how student data and analytics can be used to target intervention and improve student outcomes
TRANSCRIPT
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John GledhillProduct Director - HE
Student Insight: Student and learning analytics
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AgendaInstitutional challenges - knowing the students
Institutional self-study – trend analysis of student attainment related to student use of the VLE and library
Tailoring to meet student requirements
Focusing on student engagement – 80% from a 25 mile radius – commuter students
Development of tutoring – inclusivity rather than deficit
Diverse 23,000 strong student population42% students from BaME background
Average age: 27 years
Mixed entry tariff profile
Provision of support that has a bespoke and personalised feel
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AgendaDevelopment partnership
Objectives
• Predict student academic performance to optimise success
• Predict students at risk of non-continuation
• Build on research into link between VLE activity and academic success
• Scale data processing • Understand risk factors and
compare to cohorts
3 years of matched student and activity data used to build predictive models
Staff can use student, engagement and academic data to understand how they affect student outcomes.
Information accessible in one place on easy to understand dashboards.
Integrated with Tribal SITS:Vision and staff e:vision portal
Consultation with academic staff on presentation and design
Accuracy of module academic performance predictions 79%
Based on module academic history and demographic factors
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Student Insight
Data collection and
Data mining
Create models
Predict andunderstand
Patterns
RelationshipsTrends
Behaviours
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Student Insight
Collect
SIS integration
Student activity data
1
Identify
Outcome analysis
Risk prediction
Student tagging
2 Awareness
Monitor cohorts and tags
Student engagement
Understand risk influences
3 Act
Proactively manage progress
Record decisions and actions
Manage student interventions
4
Improve
Assess intervention effectiveness Student feedback
5
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AgendaRisk prediction
Academic performan
ce risk prediction
Course withdraw
al risk predictio
n
Monitor groups
and individu
al
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AgendaIdentify
Tag students at risk
Monitor risk by student cohort
See what
factors affect
outcomes
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AgendaUnderstand risk influences
Prediction trend
View what
influenced the
prediction
Compare to
student cohorts
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AgendaRecord decisions and actions
Log decision
s and actions
Maintain history
of decisions made
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AgendaManage student interventions
Inform student support team
Cases allocated
to student support
staff
Track cases
through to
completion
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AgendaAssess intervention effectiveness
View impact
of actions made
Take into account student
feedback
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AgendaSolution focussed
MonitorRecord SharePredictPreventSupportIntervenePersonalise
Use of Student Insight
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AgendaFit for the future
Attainment plus
Student partnership
Statutory obligations
Student academic experience
Resource management
Data and analytics
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Thank you