Although gender differences in a technological world are receiving significant research attention, much of the research and practice has aimed at how society and education can impact the successes and retention of female computer science professionals—but the possibility of gender issues within software has received almost no attention. If gender issues exist with some types of software features, it is possible that accommodating them by changing these features can increase effectiveness, but only if we know what these issues are. In this paper, we empirically investigate gender differences for end users in the context of debugging spreadsheets. Our results uncover significant gender differences in self-efficacy and feature acceptance, with females exhibiting lower self-efficacy and lower feature acceptance. The results also show that these differences can significantly reduce females ’ effectiveness.

We have been working to uncover gender differences in the ways males and females problem solve in end-user programming situations, and have discovered differences in males ’ versus females ’ use of several debugging features. Still, because this line of investigation is new, knowing exactly what to look for is difficult and important information could escape our notice. We therefore decided to bring data mining techniques to bear on our data, with two aims: primarily, to expand what is known about how males versus females make use of end-user debugging features, and secondarily, to find out whether data mining could bring new understanding to this research, given that we had already studied the data manually using qualitative and quantitative methods. The results suggested several new hypotheses in how males versus females go about end-user debugging tasks, the factors that play into their choices, and how their choices are associated with success. 1.

Abstract. Recent research has shown that gender differences exist that influence the ways that males and females work with problem-solving software. These gender differences may put females at a disadvantage in competing for jobs requiring these skills. Earlier research has shown the existence of gender differences in confidence that affects feature usage and adoption; however these findings have raised new questions. We are seeking answers to these questions through qualitative methods. The case study we present here documents our methodology and may be used as a guide for others embarking on similar qualitative analyses. 1

Cultural theory breaks with Modern analysis by rejecting traditional notions of race, gender, class and sexuality. In doing so, alternative frameworks such as Post-Feminism emerge which are useful for thinking about culture, technology and what our interactions with it mean. From a Post-Feminist perspective it can be seen how in our multi-cultural, post-industrial, digitized world, there is space to move beyond traditional ways of dividing up society such as ‘male’ and ‘female’. We are then free to re-construct our identity in light of a rich diversity of individually relevant experiences. Therefore, in order to get a better understanding of the highly nuanced cultural interactions that characterize our use of technology, this paper argues against using the inherently stereotyped lens of gender and allowing a new set of user needs to emerge.

Most of the work so far in the subfield of Gender HCI has followed a theorydriven approach. Established theories, however, do not take into account specific issues that arise in end-user debugging. We suspected that there may be important information that we were overlooking. We therefore employed a methodology change: turning to data mining techniques to find hidden patterns and relationships in females' and males ' feature usage patterns. This thesis reports two data mining studies to help discover complex ties among static, dynamic, and success data collected in end-user debugging sessions. Study 1 was our first step, and was used to derive new hypotheses about females ' and males ' strategies and behaviors. In Study 2, we then applied different data mining algorithms to a larger data set to describe, summarize, segment, and detect interesting patterns. We found that most of the factors that tied with females ' success in debugging were different than those that tied with males ' success in debugging and vice versa. The results will ultimately help Gender HCI researchers better support end-user debuggers of both genders. ©Copyright by Valentina Grigoreanu

Previous research has revealed gender differences that impact females’ willingness to adopt software features in end users ’ programming environments. Since these features have separately been shown to help end users problem solve, it is important to female end users ’ productivity that we find ways to make these features more acceptable to females. This thesis draws from our ongoing work with users to help inform our design of theory-based methods for encouraging effective feature usage by both genders. This design effort is the first to begin addressing the gender differences in the ways that people go about problem solving in end-user programming situations. ©Copyright by Shraddha R. Sorte