Significant results were found for overall search efficiency or “lostness” on the seven tasks using a repeated measures one-way ANOVA, [adjusted F (1.347, 10.779) = 4.718, p < .05]. Paired samples t-tests indicated the Atkins website (M = 21) was significantly more efficient than both Jenny Craig (M = 30) and Weight Watchers (M = 31.44). Figure 4 illustrates the search efficiency for the three sites.

Figure 4. Mean search efficiency for all seven tasks

Success

Figure 5 shows participants’ rate of successful task completion across all three sites. Participants were most successful in completing the tasks on the Atkins site, and least successful on the Jenny Craig site. No significant differences were found using a repeated measures one-way ANOVA.

Figure 5. Successful task completion on diet sites

Satisfaction

After completing each task, participants indicated their subjective satisfaction with each website. Atkins received a mean score of 30 (maximum possible = 50) which was slightly higher than Jenny Craig (M = 29.22) and Weight Watchers (M = 28). Results of a repeated measures one-way ANOVA revealed no significant differences across the three sites for satisfaction.

Preference

After all testing was completed, participants were asked to indicate which site they preferred overall. Figure 6 shows more participants chose the Atkins website as their favorite site.

Figure 6. Participant preference for diet websites (# participants choosing site as first choice)

Eye-Tracking Data

“Hot spot” maps summarizing the gaze position for each website home page were generated across users (though only 7 of the 9 participants were included due to calibration difficulty). The maps are color-coded similar to what you might expect from thermal imaging, with red being the most viewed area, graduating down to light yellow to indicate less fixation time. These hot spot maps are useful for a quick assessment of the areas of the page most frequently fixated on by participants. We also studied the user visual fixation data for experimenter-defined areas of interest (AOI’s) on each image. This enabled the collection of multiple visual fixations in a large area to be combined, so that a more precise estimation of user attention could be obtained.

Participants were asked to view the image of each site and then verbally report “what information they could expect to find on this website.” According to this data, the hot spot map of the Atkins.com site (Figure 7) indicates which specific areas received the most visual attention by participants. The most frequently and longest fixated upon AOI (M=2748ms, SD=1366ms) on this page was the rectangular area in the center of the page with the title “4 steps to a healthy new lifestyle.” The attention afforded this AOI is reasonable as it contained a block of text that explained the philosophy of the Atkins diet plan (and was targeted information). Despite the presence of the color red on the hot spot map, the ‘My Atkins’ AOI was actually fixated upon for less time (M=1365ms, SD=1412) than the navigation bar (M=2474ms, SD=2129ms) and the Atkins logo (M=1717ms, SD=1162ms). This was most likely because the other AOI’s were larger and fixation times collected in those areas exceed that for the smaller areas.

Figure 7. ‘Hot Spot’ map of the Atkins homepage

The hot spot map and AOI dwell time data for the Jenny Craig home page (Figure 8) indicate the area most fixated on to be the ‘Internet-only’ advertisement (M=2346ms, SD=1235ms), followed by the ‘Joy lost 23 lbs’ text (M=1544ms, SD=1102ms). It is interesting to note that details about the Jenny Craig diet plan and clues as to what information is available on this website located in the lower left portion of the screen was fixated upon an average of 1,000ms less than the ‘Internet-only’ ad (M=1306, SD=1108). The results for the Jenny Craig page are very similar to those of the Atkins page; participants apparently focused primarily on very specific areas of the screen for the majority of the 20 seconds. However, in both cases, it does not appear as though the areas of the page that actually contained the most information about the website and/or diet plan received the most fixation time.

Figure 8. ‘Hot Spot’ map of Jenny Craig homepage

The hot spot map for the Weight Watchers home page (Figure 9) was somewhat different. The most popular areas were the ‘Flex Points’ caption (M=2459ms, SD=1424ms), the ‘Meetings’ text directly under it (M=1985ms, SD=1229ms), and the information for online meetings below that (M=1925ms, SD=874ms). It is interesting to note, however, that on this site participants apparently fixated on areas on the left side of the screen considerably more than the right, and their hot spot areas cover more area on the screen than the other two diet sites. This suggests that no specific areas of the screen (other than the entire left side of the page) attracted the users’ attention which may have resulted in a less efficient visual search.

