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UID:ATEvent-eed436ece7e23bbcfe0d4623c4969225
SUMMARY:Ph.D. Defense of Dissertation: James Clawson
DESCRIPTION:Title: On-the-Go Text Entry: Evaluating and Improving Mobile Text Input on mini-QWERTY KeyboardsJames ClawsonHuman-Centered ComputingSchool of Interactive ComputingCollege of ComputingGeorgia Institute of Technology Date: Tuesday\, October 30\, 2012Time: 1:00-4:00pmLocation: TSRB 134 Committee:\nDr. Thad Starner\, School of Interactive Computing\, AdvisorDr. Gregory Abowd\, School of Interactive ComputingDr. Beth Mynatt\, School of Interactive ComputingDr. Scott MacKenzie\, Department of Computer Science and Engineering\, York UniversityDr. Jacob Wobbrock\, Information School\, University of Washington \nAbstract: To date\, hundreds of millions of mini-QWERTY keyboard equipped devices (miniaturized versions of a full desktop keyboard)  have been sold. Accordingly\, a large percentage of text messages originate from fixed-key\, mini-QWERTY keyboard enabled mobile phones. In this dissertation\, I present ways to improve text messaging on mini-QWERTY keyboard enabled mobile phones through the use of an automatic error correction algorithm. Over a series of three longitudinal studies I quantify how quickly and accurately individuals can input text on mini-QWERTY keyboards. I evaluate performance in ideal laboratory conditions as well as in a variety of mobile contexts. My first study establishes baseline performance measures; my second study investigates the impact of limited visibility on text input performance; and my third study investigates the impact of mobility (sitting\, standing\, and walking) on text input performance. After approximately five hours of practice\, participants achieved expertise typing almost 60 words-per-minute at almost 95% accuracy. Upon completion of these studies\, I examine the types of errors that people make when typing on mini-QWERTY keyboards. Having discovered a common pattern in errors\, I develop and refine an algorithm to automatically detect and correct errors in mini-QWERTY keyboard enabled text input. I both validate the algorithm through the analysis of pre-recorded typing data and then empirically evaluate the impacts of automatic error correction on live mini-QWERTY keyboard text input. Validating the algorithm over various datasets\, I demonstrate the potential to correct approximately a 25% of the total errors and correct up to 3% of the total keystrokes. Evaluating automatic error detection and correction on live typing results in successfully correcting 60.80% of the off-by-one errors committed by participants while increasing typing rates by almost 2 words-per-minute without introducing any distraction.\n
DTSTART:20121030T130000
DTEND:20121030T160000
CREATED:20121220T131508
DTSTAMP:20121220T131508
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