The Technovation Challenge: Increasing Girls’ Interest in Computer Science, Technology
Careers, and Entrepreneurship
Jeri Countryman, Dara Olmsted
Iridescent
The Technovation Challenge: Increasing Girls’ Interest in Computer Science, Technology
Careers, and Entrepreneurship
I. Technovation Challenge Overview
Technovation Challenge, a program that teaches high school students about entrepreneurship and
computer programming, was founded in the fall of 2009 by Dr. Anuranjita Tewary. After
attending Start-Up Weekend in San Francisco, Dr. Tewary was inspired by the empowering
experience and wanted to offer this same experience to young women- letting them participate in
a start-up company and teach them what it takes to think like a high-tech entrepreneur. Dr.
Tewary asked Iridescent, a non-profit that provides STEM (science, technology, engineering,
and math) education to underserved and underrepresented youth, to run the Technovation
Challenge.
The first pilot ran in the fall of 2010. The program has grown substantially, from a group of 45
high school girls at one site in the San Francisco Bay Area in 2010 to 520 girls at ten sites in the
San Francisco Bay Area, New York City, Boston, and Los Angeles in 2012. In the summer of
2011, Iridescent partnered with the New York Hall of Science to offer the program to a co-ed
group of 55 high school and college-aged “Explainers” who work at the museum. Since 2010,
over 800 students have participated in the Technovation Challenge.
The mission of the Technovation Challenge is to promote women in technology by giving girls
the skills and confidence they need to be successful in computer science and entrepreneurship.
The program aims to inspire girls to see themselves, not just as users of technology, but as
inventors, designers, builders, and entrepreneurs in the technology industry. By showing girls
that the high-tech world is an exciting place marked by collaboration and creativity, we hope to
encourage more women to enter the field.
The program consists of a ten-week course in which universities, technology companies, and
schools come together to support students in learning about computer science and
entrepreneurship. The participants work in teams led by teachers and professional female
mentors in technology. The ultimate goal is to create mobile phone applications (apps) using
App Inventor. App Inventor is a beginner, blocks-based programming language that helps to
hook their interest in computer programming. Through the curriculum, girls learn to think like
entrepreneurs, generate innovative ideas, do market research and customer development, create
paper prototypes, write business models, and validate their ideas. At the end of the ten weeks, the
teams pitch their app ideas and business plans to a panel of venture capitalists at a regional pitch
night event. Winners from the regional pitch nights compete in a national pitch night in the San
Francisco Bay Area. The winning team has their phone app developed professionally and sold on
the Android Market.
The program is hosted by high-tech companies and STEM labs, such as Google, Adobe,
Lawrence Berkeley National Laboratory, LinkedIn, Microsoft, and Twitter, giving the students
another point of access to the high-tech industry.
Mentorship is a key aspect of the program. We have cultivated a network of professional female
mentors in the high-tech world, and this year we have 80 mentors volunteering with us. Mentors
come from a range of high-profile companies including Google, Apple, LinkedIn, Adobe,
Microsoft, and Morgan Stanley. Each mentor is paired with a team of five participants and acts
as a coach and sounding board, helping the team navigate the business development process and
giving them insight into tech career opportunities. We work with well-connected industry groups
to recruit mentors, including Women 2.0, a national entrepreneurship group with over 25,000
members.
For many participants, the program extends beyond the ten weeks of training. When
Technovation Challenge is not running, our staff organizes field trips for the students and
mentors to high-tech companies, allowing them to get behind-the-scenes exposure to STEM
companies and make new business contacts. Recent field trips have included the design firm
IDEO, Ask.com, and EarthMine. In addition, many participants have secured internships at hightech companies or with venture capitalists due to their participation in the program.
Scaling up Technovation and sharing lessons learned is important to us. We have documented
key aspects of the program for ease of replication and successfully scaled up to six sites in 2011
and ten in 2012. We have a detailed course manual and website
(www.technovationchallenge.org) with lesson plans and presentations, videos of guest speakers,
class observation forms, and pre- and post-surveys for all participants. We have developed a set
of training materials for instructors, coaches, and mentors. Finally, we have detailed checklists
for organizing and hosting pitch night events. Ultimately, our goal for the program is that any
student, anywhere in the world can participate.
