Using an Error Detection Strategy for Improving Web Accessibility for Older Adults

Alfred Taylor, Sr. α, Les Millerα, Sree Nilakantaβ, Jeffry Sanderα, Saayan Mitraγ, Anurag Shardaα

and Bachar Chamaα

α-Department of Computer Science β-Department of LOMIS γ-Department of EECS Iowa State University College of Business Iowa State University Ames, Iowa 50011 Iowa State University Ames, Iowa 50011 Ames, Iowa 50011

Abstract

The ability to use the Internet can provide an important contribution to an older adult’s quality of life. Communication via email with family, friends and service providers has become a critical factor for improving ones ability to cope with modern society as individual’s age. The problem is that as users age, natural physical and cognitive impairments make it more difficult for them to use the required technology. The present study investigates the use of error detection as a means of improving web access amongst older adults. Specifically, error detection strategies are compared to observation as a means of identifying the impairments of Internet users.

1. Introduction The normal aging process can trigger decreases in acuity of vision and cognition as well as physical impairments, which impact Web usability, particularly if Web designs are not user-friendly [7,9]. Web design issues related to fonts, colors, graphics, background images, navigation, and search mechanisms might prevent older adult users from taking full advantage of online health resources. Web designs may also present reading comprehension barriers for the older adult, due to limitations in visual acuity, cognitive abilities, and education levels, all of which may have a consequence on Web usage [5]. The implication of better health care for older adults is a longer life [12]. It is crucial for them to be able to keep abreast of new developments in health care that can enhance their life [1]. Older adults who have access to the Internet have access to a large number of ways to find information to help them achieve this goal. It also provides an excellent means of interacting with family members which also has implications for positive health outcomes. Many older adults have problems performing daily tasks because of restricted mobility, lack of transportation, inconvenience, and fear

of crime [3]. Home computers with an Internet connection can provide access to information and services, and can also be used to manage banking and Internet shopping tasks. This can be of critical importance. Salces et al. [15] provide a detailed discussion of the effect of aging.

One means of dealing with these issues is for Website designers and Human Computer Interaction (HCI) professionals to provide services for better interfaces and Websites in order for older adults to effectively use computers and obtain information resources on-line [12]. While such an approach is viable, it restricts use of the Internet to sites that have been designed with such limitations in mind. Moreover, such an approach does not provide support for the older adults with the most serious impairments.

To provide a more general solution to the problem, it requires taking the limitations of the users into consideration. Hanson and Crayne [7] make use of user preferences. However, older adults are not as successful as younger users in making use of the preference options provided by the browser [7]. To bridge this gap, we propose the use of an error detection strategy to determine the level of impairment of user. The information on the user’s level of expected performance is stored in a user profile and then is used by the system to modify the Web page the user is working with. The information stored in the user profile can be used either at the server level [9] or downloaded to the browser [7]. The use of user profiles is not new, but it has proved to be a useful construct in our tests. We ultimately see a user profile as containing information such as font size, cognitive level (reasoning, speed of processing and locus of control), and mobility/motor measures. The present work looks the development of two profile types based on observation and our error detection strategy and focuses on vision and motor skill issues.

The key question addressed in this research is “Does error detection produce a profile of the older adults’ accessibility performance that is comparable to

2009 Second International Conferences on Advances in Computer-Human Interactions

978-0-7695-3529-6/09 $25.00 © 2009 IEEE

DOI 10.1109/ACHI.2009.34

375

a profile based on observation?” To test this question, we constructed a server based software platform that makes use of a user profile to modify Web pages. The platform was used in a user study of 25 older adults to examine our research question.

The next section briefly looks at some related work. Section 3 looks at the design of the experiment. Section 4 presents the results of our study and Section 5 provides a discussion of the results. Section 6 provides concluding remarks and thoughts about future work.

