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.

Characterizing computer input with Fitts’ law parameters—the information and non-information aspects of pointing

“…retrieving and confirming the location of the next key in human memory). Although this did not change the relative efficiency order of various stylus keyboard layouts, the existence of distance-independent time component a did mean the efficiency percentage increase due to optimization was somewhat smaller than previously estimated (Zhai et al, 2002b).…”

“…For example, my colleagues and I first used TP as the sole parameter of Fitts' law in stylus keyboard modeling, except when the user taps on the same key twice (MacKenzie and Zhang, 1999;Zhai et al, 2002a). Later we found that a non-zero a could not be ignored in actual stylus tapping from one key to another (Zhai et al, 2002b), possibly due to a non-information component (e.g. retrieving and confirming the location of the next key in human memory).…”

“…Obviously, in a computer input system, the time to click on a mouse button, the time to tap on the same key in stylus keyboard twice (MacKenzie and Zhang, 1999;Zhai et al, 2002b), is not likely to be related to Fitts' index of difficulty and should be captured by the value of a. The same is true of the ''hand-grasping adjustment'' time in Card et al's text-editing task .…”

“…For example, the range of movement in stylus keyboarding is narrower than those in menu selection on a GUI interface, given that the soft keys in a stylus keyboard are tightly packed. Zhai et al (2002b) had to conduct specific Fitts' law estimations due to this consideration. In practice, same researchers could change the set of ID values in different studies.…”

Applying mobile device soft keyboards to collaborative multitouch tabletop displays

“…A large number of virtual keyboard techniques have been proposed-Zhai et al [42] gives an excellent summary of these techniques and their performance data. However, to our knowledge, there exists no study investigating text entry performance on direct-touch tabletops using soft keyboards.…”