This document summarizes a discussion paper presented at SEDA on using data dashboards to inform decisions about widening access and participation at universities. It discusses how Ulster University collects and analyzes student data to guide educational interventions and support students. Examples of data sources and visualization dashboards are provided at the university, faculty, school, and student levels. The session promoted sharing practices for making evidence-based, data-informed decisions to improve access, participation, and student outcomes.

1. Widening Access and
Participation
Dashboards for Data
Informed Decision-
Making Discussion
Paper.
Ulster University
10th May 2019
SEDA

2. Catherine
O’Donnell
Ulster University
Professor Brian
Murphy
Ulster University
Barbara
Hunter
Ulster University
Authors
Discussion Paper Presenter at SEDA
Catherine O’Donnell

3. Aims and Objectives

4. This discussion paper session relates to the Widening Access
and Participation (WAP) theme. It aims to:
• Share how Ulster University collates, interprets and
visualises data to inform stakeholders who are seeking new
evidence-based ways of making data-informed WAP
decisions that support students during their entire student
lifecycle from pre-entry through to employability.
• Promote discussion and provide opportunities to share
practice among participants.
Aims

5. By the end of this session, delegates will be able to:
• Evaluate HE WAP policy, practice and approaches to evidence-based
data-informed decision-making that can guide educational
interventions and lead to student success, measurable by improved
student satisfaction, retention, attainment and/or learning gain.
• Explain and evaluate the key features of WAP data processes at
Ulster University and compare these features with practice elsewhere.
• Analyse and plan how the sector can share practice
regarding evidence-based ways of making data-informed decisions for
WAP objectives.
Objectives

6. PART ONE:
WAP policy, practice
and data based
decision-making
approaches at Ulster
University (and
discussions re
elsewhere).
[20 minutes]

7. § Ulster University collects and analyses a wide range of student data
in compliance with GDPR [1].
§ This data is used to make data-informed decisions that guide
educational interventions which can lead to student success and can
be measured by improved student satisfaction, retention, attainment
and/or learning gain. Widening Access and Participation Plans are
required in Northern Ireland [2, 3].
§
Introduction
Comprehensive
WAP Learner
Analytics
WAP Data
drives policy
and practice
Mission-
Defining ‘civic
contribution’
strand of
Strategic Plan
[4]

8. § Ulster University aspires to ‘be a leader in the social, economic and
cultural development of Northern Ireland’ and seeks to achieve a
‘40% participation rate by students from less affluent family
backgrounds’ by 2021 [5].
§ WAP data analysis helps drive policy and practice in the mission-
defining ‘civic contribution’ strand of the University’s Five and Fifty
Strategic Plan [6].
§ WAP data is used for decision-making related to target setting,
performance monitoring, identifying areas in need of additional
support for students and ensuring resources are allocated where they
are most needed.
Introduction

9. WAP Data Collaborative Approach
Data Analysis
& Learner
Analytic WG’s
(Directorate with responsibility for statutory
WAP Governance including planning,
monitoring, analysis & reporting)
§ Ulster adopts a collaborative
approach to meet WAP Data
objectives.
§ Access, Digital and Distributed
Learning (ADDL) the
directorate with statutory
responsibility for WAP reporting
works with planning, ISD,
faculty, working groups and
committees.
ADDL
Planning/ISD
Faculty
Working
groups
Committees
[7 & 8]

10. Data Objectives
Key data objectives at Ulster University are to:
Understand the student profile and socioeconomic backgrounds.
Analyse trends in access, participation and success.
Promote equity and inclusivity in admissions and student
performance.
Ensure consistency and robustness in defining and measuring
targets to enhance WAP.
Safeguard objective profiling of schools and areas of low
participation for support.
[9]

11. Understanding Stakeholder Data Needs
Data Analysis
& Learner
Analytic WG’s
(Directorate with responsibility for statutory
WAP Governance including planning,
monitoring, analysis & reporting)
• Different stakeholders have different data needs
• Understanding these needs helps achieve data objectives
School Level Data
• School Attainment Data
• School Leaver Data
• UCAS Data
University Level Data
• Entry Profiles
• Campus Level Data
• Faculty Level Data
• School Level Data
• Programme Level Data
• Participant
Level Data
• Retention
• Progression
• Classifications
Graduate
Outcomes &
Employability
Data
• DLHE Level Data
• Social Mobility
Data
[4]

12. Internal and External WAP DATA Streams
Internal
data
sources
Student
Info
System
Cognos
Geographical
Information
Systems
Attendance
monitoring
VLE
learner
analytics
External
data
sources
Department
of
Education
stats
NIMDM
HESA /
HeidiUCAS
Predict
[7 & 8]