2. Related Work

Several approaches have been proposed to assist older adults. A number of special purpose devices have been developed to aid users with motor and vision issues. Mice and specialized keyboards are available to

aid older adults [3,4] with declining motor skills. Special viewers to magnify the symbols on the screen are available as well. While such devices are very useful, they tend to increase the cost of computer systems and restrict where older adults can access the Internet. Moreover, older adults are less likely to be aware of special hardware [7].

Hanson et al. [6] have looked at voice browsing as a compromise. It only requires extra hardware that is easy to find. The main issues come in the form of the software’s ability to recognize commands and the confusion and frustration that results when a user’s commands are not recognized. Jonsson [11] looks at the issue of speech-based in car information systems can influence drivers. Bickmore et al. [2] are concerned with usability of conversational agents.

Figure 1. Block diagram of the software platform used by participants.

Converted Web Page

Web Page Convertor

Internet

Profile

Error Capture

Errors

Figure 2. Block diagram of the system platform for supporting development and use of a error based user profile.

Converted Web Page

Web Page Convertor

Internet

Error Capture

and Analysis

Errors

Profiles

376

The IBM research group at Watson proposed a Web solution approach to Web accessibility for older adults [9]. They employed a server to reformat Web pages based upon user preference and capability. This Gateway software was built on WebSphere Transcoding Publisher. IBM WebSphere® Transcoding Publisher Version 4.0 for Multi-platforms is server-based software that dynamically translates Web content and applications into multiple markup languages and optimizes it for delivery to mobile devices, such as mobile phones and handheld computers. The software adapts, reformats, and filters content, tailoring it for display on pervasive devices, giving companies better access to mobile employees, business partners and customers.

Another group of IBM’s researchers, Nagao et al. [14] continued research in the area of content adaptation through transcoding for accessibility for users with specific needs. Content adaptation is a type of transcoding that considers a user’s environment devices, network bandwidth, profiles, and so on [14]. In their implementation, of an annotation server annotated and changed the document contents in accordance with profiles. More recently, Hanson and Crayne [7] have started to stress the use of user defined preferences at the browser level. However, older adults are have not as successful as younger users in making use of the preference options provided by the browser [7]. Mobasher et al. [13] explored mining usage data for Web personalization. The rules are used to adapt the content served to a particular user. Collaborative filtering systems, such as Firefly [16], typically take explicit information in the form of user ratings or preferences, and through a correlation engine, return information that is predicted to closely match the users’ preferences. The next section looks at design of the experiment that serves as the basis of the this presentation

3. Experiment

3.1 Design

Twenty-five volunteers were recruited for the

study. The background and characteristics of the participants who completed the study were similar to those reported in other studies of usability for older adults. No significant differences in demographic characteristics or baseline performance were observed between participants who completed the study. Fourteen females and 11 males took part in the study. The age of the participants ranged from 62 to 97 years old with a mean age of 77. The study was conducted at a retirement community which provides services for

independent living and assisted-living. There were 24 independent living and one assisted-living participant. The sample of 25 participants was randomly selected from a pool of volunteers. They had to be willing to learn and have the ability to sit at a computer for a 30 to 60 minute session.

A block diagram of the software platform used in the experiment is shown in Figure 1. Web pages requested by the user are sent to the Web Page Convertor module. The module downloads the requested Web page and modifies it based on the contents of the user’s profile (Table 1). Each converted Web page is supplemented with JavaScript code to support error detection. The errors made by the user are captured at the server level and stored to support analysis. The study focused on two profile entry types: motor skill and vision (font size and color).

There were several types of motor and mobility errors recorded: mouse errors, scrolling, access and clicking errors. Mouse errors suggest that there were errors made as a direct consequence of problems related to using the mouse (e.g., failure to double-click immediately to complete an action). Scrolling and access fault propose an informational architecture problem that would affect the participants’ cognitive productivity. Committing a clicking error involved making a random or unnecessary mouse click unrelated to the process. The session time was also recorded in addition to specific error types.

