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
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
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
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
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
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
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
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
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
Overview of LA service – Shri (5 mins)
User voice – David Matthews (10 mins)
Overview of BI - Myles (5 mins)
The user voice - James (10 mins)
Group exercise - Shri (20 mins)
What’s coming next - Shri and Myles (5 mins)
The effective learning analytics challenge was initiated from consultation with stakeholders, senior manager and practitioners who felt the sector need support to get up to speed with learning analytics. They prioritised three main areas, a Code of Practice to address legal and ethical issues of using learning analytics; a set of basic learning analytics tools to allow institutions to get started and make informed decisions; and a network to allow institutions to share practice and learn from each other.
The current project has procured suppliers to provide a learning analytics service which are currently being tested by several institutions. This will be developed into a full service next year and provided as a new Jisc service from Sept 2017.
What do we mean by learning analytics. The service we are developing will collect data and undertake statistical analysis of historical and current data derived from the learning process to create models that allow for predictions that can be used to improve learning outcomes.
Models are developed by “mining” large amounts of data to find hidden patterns that correlate to specific outcomes
E.g. Mine VLE event data to find usage patterns that correlate to course grades
The service will provide predictive models initially for retention (identify students at risk of failing) and attainment (identifying students at risk of not achieving a specified level of attainment).
In the future we will look to offer predictive models to support employability and personal/adaptive learning.
The project consists of the learning analytics architecture (next slide), a toolkit and community.
These consist of a blog with reports and information to assist institutions with readiness to implement learning analytics and technical implementation of the Jisc service.
There are three reports all linked from the blog a Code of Practice for Learning Analytics, A report from 18 months ago that reviewed current state of learning analytics in the UK and a more recent report on the evidence base for the effectiveness of learning analytics with 12 international case studies.
If you want to be involved and keep informed about the development of the service then join the analytics jiscmail list
We also hold quarterly network meetings which are promoted via the blog and jiscmail list
Overview of learning analytics architecture.
Red items are components that will include the tools in the project (Tribal student insight, Unicon/Apereo LAP and Student Success Plan, Student App) but also alternative third party or institutional tools.
We have ~400 people on the Jiscmail list and a pipeline of interested institution's (50+ HE, 20+FE). We are actively engaging with 35 institutions, 28 in discovery institutional readiness and 12 in beta implementations.
From Sept 16 we’ll be introducing a new institutional readiness process to help institutions get ready for implementing learning analytics. This will consist of an overview workshop to introduce the service and an diagnostic assessment tool, institutions will complete the assessment tool and then undertake appropriate actions to address recommendations.
For institutions who are ready to start implementation there will be set of guidelines to get set-up with data collection and visualisations, ready to implement a predictive analytics solution and the student app.
Details will be announced via the jiscmail list – so join it to participate.
Myles
Jisc and HESA are collaborating to develop new national shared services for business intelligence, making better use of the national data landscape, reducing repetitive activities across universities, brining the benefits of BI to all Univerisits regardless of capability / expertise
Myles
HESA is a not for profit subscription organisation, so similar to Jisc in that sense. As well as a mandatory subscription, members are mandated to provide data collections covering the broad themes of Student, Staff, Destinations (of graduates) and Estates data. This is annual but in year collection is under consideration. HESA cleanse the data and provide back full data sets, published statistics and undertake bespoke analysis. Jisc and HESA membership is similar.
Myles
Heidi Plus is depicted on the left – highlight the trucks driving in to the HESA data warehouse. HESA mandates that all publicly funded HEPs provide performance data on students, destinations of leavers, staff, finance and estates. Currently an annual collection they are moving to more frequent in year collections. The data is cleansed and a new team undertake dashboard development. Quality is assured as the dashboards are offered throught the radio mast in the middle – a new national BI dashboard delivery service offered to all HESA customers (currently 180 HEPs and associated organisations and departments). Built with Jisc and launched as a HESA service in November. Includes legal framework and national training programme. Replaces a system with 6.5K users. Lowers the bar to usage through the interactive dashboards so could take BI to a woder range of staff than is currently possible.
Heidi Lab is depicted to the right. A Jisc led alpha July 15 – July 16. Highlight the trucks again and note it’s a two way street – a data sharing agreement allows HESA data into the Heidi Lab secure data processing environment. Agile analysis teams are created from multiple universities and given access. They identify commonly felt problems spaces, explore the wider national data landscape, acquire non-HESA data and cleanse, link and transform it creating new proof of concept dashboards. Highlight the trucksa driving from the Lab to the Radio Mast. Successful dashboards will be branbded produced by Jisc and delivered via Heidi Plus.
Piece in the middle is the beta service – what comes next – Heidi Plus is sustained by HESA as a service. We have proved there is real merit in Heidi Labs and will launch a beta service July 16 – July 17.
James
James
HESA’s current data delivery service is known as HEIDI (Higher Education Information Database for Institutions) developed in house in 2007. Jisc and HESA collaborated to replace this with a more up to date service. We procured Tableau, market leading data exploration software and now offer Heidi Plus
Feedback has been extremely good across the sector
Myles
Heidi Lab as a Jisc Alpha project (proof of concept) engaged with 290 individuals from 130 universities to develop a successful model of agile analysis. 50 analysts (planners, directors of planning from 44 universities volunteered to join cross institutional agile analysis teams for three Heidi Lab cycles of 3 months each at just 0.2 FTE. Teams were supported as they identified and refined widely felt problem areas (see example on the slide – covered student, staff, research, estates etc) linked to national policy. They explored the data landscape for supportive insights, recording the issues encountered in our data catalogue. Finally they produced interactive dashboards using Tableau software as proofs of concept to offer through Heidi Plus
Myles
Led by a senior staff member with knowledge of the information needs of a wide range of staff and institutions as well as national policy and what is ‘up stream’
James – adjust to suit your own experiences
James to lead
James to continue with these (as many as time permits)
James to continue with these (as many as time permits)
James to continue with these (as many as time permits)
Shri or James to lead?
Nominate a person from each table to feed back on a user story, explain we will photograph them all and feed into further cycles for consideration
Myles – just to note we are running a set of teams from the library area to prove the concept transfers
Shri – very brief note to say we are running a College Labs experiment
Myles – a new Jisc offer to explode whether there is a sustainable service in this
In case anyone wants to see anther dashboard after the session