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+ 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-4

369422?trk=my_groups-b-grp-v– http://martyncooper.wordpress.com/

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

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Nondisabled - Disabled % Complete

Disabled-Nondisabled % Complete

Number +ve 923 69%

Number -ve 415 31%

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Module level completion and pass rates for all 2010/11 presentations with at least 10 disabled students.Where the completion and pass rates are higher for disabled students the shard has a pink tip.

Where disabled students perform less well than those not declaring a disability, the shard has a grey tip.

Measure Names

Disabled

Not disabled

Disabled and Not disabled for each Milestone broken down by course_anon. Color shows details about Disabled and Not disabled.

Shard Diagrams

“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]

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

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

<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

AccessForAll 3.0

Future e-learning system

LA to focus support for Disabled Students• LA – not just data analytics

but intervention• Any LA intervention could

apply to disable students• Specific interventions for

disabled students–e.g. scaffolding for

activities with access challenges

Diagram – Doug Clow

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)

Institute of Educational Technology The Open UniversityWalton HallMilton KeynesMK7 6AA

www.open.ac.uk