II. Goals and Objectives
Through the Technovation Challenge we hope to increase students’ interest in technology careers
and/or developing their own business and increase their technical knowledge, skills, and
confidence. We expect the following participant outcomes:
Academic
 Perceive that objects in the world are designed and can be re-designed
 Learn to apply traditional academic skills in real-world settings.
 Develop a better understanding of the process of creating a business
 Develop a better understanding of design, technology, engineering and computer science
concepts
 Learn problem solving and time management skills
 Learn how to gather and analyze information
Self-awareness
 See themselves as inventors of technology, not just users of technology
 See themselves as entrepreneurs
 See the value of science and technology in everyday life.
 Develop an increased interest in STEM fields and careers
 Develop an increased interest in starting their own business
Social
 Learn teamwork and develop interpersonal skills



Work well with other students and mentoring adults
Improve their communication skills
Learn how to present to the public
III. Research and Literature that Technovation Challenge is Based On
We strongly believe in reaching out to other practitioners and researchers and adopting the best
practices in the field. The five programs we have studied closely and learned from are:
COMPUGIRLS, Exploring Computer Science, Build IT, Girl Game Company, and Techbridge.
Some of the lessons we have incorporated are: scaling up the contact hours for greater learning
gains, screening the mentors, focusing on group cohesion, exploring career and identities,
meeting participants’ social needs through pair programming (Williams, L., 2006; Resnick, M.,
2002; Karoly, L.A. and C. Panis, 2004), exposure to adult female role models, and closely
involving the parents.
The Technovation Challenge is a unique combination of the following strategies: 1) developing
mobile phone apps using App Inventor; 2) focusing on minority girls; 3) combining technology
and entrepreneurship; 4) leveraging and contributing to the open-source community to increase
impact; 5) and integrating female mentors and guest speakers from the high-tech community.
Ubiquitous Computing through Mobile Phones – App Inventor for Android
Technovation Challenge students use software called App Inventor, developed by Prof. Hal
Abelson from MIT, to create smartphone apps. App Inventor’s vision is to empower people with
no previous programming experience to design phone apps. “App Inventor enables people to
drag and drop blocks of code - shown as graphic images and representing different smartphone
capabilities - and put them together, similar to snapping together Lego blocks. The result is an
application on that person’s smartphone,” (Lohr, S., 2010). The rationale behind using App
Inventor and mobile smartphones is as follows:
 Youth use smart phones to learn about topics they care about - music, friends, sports, games,
fan culture, civic engagement, health, and nutrition (Bennett, W. L., 2008; Ito, M., et al.,
2010; Metzger, M. J. and Flanagin, A. J., 2007; McPherson, T., 2007; Salen, K., 2007;
Everett, A., 2007; Buckingham, D., 2007). Such friendship-driven and interest-driven
learning is powerful and can help integrate learning with formal education (Bransford, J.D.,
et al., 2006; Estabrook, L., et al., 2007; Warschauer, M. and Matuchniak, T., 2010)].
 Smartphones and apps are ubiquitous and their reach is only growing. Americans spend more
time using apps than using desktop computers and the mobile web and almost half of
American adults own smartphones (Anderson 2012; Smith 2012).
Broadening the Computer Science Workforce
The Technovation Challenge has the following elements that have been proven to support
persistent interest in STEM and computer science:
 We emphasize diverse interests and backgrounds, the rewards of authorship, introduce
technology via its social purpose, and include concerns about the unintended consequences
of new technology (Brunner, C., 2008; Brunner, C. and Bennett, D., 2002; Brunner, C., et al.,
1998). By focusing on the development of a mobile phone app, we establish an immediately
useful and real purpose to the programming. Further, by emphasizing the development of a






business model, we place technology in a social context, in which consequences are not only
considered, but specifically addressed.
We increase minority girls’ access to social capital by helping them interact and build
relationships with female university students as well as female faculty and professionals.
Through these interactions girls learn more about pursuing computer science and STEM
degrees and careers and perceive these paths as being more accessible (Berg, V., 2004; GrasVelazquez, A., et al., 2009; Ashby Plant, E., et al., 2009; Halpern, D., et al., 2007; Lareau,
A., 2003). Female role models share their personal accounts and stories of struggle instead of
only focusing on the professional story. They thus illustrate that proficiency in STEM and
computer science is acquirable rather than innate (Dweck, C., 2007).
