Led by Myles Danson, senior co-design manager and Shri Footring, senior co-design manager - enterprise, both Jisc. With contributions from: David Matthews, VLE development manager, Rose Bruford College of Theatre and Performance James Foster, planning analyst, University of Kent Connect more in London, 28 June 2016

2. Implementing analytics
LearningAnalytics and Business Intelligence

3. Session outline
» Overview of the Learning Analytics service
» The user voice
» Overview of the Business Intelligence project
» The user voice
» Group exercise
Learning Analytics 329/06/2016

4. Learning Analytics

5. Effective Learning Analytics Challenge
Rationale
» Universities and colleges wanted help to get started and have access to a
standard set of tools and technologies to monitor and intervene
Priorities identified
» Code of Practice on legal and ethical issues
» Develop a basic learning analytics service including an app for students
» Provide a network to share knowledge and experience
Timescale
» 2015-16 -Test and develop the tools and metrics
» 2016-17 -Transition to service (Freemium)
» Sept 2017 – Launch. Measure impact on retention and achievement
Learning Analytics 529/06/2016

6. What do we mean by Learning Analytics?
» The application of big data techniques such as machine based learning
and data mining to help learners and institutions meet their goals:
» For our project:
› Improve retention (current project)
› Improve attainment (current project)
› Improve employability (future project)
› Personalised learning (future project)
Learning Analytics 629/06/2016

7. Toolkit and community
» Blog: http://analytics.jiscinvolve.org
» Reports
› Code of Practice for Learning Analytics
› The current state of play in UK Higher and
Further Education
› Learning Analytics in Higher Education: A review
of UK and international practice
» Mailing: analytics@jiscmail.ac.uk
» Network meetings
Learning Analytics 729/06/2016

8. Learning Analytics Architecture
Learning Analytics 829/06/2016

9. Current engagement
» Expressions of interest: 85
» Engaged in activity: 35
» Discovery to Sept 16: agreed (28), completed (18), reported (17)
» Learning Analytics Pre-Implementation: (12)
» Learning Analytics Implementation: (7)
Learning Analytics 929/06/2016

10. Future Engagement
From Sept 2016
» “ReadinessToolkit” with a diagnostic set of questions and support
materials leading to implementation
» Start-up guidelines to get ready for learning implementation
Further details will be announced via analytics @jiscmail.ac.uk
Learning Analytics 1029/06/2016

11. Connect More With Jisc 2016
Embedding Learner Analytics
David Matthews
VLE Development Manager
david.Matthews@Bruford.ac.uk

12. RBC is a small specialist HEI, focusing on all aspects of
theatre/performance training.
 Large online/distance learning cohorts,
blended courses such as PGCTLHE, plus off-
campus MA and significant study abroad
elements (e.g. Erasmus and ATA).
 Want to use LA especially to help those
students who are not on campus for extended
periods of time during the working week.
 TDAP.
 Heard about the project through MASHEIN –
off-shoot of the Leadership Foundation – but
also have a long-standing relationship with
Jisc/RSC/Evan and Martin
29/06/2016 Leanring Analytics 12

13. Existing use of Learner Data
 Reporting out of Moodle – (not very good or satisfactory) – and other VLE services
 Data held in Registry, e.g. HESA returns, DLHE, NSS and first impressions etc.
 Data held by IT and LRC, e.g. logins from College usernames etc.
 Google Analytics on College corporate websites and VLE
 Data collected by programmes, e.g. end of module questionnaires, surveys
29/06/2016 Leanring Analytics 13

14. Learner Analytics Project
Joining the project…
• Introduced to Rob Wyn Jones and Paul
Bailey who have made site visits (joined by
Evan Dickerson)
• Involved Registry, Student Records and IT.
Approval from SMC – who are very keen on
the project.
• Site visits and Skype/Google Hangouts
meetings
29/06/2016 Leanring Analytics 14

15. Learner Analytics Project
Current status
 Transformation of student data into Jisc’s LA data model is now taking
place(being done in close collaboration with Jisc)
 Student data - LA data model is similar to HESA student return structure/
field specifications – documented online at
https://github.com/jiscdev/analytics-udd/
 Moodle data
o Historical data has been extracted from Moodle logs on our ULCC-
hosted Moodle (via internal Jisc software)
o This is being push into the Jisc Learning Records Warehouse
o A LIVE data plug-in for Moodle (extracting engagement data moving
forwards) has been evaluated and installed on our ULCC-hosted
Moodle
(Learning Locker screengrab)
29/06/2016 Leanring Analytics 15

