The Technovation Challenge: Increasing Girls’ Interest in Computer Science, Technology Careers, and Entrepreneurship

Jeri Countryman, Dara OlmstedIridescent

The Technovation Challenge: Increasing Girls’ Interest in Computer Science, Technology Careers, and Entrepreneurship

I. Technovation Challenge OverviewTechnovation 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 high-tech 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 ObjectivesThrough 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 OnWe 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 AndroidTechnovation 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 WorkforceThe 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; 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 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. MethodologyIn 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. AnalysisThe 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- surveysPre

meanPostmean

95% Confidence Interval of the

Difference

t df

Sig. (2-tailed)

Lower Upper1. 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 GRADEIt 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

N Mean Std. Deviation

95% Confidence Interval for Mean

Lower UpperI 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 4.10Adults 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

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 RACEIt 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 AnalysisThe 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 AssistantsAll 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 TechnovationYour technical skills n(%)

How to be an effective mentor n(%)

To engage 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 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 StepsFrom 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.

References

Williams, L. (2006). Debunking the Nerd Stereotype with Pair Programming. Computer, 39(5), 83-85.Resnick, M. (2002). Rethinking learning in the digital age. In G. Kirkham, (Eds.), The Global Information Technology Report: Readiness for the networked world. Oxford University Press.Karoly, L.A. and C. Panis. (2004). The 21st Century at Work: Forces Shaping the Future Workforce and Workplace in the United States. Santa Monica: RAND Corporation.Lohr, S. (2010). Google’s Do-It-Yourself App Creation Software, New York Times.Bennett, W.L. (2008), Civic Life Online: Learning How Digital Media Can Engage Youth. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Mizuko Ito, M., Horst, H., Antin, J., Finn, M., Law, A., Manion, A., Mitnick, S., Schlossberg, D., and Yardi, S. (2010). Hanging Out, Messing Around, and Geeking Out. Kids Living and Learning with New Media. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Metzger, M.J. and Flanagin, A.J. (2007). Digital Media, Youth, and Credibility. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT PressMcPherson, T. (2007). Digital Youth, Innovation, and the Unexpected. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Salen, K. (2007). The Ecology of Games. Connecting Youth, Games, and Learning. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Everett, A. (2007). Learning Race and Ethnicity. Youth and Digital Media. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Buckingham, D. (2007). Youth, Identity, and Digital Media. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, Massachusetts: MIT Press.Bransford, J., Vye, N., Stevens, R., Kuhl, P., Schwartz, D., Bell, P., Meltzoff, A., Barron, B., Pea, R., Reeves, B., Roschelle, J., and Sabelli, N. (2006). Learning theories and education: Toward a decade of synergy. In P. Alexander and P. Winne (Eds.). Handbook of Education Psychology. Mahwah, N.J.: Erlbaum.Estabrook, L., Witt, E., and Rainie, L. (2007). Information Searches That Solve Problems, in Pew Internet and American Life Project. Dec. 30, 2007. http://www.pewinternet.org/Reports/2007/Information-Searches-That-Solve-Problems.aspxWarschauer, M. and Matuchniak, T. (2010). New Technology and Digital Worlds: Analyzing Evidence of Equity in Access, Use, and Outcomes. Review of Research in Education. 34(1): 179-225.Anderson, J. and Rainie, L. (2012). The Future of Apps and Web. Pew Research Center.Smith, Aaron. (2012)/ Nearly Half of American Adults are Smartphone Owners. Pew Research Center.Borgman, C.L. (2008). Fostering learning in the networked world: The cyberlearning opportunity and challenge. National Science Foundation.Brunner, C., Bennett, D., and Honey, M. (1998). Girl games and technological desire. In J.

Cassell, J. and Jenkins, H. (Eds.). From Barbie To Mortal Kombat: Gender and computer games. MIT Press: Cambridge, MA: MIT Press.Brunner, C. (2008). Games and technological desire: Another decade. In Y.B. Kafai, C. Heeter, J. Denner and J.Y. Sun., (Eds). Beyond Barbie and Mortal Kombat: New perspectives on gender and gaming. Cambridge, MA: MIT Press.Brunner, C. and Bennett, D. (2002). The feminization of technology. In N. Yelland and A. Rubin, (Eds). Ghosts in the machine: Women’s voices in research with technology. (p. 71-96). Peter Lang New York: Peter Lang Publishing, Inc..Berg, V. (2004). Getting more women into computer science and engineering. Strategies ofInclusion: Gender and the Information Society. Information Society Technologies.Gras-Velazquez, A., Joyce, A., and Debry, M. (2009). Women and ICT: Why are girls still not attracted to ICT studies and careers?. CISCO.Ashby Plant, E., Baylor, A., Doerr, C., Rosenberg-Kima, R. (2009). Changing middle-school students’ attitudes and performance regarding engineering with computer-based social models. Computers and Education. 53. 209–215.Halpern, D., Aronson, J., Reimer, N., Simpkins, S., Star, J., and Wentzel, K. (2007). Encouraging Girls in Math and Science (NCER 2007-2003). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ncer.ed.gov. Lareau, A. (2003). Unequal Childhoods: Class, Race, and Family Life. Berkeley, CA: University of California Press.Dweck, C. (2007). Mindset: The new psychology of success. New York: Ballantine Books.Rusk, N., Berg, R. and Resnick, M. (2005). Rethinking Robotics: Engaging Girls in Creative Engineering. Proposal to the National Science Foundation.Turbak, F. and Berg, R. (2002). Robotic Design Studio: Exploring the Big Ideas of Engineering in a Liberal Arts Environment. Journal of Science Education and Technology. 11(3): p. 237-253.Dede, C. (2009). Comparing Frameworks for "21st Century Skills”. Cambridge, MA: Harvard Graduate School of Education.Melchior, A., Cohen, F., Cutter, T., and Leavitt, T. (2005). More than Robots: An Evaluation of the FIRST Robotics Competition Participant and Institutional Impacts. Waltham, MA: Brandeis University.Good, C., Aronson, J., and Inzlicht, M. (2003). Improving adolescents’ standardized test performance: An intervention to reduce the effects of stereotype threat. Applied Developmental Psychology, 2003. 24: p. 645–662.Good, C., Rattan, A., and Dweck, C. (2009). Why do women opt out? Sense of belonging and women’s representation in mathematics. Baruch College, Stanford University.Goode, J., Estrella, R., and Margolis, J. (2006). Lost in translation: Gender and high school computer science. In J.M. Cohoon and W. Aspray (Eds). Women in IT: Reasons on the Underrepresentation. (p. 89-114). Cambridge, MA. Seymour, E. and Hewitt, N. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.Margolis, J. and Fisher, A. (2002). Unlocking the Clubhouse: Women in Computing.Cambridge, MA: MIT Press.Singh, K., et al., Women in computer-related majors: A critical synthesis of research and theory from 1994–2005. Review of Educational Research, 2007. 77(4): p. 500-533.

Strauss, A.L., & Corbin, J. (1990). Basics of qualitative research. ThousandOaks, CA: Sage Publications.