This document discusses factors that influence the successful use of technology to aid reading. It presents research showing that while technology to support reading is available, only 20% of those eligible use text-to-speech for exams. The technology acceptance model and social factors influence adoption of assistive technologies. Barriers like stigma, competency, involvement in development, functionality, access, support and awareness must be addressed. Case studies examine providing text-to-speech for exams and designing speech for math, highlighting the need to consider an individual's strengths, tasks, resources, expertise, environment and tools in technology selection.

1. Improving the chances of
reading with technology:
factors influencing
success
Abi James
University of Southampton & BDA NTC
With contributions from:
EA Draffan, University of Southampton
Peter Cudd, University of Sheffield
Clare Bentley, University of Sheffield

2. Technology to aid reading
Readability is the ease with which
a written text can be understood by
a reader.
The readability of a particular text
depends both on its content (for
example, the complexity of its
vocabulary and syntax) and on its
typography.

3. Is the technology available? Yes
• Every major operating system has:
– Text to speech built in
– free or low cost speech apps available
– Ability to alter fonts and colours
• See…
Microsoft Office speech button
Read & Write plug-in for Chrome
ATbar for web browser and Windows
Select to speak in iPad/iPod/Mac

4. Is it being used?
20% of candidates at GCSE/A-level qualifying for a
reader are using text to speech
83% of students receiving text to speech through DSA
found it useful (Draffan et al, 2013).
Is this a reading/dyslexia problem?
Studies have shown between 30% and 70% of users
fail to make use or “adopt” their assistive technology

5. First impressions count – the voice!

6. Many factors influence technology use
• Technology Acceptance Model (Davies,1989)
• Recent study on adoption of reading technology highlighted
social factors (Deibel, 2011)

7. Barriers and Facilitators to Uptake of
Assistive Technologies
REDUCE STIGMA
IMPROVE COMPETENCY &
CONFIDENCE
INVOLVE USERS IN
DEVELEOPMENT & USE
IMPROVE FUNCTIONALITY
& DESIGN
PROVIDE TIMELY ACCESS
PROVIDE TECHNICAL
SUPPORT
IMPROVE AWARENESS
ENHANCED OUTCOMES?
BETTER EXPERIENCE FOR
USERS & PROVIDERS?
SMOOTHER TRANSITION
BETWEEN PHASES OF LIFE?
BETTER USE OF
RESOURCES?
Draffan et al (2015) Barriers and
Facilitators to Uptake of Assistive
Technologies: Summary of a
Literature Exploration.

8. Can we influence technology adoption?
Increase ease of use
Organisation
– Can I get the software
installed?
Technical
– Is the interface easy to
use?
– Will I need technical
support all the time?
Personal
– Can I learn to use this
tool?
Perceived benefits
Organisation
– Is this going to be worth
the money I spend on it?
Technical
– Is this going to bring the
benefits I need?
– Will it support the tasks I
need to undertake?
Personal
– Will I feel comfortable
using this?

9. STREET Model for selecting &
evaluating assistive technologies
Tools
Environment
Expertise
Resources
Tasks
Strengths

10. STREET Model for selecting & evaluating
assistive technologies (Draffan et al, 2016)
• Visual
• Auditory
• Kinaesthetic
Strengths
• Reading
• Writing
• Communication
Tasks
• Financial
• Procurement
• Time-frame
Resources
• Prior knowledge &
experience
• Metacognition
Expertise
• Workplace / study /
living environmentEnvironment
• Communication
• Text to speech /
e-Reading
• Speech recognition
Tools
• Dexterity & mobility
• Cognition & processing
• Receptive / expressive
language
• Health
• Organisation & Planning
• Understanding information
• Time, money & numeracy
• Daily living
• Memory & Recall
• Training
• Peer & family support
• Professional support
• Technical support
• Personal preferences /
strategies
• Confidence
• Technology skills
• Perceived benefit
• System / operating
environment
• Compatibility
• Accessibility constraints
• Security & IT Policies
• Word processing & proofing
tools
• Recording / capturing
• Graphical mapping / planning
• Reminders
• Calculators & maths
• Alarms & environmental
controls

