap increases for students
3
STEM fields for this study included mathematics, natural sciences, physical sciences,
biological and agricultural sciences, engineering and engineering technologies, and computer and
information sciences.
LOW ENROLLMENT FOR WOMEN
28
pursuing graduate-level degrees: women enrolled in engineering 50% less, and 33% less in math
and computer science (Allum & Okahana, 2015).
Low Representation of Women in Scientific and Engineering Careers
Although women make of 59% of the workforce, they are not represented proportionately
across the growing opportunities and demands of STEM careers (Canetto & Beyers-Winston,
2011). Men comprise approximately 70% of the STEM workforce, and the gender gap has been
somewhat consistent over the past 30 years, despite the national call to increase diversity in the
STEM workforce
4
(NSF, 2016). Table 3 illustrates the workforce gender gap over the last 30
years across all scientific and engineering occupations. More specifically, women comprise at
most 27% of all computer-related occupations, and at most 15% of all engineering occupations –
eliminate the industrial engineers from the grouping, and the representation of women falls to
10% or less across all other engineering occupations (Hill, Corbett, & Rose, 2010). Furthermore,
women continue to be underrepresented in scientific communities of interest. Ferreira (2009)
reported that women represent less than 5% of memberships in the national science academies.
More recently, a 2015 survey of international scientific academies reported that globally, women
represent on average 12% of overall membership in 69 national science academies; women
4
https://obamawhitehouse.archives.gov/blog/2015/07/16/stem-strength-through-diversity
https://thinkprogress.org/national-science-foundation-launches-million-dollar-initiative-to-
improve-diversity-in-stem-3f2f4183d3e
LOW ENROLLMENT FOR WOMEN
29
represent 5% in the engineering sciences (Academy of Science of South Africa, 2015). In the
United States, 13% of the National Academy of Science membership are women.
Table 3
Gender Differences in Workforce Representation: Number (in Thousands) of Men and Women in
the Scientific and Engineering Workforce from 1993 to 2013
Year Men Women
1993 2,548 755 (23%)
2003 3,548 1,269 (26%)
2013 4,080 1,670 (29%)
Note. Percentages are of total workforce. Careers include life scientists, computer and
mathematical scientists, physical scientists, psychological and social scientists, and engineers
Underlying Causes of Underrepresentation
Self-efficacy
Students with higher math and science self-efficacy enroll in STEM at higher rates (Lent
et al., 1986; Lent et al., 1991; Ong et al., 2011; Wang, 2013). Moreover, women have lower
math and science self-efficacy, which results in lower enrollment rates in STEM (Luzzo, Hasper,
Albert, Bibby, & Martinelli, 1999; Wang, 2013). Wang studied students as they progressed from
secondary education to post-secondary education. There was a strong correlation between
STEM enrollment and the students’ math self-efficacy. Wang’s conclusion was that
matriculation into a STEM degree was strongly influenced by math self-efficacy. Looking at
gender differences in self-efficacy, Huang (2013) and Williams and George-Jackson (2014)
found that men are much more confident in their math and science skills than women, and that
self-confidence has a positive effect on self-efficacy. In their study across nine large public
LOW ENROLLMENT FOR WOMEN
30
universities, Williams and George-Jackson (2014) determined men were 24% more confident in
math and science skills than women. Similarly, Huang (2013) demonstrated a gender gap in
academic self-efficacy but extended the work to demonstrate gender inequity across multiple
domains within academic self-efficacy: males scored higher in math and computer self-efficacy.
Huang demonstrated that the gap in math self-efficacy grows as students age: for all age groups
over 14, males demonstrated higher math self-efficacy than females. Specific to race, there is
also a strong relationship between self-efficacy and selecting STEM as a field of study for
undergraduate and graduate women of color (Ong et al., 2011).
Research by Wilson, Bates, Scott, Painter, and Shaffer (2015) on academic self-efficacy
in STEM students revealed a gender gap across computer science and engineering; women
reported significantly lower scores than men. Similarly, men and women studying the field of
computer science revealed a consistent trend: men had significantly higher scores in computing
self-efficacy (Beyer, 2014). Beyer found that women were more likely to say that they had no
interest in computer science, and although they did not hold negative stereotypes about computer
science, they saw the computer science stereotypes as incongruent with their values. Flores,
Navarro, Lee, and Luna (2014) explored the relationship between low engineering-related self-
efficacy in women through the lens of learning experiences, and outcome expectations. Their
work demonstrated that while self-efficacy is influenced by realistic learning experiences, the
effect is stronger for women than men. In other words, women’s self-efficacy increases as they
engage in active learning experiences. Conversely, in an early study of science and engineering
majors, Lent et al. (1986) found no difference in self-efficacy between the men and women in
their study; however, they did conclude that those students with higher self-efficacy received
LOW ENROLLMENT FOR WOMEN
31
higher grades and stayed in college longer. In addition, they discovered that that self-efficacy
influenced the career options that students believed were available.
Results from work done by Cordero et al. (2010) was consistent with other research:
Women’s math self-efficacy was significantly lower than men’s. Seeking to improve women’s
self-efficacy, the researchers attempted an intervention focused on increasing self-efficacy
through performance accomplishment and belief-perseverance techniques. Their results yielded
an increase for men, but no change for women, which may suggest that the barriers to women’s
beliefs about their abilities in math and science are a combination of internal and external
barriers such as gender bias (Cordero et al., 2010).
Gender Bias
When girls and women encounter math and science classrooms that are more gender-
inclusive, they are more interested in math and science (Fennema & Sherman, 1977; Makarova
& Herzog, 2015). Furthermore, teachers’ gender bias has been shown to create barriers to girls’
participation and learning (Li, 2004; Tiedemann, 2000). In addition, gender bias leads to gender-
biased attribution; when boys failed in math, it was attributed to their lack of effort, but when
girls failed, it was attributed to their lack of ability (Espinoza, Arêas da Luz Fontes, & Arms-
Chavez, 2014). Although Espinoza et al. only focused on high achievers, Tiedemann (2000)
confirmed similar results for low achievers. Tiedemann concluded that teachers’ gender bias
resulted in beliefs that the boys in the classroom were more capable in math than the girls.
Interestingly, of the 52 teachers that Tiedemann studied, only five were male: A teacher’s gender
did not align with their perception of the students’ capabilities.
Li (2004) confirmed that there is a correlation between what a teacher believes and what
their students believe; students’ beliefs are influenced by their teachers’ beliefs. Furthermore,
LOW ENROLLMENT FOR WOMEN
32
the relationship between students and teachers contribute to gender differences in math. Li
asserted that male and female teachers rate the importance of math concepts differently, and
these differences are reflected in their students’ beliefs. Makarova and Herzog (2015) examined
the correlation between gender and math and science for groups of students and teachers in
secondary education. They found that all teachers saw a positive correlation between all science
and males, but they saw no positive correlation between women and math; this was also true for
the students. Female students rated the terms math and physics as incongruent with being a
woman but were neutral regarding chemistry. Male students demonstrated a negative correlation
between the terms mathematics and woman (Makarova & Herzog, 2015).
Fennema, Peterson, Carpenter, and Lubinski (1990) studied a group of first grade
teachers and learned that the teachers stereotyped their students based on gender. The teachers
attributed the performance of their students based on the students’ gender, which also influenced
how they interacted with their students in the classroom. Fennema et al. suggested that the
gender inequity in the outcomes of mathematics education begins with how teachers stereotype
their students. Riegle-Crumb and Humphries (2012) extend the research on gender bias from
elementary school to the high-school environment. Their findings were consistent with Fennema
et al., Makarova and Herzog (2015), and Li (2004) – gender stereotypes shaped the teachers’
beliefs about their students, and this bias was reflected in how students selected their classes
based on their perceived capabilities. Extending the evaluation of gender bias into post-
secondary education, Riegle-Crumb, King, Grodsky, and Muller (2012) determined that
students’ achievement in their secondary education was not an indicator of choosing a STEM-
related major. Riegle-Crumb et al. determined that gender stereotypes affect students’ choices as
they determine their field of study.
LOW ENROLLMENT FOR WOMEN
33
Extending the discussion from the classroom into the home, parents’ beliefs are
consistent with previous findings: boys are better at math than girls, and these beliefs translate to
their own children. The effect of gender bias in classrooms and at home were evident by higher
performance by boys, lower self-efficacy in girls, and ultimately lower participation in
mathematics-related careers by women (Gunderson, Ramirez, Levine, & Beilock, 2012).
Attrition
Attrition rates for students pursuing STEM degrees are similar, if not slightly lower, than
non-STEM majors. However, students majoring in STEM are more likely to change majors than
students in non-STEM programs (Chen & Soldner, 2013). There are differences based on gender
in STEM attrition rates. For example, a higher percentage of men (23.7%) leave college without
a STEM degree than women (14.2%), but women pursuing STEM degrees switch fields of study
at a higher rate than men: women 32.4%, men 25.5% (Chen & Soldner, 2013). Low self-
efficacy, stereotype threat, exposure to AP-level courses, and GPA are all predictors of STEM
attrition – or contributors to STEM persistence.
Stereotype threat. In a foundational study, Spencer, Steele, and Quinn (1999),
discovered that stereotype threat influences women’s performance and persistence in math.
They performed three studies, that when combined, confirmed the existence of stereotype threat
for women – especially when the math is perceived to be a new concept or difficult. The
researchers suggested that when women are faced with difficult math, rather than confirm the
negative stereotype that they are not as qualified in math, they will avoid the situation by
dropping a class, or avoid fields of study that require significant courses involving math.
Deemer, Smith, Carroll, and Carpenter (2014) examined stereotype threat through the lens of
avoidance goals, and concluded that women are more concerned about gender stereotypes than
LOW ENROLLMENT FOR WOMEN
34
men, and that women’s procrastination is related to stereotype threat, especially in science
classrooms. Therefore, women may use performance avoidance goals to avoid confirmation of
stereotype beliefs about women in science. Stoet and Geary (2012) challenged the strength of
past research claims regarding stereotype threat contributing to the gender gap in mathematics;
they assert that the claims of past research are not as compelling, and that the media has
misrepresented the results. Nevertheless, they do concede that stereotype threat does affect
women.
GPA and exposure to AP courses. According to Ackerman, Kanfer, and Beier (2013)
the greatest predictor of persistence in STEM is the first-year GPA. In addition, successful
completion of AP courses influenced women’s math and science self-concept, which was shown
to influence persistence early in an academic program (Ackerman et al., 2013). Fewer women
pursue advanced calculus AP exams (41%), and significantly fewer women pursue computer
science AP exams (19%) than men (81%) (NSF, 2016). Finally, better preparation prior to
entering a university has been shown to increase persistence for URMs, including women (Ma &
Liu, 2015).
Classroom Environment and Pedagogy
Women leave STEM because of the chilly climate, poor classroom experiences, lack of
funding, difficult interpersonal relationships in a science setting, and discrimination (subtle and
overt) (Johnson, 2011). Therefore, programs that use a holistic approach to focus on academics,
provide a supportive environment, and exposure to research, demonstrated increased persistence
in STEM (Toven-Lindsey, Levis-Fitzgerald, Barber, & Hasson, 2015). For example, in a study
that examined the low levels of enrollment by women in introductory physics courses, Sawtelle
et al. (2012) determined that women performed much better when taking courses taught using a
LOW ENROLLMENT FOR WOMEN
35
modeling style versus a lecture style. The modeling style, a hands-on conceptual approach, led
to higher self-efficacy for women and was 6.73 times more successful than the lecture approach.
However, lecture-based instruction continues to dominate STEM instruction at the collegiate
level (Wieman, 2014).
A teaching environment that is influenced by gender bias negatively affects the outcome
of female students in math and science classrooms (Espinoza et al., 2014; Fennema et al., 1990;
Riegle-Crumb & Humphries, 2012; Tiedmann, 2000). Research suggests that math teachers
exhibit gender bias when teaching math and that their bias influences students’ performance.
Studies of elementary schools, middle schools, and high schools all concluded that teachers
perceive boys and girls differently, and that gender bias exists in the classroom (Espinoza et al.,
2014; Fennema et al., 1990; Riegle-Crumb & Humphries, 2012; Tiedmann, 2000). Reducing
gender stereotypes in math and science classrooms will result in an environment that encourages
women to pursue STEM (Betz & Hackett, 1981; Sawtelle et al., 2012).
Family Responsibilities and Culture
Women avoid pursuing STEM fields of study because they perceive these careers as
incongruent with their values, goals, interests, and ability to have a family. Beyer (2014)
revealed that even though a computer science-related career could provide financial and job
security, women did not view a computer science career as congruent with their interpersonal
values, life goals, or interests. Furthermore, when women marry or have children, they have
difficulty finding the time and energy to pursue educational or career paths due to the social
expectations that they accommodate to their husbands’ educational and work priorities, and that
they take primary responsibility for household and child care duties (Frome, Alfeld, Eccles, &
Barber, 2006; Mason & Goulden, 2004; Xie & Shauman, 2003).
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36
The Double Bind: Sexism and Racism
5
Although underrepresented minority women are just as likely to be interested in STEM as
white women, other factors influenced retention and achievement that resulted in
underrepresentation (Malcom & Malcom, 2011; Ong et al., 2011). Ong et al. discovered many
factors that influenced underrepresented female students, including a chilly STEM climate, lack
of funding, lack of mentorship and role models, family and community pressure, and lower
academic self-efficacy. Malcom and Malcom (2011) reflected on the (lack of) changes for
women of color in science that have taken place since the seminal article The Double Bind: The
Price of Being a Minority in Science (Malcom, Hall, & Brown, 1976). Although women of color
are expressing interest in science at a higher rate than their white peers, they are more likely to
pursue a career in life, social, or behavioral science – not engineering. Despite the increased
number of minority women receiving doctorate degrees, there remains a lack of diversity in the
faculty. Malcom and Malcom (2011, p. 168) challenge all institutions of higher education to
“establish sustainable, empirically based activities to support the professional advancement of
women faculty in STEM.”
5
The phrase double bind evolves from Malcom, Hall, and Brown (1976), but is used
ubiquitously to refer to the combination of sexism and racism that women face in science and
engineering
LOW ENROLLMENT FOR WOMEN
37
Factors Determining Career Choice
Researchers have explored several factors that influence women as they determined their
career choices. Social cognitive career theory (Lent, Brown, & Hackett, 1994), explored the
intersection of self-efficacy, outcome expectations, and personal goals. This section of the
literature review will explore the self-efficacy aspect of social cognitive career theory, and the
influence of family; expectancy theory will be explored in depth in a later section.
Self-efficacy
In a seminal study, Betz and Hackett (1981) determined that occupational self-efficacy
influenced women away from occupations that were perceived as traditionally male-dominated.
Women had self-efficacy scores that were significantly lower than men as they examined the
educational requirements for occupations; the greatest difference was for engineering related
fields. Specifically, 70% of males studied believed they could complete the educational
requirements for an engineering degree, but only 30% of the females studied held that same
belief. Connecting the effects of self-efficacy to science-based career choices, Lent et al. (1991)
determined that math self-efficacy influenced science-based career choices. Their study also
demonstrated that men had higher math self-efficacy than women. Furthermore, Luzzo et al.
(1999) demonstrated a link between career interest and math and science self-efficacy; through
vicarious learning- and performance accomplishment-based interventions, they demonstrated a
persistent effect on efficacy and career interest.
Focusing on gender, Zelden and Pajares (2000) explored the role of self-efficacy in
women who pursued mathematics-related majors and careers. They concluded that self-efficacy
influenced women as they made decisions about their careers. More specifically, they found that
self-efficacy sources seem to be stronger for women in male dominant careers than for women in
LOW ENROLLMENT FOR WOMEN
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traditional roles. For these women, the source of their self-efficacy was vicarious experience and
verbal persuasion; they all had family members, peers, teachers, or supervisors who influenced
them. Furthermore, women who demonstrated intent to continue in their pursuit of a STEM-
related degree also demonstrated higher ratings on the subscales of the LAESE survey (Marra,
Rodgers, Shen, & Bogue, 2009): engineering career expectations, engineering self-efficacy,
efficacy in coping with difficulties, and math expectations. Relatedly, self-efficacy and career
interests are not narrowly focused, they apply across most STEM-related career fields (Milner,
Horan, & Tracey, 2014). Thus, individuals who have high self-efficacy in STEM-related fields,
also show high levels of STEM-related career interest.
Several researchers extended the work on career interest and the effect of self-efficacy to
include communal goals, and the intrinsic value of helping others and benefiting society
(Diekman et al., 2010; Eccles, 1994). Women’s desire to help others or work with others
influenced their STEM career interest. Diekman et al. determined that the more women
endorsed their communal goals, the less they were interested in STEM. Furthermore, gender
predicted communal goal endorsement: Women are stronger endorsers than men. Although
lower math and science self-efficacy is reportedly a predictor of STEM career avoidance,
Diekman et al. concluded that the desire to help people and collaborate with others was a
stronger predictor of career choice. Similarly, Eccles (1994) concluded that career choices were
made based on the value an individual placed on the characteristics associated with an
occupation. Eccles concluded that although self-efficacy is important in the selection of a career,
it is not the only criteria used. Individuals need to feel confident about their career choice, and
place higher value on the characteristics of their choice, such as helping others and benefiting
LOW ENROLLMENT FOR WOMEN
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society. In Eccles’ study, it was clear that women placed more value on their family, and any
sacrifices necessary in support of their family.
Influence of Family
Ong et al. (2011) determined that the responsibilities of family, such as getting married,
raising a family, and supporting extended family members, can draw women away from the
pursuit of a STEM career. Conversely, once women decide to pursue a STEM career, they are
persistent in that decision (Buschor, Berweger, Frei, & Kappler, 2014); 86% of the women that
Buschor et al. studied in the quantitative portion of their study were persistent in their decisions
after two years. Buschor et al. determined that the characteristics that were predictors of
pursuing a STEM career included a deep passion for science that is linked to family influence,
role models within their social network, parental support that included providing a scientific
learning environment at home, high abilities in mathematics, and having a broad range of
interests. Furthermore, Buschor et al. discovered that any decrease in STEM career interest does
not occur as young women are matriculating into universities, but rather during their time in K-
12 education, which is when parents are able to apply the most influence on their children.
Interventions
Interventions such as mentoring, community-based programs, and summer bridge
programs have been effective at helping girls and young women understand that STEM careers
do involve helping people and collaborating with others, as well as demonstrating increased
levels of self-efficacy and STEM persistence (Diekman et al., 2010; MacPhee, Farro, & Canetto,
2013; Simon, Aulls, Dedic, Hubbard, & Hall, 2015).
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Mentoring
Several studies have found that motivational and mentoring programs improve self-
efficacy in women (Aluede, Imahe, & Imahe, 2002; Downing, Crosby, & Blake-Beard, 2005;
MacPhee et al., 2013; Simon et al., 2015). Using a combination of mentoring and exposure to
research, MacPhee et al. demonstrated a 20% increase in self-efficacy for women who
participated in the McNair mentoring program. In addition, Downing et al. (2005) determined
that mentors provided social support for women, which was more effective than role models at
influencing women to pursue a science related career (Aluede et al., 2002). Furthermore, Simon,
et al. (2015) concluded that motivational programs that focused on increasing women’s sense of
competence have the greatest impact on STEM persistence. They found that self-efficacy was an
important contributor to emotional well-being, persistence, and performance.
Community Based Programs
Community-based STEM programs have been successful at increasing STEM interest for
participants. Recent programs include the Portland Synergies project, the Chicago Science Club,
and a Canadian after-school STEM program. Falk et al. (2016) studied STEM interest and
participation data from the cohorts in the Portland Synergies project. The Synergies project is
based in an under-resourced neighborhood of Portland, and created a “STEM ecosystem” that
relied on the entire community for resources. Their assertion was that learning needs to be more
than just a classroom approach that is focused on pre-determined criteria of success – STEM
interest interventions should include the community that surrounds the children, and focus on
how the participants define participation and interest. Although the quantitative aspect of their
study did not produce results that were statistically significant, the qualitative aspect
LOW ENROLLMENT FOR WOMEN
41
demonstrated increased emotional attachment and energy that revealed promise for their
approach (Wyld, 2015).
Science Club is a community-based after-school program designed for underserved
middle-school youth in the Chicago area. The program combines mentoring and STEM
programming with an existing after-school club that provides the students with essential services
such as meals and a safe place to avoid drugs and gangs (Kennedy et al., 2016). The initial
cohort of participants in the Science Club demonstrated an increased science aptitude; the
students jumped up one aptitude level. In addition, there was a marked increase in STEM
interest as the students matriculated into college. Prior to the Science Club, only 1% of students
pursued a STEM degree; however, the first cohort demonstrated 34% STEM interest as they
chose a field of study (Kennedy et al., 2016).
Community Science Clubs (CSC) is a product of Canada’s Visions of Scientific Learning
Organization; it is a community-based program designed to increase access to STEM for youth
from low-income communities (Duodu, Noble, Yusuf, Garay, & Bean, 2017). CSC also
combines mentoring with STEM programming that includes lectures, workshops, and hands-on
STEM modules. Although CSC has yet to evaluate the student outcome because the program is
in its early stages, the researchers will be using a mixed-methods approach to evaluate the impact
of the CSC program. The challenges the team has had to overcome include community-based
issues such as gang violence, limited resources, and accommodating a wide range of youth
between the ages of 8 to 14 (Duodu et al., 2017).
Holistic University Programs
The UCLA PEERS program, a holistic approach for retaining women and
underrepresented minorities (URMs), achieved a much higher persistence rate of 84% as
LOW ENROLLMENT FOR WOMEN
42
compared to the university rate of 39% for URM students with science majors (Toven-Lindsey et
al., 2015). Most of the PEERS participants were women (68%) and URMs (70%). The PEERS
program helped the students create peer networks, established a more welcoming academic
culture, and helped students envision themselves as scientists throughout the academic calendar.
Doctoral students were the focus of Bernstein’s (2011) work: CareerWISE was developed to
address the attrition of women doctoral students pursuing a degree in a STEM field. The
CareerWISE design was an intervention and prevention model to help students persist in their
environment and to be resilient in the face of known barriers and environmental factors as they
enter the workforce. Preliminary results from randomized trials and randomized control trials
have shown statistically significant evidence that the CareerWISE program is being effective.
Specifically, participants have demonstrated “statistically significant improvement in perceived
knowledge, coping self-efficacy, and ability to apply interpersonal communication skills” as
compared to those students who did not have access to the program.
6
Summer Bridge Programs
Although there is an abundance of summer bridge programs offered by universities, there
is a limited amount of empirical evidence that has evaluated the effectiveness of these programs
(Kezar, 2000; Sablan, 2014). What limited research does exist, reports mixed results; some
programs have demonstrated improvement in skills and persistence, and others showed no
improvement (Sablan, 2014). Sablan’s review of the literature revealed a void of any published
6
Source of quote: http://careerwise.asu.edu/?q=about-careerwise
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research on programs designed specifically for women. More recently, Tomasko, Ridgway,
Waller, and Olesik (2016) and also Hughes, Nzekwe, and Molyneaux (2013) concluded that
Summer bridge programs and summer camps can be effective in increasing interest in STEM,
self-concept in science and math, and for the retention of women and URMs. Furthermore,
Hosoi and Canetto (2011) determined that recruitment rather than retention should be used to
have more of an effect on inequity for some fields of engineering; they recommend designing
interventions focused on recruiting women into engineering.
The remainder of this chapter will focus on the knowledge, motivation, and
organizational factors necessary to increase the enrollment for women at the State University
College of Engineering, Computer Science, and Technology (ECST). In particular, the
knowledge, motivation, and organizational factors that LaunchPad, an ECST summer STEM
recruitment program, will need to address to achieve the program goal of 50% matriculation into
the ECST, and contributed to the organizational goal of an increased enrollment rate for women.
The Clark and Estes (2008) Gap Analytic Conceptual Framework
Clark and Estes (2008) provide a research-tested conceptual framework for performing gap
analyses to determine the causes of performance gaps. A five-step process model is used to
describe the approach developed by Clark and Estes to perform a gap analysis, identify solutions,
and evaluate the implementation results. The first two steps of the process focus on goals. It is
important to understand the business goals that an organization has declared; equally important
are the performance goals for individuals in the organization. Step three uses the results of step
one and two to determine if a performance gap exists, and step four provides the analysis of the
gaps to determine the causes. Step five focuses on solutions to implement and is described by
Clark and Estes as three parallel activities that focus on the knowledge and skills, motivation,
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and organizational solutions to implement. Finally, step six is the performance feedback loop
where the results are evaluated, the system can be tuned, and goals can be revised.
At the core of the Clark and Estes (2008) framework is the assertion that the most
common causes of performance gaps are knowledge and skill deficits, lack of motivation, and
organizational barriers. There are four types of knowledge that can be used by individuals to
meet a performance goal: factual, conceptual, procedural, and metacognitive (Krathwohl, 2002).
Motivation is the necessary catalyst for individuals to use their knowledge to achieve a goal
(Mayer, 2011). The three facets of motivation are active choice, persistence, and mental effort
(Pajares, 2006). In addition, organizational factors such as value chains and value streams, work
processes, and material resources can also prevent individuals from achieving their performance
goals (Clark & Estes, 2008).
Stakeholder Knowledge, Motivation and Organizational Influences
Knowledge
It is important to understand what knowledge is required to achieve a goal (Rueda, 2011).
