Speakers: David Lewis, senior analytics consultant, Jisc Mike Hughes, IT director, City University, London An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.

1. Learning Analytics at City,
University of London
Mike Hughes

2. 2
Outline
The next 30 mins ...
● Intro to City, University of London
● What is Learning Analytics and why do it?
● City’s approach to Learning Analytics
● What next for your institution?

3. 3
Established 1894
City is composed of five Schools:
● Cass Business School
● The City Law School
● School of Health Sciences
● School of Mathematics, Computer Science &
Engineering
● School of Arts & Social Sciences

4. 4
What is Learning Analytics and why do it?
https://www.jisc.ac.uk/reports/learning-analytics-in-higher-education

5. What is
learning
analytics?
Our interpretation …
Learning analytics uses data about students and
their activities to help institutions understand
and improve educational processes, and
provide better support to learners.
It should be for the benefit of students, whether
assisting them individually or using aggregated
and anonymised data to help other students, or
to improve the educational experience more
generally.
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6. 6
‘Learning analytics’ Google Trends
https://trends.google.com/trends/explore?date=all&q=learning%20analytics

7. What is
learning
analytics?
Uptake in UK Higher Education sector?
2017 HeLF Report (http://bit.ly/2JKhNH7)
Some interesting findings … 53 responses
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10. 10

11. What is
learning
analytics?
Justification of investment in learning analytics
based primarily on:
○ Retention and progression
○ Student wellbeing / success
○ Timely / better access to data
but also…
○ Student experience
○ Personalised learning
○ Improve curriculum design
See http://bit.ly/2MpWgFx for some case
studies
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12. 12
https://www.city.ac.uk/about/city-information/vision-strategy-2026/performance
City’s Academic Output KPIs

13. What is
learning
analytics?
○ Demonstrating ROI is difficult, there may
be many factors
○ Sample calculation for City, based on
improving retention rate by 1% from 88%
(HESA 2015-16)
○ Potential ‘saving’ of £400K / 21 students
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ANALYSE INTERVENECOLLECT
Actor : Action : Object
Sarah : accessed : forum
John : passed : assessment
Ahmed : attended : tutorial
Anna : borrowed : book
Descriptive
76% of students have watched
the Module Introduction video
Predictive
11% of Dept X students are at
risk of withdrawal in year 1
Review by human
Determine if intervention is
required
Conversation
Dialogue between students
and staff at all levels
The Learning Analytics process

15. 1515
Learning Data Hub
Predictive Modelling
Engagement Dashboard
Student App
VLE
Student Records
System
Other data

16. Education Student Committee Paper
A paper was submitted to the Education & Student
Committee in Nov 2015 proposing to take forward
work on learning analytics and involvement with
JISC to undertake an institutional review.
2015
2014
EPIC (now gomo learning) Report
A report on City’s basic requirements for
learning analytics, suggestions for research,
and a discussion of data sources.
LEaD Research Project
A six month research project to gather further
information on the University’s processes and
to refine requirements.
2016
Learning Analytics Readiness Report
External consultants produced a ‘Learning Analytics
Readiness Report’ which was sent to Learning
Environment Committee in April 2016. The report was
endorsed by Deputy President and then sent to Education
and Student Committee.
Learning Analytics Project Business Case
A business case was put forward at end of 2016 for the
LeAP project and upon approval work started on a Privacy
Impact Assessment and identifying compliance issues.
2017
Learning Analytics Project
Work has continued throughout 2017,
resulting in an initial stakeholder consultation
with students and staff, a review of data
quality and a technology proof of concept.
2018
And Beyond
By end of 2nd year - establish a
governance structure, finalise the Learning
Analytics policy and expand stakeholder
consultation with early pilots in Schools.

17. “
LeAP aims to maximise the potential of all our students,
improving their educational experience, whilst supporting
the institution’s education performance indicators
relating to progression and attainment.
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19. Providing students with actionable information about their own
learning to help them to identify areas of strength and weakness.
They can then choose to access appropriate support mechanisms
and guidance to maximise achievement of their learning
outcomes, and potentially exceed the ‘predicted’ outcome
19
#1

20. Providing module leaders and personal tutors with
information about how students are engaging with
their courses, so they can provide the right support
when it's needed
20
#2

21. Providing programme directors with information
around student retention and progression across
their programmes, so they can provide the right
support when it's needed
21
#3

22. Pilots
Objective - to gather information in the following areas:
● reliability and validity of learning analytics insights provided by
Student Insight and Data Explorer
● nature and value of insights provided by the two systems to
Department Heads, Programme Directors, Module Leaders and
Personal Tutors
● level of training and data analysis skills required to use the
systems ethically and effectively.
● identification of any challenges that users encounter with the
systems
● identification of any additional user requirements for the systems
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23. Pilots
Pilot Participants
● Cass Business School
Accounting and Finance BSc (2 modules)
● School of Maths, Computer Science & Engineering
Computer Science BSc (5 modules)
● School of Health Sciences
Optometry BSc (4 modules)
Missing … School of Arts & Social Sciences and The Law School
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24. Pilots
How can Learning
Analytics support
progression, retention
and student success?
Module leaders given
access to Data
Explorer during Term 2
(Spring)
○ training and
requirements
gathering workshop
○ review information
at least once every
3 weeks
○ note evidence for
insights into student
engagement
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25. 25
Jisc Data Explorer /
Course overview

26. 26
Jisc Data Explorer /
Student overview

27. 27
Jisc Data Explorer /
Student overview

28. 28
Jisc Data Explorer /
Module overview

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What next for you?
Things to consider …
● Drivers / Objectives
● Stakeholders
● Data
● Policies / Processes
● Dependencies (other projects)
● Benefits
● Timescales
● Finance

30. Project team
Mimi Weiss
(LeAP Project Manager), Senior
Educational Technologist, LEaD
Julie Voce
Head of Educational Technology,
LEaD
Jo Richardson
(Policy and Stakeholder
Consultation), Senior Educational
Technologist, LEaD
Caroline Llewellyn
(Policy and Stakeholder
Consultation), Information
Compliance Officer, IT
Miriam Styrol
(Data Review), Data and Policy
Analyst, SaAS
Eleanor Cornfield
(Data Review), Business Analyst,
SaAS
Mike Hughes
(Technology Implementation),
Service Manager, IT
Ethan Henry
LeAP Senior Project Officer
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31. Thanks!
Any questions?
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JISC Effective Learning Analytics blog
https://analytics.jiscinvolve.org