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Analysis of Alternative Keyboards Using Learning Curves Allison M View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Digital Repository @ Iowa State University Industrial and Manufacturing Systems Engineering Industrial and Manufacturing Systems Engineering Publications 2009 Analysis of Alternative Keyboards Using Learning Curves Allison M. Anderson North Carolina State University Gary Mirka Iowa State University, [email protected] Sharon M.B. Jones North Carolina State University David B. Kaber North Carolina State University Follow this and additional works at: http://lib.dr.iastate.edu/imse_pubs Part of the Ergonomics Commons, Industrial Engineering Commons, and the Systems Engineering Commons The ompc lete bibliographic information for this item can be found at http://lib.dr.iastate.edu/ imse_pubs/159. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Industrial and Manufacturing Systems Engineering at Iowa State University Digital Repository. It has been accepted for inclusion in Industrial and Manufacturing Systems Engineering Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Analysis of Alternative Keyboards using Learning Curves Allison M. Anderson, North Carolina State University, Raleigh, North Carolina, Gary A. Mirka, Iowa State University, Ames, Iowa, and Sharon M. B. Joines and David B. Kaber, North Carolina State University, Raleigh, North Carolina ABSTRACT Objective: Quantify learning percentages for alternative keyboards (chord, contoured split, Dvorak, and split fixed-angle) and understand how physical, cognitive, and perceptual demand affect learning. Background: Alternative keyboards have been shown to offer ergonomic benefits over the conventional, single-plane QWERTY keyboard design, but productivity-related challenges may hinder their widespread acceptance. Method: Sixteen participants repeatedly typed a standard text passage using each alternative keyboard. Completion times were collected and subsequent learning percentages were calculated. Participants were asked to subjectively rate the physical, cognitive and perceptual demands of each keyboard and these values were then related to the calculated learning percentages. Results: Learning percentage calculations revealed the percentage for the split fixed-angle keyboard (90.4%) to be significantly different (p<0.05) from the learning percentages for the other three keyboards (chord: 77.3%, contour split: 76.9%, Dvorak: 79.1%). The average task completion time for the conventional QWERTY keyboard was 40 seconds, and the average times for the 5th trial on the chord, contoured split, Dvorak and split fixed-angle keyboards were 346, 69, 181 and 42 seconds, respectively. Conclusions: Productivity decrements can be quickly regained for the split fixed-angle and contour split keyboard but will take considerably longer for Dvorak and chord keyboards. The split fixed-angle keyboard involved physical learning while the others involved some combination of physical and cognitive learning, a result supported by the subjective responses. Application: Understanding the changes in task performance time that come with learning can provide additional information for a cost-benefit analysis when considering the implementation of ergonomic interventions. KEY WORDS: alternative keyboards, learning curve theory, ergonomic interventions, DVORAK, chord keyboards, split keyboards, musculoskeletal disorders POST-PRINT (manuscript after accepted but prior to publication) of the following published manuscript: Anderson A, GA Mirka, SMB Joines, and DB Kaber (2009) “Analysis of Alternative Keyboards Using Learning Curves”, Human Factors 51: 35-45, (DOI:10.1177/0018720808329844). 1 INTRODUCTION The basic, single-plane QWERTY keyboard has long been the conventional keyboard used in the office environment. Concerns related to the prevalence of work-related musculoskeletal disorders of the upper-extremity in computer users (e.g. Gerr et al., 2002; Gerr, Monteilh, & Marcus, 2006) have prompted designers to develop alternative keyboard designs that 1) reduce ulnar deviations of the wrists (e.g. split keyboards), 2) reduce finger motion (e.g. contoured split keyboards and chord style keyboards), and 3) make better use of the dominant fingers (2nd and 3rd digits) by altering the position of the letters on the keyboard (e.g. Dvorak keyboard). One impediment to the widespread acceptance of these alternative keyboards is the perception of a reduction in typing productivity over the short term, the long term, and/or permanently. A number of studies that have attempted to quantify this productivity impact and how, in the short term, it changes as a person becomes more proficient with the new