16. Anticipated Outcomes
 Improved use of data across the College
 Retention is already very good, but one student leaving a small cohort makes a big dent in statistics
and in funding!
 Enable Registry to function effectively/grow post-TDAP
 Better support for online/blended students and those on placements/Erasmus visits. Better or more
timely interventions
 A genuine development project that, as a small institution, we would not have been able to resource
or support ourselves
 Excited to be part of an important project in an emerging field
 Very happy to continue our working relationship with Jisc…
29/06/2016 Leanring Analytics 16

17. HESA and Jisc Business Intelligence

18. About HESA
18
Learning Analytics 1829/06/2016

19. Learning Analytics 1929/06/2016

21. Heidi Plus
 The new business intelligence service for UK Higher Education
 Replaces Heidi (which will be decommissioned in November 2016)
 Launched in November 2015 offering:
 Improved data content and functionality
 Delivery of data sets through commercial data explorer tool
 New visualisations and dashboards
 New training programme and support materials
 Available to HE institutions with a full HESA subscription
 Over 80% of current Heidi subscribers have started the Heidi Plus
application process (40% completed)
Learning Analytics 2129/06/2016

22. Heidi Lab overview
Learning Analytics 2229/06/2016

23. Secure data processing environment
Technical infrastructure bound by legal agreements to ensure data and dashboards are secure
Learning Analytics 2329/06/2016

24.  Information improvement manager UEL with;
Kent, Middlesex, Brunel, Royal Holloway
 Strategic planning and BI manager Sunderland with;
Glasgow, Glasgow Caledonian, St Andrews, Sunderland
 Director of planning, Kent with;
Birkbeck,Cardiff, Oxford, Southampton, Southampton
 Strategic Planning Manager, MMU with;
Leicester, Leicester,Cambridge, Bishop Grosseteste
Winter teams
Learning Analytics 2429/06/2016

25.  Upskilling of staff resource across sector
 Opening up of collaborative relationships
across other organisations
 Value, saving and efficiency gains from the
creation and delivery but also the actions
subsequently taken due to the insights gained
across research, student, staff and estates and
possibly internationally
 Opening up access to disparate data sets and
making sense of them in an HE context
 Possible national licensing deals for paid
access to data
Team member experiences
Learning Analytics 2529/06/2016

26. Winter team dashboards
Learning Analytics 2629/06/2016

27. Dashboard: Course Market Analysis for Institutions
What is it? An Overview Movie
Purpose:
This dashboard is designed to support a university’s strategic planner in
designing course by allowing comparison across the sector.
Use case:
As a Strategic planner when working out which courses to teach I want to
examine competition to my course offerings to ensure I target recruitment
activity most effectively.
Data sources:
National Pupil Database: http://bit.ly/224CU8I
Key Information Sets: http://bit.ly/1ZYnG5z
National Pupil Database: http://bit.ly/224CU8I
HESA Data
What needs to be done and issues Time and Effort to Market
Where there is scope for improvement:
• Generally very polished
• Some work on the interface required perhaps to sign-post the features
• Licencing issues for league table data need to be negotiated.
• Data sources would need updating each year – particularly the
school data.
Learning Analytics 2729/06/2016

28. Dashboard: University Finder for Students
What is it? An Overview Movie
Purpose:
This dashboard is targeted at students who are looking for a university course to fit
their needs. By needs we don't only mean course but also: cost, employability,
location and entry tariff.
Use case:
As a student when working out which university course offers best fit my needs, I
want to understand factors of relevance to me (course, cost, employability, location,
cost of living, rural/urban and entry tariff) to compare and match offers to my
circumstances.
Data Sources:
Key Information Sets: http://bit.ly/1ZYnG5z
HESA Data
What needs to be done and issues Time and Effort to Market
This dashboard supplies a unique perspective on data and services that are already
available to students. In some ways this is a crowded marked. So the unique selling
point of this product would need to be promoted – that is that the data already
available to students is amalgamated and drawn together to create a” wizard like
app” for students to find courses.
What would need to be done:
• Identify appropriate vehicle for delivery
• Market uniqueness of the the product
• Negotiate data licences for league table data
Learning Analytics 2829/06/2016

