Data and disadvantaged students - using learning analytics for inclusion

Niall Sclater, ConsultantData and disadvantaged students - using learning analytics for inclusion 27/02/2017

While you wait…

http://tiny.cc/data-form »If you haven’t had the

chance to do so, please take some time to look at the data and disadvantaged students pre-session googleform.

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Data and disabled students 2

http://tiny.cc/data-form

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The net of meanings - 1

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The net of meanings - 2

In this session we will…

Explore : »Ethical issues »Disability definitions and consequences»Potential scenarios »Your priorities»A real life case study

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“learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”SoLAR – Society for Learning Analytics Research

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Learning Analytics Service Toolkit Community

Jisc Learning Analytics Project

Jisc Learning Analytics 2017

86 issues in 9 groups

Group Name QuestionMain type

Importance Responsibility

2 Consent Adverse impact of opting out on individual

If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress?

Ethical 1 Analytics Committee

7 Action Conflict with study goals

What should a student do if the suggestions are in conflict with their study goals?

Ethical 3 Student

8 Adverse impact

Oversimplification How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances?

Ethical 1 Educational researcher

Group Name QuestionMain type

Importance Responsibility

2 Consent Adverse impact of opting out on individual

If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress?

Ethical 1 Analytics Committee

7 Action Conflict with study goals

What should a student do if the suggestions are in conflict with their study goals?

Ethical 3 Student

8 Adverse impact

Oversimplification How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances?

Ethical 1 Educational researcher

jisc.ac.uk/guides/code-of-practice-for-learning-analytics



Accessibility Considerations for Learning Analytics

1. Remember that learning analytics is not assessment2. Avoid the labelling of individuals and reinforcing of prejudice and stereotypes3. Maintain disabled students’ confidentiality4. Handle the inference of disabilities from the analytics appropriately5. Ensure that the analytics do not unfairly single out disabled students6. Use analytics to identify modules where there appear to be accessibility issues7. Ensure that student-facing analytics are accessible8. Ensure that interventions are worded appropriately» https://analytics.jiscinvolve.org/wp/2016/12/14/accessibility-considerations-for-lear

ning-analytics/

Jisc Learning Analytics Accessibility Webinar

https://analytics.jiscinvolve.org/wp/2016/12/14/accessibility-considerations-for-learning-analytics/

https://analytics.jiscinvolve.org/wp/2016/12/14/accessibility-considerations-for-learning-analytics/

Contacts

Paul Bailey [email protected] Niall Sclater [email protected]

Further Information: http://www.analytics.jiscinvolve.org

Join: [email protected]


mailto:[email protected]


http://www.analytics.jiscinvolve.org/

http://www.analytics.jiscinvolve.org/

http://elevator.jisc.ac.uk/

Straw poll

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The data discussion

1. How do you encourage disabled students to disclose?

2. Which of your institutional data sources might be relevant to supporting disabled students?

3. Which disabled students are visible in your data?

4. How mature is the technology?

Discussion:

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DSA EHCP

Self disclosedDiagnose

dPublic / private

Brainstorm Scenarios»Design of learner data

› What is collected (ethics, level of detail e.g. disability and other co-morbid factors, process and outcome),

› Design of interface (usability and accessibility),»Support learner progress

› Documenting/disclosing barriers provides an additional method of early identification for support by tracking progress,

› Informing course design and learner attainment › Improving learning and teaching practice› Comparing progress of disabled versus non disabled learners

»Evaluate institutional/support services› Usage of institution wide assistive technology (e.g. text to speech)› Library uptake of productivity software, ebook usage,

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Design: Stakeholder engagement:Senior manager»“We've invited a range of

stakeholders to be involved in our learning analytics steering group – including support staff and people with accessibility needs”

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Roll of roles…Senior manager

Data and disabled students

Design: Learner engagement Student Union President»“Students are actively involved

in deciding what information they see on their own personal dashboard.”

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Student union president


Design: Exam arrangementsExamination Officer»“Some people need access

arrangements for exams. It’s always involved lots of consultation and meetings. Now its just a touch of a button”

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Student union presidentExamination officer


Support: ConsistencyStudy skills tutor»Now there’s less room for

students to slip through the net because all the support services have the same information at the same time so can work together.

