The design and evaluation of a high-performance soft keyboard
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Predicting text entry speed on mobile phones
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Text input for mobile devices: comparing model prediction to actual performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An evaluation of mobile phone text input methods
AUIC '02 Proceedings of the Third Australasian conference on User interfaces - Volume 7
Model for non-expert text entry speed on 12-button phone keypads
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Model-based evaluation of expert cell phone menu interaction
ACM Transactions on Computer-Human Interaction (TOCHI)
A predictive model of menu performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Keystroke-level model for advanced mobile phone interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A cognitive simulation model for novice text entry on cell phone keypads
Proceedings of the 14th European conference on Cognitive ergonomics: invent! explore!
Learning to Text: An Interaction Analytic Study of How Seniors Learn to Enter Text on Mobile Phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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No work on mobile text messaging so far has taken into account the effect of learning on the change in visual exploration behavior as users progress from non-expert to expert level. We discuss within the domain of multi-tap texting on mobile phone and address the process of searching versus selecting a letter on the keypad interface. We develop a simulation model that forecasts the probability of letter location recall by non-expert users and thereby models learning, as the user acquires expertise in recalling, with practice, session after session. We then plugin this probability within a model of visual strategy that combines the effect of different ways visual exploration: non-expert users search for a letter while expert users select a letter. The observed non-expert non-motor time preceding a key press (for a letter) correlates extremely well with the simulation results.