KateLyn White, MSOT & Roger O. Smith, Ph.D., OT

DETERMINING ACCESSIBILITY FOR iOS APPLICATIONS:

A CHECKLIST FOR PRACTIONERS

ADOPTION OF MOBILE TECHNOLOGY• Mobile Technology

• Smart phones, tablets and PDA’s

• Uses: personal, professional, education, medical

• Applications used to customize

• Mobile Technology and Schools

• 4.5 million iPads used in classrooms across America (Apple, 2014)

• Promote education outside the classroom (Wu, 2013)

• Student centered and directed (Holzinger, 2005)

• Increased collaboration among peers and teachers (Cobcroft, 2006)

• Increased performance and satisfaction (Jung, 2002)

THE PROBLEM• Increased use of technology in schools not matched by an increase in accessibility

• 6.4 million children qualify for disability services (NCES, 2013)

• iOS devices have innate accessibility features

• VoiceOver, inverse colors, Mono Audio, guided access, etc.

• iOS application not required to be compatible with these features

• Decreased functionality for those with disabilities

• No good way to determine application accessibility for masses

• Trial and error

• Find published opinions

• Rely on app developer to publish accessibility information

THE SOLUTION• iOS Application Accessibility Checklist (iA2C)

• Tool to determine accessibility

• Tool to determine important accessibility features

• Target: educators, OT’s , AT specialists and app developers

• Organization: 4 disability categories

• Checklist

• Common cognitive tool (Winters, 2009)

• Helps with error reduction (Hales, 2006)

• Reduce variability due to background knowledge (Winters, 2009)

BASIS• W3C Web Accessibility Initiative Guidelines

• Many apps correspond to websites

• Content is perceivable, operable, understandable and can be reliably interpreted

• AUDITS

• Classroom, Syllabus, On-line course

• Checklist, layout and automatic scoring

• Apple Device Accessibility Features

• Codes to add features to apps is public domain

• Sample Questions

• Vision

• Does the app have audio output?

• Is foreground and background information distinguishable?

• Coes the app recognize invers colors?

• Hearing

• Are in app videos enabled with closed captioning?

• Do audio items allow for the modification of volume?

• Motor

• Is the app compatible with assistive touch?

• Is the app compatible with switch controls?

• Cognition

• Is information presented in a predictable pattern?

• Are headings used to assist navigation?

iA2C

METHODS

Applications

1a. TED

2a. Inventions 2

3a. Sand Timer

Session 1: Without iA2C Session 2: With iA2C

Applications

1. BrainPOP

2. Inventioneers

3. Realtimer

Novices Novices

Applications

1. 1. BrainPOP

2. Inventioneers

3. Realtimer

Experts

Applications

1a. TED

2a. Inventions 2

3a. Sand Timer

Experts

PRELIMINARY FINDINGS• Variations between experts and novices in positive features identified without the use of iA2C

PRELIMINARY FINDINGS• Variations between experts and novices in negative features identified without the use of iA2C

PRELIMINARY FINDINGS• Variations between novices and experts in positive features identified with the use of iA2C

PRELIMINARY FINDINGS• Variations between novices and experts in negative features identified with the use of iA2C

PRELIMINARY FINDINGS• There is a statistically significant difference in the mean number of positive and negative

features listed by experts and novices when neither group is using the iA2C to assess the app.

• There is not a statistically significant difference in the mean number of positive and negative features listed by experts and novices when both groups are using the iA2C to assess the app.

Paired Samples Test

Paired Differences

t dfSig. (2-tailed)Mean

Std. Deviation

Std. Error Mean

95% Confidence Interval of the

DifferenceLower Upper

Novice: Expert (without)

-3.63333 2.25215 .91944 -5.99682 -1.26985 -3.952 5 .011

Novice: Expert (iA2C) .44000 2.17928 .88969 -1.84701 2.72701 .495 5 .642

PRELIMINARY FINDINGS• There is no correlation between how experts and novices assess apps without the use of

the iA2C.

• There is a significant correlation between how experts and novices assess apps with the use of the iA2C.

Paired Samples Correlations

N Correlation Sig. Novice: Expert (without)

6 .370 .470

Novice: Expert (iA2C)6 .924 .008

CONCLUSIONS• iA2C beneficial tool

• Reduce variability between expert and novice ratings

• May reduce errors in assessment

• Could be beneficial for determining apps to use in a school setting

• Could be used to help guide app developers

LIMITATIONS• Time consuming

• Variability in interpretation

• May not need all parts of assessment

• Quantity of participants

• Further analysis to determine if app groups are matched

REFERENCES• Apple Inc, (2014). Accessibility: iOS. A wide range of features for a wide range of needs.

https://www.apple.com/accessibility/ios/

• Cobcroft RS, Towers S, Smith J & Bruns A 2006. Mobile learning in review: Opportunities

and challenges for learners, teachers, and institutions. In Proceedings, Online Learning and Teaching (OLT) Conference 2006. Brisbane: Queensland University of Technology. Available at http://eprints.qut.edu.au. Accessed 15 September 2011.

• Hales, B. & Pronovost, P. (2006). The checklist-a tool for error management and performance improvement. Journal of Critical Care. 21(3): 231-235

• Holzinger, A., Nischelwitzer, A., & Meisenberger, M., (2005) Mobile Phones as a challenge for

m-learning: examples for mobile interactive learning objects (MILOs). Proceedings ofthe 3rd Int’l Conf. on Pervasive Computing and Communications Workshops (PerCom 2005 Workshops)

• Jung, I., Choi, S., Lim, C. & Leem, J. (2002). Effects of different types of interaction on

learning achievement, satisfaction and participation in web-based instruction. Innovations

in Education and Teaching International. 39(2): 153-162.

• Winters, B., et al. (2009). Clinical review: Checklists-translating evidence into practice. Critical Care. 13(6): 210- 219. Doi: 10.1186/cc7792.

• Wu, W., Wu, Y., Chen, C., Kao, H., Lin, C., & Huang, S. (2012). Review of trends from mobile

learning studies: A meta-analysis. Computers and Education. 59: 817