keyboard (e.g. Smith & Cronin, 1993; Chen, Burastero, Tittiranonda, Hollerbach, Shih, & Denhoy, 1994; Gerard, Jones, & Wang, 1994; Swanson, Galinsky, Cole, Pan, & Sauter, 1997; Zecevic, Miller, & Harburn, 2000; Fagarasanu, Kumar, & Narayan, 2005). Zecevic et al. (2000) evaluated the performance of 16 experienced computer users with a split fixed-angle keyboard and showed that after 10 hours of use, participant’s typing speed was at about 90% of their speed with the conventional QWERTY design. In a study of a contoured split keyboard (Figure 1-top right), Gerard et al. (1994) showed that six professional typists were able to type with 72% of baseline QWERTY speed after 115 minutes of use. The split fixed-angle keyboard and contoured split keyboards described in the previous paragraph have different physical geometries than the conventional QWERTY keyboard, but still maintain the QWERTY layout of the keys. Two other keyboards have been proposed that do not employ the QWERTY layout. The Dvorak keyboard uses a more purposeful layout of the letters of the alphabet with which the right-hand does 56% of the typing (versus 43% on the QWERTY keyboard). Furthermore, about 70% of typing is performed on the home row thereby minimizing the movements of the fingers. Dvorak claimed that his keyboard layout could improve typing speed by 35% on typewriters (Dvorak, 1943). Subsequent authors have questioned these estimates (Alden, Daniels, & Kanarick, 1972; Norman & Fisher, 1982). The chord keyboard, likewise, does not use the QWERTY layout. On a chord keyboard, each letter has a specific key combination (single key or multiple keys) that is simultaneously pressed, similar to chords in music, to produce a given character (or word). Productivity of the chord keyboard approach has been explored in several studies (e.g. Gopher & Raij, 1988; Kroemer, 1992; McMulkin & Kroemer, 1994). In a study of 10 participants from a university, population Kroemer (1992) was able to show that participants were able to memorize 59 chords for a two-handed ternary chord keyboard (eight keys, one for each finger) within about 3 hours. In a subsequent performance analysis, participants showed a significant increase in typing speed (from 36 to 70 characters per minute (cpm)) after about 7 hours of typing with the chord system. Gopher and Raij (1988) tested 15 participants with no typing experience and placed them into one of three groups: QWERTY, one-handed chord keyboard, or two-handed chord keyboard (the two-handed chord was two separate one-hand chord keyboards, each could stand on their own). After 35 one-hour training sessions, chord users could type 160 cpm while QWERTY users could only type about 105 cpm. With 2 significant training, the chord keyboard appeared to yield higher productivity than the QWERTY keyboard for novices. While the above studies provide some data on change in productivity as a function of training time, none have used learning curve analysis to provide standardized measures for comparison across studies. Learning curve theory provides a structured, mathematical approach to predicting how task time decreases with repetition. It can be used to predict how long it will take for a person to reach a given level of performance on a task. As tasks become more complex, learning time increases. The standard equations used to find the learning percentage for a task is (Wright, 1936): N Yx=KX or (1) N = log (Yx/K) / log (X) (2) and Learning Percentage = 100*2N (3) Where: Yx: production time for Xth unit in sequence K: time required for first unit X: number of production units N: the slope of the line describing change in completion time (y) as a function of repetition number (x) in log-log space Accurate predictions of future productivity are important in industry, and therefore learning percentage values for many industrial tasks have been quantified in the literature (Konz and Johnson, 2000). Researchers have further explored these relationships and provided estimates for learning percentages based on the nature of the task being learned. Cognitive learning percentages have been shown to be around 70%, while more physical, motor learning percentages are about 90%, and tasks involving both physical and cognitive learning are somewhere in between (Dar-El, Ayas, & Gilad, 1995). More dramatic changes in time to complete a task in the early trials of the task are represented by a lower learning percentage (steep descent in the learning curve), while more moderate changes in these times are represented by a higher learning percentage (shallow descent in the learning curve.) Interestingly, while the research noted above has quantified changes in productivity
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