This section of the review will focus on the knowledge influences that high school students
require to demonstrate higher engineering self-efficacy and STEM career interest by the end of
the 2017 LaunchPad program.
Knowledge influences. Krathwohl (2002), in his discussion of Bloom’s taxonomy,
described four types of knowledge: factual, conceptual, procedural, and metacognitive. Factual,
conceptual, and procedural knowledge are hierarchical and build upon one another. Factual
knowledge is the foundation and includes data elements such as terms. Conceptual knowledge
builds on factual knowledge and consists of categories, principles, and theories. Procedural
knowledge builds upon factual and conceptual knowledge and is actionable; it is knowledge of
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how to perform an action. Metacognitive knowledge can be described as awareness of one’s
own knowledge and is self-reflective (Krathwohl, 2002). Knowledge is one of the three critical
factors of performance and is a necessary element in achieving performance goals (Clark &
Estes, 2008). The knowledge influences included in this review are factual, conceptual, and
metacognitive. Table 4 provides a listing of the three assumed knowledge influences that have
been derived from the research literature, the knowledge types, and how these knowledge
influences will be assessed by this study.
Students need to know the benefits of a STEM-related career. Interest can be integral to
choosing a career, even more than performance goals (Lent et al., 1994). Increasing interest in
STEM-related careers can be achieved through highlighting the benefits of such a career. In
addition, there is evidence that salary is a significant factor for women when selecting a career
(Barth, Guadagno, Rice, Eno, & Minney, 2015). The earning benefit of a STEM degree can be
as much as 23% for women (Olitsky, 2014). Olitsky confirmed that the earnings benefits for
STEM majors are large regardless of gender and suggested that the underrepresentation of
women may be due to a lack of information regarding the economic benefits of STEM majors.
In addition to increased salaries, there are additional aspects of some STEM careers that women
rated as desirable: the ability to balance career, family, and personal growth; and the security and
benefits (Quesenberry & Trauth, 2012). Although STEM careers can provide women with the
benefit of greater earning potential and increased financial security, women continue to struggle
inside STEM career tracks with pay inequity based on gender (Hill et al., 2010).
Students need to know what they will be able to achieve with a STEM degree. Women
and girls see STEM as being incongruent with their values, goals, interests, and ability to have a
family; they reject STEM careers due to their communal goals (Diekman et al., 2010).
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Therefore, women sought careers that better match their interests through providing them the
opportunity to help others, and careers that enable them to combine their desire to have a work
life balance (Beyer, 2014). When women choose to pursue a STEM-related degree, it is more
frequently within health, biological, and medical services (HBMS) rather than math, physical
engineering, and computer sciences (MPECS) (Eccles & Wang, 2016). HBMS careers are
viewed as more focused on people and humanitarian goals than MPECS careers. Changing this
perception can happen through establishing new knowledge and perceptions about how STEM
can contribute to improving the environment and enable working in partnership with others
(Diekman et al., 2010). Recruitment efforts and interventions should focus on demonstrating
that STEM degrees provide a vast number of options that can be applied towards solving real
world problems and the ability to help others (Dasgupta & Stout, 2014; Eccles & Wang, 2016).
Students need to know about their own gender stereotypes. Implicit learning is the lack
of metacognition about how an individual has learned. Gender bias can be transmitted non-
verbally and learned implicitly by girls and young women (Weisbuch & Pauker, 2011). Women
and girls internalize this bias, and it negatively affects their interest in STEM. Thus, when
women are presented with biased statements regarding their performance, their interest declines
(Thoman & Sansone, 2016). Furthermore, gender stereotypes influence students’ level of
interest in math and science (Fennema & Sherman, 1997; Makarova & Herzog, 2015). For
example, Li (2004) demonstrated that students’ beliefs are influenced by teachers’ beliefs.
Additionally, gender inequity influences students’ performance in mathematics (Fennema et al.,
1990).
Research has demonstrated that metacognitive knowledge leads to better cognitive
performance (Baker, 2006), and self-reflection can result in self-discovery based on social
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injustices of inequality and oppression (Lee & Robinson, 2014). Thus, students’ self-reflection
about stereotype influences can be effective at reducing the effects of these stereotypes. Table 4
describes the assumed knowledge, that when combined, will contribute to higher engineering
self-efficacy and STEM career interest.
Table 4
Assumed Knowledge Influences
Organizational Mission
To successfully prepare the next generation of engineering, computer science, and
technology professionals for an urban city and beyond.
Organizational Global Goal
By 2020, the enrollment rate for women in the CSULA ECST will increase from 15%
to 25%.
Stakeholder Goal
By the end of the 2017 LaunchPad program, the students will demonstrate higher
science self-efficacy and STEM career interest.
Assumed Knowledge Influence Knowledge Type
Students need to know the benefits of a STEM-related
career
Factual
Students need to know what they will be able to achieve
with STEM majors
Conceptual
Students need to know about their own gender stereotypes Metacognitive
Motivation
An individual with knowledge may or may not use it; motivation is the necessary catalyst
that individuals need to actively use and apply their knowledge (Mayer, 2011). There are three
facets of motivation in the context of individuals working towards a goal: active choice,
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persistence, and mental effort (Clark & Estes, 2008). The first element, active choice, occurs
when an individual is taking action towards achieving a goal. The second element, persistence,
occurs when an individual remains focused on a goal. The third element, mental effort, occurs
when individuals work smarter, using innovative solutions to achieve their goals. In addition,
there are five concepts of how motivation works: interest, beliefs, attributions, goals, and
partnership (Mayer, 2011). This section of the review will focus on two of Mayer’s concepts:
beliefs and interest.
Beliefs influence motivation – an individual’s belief about whether they can accomplish a
goal will influence the choices they make, and the effort they put forth (Pajares, 2006). Interest
influences motivation – students work harder on things that have more value and meaning to
them, which increases their interest (Mayer, 2011; Shraw & Lehman, 2009); value and meaning
are described by expectancy value theory (Eccles, 2006). The motivational influences affecting
the students will be the focus of this work. Specifically, how self-efficacy and expectancy values
will influence students as they achieve their goal of demonstrating higher engineering self-
efficacy and STEM career interest by the end of the 2017 LaunchPad program. Table 5
describes the assumed motivational influences that this section of the review will explore.
Self-efficacy theory. Self-efficacy is defined as the belief that a person can achieve what
he or she is attempting; higher self-efficacy has a positive impact on persistence when faced with
obstacles (Bandura, 1977). Self-efficacy is derived from four primary sources: mastery
experience, vicarious experience, social persuasions, and emotional and physiological states
(Pajares, 2006; Usher & Pajares, 2009). Mastery experience is achieved by being successful
when faced with challenging tasks and overcoming obstacles; mastery experience has the longest
lasting effects on an individual’s self-efficacy (Usher & Pajares, 2009). The observation of
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others as a comparison is known as vicarious experience. Social persuasion results from
feedback, mostly praise, individuals receive from trusted sources. Usher and Pajares (2009)
caution that self-efficacy achieved through social persuasion can be short-lived, and in some
cases, can undermine an individual’s self-efficacy. Finally, emotional and physiological states is
the fourth source of self-efficacy, and is achieved through an individual’s reaction to a task.
Self-efficacy is a significant source of motivation (Pajares, 2006).
Student self-efficacy. Students with higher math, science, and engineering self-efficacy
are likely to have higher STEM career interest, and more likely to choose STEM-related fields of
study (Beyer, 2014; Lent et al., 1986; Lent et al., 1991; Ong et al., 2011; Wang, 2013). Self-
efficacy has an influence on students’ beliefs about their career options; specifically, math self-
efficacy influences science-based career choices (Lent, et al., 1986; Lent, et al., 1991). In
addition, students with higher levels of math self-efficacy are more likely to choose STEM as a
field of study when matriculating into college (Ong et al., 2011; Wang, 2013). Furthermore,
students who have higher levels of domain specific self-efficacy, also show higher levels of
career interest in those domains. Related to STEM careers, students with higher math, science,
and engineering self-efficacy demonstrate higher levels of STEM-related career interest (Milner
et al., 2014). Moreover, women who demonstrate intent to pursue a STEM-related degree, also
revealed higher levels of engineering self-efficacy (Marra et al., 2009).
Table 5 describes the two assumed motivational influences that when combined will help
the students achieve the stakeholder goal of demonstrating higher engineering self-efficacy and
STEM career interest by the end of the 2017 LaunchPad program. The students will be
motivated to enroll in the College of Engineering with higher engineering self-efficacy and
STEM career interest.
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Expectancy value theory. Expectancy value theory describes a theoretical motivational
model that characterizes how individuals approach a task or an activity (Eccles, 2006).
Individuals are more motivated to perform well on a task if they believe they can perform the
task, and the value they attribute to completing the task (Eccles, 2006). There are four concepts
within Eccles’ expectancy value theory: (1) intrinsic value, (2) attainment value, (3) utility value,
and (4) cost. Intrinsic value, and motivation, is highest when individuals enjoy what they are
doing, and are doing things that have personal meaning to them (Eccles, 2006). Attainment
value is the connection between the task an individual is performing, and how related the task is
to the individual’s personal identity and preferences (Eccles, 2006; Pintrich, 2003). Utility value
is related to how well a task is aligned with an individual’s goals, and cost is related to the
energy required to perform the task. Eccles characterizes cost in terms of psychological cost,
time, and energy. This study will focus on attainment value as the motivational factor that
contributes to increased STEM interest for female students.
Attainment value. Students are more likely to pursue careers that are aligned with their
personal identity, sense of self, and core values (Eccles & Wang, 2016). Attainment value has
been shown to predict student’s choices; specifically, their intention to enroll in specific courses
(Pintrich, 2003). Conversely, students can be dissuaded from a specific career path if the role is
considered incongruent with their central values or personal image (Eccles, 1994). Gender
influences the value students’ place on fields of study. If women do not believe that careers are
congruent with their interpersonal values, or their core values, they will avoid the field (Beyer,
2014). Therefore, a strong predictor of career choices for women are those that are collaborative,
provide them the ability to help others (Diekman et al., 2010).
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Computer science is perceived as incongruent with women’s interpersonal values, goals,
or interests, which discourages women from pursuing computer science-related careers (Beyer,
2014). Similarly, women avoided math, physical, engineering, and computer sciences (MPECS)
and chose health, biological, and medical services (HBMS) because they were viewed as more
focused on people and humanitarian activities than MPECS (Eccles & Wang, 2016). Programs
designed to attract girls to STEM should focus on the values that are important to them: solving
real-world problems and helping people (Dasgupta & Stout, 2014; Diekman et al., 2010).
Table 5 describes the assumed motivational influences that when combined, would
contribute to the students demonstrating higher engineering self-efficacy and STEM career
interest by the end of the 2017 LaunchPad program.
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Table 5
Assumed Motivational Influences
Organizational Mission
To successfully prepare the next generation of engineering, computer science, and technology
professionals for an urban city and beyond.
Organizational Global Goal
By 2020, the enrollment rate for women in the State University College of Engineering will
increase from 14% to 25%.
Stakeholder Goal
By the end of the 2017 LaunchPad program, the students will demonstrate higher science self-
efficacy and STEM career interest.
Assumed Motivation Influences Motivation Type
Students need to believe that they can succeed in
science.
Students need to believe that they can succeed in math.
Self-efficacy
Students need to connect STEM careers with being able
to make the world a better place
Expectancy Value: Attainment Value
Organizational Influences
This section of the review will focus on the organizational influences necessary for
students to demonstrate higher engineering self-efficacy and STEM career interest by the end of
the 2017 LaunchPad program. To be successful, an organization needs to have work processes
that are efficient and effective, and an adequate supply of material resources (Clark & Estes,
2008). When diagnosing a performance gap, organizational influences must be considered.
Individuals within the organization may have the necessary knowledge to perform their tasks,
and may be highly motivated, however, if organizational barriers exist, these individuals will not
meet their performance goals (Clark & Estes, 2008; Rueda, 2011). Clark and Estes describe
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organizations as complex systems that have their own culture. This section of the review will
explore the aspects of organizational culture necessary to meet the organizational goal of
increased enrollment for women in the college of engineering.
Cultural models and cultural settings. Culture can be thought of as a set of group
assumptions that have evolved over time through the experience of resolving external problems
and applying them internally; these assumptions are passed on to new group members as
solutions to sets of problems (Schein, 2010). The influence of culture on individuals has been
divided into how an individual thinks and how an individual behaves. Cultural models are a
common set of schemas that describe how an individual believes the world works, or how they
believe the world should work (Rueda, 2011). Conversely, Rueda describes cultural settings as
the visible aspects of an individual’s behavior. Cultural models and settings are not applicable
solely to individuals, this concept can also be applied to groups and organizations. In fact, Erez
and Gati (2004) describe a reciprocal relationship that exists between the different levels of
culture: individuals are at the center of the ring, surrounded by groups, which are surrounded by
organizations. This section will explore the cultural model and cultural setting that may inhibit
students from demonstrating higher engineering self-efficacy and STEM career interest.
Cultural model: gender-inclusiveness. A barrier facing female students that have
demonstrated an interest in STEM is gender bias and the lack of a gender-inclusive learning
environment. Many studies have shown that boys and girls are perceived differently throughout
their education; from elementary school, to middle school, and through high school (Espinoza et
al., 2014; Fennema et al., 1990; Riegle-Crumb & Humphries, 2012; Tiedmann, 2000). This
perception, gender bias, has a negative effect on the performance of female students (Espinoza et
al., 2014). Furthermore, research suggests that the pedagogy, curriculum, and environment for
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STEM classrooms needs to be gender-inclusive (Brotman & Moore, 2008). Gender-inclusive
pedagogy is defined as being dynamic, incorporating cooperative learning, highlighting the
implications of science on society, and incorporating life experience of girls (Brotman & Moore,
2008). Some studies have suggested a single-sex environment has a positive effect on girls in
STEM (Cherney & Campbell, 2011; Logan, 2007; Simpson & Che, 2016). However, Hughes,
Nzekwe, and Molyneaux (2013) concluded that the quality of the pedagogy and learning
programs are more important.
As Agócs (1977) pointed out, changes that strive to reverse the “historic and systemic
nature of organizational inequality” are met with resistance (p. 920). To overcome the
resistance, to neutralize gender bias, organizations must create a culture that encourages
educators to reflect on their own bias, a culture in which candid conversations about bias can be
held, and a culture that has the courage to face the institutional barriers to achieving equity.
Cultural settings: female role models. A core component of social cognitive theory is
learning through observing, or modeling. Modeling is the demonstration of behavior or skill by
a teacher, or other individual that a student may hold in esteem (Bandura, 1987; Denler, Wolters,
& Benzon, 2006). Modeling was used to affect behavior; using television and radio dramas,
Smith (2002) changed the behavior of her viewers. Similarly, mentoring programs influenced
women to stay in STEM, and increased academic self-efficacy (Chang, 2002; MacPhee et al.,
2013). Moreover, simply having more female graduate students increased persistence for
women pursuing STEM degrees (Griffith, 2010). Extending modeling to the classroom, high
schools that have higher numbers of female teachers graduated female students that were more
inclined to major in STEM (Stearns et al., 2016). Furthermore, there continue to be a lower
representation of female faculty teaching STEM. There are only 7% tenure-track female faculty
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in engineering, and 21% in computer science and information technology (Hill, Corbett, & St.
Rose, 2010). The cultural settings of classrooms are improved for female students with an
increased number of female role models (Stearns et al., 2016). Therefore, institutions need to
establish sustainable, research-based activities to further the professional advancement of their
female faculty in STEM fields (Malcom & Malcom, 2011). Table 6 describes the organizational
influences, that when combined, will contribute to the students demonstrating higher engineering
self-efficacy and STEM career interest by the end of the 2017 LaunchPad program.
Table 6
Assumed Organizational Influences
Organizational Mission
To successfully prepare the next generation of engineering, computer science, and
technology professionals for an urban city and beyond.
Organizational Global Goal
By 2020, the enrollment rate for women in the SU ECST will increase from 14% to 25%.
Stakeholder Goal
By the end of the 2017 LaunchPad program, the students will demonstrate higher
engineering self-efficacy and STEM career interest.
Assumed Organizational Influences Organization Influence Type
Students’ learning environment in math and science
is not gender-inclusive.
Cultural Model
Students’ learning environment in math and science
does not reflect diversity of instructors or role
models.
Cultural Setting
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Conclusion
The purpose of this research project is to examine the causes of low enrollment for
women in STEM. In this chapter, a review of the related literature was conducted to provide the
reader insight into factors that influence the representation of women in STEM: low enrollment,
self-efficacy, gender bias, higher attrition rates, classroom environment and pedagogy, family,
the double bind, and interventions. The literature review, and gap analysis, concluded with a
focus on the stakeholder group, female high school students, and how increasing their
knowledge, motivation, and eliminating organizational barriers, would enable the students to
increase their interest, and matriculation into STEM fields of study. Chapter 3 will discuss these
assumed influences and the research methodology that will be used to better understand and
assess these barriers.
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CHAPTER 3: METHODOLOGY
Purpose of the Project
The purpose of this project was to conduct a needs analysis in the areas of knowledge,
motivation, and organizational factors necessary to reach the organizational performance goal of
an increase from 15% to 25% enrollment by women in the State University (SU) College of
Engineering, Computer Science, and Technology (ECST) by the year 2020. The analysis began
by generating a list of possible needs and then examined these systematically to focus on actual
or validated needs. While a complete needs analysis would focus on all stakeholders, for
practical purposes the stakeholder that is the focus of this analysis is the female high school
student.
Research Questions
The three research questions that guided this study were:
1. What are the knowledge, motivation, and organizational factors that contribute to the
STEM-related career decisions and field of study selections that female high school
students make?
2. What knowledge, motivation, and organizational factors does the LaunchPad program
influence such that the female high school student participants are more likely to enroll in
engineering or computer science, and/or pursue a career in engineering or computer
science?
3. What are the recommended knowledge, motivation, and organizational solutions that will
increase the number of female students that choose a STEM-related career, resulting in an
increased enrollment of female students in the State University College of Engineering,
Computer Science, and Technology?
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Conceptual Framework
Diversity in STEM is a limitless topic that has been studied by many: A recent query of
the USC library system on the topic returned a list of over 280,000 peer reviewed journal
articles. As a research topic, STEM diversity would be intractable without narrowing the focus.
A conceptual framework provides a lens to focus the inquiry on topics that can otherwise be too
broad (Maxwell, 2013). The conceptual framework for this study was developed using the
approach described by Maxwell: draw on personal experience, review empirical research,
perform thought experiments. The application of the knowledge, motivation, and organization
gap analysis as described by Clark and Estes (2008) was used as a guide while developing the
framework and narrowing the study to focus on a single stakeholder: female high school
students.
Figure 3 is a graphical depiction of the conceptual framework that guided this study. As
mentioned earlier, a Clark and Estes (2008) gap analysis was used to develop the conceptual
framework and to establish the assumed knowledge, motivation, and organizational influences
that will contribute to achieving the stakeholder goal. The assumed knowledge influences are:
(1) knowledge of the benefits of a STEM-related career, (2) knowledge of what can be achieved
with a STEM-related career, and (3) self-knowledge of gender stereotypes. A review of the
literature revealed that when women have this knowledge, their choice of career is influenced.
The assumed motivational influences that affect the female students are self-efficacy and
attainment value. A review of the literature revealed that increased math and science self-
efficacy and attainment value influence women’s choices in their field of study. The assumed
organizational barriers are gender bias and lack of diversity in role models. A review of the
literature revealed that when these barriers are removed, women’s choices in their field of study
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are influenced. Moreover, the literature revealed that when these influences are applied to, and
barriers removed for, female high school students, they are more likely to matriculate into a
STEM field of study, which will be necessary to achieve the organizational goal of increasing the
enrollment of women from 15% to 25% in the State University College of Engineering by 2020.
Figure 3. Conceptual Framework to describe the assumed knowledge, motivation, and organizational
influences, that, when applied to female high school students, will result in increased engineering self-
efficacy and STEM career interest.
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Participating Stakeholders and Sample Selection
State University serves over 250 high schools in the local area; these students receive
priority enrollment. The focus for this study was the female high school students that are within
the local service area of State University. More specifically, the researcher focused on female
students between their junior and senior year who attended the LaunchPad program at State
University. LaunchPad is a summer STEM recruitment program for female students from the
local area high schools who are involved in the Mathematics, Engineering, Science Achievement
(MESA) program. The following section discusses the research design, data collection
methodology, and criteria and rationale used for sample selection.
Data Collection
The research design used for this study was a transformative convergent parallel mixed
methods approach (Creswell, 2014). Transformative mixed methods use both quantitative and
qualitative data to further the research of groups that are oppressed or experience discrimination.
The purpose of this study was to further the research of women in STEM, in particular the lack
of diversity in STEM using the lens of gender inequity; therefore, a transformative study was
appropriate. The quantitative and qualitative aspects of the study were performed
simultaneously, hence the convergent parallel aspect of the design. A combination of surveys,
and document and artifact gathering were used to collect data for this study.
To validate the assumed needs in knowledge, motivation, and organization, surveys were
administered, observations were performed, and data artifacts were generated and gathered.
Surveys were administered to the students to evaluate their knowledge and motivation. Open-
ended questions were added to the surveys to gain insight into the cultural models and cultural
settings (Rueda, 2011). Observations were done as a triangulation step; the survey responses
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were compared to the observed behavior (Maxwell, 2013). Data artifacts were collected to gain
insight into the cultural settings at the students’ high schools. Combined, this information
provided insight into the factors that contribute to the STEM-related career choices and field of
study selections that female students make. Furthermore, the data collection provided insight
into the gaps that are preventing the students from achieving the stakeholder goal of
demonstrating higher science self-efficacy and STEM career interest by the end of the
LaunchPad program. Table 7 provides a mapping of the assumed influences from the conceptual
framework to the data collection approach for this study. Each row in the table represents an
assumed influence, and the columns represent a data collection method. An X in the cell of the
table indicates that the data collection method in that column will be used for the assumed
influence in that row.
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Table 7
Mapping of Assumed Influences to Data Collection Method
Surveys
Documents
and Artifacts
Knowledge
Benefit of STEM career X
What can be achieved with STEM career X X
Gender Stereotypes X
Motivation
Self-efficacy X
Attainment Value X X
Organization
Gender bias X
Diverse role models X X
Surveys
There were two written surveys and one electronic survey administered for this study. A
census approach was used to maximize participation (Johnson & Christensen, 2015): All
participants of the LaunchPad program received a survey. The initial population size for the
participants in the program was 28 students. The confidentiality of the students was maintained
by creating a unique identifier for each student and maintaining a separate file that maps the
students to their identifier. The file that maps the students’ names to their unique identifiers was
kept on the researcher’s computer and was password-protected.
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The first survey (S1) was a paper survey administered to all students during the morning
of the first day of the LaunchPad program. Prior to administering the survey, the researcher
described the survey, the purpose of the survey, and also made the students aware of the second
and third surveys (S2 and S3). All students were present for the administration of S1. Survey 2
(S2) was administered at the end of the LaunchPad program during the afternoon of the final
day. However, there were four students from a local high school that was beginning class on the
Thursday of the final week. These students would not be in attendance on the final day of
LaunchPad, so the survey was administered to three of them on Wednesday morning, and the
fourth student took the survey home with her and scanned and emailed her completed survey to
the researcher. To take advantage of having the students present at the LaunchPad program, the
researcher administered paper surveys for S1 and S2 to ensure a high response rate. Survey 3
(S3) was used to measure the long-term impact of the LaunchPad program and was administered
electronically three months after the end of the LaunchPad program. The Qualtrics software
provided by the University of Southern California was used for S3. The students were given two
weeks to complete the online survey; a reminder was sent out at the one-week point. A gift card
incentive was used to increase the response rate for each of the surveys: $5 for S1 and S2, $10
for S3.
Each survey took no longer than 30 minutes and was based on two existing survey
instruments: (1) a validated Assessing Women and Men in Engineering (AWE) Pre-College
Longitudinal Assessment of Engineering Self-Efficacy (LAESE) survey (AWE, 2008), and (2) a
pilot-tested survey developed by Dr. Lisa Flores and Dr. Rachel Navarro (L. Flores & R.
Navarro, personal communication, May 16, 2017). The LAESE survey is a pre-college survey
that measures science, engineering, and computer self-efficacy. In addition, the LAESE
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instrument also provides supplemental survey questions to measure the impact of STEM-related
activities on participants’ self-efficacy, the ability to form/identify a supportive STEM
community, and whether the activity caused participants to consider pursuing STEM studies in
higher education (AWE, 2008). The Flores and Navarro survey was designed to extend the
Social Cognitive Career Theory developed by Lent et al. (1994), and is currently being used by
the researchers in a five-year National Science Foundation (NSF) funded study (L. Flores & R.
Navarro, personal communication, May 16, 2017). There were several common questions
between the surveys: 16 common questions across all surveys, two common questions between
S1 and S2, and one common question between S2 and S3. The common questions across the
three surveys were from the LAESE instrument, and assessed the knowledge and motivation
assumed influences. The common questions between S1 and S2 explored any shift in course
enrollment pre- and post-LaunchPad. The common question between S2 and S3 focused on the
effect of LaunchPad on the students. More details regarding the surveys are provided in Table 8.
The surveys used for this study are provided in Appendix A.
