Text entry user interfaces have been a bottleneck of nontraditional computing devices. One of the promising methods is the virtual keyboard on touch screens. Various layouts have been manually designed to replace the dominant QWERTY layout. This paper presents two computerized quantitative design techniques to search for the optimal virtual keyboard. The first technique simulated the dynamics of a keyboard with "digraph springs" between keys, which produced a "Hooke's" keyboard with 41.6 wpm performance. The second technique used a Metropolis random walk algorithm guided by a "Fitts energy" objective function, which produced a "Metropolis" keyboard with 43.1 wpm performance.

Text entry has been a bottleneck of nontraditional computing devices. One of the promising methods is the virtual keyboard for touch screens. Correcting previous estimates on virtual keyboard efficiency in the literature, we estimated the potential performance of the existing QWERTY, FITALY, and OPTI designs of virtual keyboards to be in the neighborhood of 28, 36, and 38 words per minute (wpm), respectively. This article presents 2 quantitative design techniques to search for virtual keyboard layouts. The first technique simulated the dynamics of a keyboard with digraph springs between keys, which produced a Hooke keyboard with 41.6 wpm movement efficiency. The second technique used a Metropolis random walk algorithm guided by a “Fitts-digraph energy” objective function that quantifies the movement efficiency of a virtual keyboard. This method produced various Metropolis keyboards with different Shumin Zhai is a human–computer interaction researcher with an interest in inventing and analyzing interaction methods and devices based on human performance insights and experimentation; he is a Research Staff Member in the

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.

Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.

Abstract: Optimised virtual keyboards are potentially efficient and fast for expert users, but they are difficult and slow for novice users due to the visual search requirement. Introducing structure and order to the layout may ease the visual searching process. By means of a Metropolis algorithm with an added alphabetical bias term to the Fitts-digraph energy function previously used (Zhai, Hunter, & Smith, 2000), we designed a keyboard layout with alphabetical ordering tendency with little movement efficiency cost. In contrast to previous efforts that failed to find any alphabetical ordering advantage, our experiment demonstrated that the alphabetical ordering created by our design method indeed improves novice users’performance and was preferred by most participants.

It is generally recognized that today’s frontier of HCI research lies beyond the traditional desktop computers whose GUI interfaces were built on the foundation of display—pointing device—full keyboard. Many interface challenges arise without such a physical UI foundation. Text writing — ranging from entering URLs and search queries, filling forms, typing commands, to taking notes and writing emails and chat messages — is one of the hard problems awaiting solutions in off-desktop computing. This paper summarizes and synthesizes a research program on this topic at the IBM Almaden Research Center. It analyzes various dimensions that constitute a good text input interface; briefly reviews related literature; discusses the evaluation methodology issues of text input; presents the major ideas and results of two systems, ATOMIK and SHARK; and points out current and future directions in the area from our current vantage point.

With the evolving functionality in television-based (TVbased) information and entertainment appliances, there is an increased need to enable users input text through remote control devices. We present a novel text input method, The Numpad Typer (TNT), for interactive TV, multimedia home terminals or other similar applications. Embodied in a TV remote control and guided by a visual map on the TV screen, TNT was designed for consistent spatial Stimuli-Response (S-R) compatibility and consistency of use. Five users tested TNT in ten sessions of 45-minutes. This initial investigation showed that users on average could type 9.3 and 17.7 correct words per minute with TNT doing the slowest and the fastest session respectively. The study also showed that the users found the TNT method easy to grasp and fun to use. Subjectively the participants felt they mastered the method rather quickly in comparison to their actual speed improvement.

Learning a new stylus keyboard layout is time-consuming yet potentially rewarding, as optimized virtual keyboards can substantially increase performance for expert users. This paper explores whether the learning curve can be accelerated using top-down learning strategies. In an experiment, one group of participants learned a stylus keyboard layout with top-down methods, such as visuo-spatial grouping of letters and mnemonic techniques, to build familiarity with a stylus keyboard. The other (control) group learned the keyboard by typing sentences. The topdown learning group liked the stylus keyboard better and perceived it to be more effective than the control group. They also had better memory recall performance. Typing performance after the top-down learning process was faster than the initial performance of the control group, but not different from the performance of the control group after they had spent an equivalent amount of time typing. Therefore, top-down learning strategies improved the explicit recall as expected, but the improved memory of the keyboard did not result in quicker typing speeds. These results suggest that quicker acquisition of declarative knowledge does not improve the acquisition speed of procedural knowledge, even during the initial cognitive stage of the virtual keyboard learning. They also suggest that top-down learning strategies can motivate users to learn a new keyboard more than repetitive rehearsal, without any loss in typing performance.

Abstract not found

Abstract not found