13. § Ulster University is keen to develop Dashboards for WAP and
consider which tools and/or solutions might help inform them.
Building Dashboards for WAP
Tableau
online /
Tableau
server
Power BI Alteryx
In house
Solution Cognos
Black-
board
Predict
Studiosity
Qwickly
attend-
ance
[9, 12, 13, 14, 15, 16 & 17]

14. § During 2018/19 Ulster staff participated in Jisc/HESA
Analytics Labs.
§ There are five recognised key competencies associated
with taking part in analytics labs including:
Jisc/HESAAnalytics Labs
Participating in
agile
development
Visualising
data
Transforming
data
Digital
collaboration
Understanding
policy and the
data landscape
*Competencies gained should be helpful when designing WAP dashboards [9, 10 & 11]

15. • “Fewer students from socially and economically disadvantaged
backgrounds go to university, and when they do they tend not to do as
well as their more privileged peers. The influence of background
continues long after graduation”.
Social Mobility Advisory Group Final Report, 2016 (Universities UK)
http://www.universitiesuk.ac.uk/policy-and-analysis/reports/Pages/working-in-partnership-
enabling-social-mobility-in-higher-education.aspx
• “Simply because of combinations of characteristics such as income,
sex, ethnic group, and where they live, some young people are four
times more likely to enter higher education than others in their peer
group.”
UCAS End of Cycle Report, 2016
(https://www.ucas.com/corporate/data-and-analysis/ucas-undergraduate-releases/ucas-
undergraduate-analysis-reports/ucas-undergraduate-end-cycle-reports)
Why is data so important for WAP?

16. • Jisc recommends that Institutions should put available data to work as
this can: transform learning experiences, support student wellbeing and
help students achieve more.
• “Data should open up more meaningful conversations … (and) will
help staff to prepare for meetings. It tells a story but it’s not the
whole story. It’s just a really good place to start the conversation.”
• To quote Martin Lynch from the University of South Wales “this doesn’t
mean gathering more kinds of data. It simply means pulling
together what already exists, albeit in disaggregated form. Bringing
it together and presenting it clearly allows patterns to emerge.”
JISC Learning Analytics,
[21] https://www.jisc.ac.uk/news/learning-analytics-going-live-01-apr-2019
Why is data so important for WAP?

17. § Any Questions?
§ Question to consider…
ØHow do you provide (or could you provide) evidence-based
ways of making data-informed WAP decisions to support
students for WAP objectives?
ØWhat internal and external data sources do you use or
would like to use?
ØPlease discuss current practice and/or share possibilities in
small groups and answer Part 1 via
https://www.surveymonkey.com/r/SEDA now.
The presenter will collate all responses and share with interested participants after
the session.
Discussion and Sharing of Practice

18. PART TWO:
Visualisations and
Dashboards for WAP
Data informed
Decision-Making.
[15 minutes]

19. • Ulster University continuously develops many reports,
infographics, visualisations and dashboards at University,
Faculty, School, Programme and Student level for WP
decision-making for key stakeholders. A number of
examples follow.
• All visualisations ultimately seek to help Ulster staff to
make data-informed WAP decisions to benefit students
which results in learning gain.
Infographic and Visualisation Examples

20. Student Population by Postcode
Source: https://tinyurl.com/y85y8tu2
University Level

24. X Programme name here X Programme name here X Programme name here
X Programme
name here
X Programme
name here

25. Numbers at Faculty Level to reach CC2 40% KPI
283 217
411
599
397 451
463
224
574
757
346
601
142
55
65
61
-14
90
2219
1239
2626
3542
1823
2855
-500
0
500
1000
1500
2000
2500
3000
3500
4000
Faculty of Art,
Des & Built En
Faculty of Arts Faculty of Comp
& Engineering
Faculty of Life
and Health Scs
Faculty of Social
Sciences
Ulster University
Business Sch
Figure 12: FT UG Profiles by Faculty, 2016-17 and numbers
required to reach CC2 40% Q1&2 KPI
Q1 Q2 Difference to reach CC2 40% Q1&2 KPI Total Faculty Enrolments
399 more within Q1&2
at University level
overall needed to
reach CC2 40% Q1&2
725
564 579
437
743
878 779
603
117 68
58
75
3963
3774
3540
2788
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Faculty of Arts, Human &
SScs
Faculty of Comp, Engin &
BEnv
Faculty of Life and Health
Scs
Ulster University
Business Sch
Figure 13: FT UG Profiles by Faculty, 2017-18 tbc and numbers
required to reach CC2 40% Q1&2 KPI
Q1 Q2 Difference to reach CC2 40% Q1&2 KPI Total Faculty Enrolments
318 more within Q1&2
at University level
overall needed to
reach CC2 40% Q1&2
KPI
University Level
X Faculty names here X Faculty names here

