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Data and disadvantaged students - using learning analytics for inclusion

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Slides from the learning analytics webinar on Monday 27 February.

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Data and disadvantaged students - using learning analytics for inclusion

  1. 1. Niall Sclater, Consultant Data and disadvantaged students - using learning analytics for inclusion27/02/2017
  2. 2. 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. 02/03/2017 Data and disabled students 2
  3. 3. The net of meanings - 1 02/03/2017 Data and disabled students 3
  4. 4. The net of meanings - 2 02/03/2017 Data and disabled students 4
  5. 5. In this session we will… Explore : »Ethical issues »Disability definitions and consequences »Potential scenarios »Your priorities »A real life case study 02/03/2017 Data and disabled students 5
  6. 6. “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 02/03/2017 Data and disabled students 6
  7. 7. LearningAnalytics Service Toolkit Community Jisc LearningAnalytics Project Jisc Learning Analytics 2017
  8. 8. 86 issues in 9 groups Group Name Question Main 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
  9. 9. Group Name Question Main 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
  10. 10. Jisc Learning Analytics 2017
  11. 11. Jisc Learning Analytics 2017
  12. 12. Accessibility Considerations for Learning Analytics 1. Remember that learning analytics is not assessment 2. Avoid the labelling of individuals and reinforcing of prejudice and stereotypes 3. Maintain disabled students’ confidentiality 4. Handle the inference of disabilities from the analytics appropriately 5. Ensure that the analytics do not unfairly single out disabled students 6. Use analytics to identify modules where there appear to be accessibility issues 7. Ensure that student-facing analytics are accessible 8. Ensure that interventions are worded appropriately » https://analytics.jiscinvolve.org/wp/2016/12/14/accessibility-considerations-for-learning-analytics/ Jisc Learning Analytics Accessibility Webinar
  13. 13. Contacts Paul Bailey paul.bailey@jisc.ac.uk Niall Sclater niall.sclater@jisc.ac.uk Further Information: http://www.analytics.jiscinvolve.org Join: analytics@jiscmail.ac.uk Jisc Learning Analytics 2017
  14. 14. Straw poll 02/03/2017 Data and disabled students 14
  15. 15. 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: 02/03/2017 Data and disabled students 15
  16. 16. 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, 02/03/2017 Data and disabled students 16
  17. 17. 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” 02/03/2017 Data and disabled students 17 Roll of roles… Senior manager
  18. 18. Design: Learner engagement Student Union President »“Students are actively involved in deciding what information they see on their own personal dashboard.” 02/03/2017 Data and disabled students 18 Roll of roles… Senior manager Student union president
  19. 19. Design: Exam arrangements Examination 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” 02/03/2017 Data and disabled students 19 Roll of roles… Senior manager Student union president Examination officer
  20. 20. Support: Consistency Study 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. 02/03/2017 Data and disabled students 20 Roll of roles… Senior manager Student union president Examination officer Study skills tutor
  21. 21. Support: Responsiveness Head 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. 02/03/2017 Data and disabled students 21 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service
  22. 22. 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. 02/03/2017 Data and disabled students 22 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist
  23. 23. 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. 02/03/2017 Data and disabled students 23 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist
  24. 24. Evaluate:Teaching approaches Lecturer »I can see how changes to my resources and activities have impacted on everyone's engagement, and particularly benefited my disabled students. 02/03/2017 Data and disabled students 24 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer
  25. 25. Evaluate:Teaching approaches Learning 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.” 02/03/2017 Data and disabled students 25 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer Learning technologist
  26. 26. 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. 02/03/2017 Data and disabled students 26 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer Learning technologist Library manager
  27. 27. 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” 02/03/2017 Data and disabled students 27 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer Learning technologist Library manager Collections manager
  28. 28. Evaluate: blended learning E-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." 02/03/2017 Data and disabled students 28 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer Learning technologist Library manager Collections manager E-learning manager
  29. 29. Evaluate: Data planning Data 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.” 02/03/2017 Data and disabled students 29 Roll of roles… Senior manager Student union president Examination officer Study skills tutor Head of disability service Dyslexia specialist Assistive technologist Lecturer Learning technologist Library manager Collections manager E-learning manager Data analyst
  30. 30. Where this fits in your institution http://bit.ly/2lFgpwv 02/03/2017 Data and disabled students 30
  31. 31. What Can Analytics Contribute to Accessibility in e-Learning Systems and to Disabled Student’s Learning Martyn Cooper, Rebecca Ferguson and Annika Wolff
  32. 32. 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
  33. 33. The question • Can learning analytics be used to identify modules with accessibility deficits?
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. Possible Future work • Use research to find ‘critical learning paths’ to identify accessibility issues on individual modules.
  39. 39. 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. 02/03/2017 Data and disabled students 39
  40. 40. jisc.ac.uk One CastleparkTower Hill Bristol BS2 0JA customerservices@jisc.ac.uk T 020 3697 5800 Thank you for listening Subject specialists Accessibility & Inclusion Julia.Taylor@jisc.ac.uk Alistair.McNaught@ jisc.ac.uk 02/03/2017 Data and disabled students 40 Margaret.McKay@ jisc.ac.uk

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