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 7 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... if there was no one around to tell me what to do as I go. Strongly Disagree DisagreeNeither Agree Nor Disagree AgreeStrongly Agree... if I had never seen a mashup like it before. Strongly Disagree DisagreeNeither Agree Nor Disagree AgreeStrongly Agree... if I had only the software manuals for references. Strongly Disagree DisagreeNeither Agree Nor Disagree AgreeStrongly Agree... if I had seen someone else using it before trying it myself. Strongly Disagree DisagreeNeither Agree Nor Disagree AgreeStrongly Agree … 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