Figure 9. ‘Hot Spot’ map of Weight Watchers homepage

It is difficult to draw any specific conclusions from the preliminary eye-tracking data. A general observation would be that participants focused more on very specific areas of the Atkins and Jenny Craig home pages. However, upon further inspection, these areas are not necessarily those that would assist the participant in objectively determining what kinds of information might be found on the sites. Data from the Weight Watchers home page was somewhat different, in that participants seemed to both favor the left side of the screen over the right, yet did not have a specific set of targets on that side that held their attention.

DISCUSSION

Results from this study showed that users were able to complete the tasks successfully and were equally satisfied by all three sites. However, participants searched for the information more efficiently on the Atkins site and preferred this site overall.

Participants found some tasks more challenging than others across the three sites. Five users failed to correctly explain the purpose of the Jenny Craig program. This finding is complimented by the eye-tracking data, which showed participants (at least initially) fixated on graphics or large text on the home page more than the main navigation area or information near the ‘fold’. The home page was not the only problem, however, as participants did not look deep enough into the site to locate the fact that you must buy your food from Jenny Craig centers.

Many participants commented that the Atkins site was easier to use. The sitemap was a benefit noted by one user. The site was labeled as “well planned out” and “more professional” than the other sites. The titles and links were viewed as more specific resulting in an easier navigation experience. Participants commented negatively about the many areas of the Weight Watchers site that require a paid membership. The participants did not feel they could get the full scope of the site from the limited areas made available to all users.

Areas noted by participants to impact overall preference included the following:

Navigation Location: Locating the navigation at the top of the screen was seen as beneficial by many participants. However, on the Weight Watchers site, clicking the top navigation resulted in a sublevel menu directly below the clicked tab. Many users commented on the small size of the font and most missed the sublevel navigation completely on the first few tasks. Jenny Craig uses a similar structure after the home page, but the sublevel navigation is further below the clicked top level navigation. On the Atkins site, the home page has the main navigation links located at the top of the screen as well. On subsequent pages, the sublevel navigation is located to the left of the main information area. Users repeatedly ignored the left navigation. Possible explanations for this include the fact that on the home page this space is occupied by a picture; this area is also a different color and may have appeared to be an advertisement; or the user may have expected the sublevel navigation to be closer to the top level navigation.

Navigation Wording: Users complained about the wording used for the links on all three sites. The task asking about exercise was particularly troublesome because there was no consistent location for it on the sites. Several participants were looking for the key word “Exercise” which was not on any of the sites. Weight Watchers used the term “Healthy and Fit” which was more intuitive to the users than the wording used on the other sites. Wording also affected results on the task about eating out; again users expected this to be worded as “eating out” or “dining out.”

Membership Requirements: The membership requirement was a major factor affecting the opinion of the Weight Watchers’ site. The site uses a different model than the other two and requires a paid membership to access many features. The users did not feel they could get adequate information about the weight loss program without paying for a membership. Some users commented they thought they would have to go to a meeting to get information.

Page Layout: Many users preferred the home page of the Atkins site over the other two sites. The initial page of the Jenny Craig site contained little information and had two large buttons reading “Client” and “Guest.” Almost all of the participants started their activities by clicking the guest button. For tasks such as locating a recipe, they completely ignored the link labeled “Recipes” and clicked the Guest button. The Flash features on the Jenny Craig home page were also described as distracting and meaningless. The favorite feature on the Jenny Craig site was the search functions within sections of the website. The search boxes were located in a highly visible area to the right of the main text and were easy to use when locating recipes and success stories. The Weight Watchers site was reported to be “cluttered and unorganized.” Most users liked the large blue buttons located at the top of the screen on the Atkins site. As mentioned, most users completely disregarded the sublevel navigation to the left of the main text area. Other complaints on the Atkins page layout regarded the amount of space used immediately below the navigation bar. Participants did not understand the purpose of this portion of the page and often found it led them to information they were not seeking.

