Jisc aims to support UK education and research communities through digital transformation. It focuses on student digital experience, learning analytics, and providing architecture and services to support learning analytics. Learning analytics applies techniques like machine learning and data mining to help students and institutions meet their goals by improving retention, achievement, and employability. It can identify at-risk students, encourage help-seeking, and analyze which interventions are effective. Jisc's learning analytics project provides a code of practice, toolkit, and student app to support institutions in adopting learning analytics approaches.

1. Apri 2016 Jisc Learning Analytics
Sarah Davies, Head of higher education and student experience, Jisc
November 2016 Learning from your students and their data

2. About Jisc
Student digital
experience
Learning
analytics

3. Our vision and aim Aim
We aspire to be the world
class powerhouse of digital
support and transformation to
the UK teaching and research
communities
Vision
To make the UK the most
digitally advanced education
and research nation in the
world

4. We do… three main things

5. The digital experience matters to students
» Often students' first experiences
» Many courses now hybrid/blended
» Students expect university to prepare
them for digital workplaces
» Affects students' sense of wellbeing and
belonging
» Students see the digital experience as an
opportunity to contribute and get
involved
» Need experience of different groups
represented
Image by Alejandro Escamilla from https://unsplash.com/@alejandroescamilla CC-0

6. Tracker findings – learning and teaching
»72%of students believe
that when technology is used
by teaching staff it helps their
learning experience

7. Tracker findings – learning and teaching
In a 6-week period:
»9 in 10 students found
information online
»8 in 10 students produced
work in a digital format
»5 in 10 students worked online
with others
»3 in 10 students created a
personal record of their
learning

8. Students leave behind digital footprints
2/03/2016

9. More prosaically
2/03/2016

10. 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.
› Used to:
– improve retention
– improve achievement
– improve employability
2/03/2016 The case for Learning Analytics

11. Which behaviours tend to lead to the desired outcome?
2/03/2016

12. Stages of learning analytics development
Basic Analytics
What has happened
Automated Analytics
What is happening
Predictive Analytics
What might happen

13. Analytics can identify risk and shine a light
»It can flag at risk students
»Encourage students to seek
help
»Help you analyse causes of
differential outcomes
»Analyse which interventions
work
»But only systematic
interventions will make a
difference
2/03/2016 The case for Learning Analytics

14. Jisc’s learning analytics project
Architecture
and service
Toolkit Community
06/06/2017

15. Jisc learning analytics architecture

16. Service: Student app
» First version will include:
› overall engagement
› comparisons
› self declared data
› consent management
Bespoke development by Therapy Box
2/03/2016 The case for Learning Analytics

17. Toolkit: Code of practice
2/03/2016 The case for Learning Analytics

18. Summary and further information
»Learning analytics is not a silver bullet and an ethical
approach is essential, but:
› It is immediately useful to many HE providers
› Jisc is making it easier to adopt
› There is rich potential for the future
› For more information, see https://analytics.jiscinvolve.org
2/03/2016

19. Contact Jisc
06/06/2017 19
Learning analytics
setup.analytics@jisc.ac.uk
General enquiries
customer.services@jisc.ac.uk
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