Table 8
Description of Survey Instruments
Survey 1 Survey 2 Survey 3
Types of Questions
Continuous 99 78 43
Categorical 58 32 23
Open ended 1 8 2
Total Number of Questions 158 118 68
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Observation
Observations were used to triangulate the data gathered in the surveys and to provide
context for the other aspects of data collection. Triangulation will provide confidence in the
truthfulness of the students’ survey responses through observing their activities, interactions, and
conversations; and will also provide insight into any subtle factors that would not be possible to
capture in a survey or focus group (Merriam & Tisdell, 2016). The researcher assumed the
observer as participant role, and was identified to the group as an observer, which was the
primary role of the researcher. As an observer, the researcher maintained tangential membership
in the group being observed. The observations were performed each day of the two-week
LaunchPad program, and were conducted throughout the day. Although the researcher
performed a majority of the observations, two additional observers were recruited during the
second week of the program; a program administrative assistant, and an industry partner. The
additional observers provided the researcher with confidence that observer fatigue (see
Limitations section in Chapter 5) had not compromised or biased the results. The observers used
the observation checklist (as suggested by Merriam and Tisdell) in Appendix B, and maintained
detailed notes throughout the day. Beginning-of-day and end-of-day notes were taken as
necessary.
Documents and Artifacts
Documents and artifacts were gathered during the LaunchPad program, and photographs
were taken to capture the environment that the students were exposed to during the LaunchPad
program. The gathered information provided insight into gender inclusiveness of the artifacts
used during the LaunchPad program, the demographics of individuals that are portrayed in
posters, and marketing material throughout the State University environment. These documents
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and artifacts provided valuable information that can be gathered more effectively than using
interviews (Merriam & Tisdell, 2016). Additionally, the LaunchPad students were asked to write
a paragraph describing how they might affect the environment or help others by having a career
in STEM. This paragraph was captured during the surveys. This self-reflection is considered
researcher-generated documents, and allowed the researcher to gain insight into the participants’
attitudes and beliefs (Merriam & Tisdell, 2016) about how a STEM career aligns with their
personal identity, sense of self, and core values (Eccles & Wang, 2016). The researcher
transcribed the participants’ essays into electronic format and the results were coded.
Data Analysis
The researcher used Microsoft Excel to conduct descriptive statistical analyses of the
quantitative data collected from the surveys. The results from the surveys were compared for
any changes between the pre-program, post-program, and far post-program responses. The
researcher wrote specific notes during the observation period, at the beginning of the day, and at
the end of the day as appropriate. The researcher documented thoughts, concerns, and initial
conclusions about the data in relation to the conceptual framework and research questions. In the
first phase of analysis, the researcher used open coding, looking for empirical codes and applying
a priori codes from the conceptual framework. A second phase of analysis was conducted where
empirical and a prior codes are aggregated using axial coding. In the third phase of data
analysis, theoretical coding was used by the researcher to identify patterns and themes that
emerged in relation to the conceptual framework and study questions.
Credibility and Trustworthiness
Maxwell (2013) uses the concept of validity to discuss validity threat as simply a threat
that the research might be wrong. Among many, Maxwell describes two threats to validity:
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researcher bias and reactivity. Researcher bias is the influence that the researcher’s experience
and beliefs might have on the data. Reactivity is the effect that the researcher has on the
environment being observed, or the interview that is being conducted. The credibility and
trustworthiness of this study, and the researcher, was established by identifying the possible
areas of researcher bias and reactivity, and describing the process that was used to minimize the
effect of these validity threats.
Observation and open-ended survey questions were the qualitative methods used by the
researcher for this study, and the only aspect of this study vulnerable to reactivity. However, as
Maxwell (2013) points out, the setting is more likely to influence the participants’ behavior than
the observer. The researcher used an observation checklist (see Appendix B) each day to guide
documentation of the observation (Merriam & Tisdell, 2016). Because the observation duration
was only 10 days (two weeks), two of Maxwell’s (2013, pp. 125-129) checklist items will not be
applicable: Intensive, Long-Term Involvement; and Rich Data. However, the researcher
observed 100% of the LaunchPad program, thus performing a comprehensive observation of the
entire program. The results of the observation were used as a triangulation step to confirm the
correctness of the survey results. As indicated by Maxwell (2013), reactivity cannot be
eliminated completely, but it is typically not as much of an issue as researcher bias. To ensure
that researcher bias did not influence the observations, additional observers were recruited to
ensure consistency of interpretation. In addition, the researcher practiced self-reflection during
the observations making note of personal thoughts and experiences that could lead to researcher
bias. These methods, when combined, increased the credibility and trustworthiness of the data
and the study (Maxwell, 2013; Merrian & Tisdell, 2016).
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Validity and Reliability
There are three types of quantitative validity: (1) content validity: did the researcher
measure what the study was designed to measure, (2) predictive or concurrent validity: do the
results correlate, and (3) construct validity: does the instrument measure what it was designed to
measure (Creswell, 2014). Reliability is the measure of consistency over time; will the results be
consistent across multiple uses (Creswell, 2014). The instruments used in this study have been
validated through empirical studies (AWE, 2008; NSF, 2016), and are provided in Appendix A.
Although Creswell describes many possible threats to the internal validity of a study, participant
drop out is the only viable threat to internal validity for this study. The instrument was used at
the beginning of the LaunchPad program, at the end, and three months after the program ends.
There was one instance of participant drop out, and the researcher removed that participant’s
data from the analysis. In addition, the researcher ensured that all incomplete survey sets were
removed from the data analysis. There are also threats to external validity, which could lead the
researcher to reach inaccurate conclusions. Creswell (2014) describes interaction of selection
and treatment, which occurs when the participants have a unique set of characteristics that
prevent the results from being generalized across a broader population, as a threat to external
validity. The validity of this study is impacted by this external validity risk; the participants are
young women between their junior and senior year in high school, and have already shown an
interest in STEM through their membership in MESA. The results of this study will not be
applicable to all populations of female students. Nevertheless, there is value in furthering the
understanding of what motivates young women to choose STEM as a field of study, and the
conclusions of this study will be restricted to this demographic (Creswell, 2014).
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Ethics
A mixed methods study relies on quantitative and qualitative research methods.
Quantitative research focuses on testing theories by examining the correlation between variables
using statistical methods (Creswell, 2014), and qualitative research focuses on making meaning
of people’s experiences (Merriam & Tisdell, 2016). Both methods of research must be guided
by the integrity of the researcher to ensure the trustworthiness of the study results. The
University of Southern California (USC) and the State University Institutional Research Boards
(IRB) provided the guidelines that were followed for this study. USC adheres to “the ethical
principles of The Nuremberg Code, The Belmont Report: Ethical Principles and Guidelines for
the Human Subjects of Research, and the Report of the National Commission for the Protection
of Human Subjects of Biomedical and Behavioral Research.”
7
This study was approved by the
USC IRB
8
, and complies with the same guidelines. An authorization agreement was established
between the USC IRB and the State University IRB. Furthermore, the researcher acted with
integrity while following the guidelines established by the IRB, and data collection did not
commence prior to IRB approval.
Human research requires informed consent, voluntary participation, participant and data
confidentiality, and data security. In addition, the participants in this study were all under the
age of 18, so parental consent was obtained. The informed consent and parental consent forms
7
http://oprs.usc.edu/about/human-subjects-protection-program/
8
USC UPIRB #UP-17-00391
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for this study are provided in Appendix C. Participant and data confidentiality were maintained
by obfuscating the names of the participants using a unique identifier. The file that maps the
identifier to the participants was password-protected. Data security was provided by the features
of Google Drive, and access to the data will be protected using two-factor authentication.
9
In
addition, all data that resides on Google Drive is encrypted at rest using 128-bit or stronger
Advanced Encryption Standard (AES).
10
Google Drive provides the necessary features to secure
the data, and as a cloud-based service, will also reduce risk of data loss by providing data
redundancy, which mitigates the impact of a hardware failure or device theft.
The researcher’s relationship to State University is professional in nature; the researcher
completed her final quarter of corporate service on the College of Engineering, Computer
Science, and Technology (ECST) advisory board, is now an alumni representative on the board.
In addition, the dean of the ECST is a member of the researcher’s dissertation committee. As
such, the researcher is invested in State University achieving the organizational goal of increased
enrollment of women in the ECST, and the dean is invested in the success of the study as
potentially contributing to the recruiting efforts that will help achieve the ECST goal of an
increased enrollment rate for women by 2020. To mitigate any conflicts of interest or breaches
of ethical behavior, the researcher has reviewed the Ethical Issues Checklist provided by
Merriam and Tisdell (2016, pp. 264-265). In accordance with the checklist, an ethical advisor
9
https://www.google.com/landing/2step/#tab=how-it-protects
10
https://gsuite.google.com/faq/security/
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was used: the researcher’s dissertation chair served as the ethical advisor for this study. The
researcher had no relationship with the participants in the study; their participation in the
LaunchPad program and this study were completely voluntary.
As stated earlier, researcher bias is the largest threat to the external validity of the study
(Maxwell, 2013). Researcher bias is the influence that the researcher’s experience and beliefs
might have on the data. The researcher may have some internal bias resulting from personal
experience as a woman who completed a B.S. and M.S. in computer science, and has been
working in a technical role within the Aerospace industry for over 20 years. The data
interpretation step is the activity at highest risk of influence by researcher bias. To mitigate this
risk, the researcher relied on self-reflection and peer debriefing (Creswell, 2014). Furthermore,
the researcher relied on professional experience gained as a leader within a Federally Funded
Research and Development Center (FFRDC). The researcher’s role within the FFRDC required
the highest level of integrity and objectivity while providing technical advice and programmatic
recommendations on the nation’s most complex space systems.
Limitations and Delimitations
Limitations
Limitations are the influences on the study that the researcher cannot control. Mixed
methods research can be used to overcome the limitations of quantitative and qualitative
methodology (Creswell, 2014), but despite the research methodology and design, there are
aspects of the study that cannot be controlled. The participants of the study introduce levels of
variability; thus, the researcher must consider the participant-based influences that cannot be
controlled. The responses to quantitative and qualitative questions are influenced by a multitude
of factors which include: respondent cognition, interaction between the investigator and the
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participant, exaggeration, untrue responses, masked willingness to participate, and respondent
state of mind (Borg & Mohler, 1994; Iarosi, 2006). An additional limitation of this study is the
limited duration of the LaunchPad program: two weeks. Creswell discusses Intensive, Long-
Term Involvement, and Rich Data in his Validity List Checklist as strategies to mitigate specific
validity threats. These strategies were not feasible for such a short duration.
Delimitations
Delimitations are the influences on the study that the researcher can control. The
researcher chose to study the underrepresentation of women in STEM because it is a topic of
personal interest. The organization and demographic being studied were selected based on
convenience and access. The researcher was working with the dean of State University College
of Engineering, Computer Science, and Technology (ECST) and was provided access to the
LaunchPad program by the dean. The researcher anticipated that the results of this study will
contribute to the ECST goal of increasing female enrollment from 15% to 25% by 2020. A
mixed methods approach was selected by the researcher to draw on data of various forms, and
finally, the theoretical lens used by the researcher is based on a feminist perspective (Creswell,
2014). The scope of this study was constrained by the researcher through personal interest,
convenience, and access; nevertheless, the results of this study are expected to further the
literature regarding recruitment of female students into STEM fields of study.
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CHAPTER 4: RESULTS AND FINDINGS
This study evaluated the impact that the LaunchPad program had on female high school
students, and the knowledge, motivation, and organizational influences affecting the field of
study and career choices for female high school students in the State University area. The
questions that guided this study were:
1. What are the knowledge, motivation, and organizational factors that contribute to the
STEM-related career decisions and field of study selections that female high school
students make?
2. What knowledge, motivation, and organizational factors does the LaunchPad program
influence such that the female high school student participants are more likely to enroll in
engineering or computer science, and/or pursue a career in engineering or computer
science?
3. What are the recommended knowledge, motivation, and organizational solutions that will
increase the number of female students that choose a STEM-related career, resulting in an
increased enrollment of female students in the State University College of Engineering,
Computer Science, and Technology?
As discussed in Chapter 3, a mixed methods approach was used for this study (Creswell,
2014). Three surveys were administered during the study; each survey contained quantitative
and qualitative questions. In addition, documents, and artifacts were generated and collected.
When combined, this information provided insight into gaps that prevent students from
expressing interest in an engineering or computer science field of study, or a career in
engineering or computer science.
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This chapter begins by describing the stakeholders, the participants in the LaunchPad
program, and is followed by a discussion of how the surveys were administered. This is
followed by a discussion of the data analysis. The results and findings are then presented. An
analysis of the results based on the knowledge, motivation, and organizational influences is
presented first, followed by an evaluation of LaunchPad-specific results. The findings section
follows with answers to the research questions.
Participating Stakeholders
There were 28 initial participants in the State University (SU) College of Engineering,
Computer Science, and Technology (ECST) LaunchPad program. The participants were
recruited by the SU Mathematics, Engineering, Science Achievement (MESA) program
coordinator. A request was distributed to the local area high school math and science teachers
announcing the LaunchPad program and asking the teachers to nominate students for
participation. The students’ records were reviewed by the LaunchPad administrators, SU
faculty, and the ECST dean; the student’s weighted grade point average (GPA), the number of
math and science courses the student had completed, and a student essay were reviewed as
entrance criteria. The median GPA for the group of students was 3.94, with a standard deviation
of 0.51. In addition, the selected students needed to identify as female, be between their junior
and senior year in high school, and attend a high school that was considered within the SU local
service area. The goal was to select students from each of the nominating high schools, but
balance the population with groups of two or more students from each high school. The majority
of nominated students were selected to participate in LaunchPad. Table 9 describes the
population based on participants per high-school. The majority of the students identified as
Latina/Latino (61%), and Asian Pacific American (29%). Table 10 summarizes the ethnicity
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mix of the study population. The majority of participants, if they pursue a college degree, will be
first generation college graduates. See Table 11 for a description of their parents’ education.
Table 9
LaunchPad Student Population from the Local Area High Schools
High School Number of Students
Thomas 6
Boulder 3
Partnership 3
Advantage 2
Directional 5
FGM 2
Valley 6
SAH 1
Note. Pseudonyms are being used for the high school names. The student from SAH dropped out
on day three.
Table 10
Ethnicity of Participants
Ethnicity
Latina/Latino 17 61%
Asian Pacific American 8 29%
Latina/Latino and Asian Pacific American 2 7%
Latina/Latino and mix 1 3%
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Table 11
Highest Level of Education for Participants’ Parents
Graduate Studies 0
College/University 9
High School 17
Grade School 2
The LaunchPad program began with 28 participants on the first day; one student dropped
out on the third day, reducing the number of participants that completed the two-week program
to 27. The daily attendance for the first week was 100%; the attendance for the second week was
mixed due to illness, transportation issues, and a local area high school starting classes. Table 12
provides a summary of the daily attendance percentages for the two-week program. The median
attendance rate for the students was 90% overall with a standard deviation of 8%.
Table 12
Daily Attendance Percentages
Monday Tuesday Wednesday Thursday Friday
Week 1 100% 100% 100% 100% 100%
Week 2 100% 89% 78% 78% 81%
Survey Administration
As discussed in Chapter 3, a mixed-methods approach for data collection was used.
Three surveys were administered. The first survey gathered demographics and baseline data
across the assumed influences. The second and third surveys evaluated any shift in knowledge,
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motivation, organizational barriers, and also served as LaunchPad evaluations. Observation data
was captured each day of the two-week LaunchPad program, and documents and artifacts were
generated and gathered. The first survey was administered on day one of the program, and was
administered immediately after the welcome and icebreaker activity; the students had not yet
been exposed to any of the program content. Completion of the survey took between 18 and 32
minutes: The response rate was 100%. The second survey was administered to the students as
they arrived on the final day of the program and took approximately 15 minutes to complete.
Four of the students were not available on the final day, so they were administered the survey at
different times. Three of the students took the survey earlier in the week, and one student took it
home and returned her completed copy via email. The response rate for the second survey was
100%; however, one of the surveys was misplaced during analysis. The third survey was
administered three months after the LaunchPad program ended and was administered
electronically using Qualtrics. This survey was administered to all LaunchPad participants
except the student who dropped out on day three. The survey remained open for two weeks, and
a reminder email was sent at the beginning of the second week. The response rate for the third
survey was 63%, with 17 responses. There are several population sizes used throughout this
chapter: n=28 for survey 1, which was used for assessing the majority of the assumed influences;
n=26 for comparing survey 1 and survey 2, and initial feedback on the LaunchPad program; and
n=17 for discussion that includes the results from survey 3.
Data Analysis
The first and second survey were administered by the researcher during the LaunchPad
program and were paper surveys. The responses were manually entered into an Excel
spreadsheet by the researcher. The third survey was electronic and administered using Qualtrics.
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The results were exported into an Excel spreadsheet. The voluntary nature of participating in
this study was reinforced during the LaunchPad orientation and prior to administering each of the
paper surveys. For this reason, the researcher did not review the surveys as they were turned in,
and all questions in the electronic version were optional versus mandatory. During the data
analysis, the researcher performed data cleaning by checking each survey spreadsheet for wild
codes and incorrect entries (Singleton & Straits, 2010, Chapter 15).
There was some missing data, but given the voluntary aspect of the surveys, this could
not be avoided. In addition, there were some errors found for questions that constrained the
number of selections allowed. For those responses that exceeded the allowable number of
selections, the response was eliminated from the data-set to avoid skewing the results. For those
responses that did not meet the minimum requested selections, the responses were categorized as
missing data and eliminated from the analysis. The researcher created a codebook for each of
the surveys and performed data analysis using Microsoft Excel and descriptive statistics.
Frequency and percentage distributions were created for the ordinal data, and univariate analysis
was performed. The limited amount of interval data was analyzed to generate the mean, median,
mode, range, and standard deviation. The open-ended questions were coded using a combination
of manual and software-assisted coding using NVivo.
Results
The general literature review explored the underrepresentation of women in STEM, and
then narrowed to focus on the knowledge, motivation, and organizational factors that may affect
female high school students as they consider career choices and fields of study as they
matriculate into post-secondary education. The conceptual framework (see Figure 3) describes
how these factors, the assumed influences, were used to guide the focus of this study. The
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assumed knowledge influences are: knowledge of the benefits of a STEM-related career,
knowledge of what can be achieved with a STEM-related career, and self-knowledge of gender
stereotypes. The assumed motivational influences are self-efficacy and attainment value. The
assumed organizational barriers are gender bias and lack of diversity in role models. Table 13
provides a summary of the assumed influences that comprised the conceptual framework for this
study. These influences are factors that are assumed to influence female high school students
such that they are more likely to matriculate into a STEM field of study, which will be necessary
to achieve the organizational goal of increasing the enrollment of women from 15% to 25% in
the State University (SU) College of Engineering, Computer Science, and Technology (ECST)
by 2020. This chapter will discuss the results in the context of the assumed influences, the Clark
and Estes (2008) gap analysis, and the LaunchPad program.
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Table 13
Assumed Influences
Influence Type Assumed Influence
Knowledge
Factual Students need to know the benefits of a STEM-
related career
Conceptual Students need to know what they will be able to
achieve with STEM majors
Metacognitive Students need to know about their own gender
stereotypes
Motivation
Self-efficacy Students need to believe that they can succeed in
math and science.
Attainment Value Students need to connect STEM careers with being
able to make the world a better place.
Organization
Cultural Model Students’ learning environment in math and science
is not gender-inclusive.
Cultural Setting Students’ learning environment in math and science
does not reflect diversity of instructors or role
models.
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Knowledge Results
It is important to understand what knowledge is required to achieve a goal (Rueda, 2011).
This study focused on the knowledge that would be necessary for the students to demonstrate
higher engineering self-efficacy and STEM career interest by the end of the 2017 LaunchPad
program. The assumed influences are described in Table 14. Olitsky (2014) suggested that the
underrepresentation of women in STEM careers may be due to a lack of information regarding
the economic benefits of such a career. In addition, Diekman et al. (2010) suggested that women
see STEM as being incongruent with their values, goals, interests, and ability to have a family.
Establishing knowledge can change this perception. Furthermore, Lee and Robinson (2014)
suggested that self-reflection can result in self-discovery based on social injustices of inequality
and oppression. Asking the students to self-reflect about the influences of gender stereotypes
can be effective at reducing the effects of those stereotypes.
Table 14
Knowledge Influences
Knowledge Type Assumed Influence
Factual Students need to know the benefits of a STEM-related
career
Conceptual Students need to know what they will be able to achieve
with STEM majors
Metacognitive Students need to know about their own gender
stereotypes
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Students need to know the benefits of a STEM-related career. The pre-LaunchPad
survey responses reflected a very clear participant knowledge base as they entered the program.
The students had a strong understanding about the benefits of an engineering degree. Figure 4
provides of the responses for selected survey questions for this influence. Ninety-six percent of
the students responded positively when asked if a degree in engineering would result in a well-
paying job: One student responded with Don’t Know. Furthermore, all students positively
associated math to career opportunities and options. Thus, this assumed influence is not
validated: There is no gap in knowledge based on the survey responses.
Figure 4. Survey responses for Knowledge influence regarding the benefits of a STEM-related
career
4
2
14
9
9
10
17
18 1
0% 100%
Taking math courses will help me to keep my career
options open
Doing well at math will enhance my career/job
opportunities
A degree in engineering will allow me to obtain a well-
paying job
Strongly disagree Disagree Slightly disagree
Neither disagree nor agree Slightly agree Agree
Strongly agree Don't know
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This knowledge may have contributed to the number of students who indicated an interest in an
engineering or computer science degree at the beginning of the LaunchPad program (see Figure
13 and Figure 14).
Students need to know what they will be able to achieve with STEM majors. The
survey methods used to evaluate the participants’ knowledge in this area consisted of
quantitative and qualitative survey questions. The quantitative pre-LaunchPad survey questions
explored the participants’ perception of engineering and/or computer science being congruent
with their values, goals, interests, and familial environment: The results were mixed. The
participants’ responses indicated that a strong familial environment existed, but they did not see
a consistent social structure outside of their family, nor did they see engineering or computer
science as congruent with their interests. Figure 5 illustrates the mixed responses to questions
regarding this influence. Ninety-two percent of the participants responded positively that a
degree in engineering will provide a career that affords them an opportunity to be creative and
take advantage of their talents. However, the students responded with less confidence when they
were asked if a degree in engineering would allow them to obtain a job that they like; they
demonstrated a 20% reduction in positive responses. This reduction in confidence suggests that
the students are not certain that a degree in engineering would be congruent with their interests.
Although there may be additional cultural and socio-economic issues contributing to this
reduction in confidence, these results suggest that the participants would benefit from increased
conceptual knowledge in this area.
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Figure 5. Sampling of quantitative results for conceptual knowledge regarding what can be
achieved with a STEM career
The qualitative survey questions explored what the students thought they could achieve
with a degree in engineering or computer science. There was not compelling evidence that the
participants had a strong understanding of the possible contributions they could make with a
degree in engineering or computer science. This shortfall was demonstrated by the students in
their pre-LaunchPad and post-LaunchPad responses. For example, the pre-Launchpad survey
question “What computer scientists or engineers might make or invent that could make a
difference in your life” resulted in only seven of the students (27%) providing an answer that
demonstrated a strong understanding. Furthermore, in the post-LaunchPad responses, some of
the students expressed concerns about the incongruences between pursuit of a career or degree in
this area and their goals, interests, familial environment, and the impact on society. Several
5 3
5
9
9
7
10
2
2
0% 50% 100%
A degree in engineering will allow me to obtain a job
that I like
A degree in engineering will allow me to get a job
where I can use my talents and creativity
Strongly disagree Disagree Slightly disagree Neither disagree nor agree
Slightly agree Agree Strongly agree Don't know
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students’ responses indicated that the advances in technology would have a negative impact on
their lives. For example, one student was concerned that technology would further isolate her
community: “This can be a bad thing, because then more people will be glued to their electronic
devices.” In a similar response, another student thought that efficiency could lead to laziness,
and another was concerned about job security for medical staff: “I felt sad for the doctors and
cancer doctors (pathologists?) who would be out of a job if everything is automized [sic].”
These responses, combined with the quantitative responses, validated this influence. The
students did not demonstrate sufficient conceptual knowledge about what they could achieve
with a degree in engineering or computer science.
Students need to know about their own gender stereotypes. The qualitative results of
the post-LaunchPad survey revealed that a majority of the students were able to demonstrate self-
reflection about gender stereotypes in engineering and computer science. In fact, only four
students (15%) did not demonstrate any self-reflection in their responses. Many of the students’
responses indicated that they were very aware of gender stereotypes but were determined to
succeed nevertheless. One student acknowledged that engineering was a male dominant career,
but “want[ed] to show people that women are just as qualified and able to enter into these fields.”
Another student demonstrated similar determination with her awareness that there are “not a lot
of women in the engineering field which to me is frustrating. For this reason, I would want to
aspire to be a female engineer and be another example of what women are capable of.”
Furthermore, the responses from the pre-LaunchPad survey were consistent with the qualitative
responses; 96% of the students demonstrated confidence that they could cope with negative
comments about their gender and gender-based discrimination. With such an overwhelmingly
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positive display of self-reflection by the students, it is clear that these students know how to
practice self-reflection. Therefore, this assumed influence was not validated.
