Empirical Bi-Action Tables: A Tool for the Evaluation and Optimization of Text-Input Systems. Application I: Stylus Keyboards

We introduce a technique that, 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 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, Hick’s law, etc.) and full empirical testing at the end. Dominic Hughes is a computer scientist specializing in logic and mathematical foundations of computation; he is a Research Associate in the Computer Science Department of Stanford University. James Warren is a computer scientist with interests in machine learning and optimization; he is a PhD student in the Scientific Computing and Computational Mathematics Program of Stanford University. Orkut Buyukkokten is a computer scientist with interests in databases