Jisc Learning Analytics in FE and Skills Greater

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

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

2 Objectives The purpose of the day will be to: • Explore significant opportunities

2 Objectives 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 Time What 10: 00 Welcome and overview – Louise Timperley - Director

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

4 Learning Analytics What is learning analytics?

4 Learning Analytics What is learning analytics?

Learning Analytics - GMCC 16 May 2016 “learning analytics is the measurement, collection, analysis

Learning Analytics - GMCC 16 May 2016 “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” So. LAR – Society for Learning Analytics Research 5

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

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

Background and context Greater Manchester Chamber of Commerce 16 May 2016

Background and context Greater Manchester Chamber of Commerce 16 May 2016

Learning Analytics - GMCC 16 May 2016 Moodle Grade Tracker 8

Learning Analytics - GMCC 16 May 2016 Moodle Grade Tracker 8

Learning Analytics - GMCC 16 May 2016 9 Related Jisc Projects Destinations Data Project

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

Destination Data Project Business Questions: » How do the outcomes and destinations of learners

Destination Data Project Business Questions: » 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? 06/10/2015 Destinations Data Project for FE - End of Discovery Stage Review meeting 10

Example of Demographic Comparison 11

Example of Demographic Comparison 11

Example of Benchmark Comparison 12

Example of Benchmark Comparison 12

Learning Analytics - GMCC 16 May 2016 13 Data sets Local Student Demographic Prior

Learning Analytics - GMCC 16 May 2016 13 Data sets Local Student Demographic Prior Attainment In-year progress Attendance VLE access …others BIS Outcomes Based Success Measures Local contextual data Ao. C Dashboards Ofsted Dashboards …others External Labour Market Intelligence

Learning Analytics - GMCC 16 May 2016 Drivers • Learners demographic, age, ethnicity, needs,

Learning Analytics - GMCC 16 May 2016 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, … 14

Learning Analytics - GMCC 16 May 2016 Coffee – 15 mins 15

Learning Analytics - GMCC 16 May 2016 Coffee – 15 mins 15

Learning Analytics - GMCC 16 May 2016 Activity 1: User Stories A: Improve individual

Learning Analytics - GMCC 16 May 2016 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 16

Learning Analytics - GMCC 16 May 2016 Activity 1: User Stories Process 1. Add,

Learning Analytics - GMCC 16 May 2016 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 17

Learning Analytics - GMCC 16 May 2016 18 Lunch

Learning Analytics - GMCC 16 May 2016 18 Lunch

Learning Analytics - GMCC 16 May 2016 Activity 2: Data Sources Process 1. Chose

Learning Analytics - GMCC 16 May 2016 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. 19

Learning Analytics - GMCC 16 May 2016 Activity 3: Solutions and barriers Process 1.

Learning Analytics - GMCC 16 May 2016 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 20

Learning Analytics - GMCC 16 May 2016 14: 45 Coffee 21

Learning Analytics - GMCC 16 May 2016 14: 45 Coffee 21

Learning Analytics - GMCC 16 May 2016 Group feedback and next steps 22

Learning Analytics - GMCC 16 May 2016 Group feedback and next steps 22

Learning Analytics - GMCC 16 May 2016 16: 00 Finish 23

Learning Analytics - GMCC 16 May 2016 16: 00 Finish 23