1. KateLyn White, MSOT & Roger O. Smith, Ph.D., OT DETERMINING ACCESSIBILITY FOR iOS APPLICATIONS: A CHECKLIST FOR PRACTIONERS

2. 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)

3. 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

4. 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)

5. 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

6. 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

7. METHODS Applications 1a. TED 2a. Inventions 2 3a. Sand Timer Session 1: Without iA2CSession 2: With iA2C Applications 1. BrainPOP 2. Inventioneers 3. Realtimer Novices Applications 1. 1. BrainPOP 2. Inventioneers 3. Realtimer Experts Applications 1a. TED 2a. Inventions 2 3a. Sand Timer Experts

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

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

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

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

12. 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 tdf Sig. (2- tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference LowerUpper Novice: Expert (without) -3.633332.25215.91944-5.99682-1.26985-3.9525.011 Novice: Expert (iA2C).440002.17928.88969-1.847012.72701.4955.642

13. 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 NCorrelationSig. Novice: Expert (without) 6.370.470 Novice: Expert (iA2C) 6.924.008

14. 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

15. 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

16. 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