1. Knowing you’re there: analysing technological engagement to enhance retention and success
Professor Jo Smedley &
Professor Clive Mulholland
March 2014

2. 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.

3. Session Aims Using Big Data About Analytics
Case study: Learner Journey Analytics

4. Value of Big Data Analytics
Prescriptive analytics
To determine which decision and/or action will produce the most effective result against a specific set of objectives and constraints
Advanced
analytics
Predictive analytics
Leverage past data to understand why something happened or to predict what will happen in the future across various scenarios
Business
intelligence
Descriptive analytics
Mine past data to report, visualize and understand what has already happened – after the fact or in real time
Computational complexity
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

5. Case Study: Learner Journey Analytics
Belonging and attachment
Student life cycle
Learning Analytics

6. Learning Analytics Learning Analytics Target Setting Induction
Traffic Lights
Data Mining
Activity Monitoring
Learning Analytics

7. Conclusions/Further Work
Enhanced data transparency
Wider engagement
Links to:-
Admissions data
Achievement
Credit scores

8. Webpage: http://celt.southwales.ac.uk/does/sa/
Questions/Followup
Webpage:

9. Student experience surveys x n
Internal data
Module surveys x n
Student experience surveys x n
Big Data
Internal data
Activity monitoring
External data
Managing Information

10. International Student barometer
External data
NSS
PRES
PTES
HESA
DLHE
International Student barometer
Big Data
Internal data
Activity monitoring
External data
Managing Information

11. Big Data Internal data Activity monitoring External data Return
Blackboard Interactions
GlamLife interactions
Number of missed QMP Assignments
Googl Interactions
Logons from student area
Tier 4 sign-ons
(entry etc)
Estates info
Student Representation
Library interactions
Return
Big Data
Internal data
Activity monitoring
External data
Managing Information

12. Activity Monitoring Technological interactions Predictive equation
Return
Activity Monitoring
Technological interactions
BlackBoard, Googl , PC login, GlamLife
Predictive equation
Bus./Comp./Music Tech/Drama/Graphics/Acc.
Data visualisation
Managing Information

13.
Managing Information

14. Return
Managing Information

15. Return

16. Return
Target setting
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%
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

17. Return Subject groupings reflect NSS
Comparison of satisfaction levels and retention/performance levels
Most subjects fall in one faculty, others are cross faculty
Vertical scales and the baselines differ depending on the indicator being reported.
Managing Information

18. Induction Activities Impact
Return
Induction
Activities
Funded new induction activities to strengthen student sense of “belonging”
Goal: improved student achievement, success and retention
Impact
“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
”Definite bonding between them. And faster than in previous years... “ Geology
Managing Information

19. Student Life Cycle Student Life Cycle
Return
Raising Aspirations
Better Preparation
First Steps in H.E.
Moving Through
Student Success
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?
Student Life Cycle
Reference:
Managing Information

20. Learning Analytics: Techniques and Methods
Return
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
Ref: Cooper, Adam. A Brief History of Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, November 2012
Managing Information