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Study skills tutor

Support: ResponsivenessHead of disability service»Student data helps me track the

progress of students who have disclosed a barrier to learning so we can respond more swiftly.

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Study skills tutorHead of disability

service

Support: Prioritise Dyslexia specialist»I used to spend ages chasing

students who missed their appointments. Now I can instantly check their other progress and leave them alone if they’re succeeding.

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serviceDyslexia specialist


Evaluate: Support strategies Assistive technologist»As well as asking about a

learners disability we've tried to capture more specific detail about the technology strategies recommended through the assessment.

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Assistive technologist


Evaluate: Teaching approachesLecturer»I can see how changes to my

resources and activities have impacted on everyone's engagement, and particularly benefited my disabled students.

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Assistive technologistLecturer

Evaluate: Teaching approachesLearning technologist»“I can see who is using the

learning platform and how often . This makes it easy to see where content might be difficult to access.”

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Learning technologist


Evaluate: Library technology support Library manager»We can now monitor the

usage/uptake of enabling technology software in our library. This helps us to adopt a more targeted strategy for promotion of productivity tools to enhance the support we offer.

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Learning technologistLibrary manager


Evaluate: E-books and journals:

Collections manager»“I can see who is using the

resources such as e-books and e-journals. If there are any anomalies I can ask why. By exploring use by different categories of student we can plan more effective intervention and support”

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Collections manager

Evaluate: blended learningE-learning manager»“I can begin to correlate

outcomes for disabled student with online provision in different subject areas. Now I have proof that CPD in blended learning pays dividends for disabled students."

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Collections managerE-learning manager

Evaluate: Data planningData analyst»“I can begin to plan for the

future in ways that can extend what is currently possible to do. With my colleagues I can begin to shape our data to really meet the needs of a wider group of learners.”

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Collections managerE-learning manager

Data analyst


Where this fits in your institution

http://bit.ly/2lFgpwv

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What Can Analytics Contribute to Accessibility in e-Learning Systems and to Disabled Student’s Learning

Martyn Cooper, Rebecca Ferguson and Annika Wolff

Context to the research

• OU – distance educator• Larger than average no. of disabled students • Greater challenges in responding to individual needs of

disabled learners at a distance• Students can declare a disability. But don’t necessarily know

what type and no two disabled students are the same, anyway

The question

• Can learning analytics be used to identify modules with accessibility deficits?

First pass

• Look at average completion rates. – 1338 modules analysed– Can show 50% completion rate if 1 of 2 students with declared

disability drops out.– Low numbers can skew results– Solution: analyse only modules with >25 disabled students = 668

modules

Refined approach using odds ratios

• Odds ratios can determine for 2 groups whether one group is more or less likely to achieve an outcome than another group.

• It is a relative measure of the odds of one outcome occurring, given a particular criteria compared to odds of it happening in absence of the criteria

• In this case:– Outcome is success of students on the course

Using odds ratios to find accessibility issues

• A bigger odds ratio = bigger disparity between groups• But - need to find threshold above which you can say there is a problem

Threshold of > 3 looks sufficient to identify where accessibility is most likely factor to explain difference

Summary

• Low numbers make applying statistical measures very difficult• Not suitable for a large number of modules• Identifies where there might be a problem – but not how to fix

it

Possible Future work

• Use research to find ‘critical learning paths’ to identify accessibility issues on individual modules.

What next

»Follow up email with › feedback form › PDF of slides/notes› Links to Google form, Tricider votes, Niall and Julia

blog posts »Link to recording (if we remembered to press Record!)

»New blog post summarising issues and questions arising from session.

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jisc.ac.uk

One Castlepark Tower Hill Bristol BS2 0JA

[email protected]

T 020 3697 5800


Thank you for listeningSubject specialists Accessibility & Inclusion

[email protected] Alistair.McNaught@ jisc.ac.uk

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Margaret.McKay@ jisc.ac.uk


http://www.jisc.ac.uk/