Doom as an interface for process management
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Language modeling for soft keyboards
Eighteenth national conference on Artificial intelligence
What makes things fun to learn? heuristics for designing instructional computer games
SIGSMALL '80 Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tactile feedback for mobile interactions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Text Entry Systems: Mobility, Accessibility, Universality
Text Entry Systems: Mobility, Accessibility, Universality
Investigating the effectiveness of tactile feedback for mobile touchscreens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Usability guided key-target resizing for soft keyboards
Proceedings of the 15th international conference on Intelligent user interfaces
Using Amazon Mechanical Turk for transcription of non-native speech
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Sampling representative phrase sets for text entry experiments: a procedure and public resource
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards online adaptation and personalization of key-target resizing for mobile devices
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Personalized input: improving ten-finger touchscreen typing through automatic adaptation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Helping mobile apps bootstrap with fewer users
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
A personal touch: recognizing users based on touch screen behavior
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
Adaptable probabilistic flick keyboard based on HMMs
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Categorised ethical guidelines for large scale mobile HCI
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Octopus: evaluating touchscreen keyboard correction and recognition algorithms via
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
iGrasp: grasp-based adaptive keyboard for mobile devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Mobile devices often utilize touchscreen keyboards for text input. However, due to the lack of tactile feedback and generally small key sizes, users often produce typing errors. Key-target resizing, which dynamically adjusts the underlying target areas of the keys based on their probabilities, can significantly reduce errors, but requires training data in the form of touch points for intended keys. In this paper, we introduce Text Text Revolution (TTR), a game that helps users improve their typing experience on mobile touchscreen keyboards in three ways: first, by providing targeting practice, second, by highlighting areas for improvement, and third, by generating ideal training data for key-target resizing as a side effect of playing the game. In a user study, participants who played 20 rounds of TTR not only improved in accuracy over time, but also generated useful data for key-target resizing. To demonstrate usefulness, we trained key-target resizing on touch points collected from the first 10 rounds, and simulated how participants would have performed had personalized key-target resizing been used in the second 10 rounds. Key-target resizing reduced errors by 21.4%.