REFERENCES

2004 New Year’s resolutions bode well for economy. (n.d.). Retrieved December 21, 2003, from MyGoals.com Web site: http://www.mygoals.com/about/HF

Digital Equipment Corporation Limited (1986). System Usability Scale. Reading, United Kingdom: Digital Equipment Co. Ltd. ©.

Burke, Susan L. (2003). Losing weight online: The new diet evolution. Retrieved December 21, 2003, from http://www.ediets.com/news/article.cfm?article_id=8675&pg=1

Tate D.F., Wing, R.R., Winett, R.A. (2001). Using Internet technology to deliver a behavioral weight loss program. Journal of the American Medical Association, 285(9), 1172-1177.

Influence of Training and Exposure on the Usage of Breadcrumb Navigation

By Spring S. Hull

Summary: Recent studies have shown that while the use of breadcrumb trails to navigate a website can be helpful, few users choose to utilize this method of navigation. This study investigates the effects of "mere exposure" and training on breadcrumb usage and satisfaction. Findings indicate that brief training on the benefits of breadcrumb usage resulted in more efficient search behavior. 

In this study we considered the theory of mere exposure as a means of introducing participants to breadcrumb trails. According to Zajonc (1968), if the function of orienting behavior is eventually to change a novel stimulus into a familiar one, it is also its consequence to render the stimulus object eventually more attractive. Zajonc (1968) states that the mere repeated exposure of the individual to a stimulus object enhances his attitude toward it. We questioned if merely exposing participants to usage of breadcrumb trails was enough to enhance participants’ frequency of breadcrumb usage.

METHOD

Participants

Thirty-nine college students (12 males and 27 females) with an average age of 23 (range 18 to 48) volunteered for this study. All participants reported being familiar with the Internet, with 59% of participants reporting they had used the Internet for at least the past five years and 82% estimating that they spend time on the Internet at least once a day.

Materials/Procedure

The Wal-Mart® website was chosen for examination as a breadcrumb trail is one method of navigation (Figure 1).

Figure 1. Breadcrumb trail used on Wal-Mart.com

Participants were randomly assigned to one of three conditions. Condition I participants were shown the use of the breadcrumb trail twice by the experimenter prior to testing. After this exposure, participants in this condition were then given an explanation of the breadcrumb trail by the experimenter and were requested to use this method of navigation “whenever it is possible and most efficient to do so.” Condition II participants served as the mere exposure group and were shown the same usage of the breadcrumb trail as participants in Condition I. However, this group was given no explanation or instruction to use the breadcrumb trail. Condition III participants received no exposure, explanation, or instruction on the breadcrumb trail.

Participants were provided with a scenario indicating that they would be going on a camping trip and needed to purchase some items from the Wal-Mart® website for the trip (e.g., tent, sleeping bag, fishing pole, camera).

RESULTS

Participants in Condition I (Exposure & Instruction) generally used the breadcrumb trail more, the Back button less, and traversed fewer pages to purchase the items than participants in the other two conditions. However, no significant difference was found between the Condition II (Mere Exposure) and III (No Exposure or Instruction) participants in terms of the number of breadcrumb clicks, Back clicks, or total pages traversed.

Figure 2. Total time for participants to complete all tasks for each condition.

In this study, participants were tested in one of three conditions to determine the impact minimal training and exposure had on influencing usage of breadcrumb navigation. It was hypothesized that merely exposing participants to usage of breadcrumb trails would prompt participants to use the shortcut tool for navigation. We found that mere exposure alone was not enough to significantly influence their usage more than the participants receiving no exposure to the breadcrumb trail.  However, we did find that minimal training did affect participants’ usage of the breadcrumb trails and resulted in quicker completion times, visited fewer pages, and minimal use of the Back button.

