Three major principles of learning a chord keyboarding task were investigated. Five subjects were taught 18 characters on a chord keyboard, then practiced improving their keying speed for about 60 hours. The first objective of the study was to observe long-term learning on a keyboarding task. The performance, in characters typed per minute, was recorded over the entire range of the experiment. Typing skill improved quickly in the beginning and then slowed, but performance had not reached a stable peak by the end of the experiment. The second objective of this study was to determine a function that describes performance progress from initial training to a high keying speed. Five functions were evaluated; a function which predicts the logarithm of the dependent variable (characters per minute) from the logarithm of the regressor variable provided a good fit to the actual data. The final form of the equation was CPM; = e^B₀T_i^B₁ where CPM_i; = performance in characters per minute on the i-th interval, T_i = the i-th interval of practice, and B₀ and B₁ are fitted coefficients. The second objective also considered the form that T_i (from the above equation) should take. Performance can be predicted from number of repetitions such as trials, or from amount of practice such as hours. Both trials and time were used as predictor variables and both provided equally accurate predictions of typing speed. Both also provided excellent fits in conjunction with the Log-Log equation. Thus, it appears the Log-Log function is fairly robust in predicting performance from different variables. The third objective was to investigate how many trials of performance are needed before the entire learning function can be reasonably determined. In this experiment, subjects practiced for an extended period of time (about 60 hours) so a fairly complete progression of performance could be gathered. Yet, it would be more convenient to collect data for only a few hours and deduce the ensuing performance of the subject. The coefficients of the Log-Log function were determined using only the first 25, 50, 100, 150, and 200 of the initial performance points (out of about 550 total actual data points). The mean squared error (MSE) was calculated for each of these fits and compared to the MSE of the fit using all points. It appears that at least 50 performance data points are required to reduce the error to a reasonably acceptable level. / Master of Science

The information technology that is available now has been developed for other populations, such as the general public, the business community, health care organizations, or the handicapped. However, much of it can be adapted to fit the needs, desires and capabilities of the elderly and computer technology can be much "friendlier" for the elderly in both a technological and a social sense. This study examines the elderly-computer technology interface. State-of -the-art computer input devices are evaluated and an ergonomic analysis is made about the interface. Finally, a new design concept is developed to solve the problems and complications arising from the aging process in terms of accessibility, safety, efficiency, ergonomic comfort of computer technology and to introduce a new and fairly unfamiliar technology to a population group. / Master of Science

To date, hundreds of millions of mini-QWERTY keyboard equipped devices (miniaturized versions of a full desktop keyboard) have been sold. Accordingly, a large percentage of text messages originate from fixed-key, mini-QWERTY keyboard enabled mobile phones. Over a series of three longitudinal studies I quantify how quickly and accurately individuals can input text on mini-QWERTY keyboards. I evaluate performance in ideal laboratory conditions as well as in a variety of mobile contexts. My first study establishes baseline performance measures; my second study investigates the impact of limited visibility on text input performance; and my third study investigates the impact of mobility (sitting, standing, and walking) on text input performance. After approximately five hours of practice, participants achieved expertise typing almost 60 words per minute at almost 95% accuracy. Upon completion of these studies, I examine the types of errors that people make when typing on mini-QWERTY keyboards. Having discovered a common pattern in errors, I develop and refine an algorithm to automatically detect and correct errors in mini-QWERTY keyboard enabled text input. I both validate the algorithm through the analysis of pre-recorded typing data and then empirically evaluate the impacts of automatic error correction on live mini-QWERTY keyboard text input. Validating the algorithm over various datasets, I demonstrate the potential to correct approximately 25% of the total errors and correct up to 3% of the total keystrokes. Evaluating automatic error detection and correction on live typing results in successfully correcting 61% of the targeted errors committed by participants while increasing typing rates by almost two words per minute without introducing noticeable distraction.