The Technovation Challenge: Careers, and Entrepreneurship Jeri Countryman, Dara Olmsted Iridescent

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Dec 10, 2013 (3 years and 6 months ago)

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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. A
f
ter
attending Start
-
Up W
eekend 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 ma
t
h
) education to underserved and underrepresented youth, to run the Technovation
Challenge.


The first pilot ran i
n 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 5
2
0 girls at ten sites in the
San Francis
co 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 Challe
nge.


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

aim
s

to inspire girls to see themselves, not just a
s 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 profes
sional
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 inter
est in computer programming. Through the
curriculum
,

girls learn to think like
entrepreneurs, generate innova
tive 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 id
eas 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

M
a
rket
.


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 indus
try.


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 care
er 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
Technovati
on 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 high
-
tech companies

or with venture capitalists

due to their participation in the program
.


Scaling up
Technovation

and sharing lessons learned is important to u
s. 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. Fina
lly, 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.


I
I.
Goals and Objectives

Through the Te
chnovation Challenge we hope to
i
ncrease students’ interest in tech
nology

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



L
earn 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 co
mmunication skills



Learn how to present to the public



III
. Research and
L
iterature that T
echnovation
C
hallenge

is
B
ased
O
n

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 p
air 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) leverag
ing 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

Techno
vation 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 smartphon
e
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 pho
nes 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;
Ev
erett, A., 2007; Buckingham, D., 2007)
. Such friendship
-
driven and interest
-
driven
learning is powerful and can help integrate le
arning with formal education (Bransford, J.D.,
et al., 2006; Estabrook, L., et al., 2007; Warschauer, M. and Matuchniak, T., 20
10)
].



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

W
orkforce

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 conse
quences
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 establis
h 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 minor
ity 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 a
nd careers and perceive these p
aths as being more accessible
(Berg, V., 2004; Gras
-
Velazquez, 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 a
cquirable rather than innate
(Dweck, C., 2007)
.



Creating phone apps and business pla
ns

provides real
-
world challenges and deliverables.
Students learn about
real

problems

and tradeoffs such as when deciding between favo
ring
usability or aesthetics
(Rusk, N., Berg, R., and Resnick, M., 2005; Turbak, F. and Berg, R.,
2002)
. Students are abl
e to see the immediate relevance of their

learning
,

whic
h 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 c
an be improved with practice (Dwec
k, C.,
20
07; 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 dev
elop the skills and
confidence needed to present formally at high visibility events in front of top
-
tier venture
capitalists and investors.



The International Socie
ty for Technology in Education
student standards for technology and
the Computer Science Teac
hers 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 ti
meframe

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 gi
r
ls 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 p
articipants’
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 teac
hing assi
stant
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 softwa
re 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 ques
tions 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 a
nd 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
n
ine items.


Table
1.

Means for
s
tudent
p
re
-

and
p
ost
-

s
urveys


Pre

mean

Post

mean

95% Confidence
Interval of the
Difference

t

df

Sig. (2
-
tailed)

Lower

Upper

1. I am confident using technology.

3.79

3.98

-
.308

-
.073

-
3.192

167

.002

2. I know how to

write computer programs.

2.00

3.02

-
1.176

-
.860

-
12.745

167

.000

3. I am comfortable making presentations.

3.54

3.69

-
.284

-
.026

-
2.367

167

.019

4. I know about entrepreneurship.

2.62

3.41

-
.959

-
.624

-
9.337

167

.000

5. I know about the design process
that
engineers use to create products.

1.89

3.55

-
1.853

-
1.469

-
17.066

167

.000

6. I know about user interface design.

1.68

3.54

-
2.036

-
1.667

-
19.799

167

.000

8. I know what computer scientists and
computer engineers do.

2.86

3.6

-
.905

-
.571

-
8.732

167

.000

10. I have talked with someone about
her/his job in technology.

3.04

4.00

-
1.180

-
.737

-
8.547

167

.000

12. Adults have told me I should think
about a career in technology.

3.45

3.65

-
.376

-
.029

-
2.301

167

.023


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
-
s
urvey 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 12
th

grade
students are more confide
nt using technology than the other groups; the 9
th

grade students are the
least confident. For item 12,
it is most likely that adults have told the 12
th

grade students to
consider a career in technology and less likely they did so with the 9
th

grade studen
ts. Table
2

shows key findings by grade levels.


Table
2
. Mean,
s
tandard
d
eviation for the
p
ost
-
s
urvey
s
cores
a
mong the
f
our
g
rades


N

Mean

Std. Deviation

95% Confidence Interval
for Mean

Lower

Upper

I am confident using technology.

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

Adults have told me I should think
about a career in technology.

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


U
sing 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 9
th
, 10
th
, 11
th
,
and 12
th

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 12
th

grade students have the most interest in a career in computer science and computer
eng
ineering, while the 9
th

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/L
atina,
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
s
ignificant 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
know
ledge 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). T
he 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 compa
risons


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 s
ignificant
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 w
ere 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 i
n 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


Your
technical
skills n(%)

How to be an
effective
mentor n(%)

To eng
age girls in
technology n(%)

Entrepreneurship
n(%)

Your network of
women working in
technology n(%)

Strongly
Agree

5(12.2)

8(19.5)

24(58.5)

13(31.7)

15(36.6)

Agree

21(51.2)

28(68.3)

15(36.6)

21(51.2)

24(58.5)

Disagree

13 (31.7)

4(9.8)

1(2.4)

6(14.6)

1(2.4)

Strongly
Disagree

2(4.9)

1(2.4)

1(2.4)

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 entrepreneursh
ip, 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



E
nsure 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 w
ith 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 compu
ter 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 entr
epreneurship; 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 abo
ut 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
t
hree 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 t
his data interesting as the

12
th

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 younge
r
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.

O
ne of the changes
that has been

implemented to the progr
am
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
i
s to serve underrepresented minority students so t
he analysis by

race

was of particular interest. Students
from the

His
panic/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 a
re 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. A
dditionally, 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.
T
hese teachers will greatly help the retention of students so that
they can participate in the program for multiple years.

The best practices that emerged f
or the adults

in the program have

been especially helpful
.

For
the mentors, acting a
s

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 va
luable. 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 issu
es and
encouraging the girls to work as a team proved to be best practices. In summary
,

the best
practices that emer
ged
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
expectati
on of
the type of support each adult participant should bring to the program.






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