26. 41.2 41.3 50.0 51.0 42.3 40.037.4 31.5 38.6 29.8 35.6 23.0 35.5 22.9 24.7 28.8 33.3 38.5 29.5 19.3 30.0 22.0 28.6 26.139.4 39.0 18.2 22.2 21.4 75.0 100.0 100.0
0
10
20
30
40
50
60
70
80
90
100
%
Green (40% or more of Programme from Q1&2) Red (Less than 40% of Programme from Q1&2)
Amber (Close to 40% of Programme is from Q1&2 Within 1%.) Grey (Less than 20 students on programme)
40% WP target 33% 2016-17 WP Average
Programme ranks and names here
X Faculty Programmes ranked in order by % WP (Q1&2) Contribution to Faculty
Programme Overviews: % of each Programme from Q1&2
Faculty and Programme Level

27. FT UG Arts, Des & Built Environment Faculty Programmes ranked in order by % WP (Q1&2)
Contribution to Faculty
% of Programme from Q1&2 in Relation to Faculty Average and 40% WP Target
Less than 20 on
programme
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
% of Programme from Q1&2 40% WP Target 33% WP 2016/17 Faculty Average
Faculy and Programme Level
Programme ranks and names here

28. Blackboard Predict
Student Level

30. Degree Classifications for 2016/17 (NIMDM 2017)
72
328
226
17
88
339
222
27
94
342
251
21
121
334
208
20
123
342
212
20
0
100
200
300
400
1st Class Honours 2nd Class Hons Upper
Division
2nd Class Hons Lower
Division
3rd Class Honours
QUNITILE 1
Q1
2012-13
Q1
2013-14
Q1
2014-15
Q1
2015-16
Q1
2016-17
123, 18%
342, 49%
212, 30%
20, 3%
QUINTILE 1
1st 2i 2ii 3rd
116
426
192
17
130
363
170
14
149
377
180
13
184
393
160
12
185
400
161
13
0
100
200
300
400
500
1st Class Honours 2nd Class Hons Upper
Division
2nd Class Hons Lower
Division
3rd Class Honours
QUINTILE 5
Q5
2012-13
Q5
2013-14
Q5
2014-15
Q5
2015-16
Q5
2016-17
185, 24%
400, 53%
161, 21%
13, 2%
QUINTILE 5
1st 2i 2ii 3rd
University Level

31. Learning Gain DLHE 2016-17
477
161
624
179
692
235
728
224
628
174
0
200
400
600
800
Graduate Non graduate
DLHE 2016-17
Total numbers with DLHE level assigned
n=4688
(This excludes total unknown n=1026)
Q1 Q2 Q3 Q4 Q5
10.2
13.3
14.8 15.5
13.4
3.4 3.8
5.0 4.8 3.7
0.0
5.0
10.0
15.0
20.0
Q1 Q2 Q3 Q4 Q5
DLHE 2016-17
% of total with DLHE level assigned
n=4688
(This excludes total unknown n=1026)
Graduate Non graduate
78% 75% 78% 75% 76% 78%
22% 25% 22% 25% 24% 22%
0%
20%
40%
60%
80%
100%
All Q1 Q2 Q3 Q4 Q5
DLHE 2016-2017
% Overview of Graduates in Graduate & Non-Graduate Jobs within each Quintile Overall
Graduate Non Graduate
University Level

32. • Collaboration is key.
• Internal and external data sources help inform the data
landscape.
• Data security and GDPR compliance are important
considerations.
• There are a range of tools that can help provide data
insights.
• Adopting agile visualisation practices can help inform
data-driven decision making and policy decisions.
Lessons learned, progress so far and the next
stages

33. § Any Questions?
§ Some Questions to consider…
ØDo you already implement similar or different approaches?
ØHow do you already (or how would you like to) visualise
data and seek to prove WAP impact?
ØPlease discuss current practice and/or share possibilities in
small groups and answer Part 2 via
https://www.surveymonkey.com/r/SEDA now.
Presenters will collate all responses and share with interested participants after the session.
.
Discussion and Sharing of Practice

34. PART THREE:
Action Planning for
WAP Based
Decision-Making.
[10 minutes]

35. § Do you think any of the approaches discussed so far may
be transferrable across institutions? If so, in what ways?
§ Please discuss and share current practice or possibilities
in small groups and answer Part 3 via
https://www.surveymonkey.com/r/SEDA now.
Presenters will collate all responses and share with interested participants after
the session.
§ Plan actions as to how WAP data informed decision-
making could be deployed/adapted in your institutions in
light of discussions.
Action Planning for WAP Based Decision-
Making.