Summary of knowledge results. Of the three assumed knowledge influences for this
study, only one was validated: Students need to know what they will be able to achieve with
STEM majors. Although the results were mixed, there were aspects of the students’ responses
that demonstrated a gap in their conceptual knowledge in this area. Conversely, the students
demonstrated sufficient knowledge of the benefits of a STEM career and were able to
demonstrate through exemplars that they are capable of self-reflection regarding gender
stereotypes. Therefore, these knowledge influences were not validated. Table 15 provides a
summary of the results for the assumed knowledge influences.
Table 15
Validation Status for Knowledge Influences
Knowledge Type Assumed Influence Validated
Factual Students need to know the benefits of a STEM-related
career
No
Conceptual Students need to know what they will be able to achieve
with STEM majors
Yes
Metacognitive Students need to know about their own gender
stereotypes
No
Motivation Results
An individual with knowledge may or may not use it; motivation is the necessary catalyst
that individuals need to actively use and apply their knowledge (Mayer, 2011). This study
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focused on the motivation necessary for the students to demonstrate higher math and engineering
self-efficacy and STEM career interest by the end of the 2017 LaunchPad program. The
assumed influences are described in Table 16. Students with higher math, science, and
engineering self-efficacy are likely to have higher STEM career interest, and more likely to
choose STEM-related fields of study (Beyer, 2014; Lent et al., 1986; Lent et al., 1991; Ong et al.,
2011; Wang, 2013). In addition, students are more likely to pursue careers that are aligned with
their personal identity, sense of self, and core values (Eccles, 2006).
Table 16
Motivation Influences
Motivation Type Assumed Influence
Self-efficacy Students need to believe that they can succeed in math
and science.
Expectancy Value:
Attainment Value
Students need to connect STEM careers with being
able to make the world a better place.
Students need to believe that they can succeed in math and science. The pre-
LaunchPad survey results were somewhat mixed for the assumed influence of self-efficacy. The
respondents demonstrated strong self-efficacy in math and science for most of the questions.
Figure 6 illustrates one example of a strong self-efficacy response; there was 100% positive
response when asked about their perception of success regarding their math courses.
Furthermore, 84% of those respondents indicated that succeeding in their math courses was Very
Important, the remaining responses assigned the value Important to the response. The responses
were even stronger when asked if they would succeed in science courses. Although students
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demonstrated strong self-efficacy regarding their abilities to succeed in their math and science
classes, they demonstrated lower confidence in their ability to do the work required to enter a
career in engineering or computer science. In addition, they demonstrated lower social self-
efficacy, which can have an impact on academic performance (Sherer et al., 1982). Figure 7
illustrates these responses; 39% of the respondents believed or were unsure that they would fit in
socially with other students, and 39% of the respondents were concerned that an engineering or
computer science career path would require too much time or schooling. Although the students
had high self-efficacy regarding individual classes, their motivation would be improved through
increased confidence in their ability to complete the requirements of an engineering or computer
science degree, and the belief that they would be able to fit in to the university environment.
Therefore, this influence is considered validated.
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Figure 6. Strong indicators of math, engineering, and science self-efficacy dominated the survey
responses for this assumed influence.
3 8
1
11
3
I think I will succeed (earn an A or B) in my math courses
Very Important
Important
Slightly Agree
Neither Important
nor unimportant
Unimportant
Very Unimportant
Agree
Strongly Agree
Neither Disagree
or Agree
Slightly Disagree
Disagree
Strongly Disagree
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Figure 7. Lower self-efficacy appeared in a few areas of the survey responses.
Students need to connect STEM careers with being able to make the world a better
place. The methodology used to evaluate the assumed influence of attainment value consisted of
quantitative and qualitative methods. The survey questions used to analyze this assumed
influence were from the pre-, post-, and 3-month post-LaunchPad surveys. The questions were
open- and closed-ended, and the researcher used observations, and documents and artifacts to
assess this assumed influence. The quantitative questions were direct, asking the students about
topics such as personal identity, sense of self, or core values; the results discussed here are from
the pre-LaunchPad survey and provide a baseline assessment of the students’ perceptions prior to
being exposed to the LaunchPad program. The open-ended questions evaluated more subtle
aspects of attainment value by exploring the students’ ability to describe how they would affect
8
8
9
9
2
9
9
2
0% 50% 100%
I would worry that engineering or computer science as a
career path would require too much time or schooling.
I don't fit in socially with other students in engineering or
computer science.
Strongly disagree Somewhat disagree Not sure Somewhat agree Strongly agree
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the environment or help others by having a career in computer science or engineering. These
questions were evaluated manually versus coding the responses using NVivo; this was done
because the responses were very short and easy to tally. A response was considered insufficient
if the student parroted the question or demonstrated a lack of understanding.
There were two open-ended questions that explored attainment value. The first question
was asked in each of the three surveys, and the responses demonstrated an inability by the
students to provide a reasonable description of how having a career in computer science or
engineering would enable them to make the world a better place or help others. The pre-
LaunchPad survey responses revealed that 54% of the students were unable to respond
appropriately. In the post-LaunchPad survey, this gap was reduced to 35% (19% less); however,
the gap increased to 40% in the 3-month post-LaunchPad survey. In addition, a second question
was asked in the post-LaunchPad survey, which resulted in 42% of students unable to respond
sufficiently. Table 17 describes the results of the qualitative responses in more detail.
Table 17
Qualitative Results for the Motivation Influence of Attainment Value
Percent of respondents unable to respond appropriately
Survey 1 (n=28) Survey 2 (n=26) Survey 3 (n=17)
Questions: 78, 22, 10 54% 35% 40%
Question 32 42%
Note. The percentages in this table represent the number of students who were unable to respond
to the open-ended question appropriately. Blank responses did not contribute to these
percentages.
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Figure 8. Career importance: Students assigned strong importance on careers that are congruent
with family and community.
The quantitative questions led to mixed findings. The students assigned high value to
work that allows them to help their community and to spend time with their family as illustrated
in Figure 8. However, as illustrated in Figure 9, when asked if a degree in engineering would
allow them to feel like “part of the group” if they entered engineering, only 39% responded
positively, 50% either did not know or were neutral, and 10% disagreed with the statement.
When asked if doing well in math would increase their self-worth, 60% of the students
responded positively, however, 40% of the students responded either negatively or neutrally (see
Figure 9). Combined, the qualitative and quantitative results suggest that the students do not
believe that a career in computer science or engineering is aligned with their core values or is
congruent with being able to make a positive impact in the world around them. Therefore, this
influence is considered validated.
2 4
6
22
22
0% 25% 50% 75% 100%
Work that allows me to have time with family
Work that allows me to help my community and/or
society
Not Important Somewhat Important Very Important
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Figure 9. A lack of confidence in an engineering career is consistent with placing less value on
things that are incongruent with personal identity, sense of self, or core values.
Summary of motivation influence results. Both of the assumed motivation influences
for this study were validated. Although there were some strong responses regarding the
students’ self-efficacy, there was evidence that they would benefit from increased confidence in
their ability to complete the academic requirements for this career path. Additionally, an
analysis of the quantitative and qualitative data concluded that the students do not believe that a
career in computer science or engineering is aligned with their core values, and they do not
believe that it is congruent with being able to make a positive impact in the world around them.
Table 18 provides a summary of the results for the assumed motivation influences.
1 1
1
1
2
6
8
6 8
7
3
4
2
6
0% 25% 50% 75% 100%
Doing well at math will increase my sense of self-worth
I will feel “part of the group” on my job if I enter
engineering
Strongly disagree Disagree Slightly disagree
Neither disagree nor agree Slightly agree Agree
Strongly agree Don't know
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Table 18
Validation Status for Motivation Influences
Motivation Type Assumed Influence Validated
Self-efficacy Students need to believe that they can succeed in math
and science.
Yes
Expectancy Value:
Attainment Value
Students need to connect STEM careers with being
able to make the world a better place.
Yes
Organizational Results
Individuals within the organization may have the necessary knowledge to perform their
tasks, and may be highly motivated; however, if organizational barriers exist, these individuals
will not meet their performance goals (Clark & Estes, 208; Rueda, 2011). This study focused on
the organizational barriers that would need to be mitigated for the students to demonstrate higher
math and engineering self-efficacy and STEM career interest by the end of the 2017 LaunchPad
program. The assumed influences are described in Table 19.
Gender bias and the lack of a gender-inclusive learning environment is an organizational
barrier facing female students (Brotman & Moore, 2008; Espinoza et al., 2014). Furthermore,
increasing the number of female role models will have a positive effect on women pursuing
engineering and computer science (Stearns et al., 2016).
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Table 19
Organizational Influences
Type Assumed Influence
Cultural Model Students’ learning environment in math and science is
not gender-inclusive.
Cultural Setting Students’ learning environment in math and science
does not reflect diversity of instructors or role models.
Students’ learning environment in math and science is not gender-inclusive. The
responses to the pre-LaunchPad survey indicated that a majority of the students have been
exposed to gender bias in the classroom. As illustrated by Figure 10, 18% of the students had
experienced discrimination because of their gender, 43% had experienced negative comments
about their gender, and 29% of the students had been treated differently because of their gender.
Figure 10. Gender bias: The majority of responses indicate that the students have encountered
gender bias in their learning environment.
9
7
11
6
8
6
5
1
6
8
10
5
2
0% 50% 100%
At school, or during a learning activitiy, I have been treated
differently because of my sex.
At school, or during a learning activitiy, I have eperienced
negative comments about my sex (such as insults or rude
jokes).
At school, or during a learning activity, I have experienced
discrimination because of my sex
Strongly disagree Somewhat disagree Not sure Somewhat agree Strongly agree
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These results were confirmed by the observations made during the LaunchPad program. In 48%
of the LaunchPad sessions, the observers documented the faculty or administrators using gender-
biased language. They used phrases such as “you guys” or “ok guys” when addressing a room
full of female students, even after a comment in the Day 1 end-of-day summary email was sent
to the faculty and administrators making note of the gender-bias language observed.
Furthermore, when asked if they expected to be treated fairly on the job if they chose
engineering, 30% of the students were unsure or disagreed with this statement. Therefore, this
influence is considered validated.
Students’ learning environment in math and science does not reflect diversity of
instructors or role models. Responses to the pre-LaunchPad survey indicated that 50% of the
students did not have access to a role model, and 39% of the students did not have access to a
mentor. In addition, 21% of the students did not believe that there were “people like me” in the
field of engineering. The responses to these selected survey questions are illustrated in more
detail in Figure 11. The responses to the open-ended survey questions provided were consistent
with these results and will be discussed further in the LaunchPad section.
Figure 11. A significant number of students did not have access to a role model or a mentor.
1
2
2
2
5
4
8
7
16
9
9
6
8
5
0% 100%
I feel that there are people "like me" in this field.
I would have access to a "mentor" who could offer me
advice and encouragement.
I have access to a "role model" in the field (i.e.,
someone you can look up to and learn from
observing).
Strongly disagree Somewhat disagree Not sure Somewhat agree Strongly agree
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Summary of organization influence results. Both of the assumed organization
influences for this study were validated. A majority of the students had been exposed to gender
bias in their learning environment, and do not have access to a mentor or role model in the field.
Table 20 provides a summary of the results for the assumed organizational influences.
Table 20
Validation Status for Organizational Influences
Type Assumed Influence Validated
Cultural Model Students’ learning environment in math and science is
not gender-inclusive.
Yes
Cultural Setting Students’ learning environment in math and science
does not reflect diversity of instructors or role models.
Yes
Summary of Knowledge, Motivation, and Organization Results
For an assumed influence to be considered validated, the results need to have provided
evidence that the students have a gap in knowledge, are lacking motivation, and are experiencing
an organizational barrier. Table 21 provides a comprehensive listing of the assumed influences
and their validation status.
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Table 21
Summary of Assumed Influences and Validation Status
Influence Type Assumed Influence Validated
Knowledge
Factual Students need to know the benefits of a STEM-
related career
No
Conceptual Students need to know what they will be able to
achieve with STEM majors
Yes
Metacognitive Students need to know about their own gender
stereotypes
No
Motivation
Self-efficacy Students need to believe that they can succeed in
math and science.
Yes
Attainment Value Students need to connect STEM careers with being
able to make the world a better place.
Yes
Organization
Cultural Model Students’ learning environment in math and science
is not gender-inclusive.
Yes
Cultural Setting Students’ learning environment in math and science
does not reflect diversity of instructors or role
models.
Yes
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LaunchPad Results
The LaunchPad program is a summer STEM recruitment program in its inaugural year;
the program is focused on increasing the enrollment of women into the State University (SU),
College of Engineering, Computer Science, and Engineering (ECST) to meet the ECST goal of
25% female enrollment by 2020. The LaunchPad schedule and agenda are provided in Appendix
D. The program objectives were to allow the women to gain experience in multiple areas of
engineering, explore possible career options, connect to faculty and peer mentors, and to have
fun and make new friends. These goals are consistent with the assumed influences that emerged
from the literature review. The LaunchPad surveys evaluated the impact the LaunchPad program
had on the participants in the context of the engineering and computer science intent, college
intent; and knowledge, motivation, and organizational influences. Furthermore, survey questions
measured the participants’ intent to enroll in college, an engineering and/or computer science
field of study, and State University.
College and engineering intent. The majority of survey participants indicated positive
intention on enrolling in college. When asked what they expected to do after graduating from
high school, 93% responded with the intent to attend a four-year college, one intended on
attending a technical school, and one intended on joining the military. Figure 12 illustrates these
responses across the first two surveys and also all three surveys
11
.
11
n=26 for survey two and n=17 for survey three
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Figure 12. College intention: The students demonstrate strong intentions on attending college
after graduating high school.
When asked about engineering and computer science intent, the students’ responses to
two questions demonstrated increased interest between the pre- and post-LaunchPad surveys.
Responses to the first question regarding a future career in computer science or engineering,
resulted in a 54% positive response in the pre-LaunchPad survey, which increased to 65% in the
post-LaunchPad survey. Responses to the second question regarding college and the pursuit of a
career in a computer or engineering-related field also showed an increase in positive responses
between the pre- and post-LaunchPad surveys. The pre-LaunchPad survey response resulted in
0 5 10 15 20 25 30
Get a full-time job
Join the military
Don't know
Other: (open ended)
Attend a technical school (for example: business school, beauty
school, technology school, etc.)
Go to a college or university
Pre-Event Post-Event
0 2 4 6 8 10 12 14 16 18
Get a full-time job
Join the military
Don't know
Other: (open ended)
Attend a technical school (for example: business school, beauty
school, technology school, etc.)
Go to a college or university
3-month Post-Event Pre-Event Post-Event
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50% positive responses, which increased to 65% in the post-LaunchPad survey. Figure 13
provides more detail regarding these responses. Note that this set of questions was not part of
the 3-month post-LaunchPad survey.
Figure 13. Pre-LaunchPad and Post-LaunchPad responses to questions regarding future and
collegiate career intent.
The final question set in this section explores the students’ college, engineering or
computer science, and State University (SU) intentions. The results were consistent across all
three surveys when asked about college and SU enrollment: 100% of the students intend to go to
college, and 50% intend to attend SU. The students were asked to select a percentage on a
sliding scale for each of these questions. The score increased from 65% to 78% between the pre-
LaunchPad and post-LaunchPad surveys (n=26), and from 75%, to 88%, and 96% respectively
across all three surveys (n=17). Figure 14 illustrates these results, and Table 22 provides a
statistical analysis.
17
14
17
15
3
4
2
4
6
10
7
9
0% 20% 40% 60% 80% 100%
Post-Event
Pre-Event
If you go to college, do you think you will pursue a career
in computer or engineering-related field?
Post-Event
Pre-Event
In you future, do you think you want to be a computer
scientist, computer engineer, or engineer?
Yes No Don't Know
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Figure 14. Student intent - comparative results. The students indicated that there is 100% chance
that they will go to college, and a 50% chance that they will enroll in State University. This
response was consistent across all three surveys. They indicated an increased chance that they
would pursue engineering or computer science if they enroll in college. Note: n=26 for the pre-
and post-LaunchPad surveys; n=17 across the pre-, post-, and 3-month post-LaunchPad surveys
due to the response-rate for the 3-month post-LaunchPad survey. See Table 22 for a statistical
analysis of these results.
0
20
40
60
80
100
120
What is the chance that you
will go to college?
If you decide to go to a
college or university, what
is the chance that you will
study engineering or
computer science?
What is the chance that you
will go to State University?
Percentages
Pre-Event Post-Event
0
20
40
60
80
100
120
What is the chance that you
will go to college?
If you decide to go to a
college or university, what
is the chance that you will
study engineering or
computer science?
What is the chance that you
will go to State University?
Percentages
Pre-Event Post-Event 3-month Post-Event
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Table 22
Statistical Results for LaunchPad Responses to Questions Regarding College, Engineering or
Computer Science, and State University Intent
Pre- Post-LaunchPad: n=26 Pre-, Post-, 3-month Post-LaunchPad: n=17
Pre: Q1 Q2 Q3 Pre: Q1 Q2 Q3
Mean 98 66 42 Mean 97 69 47
Median 100 65 50 Median 100 75 50
Mode 100 50 50 Mode 100 50 50
Range 30 75 65 Range 30 65 60
Standard Deviation 7 23 20 Standard Deviation 8 23 18
Responses 28 27 19 Responses 17 17 11
Post: Post:
Mean 99 72 49 Mean 99 79 48
Median 100 78 50 Median 100 88 50
Mode 100 50 50 Mode 100 100 50
Range 18 98 90 Range 17 60 80
Standard Deviation 5 25 30 Standard Deviation 4 22 26
Responses 26 26 17 Responses 17 17 10
3-month Post:
Mean 99 81 45
Median 100 96 50
Mode 100 100 50
Range 10 75 78
Standard Deviation 2 24 23
Responses 17 17 16
Note. Q1: What is the chance that you will go to college? Q2: If you decide to go to a college or
university, what is the chance that you will study engineering or computer science? Q3: What is
the chance that you will go to State University?
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Knowledge. The survey responses indicated that the participants in the LaunchPad
program demonstrated increased knowledge in two areas: (1) understanding engineering and
computer science, and (2) career goals. Figure 15 illustrates selected responses to the post-
LaunchPad survey. The responses were 100% positive to the questions exploring the impact
LaunchPad had on the students understanding in engineering, computer science, civil
engineering, and career goals.
Figure 15. Post-LaunchPad survey results exploring the knowledge impact of the LaunchPad
program. The results from the post-LaunchPad survey. (n=26)
A comparison of the responses from the post-LaunchPad survey and the 3-month post-
LaunchPad survey indicate that the students’ perception of the impact LaunchPad had in these
areas persisted beyond the immediacy of the program. Figure 16 provides a comparison of the
students’ responses to these same questions. The responses remained 100% positive, although in
two instances there was a shift from the response A Great Deal to Moderately. Note that the
strength of the responses regarding the students’ understanding of civil engineering increased
over time.
4
3
1
1
8
9
6
3
14
14
19
22
0% 50% 100%
Helped me understand civil engineering better.
Led me to better understanding of my own career
goals.
Helped me understand computer science better.
Helped me understand engineering better.
How much did participating in LaunchPad impact each of the following?
Not at All Slightly Moderately A Great Deal
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Figure 16. Survey results exploring the knowledge impact of the LaunchPad program. The
results for the post-LaunchPad and 3-month post-LaunchPad responses. (n=17)
The qualitative responses support these results. Coding the open-ended questions resulted in 29
instances of positive statements about an increased understanding in engineering. The students
used phrases such as “opened up my mind about engineering” and “provided me insight on the
many disciplines of engineering and computer science.”
Motivation. The survey responses indicated that the participants in the LaunchPad
program demonstrated increased motivation in two areas: (1) confidence in their ability to
2
2
1
1
1
1
1
6
8
5
5
5
4
4
1
9
7
11
11
11
12
13
15
0% 20% 40% 60% 80% 100%
3-month Post-Event
Post-Event
Helped me understand civil engineering better.
3-month Post-Event
Post-Event
Led me to better understanding of my own career
goals.
3-month Post-Event
Post-Event
Helped me understand computer science better.
3-month Post-Event
Post-Event
Helped me understand engineering better.
How much did participating in LaunchPad impact each of the following?
Not at All Slightly Moderately A Great Deal
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participate in and succeed in computer science and engineering, and (2) interest in studying
computer science or engineering in college. The responses in these areas were 100% positive,
with over 50% of those responses being the strongest: A Great Deal. Figure 17 provides more
insight into these results. This magnitude of the response increased to 65% in the 3-month post-
LaunchPad survey, as described in Figure 18. The results indicate an increase in the strength of
the responses regarding the students’ ability to succeed in engineering, and an increased interest
in studying computer science or engineering.
Figure 17. Post-LaunchPad survey results exploring the motivation impact of the LaunchPad
program. The results from the post-LaunchPad survey. (n=26)
5
4
3
7
8
6
14
14
17
0% 50% 100%
Increased my interest in studying computer science or
engineering in college.
Made me more confident in my ability to succeed in
computer science or computer engineering.
Increased my confidence in my ability to participate in
computer science and engineering projects or
activities
How much did participating in LaunchPad impact each of the following?
Not at All Slightly Moderately A Great Deal
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Figure 18. Survey results exploring the motivation impact of the LaunchPad program. The
results for the post-LaunchPad and 3-month post-LaunchPad responses. (n=17)
The survey responses were reinforced by the qualitative responses to an open-ended
question. There were four open-ended questions, three from the post-LaunchPad survey (n=26),
and one from the 3-month post-LaunchPad survey (n=17). The questions explored the students’
thoughts on their participation in LaunchPad: how they liked it, how would they change it, would
they recommend it, and how did it affect them. Overall, there were 14 positive statements that
were coded as motivation: 10 responses from the 3-month post-LaunchPad survey, and four from
the post-LaunchPad survey. The majority of the responses were coded as self-efficacy, with
eight occurrences in the 3-month post-LaunchPad survey. The students reported that their
participation in LaunchPad increased their confidence, and helped them decide what they wanted
to do after high school. They used phrases such as “boosted my confidence” and “empowered
2
2
2
3
2
4
5
4
6
6
4
11
10
11
8
11
11
0% 20% 40% 60% 80% 100%
3-month Post-Event
Post-Event
Increased my interest in studying computer science or
engineering in college.
3-month Post-Event
Post-Event
Made me more confident in my ability to succeed in
computer science or computer engineering.
3-month Post-Event
Post-Event
Increased my confidence in my ability to participate in
computer science and engineering projects or activities
How much did participating in LaunchPad impact each of the following?
Not at All Slightly Moderately A Great Deal
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me to pursue a career in the engineering field.” Table 23 provides the details of the coding
results for motivation.
Table 23
Coding Results Related to Motivation
Post-LaunchPad Survey 3-month Post-LaunchPad
Survey
Question 14a Question 14b Question 15 Question 14
Motivation 0 0 0 1
Attainment Value 0 0 1 1
Self-efficacy 2 0 1 8
Organization. The survey responses indicated that the participants in the LaunchPad
program were positively impacted in two areas that reduced organizational barriers: (1) female
panelists that were brought in during the first and second week of the program, which
represented an increase in diverse role models; and (2) the program used gender-inclusive
pedagogy. There were five references to the panelists in the open-ended questions; the
comments suggest that providing diverse role models had a positive impact on the students. One
student in particular stated that:
The female engineers that spoke to us showed us that it was possible to pursue an
engineering career even if you're a female. They gave me the confidence and comfort to
know that there will be people in college who support me and help me with my
academics.
In addition, the students appreciated the hands-on aspect of the program, and the ability
to collaborate with one another. When asked what they liked about the program, 27% made
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explicit statements about the hands-on activities, and when asked how the program could be
improved, 19% of the students suggested more hands-on activities. Providing female role
models and exposing female students to hands-on engineering learning activities are both
strategies that have been shown to have a positive impact on women in engineering and
computer science (Stearns et al., 2016; Williams & George-Jackson, 2014).
Summary of LaunchPad Results
The LaunchPad program showed promising results as a pilot program. The participants
demonstrated increased knowledge in several areas: understanding engineering and computer
science, and career goals. In addition, they demonstrated increased confidence in their ability to
succeed in computer science or engineering, and increased interest in studying computer science
or engineering in college. Furthermore, they were positively impacted by the female panelists
that were brought in to discuss their experiences, and the gender-inclusive pedagogy.
Findings
This section will provide answers to the two of research questions that guided this
study; the study results will inform these answers:
1. What are the knowledge, motivation, and organizational factors that contribute to the
STEM-related career decisions and field of study selections that female high school
students make?
2. What knowledge, motivation, and organizational factors does the LaunchPad program
influence such that the female high school student participants are more likely to enroll in
engineering or computer science, and/or pursue a career in engineering or computer
science?
The third research question will be discussed in Chapter 5.
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Research Question #1 Findings
Research question #1 asks about the knowledge, motivation, and organizational factors
that contribute to the STEM-related career decisions and field of study choices that female high
school students make. This study evaluated this question in the context of the assumed
influences identified in Chapter 2.
Knowledge
The knowledge that the students need to further influence their STEM-related career
decisions and field of study choices is conceptual; they need to know what they will be able to
achieve with a STEM major. Although the quantitative results were mixed, there was evidence
that the students were not confident that a degree in engineering would be congruent with their
interests. The qualitative results further supported this finding; only 27% of the students were
able to describe an engineering or computer science invention that could make a difference in
their life. One student saw technological progress as a risk to society “because then more people
will be glued to their electronic devices.” These combined findings are consistent with Diekman
et al. (2010), who suggested that women reject STEM careers due to their perception of STEM
being incongruent with their values, goals, interests, and ability to have a family. Diekman et al.
suggested that changing these perceptions can lead to increased interest in STEM careers and
fields of study.
