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Soft Keyboard Evaluations: Integrating User’s Background in Predictive Models
Abstract
Predictive models, based on cognitive and motor laws (modeling some aspect of human behaviors), were supposed to provide an efficient tool to compare quickly soft keyboards performances, but these models are confronted with some limits. Moreover, they failed to predict efficiently the performances during the first usage that got an important impact on soft keyboard acceptability. To improve these models, the authors integrated into them the character search strategies oriented by the user’s background. They illustrate their purpose by modeling three keyboards and comparing the results with experimental ones. The models integrating the user’s background predicted results close to the experimental ones. However, those models must be adapted to the function of the keyboard and the targeted population. The applicability of the models as a rapid comparison tool for soft keyboards must be questioned.
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