36. • Thank you for listening
• Any further questions?
Conclusions
Professor Les Ebdon commented that universities should use learning
analytics to boost fair access and retention as
“an effective approach to access should not stop at the front door
when a person enters higher education.” [20]

37. For further information please contact:
• For further information please contact:
Catherine O’Donnell
Email: c.odonnell@ulster.ac.uk
Phone: +44 (0)28 90 368513
Please give us feedback or request info now via:
https://www.surveymonkey.com/r/SEDA

38. References
Data Analysis
& Learner
Analytic WG’s
(Directorate with responsibility for statutory
WAP Governance including planning,
monitoring, analysis & reporting)
1. GDPR information retrieved from URL: https://www.gov.uk/government/publications/guide-to-the-
general-data-protection-regulation [Last accessed April 2019].
2. DFE website retrieved from URL: https://www.economy-ni.gov.uk/articles/higher-education-
widening-participation [Last accessed April 2019].
3. Ulster University Annual WAP plan retrieved from URL: http://addl.ulster.ac.uk/wap/plan201617
[Last accessed April 2019]
4. O'Donnell, C., Murphy, B. and Hunter, B. (2017), Evidence-Based Widening Access and
Participation Policy at the Core of Higher Education Strategy, Leadership and Decision Making-
Iceri2017 Proceedings, 10th annual International Conference of Education, Research and
Innovation, Seville, Spain. 16-18 November, 2017. ISBN: 978-84-697-6957-7 / ISSN: 2340-1095
doi: 10.21125/iceri.2017. Publisher: IATED
5. Ulster University Civic Contribution retrieved from URL: https://www.ulster.ac.uk/fiveandfifty/civic-
contribution [Last accessed April 2019].
6. Ulster University’s Five and Fifty Strategic Plan retrieved from URL:
https://www.ulster.ac.uk/fiveandfifty [Last accessed April 2019].
7. O'Donnell, C., Murphy, B. and Hunter, B. (2017), ‘Participation by Numbers: WAP presentation at
the core of strategy, leadership, and change management’ paper presentation at Concepts of
Value and Worth, National and International Perspectives on Widening Access and Participation
Conference, Glasgow.

39. References
Data Analysis
& Learner
Analytic WG’s
(Directorate with responsibility for statutory
WAP Governance including planning,
monitoring, analysis & reporting)
8. O'Donnell, C., Murphy, B. and Hunter, B. (2018), ‘Participation by Numbers: WAP at the core of
strategy, leadership, and change management’ paper in the Concepts of Value and Worth,
National and International Perspectives on Widening Access and Participation, (Broadhead et.
al. 2018), 111-136. ISBN: 978-0-9954922-2-6.
9. O'Donnell, C., Murphy, B. and Hunter, B. (2018), 4D: DEVELOPING DASHBOARDS FOR DATA-
DRIVEN DECISION-MAKING Paper, ICERI 2018
10. JISC/HESA analytics labs information retrieved from URL:
https://www.jisc.ac.uk/rd/projects/business-intelligence-project [Last accessed April 2019].
11. JISC article retrieved from URL: https://www.jisc.ac.uk/news/jisc-and-hesa-analytics-project-in-
the-running-for-a-national-technology-award-22-mar-2018 [Last accessed April 2019].
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13. Tableau website retrieved from URL: https://www.tableau.com/trial/tableau-software [Last
accessed April 2019].
14.Tableau website retrieved from URL: https://www.tableau.com/trial/tableau-software [Last
accessed April 2019].
15. Power Bi website retrieved from URL: https://powerbi.microsoft.com/en-us/ [Last accessed April
2019].
16. Blackboard Predict website retrieved from URL: https://www.blackboard.com/education-
analytics/blackboard-predict.html [Last accessed April 2019].

40. References
Data Analysis
& Learner
Analytic WG’s
(Directorate with responsibility for statutory
WAP Governance including planning,
monitoring, analysis & reporting)
14. Studiosity website retrieved from URL: https://www.studiosity.com/ [Last accessed April 2019
15. Qwickly website retrieved from URL: https://www.goqwickly.com/attendance/ [Last accessed April
2019].
16. NIMDM 2017 information retrieved from URL:
https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-
2017-nimdm2017 [Last accessed April 2019].
17. Professor Les Ebdon (JISC) retrieved from URL: https://www.jisc.ac.uk/blog/how-universities-
can-use-learning-analytics-to-boost-fair-access-and-retention-11-apr-2017 [Last accessed April
2019].
18.JISC Learning Analytics, https://www.jisc.ac.uk/news/learning-analytics-going-live-01-apr-2019.
Last accessed April 2019].