Motivation
The motivation that the students need to further influence their STEM-related career
decisions and field of study choices is self-efficacy and attainment value; they need to believe
that they can succeed, and that a degree or career in engineering or computer science will help
make the world a better place. Although the participants in this study demonstrated confidence
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in their ability to succeed in math and science, they were concerned that engineering and
computer science would require too much time or schooling. As illustrated in Figure 7, 39% of
the students agreed with the survey statement: “I would worry that engineering or computer
science as a career path would require too much time or schooling.” In addition, they struggled
with feeling like they could fit in with other students in engineering and computer science.
Students with higher math, science and engineering self-efficacy are likely to have higher STEM
career interest, and more likely to choose STEM-related fields of study (Beyer, 2014; Lent et al.,
1986; Lent et al., 1991; Ong et al., 2011; Wang, 2013). Furthermore, Rice, Barth, Guadagno,
Smith, and McCallum (2013) concluded that social support is a factor in increased levels of self-
efficacy.
Organization
The organizational barriers that need to be remediated such that the students’ STEM-
related career decisions and field of study choices will not be obstructed are gender bias, and the
lack of diverse role models. As illustrated in Figure 10, 43% of the students had experienced
gender bias in the classroom. Moreover, 50% of the students did not have access to a role
model. Eliminating gender bias (Espinoza et al., 2014) and increasing the number of female
teachers (Stearns et al., 2016) will graduate female students that are more inclined to major in
STEM.
Summary of Findings for Research Question #1
A thorough literature review resulted in a culmination of factors that affect the
underrepresentation of women in STEM. Using these factors, a conceptual framework was
developed that focused the methodology of this study to evaluate the factors that were assumed
to affect the specific stakeholder group that this study is focused on – the female high school
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student. A Clark and Estes (2008) gap analysis was performed, which validated all but two of
the assumed influences. The validated influences represent the gaps in knowledge, motivation,
and organizational barriers, that if alleviated, would positively affect the STEM-related career
decisions and field of study choices that female high school students make. The validated
influences are described in Table 24.
Table 24
Validated Influences
Influence Type Assumed Influence
Knowledge
Conceptual Students need to know what they will be able to achieve with STEM
majors
Motivation
Self-efficacy Students need to believe that they can succeed in math and science.
Attainment Value Students need to connect STEM careers with being able to make the
world a better place.
Organization
Cultural Model Students’ learning environment in math and science is not gender-
inclusive.
Cultural Setting Students’ learning environment in math and science does not reflect
diversity of instructors or role models.
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Research Question #2 Findings
Research question #2 asks about the knowledge, motivation, and organizational factors
that the LaunchPad program influenced such that the female high school students are more likely
to enroll in engineering or computer science, and/or pursue a career in engineering or computer
science. This study evaluated this question in the context of the assumed influences identified in
Chapter 2.
Knowledge
The LaunchPad participants asserted that they had a better understanding of engineering
and computer science as a result of the program; their claims persisted in the 3-month post-
LaunchPad survey. As previously illustrated in Figure 15, the responses were 100% positive to
questions about how LaunchPad helped the students understand engineering and computer
science better. In addition, the responses to the same questions on the 3-month post-LaunchPad
survey were also 100% positive. The participants were consistent in their discussion in the open-
ended survey questions. The coding produced 30 instances of statements regarding a better
understanding of engineering and computer science; the students attributed their increased
understanding to LaunchPad. One student stated that what she liked about the program was “that
I was able to learn more about the different engineering fields. It allowed me to understand
engineering better to decide which field to go into.”
Motivation
The LaunchPad participants asserted that they had increased confidence in their ability to
succeed in, and to participate in, engineering and computer science. Their responses to the
survey questions indicated an increased level of confidence between the post-LaunchPad survey
and the 3-month post-LaunchPad survey (see Figure 18). The students attributed this increased
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confidence to their participation in the LaunchPad program. When asked how their experience at
LaunchPad affected them, eight of the students responded with a discussion of increased
confidence. For example, one student claimed “after LaunchPad, I became more confident in my
math skills,” another asserted that “I am also more confident in my abilities to become an
engineer.”
Organization
The organizational barrier that was reduced for the LaunchPad participants was the lack
of diverse role models. As discussed earlier, the students’ responses to the open-ended questions
claim that the panelists made a positive impact by showing them “that it was possible to pursue
an engineering career even if you’re female.” In addition, there were nine responses that
discussed the networking opportunities that were made possible through the LaunchPad program.
One student discussed that she “was able to meet people with interests like mine,” and another
“liked being able to talk with others with similar interests.”
Summary of Findings for Research Question #2
The knowledge, motivation, and organizational factors that the LaunchPad program
influenced were an increased understanding of engineering and computer science, increased
confidence, diverse role models, and the ability to establish peer networks. The impact that the
LaunchPad program had on the students is consistent with the assumed knowledge, motivation,
and organizational influences that guided this study, and supported by the literature review. For
example, an increased understanding of engineering and computer science will add to the
students’ conceptual knowledge regarding what can be achieved with a degree in these fields of
study. Increased confidence will positively influence the students’ self-efficacy, and access to
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diverse role models and the ability to establish peer networks reduces the organizational barriers
that the students experience in their learning environment.
All of these influences are factors that make a positive impact on a female high school
student’s likelihood to enroll in, or pursue a career in, engineering or computer science. As such,
the student participants in the LaunchPad program demonstrated an increase in their intentions to
study engineering or computer science. Figure 19 illustrates an increased likelihood in the
students’ responses as they are surveyed at three different stages of the program: before,
immediately afterwards, and 3-months afterwards. Furthermore, 70% of LaunchPad participants
submitted their college application to State University: 58% of the applicants applied to ECST,
32% applied to other STEM majors, and 11% applied to non-STEM majors. Figure 20 provides
insight into the applicants’ field of study choice.
Figure 19. Increased engineering or computer science intention.
75 88 96
0
20
40
60
80
100
120
If you decide to go to a college or university, what is the chance that you will study
engineering or computer science?
Pre-Event Post-Event 3-month Post-Event
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Figure 20. Fall 2018 LaunchPad participants’ State University application data.
These factors, increased understanding of engineering and computer science, increased
confidence, diverse role models, and the ability to establish peer networks, should be considered
when making recommendations for developing STEM recruitment programs; they will be
explored further in Chapter 5.
Summary
The participants in the LaunchPad program began the program as high performing
students. Their median weighted GPA was 3.94, and 93% of the participants intended on
enrolling in college upon completion of high school. In addition, they indicated interest in
learning more about STEM by their voluntary enrollment in a STEM recruitment program –
LaunchPad. Nevertheless, the quantitative and qualitative results of this study validated five of
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the seven assumed influences. Although the students knew about the benefits of a STEM career,
and recognized gender stereotypes associated with women in STEM, they demonstrated a gap in
conceptual knowledge by being unable to describe what they could achieve by obtaining a
degree in a STEM-related field. Furthermore, there were pockets of reduced self-efficacy and
gaps in attainment value. The students lacked confidence in their ability to fit in socially with
other students and were unable to describe how a career in computer science or engineering
could make a positive difference in the world. Moreover, a majority of the students had
experienced gender bias in their learning environment, and they lacked access to a mentor or a
role model. A complete traceability matrix that maps each survey question to an assumed
influence is provided in Appendix E.
The LaunchPad program showed promising results as a pilot program. The participants
demonstrated increased knowledge in several areas: understanding engineering and computer
science, and career goals. In addition, they demonstrated increased confidence in their ability to
succeed in computer science or engineering, and increased interest in studying computer science
or engineering in college. Furthermore, they were positively impacted by the female panelists
that were brought in to discuss their experiences.
The results and findings provided answers to the first two research questions by exploring
the knowledge, motivation, and organizational factors that contribute to the STEM-related career
decisions and field of study choices that female high school students make; and by exploring the
knowledge, motivation, and organizational factors that the LaunchPad program influenced such
that the students are more likely to enroll in engineering or computer science, and/or pursue a
career in engineering or computer science. Chapter 5 will provide the answer to the third
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research question by providing recommended solutions, based on these results and findings, that
will increase the number of female high school students that enroll in the college of engineering.
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CHAPTER 5: RECOMMENDATIONS
Introduction
This study explored the knowledge, motivation, and organizational influences that
affected female high school students entering the LaunchPad program. These influences are
those that have the potential to shape the decisions young women make as they consider
engineering and computer science as a field of study and career field. In addition, this study
evaluated the impact that the LaunchPad program had on these students during its inaugural year.
Chapter 4 presented an analysis of the data collected by this study, determined which of the
assumed influences were validated (see Table 25), and described the positive impact that the
LaunchPad program had on the participants. The findings from both aspects of this study are
aligned along these validated influences, which are the gaps in knowledge, motivation, and the
organizational barriers that are affecting their field of study choices and career aspirations. The
LaunchPad program demonstrated that it has the potential to address these needs.
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Table 25
Description of the Validated Influences
Influence Type Assumed Influence
Knowledge
Conceptual Students need to know what they will be able to achieve with STEM
majors
Motivation
Self-efficacy Students need to believe that they can succeed in math and science.
Attainment Value Students need to connect STEM careers with being able to make the
world a better place.
Organization
Cultural Model Students’ learning environment in math and science is not gender-
inclusive.
Cultural Setting Students’ learning environment in math and science does not reflect
diversity of instructors or role models.
Chapter 5 provides recommendations to build upon the success of the inaugural
LaunchPad program. These recommendations, if implemented, have the potential to further
influence students’ field of study decisions, and career aspirations in the area of engineering and
computer science., and can contribute to ECST goal of an increased enrollment rate for women.
These recommendations are based on theoretical principles and were developed using the New
World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016).
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This chapter will present the study recommendations organized by knowledge,
motivation, and organization. Following the recommendations, a description of the Kirkpatrick
and Kirkpatrick (2016) evaluation framework will be presented with a sample evaluation plan.
Finally, the strengths and weaknesses of the study, the limitations and delimitations of the study,
and future research will be described.
Recommendations for Practice to Address KMO Influences
Knowledge Recommendations
Introduction. Table 26 contains the knowledge influences that were presented in Chapter
2 and validated by this study. Krathwohl (2002), in his discussion of Bloom’s taxonomy,
described four types of knowledge: factual, conceptual, procedural, and metacognitive. The
validated knowledge influence type is conceptual, which is defined as being able to discern
relationships amongst elements within a larger structure. Knowledge is a necessary component
for stakeholders to achieve their performance goal, and it is important to understand what
knowledge is required to achieve that goal (Rueda, 2011; Schraw and McCrudden, 2006). Table
26 contains the recommendation to improve stakeholder performance and the theoretical
principle that the recommendation is based upon.
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Table 26
Summary of Validated Knowledge Influences and Recommendations
Validated Knowledge Influence Principle and Citation Context-Specific
Recommendation
Students need to know what
they will be able to achieve
with STEM majors
(Conceptual)
Learning connected to
individual interests will
encourage meaningfulness
(Schraw & McCrudden, 2006).
Knowledge is one of the three
critical factors of performance,
and is a necessary element in
achieving performance goals
(Clark & Estes, 2008).
Provide the students
information about the
accomplishments that they will
be able to achieve if they major
in STEM.
Conceptual knowledge solution. To increase interest in engineering and computer
science, the students will need to know what they can achieve by pursuing a degree in a STEM-
related field of study. Women and girls see STEM as being incongruent with their values, goals,
interests, and ability to have a family; they reject STEM careers due to their communal goals
(Diekman et al., 2010). Thus, women seek careers that better match their interests through
providing them the opportunity to help others, and careers that enable them to combine their
desire to have a work life balance (Beyer, 2014). Schraw and McCrudden (2006) suggested that
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interest is a precursor to obtaining knowledge, and knowledge is one of the three critical factors
of performance (Clark & Estes, 2008). As such, increasing conceptual knowledge about
accomplishments that can be achieved with a STEM major will result in increased interest in
STEM.
Therefore, the recommendation is to increase the students’ conceptual knowledge by
providing them information about the accomplishments that are possible if they major in STEM.
This activity should focus on demonstrating that STEM degrees provide a vast number of options
that can be applied towards solving real world problems and the ability to help others (Dasgupta
& Stout, 2014; Eccles & Wang, 2016). Increasing the students’ conceptual knowledge will lead
to increased enrollment in engineering and computer science.
Motivation Recommendations
Introduction. Table 27 represents the list of motivation influences that were presented
in Chapter 2 and validated by this study. As discussed previously, motivation is the necessary
catalyst that individuals need to actively use and apply their knowledge (Mayer, 2011). There
are three facets of motivation in the context of individuals working towards a goal: active choice,
persistence, and mental effort (Clark & Estes, 2008). In addition, there are five concepts of how
motivation works: interest, beliefs, attributions, goals, and partnership (Mayer, 2011). The
validated motivation influences are aligned with the concepts of interest and belief. Table 27
also shows the recommendations to improve stakeholder performance and the theoretical
principle that the recommendation is based upon.
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Table 27
Summary of Validated Motivation Influences and Recommendations
Motivation Influence
Principle and Citation Context-Specific
Recommendation
Students need to believe that
they can succeed in math
and science (Self-efficacy).
Higher self-efficacy has a
positive impact on
persistence when faced with
obstacles (Bandura, 1977).
Higher self-efficacy leads to
greater persistence and
mental effort (Rueda, 2011).
Modeling increases self-
efficacy (Pajares, 2006).
Motivation is higher in
individuals with higher self-
efficacy (Pajares, 2006).
Scaffolded instruction by the
ECST faculty, or subject
matter expert, will be
provided to the students with
frequent opportunities for
hands-on practice.
Positive role models who
have successful careers and
are passionate about their
STEM careers will be
introduced to the students in
the format of speaker panels.
Students need to connect
STEM careers with being
able to make the world a
better place (Attainment
value).
Intrinsic value, and
motivation, is highest when
individuals enjoy what they
are doing, and are doing
things that have personal
meaning to them (Eccles,
2006).
Individuals are more
motivated, and will persist if
the task is valued (Rueda,
2011).
The materials and examples
used during the LaunchPad
program will focus on
solving real-world problems
and helping people.
Self-efficacy. Female students have low science and math self-efficacy, which inhibits
them from pursuing a degree in the STEM field. Individuals with higher self-efficacy
demonstrate higher persistence when faced with obstacles (Bandura, 1977; Rueda, 2011), and
exert more mental effort on tasks. There are two recommendations to improve self-efficacy.
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The first is to provide scaffolded instruction to the students that includes frequent opportunities
for hands-on practice. This activity will increase the students’ self-efficacy through mastery
experience, one of the four sources of self-efficacy (Pajares, 2006; Usher & Pajares, 2009). In
addition, modeling has a positive effect on self-efficacy (Pajares, 2006), and motivation is higher
with individuals that have higher self-efficacy. The second recommendation is to expose the
students to positive models who have successful careers and are passionate about their STEM
careers; this could be done in the format of speaker panels.
Researchers have concluded that students with higher math, science, and engineering
self-efficacy are more likely to demonstrate higher STEM career interest, and more likely to
choose STEM-related fields of study (Beyer, 2014; Lent et al., 1986; Lent et al., 1991; Ong et al.,
2011; Wang, 2013). Furthermore, students who have higher levels of domain-specific self-
efficacy demonstrate higher levels of career interest and are likely to choose STEM as a field of
student when matriculating into college (Milner et al., 2014; Ong et al., 2011). Thus, providing
the students scaffolded instruction and positive role models who are successful and passionate
about their STEM careers will increase the students’ self-efficacy through mastery and modeling
(Pajares, 2006).
Attainment value. Female students perceive STEM careers to be incongruent with their
central values or personal interest (Diekman et al., 2010). Eccles (2006) concluded that intrinsic
value, and motivation, is highest when individuals enjoy what they are doing, and are doing
things that have personal meaning to them. Furthermore, individuals are more motivated, and
will persist if the task is valued (Rueda, 2011). Therefore, the recommendation for the
LaunchPad program is to use materials and examples that focus on how individuals who are
working in a STEM career have solved real-world problems and are actively helping people.
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For example, solving real-world problems will contradict the perception that computer
science is incongruent with women’s interpersonal values, goals, and interests (Beyer, 2014). In
a similar study, Eccles and Wang (2016) found that women avoided math, physical, engineering,
and computer sciences and chose health, biological, and medical services because they were
viewed as more focused on people and humanitarian activities. Researchers agree that programs
designed to attract girls to STEM should focus on the values that are important to them: solving
real-world problems and helping people (Dasgupta & Stout, 2014; Diekman et al., 2010).
Therefore, engaging the LaunchPad participants in activities that provide examples of women
who are working in a STEM field, and who have solved real-world problems resulting in helping
people, will increase their motivation (Eccles, 2006; Rueda, 2011). Increased motivation will
lead to increased enrollment in engineering or computer science.
Organization Recommendations
Introduction. Table 28 contains the organizational influences that were presented in
Chapter 2 and validated by this study. When diagnosing a performance gap, organizational
influences must be considered. Individuals may have the necessary knowledge to perform their
tasks, and may be highly motivated, however, if organizational barriers exist, these individuals
will not meet their performance goals (Clark & Estes, 2008; Rueda, 2011). The influence of
culture on individuals has been divided into how an individual thinks and how an individual
behaves. Cultural models are a common set of schemas that describe how an individual believes
the world works, or how they believe the world should work (Rueda, 2011). Conversely, Rueda
describes cultural settings are the visible aspects of an individual’s behavior. Table 28 contains
the recommendations to improve stakeholder performance, and the theoretical principle that the
recommendation is based upon.
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Table 28
Summary of Validated Organization Influences and Recommendations
Validated Organization
Influence
Principle and Citation
Context-Specific
Recommendation
Students’ learning
environment in math and
science is not gender-
inclusive (Cultural model).
Pedagogy, curriculum, and
environment for STEM
classrooms needs to be
gender-inclusive (Brotman &
Moore, 2008).
Gender bias has a negative
effect on the performance of
female students (Espinoza et
al., 2014).
Provide training on gender-
inclusive instruction to the
ECST faculty, administrators,
and volunteers that will be
involved in the LaunchPad
program.
Students’ learning
environment in math and
science does not reflect
diversity of instructors or role
models (Cultural setting).
Organizations that provide
diverse role models for their
STEM students achieve
increased interest and
persistence in STEM (Chang,
2002).
The existence of female
graduate students increases
persistence for women
pursuing STEM degrees
(Griffith, 2010).
The cultural settings of
classrooms are improved for
female students with an
increased number of female
role models (Stearns et al.,
2016).
Ensure that the majority of
faculty and graduate students
participating in the
LaunchPad program are
female.
Cultural model. Female high school students face gender bias in their learning
environment; this is especially evident in math and science courses. The lack of a gender-
inclusive learning environment has a negative effect on the performance of female students
(Espinoza et al., 2014). Furthermore, research suggests that the pedagogy, curriculum, and
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environment for STEM classrooms needs to be gender-inclusive (Brotman & Moore, 2008).
Gender-inclusive pedagogy is defined as being dynamic, incorporating cooperative learning,
highlighting the implications of science on society, and incorporating life experience of girls
(Brotman & Moore, 2008). Therefore, the recommendation is to provide gender-inclusive
training to LaunchPad faculty, administrators, and volunteers; this will reduce the organizational
barrier of gender-bias.
Many studies have shown that boys and girls are perceived differently throughout their
education; from elementary school, to middle school, and through high school (Espinoza et al.,
2014; Fennema et al., 1990; Riegle-Crumb & Humphries, 2012; Tiedmann, 2000). This
perception, gender bias, has a negative effect on the performance of female students (Espinoza et
al., 2014). To neutralize gender bias, organizations must create a culture that encourages
educators to reflect on their own bias, a culture in which candid conversations about bias can be
held, and a culture that has the courage to face the institutional barriers to achieving equity.
Therefore, reducing gender-bias will lead to a more gender-inclusive environment for the
students.
Cultural settings. The math and science learning environment that female students
encounter does reflect diversity of instructors or role models (Hill et al., 2010). Chang (2002)
determined that providing diverse role models for STEM students achieved increased interest
and persistence in STEM. In addition, Bottia, Stearns, Mickelson, Moller, and Valentino (2016)
concluded that an increase in female math and science teachers has a positive impact on female
high-school students; they are more likely to pursue a STEM degree upon graduation. Therefore,
the recommendation to ensure that the majority of faculty and graduate students that participate
in the LaunchPad program are female, will lead to an increased interest in STEM.
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A core component of social cognitive theory is learning through observing, or modeling.
Modeling is the demonstration of behavior or skill by a teacher, or other individual that a student
may hold in esteem (Bandura, 1977; Denler et al., 2006). Modeling through mentoring programs
has successfully influenced women to stay in STEM, and increased academic self-efficacy
(Chang, 2002; MacPhee et al., 2013). Furthermore, extending modeling to the classroom, high
school students that were exposed to higher numbers of female teachers were more inclined to
major in STEM (Bottia et al., 2016; Stearns et al., 2016). Thus, increasing the representation of
female role models and instructors in the LaunchPad program will result in an improved cultural
setting for the students, and result in increased rates of engineering and computer science
enrollment.
Integrated Implementation and Evaluation Plan
Implementation and Evaluation Framework
This implementation and evaluation framework was developed using the New World
Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016) as illustrated in Figure 21. This model is
rooted in organizational training; however, it has been adapted for use in conjunction with the
Clark and Estes (2008) gap analysis. Although the New World Kirkpatrick Model was originally
designed for training evaluation, methods and timing have been added to facilitate the
development of an integrated implementation and evaluation plan. This integrated plan provides
a recommended approach to improve upon the LaunchPad program that focuses on the validated
influences (performance gaps) identified by this study. The model consists of four evaluation
levels, which have been adapted based on the study recommendations; the adapted levels are
described in Table 29.
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Table 29
The New World Kirkpatrick Model Four Levels of Evaluation
Evaluation Level Description
4 Measures the results of the LaunchPad program in the context of the
organizational goals.
3 Measures the critical behaviors that students will demonstrate after
completing the LaunchPad program.
2 Measures the learning goals established for the Launchpad program
1 Measures student reaction: How engaged are they during the LaunchPad
program? How relevant is the program to their interest in engineering and
computer science? And how satisfied they are with the program?
Kirkpatrick and Kirkpatrick (2016) recommend developing the evaluation plan starting at
Level 4, which establishes a focus on the leading indicators and desired results. The subsequent
levels are then decomposed using the organizational results as a guide. This reverse order
establishes the focus on the results, which are the most important aspect of any training or
intervention program (Kirkpatrick & Kirkpatrick, 2016).
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Figure 21. The New World Kirkpatrick Model. Reprinted from Kirkpatrick’s Four Levels of
Training Evaluation (p. 11), by J. D. Kirkpatrick and W. K. Kirkpatrick, 2016, Alexandria, VA:
ATD Publications. Copyright 2016 by Kirkpatrick Partners, LLC www.kirkpatrickpartners.com.
Organizational Purpose, Need and Expectations
The mission of the State University (SU) College of Engineering, Computer Science, and
Technology (ECST) is to successfully prepare the next generation of engineering, computer
science, and technology professionals for an Urban City and beyond. The focus of this study
was to explore the knowledge and skills, motivation, and organizational barriers that were
preventing the ECST from achieving an increase in female enrollment. The organizational goal
of the ECST is to increase the enrollment rate for women from 15% to 25% by the year 2020.
The LaunchPad program is designed to recruit female students from the local area high school,
contributing to the ECST goal. LaunchPad will provide the students with the knowledge
necessary to understand what they will be able to achieve if they pursue a STEM career. The
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scaffolded instruction, role models, and activities that will increase their motivation to enroll in
the college of engineering. In addition, a more gender-inclusive environment will reduce the
organizational barriers that are inhibiting women from enrolling in the ECST.
Level 4: Results and Leading Indicators
Table 30 describes the proposed Level 4 evaluation, Results, and Leading Indicators, in
the form of outcomes, metrics and methods for both external and internal outcomes for
LaunchPad. If the LaunchPad program achieves the external outcomes described in Table 30,
and the student participants achieve the internal outcomes, then the organizational goals for the
ECST can be met.
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Table 30
Outcomes, Metrics, and Methods for External and Internal Outcomes
Outcome Metric(s) Method(s)
External Outcomes
The LaunchPad
environment is more
gender-inclusive.
The number of females that are
displayed in posters and photos
within the laboratories and
hallways of the ECST.
Observations during
LaunchPad.
The pedagogy used by the
LaunchPad faculty and
administrators is more
gender-inclusive.
The number of examples used in
the LaunchPad sessions that
involved the implications of
math and/or science on society.
Observations during
LaunchPad.
An increased number of
female role models for the
students.
The number of female faculty,
graduate students, and panelists
participating in the LaunchPad
program.
Observations during
LaunchPad.
Internal Outcomes
Students demonstrate
increased interest in
pursuing a degree in
engineering or computer
science.
Quantitative and qualitative
results from specific questions
on LaunchPad participant
survey.
Surveys administered at the
beginning of the LaunchPad
program, at the end, and 3-
months afterwards.
Students demonstrate
increased engineering and
computer science career
interest.
Quantitative and qualitative
results from specific questions
on LaunchPad participant
survey.
