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Jisc learning analytics update-nov2016
- 1. The Open University,2nd November 2016 8th UK Learning Analytics Network Meeting
- 2. Programme Jisc Learning Analytics2016 10:25 – 11:15 Update on Jisc’s learning analytics programme 11:15 – 11:30 Tea / coffee 11:30 – 12:30 Learning design meets learning analytics, Dr Bart Rienties, Open University 12:30 – 13:30 Lunch 13:30 – 14:15 Parallel session 1: Legal issues for learning analytics, Andrew Cormack, Jisc Parallel session 2: Addressing the challenges , Il-Hyun Jo, Ewha Womans University 14:15 – 15:00 Parallel session 1:The potential of blockchain , Prof John Domingue, Knowledge Media Institute, OU The design and deployment of a learning analytics dashboard, David Evans, NorthWarwickshire & Hinckley College 15:00 – 15:15 Tea / coffee – Juniper/Medlar Room,The Hub 15:15 – 15:55 The Learning Analytics Community Exchange, Dr Doug Clow, Institute for Educational Technology, OU
- 3. Paul Bailey, SeniorCodesign Manager, Research and Development Jisc learning analytics service http://www.slideshare.net/paul.bailey/
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- 6. Effective Learning AnalyticsChallenge Jisc Learning Analytics 2016 Rationale »Organisations wanted help to get started and have access to standard tools and technologies to monitor and intervene Priorities identified »Code of Practice on legal and ethical issues »Develop basic learning analytics service with app for students »Provide a network to share knowledge and experience Timescale »2015-16—test and develop the tools and metrics »2016-17—transition to service »Sep 2017—launch, measure impact: retention and achievement
- 7. Jisc’s Learning AnalyticsProject Three core strands: Learning Analytics Service Toolkit Community Jisc Learning Analytics Jisc Learning Analytics 2016
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- 9. Analytics – thebigger picture https://docs.google.com/presentation/d/1AdBkYHO3hqEJ7W2McYIsAKzF4EgFNYJM9X GfDOTRYek/edit?usp=sharing Jisc Learning Analytics 2016 Michael Webb
- 10. Descriptive Analytics what happened? Diagnostic Analytics why did it happen? Predictive Analytics whatwill happen? Prescriptive Analytics what should I do? Automated Decision making It's done Analytics maturity
- 11. Descriptive Analytics what happened?How do I compare? Prescriptive Analytics what should I do? Predictive what will happen? Automated it’s done Data Diagnostic Analytics why did it happen? Ordered Data Sector Transformation Awareness Experimentation Organisation support Organisational transformation Analytics without a national approach
- 12. Sector Transformation Awareness Experimentation Organisation support Organisational transformation Descriptive Analytics what happened?How do I compare? Predictive Analytics what will happen? Prescriptive Analytics what should I do? Automated it’s done Data Diagnostic Analytics why did it happen? Ordered Data Standardised Data Analytics with a national approach
- 13. Sector Transformation Awareness Experimentation Organisation support Organisational transformation Descriptive Analytics what happened?How do I compare? Predictive Analytics what will happen? Prescriptive Analytics what should I do? Automated it’s done Data Diagnostic Analytics why did it happen? Ordered Data Standardised Data Adaptive learning etc. Recommendation engines etc. Predictive models, Intervention management etc Data exploration tools, processes etc Dashboards, Benchmarking etc. Data Warehouse, data stores Data connectors Analytics with a national approach
- 14. Descriptive Analytics Predictive Analytics Prescriptive Analytics AutomatedDiagnostic Analytics Standardised Data Learning Records Warehouse xAPI Plugins Data transformation tools Data andAPI Standards Jisc Services Other Provider Services Basic dashboards Student App Analytics Labs Benchmarking services College Analytics Basic predictive modelling and intervention management Procurement frameworks Integration tools Services for researchers Pilot projects Services for researchers Pilot projects Institutional Dashboards Data visualisation tools Data exploration tools Advanced predictive modelling Integrated intervention management ??? ???
