ACT-R Based Models For Learning Interactive Layouts
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This dissertation presents research on learning of interactive layouts. I develop two models based on a theory of cognition known as ACT-R (Adaptive Control of Thought–Rational). I validate them against experimental data collected by other researchers. The first model is a simulation model that emulates the transition from novice to expert level in text entry. The model transcribes the presented English letters on a traditional phone keypad. It predicts the non-movement time to copy a pre-cued letter. It explains the visual exploration strategy that a user may employ in the novice to expert continuum. The second model is a closed-form model that accounts for the combined effect of practice, decay, proactive interference and mental effort on task completion time while practicing target acquisition on an interactive layout. The model can quantitatively compare a set of layouts in terms of the mental effort expended to learn them. My first model provides insight into how much practice is needed by a learner to progress from novice to expert level for an interactive layout. My second model provides insight into how effortful is it to learn a layout relative to other layouts.