june 21 learning analytics overview
DESCRIPTION
presentation to OU alumni 21 JuneTRANSCRIPT
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Learning Analytics and Higher
Education: a brief introduction
Sharon Slade
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Overview of session
• Brief background
• How learning analytics is being used in Higher Education
• What the OU is doing
• Things to think about
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Our students leave information about themselves every time they interact with usWith no realization or understanding of what we do with that information
So, how do we use that data – and does it matter?
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Learning analytics is the measurement, collection, analysis and reporting of data about learners to increase our understanding of them and their learning needs, and to use that understanding to influence their learning.
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What do we mean by data about learners?
Background
Disability
Gender
Ethnicity
Learning behaviours
Study history
Learning style
Funding issues
Assignment/test scores
Websites visited
Hitting study milestones
Study goals
Age
Location
Working status
Family income
LanguageLog in frequency
Posting to forums - frequency
Frequency of contact with tutor
Posting to forums - content
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What’s going on in learning analytics?• Many universities are using student data to trigger
interventions– Some are automated and direct to students– Others are delivered via the tutor or support staff– Most focus on online engagement, assessment and
demographics• Broader studies are looking at modifying student
learning as well as providing student support• Lots of newer work around social networking and how
students engage with each other (and how that impacts on their success)
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Purdue’s Course signals• Uses a predictive model based on
– online activity and assessment scores– Previous academic history and demographic data
• Has created an ‘early warning’ system which– Identifies students ‘at risk’ of not completing a
course– Deploys an intervention to increase chances of
success• System automates the intervention process
– Student gets ‘traffic light’ alert via their online student page, and
– an email/message suggesting corrective action
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Knewton (Arizona State Univ)• A continuously adaptive online learning
platform• Logs data about student behaviour and
performance (e.g. keystrokes, scores, speed, etc)
• Analyses behavioural and performance data, comparing it with similar students and assessing relevance of educational content to students
• Serves each individual student the most appropriate learning activity for them at a particular moment in time
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University of Maryland’s ‘Check my activity’ Tool
• Allows students to compare their use of the VLE against that of other students
• Results indicate that students with lower usage score less well
• http://www.screencast.com/t/jmZzozpPRZiG
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The OU and Student Support Teams
• From Feb 2014, supporting students by curriculum rather than geography
• Ensuring that students get proactive support based on their characteristics and study behaviours
• Underpinned by a standard ‘service level agreement’ to ensure equitable treatment and the maintenance of high standards of support
• The opportunity for development of expertise
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The OU Student Support Tool
• A monitoring tool to be used by Student Support Teams
• Pulls in data relevant to the student• Aims to identify and track student progression against
key milestones across a curriculum area• Links to interventions direct to students or via other
staff
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Select the SST, quals, pathways, modules, levels, regions of interest
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Get a summary view of student numbers
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Run a report to identify students who meet certain characteristics
Students aged 50+
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Review list of students
Can sort and search each column
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Send an intervention if needed
Can opt to exclude certain student types
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Student Support Teams Pilots• This approach has been piloted across faculties for the
last few years • Has led to greater understanding of drivers of student
success• Support staff feel more knowledgeable• Improvements in student retention and progression, as
well as increased student satisfaction
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So learning analytics can help us to really understand our students.
Sounds great, yes?
Most research has focused on data protection and privacy issues, but is there more to it than that?
What other issues might we be concerned about?
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Privacy
Do students appreciate that information is being
gathered about them?
Are we explicit about what we might do with that
information?
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Transparency and robustness
Who can see the data collected?
Who can see/influence the models?
How reliable and robust are the models?
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Power
Who gets to decide what happens next?
Who can choose which students get more support?
Do teachers, learners, and administrators have the same authority/rights to determine what support is provided?
------ less
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Ownership issues
Who can mine our data for other purposes?
Can students opt out of having their information used?
… and what are the consequences of that?
How long is data kept for?
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Responsibilities
Is there a shared responsibility to ensure that
information is accurate? Can students opt to
disguise themselves online?
Do we have a responsibility to ensure
equitable treatment of students based on what
we know? (or despite what we know)
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Any questions?