© University of South Wales
Enhancing retention through
learning analytics
Dr Jo Smedley
University of South Wales
September 2013
© University of South Wales
“University learners sometimes encounter challenges with their
learning which can lead them to quit. To enhance retention and
success of all students, information technology has enabled the
analytical review of considerable quantitative and qualitative
learning data. This has informed the identification of several key
factors with differential applications, for example, between
subjects, between student age groups, which has led to the
enhanced targeting of continuing initiatives to maximise overall
achievement.”
Abstract
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Is your organisation maximising its
information potential?
Refining data rich, information poor (DRIP) systems to
enhance client experiences
Enhancing client
experiences
Data management
Adjusting categories (JACS codes)
Adjusting reporting times
Cross-University initiative
++++++
External survey data
UCAS Admissions
Student Experience
National Student Survey
International Student barometer
Internal survey data
Module feedback
Retention
Success
Activity Monitoring
Virtual Learning Environments
Estates
Induction
++++++
Dr Jo Smedley Email: [email protected]
Collaborative opportunities Practitioner
case studies
Ideas for development
Feedback on existing
work
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Retention
Induction Activities
Internal Survey Data (module feedback, student
representation, student experience surveys)
Activity Monitoring (Blackboard Interactions, GlamLife
Interactions, Missed QMP Assignments, Googlemail Interactions, Logons from
student area, Tier 4 Signons, Estates info, Library info)
Data Management (Target Setting, Data Sharing)
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Success & Satisfaction
External Survey Reporting (NSS, PRES, International Student
Barometer, DLHE, HESA)
Data Management
(JACS coding)
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The Undergraduate Learner Journey
UCAS Admissions
Module feedback x n
Student representation
End of year surveys x n
National Student Survey
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The Postgraduate Learner Journey
Admissions
Module feedback x n
Student representation
End of year surveys x n
PRES
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The International Learner Journey
Admissions
Module feedback x n
Student representation
End of year surveys x n
International Student Barometer
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Big Data
• Internal data
• Activity monitoring
• External data
Activity monitoring
Blackboard Interactions
GlamLife interactions
Number of missed QMP Assignments
Googlemail Interactions
Logons from student area
Tier 4 sign-ons
Estates info
(entry etc)
Student Representation
Library interactions
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Internal data
Module surveys x n
Student experience surveys x n
Big Data
• Internal data
• Activity monitoring
• External data
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External data
NSS
PRES
HESA DLHE
International Student
barometer Big Data
• Internal data
• Activity monitoring
• External data
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Predictive modelling: retention
)MonitoringActivity (
data) Internal(
Retention
g
f
where:- • f and g are a multiplying factors to be determined through data analysis • internal data comprises reported formal and informal data from internal surveys, e.g. module feedback, student experience surveys • activity monitoring comprises data gathered from student interactions, e.g. VLE, Googlemail, Library, Estates
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Predictive modelling: success/satisfaction
)data External(
tisfactionSuccess/Sa
h
where:- • h is a multiplying factor to be determined through data analysis • external data comprises reported data in external league tables, e.g. NSS, PRES, International Barometer, HESA
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Continuing Work
• Analyse categories of existing data to determine model factors
• Collaboration
– “What works” initiative
• Impact
• Further dissemination
14 Email: [email protected]