Creating phone apps and business plans provides real-world challenges and deliverables.
Students learn about real problems and tradeoffs such as when deciding between favoring
usability or aesthetics (Rusk, N., Berg, R., and Resnick, M., 2005; Turbak, F. and Berg, R.,
2002). Students are able to see the immediate relevance of their learning, which is motivating
and exciting (Dede, C., 2009). Finally, students are given opportunities to develop their
leadership skills, make important decisions that affect the team, and take on responsibilities
such as pitching a business plan to potential investors (Melchior, A., et al., 2005).
We create a growth mindset environment so that girls are more likely to believe that
computer programming is an acquirable skill that can be improved with practice (Dweck, C.,
2007; Halpern, D., et al., 2007; Good, C., et al., 2009; Goode, J., et al., 2006; Seymour, E.
and Hewitt, N., 1997). Technovation also provides multi-year programming support to the
girls to decrease the feeling of being misfits in a male-dominated environment (Margolis, J.
and Fisher, A., 2002; Singh, K., et al., 2007).
We provide a structured path from a woman-centered, safe, learning space to high visibility
technical presentation events. Through gradual preparation, students develop the skills and
confidence needed to present formally at high visibility events in front of top-tier venture
capitalists and investors.
The International Society for Technology in Education student standards for technology and
the Computer Science Teachers Association K-12 Computer Science Standards form the
basis of the curriculum. In particular we emphasize:
o identifying and defining authentic problems - teams define and narrow the scope of
the problem so that it can be feasibly addressed in a short timeframe
o using diverse perspectives to explore alternative solutions - students brainstorm
together, elicit and consider different perspectives, and finally identify one app idea
o creating original works as a means of group expression - teams work together to
invent new mobile phone app solutions to problems they face
o identifying trends and forecasting possibilities - teams do market research
o troubleshooting systems and applications - team learn to debug systems
We leverage media coverage of the Pitch Night events to: 1) show that girls can be very
successful in male-dominated fields such as computer science; 2) spread the message that
girls are particularly wanted as computer science and engineering students; and 3) make
more girls aware of the program (Berg,V., 2004).
IV. Methodology
In 2011, we evaluated the program to document the implementation of the Technovation
Challenge program and determine its impact on high school girls, mentors, instructors, and
teaching assistants (TAs). The focus of the data collection is to (1) document participants’
background information and feedback on the implementation of the program and (2) measure its
impact on participants. Five surveys were used to collect the data: one instructor post-survey
(n=5), one mentor post-survey (n=41), one teaching assistant post-survey (n=46), one student
pre-survey (168), and one student post-survey (179).
Researchers employed a mix of qualitative and quantitative methodologies to analyze the data.
For the quantitative data, researchers used SPSS, a statistical software package, to conduct
descriptive analysis, analysis of variance (ANOVA), and analysis of covariance (ANCOVA)
where appropriate. While the ANOVA was used to compare the means of groups of
measurement data, the ANCOVA allowed the comparison of one variable in two or more groups
taking into account (or to correct for) variability of other variables, called covariates. For the
qualitative data, researchers used a grounded theory approach (Strauss & Corbin, 1990). They
read and coded the open-ended survey questions to identify and document the salience and
substance of the themes and sub-themes that surfaced about changes in participants’ experiences
of the program and its impact on them.
V. Analysis
The analysis of the data has found a significant difference in the girls’ knowledge of computer
science, the design process, entrepreneurship, and what a career in computer science looks like
after taking the course. Their confidence making presentations also increased. There were 12
items in both the student pre and post- surveys. A paired t-test was conducted to compare the
pre- and post-survey differences in participants’ answers. There is statistically significant
increase in the post-survey scores on all items below. Table 1 shows results for the significant
nine items.
Table 1. Means for student pre- and post- surveys
Pre
mean
1. I am confident using technology.
2. I know how to write computer programs.
3. I am comfortable making presentations.
4. I know about entrepreneurship.
5. I know about the design process that
engineers use to create products.
6. I know about user interface design.
8. I know what computer scientists and
computer engineers do.
10. I have talked with someone about
her/his job in technology.
12. Adults have told me I should think
about a career in technology.
