The paper examines a computational design approach for improving user interface designs for people with sensorimotor and cognitive impairments. In ability-based optimization, designs are created by an optimizer and evaluated against model of an individual performing tasks. Alternative designs can be explored and adapted to an individual's abilities. In this paper, we explore text entry on touchscreen devices as the case. Individual abilities are parametrically expressed as part of a task-specific cognitive model, and the model estimates how the individual might adapt her interaction to the task. Optimized designs can potentially improve speed and reduce error for users with tremor and dyslexia. Ability-based optimization does not necessitate extensive data-collection and could be applied both automatically and manually by users, designers, or caretakers.
Predicting how users learn new or changed interfaces is a longstanding objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.
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