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Jisc learning analytics mar2017

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Overview Jisc learning analytics service

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Jisc learning analytics mar2017

  1. 1. Paul Bailey, Senior Codesign Manager, Research and Development Jisc learning analytics service http://www.slideshare.net/paul.bailey/
  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. Learning Analytics Sophistication ModelLearning Analytics Service
  5. 5. Analytics categories by intervention Learning Analytics Service Improve individual student performance - interventions aimed directly at learners Improve teaching and learning quality - interventions aimed at curriculum design Improve support systems and process - interventions aimed at support staff and the process around support staff and students. Develop strategy - interventions required to improve the performance of the institution
  6. 6. 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 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 (freemium) »Sep 2017—launch, measure impact: retention and achievement
  7. 7. What do we mean by Learning Analytics? The application of big data techniques such as machine based learning and data mining to help learners and institutions meet their goals: For our project: » Improve retention (current project) » Improve attainment (current project) » Improve employability (future project) » Personalised learning (future project) Learning Analytics Service
  8. 8. Learning Analytics Service 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
  9. 9. Descriptive Analytics Predictive Analytics Prescriptive Analytics AutomatedDiagnostic Analytics Standardised Data Learning Records Warehouse xAPI Plugins Data transformation tools Data and API 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 ??? ???
  10. 10. AI and analytics Learning Analytics Service
  11. 11. Jisc’s Learning Analytics Project Three core strands: Learning Analytics Service Toolkit Community Jisc Learning Analytics Learning Analytics Service
  12. 12. Community: Project Blog, mailing list and network events Blog: http://analytics.jiscinvolve.org Mailing: analytics@jiscmail.ac.uk Learning Analytics Service
  13. 13. Toolkit: Code of Practice Learning Analytics Service  Code of Practice http://www.jisc.ac.uk/guides/code-of-practice-for-learning- analytics  Literature Review http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A- _Literature_Review.pdf  Template Learning Analytics Policy https://analytics.jiscinvolve.org/wp/2016/11/29/developing- an-institutional-learning-analytics-policy/  Guidance on consent for learning analytics https://analytics.jiscinvolve.org/wp/2017/02/16/consent-for- learning-analytics-some-practical-guidance-for-institutions/
  14. 14. Learning Analytics Service Architecture Learning Analytics Service
  15. 15. Dashboards Dashboards for different users of the analytics  Administrators to see over all activity  Course tutors to view and compare students  Student view to see engagement activity Based on either commercial tools from Tribal (Student Insight) or open source tools from Unicon/Marist or other providers of learning analytics products Learning Analytics Service
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  24. 24. First version will include: » Overall engagement » Comparisons » Self declared data » Consent management Bespoke development by Therapy Box Student App Learning Analytics Service
  25. 25. Learning Analytics Service Stats – Provides an engagement and attainment overview and drilling down to gives comparative activity graphs. Log – Allows you to log time spent on specified activities e.g. reading for an assignment Target – Allows you set personal targets to improve your engagement e.g. study for 10 hours this week
  26. 26. Alert and Intervention System Tools to allow management of interactions with students once risk has been identified: » Case management » Intervention management » Data fed back into model » etc… Based on open source tools from Unicon/Marist (Student Success Plan) Learning Analytics Service
  27. 27. Learning Analytics Service
  28. 28. On-boarding Process Stage 1: Orientation Stage 2: Discovery Stage 3: Culture and Organisation Setup Stage 4: Data Integration Stage 5: Implementation Planning Learning Analytics Service https://analytics.jiscinvolve.org/wp/on-boarding/
  29. 29. 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
  30. 30. Learning Analytics Service Data collection About the student Activity data TinCan (xAPI)ETL
  31. 31. Jisc Learning Analytics 2017 Initial Implementation Approach Historic Data Model Live Data Analyse Results Analysis and Pilot Choose full product Suppliers Institution Team End Users Deploy Study Goal
  32. 32. Jisc Learning Analytics 2017 Current Implementation Approach Analyse and Pilot Choose full Product Historic Data Analyse Results Live Data Deploy dashboards/other tools Suppliers Institution Team End Users Model Build Deploy Study Goal Deploy DataX Analyse and Pilot
  33. 33. Learning Analytics Service On-boarding Process Data Explorer Visualisation Tools Ready to implement Ready to implement
  34. 34. Contacts Paul Bailey paul.bailey@jisc.ac.uk Further Information: http://www.analytics.jiscinvolve.org Join: analytics@jiscmail.ac.uk Learning Analytics Service

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