marketing & communications – the first 90...
TRANSCRIPT
Data is more important than ever for higher education
We are moving from a funding-driven to a data-driven system of regulation
Advances in technology and changes in policy are
leading to increased demand for data
• The sector is more diverse
• Students have more choice
• Economic return is higher priority
The Government has a positive agenda for data
and digital services
• Open data
• Open source tools
• Registers
• APIs
• ‘Government as a platform’
Higher Education Providers need to respond to this challenge
• Improved data capability
• Better data integration
• Better, faster decision-making
• Improved targeting of resources
Data Futures
• Improving the collection platform
• Standardising the data model
• Increasing efficiency and speed of operation
Detailed Design Phase
Detailed Design
Phase Implementation/Build Finalise Procurement
Alpha Pilot Beta Pilot
Feb
2017
July
2017
HESA Board
Data Futures Programme Board
Data Futures Delivery Group (SMT & DF-PMO)
Data Futures Delivery Team • Data Policy
• Data Management
• Development and
Technical
• Supplier
Programme
Leadership and
Sponsors
Programme
Steering every 4
weeks
Delivery
Team
Data Futures Management Structure
Roadmap for the changes ahead
• Improved collection platform
• Better data linking
• Advanced analytical tools
Apri 2016 Jisc Learning Analytics
Sarah Davies, Head of higher education and student experience, Jisc
November 2016 Learning from your students and their data
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
Tracker findings – learning and teaching
•72% of students believe
that when technology is used by teaching staff it helps their learning experience
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
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
Stages of learning analytics development
Basic Analytics
What has
happened
Automated
Analytics
What is
happening
Predictive
Analytics
What might
happen
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
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
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
• Or email [email protected]
2/03/
2016