Two approaches to constructing the user profile have been used in the reported work. In the first approach participants were observed while they were completing the task set. To ensure consistency, an observation evaluation form was developed with the help of a psychologist. Moreover, all observations were conducted by the same reviewer to reduce any observer biases. The motor skill score was assigned a value in the range 1-5 after the observation evaluation form was completed and evaluated by the observer. The font size and color values were set by observing the difficulties that the participants had with different font sizes and colors used during the tasks.

The second approach is based on the use of the errors generated by users as they worked their way through a set of training tasks used by the system to determine the skill set of the user. Figure 2 shows the block diagram of the modified platform. During the training tasks, the errors that are captured are analyzed and used to modify the current profile when the number of errors is above a preset threshold. The process continues until the system sees no additional change in the performance of the user. As in the observation approach, the number of clicks around a button or link are counted to determine the motor skill score. The font size and color are set based on the user’s

377

performance (correct step in the task set) based on giving the users different font sizes and colors to work with.

3.2 Procedure

Participants were placed approximately 25 inches from a 20-inch viewable Dell monitor display screen. Screen resolution was set at 1024 × 768 pixels, with a 32 bit-color setting. The icon and the target folders sizes were 36.8 mm (diagonal distance) based on the

Table 1. The portion of the user profile used in the current study.

User name Date changed Font size Font color Motor skill score 1..5 (poor to high)

Table 2. Error means and standard deviations for the 3 approaches tested in the study.

findings from Jacko et al., (2001). To perform the task, participant used an IBM Pentium computer. The operating system was Microsoft Windows XP Professional. The computer used a Digital Subscriber Line (DSL) for Internet access.

4. Results Performance in the experiment was measured based on the number of errors that participants made while completing the task set. Errors were chosen over time due to our belief that the critical issue for the older

adults was successful navigation rather than speed of performance. Table 2 shows the mean and standard deviation of the errors made for the cases where the Web pages were converted using a profile based on observation, conversion using a profile based on error detection, and no conversion of the Web pages.

Table 3. Paired Samples Correlation.

Figures 3 and 4 show the distributions of the errors made using the observation and error detection profiles, respectively.

To consider the question, “Does error detection produce a profile for older adults’ accessibility that is comparable to a profile based on observation?”, we used the paired samples t-test.

Table 3 shows the paired samples correlation. The value of .963 shows that the results from the two profile types are highly correlated. The paired sample t-test result is not significant at .161 (Table 4). The result indicates that there is not a significant difference in our study between the two methods of developing user profiles.

5. Discussion The result shown in Table 4 indicates that there wasn’t a significant difference in our study between creating the user profile based on observation or on error detection. The importance of this result comes from the work required to create the profiles. Observation is very labor intensive and is difficult to

Observation Error Detection None Mean 7.12 6.80 57.8 S.D. 3.621 4.010 13.952

N Correlation Sig.Errors made using Observation profile & errors using Error detection

25 .963 .000

Errors made using Observation profile – errors using Error detection

Mean

0.320

S. D.

1.108

Std. Error Mean

0.673

Paired Differences

Table 4. Paired Samples t-test: observation and error detection.

1.445

t

df

24

Sig. (2-tailed)

0.161

378

Figure 4: Errors Made in Profile Steady State

use with very many users. The use of error detection, on the other hand, places the burden on the computer system. It can be applied to any number of users and is not site specific. Moreover, targeting the accessibility skills of an older adult is not a static target. The physical and cognitive limitations of older adults tend to increase as they age. The dynamic nature of using an error detection strategy allows the profile contents to dynamically change as the user changes.

6. Conclusion A user study consisting of 25 older adults was developed and performed to test the hypothesis that error detection strategies could provide the same level of performance (measured by the number of errors that a user made on a task set) as with observation. A server based platform was developed for the user study. The platform used a user profile that contained a measurement of the user’s impairments for motor skills and vision. The server converted any Web page that

HOW MANY ERRORS WERE MADE USING OBSERVATION20151050

Frequency

8

6

4

2

0 Mean =7.12Std. Dev. =3.621

N =25

HOW MANY ERRORS WERE MADE USING ERROR DETECTION2015105 0

Frequency

10

8

6

4

2

0

HOW MANY ERRORS WERE MADE IN PROFILE 4 STEADY STATE

Mean =6.8Std. Dev. =4.01

N =25

Figure 3. Errors made using observation profile.

