© university of south wales knowing you’re there: analysing technological engagement to enhance...
TRANSCRIPT
© University of South Wales
Knowing you’re there: analysing technological engagement to enhance retention and success
Professor Jo Smedley & Professor Clive Mulholland
March 2014
© University of South Wales
Abstract
Student engagement is an important indicator of all types of academic attachment demonstrating active citizenship with their learning “world” (Barnett and Coate (2005), Krause and Coates, 2008). Learning
analytics on technological activity data provide early predictors of change impacting on retention,
achievement and success. From this learner behaviour “window”, outcomes are informing student-centred
initiatives at various stages of their learner journeys.
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Session Aims
• Using Big Data
• About Analytics
• Case study: Learner Journey Analytics
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Value of Big Data Analytics
• The goal of all organizations with access to large data collections should be to harness the most relevant data and use it for better decision making
Descriptive analyticsMine past data to report, visualize and understand what has already happened – after the fact or in real time
Computational complexity
Predictive analyticsLeverage past data to understand why something happened or to predict what will happen in the future across various scenarios
Prescriptive analyticsTo determine which decision and/or action will produce the most effective result against a specific set of objectives and constraints
Advancedanalytics
Businessintelligence
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Case Study: Learner Journey Analytics
• Belonging and attachment
• Student life cycle
• Learning Analytics
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Learning Analytics
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Target Setting
Induction
Traffic Lights
Data Mining
Activity Monitoring Learning
Analytics
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Conclusions/Further Work
• Enhanced data transparency
• Wider engagement
• Links to:- – Admissions data– Achievement– Credit scores
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Questions/Followup
Webpage: http://celt.southwales.ac.uk/does/sa/
Email: [email protected]@southwales.ac.uk
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Internal data
Module surveys x n
Student experience surveys x n
Big Data
• Internal data
• Activity monitoring
• External data
Managing Information
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External data
NSS
PRES
PTES
HESA
DLHE
International Student
barometer
Big Data
• Internal data
• Activity monitoring
• External data
Managing Information
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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 areaTier 4 sign-ons
Estates info(entry etc)
Student Representation
Library interactions
Managing Information 11
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Activity Monitoring
• Technological interactions– BlackBoard, Googlemail, PC login, GlamLife
• Predictive equation– Bus./Comp./Music Tech/Drama/Graphics/Acc.
• Data visualisation
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2012-2013
Managing Information 13
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2012-2013
Managing Information 14
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Target setting
• Comparison of retention targets with actual performance in 2011/12 and 2012/13, based on agreed retention target formula
• Generation of new targets for 2013/14
Managing Information 16
Status Quo Scale of improvement
Actual Return Rate 90%+ Target return rate same
Actual Return Rate 83% to 89% Target return rate 90%
Actual Return Rate 80% to 82% Target return rate – increase actual return rate by 5%
Actual Return Rate 70% to 79% Target return rate – increase actual return rate by 10%
Actual Return Rate below 70% Target return rate – increase actual return rate by 20%
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Induction
• Activities– Funded new induction activities to strengthen
student sense of “belonging”– Goal: improved student achievement, success and
retention
• Impact
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”Definite bonding between them.
And faster than in previous years... “
Geology
“The students have bonded particularly well,
they have been much more willing to approach staff and confident in how
they interact with us” Chemistry
“Decrease in student
withdrawals attributed to the
induction activity” Forensic Science
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Student Life Cycle
Raising Aspiratio
ns
Better Preparati
on
First Steps in
H.E.
Moving Through
Student Success
Managing Information 19
Student Life Cycle
• Are you aware of the main reasons why students withdraw from your programme?
• Are you aware of the steps they have to take in order to officially withdraw?
• What advice would you give to a student contemplating withdrawal?
Reference: http://www.ulster.ac.uk/star/resources/Anagnostopoulou_Parmar.pdf
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Learning Analytics: Techniques and Methods
• Statistics: hypothesis testing• Business Intelligence: effective reporting • Web analytics: technological interactions• Artificial intelligence/data mining: data patterns • Operational research: statistical methods• Social Network Analysis: online/offline links • Information visualisation: making sense of data
Managing Information 20
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Ref: Cooper, Adam. A Brief History of Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, November 2012