The document discusses Ayrshire College's efforts to improve student retention rates through predictive analytics. After a college merger, retention rates dropped and were the worst in Scotland. The college developed an internal retention tool using basic Excel to highlight at-risk students based on attendance. This led to the development of an online retention tool using predictive attributes from student data to assign risk scores and target at-risk students. The tool was improved over multiple versions. After implementing these predictive analytics approaches, the college saw a 2.4% improvement in early withdrawals and improved their retention rate ranking among Scottish colleges.

1. Ayrshire College – Improving
Retention through Predictive
Analytics
Mark Sanger – Data Analyst
Richard Simson – Performance and Planning Manager

2. Background
College merger August 2013 (Kilmarnock College, Ayr College and James Watt
College – Kilwinning Campus)
Three separate Unit-e Databases August 2013-August 2014
Restructure and reorganisation August 2013 – April 2014
Legacy processes or no processes in place
Single Unit-e Database started August 2014
Size and scale of new college a challenge
Small College Thinking – Systems, Reports, Communication
Result
Key Performance Indicators 2014-15 worst in Scottish Sector FE & HE

3. Further Education Results 2014-15
College
Rank
2014-15
Rank
2013-14
Rank
2012-13
Rank
2011-12
Newbattle 1 9 1 17
Dundee & Angus College 2 2 4 5
City of Glasgow 3 4 16 7
South Lanarkshire 4 6 2 3
Forth Valley 5 5 6 4
Borders 6 1 5 1
SRUC 7 3 3 2
Glasgow Clyde 8 15 15 15
D & G 9 17 14 12
Glasgow Kelvin 10 12 13 13
West Lothian 11 14 9 10
New College Lanarkshire 12 16 12 11
NE Scotland 13 7 10 14
Fife College 14 13 17 16
West College Scotland 15 11 11 9
Edinburgh College 16 10 8 6
Ayrshire College 17 8 7 8

4. What was the issue.
Unit-e reports running slow.
Reports complex and difficult to navigate
Provided attendance information but didn’t highlight
who was at risk.
Disconnect – MIS Reports fit for purpose/ Performance
and Planning not fit for purpose.
Result
Alternative solution sought - Internally Developed
Retention Tool v1

5. Retention Tool v1.
Basic
Excel with VBA
Colour coded.
Built to highlight student
with poor attendance
Notes recorded centrally
CRM
Communicate from the tool
email/SMS

7. Data Cycle

8. Withdrawal Risk
High Risk of
Withdrawal
Low Risk of
Withdrawal
Predictive Attributes
Virtual Learning Environment Demographics
Student FundingStudent Attendance

10. Decision Trees

16. Online Retention Tool Interface – Level 1
Technical Specification
• Classic ASP
• Bootstrap Framework (CSS)
• MS Access DB
• J Query
• IS Server

17. Online Retention Tool Interface – Level 2

18. Predictive
Score
Percentage
Attendance
Online Retention Tool Interface – Level 3

26. FE Results
College
Rank
2015-16
Rank
2014-15
Rank
2013-14
Rank
2012-13
Rank
2011-12
Dundee & Angus College 1 2 2 4 5
City of Glasgow 2 3 4 16 7
SRUC 3 7 3 3 2
Forth Valley 4 5 5 6 4
Newbattle 5 1 9 1 17
South Lanarkshire 6 4 6 2 3
NE Scotland 7 13 7 10 14
Glasgow Kelvin 8 10 12 13 13
Borders 9 6 1 5 1
Edinburgh College 10 16 10 8 6
Glasgow Clyde 11 8 15 15 15
West Lothian 12 11 14 9 10
West College Scotland 13 15 11 11 9
Fife College 14 14 13 17 16
Ayrshire College 15 17 8 7 8
D & G 16 9 17 14 12
New College Lanarkshire 17 12 16 12 11
FE Full-time (2015-16 Second semester)
FE Full-time (2016-17 First semester)
2.4% Improvement in early withdrawals

27. http://educationaldatamining.org/EDM2014/u
ploads/procs2014/short%20papers/209_EDM-
2014-Short.pdf
Building a Student At-Risk Model:
An End-to-End Perspective
New York Institute of Technology