Surveys administered at the
beginning of the LaunchPad
program, at the end, and 3-
months afterwards.
Student engagement during
LaunchPad activities.
Observations during
LaunchPad.
The students understand
that pursuing a STEM
career will give them
opportunities to help
others
Quantitative and qualitative
results from specific questions
on LaunchPad participant
survey.
Surveys administered at the
beginning of the LaunchPad
program, at the end, and 3-
months afterwards.
The students are more
confident that they can do
the work required of a
STEM degree.
Quantitative and qualitative
results from specific questions
on LaunchPad participant
survey.
Surveys administered at the
beginning of the LaunchPad
program, at the end, and 3-
months afterwards.
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Level 3: Behavior
Critical behaviors. There are four critical behaviors necessary for the LaunchPad
participants to achieve their performance goal. The first critical behavior is that the students
must be able to discuss the impact of engineering or computer science on the environment or
humanity. The second critical behavior focuses on the various roles that engineers and computer
scientists have. The students will be able to articulate the roles, careers, or functions of
engineers or computer scientists. The third critical behavior is the confidence that the students
have regarding their ability to complete the requirements of a degree in engineering or computer
science. Finally, the fourth critical behavior is the confidence that the students have that they can
overcome the gender bias that they will encounter during the pursuit of their degree, and in the
workplace. The metrics necessary to measure, the methods for the measurement, and the timing
for the measurements are described in Table 31.
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Table 31
Critical Behaviors, Metrics, Methods, and Timing for Students
Critical Behavior Metric(s) Method(s) Timing
1.The students
articulate how
engineering and
computer science
professionals make a
difference in the
environment and/or
help people.
The number of
computer science or
engineering projects
that the students
identify that make a
difference in the
environment.
LaunchPad faculty will
poll the students during
instructional sessions to
identify current projects
that they have been
exposed to via the news,
social media, or other
methods.
A daily session
activity.
2. The students are
able to articulate the
roles, careers, or
functions of engineers
or computer scientists.
The number of
individuals that the
students can identify
who rely on computer
science or engineering
in their work or civic
role.
The students will keep a
journal during
LaunchPad, and in an
interactive session with
the students, the faculty
will ask the students to
share how many
additional names have
been added to their
journal.
A daily session
activity.
3. The female students
are confident that they
can perform the work
required of a degree in
engineering or
computer science.
The number of female
students that enroll in
AP-level math,
engineering, or
computer science
courses.
The ECST LaunchPad
administrator will
monitor the enrollment
numbers.
A frequency
congruent with the
school district’s
enrollment
schedule.
4. The female students
demonstrate
confidence that they
can overcome gender
bias.
4a. The number of
incidents of gender
bias during
LaunchPad sessions.
4b. The number of
times that the female
students appropriately
respond to gender bias
when encountered.
The LaunchPad
panelists will discuss
strategies for reducing
and overcoming gender
bias in the students’
environment
Daily metrics will
be captured by the
LaunchPad
observer.
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Required drivers. Female students require support from their learning environment,
teachers, and admissions counselors to achieve the critical behaviors described in Table 31.
Table 32 describes the critical drivers that are necessary for these outcomes.
Table 32
Required Drivers to Support Students’ Critical Behaviors
Method(s) Timing
Critical Behaviors Supported
1, 2, 3 Etc.
Reinforcing
A current events board that is
maintained during the
LaunchPad program.
Duration of LaunchPad
program
1, 2
Encouraging
Students meet in small groups
to share their journals and to
provide peer-review.
Weekly 2
The admissions counselors
hold scheduled information
sessions to discuss the
enrollment requirements for
State University.
Twice during LaunchPad, and
Quarterly post-LaunchPad
3
Rewarding
Real-time feedback on
assignments with verbal
feedback on the students’
work.
Ongoing 2
Monitoring
Faculty provide opportunities
for the students to share
success stories
Weekly 3, 4
Female engineering and/or
computer science alumni
speak on panels discussing
their accomplishments in their
pursuit of a degree.
Weekly 3
Organizational support. To ensure that the required drivers are implemented, the
LaunchPad administrators will need to provide the support described in Table 32. A current
events board will need to be funded and maintained within the LaunchPad main classroom. This
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board will be populated by the LaunchPad administrators and will need to include articles with
women and minorities as the focal point. In addition, sufficient staffing levels for admissions
counselors to maintain regular sessions with female students to discuss enrollment requirements
for the State University, and to hold follow-up sessions. These sessions will need to be held
twice during LaunchPad, and quarterly afterwards to provide ample opportunity for the students
to understand the requirements. Furthermore, the LaunchPad administrators will need to work
cooperatively with the College of Engineering, Computer Science, and Technology to field
panels of alumni who can discuss their experiences and accomplishments as they pursued a
degree in engineering or computer science. To be effective, there should be two panels during
the LaunchPad program.
Level 2: Learning
Learning goals. The goals described in this section are categorized using the Kirkpatrick
and Kirkpatrick (2016) evaluation components of learning: skills, knowledge, attitude,
confidence, and commitment. The goals are aligned with the validated gaps described in Chapter
4. Following completion of the recommended solutions, the students will be able to:
1. Correctly identify an engineering or computer science professional that is making a
positive impact on the environment or society (Knowledge - Conceptual)
2. Indicate confidence that they can perform the work required of a degree in engineering or
computer science (Confidence - Self-efficacy)
3. Characterize the benefits of a STEM career (Knowledge - Conceptual)
4. Commit to pursuing a degree in engineering or computer science (Commitment)
5. Value the impact that a degree in engineering or computer science can have on the
environment or on people’s lives (Attitude – Attainment value)
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Program. The learning goals listed in the previous section, will be achieved during
LaunchPad program., a summer STEM recruitment program for young women between their
junior and senior year in high school. LaunchPad is sponsored by the State University College
of Engineering, Computer Science, and Technology with financial and personnel support from
faculty instructors, student mentors, and industry partners. The students will identify as female
and will be between their junior and senior year of high school. They will be exposed to a range
of STEM topics, lab tours, and relevant field trips. The pilot program consisted of two computer
science modules, two civil engineering modules, one mechanical engineering module, two lunch-
time panels, a lab tour, a field trip, and final presentations to the students’ families. The duration
of the program was two weeks from 9am to 4pm daily. Subsequent programs may be extended
an additional week to expose the students to a broader array of engineering and computer science
fields and to incorporate more complex activities.
The computer science modules are designed to teach the students some basic principles
of computer programming and to introduce them to basic constructs of the Python programming
language. The second module introduces data science. During each of the modules, the faculty
discuss the industry demand for computer scientists, and in particular the growing demand and
higher salaries for those computer scientists that are familiar with data science. In addition, the
faculty provide examples of data science applications that have been used for preventive health
and patient monitoring; two applications that are designed to help others. The students are
introduced to a free online python programming environment, a workbook that contains a
description of the Python constructs for reference, and code samples for use during the modules.
The first civil engineering module focuses on the atmosphere and the faculty guide the
students through several simulations to determine the rate of CO
2
growth on the planet, and how
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small changes can affect the growth. The second civil engineering module focuses on
hydrology. There are two hands-on activities: 1) building a water filter, and 2) running
simulations to determine how individual actions can impact the amount of water a family uses in
a week.
The mechanical engineering module is focused on a hands-on activity in which the
students build a robot using their cell phone. They use the coding knowledge they gained during
the computer science modules and apply those skills to control the robot. In addition, they all
have the opportunity to use a soldering iron, assemble an electrical circuit board, and assemble
the frame of the robot.
Components of learning. As described in the literature review, the combination of
knowledge and motivation are necessary contributors to improving stakeholder performance.
Therefore, it is important to evaluate the learning for conceptual knowledge and motivation to
ensure that the students have the information they need, and also to ensure that they are
motivated by seeing the value of their decisions. The students need to have the self-efficacy
necessary to apply the newly obtained knowledge as they determine their college and career
choices. As such, Table 33 lists the evaluation methods and timing for these components of
learning.
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Table 33
Components of Learning for the Program
Method(s) or Activity(ies) Timing
Declarative Knowledge “I know it.”
Knowledge assessment using survey On the last day of the program.
Verbal knowledge checks during the sessions Periodically during the program and
documented via observation notes.
Procedural Skills “I can do it right now.”
Demonstration by individuals during the
session.
Periodically during the program and
documented via observation notes.
Hands-on group activity led by the faculty and
graduate student assistants.
During the program, verbal feedback will be
provided during the activity by the leaders.
Attitude “I believe this is worthwhile.”
Team presentation about an innovative idea
that they will develop in the future that will
benefit the environment or people’s lives.
As a closing activity for the program.
Open ended survey question On the last day of the program.
Confidence “I think I can do it”
Survey items using scaled items Pre- and Post-program survey
Commitment “I will enroll.”
Retrospective pre- and post-program
assessment.
Pre- and Post-program survey.
Level 1: Reaction
Level 1 evaluation consists of engagement, relevance, and customer satisfaction
(Kirkpatrick & Kirkpatrick, 2016), and will provide the basic, but necessary, feedback on the
faculty instructors and the quality of the program. Table 34 provides insight into the methods
and tools that will be used for Level 1 evaluation of the LaunchPad program.
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Table 34
Components to Measure Reactions to the Program
Method(s) or Tool(s) Timing
Engagement
Completion of hands-on activities during the
program
Ongoing during different sessions of the
program.
Observation by a dedicated observer During the program
Attendance During the program
Program evaluation On the last day of the program, and three
months after the program.
Relevance
Brief pulse-check with participants via
discussion (ongoing)
After every module in the program
Evaluation of the topics that the students select
for their final program presentation.
During the program, with the presentation on
the last day.
Program evaluation On the last day of the program, and also three
months post-program.
Customer Satisfaction
Brief pulse-check with participants via
discussion (ongoing)
After every module in the program
Program evaluation On the last day of the program.
Evaluation Tools
Immediately following the program implementation. On the last day of the
LaunchPad program, the second survey will be administered. The results of the survey will
provide insight into how each of the LaunchPad modules was received by the students, how
engaged the students were during the program, and the impact the program had on the students’
knowledge, attitude, and commitment to engineering or computer science as a field of study
and/or career. This survey will evaluate Kirkpatrick and Kirkpatrick (2016) Level 1 and 2
categories. In addition to the end-of-program survey, there will also be Level 1 and Level 2
assessments being performed during the program; these were previously discussed and are
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described in Table 34. Table 35 provides representative questions from the second LaunchPad
survey.
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Table 35
Representative Questions from the LaunchPad End of Program Survey, the Kirkpatrick Level,
and Type of Question
Kirkpatrick
Level
Type of Question Sample Questions
1 Likert Scale:
Strongly Disagree
Disagree
Agree
Strongly Agree
Help us improve LaunchPad by telling us about your
experience with the overall program:
a) The information I received about the
activity before it began helped me to
participate successfully
b) I found the instructions and information I
received during the activity to be effective
and helpful
c) The leaders for this activity were prepared
d) This activity was well organized
e) My goals for participating in this activity
were met
2 Likert Scale:
Not at All
Slightly
Moderately
A Great Deal
How much did participating in LaunchPad impact each of
the following?
a) Helped me understand computer science
better
b) Helped me understand civil engineering
better
c) Helped me understand engineering better
d) Led me to a better understanding of my
own career goals.
e) Increased my interest in studying
computer science or engineering in
college.
f) Made me think more about what I will do
after graduating from high school
g) Made me decide to work harder in school.
h) Made me decide to take different classes
in school than I had planned to.
i) Made me more confident in my ability to
succeed in computer science or
engineering.
j) Increase my confidence in my ability to
participate in computer science or
engineering projects or activities.
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Delayed for a period after the program implementation. Approximately three months
after the LaunchPad program, the program administrators will administer an electronic survey.
The survey will use the Blended Evaluation
Ⓡ
approach, which integrates the evaluation across
the Kirkpatrick and Kirkpatrick (2016) levels. This will ensure that the program is evaluated
using a comprehensive approach. Level 1 measurements will assess the students’ satisfaction;
Level 2 will measure the students’ confidence, knowledge, attitude, and commitment; Level 3
will measure the students’ behavior; and Level 4 will measure the results of the LaunchPad
program. Table 36 provides sample questions from the post-program survey. The actual survey
is provided in Appendix A
Table 36
Sample Questions for a Post-LaunchPad Survey, the Kirkpatrick Level, and the Type of Question
Kirkpatrick
Level
Type of Question Sample Question
1 Likert Scale:
Not at all
Slightly
Moderately
A Great Deal
How much did participating in LaunchPad impact each of
the following? My participation in the program:
- Led me to a better understanding of my career
goals
- Increased my interest in studying computer
science or engineering in college
2 Open ended What do you think computer scientists or engineers might
make or invent that could make a difference in your life?
3 Open ended Now that time has passed since you participated in
LaunchPad, tell us how your experience in LaunchPad
affected you.
4 Rating scale (0-100) What is the likelihood you will enroll in State
University?
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Data Analysis and Reporting
The Level 4 goals for the students, as previously discussed (see Table 30), are to
demonstrate increased interest in engineering and/or computer science, to have increased
knowledge of the benefits that an engineering and/or computer science career has to offer, and to
demonstrate increased confidence that they can do the work required of an engineering or
computer science degree. These goals should be measured immediately after the LaunchPad
program, and 3-months after the program. In addition, a baseline measurement should be taken
on the first day of the program. A data archive will need to be maintained for monitoring and
accountability. Figure 22 provides a sample Level 4 dashboard for interest data; similar
dashboards will be developed to monitor performance of the Level 1, 2, and 3 goals.
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Figure 22. Measures of Career and College Intent Compared Across Stages of the LaunchPad
Program.
Summary
The New World Kirkpatrick Model (Kirkpatrick and Kirkpatrick, 2016) was used to
develop a focused set of recommendations that were informed by the results and findings of this
study. The goals established, and questions asked, will provide SU ECST with the answers to
Kirkpatrick and Kirkpatrick’s three important questions surrounding data analysis: 1) Did the
LaunchPad program meet expectations? 2) If no, why not? 3) If so, why? The results from the
Clark and Estes (2008) gap analysis performed by this study informed the application of the
Kirkpatrick and Kirkpatrick model. The Level 4 analysis focused on the results of the
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LaunchPad program by establishing leading indicators, metrics, and methods of evaluation. The
Level 3 assessment focused on the critical behaviors that would be expected of the students after
a successful LaunchPad program. The Level 2 analysis focused on the students’ learning,
providing recommended assessments of the learning components of the LaunchPad program.
The Level 1 recommendations focused on the students’ reaction to the LaunchPad program. The
integration of the Clark and Estes (2008) gap analysis and the Kirkpatrick and Kirkpatrick (2016)
model provides a holistic approach to discovering the barriers that are preventing an organization
from meeting established goals, developing an intervention to remediate the performance
barriers, and developing an evaluation plan that provides an accountability mechanism for
sustained performance.
Strengths and Weaknesses of the Approach
As previously mentioned, the combined approach of using the Clark and Estes (2008)
Gap Analytic Framework with the New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick,
2016) is a holistic approach to analyzing a performance problem and developing a solution and
an evaluation plan that are based on empirical results. However, this approach can also be overly
constrained and narrowly focused as the solution space is explored. The New World Kirkpatrick
Model is focused on training, which limits the exploration of solutions to only the knowledge
aspect of the performance gap. As discussed by Mayer (2011), an individual with knowledge
may or may not use it; motivation is the necessary catalyst that individuals need to actively use
and apply their knowledge. Furthermore, individuals within an organization may have the
necessary knowledge to perform their tasks, and may be highly motivated, however, if
organizational barriers exist, these individuals will not meet their performance goals (Clark &
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Estes, 2008; Rueda, 2011). Solutions to motivational gaps and organizational barriers are not
isolated to training (Clark & Estes, 2008).
Limitations and Delimitations
Limitations
Limitations are the influences on the study that the researcher cannot control. Mixed
methods research can be used to overcome the limitations of quantitative and qualitative
methodology (Creswell, 2014), but despite the research methodology and design, there are
aspects of the study that cannot be controlled. The participants of the study introduce levels of
variability; thus, the researcher must consider the participant-based influences that cannot be
controlled. The responses to quantitative and qualitative questions are influenced by a multitude
of factors which include: respondent cognition, interaction between the investigator and the
participant, exaggeration, untrue responses, masked willingness to participate, and respondent
state of mind (Borg & Mohler, 1994; Iarosi, 2006). An additional limitation of this study is the
limited duration of the LaunchPad program: two weeks. Creswell discusses Intensive, Long-
Term Involvement, and Rich Data in his Validity List Checklist as strategies to mitigate specific
validity threats. Furthermore, individual interviews with the students could have further
explored their high-school learning environment and experience with gender bias. These
strategies were not feasible for such a short duration.
Although the instruments used in this study were validated, they were developed by other
researchers for different studies. Using a tool developed for a different study resulted in a
challenging process as the questions were mapped to the assumed influences identified by this
study. However, the benefit of having a valid and reliable instrument available outweighed the
resources that would have been required to develop and validate a unique instrument for this
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study. The assumed influences for this study were well-known and supported by multiple
sources of empirical research.
Observation fatigue is experienced when the activity becomes mechanical and the
observer is not able to maintain their “perceptiveness and enthusiasm” (O’Keefe, 1968, p. 342).
This was experienced by the researcher during the second week of the LaunchPad program, but
was mitigated by recruiting two additional observers to help ensure that the results captured in
the observation forms was consistent. In addition to observer fatigue, the researcher was also
pulled away to respond to urgent work email requests during the second week of LaunchPad,
which increased her cognitive load and likely contributed to observer fatigue.
Delimitations
Delimitations are the influences on the study that the researcher can control. The
researcher chose to study the underrepresentation of women in STEM because it is a topic of
personal interest. The organization and demographic being studied were selected based on
convenience and access. The researcher was working with the Dean of State University College
of Engineering and was provided access to the LaunchPad program by the Dean. The researcher
anticipated that the results of this study will contribute to the College of Engineering’s goal of
increasing female enrollment from 15% to 25% by 2020. A mixed methods approach was
selected by the researcher to draw on data of various forms, and finally, the theoretical lens used
by the researcher is based on a feminist perspective (Creswell, 2014). The scope of this study
was constrained by the researcher through personal interest, convenience, and access;
nevertheless, the results of this study are expected to further the literature regarding recruitment
of female students into STEM fields of study.
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Future Research
The participants in this study were female high school students that were recommended
by their high school math and science teachers for inclusion. The students had demonstrated an
interest in engineering and computer science through their involvement in the MESA program.
They were accepted into the program as high-performing students who had completed quite a bit
of advanced math while maintaining higher GPAs. In addition, 71% of the students identified as
Latina or Latina mix. Increasing the opportunity for more students to apply to the LaunchPad
program would provide a larger pool of applicants such that a randomized selection process
could be used. This would facilitate results that could be generalized to the population
(Creswell, 2014).
This study was a cross-sectional design limited to the two-week LaunchPad program, and
a follow-on survey 3-months after the program concluded. Extending this study into a
longitudinal design would allow researchers to follow cohorts of students as they participate in
LaunchPad, enroll and attend college, and enter the workforce. This would facilitate a deeper
understanding of the causal relationships of potential influences on the students’ field of study
and career choice (Singleton & Straits, 2010, Chapter 9).
The mixed method design of this study was a convergent parallel mixed methods design
using a transformative framework (Creswell, 2014). The mixed method components were
performed simultaneously using qualitative and quantitative questions in each of the three
surveys. This methodology was selected due to time constraints. The weakness of a convergent
design is the inability to explore any divergence between the quantitative and qualitative results
(Creswell, 2014). Although the students’ responses to the open-ended questions did provide
insight into emerging themes, the researcher was unable to further explore and interpret these
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findings. For example, the students discussed the ability to establish peer networks as a benefit
of the LaunchPad program, but the researcher was unable to explore this finding any further. An
explanatory sequential mixed methods design that deploys the methods in two phases
(quantitative followed by qualitative) would facilitate a deeper understanding of the quantitative
results. The qualitative aspect is informed by the first phase, which provides the researcher with
an opportunity to better interpret the quantitative results.
Finally, investigating the added effect that a social media-based cohort model might have
on the knowledge, motivation, and organizational influences explored by this study may provide
additional insight into methods that could neutralize the factors that are deterring female high
school students from choosing engineering and computer science as a field of study and career
choice.
Conclusion
Women are underrepresented in the ECST student population. The purpose of this study
was to evaluate the impact that the LaunchPad program had on female high school students, and
the knowledge, motivation, and organizational influences affecting the field of study and career
choices for female high school students in the State University area. The Clark and Estes (2008)
gap analysis framework was used, and these three questions guided the study:
1. What are the knowledge, motivation, and organizational factors that contribute to the
STEM-related career decisions and field of study selections that female high school
students make?
2. What knowledge, motivation, and organizational factors does the LaunchPad program
influence such that the female high school student participants are more likely to enroll in
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engineering or computer science, and/or pursue a career in engineering or computer
science?
3. What are the recommended knowledge, motivation, and organizational solutions that will
increase the number of female students that choose a STEM-related career, resulting in an
increased enrollment of female students in the State University College of Engineering,
Computer Science, and Technology?
This study concluded that the students demonstrated sufficient knowledge about the
benefits of a STEM career and recognized gender stereotypes associated with women in STEM.
However, the following gaps were identified: a gap in conceptual knowledge by being unable to
describe what they could achieve by obtaining a degree in a STEM-related field, a lack of
confidence in their ability to fit in socially with other students, and an inability to describe how a
career in computer science or engineering could make a positive difference in the world.
Furthermore, a majority of the students had experienced gender bias in their learning
environment, and they lacked access to a mentor or a role model.
The LaunchPad program showed promising results as a pilot program. The participants
demonstrated increased knowledge in several areas: understanding engineering and computer
science, and career goals. In addition, they demonstrated increased confidence in their ability to
succeed in computer science or engineering, and increased interest in studying computer science
or engineering in college. Furthermore, they were positively impacted by the female panelists
that were brought in to discuss their experiences.
The New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016) was used to
develop recommendations for improving the LaunchPad program. If implemented, it is expected
that the LaunchPad program will result in increased interest in engineering and computer science
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by the participants, which will in turn contributed to the ECST organizational goal of increased
enrollment by women.
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Zelden, A. L. & Pajares, F. (2000). Against the odds: Self-efficacy beliefs of women in
mathematical, scientific, and technological careers. American Educational Research
Journal, 37(1), 215-246.
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APPENDIX A
Survey Items
This appendix contains six survey instruments that will be administered for this study.
Table A1 below provides the name of the instrument, and when it will be administered. As
described in Chapter 3 of the study, there will be surveys administered at the beginning of the
LaunchPad program, at the end of the program, and three months after the end of the program.
Table A1
Schedule for Survey Administration
Beginning of the Program
• Pre-College Self-Efficacy Survey
• Pre-Activity Survey for High School-
aged Participants, Computer and
Engineering
• Rating Scale for Recruiting
• Flores Navarro
End of the Program • Immediate Post-Activity Survey for
High School-aged Participants,
Computer and Engineering
• Rating Scale for Recruiting
Three months after the end of the Program • 3-6 Month Post-Activity Survey for
High School-aged Participants,
Computer and Engineering
• Rating Scale for Recruiting
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LaunchPad Survey 1of 3
This survey is designed to gain insight into your knowledge of engineering and computer science
careers, your comfort level with math, engineering, and computer science, and any barriers you
have encountered at school. Although it is 16 pages long, the questions are fairly straightforward
and it should not take too long to complete.
Name:
(Please PRINT your first and last name)
Email Address:
School Name:
Today’s Date:
As of today, what do you expect to do after graduating from high school? (Check one)
¨ Attend a 2-year college ¨ Join the military
¨ Attend a 4-year college ¨ Stay at home
¨ Attend a vocational or technical school ¨ Not Sure
¨ Get a job ¨ Other _____________________________
If you will pursue an engineering career, what type of engineering? (Check one or two)
¨ Aerospace ¨ Environmental
¨ Agricultural ¨ General Engineering
¨ Architectural ¨ Industrial
¨ Bioengineering ¨ Materials
¨ Chemical ¨ Mechanical
¨ Civil ¨ Nuclear
¨ Computer Engineering ¨ Petroleum
¨ Computer Science ¨ Undecided
¨ Electrical ¨ Other: ________________________________
Gender:
¨ Male ¨ Female
Ethnicity/Citizenship (check a maximum of two):
¨ Black/African American ¨ Latina/Latino/Hispanic American
¨ American Indian/Alaskan Native ¨ White American
¨ Asian & Pacific American ¨ Other: ________________________
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1. My experience of the work required in high school classes is (check one):
¨ It is very easy for me to get the grade I want in all my classes
¨ With a few exceptions, it is easy for me to get the grade I want in my classes
¨ I have to work some, but not all that hard to get the grade I want in my classes
¨ I have to work hard to get the grade I want in my classes
2. If I go to college, I expect (choose one):
¨ I will have to work less than I did in high school to get the grades I want
¨ I will have to work the same amount as I did in high school to get the grades I want
¨ I will have to work harder than I did in high school to get the grades I want
3. At the present time, how satisfied are you with your plans for what you will do after
graduating from high school? (Circle a number from the scale below)
Very dissatisfied Dissatisfied Neither satisfied
nor dissatisfied
Satisfied Very satisfied
0 1 2 3 4
4. At the present time, how confident are you that you will stick to your plan? (Check one from
the items below)
¨ Not at all confident; I am already planning to change
¨ Not very confident; it is highly likely that I will change
¨ There’s about a 50% chance that I’ll change
¨ I’m fairly confident that I will keep my current plan
¨ I’m very confident that I will keep my current plan
5. At the present, are you exploring other possible plans?
¨ Yes
¨ No
If so, what are the other plans you are considering? (Please list)
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6. What sources of information did you use in order to decide on your current plan? (Check all
that apply)
¨ Campus visits or university “open houses”
¨ Other activities sponsored by a college or university
¨ Activities sponsored by your high school
¨ Employers
¨ Parents
¨ Other family members
¨ High school teachers
¨ High school counselors
¨
Other (please specify) ____________________________________________________
7. The following is a list of college planning and career exploration activities.
Check all the activities that you have participated in at least once during the past year:
¨ An organized social club or sorority
¨ A sports team
¨ A program to prepare you for college or university
¨ A program to prepare you for a job after high school
¨ Another activity _______________________________________________________
¨ Another activity _______________________________________________________
Directions: For the situations described in items 8 through 11, use the numbers 1, 2, and 3
(where 1 is your first choice and 3 would be your last choice) to RANK NO MORE THAN 3
ACTIONS that best describe how you would react to the situation.