Post
mean
3.79
3.98
2.00
3.02
3.54
3.69
2.62
3.41
1.89
3.55
1.68
3.54
2.86
3.6
3.04
4.00
3.45
3.65
95% Confidence
Interval of the
Difference
Sig. (2-tailed)
Lower
-.308
Upper
-.073
t
-3.192
-1.176
-.860
-12.745 167
.000
-.284
-.026
-2.367
167
.019
-.959
-.624
-9.337
167
.000
-1.853
-1.469
-17.066 167
.000
-2.036
-1.667
-19.799 167
.000
-.905
-.571
-8.732
167
.000
-1.180
-.737
-8.547
167
.000
-.376
-.029
-2.301
167
.023
df
167
.002
By GRADE
It was speculated that GRADE might make a difference in participants’ survey scores. GRADE
in this study includes 9, 10, 11, and 12. Using GRADE as the co-variate for the post-survey
score, the one-way analysis of variance (ANOVA) was conducted to compare the difference in
the post-survey scores among the 4 different grades.
There are significant differences for item 1 [I am confident using technology.] and item 12
[Adults have told me I should think about a career in technology.]. For item 1, the 12th grade
students are more confident using technology than the other groups; the 9th grade students are the
least confident. For item 12, it is most likely that adults have told the 12th grade students to
consider a career in technology and less likely they did so with the 9th grade students. Table 2
shows key findings by grade levels.
Table 2. Mean, standard deviation for the post-survey scores among the four grades
95% Confidence Interval
for Mean
I am confident using technology.
Adults have told me I should think
about a career in technology.
N
Mean
Std. Deviation
Lower
Upper
9
28
3.75
.844
3.42
4.08
10
53
4.06
.908
3.81
4.31
11
59
3.86
.753
3.67
4.06
12
28
4.29
.713
4.01
4.56
Total
168
3.98
.826
3.85
4.10
9
28
3.36
1.193
2.89
3.82
10
53
3.87
1.161
3.55
4.19
11
59
3.37
1.496
2.98
3.76
12
28
4.11
.916
3.75
4.46
Total
168
3.65
1.286
3.45
3.84
Using GRADE as co-variate, controlling the pre-survey score difference, a one-way analysis of
covariance (ANCOVA) was conducted to find out if GRADE makes a significant difference in
the post-survey scores. The independent variable, the girls’ grade level, included 9th, 10th, 11th,
and 12th grades. The dependent variable was girls’ post-survey result and the covariate was their
pre-survey scores. The result is significant for interested in a career in computer
science/computer engineering, by controlling the pre-survey differences among different grades,
the 12th grade students have the most interest in a career in computer science and computer
engineering, while the 9th grade has the least interest in a career in computer science and
computer engineering.
By RACE
It was speculated that RACE might make a difference in participants’ survey scores. RACE in
this study includes Asian, Hispanic/Latina, White, and Other. For the participants who completed
both pre- and post- surveys, American Indian/Alaska Native only had 2, African American/Black
had 12, and Other had 4. These 3 categories were combined as one category – Other in the
analysis. Each cell had to have at least 20 subjects for the analysis to hold meaningful results.
Using RACE as a covariate, ANOVA and ANCOVA were conducted to determine if there were
significant differences among the following four different ethnic groups: Asian, Hispanic/Latina,
White, and Other.
The results from the ANOVA shows that there are statistical significances among the four
different ethnic groups for the following items:
o Item 5: I know about the design process that engineers use to create products. There were
significant differences among the four ethnic groups for Item 5 (F(3, 164) = 2.94, p=.035).
The multiple comparisons – Tukey HSD test showed that Asian (mean=3.62),
Hispanic/Latina (mean=3.66), and White (mean=3.65) participants have significant higher
knowledge scores on this item than participants in the Other ethnic category (mean=2.89).
o Item 8: I know what computer scientists and computer engineers do. There were significant
differences among the four ethnic groups for Item 8 (F(3,164) = 4.12, p=.008). The multiple
comparisons – Tukey HSD test showed significant differences among the following groups:
Asian participants (mean = 3.82) have significant higher knowledge on this item than
Hispanic/Latina participants (mean =3.29) with p = .03 and participants in the Other ethnic
category (mean = 3.17) with p=.01.
o Item 11: I have been encouraged to take advanced classes in math and science. There were
significant differences among the four ethnic groups for Item 11 (F(3,164) = 5.31, p=.002).
