data and disadvantaged students - using learning analytics for inclusion
TRANSCRIPT
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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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
Jisc Learning Analytics 2017
Jisc Learning Analytics 2017
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
Contacts
Paul Bailey [email protected] Niall Sclater [email protected]
Further Information: http://www.analytics.jiscinvolve.org
Join: [email protected]
Jisc Learning Analytics 2017
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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Roll of roles…Senior manager
Student union president
Data and disabled students
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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Roll of roles…Senior manager
Student union presidentExamination officer
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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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Roll of roles…Senior manager
Student union presidentExamination officer
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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Roll of roles…Senior manager
Student union presidentExamination officer
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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
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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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologist
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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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologistLecturer
Learning technologist
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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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologistLecturer
Learning technologistLibrary manager
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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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologistLecturer
Learning technologistLibrary manager
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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologistLecturer
Learning technologistLibrary manager
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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Roll of roles…Senior manager
Student union presidentExamination officer
Study skills tutorHead of disability
serviceDyslexia specialist
Assistive technologistLecturer
Learning technologistLibrary manager
Collections managerE-learning manager
Data analyst
Data and disabled students
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
T 020 3697 5800
Data and disabled students
Thank you for listeningSubject specialists Accessibility & Inclusion
[email protected] Alistair.McNaught@ jisc.ac.uk
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Margaret.McKay@ jisc.ac.uk