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Russell Drummond

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Publications by Russell Drummond (bibliography)

» 2008 «

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Subrahmaniyan, Neeraja, Beckwith, Laura, Grigoreanu, Valentina, Burnett, Margaret, Wiedenbeck, Susan, Narayanan, Vaishnavi, Bucht, Karin, Drummond, Russell and Fern, Xiaoli (2008): Testing vs. code inspection vs. what else?: male and female end users' debugging strategies. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 617-626. Available online

“Little is known about the strategies end-user programmers use in debugging their programs, and even less is known about gender differences that may exist in these strategies. Without this type of information, designers of end-user programming systems cannot know the "target" at which to aim, if they are to support male and female end-user programmers. We present a study investigating this issue. We asked end-user programmers to debug spreadsheets and to describe their debugging strategies. Using mixed methods, we analyzed their strategies and looked for relationships among participants' strategy choices, gender, and debugging success. Our results indicate that males and females debug in quite different ways, that opportunities for improving support for end-user debugging strategies for both genders are abundant, and that tools currently available to end-user debuggers may be especially deficient in supporting debugging strategies used by females.”

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» 2007 «

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Stumpf, Simone, Rajaram, Vidya, Li, Lida, Burnett, Margaret, Dietterich, Thomas G., Sullivan, Erin, Drummond, Russell and Herlocker, Jonathan (2007): Toward harnessing user feedback for machine learning. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 82-91. Available online

“There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource -- the users themselves -- could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.”

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