- 15. - Sector Dataused in mashups: - NSS - SCONUL - LiDP - HESA - Open Access Reporting/Deposit, - JUSP / IRUS - IRUS - IMD - Altmetrics - H index - Impact Factor - REF metrics - Jisc Collections bands & Subscription data Jisc Learning Analytics 2016 Library Labs: 6 teams, 33 participants drawn from Libraries
- 16. Library Analytics Jisc LearningAnalytics 2016 Library Labs - BUT also analytics on institutional data: - e-resource usage by type & department - e-resource cost benchmarking - EZProxy logs - Loans - Gate entries - Acquisitions - Counter reports - Capita Decisions - Journal Citation Reports
- 17. Library Analytics Jisc LearningAnalytics 2016 Library Labs Birkbeck,University of London Sheffield Hallam University University of Edinburgh University of Warwick The University of Manchester University of Salford Liverpool John Moores University Newcastle University Southampton Solent University Anglia Ruskin University Library University of South Wales University of Nottingham Brunel University London Kingston University Teesside University Bodleain Libraries, University of Oxford University of Wolverhampton University of Leicester University of Reading Manchester Metropolitan University University of Bath De Montfort University
- 18. Library Analytics - Mashingup Library data was difficult – SCONUL is not HESA - Many different internal systems, comparative analytics difficult - Proof of concept dashboards stimulating institutions (traffic lights) - More interest and contributions to recipes at http://github.com/jiscdev/xapi-lib - New verbs! Eduroam, presence - Data Sharing Agreements and an experimental area in the Heidi Lab - Scope for more librarians alongside planners on Jisc’s beta BI project Jisc Learning Analytics 2016
- 19. Where are wenow…
- 20. Community: Project Blog, mailinglist and network events Blog: http://analytics.jiscinvolve.org – over 30 blog posts Mailing: analytics@jiscmail.ac.uk – 422 members (182 organisations) 8th Network Meeting ~600+ participants Jisc Learning Analytics 2016
- 21. http://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics Code of Practice JiscLearning Analytics 2016 http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf
- 22. Learning Analytics ServiceArchitecture Library Analytics Service
- 23. Learning analytics productsand tools Learning records warehouse – active Data Explorer – basic visualisations Student Unified Data Definition – version 1.2.7 and examples major SRS and validation too VLE – xAPI recipe and plugins for Blackboard and Moodle Attendance tracking – xAPI recipe (being piloted soon) Student App – release 1 Dec 2016 Jisc Learning Analytics 2016 Tribal Student Insights (10) Open Learning Analytics Processor (4) Further learning analytics product pilots (tbc)
- 24. UDDValidatorTool • Customer-side UDDvalidation (web-based, secure access) • UDD data preparation tool for institutions • Jisc will load the historical data (once validated) • Covers current & future UDD - 1.2.7, 1.2.x, 1.3.0 etc • Links directly to UDDGitHub site (dynamic updates) • Agile approach to software functionality/ release • V1.0 - hard validation (UDD structure, optional/ mandatory fields, field contents) • Relational entities – integrity checks • Soft validation - data quality and concentration/ coverage (working withTribal/ Unicon Marist) • Focus on key fields for predictive modelling purposes, student app • Gives control & flexibility to our members – rapidly quick data validation (Azure Cloud) Jisc Learning Analytics 2016
- 25. Implementations Profile Aims ToolsNo Data Sources Teaching and research led Universities Student retention and success Tribal student insight/data warehouse 7 VLE (Moodle and Blackboard), student records and attendance Teaching and research led Universities Success and engagement Student app 4 VLE (Moodle and Blackboard), student records Teaching led Universities Student retention Open source processors/data warehouse 4 VLE (Moodle and Blackboard), student records and attendance FE Colleges Student retention Tribal student insight 2 VLE (Moodle), student records and attendance Jisc Learning Analytics 2016
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- 27. On-boarding Process Stage 1:Orientation Stage 2: Discovery Stage 3: Culture and Organisation Setup Stage 4: Data Integration Stage 5: Implementation Planning Jisc Learning Analytics 2016 https://analytics.jiscinvolve.org/wp/on-boarding/
- 28. Stage 1: Orientation JiscLearning Analytics 2016 Stage 1. Orientation 1. Sign up to the analytics mailing list Evidence required: A list of people in your institution signed up to the mailing list 2. Review the learning analytics blog post and relevant reports Evidence required: Notes on useful articles and posts you have found 3. Attend a Jisc webinar, network meeting or workshop Evidence required: Notes from attending a recent event