29. Dashboard: Finding Comparable Institutions
What is it? An Overview Movie
Purpose:
This dashboard can be used to identify a university’s relative performance against
a benchmark of similar institutions.
Use case:
As a Planning Manager I want to select similar institutions based on metrics I
choose so that I can determine the best institutions to compare with my own
university to understand if our performance is relatively good or bad
Data Sources:
HESA data from Heidi
Key Information Sets: http://bit.ly/1ZYnG5z
League Table Data – will require licensing
What needs to be done and issues Time and Effort to Market
Where there is scope for improvement:
• Data – a relatively narrow data set was used for prototyping; a production
version could accommodate a far more comprehensive data set.
• Filters – searching and filtering could be enhanced
• Licencing – Makes use of some league table data to benchmark against entry
tariff. Licence for this need to be negotiated.
Learning Analytics 2929/06/2016

30. BI Group exercise
Learning Analytics 3029/06/2016

31.  As a: Strategic Planning Manager
 When: Reviewing current course provision
 I want to: Enable course/curriculum management planning to match national
and local demand
 So I can: Grow or at least maintain student recruitment
Data Sources:
 HESA student, DLHE, Award data, KIS,CUG
 School/College performance data (A level results and numbers, School Age
Populations Forecasts, etc.)
 Labour market data from NOMIS (Employment rates, earnings, SOC, SIC…
Group exercise
Learning Analytics 3129/06/2016

32. Feeding back
29/06/2016 Learning Analytics 32

33. What next?
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34. Library analytics labs
» Teams working on Library BI Stories at 0.2 FTE, total estimated effort 15
days from July - Oct 2016
» Both Product Owners and Sector Data Experts invited:
› Product Owner from the sector to steer which stories are of interest
› Sector Experts to understand what data sources are available and what
is in the data
› Jisc Contracted Data transformation specialist (CETIS)
› JiscAgile Scrum Master andTableau User
» Teams receive experience and guidance of Agile working
» Option forTableau Desktop training to help with creating visualisations
» Apply at http://bit.ly/jisc_library_data_labs_applications
» Queries to siobhan.burke@jisc.ac.uk or myles.danson@jisc.ac.uk
Learning Analytics 3429/06/2016

35. FE analytics labs
» Shri- can you add an overview and description of the three clusters
Learning Analytics 3529/06/2016

36. Analytics academy – a Jisc beta service - October 2016
» Business intelligence offers value, savings and efficiencies to Universities
through data informed enhanced planning / decision making
» Many problem spaces are commonly felt, while the data landscape to
support insights is vast
» Some universities have little access to good BI at all, while those with
capability are often duplicating effort
» There is no higher education focused CPD offer to train up BI expertise
» Analytics academy addresses these problems by providing expertise and
tools for analysts (planning officers and others) to identify suitable
problem areas (student, staff, research, estates etc), exploring the data
landscape for insights and producing interactive dashboards for the sector
Learning Analytics 3629/06/2016

37. Keep in touch
» business-intelligence.ac.uk
» Subscribe via jiscmail.ac.uk/JISC-HESA-BUSINESS-INTEL
» Twitter @HESA @jisc #hesajiscbi
Learning Analytics 3729/06/2016

38. Dashboard: University Research Benchmarking
What is it? An Overview Movie
Purpose:
This dashboard is designed to answer a range of questions around the
university’s research profile and potential vulnerability/strength.
Use case:
As a Research Planning Officer, when influencing research policies, I want to
assess individual cost centres' relative research strengths against
national/mission group norms; so that I can help support the financial
sustainability of the institution.
Data Sources
HESA data from Heidi
Key Information Sets: http://bit.ly/1ZYnG5z
What needs to be done and issues Time and Effort to Market
The research dashboard was a proof of concept using the limited research
data that was available to the team. Identifying further data sources would
make this into a powerful tool.
• Low level HESA data on research
• Research Grant Data from
• Funding Councils
• EU
• Other
• Ref Data
Learning Analytics 3829/06/2016

39. jisc.ac.uk
Myles Danson
Senior co-design manager
Shri Footring
Co-design manager for enterprise
Myles.Danson@jisc.ac.uk
Shri.Footring@jisc.ac.uk
29/06/2016 Learning Analytics 39