Enhancing retention through learning analytics

© University of South Wales

Enhancing retention through

learning analytics

Dr Jo Smedley

University of South Wales

September 2013


“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

© University of South Wales 3

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


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)


Success & Satisfaction

External Survey Reporting (NSS, PRES, International Student

Barometer, DLHE, HESA)

Data Management

(JACS coding)


The Undergraduate Learner Journey

UCAS Admissions

Module feedback x n

Student representation

End of year surveys x n

National Student Survey


The Postgraduate Learner Journey

Admissions

Module feedback x n



PRES


The International Learner Journey

Admissions

Module feedback x n



International Student Barometer


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


Internal data

Module surveys x n

Student experience surveys x n

Big Data

• Internal data


• External data


External data

NSS

PRES

HESA DLHE

International Student

barometer Big Data

• Internal data


• External data


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


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


Continuing Work

• Analyse categories of existing data to determine model factors

• Collaboration

– “What works” initiative

• Impact

• Further dissemination

14 Email: [email protected]

Enhancing retention through learning analytics

Education

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