Analytics & ILL
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Keynote presentation on learning analytics given to the FIL (Forum for Interlanding and Information Delivery) conference in Belfast, 25th June 2019
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Analytics & ILL
- 1. Analytics & ILL Kerr Gardiner FIL Conference 2019
- 2. Contents Analytics Current activity Analytics and ILL Opportunities Considerations Futures
- 7. Learning Analytics – a definition “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens, 2012)
- 10. Learning Analytics aren’t new! learning technology & space
- 11. Types of learning analytics? Management Reading lists Who’s using the service Trends in departments KPIs Learner Focussed on individual Help them make choices Help them study effectively Curriculum Improved learning activities Identify areas that work Quality rather than quantity Wellbeing Pastoral Mental health
- 12. VLE data + Student record system + Attendance data + Library data Buildings data + Learning space data + Location data Teaching quality data + Assessment data + Curriculum design data Content data + Learning pathways data Better retention and attainment A more efficient campus Improved teaching & curricula Personalised and adaptive learning Now Learning analytics Institutional analytics Educational analytics Cognitive Analytics and AI Future Jisc Learning Analytics Service Retention and attainment Efficient campus Improving teaching & curricula
- 13. Universities have lots of data – but in silos By department By service By student cohort By location
- 15. Current activity
- 16. Current activity • Commercials: • VLE • LMS • eBook suppliers • Jisc: • Learning Analytics Service • Project work – e.g. LA Cymru • Working with suppliers • Data Hub
- 18. Current activity • Commercials: • VLE • LMS • eBook suppliers • British Library • Jisc: • Learning Analytics Service • Project work – e.g. LA Cymru • Working with suppliers • Data Hub
- 19. Data Collection Data Storage and Analysis Presentation and Action Jisc Learning Analytics open architecture: core Alert and Intervention system Other Staff Dashboards Student Consent Student App: Study Goal Jisc Learning Analytics Predictor Learning Data Hub Student Records VLE Library Staff dashboards in Data Explorer Self Declared Data Attendance, Presence, Equipment use etc…. Data Aggregator UDD Transformation Toolkit Plugins and/or Universal xAPI Translator 19 Jisc Learning Analytics Service
- 20. What does it mean for ILL?
- 21. Why bother? Know where you are Keep relevant Improve service
- 22. LA from an ILL perspective Need to understand your institutional perspective Libraries are not always part of the discussion Types of ILL analytics
- 23. LA from an ILL perspective Data for management • Informed decisions on document management • Impact and value • Improve the user experience Data for learning • Usage patterns • Engagement • Citation use
- 24. Opportunities Re-think current data What other data could you get Engage with suppliers Digital landscape Be part of your institutional discussion You’re best placed to know what could be achieved
- 25. Considerations
- 26. Where to start! LEGAL ETHICAL DATA QUALITY INTERPRETATION INSTITUTIONAL BARRIERS RESPONSIBILITIES STUDENT VIEW
- 27. Legal & Ethical GDPR Privacy Data ownership Transparency Consent Anonymity ‘I’m not a number’ Change student behaviour Student motivation Built in bias
- 28. USW “learning analytics can be the basis for better quality conversations with students about their expectations and aspirations.”
- 29. University of Gloucestershire • We will use Learning Analytics to help all students reach their full academic potential. • We will be transparent about data collection, sharing, consent and responsibilities. • We will abide by ethical principles and align with our university strategy, policies and values. • Learning Analytics will not be used to inform significant action at an individual level without human intervention.
- 30. University of Greenwich • We will use Learning Analytics to help all students reach their full academic potential. • We will be transparent about data collection, sharing, consent and responsibilities. • We will abide by ethical principles and align with our university strategy, policies and values. • Learning Analytics will be supported by focused staff and student development activities. • Learning Analytics will not be used to inform significant action at an individual level without human intervention. • We will actively work to recognise and address any potential negative impacts from Learning Analytics.
- 31. Data considerations IS IT AVAILABLE? IS IT COMPLETE? IS IT CORRECT? DISPARATE SYSTEMS MERGING WITH OTHER DATA
- 32. Data interpretation • Correlation ≠ Causation • Quality of algorithms • What is it telling me? • Visualisation • Decision making • Predictions
- 33. Barriers & Responsibilities Institutional barriers • Lack of conversations • Lack of headspace • Not a priority Responsibilities • What are the institutional and personal responsibilities if the data shows ‘something’
- 34. Futures
- 35. What’s happening DIGITAL EXPANSION CURRENT FOCUS
- 36. Digital Expansion More Devices More connected More data Student expectations Open access
- 37. Current Focus Government Jisc & Suppliers Institutional Students & Parents
- 38. The Big Questions What does the digital landscape mean for ILL? What does open access mean for ILL? Should ILL engage with this trend of LA?
- 39. Starting the journey…. Today has been about raising awareness You are the specialists Have conversations Ask your systems people what is possible
- 40. The times they are a changing… Will you?
- 41. Useful readings • Learning Analytics Explained / Niall Sclater • https://www.jisc.ac.uk/reports/learning-analytics-in-higher-education • https://www.jisc.ac.uk/learning-analytics • http://cdn.nmc.org/media/2015-nmc-horizon-report-library-EN.pdf