8. If I were having difficulties with one of my teachers, I would:
Rank no more than three items: (1-3)
_____ a. Talk to a friend (peer) about it
_____ b. Talk to the teacher about it
_____ c. Talk to an adult friend about it
_____ d. Talk to a parent about it
_____ e. Do nothing
_____ f. Other (please specify) __________________________________
9. If I were having difficulties deciding what classes to choose for next semester, I would:
Rank no more than three items: (1-3)
_____ a. Talk to peers/friends
_____ b. Talk to a teacher about it
_____ c. Talk to an adult friend about it
_____ d. Talk to a parent about it
_____ e. Make the best decision on my own
_____ f. Other (please specify) __________________________________
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10. If I were on a student team and having difficulties with one or more of my team members, I
would:
Rank no more than three items: (1-3)
_____ a. Gather the entire team and try to solve the problem
_____ b. Talk to classmates who aren’t in my team
_____ c. Try to switch into another team
_____ d. Talk to the teacher
_____ e. Do the best I can to work effectively on the team
_____ f. Do nothing
_____ g. Other (please specify) __________________________________
11. If I just found out that I had performed poorly on an exam in a class that is critical to
graduate, I would:
Rank No More Than Three items: (1-3)
_____ a. Talk to a friend about it
_____ b. Talk to a teacher about it
_____ c. Talk to a parent about it
_____ d. Do nothing
_____ e. Other (please specify) _______________________________
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Directions:
Below are statements about studying engineering. To the left of each statement indicate
whether you Strongly Disagree, Slightly Disagree, Neither Disagree nor Agree, Slightly Agree,
Agree, Strongly Agree, or Don’t Know by circling the appropriate number.
To the right of each statement circle the appropriate number to indicate whether the statement
is Very Unimportant, Unimportant, Neither Important nor Unimportant, Important, or Very
Important to you in terms of completing an engineering program.
To what extent do you AGREE? How IMPORTANT is this?
0 = Strongly Disagree 0 = Very unimportant
1 = Disagree 1 = Unimportant
2 = Slightly Disagree 2 = Neither Important
3 = Neither Disagree nor Agree nor Unimportant
4 = Slightly Agree 3 = Important
5 = Agree 4 = Very Important
6 = Strongly Agree
? = Don’t Know
12. 0 1 2 3 4 5 6 ? I can relate to the people around me in my classes 0 1 2 3 4
13. 0 1 2 3 4 5 6 ? I think I can succeed in an engineering curriculum 0 1 2 3 4
14. 0 1 2 3 4 5 6 ? I have a lot in common with the other students in my classes 0 1 2 3 4
15. 0 1 2 3 4 5 6 ? Someone like me can succeed in an engineering career 0 1 2 3 4
16. 0 1 2 3 4 5 6 ? The other students in my classes share my personal interests 0 1 2 3 4
17. 0 1 2 3 4 5 6 ? I can succeed in an engineering curriculum while not having to
give up participation in my outside interests (e.g.,
extracurricular activities, family, sports)
0 1 2 3 4
18. 0 1 2 3 4 5 6 ? I can relate to the people around me in my extracurricular
activities
0 1 2 3 4
19. 0 1 2 3 4 5 6 ? I think I will succeed (earn an A or B) in my science courses 0 1 2 3 4
20. 0 1 2 3 4 5 6 ? I think I will succeed (earn an A or B) in my math courses 0 1 2 3 4
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Directions: For each statement below indicate whether you Strongly Disagree, Disagree, Slightly
Disagree, Neither Disagree nor Agree, Slightly Agree, Agree, Strongly Agree, or Don’t Know by
circling the appropriate number or symbol.
I am confident that …
Strongly
Disagree
Disagree
Slightly
Agree
Neither
Disagree nor
Agree
Slightly
Agree
Agree
Strongly
Agree
Don’t
Know
21. I can complete the math requirements for
most engineering majors
0 1 2 3 4 5 6 ?
22. Doing well at math will enhance my
career/job opportunities
0 1 2 3 4 5 6 ?
23. A degree in engineering will allow me to
obtain a well-paying job
0 1 2 3 4 5 6 ?
24. I can excel in engineering 0 1 2 3 4 5 6 ?
25. I will be treated fairly on the job. That is, I
expect to be given the same opportunities
for pay raises and promotions as my
fellow workers if I enter engineering
0 1 2 3 4 5 6 ?
26. I can complete an engineering degree 0 1 2 3 4 5 6 ?
27. I can cope with not doing well on a test 0 1 2 3 4 5 6 ?
28. A degree in engineering will give me the
kind of lifestyle I want
0 1 2 3 4 5 6 ?
29. I can make friends with people from
different backgrounds and/or values
0 1 2 3 4 5 6 ?
30. Doing well at math will increase my sense
of self-worth
0 1 2 3 4 5 6 ?
31. I will feel “part of the group” on my job if I
enter engineering
0 1 2 3 4 5 6 ?
32. I can complete the science requirements
for most engineering majors
0 1 2 3 4 5 6 ?
33. Taking math courses will help me to keep
my career options open
0 1 2 3 4 5 6 ?
34. I can cope with friends’ disapproval of my
chosen career
0 1 2 3 4 5 6 ?
35. A degree in engineering will allow me to
get a job where I can use my talents and
creativity
0 1 2 3 4 5 6 ?
36. I can cope with being the only person of
my race/ethnicity in a class
0 1 2 3 4 5 6 ?
37. I can cope with being the only person of
my gender in a class
0 1 2 3 4 5 6 ?
38. I can adjust to life as a college or
university student
0 1 2 3 4 5 6 ?
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39. A degree in engineering will allow me to
obtain a job that I like
0 1 2 3 4 5 6 ?
40. At the present time, how confident are you that you will be enrolled in an engineering
program in the next 5 years? (Check one)
¨ Not at all confident; I am already planning not to pursue engineering
¨ There’s about a 50% chance that I’ll be in engineering
¨ I’m fairly confident that I will be in engineering then
¨ I’m very confident that I will be in engineering then
41. At the present time, how confident are you that you will complete any engineering program?
(Check one)
¨ Not at all confident; I am already planning not to pursue engineering
¨ Not confident; it is highly likely I will not complete an engineering program
¨ There’s about a 50% chance that I’ll complete an engineering program
¨ I’m fairly confident that I will complete an engineering program
¨ I’m very confident that I will complete an engineering program
42. At the present time, how confident are you that you will complete any degree in college?
(Check one)
¨ Not at all confident; I am already planning not to attend college or to drop out of
college
¨ Not confident; it is highly likely I will not complete any college degree
¨ There’s about a 50% chance that I’ll complete a college degree
¨ I’m fairly confident that I will complete a college degree
¨ I’m very confident that I will complete a college degree
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43. What is the chance that you will go to college?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
44. If you decide to go to a college or university, what is the chance that you will study
engineering or computer science?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
45. What is the chance that you will go to State University?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
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Learning Environment Barriers
Please respond to each statement using the following answers:
1: Strongly disagree 2: Disagree 3: Not Sure, 4: Agree, 5: Strongly Agree
How much do you agree or disagree with this sentence?
Strongly
Disagree
Somewhat
Disagree
Not
Sure
Somewhat
Agree
Strongly
Agree
46 At school, or during a learning activity, I
have been treated differently because of my
sex.
o o o o o
47 At school, or during a learning activity, I
have experienced negative comments about
my sex (such as insults or rude jokes).
o o o o o
48 At school, or during a learning activity, I
have experienced discrimination because of
my sex.
o o o o o
Learning Environment Coping
Please rate your degree of confidence that you could overcome each of the potential barriers
listed below.
On a scale from 1 to 5, How much do you agree or disagree that you can overcome the following
if you experienced it at school, or during a learning activity:
Not at all
Confident
Somewhat
Confident
Confident
Very
Confident
Extremely
Confident
I can overcome the following if experienced
at school or during a learning activity:
1 2 3 4 5
49 Discrimination due to my gender o o o o o
50 Discrimination due to my ethnicity o o o o o
51 Negative comments about my sex (insults,
rude jokes).
o o o o o
52 Negative comments about my racial/ethnic
background (insults, jokes).
o o o o o
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Engineering Supports and Barriers
Many factors can either support or hinder a student's education. We are interested in learning
about the types of situations that help or hinder your education if you plan on pursuing computer
science or engineering.
Using the scale below, please answer the following questions:
1: Strongly Disagree, 2: Disagree, 3: Not Sure, 4: Agree, 5: Strongly Agree
How much do you agree or disagree with this sentence?
Strongly
Disagree
Somewhat
Disagree
Not
Sure
Somewhat
Agree
Strongly
Agree
53 I have access to a "role model" in the field
(i.e., someone you can look up to and learn
from observing).
o o o o o
54 I would feel support if I chose to pursue
engineering or computer science from
important people in your life (e.g., teachers).
o o o o o
55 I feel that there are people "like me" in this
field.
o o o o o
56 I can get helpful assistance from a tutor, if I
felt like I needed such help.
o o o o o
57 I would get encouragement from my friends
for pursing engineering or computer science.
o o o o o
58 I would get helpful assistance from a
counselor or mentor.
o o o o o
59 My family members would support my
decision to pursue engineering or computer
science
o o o o o
60 My close friends or relatives would be proud
of me for pursuing engineering or computer
science.
o o o o o
61 I would have access to a "mentor" who
could offer me advice and encouragement.
o o o o o
62 I would receive negative comments or
discouragement about engineering or
computer science from family members.
o o o o o
63 I would worry that engineering or computer
science as a career path would require too
much time or schooling.
o o o o o
64 I don't fit in socially with other students in
engineering or computer science.
o o o o o
65 I would receive negative comments or
discouragement about engineering or
computer science from my friends.
o o o o o
66 I would feel pressure from my parents or
other important people to change your
major, if I chose engineering or computer
science, to some other field.
o o o o o
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Pre-Activity Survey for High School-aged Participants
Computer and Engineering
67. Education: Check the grade that you will enter next fall.
Check only one.
a. o 9
th
o 10
th
o 11
th
o 12
th
68. From the list below, check the classes that you plan to take in your next school year.
Check all that apply.
a.
o Algebra I o Foreign Language
o Algebra II o General Math
o Calculus o Geometry
o Chemistry o History/Social Studies
o Computer Applications o Music
o Computer Science o Physics
o Drafting or CAD (Computer-Aided Drawing) o Pre-Calculus
o Earth or Physical Science o Technology Education
o Engineering o Other math, engineering or science courses:
_______________________ o English
For the upcoming school year: Yes No
Not
Available
b. Are you currently enrolled in honors or advanced classes? ¨ ¨ ¨
c. Have you been encouraged to enroll in honors or advanced
classes?
¨ ¨ ¨
d. Are you enrolled in a special engineering or science curriculum? ¨ ¨ ¨
e. Do you plan to enroll in honors or advanced classes next year? ¨ ¨ ¨
69. Has anyone talked to you about the importance of . . .
a. Taking classes that will prepare you for college? o Yes o No
b. Math to your future career? o Yes o No
70. What do you plan to do when you graduate from high school? Check only one.
o Go to a college or university
o Attend a technical school (for example: business school, beauty school, technology school, etc.)
o Get a full-time job
o Join the military
o Don’t know
o Other: _______________________________________
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71. What is the highest level of formal education completed by your parents?
o High School
o College/University
o Graduate Studies
o Other: ________________________________________
72. What do computer scientists or computer engineers do?
Read the following statements about what computer scientist or computer engineers might do
and indicate your agreement or disagreement with each statement.
Computer Scientist or Computer Engineers: Disagree Don’t
Know
Agree
a) Mainly work on machines and computers o o o
b) Mainly work with other people to solve problems
o o o
c) Work on things that help the world o o o
d) Can choose to do many different kinds of jobs
o o o
e) Mainly work on things that have nothing to do with
me
o o o
f) I don’t know what computer scientists or computer
engineers do
o o o
g) Other (please write in whole sentences):
73. What do engineers do?
Read the following statements about what engineers might do and indicate your agreement or
disagreement with each statement.
Engineers: Disagree Don’t
Know
Agree
a) Mainly work on machines and computers o o o
b) Mainly work with other people to solve problems
o o o
c) Can choose to do many different kinds of jobs o o o
d) Have lots of choices about what they can do in
their jobs
o o o
e) Mainly work on things that have nothing to do with
me
o o o
f) I don’t know what engineers do o o o
g) Other (please write in whole sentences):
74. If you go to college, do you think you will pursue a career in a computer or engineering-related field?
o Yes o No o Don’t Know
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75. In your future, do you think you want to be a computer scientist, computer engineer, or engineer?
o Yes o No o Don’t Know
76. Has anyone talked to you about becoming a computer scientist, computer engineer, or engineer?
o Yes o No
If yes, put a check by everyone who has talked to you about this:
o Engineering or technology teacher o Math teacher
o Family members o Science teacher
o Family friends o Computer teacher
o
Guidance counselor
o Other (provide kind of person or teacher,
not name):
_____________________________
Tell us about your goals
77. The following statements describe work or jobs you might do in the future. Tell us how important each
of the items below is to you in your future work.
How important is it to you to do . . . Not
Important
Somewhat
Important
Very
Important
a) Work that makes me think o o o
b) Work that allows me to make lots of money o o o
c) Work that allows me to use math, computer,
engineering or science skills
o o o
d) Work that allows me to tell other people what to
do
o o o
e) Work that allows me to help solve problems and
create solutions
o o o
f) Work that is fun to do o o o
g) Work that allows me to have time with family o o o
h) Work that allows me to help my community
and/or society
o o o
i) Work that makes people think highly of me o o o
j) Work that is satisfying to me o o o
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78. What do you think computer scientists or computer engineers might make or invent that could make a
difference in your life (either good or bad)? Make a list in the space below. (Please write in whole
sentences).
Tell us what you do
79. If you encounter a math homework problem that you don’t know how to solve, what are you most
likely to do? Check no more than 3 options below.
o Ask a parent or other family member for help with the problem
o Call or meet with a friend who you know is good at math and ask her or him for help so you
can solve it
o Contact Homework Hotline or similar resource
o Get help from your math teacher on this problem
o Work it out with my study group
o Go to the teacher's Web page for help
o Copy the answer from one of my friends
o Search the Internet for help
o Take some time and try to figure out how to best approach solving this problem
o Other (please specify):
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80. The table lists things you can do when you are working on school activities or assignments. Check
the appropriate box to tell us how often you do each of these things.
Never Sometimes Very Often Always
a) When I see a new math problem, I can use what
I have learned to solve the problem.
o o o o
b) I can use what I know to design and build
something mechanical that works.
o o o o
c) In lab activities, I can use what I have learned to
design a solution.
o o o o
d) I can effectively lead a team to design and build
a hands-on project.
o o o o
e) I know where I can find the information that I
need to solve difficult problems.
o o o o
f) I can use what I have learned to teach myself
how to program a computer game.
o o o o
g) I can explain math or science to my friends to
help them understand.
o o o o
h) I can get good grades in math. o o o o
i) I can get good grades in science. o o o o
Tell us what you think
Here is a list of statements. Tell us what you think about them. Select a response that indicates your
level of agreement.
How much do you agree or disagree with this sentence?
Strongly
Disagree
Somewhat
Disagree
Somewhat
Agree
Strongly
Agree
a) I look forward to science class in school. o o o o
b) I look forward to math class in school. o o o o
c) I would rather solve a problem by doing an
experiment than be told the answer.
o o o o
d) More time should be spent on hands-on
projects in science or technology activities
at school.
o o o o
e) I would like to (or already do) belong to a
science or technology activities club.
o o o o
f) I get bored when I watch programs on
channels like Discovery Channel, Animal
Planet, Nova, Mythbusters, etc.
o o o o
g) I like to get science books or science
experiments kits as presents.
o o o o
h) I like learning how things work. o o o o
i) Science is too hard when it involves math. o o o o
j) Science is a difficult subject. o o o o
k) Doing experiments in science class is
frustrating.
o o o o
l) I feel comfortable with using a computer to
make graphs and tables.
o o o o
m) I am interested in learning more about how
computers work.
o o o o
n) I like to learn to use new computer software. o o o o
Tell us why you are here
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81. Why did you choose to attend this activity? Check all that apply.
o Have fun o Have something to do
o Learn more about California State
University, Los Angeles
o Learn more about different majors in college
(e.g., engineering, science, computers, etc.)
o Learn about what computer scientists and
computer engineers do
o Make my parents/guardians happy
o Meet others with interests similar to mine o Help me to do well in school
o Not sure o Other:
_______________________________
82. How did you hear about this activity? Check all that apply.
o A guidance counselor at my school told me about it o I saw a newspaper or other advertisement
o A teacher at my school told me about it o My parents told me about it
o I or my parents did a Web/Internet search o I received something in the mail
o Someone who went to this college told me about it o Other: _______________________________
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Immediate Post-Activity Survey for High School-aged Participants –
Computer and Engineer
Thank you so much for taking the time to fill out this survey – it should take about 10 to 15 minutes to complete. It
has been an honor to be entrusted with your permission to observe this two-week period. My goal is to use your
experience to benefit other women that would benefit from a degree in engineering or computer science
Warm regards,
Cheryl
Name:
(Please PRINT your first and last name)
E-mail:
Tell us about you
1. Gender:
o Female
o Male
2. Ethnicity: (You may check more than one, as appropriate).
o African/Black American
o American Indian/Alaskan Native
o Asian & Pacific American
o Latina/Latino/Hispanic American
o White American
o Other: ______________________________
3. Education: Check the grade that you are in now or, if it is summer, check the grade you will enter
next fall.
Check only one.
3a. o 9
th
o 10
th
o 11
th
o 12
th
o Graduating
3b.
Name of High School:
If home schooled, write “home schooled”
3c.
Year You Graduate from High School:
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4. From the list below, check which classes that you plan to take in your next school year.
Check all that apply.
o Algebra I o Foreign Language
o Algebra II o General Math
o Calculus o Geometry
o Chemistry o History/Social Studies
o Computer Applications o Music
o Computer Science o Physics
o Drafting or CAD (Computer-Aided Drawing) o Pre-Calculus
o Earth or Physical Science o Technology Education
o Engineering o Other math, engineering or science courses:
_______________________ o English
Yes No
Not
Available
4a. Will you currently enrolled in honors or advanced classes? ¨ ¨ ¨
4b. Have you been encouraged to enroll in honors or advanced
classes?
¨ ¨ ¨
4c. Will you enroll in a special engineering or science curriculum? ¨ ¨ ¨
5. Has anyone talked to you about the importance of . . .
5a. Taking classes that will prepare you for college? o Yes o No
5b. Math to your future career? o Yes o No
6. What do you plan to do when you graduate from high school? Check only one.
o Go to a college or university
o Attend a technical school (for example: business school, beauty school, technology school, etc.)
o Get a full-time job
o Join the military
o Don’t know
o Other: _______________________________________
Tell us what you think about the LaunchPad progam
7. My goals for participating in this activity were: (Check all that apply)
o Have fun o Have something to do
o Learn more about California State University, Los
Angeles
o Learn more about different majors in college
(e.g., engineering, science, computers, etc.)
o Learn about what computer scientists and
engineers do
o Make my parents/guardians happy
o Meet other kids with interests similar to mine o Prepare me to do well in school
o Not sure o Other: _________________________
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8. Help us improve LaunchPad by telling us about your experience with the overall program:
Strongly
Disagree
Disagree Agree
Strongly
Agree
a) The information I received about the activity before
it began helped me to participate successfully
o o o o
b) I found the instructions and information I received
during the activity to be effective and helpful.
o o o o
c) If I needed help in solving a problem or had a
question during the activity, it was readily available.
o o o o
d) I found it easy to get to know the other participants
in this activity.
o o o o
e) The leaders for this activity were prepared.
o o o o
f) This activity was well organized.
o o o o
g) My goals for participating in this activity were met.
o o o o
h) Other feedback?
9. Help us improve the Computer Science and Data Science session by telling us about your
experience:
Strongly
Disagree
Disagree Agree
Strongly
Agree
a) The information I received about the activity before
it began helped me to participate successfully
o o o o
b) I found the instructions and information I received
during the activity to be effective and helpful.
o o o o
c) If I needed help in solving a problem or had a
question during the activity, it was readily available.
o o o o
d) I found it easy to get to know the other participants
in this activity.
o o o o
e) The leaders for this activity were prepared.
o o o o
f) This activity was well organized.
o o o o
g) My goals for participating in this activity were met.
o o o o
h) Other feedback?
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10. Help us improve the Civil Engineering – Atmosphere session by telling us about your experience:
Strongly
Disagree
Disagree Agree
Strongly
Agree
a) The information I received about the activity before it
began helped me to participate successfully
o o o o
b) I found the instructions and information I received
during the activity to be effective and helpful.
o o o o
c) If I needed help in solving a problem or had a
question during the activity, it was readily available.
o o o o
d) I found it easy to get to know the other participants in
this activity.
o o o o
e) The leaders for this activity were prepared.
o o o o
f) This activity was well organized.
o o o o
g) My goals for participating in this activity were met.
o o o o
h) Other feedback?
11. Help us improve the Civil Engineering – Hydrology session by telling us about your experience:
Strongly
Disagree
Disagree Agree
Strongly
Agree
a) The information I received about the activity before
it began helped me to participate successfully
o o o o
b) I found the instructions and information I received
during the activity to be effective and helpful.
o o o o
c) If I needed help in solving a problem or had a
question during the activity, it was readily available.
o o o o
d) I found it easy to get to know the other participants
in this activity.
o o o o
e) The leaders for this activity were prepared.
o o o o
f) This activity was well organized.
o o o o
g) My goals for participating in this activity were met.
o o o o
h) Other feedback?
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12. Help us improve the Mechanical Engineering – Robotics session by telling us about your experience:
Strongly
Disagree
Disagree Agree
Strongly
Agree
a) The information I received about the activity before
it began helped me to participate successfully
o o o o
b) I found the instructions and information I received
during the activity to be effective and helpful.
o o o o
c) If I needed help in solving a problem or had a
question during the activity, it was readily available.
o o o o
d) I found it easy to get to know the other participants
in this activity.
o o o o
e) The leaders for this activity were prepared.
o o o o
f) This activity was well organized.
o o o o
g) My goals for participating in this activity were met.
o o o o
h) Other feedback?
13. How much did participating in LaunchPad impact each of the following?
My participation in this activity:
Not at All Slightly Moderately
A Great
Deal
a) Helped me understand computer science better.
o o o o
b) Helped me understand civil engineering better.
o o o o
c) Helped me understand engineering better.
o o o o
d) Led me to a better understanding of my own career
goals.
o o o o
e) Increased my interest in studying computer science
or engineering in college.
o o o o
f) Made me think more about what I will do after
graduating from high school.
o o o o
g) Made me decide to work harder in school.
o o o o
h) Made me to decide to take different classes in
school (including college) than I had planned to.
o o o o
g) Made me more confident in my ability to succeed in
computer science or computer engineering.
o o o o
h) Increased my confidence in my ability to participate
in computer science and engineering projects or
activities.
o o o o
14. Please respond to the following two-part question regarding your participation in this activity:
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a) What did you like best about this activity?
b) If you were in charge, how would you change this activity?
15. Will you recommend that your friends participate in this activity? ¨ Yes ¨ No
Please explain why or why not:
16. What do computer scientists or computer engineers do?
Read the following statements about what computer scientists or computer engineers might do
and indicate your agreement or disagreement with each statement.
Computer Scientists or Computer Engineers: Disagree Don’t
Know
Agree
a) Mainly work on machines and computers o o o
b) Mainly work with other people to solve problems o o o
c) Work on things that help the world o o o
d) Can choose to do many different kinds of jobs o o o
e) Mainly work on things that have nothing to do with
me
o o o
f) I don’t know what computer scientists or computer
engineers do
o o o
g) Other:
17. What do engineers do?
Read the following statements about what engineers might do and indicate your agreement or
disagreement with each statement.