REFERENCES

Bowler, D., Ng, W., & Schwartz, P. (2001). Navigation bars for hierarchical websites. Retrieved 01/20/03 from University of Maryland, Student HCI Online Research http://www.otal.umd.edu/SHORE2001/navBar/index.html

Lazar, N., & Eisenbrey, M. (2000). Website structural navigation. Retrieved April 29, 2003, from University of Maryland, Student HCI Online Research http://www.otal.umd.edu/SHORE2000/webnav/index.html.

Lida, B., Hull, S., & Pilcher, K. (2003). Breadcrumb navigation: An exploratory study of usage. ../usabilitynews/51/breadcrumb.htm

Lida Rogers, B., & Chaparro, B. (2003). Breadcrumb navigation: Further investigation of usage.
../usabilitynews/52 /breadcrumb.htm

Maldonado, C. A., & Resnick, M.L. (2002). Do common user interface design patterns improve navigation? Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting, 1315-1319.

Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction. (3rd ed.). Reading, MA: Addison-Wesley.

Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology Monographs, 9(2, Pt. 2).

Does Background Music Impact Computer Task Performance?

by Christine Phillips

Summary: The effects of music on performance on a computer-mediated problem-solving task were examined. Participants completed the task in anonymous dyads as they were exposed to either Classical music, Punk music, or No Music. Results indicate that those in the Classical music condition performed better on the problem solving-task than those in the Punk music or No Music conditions. However, those listening to the Classical music offered more off-task comments during the task than those listening to No Music. Implications for website designers are discussed.  

The purpose of the current study was to determine how websites designed for older adults currently adhere to the senior-friendly guidelines. Using heuristic evaluation techniques, raters identified whether each website conformed to the basic principles of usability following the NIA guidelines.

Method

Participants

Four members of the Software Usability Research Lab (SURL) at Wichita State University volunteered to participate in the research project. The evaluators had practical and research knowledge with website design and usability.

Procedures

Each of the heuristic evaluators were given a packet of information containing the following: a consent form, instructions, a list of websites to visit, the “Senior-Friendly” guidelines, and a blank guideline rating checklist for each website.  The assessments were done over a two-week period to allow the evaluators ample time to familiarize themselves with each website. The websites chosen for guideline evaluation were obtained from common search engines (i.e., MSN, Google, etc.) using keywords such as “seniors,” “elderly” and “older adults.” The websites selected were commonly listed within the first twenty search results and were required to have a statement about the site “targeting” or “designed for” those 50 years or older. Websites were excluded from the sample if they appeared to be search engines or portal sites that directed users to other sites.

Results

A total of 36 websites were included in the analysis. A two-way mixed effect model for consistency was used to determine if there were differences between the four individual raters, which needed to be addressed before interpreting the other results, as suggested by Yaffee (1998).  The average measured intraclass correlation (ICC) for the four raters was 0.8117, F (881, 2643) = 5.31, p < .0001.

Each of the twenty-five checklist items were rated on a 4-point scale to determine if the “Senior-Friendly” guidelines were followed or used by the particular site; rated as 1= Never, 2= Sometimes, 3= Frequently, and 4= Always. The scores from all four raters were combined to form a “Total Score” comprised of the number of guidelines rated as frequently or always present on the senior websites. After the total points were calculated, the websites were classified into three groups; Most (greater than 70% guideline adherence), Medium (between 51-69% adherence), and Least compliant to the guidelines (less than 50% adherence). The 36 websites’ total scores ranged from 13 to 23 points (M= 18.7, SD= 2.68); 22% of the websites had 13 -16 points (Least), 47% had 17 to 20 points (Medium), and 30.5% of the senior websites scored 21 to 24 points (Most). The ratings for the evaluated websites are listed in Table 1.