The multiple comparisons – Tukey HSD test showed significant differences among the
following groups: Asian participants (mean = 4.35) have significant higher knowledge on
this item than Hispanic/Latina participants (mean =3.71) with p = .01. Asian participants
(mean = 4.35) have significant higher knowledge on this item than participants in the Other
ethnic category (mean = 3.44) with p=.008.
Post Survey Analysis
The results from the descriptive analysis of the girls’ post-surveys show that the program has a
very positive impact on them. They said that as a result of participating in the Technovation
Challenge program, they believe a career in technology is a good career for women (94%), know
more about different kinds of careers in technology (89%), learn that team work is good for
solving problems (89%), know more about programming concepts (88%), feel more confident
(81%), are more comfortable troubleshooting problems (79%), can see themselves in a career in
technology (75%), are more interested in working in a career in technology (75%), are
considering studying computer science or engineering in college (70%), and know more about
how to prepare for college (68%).
A one-way analysis of variance (ANOVA) was used to compare if there are significant
differences among RACE (Asian, Hispanic/Latina, White, Other) on post survey questions. The
results from the ANOVA show that there were statistical significances on the following items:
o Item: Because of the Technovation Challenge, I know more about programming concepts.
There were statistically significant differences among the four ethnic groups (F(3, 175) =
3.315, p = .021 <.05). The post hoc tests show that Asian participants (mean=1.89) have
significantly higher score on this item than the Hispanic/Latina participants (mean=1.47).
o Item: Because of the Technovation Challenge, I am considering studying computer
science or engineering in college. There were statistically significant differences among the
four ethnic groups on this item (F(3, 175) = 6.84, p = .00 <.05). The post hoc tests show that
Hispanic/Latina participants (mean=1.84) have significantly lower score on this item than
participants from all the other three ethnic groups (Asian=2.31, White=2.65, Other=2.04).
o Item: Because of the Technovation Challenge, I believe a career in technology is a good
career for women. There were statistically significant differences among the four ethnic
groups on this item (F(3, 175) = 3.145, p = .027 <.05). The post hoc tests show that Asian
participants (mean=1.73) have significantly higher score on this item than Hispanic/Latina
participants (mean=1.39).
Best Practices by Mentors, Instructors, and Teaching Assistants
All the adult participants were given post surveys at the end of the program. While the main
goal of the program is to increase girls’ interest in the tech industry, most of the mentors
indicated that the Technovation Challenge program helped them improve their skills. It offered
the opportunity to engage girls in technology (95%), network with women working in
technology (95%), increase their knowledge of entrepreneurship (83%), learn to be effective
mentors (88%), and improve their technical skills (63%). The results are summarized in Table 3.
Table 3. Mentor’s skills development through Technovation
Strongly
Agree
Agree
Disagree
Strongly
Disagree
Your
technical
skills n(%)
5(12.2)
How to be an
effective
mentor n(%)
8(19.5)
To engage girls in
technology n(%)
Entrepreneurship
n(%)
24(58.5)
13(31.7)
Your network of
women working in
technology n(%)
15(36.6)
21(51.2)
13 (31.7)
2(4.9)
28(68.3)
4(9.8)
1(2.4)
15(36.6)
1(2.4)
1(2.4)
21(51.2)
6(14.6)
1(2.4)
24(58.5)
1(2.4)
1(2.4)
In the survey the students, mentors, instructors and teaching assistants were asked how the other
adults in the program supported the program and how they could do better.
