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Considering People with Disabilities
as Überusers
for Eliciting Generalisable Coping Strategies
on the Web
Markel Vigo1 & Simon Harper2 University of Manchester (UK)
1: @markelvigo
2: @sharpic
ACM Web Science 2013
markel.vigo@manchester.ac.uk
simon.harper@manchester.ac.uk
http://dx.doi.org/10.6084/m9.figshare.695072
Coping
The cognitive and behavioral efforts to manage
demands that exceed the resources of a person.
Lazarus & Folkman, 1984
ACM Web Science 20132 May 2013 2
Problem
We do not know the coping strategies
employed on the Web
ACM Web Science 20132 May 2013 3
Why is this important
If we are able to automatically detect coping
we can provide the means to overcome the
situation
ACM Web Science 20132 May 2013 4
What do we propose
ACM Web Science 20132 May 2013
Transferring the identified strategies from
populations who cope more frequent and
overtly to general audiences
5
Why is it challenging
Coping occurs seldom
Once every 75 minutes.
Novick et al., 2007
112 minutes for sighted users
95 for visually impaired
Vigo and Harper, 2013
ACM Web Science 20132 May 2013 6
Why is it costly
Significant amount of observations
in the wild are required
ACM Web Science 20132 May 2013 7
What do we propose:
Step 1. Observation &
Identification of Strategies
ACM Web Science 20132 May 2013 8
1. Observation
What do we propose:
Step 2. Implementation of
algorithms to detect
strategies
ACM Web Science 20132 May 2013 9
1. Observation 2. Algorithms
ACM Web Science 20132 May 2013
What do we propose:
Step 3. Deployment in the wild
10
1. Observation 2. Algorithms 3. Deployment
What do we propose:
Step 4. Run user studies
ACM Web Science 20132 May 2013 11
1. Observation 2. Algorithms 3. Deployment 4. User studies
What do we propose:
Refine algorithms
go to step 2
ACM Web Science 20132 May 2013 12
1. Observation 2. Algorithms 3. Deployment 4. User studies
Case study
Step 1. Observation and analysis
• 2 independent studies/datasets generated
from ethnographic studies and user tests
• 24 screen reader and screen magnifier
users
• 17 coping strategies were identified
ACM Web Science 20132 May 2013 13
Case study
Step 2. Implementation
- Retracing: users retrace the steps in a
sequence of pages.
- Re-checking: fast revisitations.
ACM Web Science 20132 May 2013 14
Case study
Step 3. Deployment
• Algorithms deployed in a Firefox add-on
ACM Web Science 20132 May 2013 15
Case study
Step 4. User study
• 18 sighted participants, 10 days
• 126 retraces and 67 rechecks
• Tabbed browsing was interfering
• Feedback on false positives:
– “I’m browsing across tabs”
– “I’m comparing different web pages”
– “I’m navigating through different tabs”
ACM Web Science 20132 May 2013 16
Case study
Refinement; then iterate.
Tab browsing breaks the interaction context
re-checking:
webpagei  wpj  wpi  wpj
ACM Web Science 20132 May 2013 17
NON-TABBED NON-TABBED NON-TABBED
False positive rate (less is better)
ACM Web Science 20132 May 2013
study 1 study 20.0
0.2
0.4
0.6
0.8
1.0
study 1 study 20.0
0.2
0.4
0.6
0.8
1.0
Retracing Re-checking
18
• 2nd study: 20 sighted participants, 10 days
• 24 retraces, 16 rechecks
Conclusion
There is an overlap between the coping
strategies of different populations
ACM Web Science 20132 May 2013 19
Follow up
ACM Web Science 20132 May 2013 20
Contact
@markelvigo | markel.vigo@manchester.ac.uk
Presentation DOI
http://dx.doi.org/10.6084/m9.figshare.695072
Source code
https://bitbucket.org/mvigo/cope
Datasets
http://wel-data.cs.manchester.ac.uk/studies/3

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Considering People with Disabilities as Überusers for Eliciting Generalisable Coping Strategies on the Web