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Engineers: Disagree Don’t
Know
Agree
a) Mainly work on machines and computers o o o
b) Mainly work with other people to solve problems o o o
c) Work on things that help the world o o o
d) Can choose to do many different kinds of jobs o o o
e) Mainly work on things that have nothing to do with
me
o o o
f) I don’t know what engineers do o o o
g) Other (please write in whole sentences):
18. If you go to college, do you think you will pursue a career in a computer or engineering-related field?
o Yes o No o Don’t Know
19. In your future, do you think you want to be a computer scientist or engineer?
o Yes o No o Don’t Know
20. Has anyone talked to you about becoming a computer scientist or engineer?
o Yes o No
If yes, put a check by everyone who has talked to you about this:
o Computer teacher o Math teacher
o Family members o Science teacher
o Family friends o Engineering or technology teacher
o Guidance counselor o Other (provide kind of person or teacher,
not name):
______________________________
Tell us about your goals
21. The following statements describe work or jobs you might do in the future. Tell us how important each
of the items below is to you in your future work.
How important is it to you to do . . . Not
Important
Somewhat
Important
Very
Important
b) Work that makes me think o o o
b) Work that allows me to make lots of money o o o
c) Work that allows me to use math, computer,
engineering or science skills
o o o
d) Work that allows me to tell other people what to
do
o o o
e) Work that allows me to help solve problems and
create solutions
o o o
f) Work that is fun to do o o o
g) Work that allows me to have time with family o o o
h) Work that allows me to help my community
and/or society
o o o
i) Work that makes people think highly of me o o o
j) Work that is satisfying to me o o o
22. What do you think computer scientists or engineers might make or invent that could make a
difference in your life (either good or bad)?
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Tell us what you do when:
23. If you encounter a math homework problem that you don’t know how to solve, what are you most
likely to do? Check no more than 3 options below.
¨ Ask a parent or other family member for help with the problem
¨ Call or meet with a friend who you know is good at math and ask her or him for help so I can
solve it
¨ Contact Homework Hotline or similar resource
¨ Get help from my math teacher on this problem
¨ Work it out with my study group
¨ Go to the teacher's Web page for help
¨ Copy the answer from one of my friends
¨ Search the Internet for help
¨ Take some time and try to figure out how to best approach solving this problem
¨ Other (please specify):
24. The table lists things you can do when you are working on school activities or assignments. Check
the appropriate box to tell us how often you do each of these things.
Never Sometimes Very Often Always
a) When I see a new math problem, I can use what
I have learned to solve the problem.
o o o o
b) I can use what I know to design and build
something mechanical that works.
o o o o
c) In lab activities, I can use what I have learned to
design a solution to a problem.
o o o o
d) I can effectively lead a team to design and build
a hands-on project.
o o o o
e) I know where I can find the information that I
need to solve difficult problems.
o o o o
f) I can use what I have learned to teach myself
how to program a computer game.
o o o o
g) I can explain math or science to my friends to
help them understand.
o o o o
h) I can get good grades in math. o o o o
i) I can get good grades in science. o o o o
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Tell us what you think
25. Here is a list of statements. Tell us what you think about them. Select a response that indicates your
level of agreement.
How much do you agree or disagree with this sentence?
Strongly
Disagree
Somewhat
Disagree
Somewhat
Agree
Strongly Agree
a) I look forward to science class in
school.
o o o o
b) I look forward to math class in
school.
o o o o
c) I would rather solve a problem by
doing an experiment than be told
the answer.
o o o o
d) More time should be spent on
hands-on projects in science or
technology activities at school.
o o o o
e) I would like to (or already do) belong
to a science or technology activities
club.
o o o o
f) I get bored when I watch programs
on channels like Discovery Channel,
Animal Planet, Nova, Mythbusters,
etc.
o o o o
g) I like to get science books or
science experiments kits as
presents.
o o o o
h) I like learning how things work. o o o o
i) Science is too hard when it involves
math.
o o o o
j) Science is a difficult subject. o o o o
k) Doing experiments in science is
frustrating.
o o o o
l) I feel comfortable with using a
computer to make graphs and
tables.
o o o o
m) I am interested in learning more
about how computers work.
o o o o
n) I like to learn to use new computer
software.
o o o o
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Rating Scales for Recruiting to a STEM Career and/or Institution
26. What is the chance that you will go to college?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
27. If you decide to go to a college or university, what is the chance that you will study
engineering or computer science?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
28. What is the chance that you will go to State University?
Indicate by marking an “x” on the line below. If you do not know, check here o:
No Chance Definitely
0% 25% 50% 75% 100%
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Essay Section
29. How do your expectations of yourself as a woman align with a career in engineering or
computer science?
30. In what way do the expectations of your family align with a career in engineering or
computer science?
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31. Were there any other ways in which an engineering or computer science career did not fit
those expectations?
a. Personal
b. Familial?
c. Friends/Peers?
32. How would you affect the environment or help others by having a career in computer
science or engineering?
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LaunchPad - 3-month post event survey
12
Dear LaunchPad participant,
As I discussed during LaunchPad, I am following up with a post-event survey. I want to thank
you in advance for taking the time to complete the survey, which contains 15 questions and
should take approximately 10 minutes to complete.
If you have any questions at all please call, text, or email me at: cdematte@usc.edu or
562.477.3022.
Please double-check your email address as you enter it below - I will be sending a $10 Starbucks
gift card to this address as a token of my appreciation for your participation in the 3 LaunchPad
surveys, and for helping me achieve my goal of completing my doctoral program.
I hope you are doing well!
Many Thanks,
Cheryl
12
Survey 3 is a Qualtrics export
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1. Name (1) ________________________________________________
2. Email Address (2) ________________________________________________
Q2 What do you plan to do when you graduate from high school?
3. Go to a college or university (1)
4. Attend a technical school (for example: business school, beauty school, technology
school, etc.) (2)
5. Get a full-time job (3)
6. Join the military (4)
7. I don't know (5)
8. Click to write Choice 7 (6)
9. Other: (7) ________________________________________________
Q3
Has anyone talked to you about the importance of...
Yes (1) No (2)
Taking classes that would
prepare you for college? (1)
10. 11.
Math to your future career?
(2)
12. 13.
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Q4 What do computer scientists or computer engineers do?
Computer Scientists or Engineers:
Disagree (1)
Don't Know
(2)
Agree (3)
Mainly work on
machines and
computers (1)
14. 15. 16.
Mainly work with
other people to solve
problems (2)
17. 18. 19.
Work on things that
help the world (3)
20. 21. 22.
Can choose to do
many different kinds
of jobs (4)
23. 24. 25.
Mainly work on
things that have
nothing to do with
me (5)
26. 27. 28.
I don't know what
computer scientists
or computer
engineers do (6)
29. 30. 31.
Other: (7)
32. 33. 34.
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Q5 What do engineers do?
Engineers:
Disagree (1)
Don't Know
(2)
Agree (3)
Mainly work on
machines and
computers (1)
35. 36. 37.
Mainly work with
other people to solve
problems (2)
38. 39. 40.
Work on things that
help the world (3)
41. 42. 43.
Can choose to do
many different kinds
of jobs (4)
44. 45. 46.
Mainly work on
things that have
nothing to do with
me (5)
47. 48. 49.
I don't know what
engineers do (6)
50. 51. 52.
Other: (7)
53. 54. 55.
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Q6
Yes (1) No (2)
Don't Know
(3)
If you go to college,
do you think you will
pursue a career in a
computer or
engineering-related
field? (1)
56. 57. 58.
In your future, do you
think you want to be
a computer scientist
or engineer? (2)
59. 60. 61.
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Q7 Has anyone talked to you about becoming a computer scientist or engineer?
62. Yes (1)
63. No (2)
Display This Question:
If Q7 = Yes
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Put a check by everyone who has talked to you about becoming a computer scientist or
engineer
▢ Computer teacher (1)
▢ Family members (2)
▢ Family friends (3)
▢ Guidance counselor (4)
▢ Math teacher (5)
▢ Science teacher (6)
▢ Engineering or technology teacher (7)
▢ Other (provide kind of person or teacher, not name) (8)
________________________________________________
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Q8 How much did participating in LaunchPad impact each of the following?
My participation in this activity:
Not at All (1) Slightly (2) Moderately (3) A Great Deal (4)
Helped me understand
computer science better.
(1)
64. 65. 66. 67.
Helped me understand
civil engineering better.
(2)
68. 69. 70. 71.
Helped me understand
engineering better. (3)
72. 73. 74. 75.
Led me to a better
understanding of my own
career goals. (4)
76. 77. 78. 79.
Increased my interest in
studying computer
science or engineering in
college. (5)
80. 81. 82. 83.
Made me think about
what I will do after
graduating from high
school. (6)
84. 85. 86. 87.
Made me decide to work
harder in school. (7)
88. 89. 90. 91.
Made me decide to take
different classes in school
than I had planned to. (8)
92. 93. 94. 95.
Made me more confident
in my ability to succeed in
computer science or
engineering. (9)
96. 97. 98. 99.
Increased my confidence
in my ability to participate
in computer science and
engineering projects or
activities. (10)
100. 101. 102. 103.
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Tells us about your goals
Q9 The following statements describe work or jobs you might do in the future. Tell us how
important each of the items below is to you in your future work.
How important is it to you to do...
Not Important (1)
Somewhat
Important (2)
Very Important (3)
Work that makes me
think (1)
104. 105. 106.
Work that allows me to
make lots of money (2)
107. 108. 109.
Work that allows me to
use math, computer,
engineering, or science
skills (3)
110. 111. 112.
Work that allows me to
tell other people what
to do (4)
113. 114. 115.
Work that allows me to
help solve problems
and create solutions (5)
116. 117. 118.
Work that is fun to do
(6)
119. 120. 121.
Work that allows me to
have time with family
(7)
122. 123. 124.
Work that allows me to
help my community
and/or society (8)
125. 126. 127.
Workk that makes
people think highly of
me (9)
128. 129. 130.
Work that is satisfying
to me (10)
131. 132. 133.
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Q10 What do you think computer scientists or engineers might make or invent that could make a
difference in your life (either good or bad)?
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
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Tell us what you do when:
Q11 If you encounter a math homework problem that you don't know how to solve, what are you
most likely to do? (Check no more than 3 options below)
▢ Ask a parent or other family member for help with the problem (1)
▢ Call or meet with a friend who you know is good at math and ask her or him for help so
you can solve it (2)
▢ Contact Homework Hotline or similar resource (3)
▢ Get help from your math teacher on this problem (4)
▢ Work it out with your study group (5)
▢ Go to the teacher's web page for help (6)
▢ Copy the answer from one of your friends (7)
▢ Search the Internet for help (8)
▢ Take some time and try to figure out how to best approach solving the problem (9)
▢ Other (please specify): (10) ________________________________________________
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Q12 Below are lists of things you can do when you are working on school activities or
assignments. Check the appropriate response to tell us how often you do each of these things.
Never (1) Sometimes (2) Very Often (3) Always (4)
When I see a new math
problem, I can use
what I have learned to
solve the problem. (1)
134. 135. 136. 137.
I can use what I know
to design and build
something mechanical
that works. (2)
138. 139. 140. 141.
In lab activities, I can
use what I have learned
to design a solution to
a problem. (3)
142. 143. 144. 145.
I can effectively lead a
team to design and
build a hands-on
project. (4)
146. 147. 148. 149.
I know where I can
find the information
that I need to solve
difficult problems. (5)
150. 151. 152. 153.
I can use what I have
learned to teach myself
how to program a
computer game. (6)
154. 155. 156. 157.
I can explain math or
science to my friends
to help them
understand. (7)
158. 159. 160. 161.
I can get good grades
in math. (8)
162. 163. 164. 165.
I can get good grades
in science. (9)
166. 167. 168. 169.
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Tell us what you think
Q13 Here is a list of statements. Tell us what you think about them. Select a response that
indicates your level of agreement.
How much do you agree or disagree with this
sentence?
Strongly
Disagree (1)
Somewhat
Disagree (2)
Somewhat
Agree (3)
Strongly
Agree (4)
I look forward to science class in school.
(1)
170. 171. 172. 173.
I look forward to math class in school.
(2)
174. 175. 176. 177.
I would rather solve a problem by doing
an experiment than be told the answer.
(3)
178. 179. 180. 181.
More time should be spent on hands-on
projects in science or technology
activities in school. (4)
182. 183. 184. 185.
I would like to (or already do) belong to
a science or technology activities club.
(5)
186. 187. 188. 189.
I get bored when I watch programs on
channels like Discovery Channel,
Animal Planet, Nova, Mythbusters, etc.
(6)
190. 191. 192. 193.
I like to get science books or science
experiment kits as presents. (7)
194. 195. 196. 197.
I like learning how things work. (8)
198. 199. 200. 201.
Science is too hard when it involves
math. (9)
202. 203. 204. 205.
Science is a difficult subject. (10)
206. 207. 208. 209.
Doing experiments in science is
frustrating. (11)
210. 211. 212. 213.
I feel comfortable with using a computer
to make graphs and tables. (12)
214. 215. 216. 217.
I am interested in learning more about
how computers work. (13)
218. 219. 220. 221.
I like to learn to use new computer
software. (14)
222. 223. 224. 225.
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Q14 Now that time has passed since you participated in LaunchPad, tell us how your experience
in LaunchPad affected you.
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
Rating Scales for Recruiting to a STEM career and/or Institution
Q15 Move the slider to the appropriate position. If you do not know, please type "I don't know"
in the text box.
What is the chance that you will go to
college? (1)
If you decide to go to a college or
university, what is the chance you will study
engineering or computer science? (2)
What is the chance you will go to
State University? (3)
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APPENDIX B
Observation Checklist
This observation checklist will be used to guide the documentation of the observations.
The following items will be documented throughout each day of observation (Merriam &
Tisdell, 2016, p. 141).
1. The physical setting
2. The participants
3. Activities and Interactions
4. Conversations
5. Subtle Factors
6. The researcher’s behavior
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Peer Observation Form
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APPENDIX C
Informed Consent/Information Sheet
University of Southern California
Rossier School of Education,
3470 Trousdale Parkway, Los Angeles, CA 9009
YOUTH ASSENT-PARENTAL PERMISSION FOR NON-MEDICAL RESEARCH
This form will also serve as the “Youth Assent” and “Consent/Permission form for the
Youth to Participate in Research.” In this case, “You” refers to “your child.”
MITIGATING THE LOW ENROLLMENT RATES FOR
WOMEN IN ENGINEERING AND COMPUTER SCIENCE
You are invited to participate in a research study conducted by Cheryl K. DeMatteis, M.S.
Computer Science and Dr. Lawrence O. Picus, Ph.D., from the University of Southern
California. There is NO FUNDING supporting this study. Your participation is voluntary. You
should read the information below, and ask questions about anything you do not understand
before deciding whether to participate.
Please take as much time as you need to read the consent form. Your child will also be asked
his/her permission. Your child can decline to participate, even if you agree to allow
participation. You and/or your child may also decide to discuss it with your family or friends. If
you and/or your child decide to participate, you will both be asked to sign this form. You will be
given a copy of this form.
PURPOSE OF THE STUDY
The purpose of this study is to evaluate the factors that influence young women to pursue
engineering and computer science.
STUDY PROCEDURES
If you agree to participate, you will be asked to participate in the following activities:
Surveys:
There will be three surveys. Two of the surveys will be administered during the LaunchPad
program; one at the beginning, and one at the end. The third survey will be an online survey
and will be available three months after LaunchPad program ends. Each survey will take
about 30 minutes. You do not have to answer any questions that you don’t want to.
Focus Groups:
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You may also be asked to participate in an audio-taped focus group interview. The focus
group consists of two or more people and will take about 30-45 minutes to complete. If you
don’t want to be taped, you cannot participate in the focus group.
Observation:
There will be an observer documenting the activities during the entire two weeks of the
LaunchPad program.
POTENTIAL RISKS AND DISCOMFORTS
There are no anticipated risks or discomforts to your participation.
POTENTIAL BENEFITS TO PARTICIPANTS AND/OR TO SOCIETY
There are no anticipated benefits to your participation. We hope that this study will help
researchers learn more about what influences young women to choose engineering or computer
science as a field of study
PAYMENT/COMPENSATION FOR PARTICIPATION
You will receive a $5 Starbucks gift card for completing each of the in-person surveys, and a $10
gift card for completing the online survey. You do not have to answer all questions to receive
the card. The gift card will be given to you when you return the in-person surveys, and mailed to
you when you respond to the online survey.
CONFIDENTIALITY
We will keep your records for this study confidential as far as permitted by law. However, if we
are required to do so by law, we will disclose confidential information about you. The members
of the research team and the University of Southern California’s Human Subjects Protection
Program (HSPP) may access the data. The HSPP reviews and monitors research studies to protect
the rights and welfare of research subjects.
Upon completion of the data collection and data entry, all hard copies (consent documents,
surveys, etc.) will be destroyed. The data, including the audio recordings, will be stored
electronically by the researcher on Google Drive, which is password protected and encrypted; the
data will be kept online for three years and then destroyed. The participants name will be stored
separately from the survey data, and the file that maps the participants to the data will be password
protected.
PARTICIPATION AND WITHDRAWAL
Your participation is voluntary. Your refusal to participate will involve no penalty or loss of
benefits to which you are otherwise entitled. You may withdraw your consent at any time and
discontinue participation without penalty. You are not waiving any legal claims, rights or remedies
because of your participation in this research study.
ALTERNATIVES TO PARTICIPATION
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226
If you don’t want to participate in this study, you will not receive a survey, nor will the researcher
record any information about you in the observation notes.
INVESTIGATOR’S CONTACT INFORMATION
If you have any questions or concerns about the research, please contact:
Cheryl DeMatteis Dr. Lawrence Picus
562.477.3022 213.740.2175
cdematte@usc.edu lpicus@rossier.usc.edu
University of Southern California
Rossier School of Education
3470 Trousdale Parkway
Los Angeles, CA 9009
RIGHTS OF RESEARCH PARTICIPANT – IRB CONTACT INFORMATION
If you have questions, concerns, complaints about your rights as a research participant or the
research in general and are unable to contact the research team, or if you want to talk to someone
independent of the research team, please contact the University Park Institutional Review Board
(UPIRB), 3720 South Flower Street #301, Los Angeles, CA 90089-0702, (213) 821-5272 or
upirb@usc.edu
SIGNATURE OF RESEARCH PARTICIPANT (If the participant is 14 years or older)
I have read the information provided above. I have been given a chance to ask questions. My
questions have been answered to my satisfaction, and I agree to participate in this study. I have
been given a copy of this form.
Name of Participant
Signature of Participant Date
LOW ENROLLMENT FOR WOMEN
227
SIGNATURE OF PARENT(S)/LEGALLY AUTHORIZED REPRESENTATIVE
I have read the information provided above. I have been given a chance to ask questions. My
questions have been answered to my satisfaction, and I agree to allow my child participate in this
study. I have been given a copy of this form.
AUDIO/VIDEO/PHOTOGRAPHS
□ I agree to be audio recorded
□ I do not want to be audio recorded
Name of Parent/Legally Authorized Representative
Signature of Parent/Legally Authorized Representative Date
SIGNATURE OF INVESTIGATOR
I have explained the research to the participant and his/her parent(s)/Legally Authorized
Representative, and answered all of their questions. I believe that the parent(s) understand the
information described in this document and freely consents to participate.
Cheryl K. DeMatteis
Name of Person Obtaining Consent
Signature of Person Obtaining Consent Date
LOW ENROLLMENT FOR WOMEN
228
APPENDIX D
LaunchPad Schedule and Agenda
Monday Tuesday Wednesday Thursday Friday
Week 1
Morning
9am – 12pm
1. Welcome
2. Ice
Breaker
3.
Engineering
Activity
CS: Python
Basics I
CS: Python
Basics II
CS:
Python
Basics III
Field Trip –
CA Science
Center
“Dream Big
–
Engineering
Our World”
Lunch
12pm – 1pm
Industry
Guest
Panel
Afternoon
1pm – 4pm
CE:
Atmosphere
I
CE:
Atmosphere
II
CE:
Hydrologic
I
CE:
Hydrologic
II
Field trip –
Disney
Imagineering
Week 2
Morning
9am – 12pm
CS: Data
Science and
Python
CS: Data
Science and
Python
CS: Data
Science
and Python
CS: Data
Science
and Python
Presentation
Prep
Lab Tours
Lunch
12pm – 1pm
Industry
Guest
Panel
Afternoon
1pm – 4pm
ME:
Robotics I
ME:
Robotics II
ME:
Robotics
III
ME:
Robotics
IV
Presentation
Rehearsal
Final presentations and Banquet will be held on Friday, August 11, 2017 from 4:30pm to 6:30pm
LOW ENROLLMENT FOR WOMEN
229
APPENDIX E
Table 37
Legend for Traceability Matrix
Acronym Definition
Motivation
M: SE Self-efficacy
M: SE:SS Self-efficacy - social support
M: SE:C Self-efficacy - Coping
M: EV Expectancy Value - Attainment Value
Knowledge
K: B Benefit of a STEM career
K: A What can be achieved with a STEM Career
K: GS Gender Stereotypes
Organization
O: GB Gender Bias
O: DRM Lack of Diversity
Other
LP LaunchPad Eval
D Demographics
ECS: I Engineering or computer science intent
U: ECS Understanding of engineering and computer science
ECS: INT Engineering or computer science interest
LOW ENROLLMENT FOR WOMEN
230
Traceability Matrix
Question number
Question number
Question number
KMO
Influence Survey1 Survey 2 Survey 3
KMO
Influence Survey1 Survey 2 Survey 3
KMO
Influence Survey1 Survey 2 Survey 3
Motivation
Motivation
Organization
M: EV
35
M: SE:SS
6
O: DRM
53
M: EV
39
M: SE:SS
7
O: DRM
55
M: EV
77 21 9
M: SE:SS
8
O: DRM
61
M: EV
78 22 10
M: SE:SS
9
M: EV
32
M: SE:SS
10
O: GB 25
M: SE:SS
11
O: GB 46
M: SE
15
M: SE:SS
69a 5a 3a
O: GB 47
M: SE
31
M: SE:SS
69b 5b 3b
O: GB 48
M: SE
13
M: SE:SS
76 20
7
Other
M: SE
17
M: SE:SS
79 23 11
ECS: I 40
M: SE
19
M: SE:SS
54
ECS: I 41
M: SE
20
M: SE:SS
56
ECS: I 74 18 6a
M: SE
21
M: SE:SS
57
ECS: I 75 19 6b
M: SE
24
M: SE:SS
58
ECS: I 44 27 15b
M: SE
26
M: SE:SS
59
M: SE
32
M: SE:SS
60
U: ECS 72 16 4
M: SE
27
M: SE:SS
64
U: ECS 73 17 5
M: SE
29
M: SE:SS
65
M: SE
34
M: SE:SS
66
ECS:INT 42
M: SE
36
M: SE:SS
12
ECS:INT 43 26 15a
M: SE
38
M: SE:SS
14
ECS:INT 45 28 15c
M: SE
22
M: SE:SS
16
M: SE
30
M: SE:SS
18
LP 13 8
M: SE
33
Demographics
Knowledge
M: SE
1
D
A
K: A
59
M: SE
2
D
B
K: A
60
M: SE
5
D
C
K: A
64
M: SE
81 25 13
D
D
K: A
65
M: SE
62
D
71
K: A
66
M: SE
63
D
3
K: A
35
LOW ENROLLMENT FOR WOMEN
231
Question number
Question number
Question number
KMO
Influence Survey1 Survey 2 Survey 3
KMO
Influence Survey1 Survey 2 Survey 3
KMO
Influence Survey1 Survey 2 Survey 3
Motivation
Demographics
Knowledge
M: SE
37
D
4
K: A
39
M: SE
80 24 12
D
67 3a
D
68 4
K: B
23
M: SE:C 49
D
82
K: B
28
M: SE:C 50
D
83
K: B
22
M: SE:C 51
D
70 6 2
K: B
33
M: SE:C 52
K: GS
29
K: GS
30
K: GS
31
Abstract (if available)
Abstract
Women are underrepresented in in engineering and computer science fields of study, and in the workforce (National Science Foundation, 2016). Furthermore, although women enroll in college at higher rates than men, they declare engineering as a major 10% less than men. The purpose of this study was to evaluate the State University, College of Engineering, Computer Science, and Technology LaunchPad program, and to perform a Clark and Estes (2008) gap analysis of the assumed knowledge, motivation, and organizational factors that affect the lower enrollment rates for women in engineering and computer science. The assumed influences were developed by a thorough review of the literature and validated using a transformative mixed methods study. Surveys, observations, document review, and artifact collection were used to validate the assumed influences.The study participants were female high school students between their junior and senior year. Findings from this study concluded that the participants demonstrated lower self-efficacy and attainment value
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Asset Metadata
Creator
DeMatteis, Cheryl Kaye
(author)
Core Title
Mitigating the low enrollment rates for women in engineering and computer science
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Organizational Change and Leadership (On Line)
Publication Date
03/09/2018
Defense Date
01/17/2018
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
diversity,engineering and computer science,gender bias,low enrollment,OAI-PMH Harvest,recruiting,retention,role models,self-efficacy,STEM,underrepresentation
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Picus, Lawrence (
committee chair
), Allen, Emily (
committee member
), Krop, Cathy (
committee member
)
Creator Email
cdematte@usc.edu,Cheryl.K.DeMatteis@aero.org
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484593
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DeMatteis, Cheryl Kaye
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texts
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(contributing entity),
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Tags
engineering and computer science
gender bias
low enrollment
recruiting
retention
role models
self-efficacy
STEM
underrepresentation