The document discusses how the University of South Wales employs learning analytics to improve student retention and success by analyzing both quantitative and qualitative data. It highlights the identification of key factors affecting student achievement, adaptation of data management practices, and collaborative initiatives to enhance educational outcomes. The focus is on using both internal activity monitoring and external survey data for predictive modeling and ongoing analysis in order to refine educational strategies.

1. © University of South Wales
Enhancing retention through
learning analytics
Dr Jo Smedley
University of South Wales
September 2013

2. © 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

3. © University of South Wales
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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: jo.smedley@southwales.ac.uk
Collaborative
opportunities Practitioner
case studies
Ideas for
development
Feedback on
existing
work

4. © University of South Wales
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)

5. © University of South Wales
Success & Satisfaction
External Survey Reporting (NSS,
PRES, International Student
Barometer, DLHE, HESA)
Data Management
(JACS coding)

6. © University of South Wales
The Undergraduate Learner Journey
UCAS
Admissions
Module
feedback x n
Student
representation
End of year
surveys x n
National
Student Survey

7. © University of South Wales
The Postgraduate Learner Journey
Admissions
Module
feedback x n
Student
representation
End of year
surveys x n
PRES

8. © University of South Wales
The International Learner Journey
Admissions
Module
feedback x n
Student
representation
End of year
surveys x n
International
Student
Barometer

9. © University of South Wales
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

10. © University of South Wales
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Internal
data
Module
surveys x n
Student
experience
surveys x n
Big Data
• Internal data
• Activity monitoring
• External data

11. © University of South Wales
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External
data
NSS
PRES
HESADLHE
International
Student
barometerBig Data
• Internal data
• Activity monitoring
• External data

12. © University of South Wales
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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

13. © University of South Wales
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Predictive modelling: success/satisfaction
)dataExternal(
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

14. © University of South Wales
Continuing Work
• Analyse categories of existing data
to determine model factors
• Collaboration
– “What works” initiative
• Impact
• Further dissemination
14Email: jo.smedley@southwales.ac.uk