Movement model, hits distribution and learning in virtual keyboarding

Abstract

In a ten-session experiment, six participants practiced typing with an expanding rehearsal method on an optimized virtual keyboard. Based on a large amount of in-situ performance data, this paper reports the following findings. First, the Fitts-digraph movement efficiency model of virtual keyboards is revised. The format and parameters of Fitts' law used previously in virtual keyboards research were incorrect. Second, performance limit predictions of various layouts are calculated with the new model. Third, learning with expanding rehearsal intervals for maximum memory benefits is effective, but many improvements of the training algorithm used can be made in the future. Finally, increased visual load when typing previously practiced text did not significantly change users' performance at this stage of learning, but typing unpracticed text did have a performance effect, suggesting a certain degree of text specific learning when typing on virtual keyboards