1. Greater Manchester Chamber of Commerce 16 May 2016 Jisc Learning Analytics in FE and Skills

2. Objectives 2 The purpose of the day will be to: Explore significant opportunities for improving access to data, and analytical capacity, in the face of the significant changes that are taking place across further education and training, skills commissioning and apprenticeship provision and funding. Explore technical priorities but also as key strategic issues, of concern to those in leadership positions. Prioritise the learning analytics user stories/models for FE and Skills. We currently implement learning analytics models to predict students at risk of failing or not achieving their potential. Identify and prioritise the data sources and systems we will need to integrate to undertake the analytics user stories. Identify strategic issues/concerns that need to be address in readiness for implementation of analytics services. Identify next steps and how colleges/providers can start to engage with Jisc

3. Agenda 3 Time What 10:00 Welcome and overview – Louise Timperley - Director of Skills, Manchester Chamber of Commerce, Martin Hall, Consultant 10:15Introduction and Ice breaker, Paul Bailey, Jisc 10:30Context– Martin Hall, Shri Footring and Sue Attewell, Heads of FE and Skills, Jisc 11:00Coffee 11:15Activity 1: User Stories – what are the Analytics Questions we most want to be able to answer 12:30Lunch 13:15Activity 2: Identifying data sources required to implement each of the groups of user stories 13:45Activity 3: Solutions and barriers 14:45Coffee 15:00Feedback from groups 15:30Next steps and getting involved 16:00Finish

4. Learning Analytics What is learning analytics? 4

5. 5 “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 - GMCC 16 May 2016

6. 6 Learning Analytics Sophistication Model (FE) Learning Analytics - GMCC 16 May 2016

7. Greater Manchester Chamber of Commerce 16 May 2016 Background and context

8. 8 Moodle Grade Tracker Learning Analytics - GMCC 16 May 2016

9. Related Jisc Projects Destinations Data Project for FEBusiness Intelligence Project (HE) 9Learning Analytics - GMCC 16 May 2016

10. Destination Data Project » How do the outcomes and destinations of learners at this college compare › Across different vocational areas? › With those of other colleges both locally and nationally? › Across different groups of learners? Business Questions: 06/10/2015 Destinations Data Project for FE - End of Discovery Stage Review meeting 10

11. Example of Demographic Comparison 11

12. Example of Benchmark Comparison 12

13. 13 Data sets Student Demographi c Prior Attainment In-year progress AttendanceVLE access…others Labour Market Intelligence BIS Outcomes Based Success Measures Local contextual data AoC Dashboards Ofsted Dashboards …others Local External Learning Analytics - GMCC 16 May 2016

14. 14 Drivers Learners demographic, age, ethnicity, needs, … widening participation agenda Funding success rates, positive destinations, … Local Job Market labour market intelligence, … Growth Potential capacity for developing specific areas of provision, apprenticeships, … Learning Analytics - GMCC 16 May 2016

15. 15 Coffee – 15 mins

16. Activity 1: User Stories A: Improve individual student performance - user stories from (e..g staff, employers, parents, learners) that generate interventions aimed directly at learners - but to look at different user types e..g apprentices tec. B: Improve teaching and learning quality - users stories that generate interventions aimed at teaching staff to improve quality across groups of students C: Improve college support systems and process - user stories that generate interventions aimed at support staff and the process around support staff and students. Acknowledge resource restraints of colleges, limited support staff (in IT and LT) etc. D: Develop college strategy - user stories that inform the strategic priorities and interventions required to improve the performance of the college Learning Analytics - GMCC 16 May 201616

17. Activity 1: User Stories Process 1. Add, review edit user stories on your table. 2. Max 10 user stories – add one, you lose one 3. Prioritise (Important, Useful, Nice to have) – rank 1-10 4. Circulate Round 1 – 25 mins, Round 2 – 20 mins Round 3 – 10 mins, Round 4 – 10 mins Learning Analytics - GMCC 16 May 201617

18. Learning Analytics - GMCC 16 May 201618 Lunch

19. Activity 2: Data Sources Process 1.Chose a group 2.Use post-its to identify data sources you will need to answer the user stories 3. Learning Analytics - GMCC 16 May 201619

20. Activity 3: Solutions and barriers Process 1.Chose a group – select one/group of user stories. 2.Use flipchart paper - Existing solutions or opportunities that could be developed - Technical challenges - Cultural challenges - Three essential features that the solution must include Learning Analytics - GMCC 16 May 201620

21. Learning Analytics - GMCC 16 May 201621 14:45 Coffee

22. Group feedback and next steps Learning Analytics - GMCC 16 May 201622

23. Learning Analytics - GMCC 16 May 201623 16:00 Finish