Learning Analytics and Disabled
Students / Accessibility
Martyn Cooper (IET)
The OU Leading the Way!
• World leaders in applying learning analytics to benefit
disabled students in particular
– 19,000...
LA for
Accessibility
• Hypothesis – if there is a
module or learning activity
on which disabled students
consistently doin...
Shard Diagrams
Shard Diagrams
“Clickometrics”
• The VLE records every
“click” of every student
– So know:
• How long they have been
online
• How long en...
Data Issues
• Noise
–Disabled students subject to all the other factors that
might impact on completion or pass rates
• Di...
User Modelling
• Learning analytics for
disabled students is only
viable because the
university knows which
students decla...
<Disability type>
• Based on data collected
for HESA
• Does not map to
accessibility needs and
preferences
No. Descriptor
...
AccessForAll 3.0
Future e-learning system
Accessibility of Learning Analytics
• If learning analytics (in
general and special
support for disabled
students) is goin...
Institute of Educational Technology
The Open University
Walton Hall
Milton Keynes
MK7 6AA
www.open.ac.uk
Learning analytics and disabled students (IET-OU LA workshop May 15 2014)
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Learning analytics and disabled students (IET-OU LA workshop May 15 2014)

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Presentation on role of learning analytics in supporting disabled students and identifying accessibility deficits

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Learning analytics and disabled students (IET-OU LA workshop May 15 2014)

  1. 1. Learning Analytics and Disabled Students / Accessibility Martyn Cooper (IET)
  2. 2. The OU Leading the Way! • World leaders in applying learning analytics to benefit disabled students in particular – 19,000+ disabled students a key driver in this • Main Actors: – Martyn Cooper (IET); Rebecca Ferguson (IET); Annika Wolff (KMi) • Forums: – https://www.linkedin.com/groups/Learning-Analytics-Accessibility- 4369422?trk=my_groups-b-grp-v – http://martyncooper.wordpress.com/
  3. 3. LA for Accessibility • Hypothesis – if there is a module or learning activity on which disabled students consistently doing worse than non-disabled students – it might indicate accessibility deficits • Challenges: • Data issues • Disabled students often do better than non-disabled • Variable results comparing successive presentations • Subject differences • Even if it indicates where a deficit is it does not say anything about what it is -60.0 -40.0 -20.0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 1 62 123 184 245 306 367 428 489 550 611 672 733 794 855 916 977 1038 1099 1160 1221 1282 1343 1404 Nondisabled - Disabled % Complete Disabled-Nondisabled % Complete Number +ve 923 69% Number -ve 415 31%
  4. 4. Shard Diagrams Shard Diagrams
  5. 5. “Clickometrics” • The VLE records every “click” of every student – So know: • How long they have been online • How long engage with a given activity • Which activities, forums etc. they interacted with • Huge volume and velocity of data – but what can we measure with it? • KMi has been doing work on retention using this data – Level of “clicking” not a measure of student in trouble but changes in level a good predictor – Use as a launch for intervention [Wolff, A., Zdrahal, Z., Herrmannová, D. and Knoth, P. (2013) Predicting Student Performance from Combined Data Sources, in eds. Alejandro Peña-Ayala, Educational Data Mining: Applications and Trends, 524, Springer]
  6. 6. Data Issues • Noise –Disabled students subject to all the other factors that might impact on completion or pass rates • Disabled students are far from a homogenous group • User modelling is currently very crude • Accessibility would be better assessed if we knew the nature of the content but this not readily available without going back to Module Teams • Data not always robust and technical issues getting to it live
  7. 7. User Modelling • Learning analytics for disabled students is only viable because the university knows which students declare a disability • <disability flag? (binary) • <disability type> (12 medical model categories) • Functional models – Could be based on IMS AccessForAll 3.0 – Model access needs not personal traits – Needs students to complete web form
  8. 8. <Disability type> • Based on data collected for HESA • Does not map to accessibility needs and preferences No. Descriptor 1 Sight 2 Hearing 3 Mobility 4 Manual skills 5 Speech 6 Specific learning difficulty e.g. dyslexia 7 Mental health 8 Personal care 9 Fatigue/pain 10 Other 11 Unseen disability e.g. diabetes, epilepsy, asthma 12 Autistic Spectrum Disorder
  9. 9. AccessForAll 3.0
  10. 10. Future e-learning system
  11. 11. Accessibility of Learning Analytics • If learning analytics (in general and special support for disabled students) is going to be of use then dashboards must be accessible – General accessibility guidelines apply (WCAG 2.0 / OU Guidelines)
  12. 12. Institute of Educational Technology The Open University Walton Hall Milton Keynes MK7 6AA www.open.ac.uk

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