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Learning Analytics
the Integrated Way
Sandra Stevenson-Revill & Ruth Grindey
University of Derby
Introduction
Update on our first-hand experience of implementing
Blackboard’s own Learning Analytics Solution.
With the hindsight of having used our own Analytics for many
years.
2
What is UDOL
3
• Improve the range of analytics available, specifically for UDOL
– How is the system performing for student
– What tools are being used? – Can we refocus training?
– Who need us? – Where can we best spend our time?
– Why - Is there a pattern to the support requirements, can we make
changes to reduce load
– Where – Are issues focused in one area, physical/subject?
– When – Are we providing support when are students need it.
Increasing reporting capacity to ensure that our eye is always on
the ball.
4
The aim
• 2011 – Leeds Blackboard TLC – Do you remember!
5
• Number of active users, staff & students
• Types of content uploaded
• Content Areas used
• Module/Programme areas used/not used.
Before
Before
• Focused on
– Modules with out any content
– Modules that had not been granulated
– Broad student and staff usage figures
• How
– External reporting tool, Cognos
– via Data Warehouse
– No VLE system impact
– New reports created as required
– Blending data from SIS and VLE
6
Why change?
• Growth in requirement
• Increased variety of tools
• Restructure
– UDOL became a separate Business Unit
• Managed warehouse
• More expansion than change
7
How did it go
• Slowly – longer than expected – slow burn
• Still in it’s infancy
• More questions than answers. – We will be
back next year
• How will we manage the quantity of data
• Academics are excited
– But concerned. Big Brother is watching!
8
What to expect?
• Regular calls with project managers
and the implementation team
• Start up meeting to get the basics
down
• 3 days onsite to work through the
nitty gritty
– What additional reports will we need.
Bespoke
• Academic scepticism
9
Benefits
• Visibility – A real first.
• Trend data for 7 years across the
business for UDOL the life of the
business.
• Cause and effect analysis is now
possibility
• Correlation activities can take
place
10
Did we have a crystal ball?
• Keeping the ‘big’ data for that long – so Analytics can use it
• Having 7 years of data
• Test and production match
• Quality of the data
• Ability to align with SIS data
11
Analytics for students
• Not this academic year
• Need to focus on the management reports
• Assessing the students reaction
• Light touch foot print
12
Questions
13
Get in touch
14
• Sandra Stevenson-Revill,
s.revill@derby.ac.uk
• Ruth Grindey,
r.m.grindey@derby.ac.uk

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Learning Analytics the Integrated Way

  • 1. Learning Analytics the Integrated Way Sandra Stevenson-Revill & Ruth Grindey University of Derby
  • 2. Introduction Update on our first-hand experience of implementing Blackboard’s own Learning Analytics Solution. With the hindsight of having used our own Analytics for many years. 2
  • 4. • Improve the range of analytics available, specifically for UDOL – How is the system performing for student – What tools are being used? – Can we refocus training? – Who need us? – Where can we best spend our time? – Why - Is there a pattern to the support requirements, can we make changes to reduce load – Where – Are issues focused in one area, physical/subject? – When – Are we providing support when are students need it. Increasing reporting capacity to ensure that our eye is always on the ball. 4 The aim
  • 5. • 2011 – Leeds Blackboard TLC – Do you remember! 5 • Number of active users, staff & students • Types of content uploaded • Content Areas used • Module/Programme areas used/not used. Before
  • 6. Before • Focused on – Modules with out any content – Modules that had not been granulated – Broad student and staff usage figures • How – External reporting tool, Cognos – via Data Warehouse – No VLE system impact – New reports created as required – Blending data from SIS and VLE 6
  • 7. Why change? • Growth in requirement • Increased variety of tools • Restructure – UDOL became a separate Business Unit • Managed warehouse • More expansion than change 7
  • 8. How did it go • Slowly – longer than expected – slow burn • Still in it’s infancy • More questions than answers. – We will be back next year • How will we manage the quantity of data • Academics are excited – But concerned. Big Brother is watching! 8
  • 9. What to expect? • Regular calls with project managers and the implementation team • Start up meeting to get the basics down • 3 days onsite to work through the nitty gritty – What additional reports will we need. Bespoke • Academic scepticism 9
  • 10. Benefits • Visibility – A real first. • Trend data for 7 years across the business for UDOL the life of the business. • Cause and effect analysis is now possibility • Correlation activities can take place 10
  • 11. Did we have a crystal ball? • Keeping the ‘big’ data for that long – so Analytics can use it • Having 7 years of data • Test and production match • Quality of the data • Ability to align with SIS data 11
  • 12. Analytics for students • Not this academic year • Need to focus on the management reports • Assessing the students reaction • Light touch foot print 12
  • 14. Get in touch 14 • Sandra Stevenson-Revill, s.revill@derby.ac.uk • Ruth Grindey, r.m.grindey@derby.ac.uk

Editor's Notes

  1. Comparison old reports new reports that match