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Jisc learning analytics service core slides


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Overview of the Jisc learning analytics service

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Jisc learning analytics service core slides

  1. 1. Paul Bailey, Senior Codesign Manager, Research and Development Jisc learning analytics service
  2. 2. Learning Analytics What is learning analytics? Learning Analytics Service
  3. 3. “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 Learning Analytics Service
  4. 4. Agenda Learning Analytics Service Predictive models identify students at risk Timely intervention by teaching or support staff Increased retention Better understanding of the effectiveness of interventions Rich data on student activity and attainment Data shared with student prompting them to change own behaviour Better student outcomes Data can be explored to understand patterns of behaviour Better understanding of the behaviours linked to differential outcomes
  5. 5. Learning Analytics Service 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 Retention and attainment A more efficient campus Improved teaching & curricula Personalised and adaptive learning Efficient campus Improving teaching & curricula Now Learning analytics Institutional analytics Educational analytics Cognitive Analytics and AI Future
  6. 6. Paul Bailey, Senior Codesign Manager, Research and Development Jisc learning analytics service
  7. 7. Effective Learning Analytics Challenge Learning Analytics Service 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 a core learning analytics service with app for students »Provide a network to share knowledge and experience Timescale »2015-17 Development »2017-18 Beta Service »Aug 2018 Full Service
  8. 8. Community: Project Blog, mailing list and network events Blog: Docs: Mailing: Learning Analytics Service
  9. 9. Toolkit: Code of Practice Learning Analytics Service  Code of Practice analytics  Literature Review _Literature_Review.pdf  Template Learning Analytics Policy an-institutional-learning-analytics-policy/  Guidance on consent for learning analytics learning-analytics-some-practical-guidance-for-institutions/
  10. 10. Legal and ethical: consent and GDPR Learning Analytics Service Advice is  Make sure your collection notice covers the use of data to support the student learning and wellbeing  Not ask for consent for the use of non-sensitive data for analytics (our current understanding is that this can be considered as of legitimate interest or public interest)  Ask for consent for use of sensitive data (which, under the GDPR, is called “special category data”)  Ask for consent to take interventions directly with students on the basis of the analytics
  11. 11. Data Collection Data Storage and Analysis Presentation and Action Jisc Learning Analytics open architecture: core Alert and Intervention system Other Staff Dashboards Consent Service (tbc) 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
  12. 12. Learning Analytics Service Data collection About the student Activity data TinCan (xAPI)ETL Learning Data Hub Attendance StudyGoal
  13. 13. Products and dashboards Data Explorer: Learning Analytics dashboards for staff, focussing on showing learning analytics data to staff based on their role. Study Goal: An app for students - allowing them to view their learning analytics data, and set measurable actions to support their success. Learning Analytics Predictor: A predictive model designed to do one thing well - predict success at course level. Output can be viewed in Data Explorer or any other system that can integrated in the Learning Data Hub. Traffic Lights Calculator: A straightforward rules based engine, allowing RAG status to be calculated for online activity, attendance and achievement, at module level. Output fromTLC can viewed in data explorer or any other system that can integrated in the learning data hub. Learning Data Hub: the core of Jisc's learning analytics service, holds data about students, works in conjunction with an institutions data warehouse, rather than replace it, to share data between applications in a standard way, a collection point for semi-structured learning data such as student activity. Learning Analytics Service
  14. 14. Data Explorer  Data Explorer Release 1.0  View data in learning records warehouse  Site Overview – overview of all data  My Students and My Modules  RAG Status and predictive models  User Guide and videos  ata-explorer/Home Jisc Learning Analytics 2017
  15. 15. Learning Analytics Service
  16. 16. Study Goal  Study Goal aims  Social learning app with gamification  Setting targets and logging self-declared activity (fitbit model)  View activity and attainment data  Attendance check-in  Guides and videos goal/Home Jisc Learning Analytics 2017
  17. 17. Who we are working with…. Jisc learning analytics service
  18. 18. On-boarding Process Stage 1: Orientation – get more info Stage 2: Discovery – DIY and/or paid for consultancy Stage 3: Culture and Organisation Setup – sign up for Jisc service and/or supplier products Stage 4: Data Integration - push data to learning data hub Stage 5: Implementation Planning Learning Analytics Service
  19. 19. Discovery readiness Topic ID Question Commentary Response Score Leadersh ip 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 Leadersh ip 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 Leadersh ip 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 Learning Analytics Service A supported review of institutional readiness
  20. 20. Engaging institutions 2017-18 - Currently working with 20+ institutions (HE and FE) on beta service Deadline for beta service implementation is April 2018 (12 slots) Target 40 institutions signed up to the learning analytics service byAug 2018 Learning Analytics Service
  21. 21. Institutional engagement (pathfinders) » Plymouth University » Aberystwyth University » University of East Anglia » Cardiff Metropolitan University » University of Greenwich » University of Gloucestershire » Oxford Brookes University » City ofWolverhampton College » Newman University » University of Chester » Dumfries & Galloway College » Aston University » University of SouthWales » University of Brighton » University of Abertay, Dundee » Glasgow Caledonian University » City, University of London » Regent College University » Bath Spa University » Milton Keynes College
  22. 22. Learning Analytics Purchasing Service – How we are working with suppliers of LA solutions  USPs for Institutions:  Marketplace for LA product & services compatible with the core Jisc service  Procurement Framework – mini competitions can be easily initiated  Mandatory clauses included – ensures a consistent & safe approach to data protection  Institutions will control and own the contracts directly  Framework will available to institutions from 18th September 2017  Three categories of supplier services will be offered: 1. Learning Analytics Solutions 2. Learning Analytics Services 3. Learning Analytics Infrastructure  Service Jisc Learning Analytics 2017
  23. 23. Vendor engagement Learning Analytics Solution and Service Providers › Altis, HT2, Phoenix Software, SolutionPath, Civitas Learning,Tribal, Unicon-Marist, Kortex Data Sources including › Tribal Education, Agresso (UNIT4), HESA, Turnitin, Blackboard, Canvas, ExLibris, OCLC (Online Computer Library Service), Capita,Thales,TDS Student, Kortex
  24. 24. Learning Analytics Workshops/Consultancy Examples »Discovery- helps you assess readiness for implementing learning analytics. Culture, Data, technology and strategy »Legal and ethical issues – explores data protection, consent, GDPR »Intervention planning to review data to plan interventions with students and usingdata to enhance the curriculum 26/11/2013 Jisc Co-design 24
  25. 25. Pricing formula from 2018-19 Learning Analytics Service Formula per annum £5K charge + £1.80 per student for first 15,000 students + 50p per student thereafter Examples ~5,000 students, £14k per annum ~10,000 students, £23K per annum ~18,000 students, £33K per annum ~27,000 student, £39K per annum
  26. 26. Contacts Paul Bailey Further Information: Join: Learning Analytics Service