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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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