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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.
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• 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.
The aim
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• 2011 – Leeds Blackboard TLC – Do you remember!
• Number of active users, staff & students
• Types of content uploaded
• Content Areas used
• Module/Programme areas used/not used.
Before
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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
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Why change?
• Growth in requirement
• Increased variety of tools
• Restructure– UDOL became a separate Business Unit
• Managed warehouse
• More expansion than change
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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!
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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
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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
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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
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Analytics for students
• Not this academic year
• Need to focus on the management reports
• Assessing the students reaction
• Light touch foot print