Seven guidelines were scored as “frequently” or “always” present at 95% of the sites and another four guidelines were only followed by 25% or less of the selected senior websites. The other guidelines did not appear to be consistently present (or lacking) across the different website designs. The guidelines and percentage of adherence are listed in Table 2.

Table 2. Guideline Adherence (% of sites adhering to guideline)

Results from this analysis show a wide range in the level of compliance across sites. In general, the majority of the sites complied to guidelines related to basic navigation and content phrasing and style but not to guidelines specifying text size, text weight, line spacing, textual links with graphics, or site map availability.  The lack of consistency with regard to the formatting of the textual content is surprising, given the fact that one of the most fundamental guidelines for developing reading materials for older adults is to provide enlarged and highly contrasted text.   

The impact of compliance to these guidelines on actual performance and satisfaction with a site is not clear. One may hypothesize that the more compliant a site is, the more usable it is. However, preliminary results from our research suggest that the sites most compliant are not always the most efficient or preferred. Specific results of this research will be available in the next issue of Usability News.

REFERENCES

Chaparro, A., Bohan, M., Fernandez, J. E., Choi, S. D., & Kattel, B. (1999). The impact of age on computer input device use: psychophysical and physiological measures. International Journal of Industrial Ergonomics, 24, 503-513.

Cortese, A. (1997, May 5). A census in cyberspace. Business Week, 84-85.

Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers. Journal of Gerontology, 53B (5), 329-340.

Echt, K. V., Morrell, R. W., & Park, D. C. (1998). Effects of age and training formats on basic computer skill acquisition in older adults. Educational Gerontology, 24, 3-25.

Ellis, R. D., & Allaire, J. C. (1999). Modeling computer interest in older adults: The role of age, education, computer knowledge, and computer anxiety. Human Factors, 41(3), 345- 356.

Liao, C., Groff, L., Chaparro, A., Chaparro, B. S., & Stumpfhauser, L. (2000). A comparison of web site usage between young adults and the elderly. Proceedings of the IEA 2000/HGES 2000 Congress. San Diego, CA: Human Factors and Ergonomics Society.

Mediamark Research Inc. (September 2002 – October 2003). MRI Cyberstats, Fall 2003. Retrieved on November 18, 2003, from www.mediamark.com

Melenhorst, A. S., Rogers, W. A., & Caylor, E. C. (2001). The use of communication technologies by older adults: exploring the benefits from the user’s perspective. Proceedings of the Human Factors and Ergonomics Society 45th Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

National Institute on Aging (2002). Older adults and information technology: A compendium of scientific research and web site accessibility guidelines. Washington, DC: U.S. Government Printing Office.

Rogers, W. A., Meyer, B., Walker, N., & Fisk, A. D. (1998). Functional limitations to daily living tasks in the aged: A focus group analysis. Human Factors, 40(1), 111-125.

Selvidge, P. R. (2003). The effects of end-user attributes on tolerance for world wide web delays. Unpublished doctoral dissertation, Wichita State University, Wichita, Kansas.

The Effects of Perceptual Grouping on Text Entry Performance

Christopher J. Hamblin, Michael Bohan, & Alex Chaparro

Summary: One of the primary challenges confronting designers of mobile computing devices is the issue of efficient text entry. One potential solution is to group multiple letters onto single keys, similar to the T9 keyboard currently used on telephones. Two experiments examined the effects of perceptual grouping on soft keyboard transcription rates. Results from Experiment 1 showed significantly slower transcription rates for QWERTY keyboards with grouped keys. Results from Experiment 2 showed various levels of perceptual interference due to the different Gestalt grouping effects. These results indicate that perceptual grouping can negatively affect text entry performance, and placing multiple letters onto single keys reduces the speed at which users can transcribe words.

INTRODUCTION

Technological advances in the computing industry have lead to a dramatic increase in the accessibility of mobile computing devices such as pocket PCs, personal digital assistants, pen tablets, web-enabled cellular phones, and two-way pagers (Mobile.com, 2002; Legard, 2000). As these devices continue to provide increased functionality while at the same time becoming progressively smaller, data entry for these devices becomes a critical issue.

