Gender HCI is a subfield of human-computer interaction that focuses on the design and evaluation of interactive systems for humans, with emphasis on differences in how males and females interact with computers.
Gender HCI research has been conducted in the following areas (among others):
- Biases in perceptions of gendered computerized partners
- The effects of confidence and self-efficacy on both genders’ interactions with software.
- The design of gender-specific software, such as video games created for females.
- The design of display screen sizes and how they affect both genders.
- The design of gender-neutral problem-solving software.
Gender HCI investigates ways in which attributes of software (or even hardware) can interact with gender differences. As with all of HCI, Gender HCI is a highly interdisciplinary area. Findings from fields such as psychology, computer science, marketing, neuroscience, education, and economics strongly suggest that males and females problem solve, communicate, and process information differently. Gender HCI investigates whether these differences need to be taken into account in the design of software and hardware.
The term Gender HCI was coined in 2004 by Laura Beckwith, a PhD candidate at Oregon State University, and her advisor Margaret Burnett. They discovered that, although there had been some activity that could be characterized as Gender HCI work, people did not know about each other's work. The relevant research reports were isolated and scattered about various fields. Since that time, they and others have worked to help researchers know about each other's work and practitioners to be aware of the findings, so as to allow this area to mature as a subarea of HCI.
The following are a brief set of milestones in the history of this emerging subarea.
- 1987: Games designed as "gender neutral" look like games designed for boys. (Chuck Huff).
- 1989: Ethnographic research exploring women, programming, and computers (Sherry Turkle).
- 1995: Gender differences in self-efficacy and attitudes toward computers (Tor Busch).
- 1998: Gender factors in the design of video games (Justine Cassell).
- 2002: Wider displays more beneficial to all users, especially females (Mary Czerwinski, Desney S. Tan, George G. Robertson).
- 2004: The concept Gender HCI made explicit (Laura Beckwith, Margaret Burnett).
- 2006: A research workshop on Gender HCI.
Selected Gender HCI Findings
Here are some results from the Gender HCI research conducted to date - ordered from most to least recent, within categories:
- "Reward Expectations of Gendered Computers."
- In one experiment, subjects worked on a task with a computerized partner that was named James or Julie. The task was gender-neutral, meaning that it was not directly relevant to being a man or woman. The results showed that subjects behaved the same way toward a computer named James or Julie. Despite these similarities in behavior, subjects estimated that a computer named James would cost them significantly more than one named Julie. The findings show gender shape user perceptions of their computers, which lack the human features that define the characteristic of gender.
- Confidence-Related Findings.
- For spreadsheet problem-solving tasks, (1) female end users had significantly lower self-efficacy than males and (2) females with low self-efficacy were significantly less likely to work effectively with problem-solving features available in the software. In contrast, males’ self-efficacy did not impact their effectiveness with these features.
- In a study of the computer attitudes and self-efficacy of 147 college students, gender differences existed in self-efficacy for complex tasks (such as word processing and spreadsheet software), but not simpler tasks. Also, male students had more experience working with computers and reported more encouragement from parents and friends.
- Software Feature Related Findings.
- In spreadsheet problem-solving tasks, female end users were significantly slower to try out unfamiliar features. Females significantly more often agreed with the statement, "I was afraid I would take too long to learn the [untaught feature]." Even if they tried it once, females were significantly less likely to adopt new features for repeated use. For females, unlike for males, self-efficacy predicted the amount of effective feature usage. There was no significant difference in the success of the two genders or in learning how the features worked, implying that females’ low self-efficacy about their usage of new features was not an accurate assessment of their problem-solving potential, but rather became a self-fulfilling prophecy.
- Behavior Related Findings.
- In spreadsheet problem-solving tasks, tinkering (playfully experimenting) with features was adopted by males more often than females. While males were comfortable with this behavior, some did it to excess. For females, the amount of tinkering predicted success. Pauses after any action were predictive of better understanding for both genders.
- Males viewed machines as a challenge, something to be mastered, overcome, and be measured against. They were risk-takers, and they demonstrated this by eagerly trying new techniques and approaches. Females rejected the image of the male hacker as alienating and depersonalizing. Their approach to computers was "soft;" tactile, artistic, and communicative.
- Hardware Interface Findings.
- Video Games Findings.
- Several findings were reported about girls’ interests that relate to video games, with interpretations for the video game software industry.
- Several researchers explored what girls seek in video games, and implications for video game designers. Among the implications were collaboration vs. competition preferences, and use of non-violent rewards versus death and destruction as rewards. These works argue both sides of the question as to whether or not to design games specifically for girls.
- Other Related Findings About Gender and Computers.
- In a study of the way people interacted with conversational software agents in relation to the sex of the agent, the female virtual agent received many more violent and sexual overtures than either the male one or the gender-free one (a robot).
- In the home, where many appliances are programmable to some extent, different categories of appliance were found to be more likely to be programmed by men (e.g. entertainment devices) and by women (e.g. kitchen appliances). There is often one member of a household who assumes responsibility for programming a particular device, with a "domestic economy" accounting for this task.
- Males and females had different perceptions for whether a web page would be appropriate for his/her home country, and further, females more often than males preferred more information on all web pages viewed during a study.
- Women who entered mathematics, science, and technology careers had high academic and social self-efficacy. Their self-efficacy was based on vicarious experiences and verbal persuasion of significant people around them.
