Award Abstract # 1203234
GSE/RES Beyond the Deficit Model: Gender Schemas, Computing, Preferences, and IT Careeer Choices

NSF Org: HRD
Division Of Human Resource Development
Awardee: UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
Initial Amendment Date: November 29, 2011
Latest Amendment Date: March 3, 2016
Award Number: 1203234
Award Instrument: Standard Grant
Program Manager: Jolene Jesse
jjesse@nsf.gov
 (703)292-7303
HRD
 Division Of Human Resource Development
EHR
 Direct For Education and Human Resources
Start Date: July 1, 2011
End Date: September 30, 2016 (Estimated)
Total Intended Award Amount: $497,504.00
Total Awarded Amount to Date: $497,504.00
Funds Obligated to Date: FY 2010 = $497,504.00
History of Investigator:
  • Zeynep  Tufekci (Principal Investigator)
    zeynep@unc.edu
  • Sandra  Hughes-Haskell (Co-Principal Investigator)
Awardee Sponsored Research Office: University of North Carolina at Chapel Hill
104 AIRPORT DR STE 2200
CHAPEL HILL
NC  US  27599-1350
(919)966-3411
Sponsor Congressional District: 04
Primary Place of Performance: University of North Carolina at Chapel Hill
NC  US  27599-1350
Primary Place of Performance
Congressional District:
04
DUNS ID: 608195277
Parent DUNS ID: 142363428
NSF Program(s): RES ON GENDER IN SCI & ENGINE
Primary Program Source: 040106 NSF Education & Human Resource
Program Reference Code(s): 9178, SMET
Program Element Code(s): 1544
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

Intellectual Merit: This mixed-method study will investigate how different aspects of computing are embedded within masculine and feminine gender schemas, and how this embedding interacts with students' perceptions of these aspects, as well as with course-taking and career intentions. Computing differs from other STEM fields in that it is associated with a pervasive everyday object which people encounter from an early age. The computer, moreover, incorporates a complex set of possible practices which are deeply entangled with gender schemas. For example, interest in "looking under the hood" and approaching an artifact as interesting per se, as if it were a toy, is associated with masculinity in Western cultures, whereas using objects as a tool within a social context, with attention to social rewards, is associated with femininity. This toy vs. tool duality may manifest in computing with boys showing interest in fixing or hacking the computer, and with girls being attracted to blogs and social networking. It is these dualities, and the specific uses of computing associated with them, that maintain relatively stable gender associations. While boys and girls both use computers, the modalities of computer use that continue to be associated with computer courses, majors and careers appear to be embedded within a masculine cultural schema. Thus, everyday use can equalize across genders while career-oriented attitudes and behaviors see an increasing gender disparity. This research begins with a qualitative exploration and then develops theoretically based, updated scales which measure how current computing practices of middle school students are lived and gendered. These new measures will be used in a comprehensive survey to be administered to 2,000 students in the Howard and Prince George's County, Maryland, public schools. The study incorporates two potentially transformative concepts: recognition of the importance of the cultural context of computing and development of updated measures which operationalize theoretical concepts that have not been quantitatively explored in this context. The research also moves away from a "deficit model" where girls' lack of enthusiasm is seen as a result of deficiencies in girls, and, focus on why computing majors are failing to attract girls.

Broader Impacts: The findings and the new measures will help educators and researchers understand what girls find unattractive about computer courses and occupations. This is important for design and implementation of computer science curricula in high schools and for efforts to increase inclusive practices in computing majors and professions. The findings will also provide guidelines for designing curricula in high school that are attractive to girls as well as boys, and for publicizing aspects of computing jobs that are not strictly associated with masculine gender schemas. A diverse group of undergraduate and graduate students will develop research skills and an understanding of opportunities in higher education. UMBC's partnership with school districts will be enhanced. Middle school students will be exposed to research and a diverse group of researchers. Findings will be disseminated broadly, across sociology, education and IT disciplines and to educators as well as parents.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project titled “Beyond the Deficit Model: Gender Schemas, Computing, Preferences, and IT Careeer Choices” set out to understand the mechanisms that influence how and whether middle school students move toward computing classes, majors and careers. Our goal was to update our conceptual models and scales and instruments on this fast moving field: today’s children grow up with computers all around them, and many of them own powerful pocket computers—smart phones—by the time they enter high school. However, despite decades of effort, computing careers have few women or African-Americans or Hispanics, and few people from poorer backgrounds. We wanted to understand the dynamics and the context that motivates and supports girls, children of color and poorer children towards a path that can result in a career in computing fields, and what discourages or blocks such paths. Most importantly, we wanted to understand both the obstacles and the aspirations of the children themselves.

To accomplish all this, we started with in-depth interviews with more than 100 middle school students and using those interviews to capture their language and mental models. We proceeded to develop scales that measure important dimensions of the mechanisms that influence our research question. After piloting and testing these scales, we developed an 18 page high-quality survey instrument that included almost every dimension that was found to be relevant in the literature. The survey also included our new scales which reflected an updated understanding of this age group and their relationship to computing. We proceeded to gather survey data using this instrument. Our resulting sample of about 5200 surveys far exceeded our initial goal of 2000 and included middle school children from a variety of backgrounds. Our sample has a range of schools ranging from very well-off schools ones to poorer schools across a range of racial and ethnic compositions. The advantage of such a large sample is that it will allow us to do truly intersectional analyses: we can try see how race, gender and class interact as we have sufficient subsamples of many groups who are traditionally hard to analyze because they don’t show up in large numbers in smaller surveys.  

Our findings will be published in a wide range of academic journals and will be communicated to the public, practitioners and stakeholders. Through our analyses, we hope to be able to help schools design better curriculum that can be more attractive to demographic groups who are underrepresented in computing classes. Our data can be used to design more inclusive summer camps and after school programs in computing, and understand which paths are more likely to encourage children to pick up computing classes, major and careers. Our publications should also help guide technology companies better understand how children conceptualize careers in computing, and whether they need to change the way they advertise and publicize what such work entails.

Since we began our work, the White House announced a “computing for all” initiative for high school students. We hope that our data can be used to guide how to make these computing classes truly work for “all” students rather than ones who were already on a path to such careers.

Our survey instruments and scales should help other researchers looking at these questions. Many of our existing theories and instruments predate the explosion of computers in everyday life. Thanks to this NSF funding, we were able to catch up in this very fast-moving field.

Our findings should help teachers understand how children think of their classes in computing, and how to try to frame them to appeal to broader groups.

Our findings also include data about the importance of school infrastructure in how children think of computing careers. Many times, the first computer technician they see is the one who works in their schools, and they may think of computing careers within the framework of their school experience.

Our findings should be helpful to parents as we asked students about their views on skill levels of their parents, as well as the role of parental careers. Parents can also use our findings to understand the role computing classes and other activities play in encouraging children to take interest in computing careers.

Our survey has in-depth data in many other dimensions, ranging from the role of games to students’ sense of their skills.

We hope that our findings will further our theoretical understanding of the role of gender, race and class in computing careers. We also hope our findings will help practitioners design better extra-curricular programs; help policy-makers appreciate the role of infrastructure; help parents have a better sense of which computing activities contribute to educational outcomes and how; help teachers understand their classroom choices better; and help people in the technology companies have more options in recruiting and fostering a more diverse workforce.

 

 

 

 


Last Modified: 01/27/2017
Modified by: Zeynep Tufekci

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