Previous research has shown that novice users are typically faster and more accurate using a QWERTY keyboard than with alternative keyboard design (e.g. Bohan, Phipps, Chaparro, & Halcomb, 1999; McQueen, MacKenzie, Nonnecke, & Riddersma 1994; MacKenzie, Zhang, & Souckoreff, 1999). Thus, shrinking the QWERTY keyboard to accommodate mobile devices provides familiarity at the expense of speed.

Altering keyboards to optimize data entry often results in a loss of immediate usability. Several “high-performance” keyboard designs such as the OPTI (MacKenzie & Zhang, 1999) and the Fitaly (Broida, 1998) have been proposed to minimize travel distance between keys. MacKenzie and Zhang demonstrated that users were able to achieve tapping rates upwards of 44 wpm on the OPTI keyboard after approximately 15 hours of use compared to 40 wpm for the standard QWERTY keyboard with the same amount of practice. However, initial performance of novice typers was considerably faster with the more familiar QWERTY (28 wpm) than the OPTI keyboard (17 wpm).

Additional keyboards have been proposed that attempt to achieve a balance between more efficient input and ease of use. Hashimoto and Togasi (1995) attempted to maximize the performance of a virtual QWERTY keyboard by adjusting its shape to better accommodate the biomechanical attributes of the user’s wrist and fingers. The authors demonstrated that participants could point to the designated keys significantly faster on the oval keyboard than on the standard rectangular keyboard layout.

The T9 keyboard (Tegic communication, 1998) incorporates the familiar alphanumeric layout of touchtone telephones where 3-4 letters are grouped onto a single button in alphabetical order. Applying Fitts’ Law (Fitts & Peterson 1964; Fitts, 1954), this keyboard is potentially better suited for touch tapping in small spaces because it reduces the number of keys in addition to reducing travel time between keys. In this case, tapping sequential letters that are grouped on the same key would minimize travel time. Bohan, et al. (1999) investigated the immediate usability of the T9 keyboard relative to the standard QWERTY keyboard of the same overall size. It was expected that participant’s performance would be faster and more accurate using the T9 keyboard due to the larger blocked keys and the ability to make multiple taps on a single key when selecting letters within the same block; however, transcription rates were actually higher for the QWERTY keyboard (23 wpm) than for the T9 keyboard (17 wpm).

There are at least two interpretations of Bohan, et al.(1999) results. One interpretation is that the slower performance of the T9 keyboard relative to the QWERTY keyboard reflects differences in the participant’s familiarity with the respective keyboard. Alternatively, performance may have been hindered due to perceptual interference in accordance with the Gestalt principle of common region (Palmer, 1992) which states that elements will be perceived as a group together if they are located within a common region of space or an enclosed boundary. This grouping effect may in turn make it more difficult to process the individual elements within a group. The letters on the telephone keypad are arranged on the buttons in groups of three or four, which will cause them to be perceptually grouped. The resulting group would then have to be decomposed into its requisite component letters before a target letter could be identified. If perceptual grouping does negatively impact transcription performance in this way, the effects may be strong enough to counteract the motor advantages supported by larger keys (Bohan, et al. 1999). The following experiments address both explanations.

EXPERIMENT 1

The first experiment compared the transcription rates of participants using a single key (Qs) keyboard and a modified keyboard (Qm) in which the arrangement of the letters were not altered; however, keys were merged so that the width of the keys were increased by approximately four fold with up to three letters on a single key (see Figure 1). Participants also transcribed words using single- and multiple-key versions of the ABC keyboard layout (As and Am, respectively). It was reasoned that perceptual grouping would have a greater impact on transcription performance when participants are less familiar with the spatial arrangement of a particular keyboard thus having to rely a greater extent on visual scanning to find the target letter. Whereas with experience the participants may rely more on remembered spatial locations and kinesthetic positional cues rather than visual scanning to find the target letter.