11. Case study: provision of text to speech in
exams
School Student
Awarding
body Regulator

12. STREET: influence on using text to
speech in exams
Strengths
• Reading
• WritingTasks
• Financial
• Procurement
• Time-frame
Resources
• Prior knowledge &
experience
• Metacognition
Expertise
Environment
• Text to speech /
eReading
Tools
• Understanding information
• Training
• Peer & family support
• Professional support
• Technical support
• Personal preferences /
Strategies
• Confidence
• Technology skills
• Perceived benefit
• System /Operating
Environment
• Compatibility
• Accessibility constraints
• Security & IT Policies
• Word processing & proofing
tools

13. STREET: influencing factors on using
text to speech in exams
• Professional support
• Technical supportResources
Expertise
Environment
• Training
• Peer & family support
• Financial
• Procurement
• Time-frame
• Confidence
• Perceived benefit
• Metacognition
• Prior knowledge &
experience
• Personal preferences
• Technology skills
• System /Operating
Environment
• Security & IT Policies
• Accessibility constraints
• Compatibility
The student

14. How do we increase use of text to
speech in exams?
• Supportive policy environment
• Engage senior managers to secure the necessary
resource, particularly
– Staff time from IT
– Time for students to learn and use the technology
• Capture the benefits for the organisation as a whole
– Cost benefits
– Student outcomes
• Engage with the student to improve skills and confidence

15. Case study: designing text to speech
for maths - STEMReader project
• AIM: to develop a usable, sustainable tool for reading aloud
maths notation
• Incorporate user feedback within the software design
open bracket, 4 x
minus 3 y, close
bracket, plus 7 open
bracket, x plus y, close
bracket.

16. STEMReader: Making Maths Readable
• Assist with accessing,
creating and
understanding STEM
learning materials
• Read aloud maths
accurately personalised
for the learner
• Increase confidence
with maths notation
• www.stemreader.org.uk
• info@stemreader.org.uk

17. Case study: designing text to speech for
maths
• Visual
• Auditory
• Kinaesthetic
Strengths
• Reading
• WritingTasks
• Financial
• Procurement
• Time-frame
Resources
• Prior knowledge &
experience
• Metacognition
Expertise
• Workplace / study /
living environmentEnvironment
• Text to speech /
e-ReadingTools
• Dexterity & mobility
• Cognition & processing
• Receptive / expressive
language
• Health
• Time, money & numeracy
• Training
• Peer & family support
• Professional support
• Technical support
• Personal preferences /
strategies
• Confidence
• Technology skills
• Perceived benefit
• System / operating
environment
• Compatibility
• Accessibility constraints
• Security & IT Policies

18. Conclusion
• Successful use of assistive and supportive
technology is impacted by many factors
• Not all of them are technology related!
• Think about what factors you can influence when
selecting and introducing technology
‘a mismatch [of technology] can
hamper the students ability to
use coping strategies to manage
their dyslexia’ Stacy (1998)

19. Contact details
Abi James
a.james@soton.ac.uk
https://access.ecs.soton.ac.uk/
ATbar: www.atbar.org
STEMReader: www.stemreader.org.uk

20. References
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user
information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of
Technology).
Deibel, K. (2011). Understanding and Supporting the Adoption of Assistive Technologies by
Adults with Reading Disabilities (Doctoral dissertation).
Draffan, E. A., James, A., Wald, M., & Idris, A. (2016). Framework for selecting assistive
technology user-participation methods. Journal of Assistive Technologies, 10(2).
Draffan EA, James A, Cudd P, Bentley C (2015) Barriers and Facilitators to Uptake of
Assistive Technologies: Summary of a Literature Exploration. Pp 350 – 356. Studies in Health
Technology and Informatics. Volume 217: Assistive Technology
Draffan, E. A., James, A., Wilkinson, S., & Viney, D. (2013) Assistive technology and
associated training: a survey of students who have received the Disabled Students’
Allowances. Journal od Inclusive Practice in Further and Higher education. Issue 5.1 Special
Edition, 5-10.
Stacey, G. (1998) Equal opportunities and staff concerns, paper presented at the 2nd
International Conference on Dyslexia in Higher Education, University of Plymouth.