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.