Participants transcribed words presented at systematically varying rates using soft keyboards graphically displayed on a touch-screen. The average maximum transcription rate for each keyboard was calculated and used to compare text entry performance. According to the perceptual grouping hypothesis, transcription rates for the Qs should be as high or higher than those for the Qm due to the additional visual processing. The alternative hypothesis is that grouping letters on a single key does not introduce additional visual processing resulting in significantly faster transcription rates for the Qm due to the key’s larger target size and the ability to make multiple taps on a single key. In addition, transcription rates for the QWERTY keyboards would be higher than those of the ABC keyboards due to difference in familiarity. In addition, transcription rates for the As keyboard should be higher than those for the Am keyboard.

Figure 1. Qm keyboard with multiple letters grouped onto single keys.

METHOD

Calculation of Transcription Rate

To measure participant performance, a mathematical formula originally developed to measure reading rates was used (Legge, Pelli, Rubin, & Schleske, 1985). Transcription rates were calculated by multiplying the percentage of correctly transcribed words by the text presentation rate (wpm). Maximum average transcription rates were estimated by fitting each participant’s performance curve with a second order polynomial function (i.e. parabola). The peak of the function was estimated by solving for the first-order derivative of the function. This method minimizes the potential impact of experimenter bias in estimating the peak when the function was relatively flat. Maximum average transcription rates were used to measure the mean differences between each of the keyboard formats.

Participants

Ten right-handed participants, ages 28-45 (M = 34) participated in the study. All participants reported having 20/20 corrected/uncorrected vision. All participants had extensive computer experience and four had used a PDA.

Apparatus

A custom computer program written in Microsoft Visual Basic 6.0 was created to display the various keyboards and collect the data. A land-on touch selection strategy was used to activate the “buttons” of the soft keyboard. For these experiments the actual size of the keys were not a concern as the purpose of the experiments was to determine if there was an effect for grouping; as such the size of the keys were four times larger than a traditional keyboard in an effort to minimize inadvertent errors. With participants seated approximately 24 cm from the screen single key targets subtended 2.5˚ visual angle with alphanumeric characters within the keys subtending 1.67˚ visual angle. Grouped keys employed the same size characters; however, the key target width subtended to 6.95˚ visual angle to include the additional letters. The program ran on a 233 MHz IBM-compatible computer and stimuli displayed on a 15-inch diagonal 3M Microtouch CRT Touch Monitor (1024 x 768 pixel resolution) with a resistive touch-sensitive screen.

Procedure

The height of the monitor was adjusted to center the screen approximately at eye level. Participants started each trial by tapping a “START” button displayed on the screen. Upon starting the trial ten four-letter words, randomly selected from a database, appeared individually at the top of the screen. Participants were required to transcribe the word using the soft keyboard displayed on the touch-sensitive screen. The participant’s transcriptions were displayed in a window directly below the target word. In trials involving grouped keys the software automatically determined the correct letter to display in the transcription window by comparing the sequencing of the tapped keys to the letters in each word. For example, if the word “BIRD” was displayed at the top of the screen, the participant would correctly tap the “VBN” key for the letter B, the “UIO” key for the letter "I" and so on. If the tapped key contained the required letter the program tallied a correct response and displayed the letter in the transcription window. If the tapped key did not contain the required letter the program tallied a miss and a hyphen was displayed in the transcription window. Each trial presented the words at different rates (10, 15, 20, 25, and 30 words per minute). Participants performed 5 trials at each presentation rate for each keyboard resulting in 25 trials per keyboard. The order of presentation rate and order of keyboard layout was randomized for each participant. Each experiment took approximately 25 minutes.

