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Review

Review. Empirical evaluations Usability testing Think-aloud studies Statistical studies. Gender HCI. CS352 Usability Engineering Summer 2010. A couple of numbers…. 1984 – 34% 2007 - 12% The answer: http://www.youtube.com/watch?v=Be7b2IQap4k. Gender issues in technological world.

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Review

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  1. Review • Empirical evaluations • Usability testing • Think-aloud studies • Statistical studies

  2. Gender HCI CS352 Usability Engineering Summer 2010

  3. A couple of numbers… • 1984 – 34% • 2007 - 12% • The answer: http://www.youtube.com/watch?v=Be7b2IQap4k

  4. Gender issues in technological world • Most research and practice has focused on… • Retention of female computer science professionals • What we focus on… • Gender differences within software environments

  5. Gender HCI • Who: • Dr. Margaret Burnett @ EECS.OSU • Laura Beckwith’s Ph.D. dissertation • Many others including myself • Projects involved identifying and closing gender gaps in: • Spreadsheets • Mashups • Visual Studio • … …

  6. Spreadsheets • Forms/3, Excel

  7. Mashups www.weatherbonk.com Google Maps

  8. Mashups • MS Popfly • Yahoo!Pipes(http://pipes.yahoo.com/pipes/) • Intel Mashmaker • … …

  9. Findings • Self-efficacy • Feature acceptance • Tinkering • Debugging strategies

  10. Self-efficacy • Females (both computer science majors and end users) have lower self-confidence than males in their computer-related abilities • Self-efficacy: • is a person’s judgment about his or her ability to carry out a certain task • is related to the task • Two factors affect task performance [Bandura] • Necessary skills • Self-efficacy!!!

  11. Gender gap in self-efficacy • Females’ SE < Males’ SE (10+ studies, 1,000+ participants) ftp://ftp.cs.orst.edu/pub/burnett/chi05.gender.pdf

  12. How was SE measured? • Modified versions of Compeau and Higgins’s computer self-efficacy questionnaire The following questions ask you to indicate whether you could use a mashup environment under a variety of conditions. For each of the conditions please indicate whether you think you would be able to complete the job using the system. Given a description of what a mashup should do, I could figure out how to create the mashup:

  13. Self-efficacy predicting performance ftp://ftp.cs.orst.edu/pub/burnett/chi05.gender.pdf

  14. Lessons • Females had lower SE than males • Females’ SE had an influence on task performance whereas males did not

  15. Findings • Self-efficacy  Feature acceptance • Tinkering • Debugging strategies

  16. Feature creep

  17. Who is more open to unfamiliar features? • Time to approach new features 3 types of features: (1) Familiar - the ability to edit Formulas (2) Taught - checkmarks and arrows (3) Untaught - X-mark feature ftp://ftp.cs.orst.edu/pub/burnett/chi05.gender.pdf

  18. Who is more open to unfamiliar features? ftp://ftp.cs.orst.edu/pub/burnett/chi05.gender.pdf

  19. Who is more open to unfamiliar features? ftp://ftp.cs.orst.edu/pub/burnett/chi10-genderMashupDesign.pdf

  20. Lessons • Females were less willing to use unfamiliar features • Self-efficacy predicted feature usage for females but not for males

  21. Why were women less willing to approach unfamiliar features? • Some possible explanations: • Risk perception (women are more risk-averse than men) • Perceived ease of use (influence women) vs. perceived usefulness (influence men)

  22. Why were women less willing to approach unfamiliar features? • Some possible explanations: • Perception of technology

  23. Findings • Self-efficacy • Feature acceptance  Tinkering • Debugging strategies

  24. Tinkering • Paper can be found here: ftp://ftp.cs.orst.edu/pub/burnett/chi06-genderTinker.pdf • Stereotypically related to males • Males tinkered more than females in this spreadsheet environment

  25. Findings • Self-efficacy • Feature acceptance • Tinkering  Debugging strategies

  26. Debugging strategies • Complete list of strategies can be found in this paper: ftp://ftp.cs.orst.edu/pub/burnett/chi08-genderStrategies.pdf • Males preferred dataflow • Females preferred code inspection

  27. Many gender gaps within software environments • Self-efficacy • Feature acceptance • Tinkering • Debugging strategies - Every gap seems to be working against the females. What do we do?

  28. What do we do about them? • Our approach: • Feature design to bridge the gap • Goal to remove barriers not to create a pink vs. blue version of any software • One example study…. • StratCell study

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