- Factors affecting low retention of women in computer science majors in college included women’s lower previous experience in computing compared to men, their low self-perceived ability, discouragement by the dominant male peer culture, and lack of encouragement from faculty.
- Human-computer interaction
- Topics in human-computer interaction
- Usability engineering
- Posard, Marek (August 2014). "Status processes in human-computer interactions: Does gender matter?". Computers in Human Behavior 37: 189–195.
- Beckwith, L. and Burnett, M. Gender: An important factor in end-user programming environments?, In Proc. Visual Languages and Human-Centric Computing Languages, IEEE (2004), 107-114.
- De Angeli, A. and Bianchi-Berthouze, N. Proceedings of Gender and Interaction, Real and Virtual Women in a Male World Workshop, Venice, May 23, (2006).
- Beckwith, L. Burnett, M., Wiedenbeck, S., Cook, C., Sorte, S., and Hastings, M. Effectiveness of end-user debugging software features: Are there gender issues? ACM Conference on Human Factors in Computing Systems (2005), 869-878.
- Busch, T. Gender differences in self efficacy and attitudes towards computer, Journal of Educational Computing Research 12,(1995)147-158.
- Beckwith, L. Kissinger, C., Burnett, M., Wiedenbeck, S., Lawrance, J., Blackwell, A., and Cook, C. Tinkering and gender in end-user programmers' debugging, ACM Conference on Human Factors in Computing Systems, (2006), 231-240.
- Turkle, S. Computational reticence: Why women fear the intimate machine. In Technology and Women's Voices, Cheris Kramerae (ed.), (1988), 41-61.
- Czerwinski, M., Tan, D., and Robertson, G., Women take a wider view, In Proc. CHI 2002, ACM Press (2002), 195-202.
- Tan, S., Czerwinski, M., and Robertson, G., Women go with the (optical) flow, In Proc. of CHI 2003, Human Factors in Computing Systems, (2003), 209-215.
- Gorriz, C. and Medina, C. Engaging girls with computers through software games. Communications of the ACM, (2000), 42-49.
- Cassell, J. Genderizing HCI, MIT Media Lab, (1998).
- Cassell, J. and Jenkins, H. (Eds.), From Barbie to Mortal Kombat: Gender and Computer Games, Cambridge, MA: MIT Press, (1998).
- De Angeli, A. and Brahnam, S. Sex stereotypes and conversational agents. In Proc. of Gender and Interaction, Real and Virtual Women in a Male World Workshop, (2006).
- Rode, J.A., Toye, E.F. and Blackwell, A.F., The Fuzzy Felt Ethnography - understanding the programming patterns of domestic appliances. Personal and Ubiquitous Computing 8, (2004), 161-176.
- Simon, S., The impact of culture and gender on web sites: An empirical study, The Data Base for Advances in Information Systems, 32(1), (2001), 18-37.
- Zeldin, A. and Pajares, F., Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 37, (2000), 215-246.
- Margolis, J., and Fisher, A. Unlocking the Clubhouse: Women and Computing. Cambridge, MA, MIT Press, (2001).
- de Ribaupierre, H. La différence entre les genres dans le processus d'adoption d'un logiciel de dessin à partir du modèle de l'acceptabilité des nouvelles technologies (TAM) . Master thesis, (2009).
- Beckwith, L. Burnett, M., Grigoreanu, V., and Wiedenbeck, S. Gender HCI: What about the software? IEEE Computer, (2006), 97-101.
- Beckwith, L. Sorte, S., Burnett, M., Wiedenbeck, S., Chintakovid, T., and Cook, C. Designing features for both genders in end-user software engineering environments, IEEE Symposium on Visual Languages and Human-Centric Computing,(2005) 153-160.
- Brewer, J. and Bassoli, A. Reflections of gender, reflections on gender: Designing ubiquitous computing technologies. In Proc. of Gender and Interaction, Real and Virtual Women in a Male World Workshop, (2006).
- Cottrell, J. I'm a stranger here myself: A consideration of women in computing. In Proc. ACM SIGUCCS User Services Conference, (1992), 71-76.
- Fisher, A., Margolis, J., and Miller, F. Undergraduate women in computer science: Experience, motivation, and culture. In Proc. SIGCSE Technical Symposium on Computer Science Education, ACM Press (1997), 106-110.
- Grigoreanu, V., Beckwith, L., Fern, X., Yang, S., Komireddy, C., Narayanan, V., Cook, C., Burnett, M. Gender differences in end-user debugging, revisited: What the miners found, IEEE Symposium on Visual Languages and Human-Centric Computing, (2006), 19-26.
- Hartzel, K. How self-efficacy and gender issues affect software adoption and use. Communications of the ACM, (2003), 167-171.
- Huff, C. and Cooper, J. Sex bias in educational software: The effect of designers' stereotypes on the software they design. Journal of Applied Social Psychology, 17, (1987), 519-532.
- Kelleher, C. and R. Pausch. Lessons Learned from Designing a Programming System to Support Middle School Girls Creating Animated Stories. 2006 IEEE Symposium on Visual Languages and Human-Centric Computing.
- Nass, Clifford, Youngme Moon, and Nancy Green. "Are Machines Gender Neutral? Gender‐Stereotypic Responses to Computers With Voices." Journal of applied social psychology 27.10 (1997): 864-876.
- Posard, Marek N. "Status processes in human-computer interactions: Does gender matter?." Computers in Human Behavior 37 (2014): 189-195.