379

the user requested based on the contents of the user profile. The results of the study were promising. Using the paired Samples t-test, the study indicated that there was no statistical difference between the means of the results of the observation-based profiles and the results of the error detection-based profiles. This is an interesting result in that doing in depth observations of the potential users is very labor intensive and error detection places the burden on the computer system. Currently, we are looking at the cognitive phase of our project. 7. Acknowledgements: We would like to thank Jennifer Margrett for her help on this project. 8. References [1] Becker, S. (2004). A Study of Older adults Usability for Older Adults Seeking Online Health Resources. [2] Bickmore, T.W., L. Caruso, and K. Clough-Gorr. (2005). Acceptance and usability of a relational agent interface by urban older adults. CHI ’05 Extended Abstracts on Human Factors in Computing Systems. CHI ’05. Pages 1212-15. [3] Czaja, S., & Lee, C. (2003). Designing Computer Systems for Older Adults. The Human Computer Interaction Handbook 21, 413-427. [4] Czaja, S., & Lee, C. (2001). The Internet and older adults: design challenges and opportunities. Communication, Technology and Aging; Opportunities and Challenges for the Future. [5] Davis, T. C., Michielutte, R. M. , Askov, E. N., Williams, M.V., and Weiss, B. D. . (1998). Practical assessment of adult literacy in health care. Health Education. Behavior 25, 613-624. [6] Hanson, Vicki, John T. Richards, and Chin Chin Lee. (2007). Web Access for Older Adults: Voice Browsing? HCI (5). Pages 904-913. [7] Hanson, Vicki and Susan Crayne. (2005). Personalization of Web browsing: adaptations to meet the needs of older adults. Journal of Universal Access

in the Information Society. Volume 4, Number 1. Pages 46-58. [8] Hanson, Vicki. (2004). The user experience: designs and adaptations. Proceedings of the international cross-disciplinary workshop on Web accessibility (W4A). New York City, New York. Pages: 1 – 11. [9] Hanson, V. et al. (2001). Older adults Access for Older adults Citizens. Pub: WUAUC, ACM 1-58113-424-X, 14-18. [10] Jacko, J., Rosa, R., Scott, U., Pappas, C., & Dixon, M. (2001). Visual impairment: The use of visual profiles in evaluations of icon use in Computer-based tasks. International Journal of Human-Computer Interaction, 12 (1), 151-164. [11] Jonsson, I., M. Zajicek, H. Harris, and C. Nass. (2005). Thank you, I did not see that: in-car speech based information systems for older adults. CHI ’05 Extended Abstracts on Human Factors in Computing Systems. CHI ’05. Pages 1953-56. [12] Keegan, C., Gross, S., Fisher, L., & Remez, S. (2004). Boomers at Midlife 2004. AARP Life Stage Study Research Report, 100. [13] Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data. Proc. of the IJCAI 2001Workshop on Intelligent Techniques for Web Personalization (ITWP01). [14] Nagao, K., Shirai, Y., & Squire, K. (2001). Semantic Annotation and Transcoding: Making Older adults Content More Accessible. Older adults Engineering, 1070(986X), 69-81 [15] Salces, Fausto J. Sainz, Michael Baskett, David Llewellyn-Jones and David England. (2006). Ambient Interfaces for Elderly People at Home. Ambient Intelligence in Everyday Life. Springer. Berlin. Pages 256-284. [16] Shardanand, U., & Maes, P. (1995).Social information filtering: algorithms for automating “word of mouth.”In Proceedings of Workshop on Research Issues in Data Engineering.

380