This presentation was delivered at the STEM 2016 conference in July 2016 at Leicester University. It describes a system for identifying students at risk on computer science programmes and then helping them.

1. STEWART GREEN
NICK PLANT
COURTNEY CHAN
A student monitoring and remedial
action system for improving retention on
computer science programmes

2. Presentation Structure
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 The problem
 The solution approach
 The system
 The results
 A discussion
 The future
 Including a more comprehensive retention framework

3. The problem
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 Retention is a problem for CSCT programmes at
UWE, Bristol, as it is nationally (Woodfield, 2014))
 E.g. for BSc (Hons) Computer Science at UWE:
 2012-13:
 inchoate retention system: 11%
 2013-14:
 no retention system: 18%
 2014-15:
 personal retention system: 12%
 2015-16:
 department-wide system based on the personal
system: ??

4. Solution Approach
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 Problem addressed by UWE, including FET and
CSCT
 Resourced initially by the Widening Participation initiative
 CSCT addressing the problem via:
 Student-at-risk-identification-and-remedial-action system
 Research into retention
 Investigating module performance
 The focus of this presentation is on the Students-at-risk-
identification-and-remedial-action system
 Programme-focused
 Local variations permitted

5. The System 1
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 Retention coordinator (intern)
 Acquires for every student on the 10 CSCT programmes:
 Attendance data (a proxy for engagement)
 Other engagement proxies, e.g. VLE use
 Formative and summative assessment results (measures of academic
performance)
 Maintains:
 Spreadsheet per programme identifying:
 Students at risk (SAR)
 Judged subjectively: some academic performance failures and/or
attendance failures
 Students at critical risk
 Judged subjectively: nearly all academic performance failures and
attendance failures

6. The System 2
 Programme Leader, Year-One Academic Personal
Tutor and Retention Coordinator periodically:
 Review SAR data for their programme
 Help students at risk
 Supported by Student Support Advisors
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7. Help
 Help may include:
 An email or face-to-face chat about:
 The student’s main problems
 How to tackle those problems
 Encouragement to attend:
 lectures, tutorials and lab sessions
 Appointments for:
 Peer Assisted Learning sessions
 Advice to attend catch-up coding sessions
 Provision of advice to engage with, and seek help from, academic staff
 Referral to Student Support Advisors for extremely critical
cases
 e.g. suicidal or depressed student
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8. The Results 1: Retention Rate:All Programmes
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Programme
name
Number of
students 2014-15
Predicted loss
2014-15
Number of
students 2015-
16
Predicted loss
2015-16
Change
Computing 29 29% 28 34% -5%
Broadcast Audio &
Music technology
14 29% 6 0% +29%
Audio and Music
Technology
78 26% 52 27% -1%
IT Management for
Business
47 17% 29 7% +10
Computer Systems
Integration
21 16% 18 0% +18%
Forensic Computing
and Security
32 13% 42 15% -2%
Games Technology 83 13% 64 26% -13%
Computer Science 93 12% 91 11% +1%
Creative Music
Technology
20 10% 25 24% -14%
Digital media 25 8% 25 20% -12%

9. Programme Leader Buy-in Varied
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 Results of a questionnaire indicated:
 Programme Leaders varied in the way they enacted the system
 Little or no engagement (1 from 10)
 Full engagement (1 from 10)
 Somewhere in between (8 from 10)

10. Results 2: Causes of At-Riskiness: BSc (Hons)
Computer Science Programme
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Student identifier Number of core modules
failed (max = 4)
Reason
S1 4 Fee problem
S2 4 Transferring to Korean Studies at Sheffield University
S3 4 Full-time job to support parents
S4 4 Late start due to accommodation problem; transferring to Banking
S5 4 Severe depression
S6 4 Not known yet.
S7 4 End of a long-term relationship.
S8 4 Undisclosable personal reasons.
S9 3 Not yet known.
S10 3 Not yet known.
S11 3 Transferring to Digital media at UWE.
S12 ECA TO
S13 ECA TO: Transferring course.
S14 ECA RWD: 10/12/15: Suicidal: required to withdraw
S15 WD: 18/12/15: Preferred employment to university
S16 0 Success!
S17 1 Success!

11. Discussion of Results for BSc (Hons) Computer
Science
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 Face-to-face meetings identified a wide range of “causes” for students
being at risk
 Many causes are unrelated to the students attitude towards academic
work, which may be positive
 Could more have been done to “save” more Computer Science
students?
 Apart from the three “Not yet knowns”, all students arguably had
valid non-academic reasons for
 engaging poorly &
 performing poorly academically
 Could demographic analytics tools have predicted these non-academic
contingencies?
 Arguably no
 But, we remain open to testing their use

12. Other Related Retention Initiatives in 2015-16
 Pre-joining study advice to new Computer Science
and Computing students
 Provided in a joining-letter to new students over the 2015
summer
 Well received by the students
 Seemed to improve initial programming ability of the cohort
 More focused and directed advice for 2016-17
 And practice spreading now to other programmes
 Read three key retention reports (see references)
 Influenced the development of a retention framework
 Trying to see student as a whole person
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13. For 2016-17 and Beyond 1: Retention Framework
 Interactions with students at key stages critically
impact likelihood of high retention (Thomas, 2012),
including:
 Attracting students
 Open Days
 Applicant days
 Pre-arrival joining instructions
 Pre-arrival activity events
 Induction
 Year-one, -two and -three
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14. Retention Framework: Retention Mechanisms
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 At each stage, specific mechanisms can help address
problems, or exploit opportunities, in order to facilitate
retention (Thomas, 2012; Gordon, 2016).
 For example:
 Open Days can:
 Provide course information in order to inculcate appropriate course
expectations
 Use female student ambassadors in order to help to tackle the gender
gap
 Enable staff-student and student-student interaction in order to
begin building feelings of belonging
 Provide opportunities to invite prospective students to a dedicated
Facebook site to help build belonging, provide information, set
appropriate expectations, etc.

15. For 2016-17 and beyond 2: System to Continue
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 It seems very likely that management will resource
the running of the student-at-risk-identification-
and-remedial-action system in 2016-17
 Retention Coordinator role expands:
 From data collection and analysis
 To more engagement with:
 Programme Teams
 External units, e.g. Business Intelligence

16. For 2016-17 and Beyond 3: IT Tools for
Improving Retention
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 The University is continuing to investigate tools that
support aspects of retention e.g.:
 Demographic analytics:
 UniqueInsights
 Learning analytics:
 SEAtS, Tribal
 Attendance monitoring
 SEAtS, CAMPUSM

17. Resources
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 Building student engagement and belonging in
Higher Education at a time of change, 2012, Liz
Thomas
 Undergraduate retention and attainment across the
disciplines, 2014, Ruth Woodfield
 Issues in retention and attainment in Computer
Science, 2016, Neil Gordon, University of Hull
 From March ‘16 retention webinar
 Reports from six different academic disciplines
 Presentations from three of the six disciplines