Finding Gender Differences in End-User Debugging: A Data Mining Approach

We are currently investigating what types of end user personas (or homogeneous groups in the population) exist and what works for or hinders each in end-user debugging. These personas will be determined using data mining methods such as cluster analysis to see how static (background and self-efficacy), behavioral, and success variables interact for each cluster of users. This research will help provide a better understanding of the needs of end users and the tools that are necessary for supporting both male and females in debugging tasks.