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 Grigorean
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