RESULTS AND DISCUSSION

Maximum average transcription rates were subjected to a 2 (grouping) X 2 (keyboard layout) within-subjects ANOVA. Analysis showed a significant main effect for keyboard layout, F (1, 9) = 437.81, p < .001, as transcription rates were significantly faster for the QWERTY keyboards than for the ABC keyboards. There was not a significant main effect of grouping, F (1, 9) = 1.23, p = .285; however, there was a significant interaction between keyboard layout and grouping F (1, 9) = 14.122, p = .004. Paired sample t-tests comparing transcription rates within keyboard layouts showed a significant difference between Qs and Qm keyboards, t (9) = 3.29, p = .009 (see Table 1).

Table1. p-values for pairwise comparisons of transcription rates in Experiment 1.

The results of this experiment showed significant differences in transcription rates. In the case of the ABC keyboard, the results were likely indicative of a floor effect. That is, the novelty of the ABC keyboard caused search times long enough that any additional searching induced by the grouping effect would not have a significant affect. The results support the hypothesis that perceptual grouping significantly affects transcription rates. The results also support Bohan, et al.’s (1999) previous findings that transcription rates for the QWERTY keyboard were faster than the T9 keyboard despite the larger blocked keys.

EXPERIMENT 2

Participants

Ten right-handed participants ages 29-49 (M = 36) participated in the study.  All participants reported having 20/20 corrected/uncorrected vision. All participants had extensive computer experience and three had used a PDA.

Apparatus

The task and apparatus were identical to those used in Experiment 1 with the exception of the keyboards used. Like the first experiment, the single-key and multiple-key QWERTY keyboards were used in addition to a keyboard grouped by proximity. The proximity keyboard (Qp) consisted of letters grouped together in groups of 1 to 3 by spatial distances between keys; that is, there were not box outlines to contain the letters (see Figure 2).

Figure 2. Screen shot of the Qp keyboard with multiple letters keys grouped by spatial proximity.

RESULTS

A significant main effect was found for grouping type, F (1, 9) = 5.87, p = .006. Pairwise comparison t-tests showed significant differences between the Qs keyboard and the grouped keyboards: Qm (p = .05), Qp (p = .043), Qc (p = .021). Significant differences between the grouped keyboards are shown in Table 2.  Interestingly, transcription rates for the Qc condition was significantly different from all the other conditions suggesting that the Qc condition has a much stronger perceptual grouping effect that grouping by common border or proximity. The reason for this effect is unknown but will be the focus of continuing research.

 DISCUSSION

Tapping on a miniaturized virtual QWERTY keyboard has been shown to be the optimal solution for data entry because of its widespread familiarity (MacKenzie, Zhang & Soukoreff, 1999). Thus, a logical approach to finding a balance between these tradeoffs is to maximize the performance of a familiar text entry method like the QWERTY keyboard (Hashimoto & Togasi, 1995).

Given what is known about speed/accuracy tradeoffs in human performance, one way to improve performance on soft keyboards is to increase the size of the keys. However, size constraints of mobile computing devices prevent this option. Alternatively, placing multiple letters on a single key reduces the number of keys required and minimizes the space required. However, this study suggests that grouping letters onto a single-key may decrease transcription performance due to additional perceptual processing steps despite a four-fold increase in target size.

The results of these experiments illustrate that the need to visually parse groups of letters into their constituents before a target letter can be identified will result in a significant decrease in text entry speed. In addition, comparison of mean transcription rates in the second experiment suggest that strength of the perceptual interference on text input is consistent between the types of grouping effects tested. Ongoing research is currently investigating the effects of other grouping effects. 

REFERENCES

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Evaluating the CombiMouse™: A New Input Device
for Personal Computers

Jeremy Slocum, Shelby Thompson, Barbara Chaparro, and Michael Bohan

INTRODUCTION

Intensive mouse and keyboarding use has been associated with increased risk of upper extremity musculoskeletal disorders (see Fagarasanu & Kumar, 2003 for a review). As a result, input devices (e.g. ergonomic keyboards, trackball or joystick) are often recommended as a therapeutic intervention. However, use of these alternative devices may result in a trade-off between comfort and performance.