The responses were analyzed and a list of best practices was created for the mentors, instructors,
and teaching assistants. The mentors doubled as project managers for each team, provided
support to the TAs and girls, and acted as great role models. The best practices that emerged for
mentors are the following:



Provide leadership
Manage team dynamics
Act as a role model and coach to the
girls



Provide one-to-one interaction and
feedback to the girls
Stay neutral during class discussion
Assist girls after class
The Technovation program increased or refreshed the instructors’ technical skills, provided them
support on how to be an effective instructor, increased their ability to engage girls in technology,
increased their knowledge about entrepreneurship, and expanded their network of women
working in technology. They contributed to the successful implementation of the program by
delivering clear instruction, being responsive to participants’ questions, sharing information
effectively, being caring and supportive, and providing classroom management skills. The best
practices that emerged for instructors are the following:

Be enthusiastic and knowledgeable
about the instructional material

Provide one-to-one interaction and
feedback to the girls



Ensure the girls have all the tools
and resources needed for the class
Keep the information flow thorough
and timely
Keep the girls focused on their tasks



Troubleshoot problems with App
Inventor
Simplify programming concepts for
the girls
Deal effectively with classroom
management issues
The Technovation program helped the teaching assistants network with women working in
technology, expand their knowledge about technical careers, engage girls in technology, increase
their knowledge of entrepreneurship, become effective teaching assistants, and improve their
technical skills. Specifically, it improved their leadership skills, technology skills,
communication skills, teaching skills, understanding of girls and technology, and knowledge of
technology and business careers. The best practices that emerged for teaching assistants are the
following:






Provide programming resources
Provide team guidance
Help with time management
Encourage teams to stay on task
Help in the management of classes
Communicate with instructors,
mentors, and other TAs




Delegate tasks
Support interactive sessions
Act as a coach to the girls
Encourage the girls to work
collaboratively and participate in
class
VI. Conclusion and Next Steps
From the analysis, it can be seen that by participating in the Technovation program, the girls
improved in the following three STEM areas:
 Felt more confident using technology. This was especially true for the 12th grade students.
 Learned more about computer programming, engineering design, and user interface design.
The Hispanic/Latina, White, and Asian students demonstrated high levels of knowledge
about the engineering design process.
 Learned more about computer scientists, computer engineers, and entrepreneurship; and were
encouraged to think about a career in technology. As a result, the 9th and 11th grade students
indicated that they are more interested in working in a career in technology. The Asian
students demonstrated high levels of knowledge about the careers of computer engineers and
computer scientists, and programming concepts. They also believe that a career in technology
is a good career for women, and felt that they have been encouraged to take advanced classes
in math and science.
It was found that grade made a significant difference in three of the survey items [I am confident
using technology], [Adults have told me I should think about a career in technology], and [I am
interested in a career in computer science/engineering]. We find this data interesting as the 12th
grade students who are closest to choosing a college major are the most confident with
technology, have been told by adults to consider a career in tech, and have the most interest in
computer science and computer engineering.
One of our goals from the start has been to support students over multiple years by sharing
STEM educational opportunities provided by other organizations. This will help these younger
students to continue to build technology confidence, meet caring adults that encourage them to
consider a technology career, and hopefully increase their interest in computer
science/engineering. One of the changes that has been implemented to the program based on this
data is to allow the girls to repeat multiple years in the program. Each year a new challenge will
be announced so that girls can come back year-after-year to build on their tech skills and meet
more caring adults that can encourage them to consider a technology related career.
One of the goals of the program is to serve underrepresented minority students so the analysis by
race was of particular interest. Students from the Hispanic/Latina and Other (which was
comprised of mostly African American students) categories had significantly lower scores
compared to the students in the Asian and White categories on a number of the items from
survey. This data shows that students who are Latina or African American are in greatest need
of the program.
The percentage of African American and Latina students enrolled in the program in 2011 was
lower than expected by staff since a number of girls from these ethnic groups had applied to be a
part of the program but then never attended the program. When the students who could be
reached were asked why they never attended, issues of transportation and parental permission
were given as reasons. One of the changes to the 2012 program is an effort to recruit from high
schools that have a student population that is comprised of at least 60% underrepresented
minorities. Additionally, to help recruit and retain students, teachers have been hired from
schools to serve as coaches to the students and to help arrange transportation from the schools to
the corporate sites each week. These teachers will greatly help the retention of students so that
they can participate in the program for multiple years.
The best practices that emerged for the adults in the program have been especially helpful. For
the mentors, acting as project manager was an important role for the teams, and as Pitch Night
approached, scheduling time to meet outside of class time to continue to work on the projects
was extremely valuable. For the instructors, being enthusiastic, knowledgeable about the
content, and keeping the flow of the instruction and information on schedule were best practices
for leading each session. For the teaching assistants, helping with programming issues and
encouraging the girls to work as a team proved to be best practices. In summary, the best
practices that emerged for the adult participants are to act as a role model, manage teams, be
enthusiastic, encourage girls to work collaboratively, provide programming support, and provide
leadership. This data was shared and modeled during the 2012 trainings to set the expectation of
the type of support each adult participant should bring to the program.
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