We introduce a technique which, given any text input system A and novice user u, will predict the peak expert input speed of u on A, avoiding the costly process of actually training u up to expert level. Here peak refers to periods of ideal performance, free from hesitation or concentration lapse, and expert refers to asymptotic competence (e.g. touch-typing, in the case of a two-handed keyboard). The technique is intended as a feedback mechanism in the interface development cycle between abstract mathematical modeling at the start (Fitts Law, Hicks Law, etc.) and full empirical testing at the end.
The technique is parametric in the character map, i.e., in the map from
actions (keystrokes, gestures, chords, etc.) to characters. Therefore
standard heuristic algorithms can be employed to search for optimal
character maps (e.g. keyboard layouts). We illustrate the use of our
technique for evaluation and optimization in the context of stylus
keyboards, first benchmarking a number of stylus keyboards relative to a
simple alphabetic layout, then demonstrating the use of an ant algorithm
to obtain a machine-optimized layout.