- 29. Stage 2: DiscoveryReadiness Jisc Learning Analytics 2016 Stage 2. Discovery 4. Decide on institutional aims for learning analytics Evidence required: A prioritised list of your aims for learning analytics 5. Strategic alignment, senior management approval and you have a nominated project lead Evidence Required: Named sponsor from the senior management team, Named project lead and contact details, Named technical lead and contact leaded, A list of members of your working/management group 6. Undertake the readiness assessment Evidence required :A completed readiness assessment questionnaire with your commentary on the answers 7. Arrange a verification meeting with Jisc to discuss the outcomes and possible next steps Evidence required: Date of meeting, documentation to share and a list of people attending
- 30. Discovery readiness Topic IDQuestion Commentary Response Score Leadership 1 The institutional senior management team is committed to using data to make decisions Please provide a commentary on you response to each question where appropriate 0 - Hardly or not at all 1 - To some extent 2 - To a great extent Leadership 2 Our vice-chancellor / principal has encouraged the institution to investigate the potential of learning analytics 0 - Hardly or not at all 1 - To some extent 2 - To a great extent Leadership 3 There is a named institutional champion / lead for learning analytics 0 - No 2 - Yes Vision 4 We have identified the key performance indicators that we wish to improve with the use of data 0 - Hardly or not at all 1 - To some extent 2 - To a great extent Jisc Learning Analytics 2016 A supported review of institutional readiness https://analytics.jiscinvolve.org/wp/on-boarding/step-6-readiness-assessment/
- 31. Stage 3: Cultureand Organisation Setup Jisc Learning Analytics 2016 Stage 3. Culture and Organisation Setup 8. Start to address readiness recommendations Evidence required: Action plan to address readiness recommendations 9. Legal and ethical policy considerations in hand Evidence required: List of institutional policies relevant to learning analytics; Plan to update/create policies to cover learning analytics 10. Decision on learning analytics products to pilot Evidence required: A documented list of products with an agreed rational for choices 11. Data processing agreement signed Evidence required: Signed Data Processing Agreement 12. Select student groups for the pilot and engage staff/students Evidence required: List of student groups/cohorts and numbers of students involved
- 32. Stage 4: DataIntegration Jisc Learning Analytics 2016 Stage 4. Data Integration 13. Undertake a data and systems audit 14. Contact Jisc to start data integration 15. Install and evaluate the VLE data plugin(s) on a test system at your institution 16. Extract student data, transform to UDD and validate. 17. Extract historical VLE (or other activity) data 18. InstallVLE (or other activity) data plugin(s) on live system, activate for live data upload to LRW 19.View uploaded LRW data using data explorer to check quality
- 33. Jisc Learning Analytics2016 Stage 4: Data collection About the student Activity data TinCan (xAPI)ETL
- 34. Stage 5: ImplementationPlanning Jisc Learning Analytics 2016 Stage 5. Implementation Planning 20: Move to implementation Stage Evidence required: An implementation plan with agreed timescales
- 35. Jisc Learning Analytics2016 On-boarding Process Data Visualisation Dashboards Ready to implement Ready to implement
- 36. On-boarding – getstarted Stage 1: Orientation – review/done Stage 2: Discovery – mostly self-support Stage 3: Culture and Organisation Setup – Jan 2017 Stage 4: Data Integration – slots from early 2017 Stage 5: Implementation Planning - slots from early 2017 Jisc Learning Analytics 2016
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- 38. Co-design challenges 2017 Exploreour co-design challenges Help steer our innovation work by exploring the next big ideas for technology in education and research. Jisc Learning Analytics 2016
- 39. Jisc Learning Analytics2016 Data driven learning gains Next generation research environment Digital skills for research Should we gather more data on students, staff and buildings that would allow us to deliver better experiences? We think it is time for a new type of learning environment, but what would this look like? We think it is time for a new type of learning environment, but what would this look like? What would a truly digital apprenticeship look like? Can we make better use of data to improve learning, teaching and student outcomes? How do we equip researchers and related staff with the skills they need for the future of research? The intelligent campus The digital apprentice Next generation learning environment
- 40. Jisc Learning Analytics2016 1 Discuss emerging challenges 2 Prioritise ideas 3 Announce successful ideas 4 Report progress Identify ideas 31st Oct – 24th Nov 4th Jan– 30th Jan 6th Feb Apr/May Release 6 challenge areas and invite Jisc members and other experts to discuss Audience: managers, consumers, some leaders, other experts Present ideas for activities Jisc could do and ask members which they support Audience: managers, consumers, some leaders Release 6 challenge areas and invite Jisc members and other experts to discuss Audience: everyone who followed the challenge Release 6 challenge areas and invite Jisc members and other experts to discuss Audience: everyone who followed the challenge
- 41. Contacts Paul Bailey paul.bailey@jisc.ac.uk FurtherInformation: http://www.analytics.jiscinvolve.org Join: analytics@jiscmail.ac.uk Jisc Learning Analytics 2016
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