  • 1. Considering People with Disabilities as Überusers for Eliciting Generalisable Coping Strategies on the Web Markel Vigo1 & Simon Harper2 University of Manchester (UK) 1: @markelvigo 2: @sharpic ACM Web Science 2013 markel.vigo@manchester.ac.uk simon.harper@manchester.ac.uk http://dx.doi.org/10.6084/m9.figshare.695072
  • 2. Coping The cognitive and behavioral efforts to manage demands that exceed the resources of a person. Lazarus & Folkman, 1984 ACM Web Science 20132 May 2013 2
  • 3. Problem We do not know the coping strategies employed on the Web ACM Web Science 20132 May 2013 3
  • 4. Why is this important If we are able to automatically detect coping we can provide the means to overcome the situation ACM Web Science 20132 May 2013 4
  • 5. What do we propose ACM Web Science 20132 May 2013 Transferring the identified strategies from populations who cope more frequent and overtly to general audiences 5
  • 6. Why is it challenging Coping occurs seldom Once every 75 minutes. Novick et al., 2007 112 minutes for sighted users 95 for visually impaired Vigo and Harper, 2013 ACM Web Science 20132 May 2013 6
  • 7. Why is it costly Significant amount of observations in the wild are required ACM Web Science 20132 May 2013 7
  • 8. What do we propose: Step 1. Observation & Identification of Strategies ACM Web Science 20132 May 2013 8 1. Observation
  • 9. What do we propose: Step 2. Implementation of algorithms to detect strategies ACM Web Science 20132 May 2013 9 1. Observation 2. Algorithms
  • 10. ACM Web Science 20132 May 2013 What do we propose: Step 3. Deployment in the wild 10 1. Observation 2. Algorithms 3. Deployment
  • 11. What do we propose: Step 4. Run user studies ACM Web Science 20132 May 2013 11 1. Observation 2. Algorithms 3. Deployment 4. User studies
  • 12. What do we propose: Refine algorithms go to step 2 ACM Web Science 20132 May 2013 12 1. Observation 2. Algorithms 3. Deployment 4. User studies
  • 13. Case study Step 1. Observation and analysis • 2 independent studies/datasets generated from ethnographic studies and user tests • 24 screen reader and screen magnifier users • 17 coping strategies were identified ACM Web Science 20132 May 2013 13
  • 14. Case study Step 2. Implementation - Retracing: users retrace the steps in a sequence of pages. - Re-checking: fast revisitations. ACM Web Science 20132 May 2013 14
  • 15. Case study Step 3. Deployment • Algorithms deployed in a Firefox add-on ACM Web Science 20132 May 2013 15
  • 16. Case study Step 4. User study • 18 sighted participants, 10 days • 126 retraces and 67 rechecks • Tabbed browsing was interfering • Feedback on false positives: – “I’m browsing across tabs” – “I’m comparing different web pages” – “I’m navigating through different tabs” ACM Web Science 20132 May 2013 16
  • 17. Case study Refinement; then iterate. Tab browsing breaks the interaction context re-checking: webpagei  wpj  wpi  wpj ACM Web Science 20132 May 2013 17 NON-TABBED NON-TABBED NON-TABBED
  • 18. False positive rate (less is better) ACM Web Science 20132 May 2013 study 1 study 20.0 0.2 0.4 0.6 0.8 1.0 study 1 study 20.0 0.2 0.4 0.6 0.8 1.0 Retracing Re-checking 18 • 2nd study: 20 sighted participants, 10 days • 24 retraces, 16 rechecks
  • 19. Conclusion There is an overlap between the coping strategies of different populations ACM Web Science 20132 May 2013 19
  • 20. Follow up ACM Web Science 20132 May 2013 20 Contact @markelvigo | markel.vigo@manchester.ac.uk Presentation DOI http://dx.doi.org/10.6084/m9.figshare.695072 Source code https://bitbucket.org/mvigo/cope Datasets http://wel-data.cs.manchester.ac.uk/studies/3

Editor's Notes

  1. ethno
  2. caption, lower better
  3. TransferabilityExtent and magnitude