Jisc provides a learning analytics service to help higher education institutions use student data to improve outcomes. The service includes predictive models, dashboards, and an app to provide insights to staff and students. It integrates various institutional data sources. Over 30 institutions are using the service to enhance student success, curriculum, employability, and digital apprenticeships through analytics.

1. Paul Bailey, Senior Codesign Manager, Research and Development
Supporting learning analytics in the sector
http://www.slideshare.net/paul.bailey/

2. Outline
Learning analytics
 Service
 Toolkit, community, consultancy
 Purchasing service
Learning analytics – service development
 Student success
 Curriculum enhancement
 Employability
 Digital apprentices
 Intelligent campus
Learning Analytics Service

3. Effective Learning Analytics Challenge
Learning Analytics Service
Rationale
»Organisations wanted help to get started and have access to standard
tools and technologies to monitor and intervene
Priorities identified
»Code of Practice on legal and ethical issues
»Develop a core learning analytics service with app for students
»Provide a network to share knowledge and experience
Timescale
»2015-17 Development
»2017-18 Beta Service
»Aug 2018 Full Service

4. Take-up of Jisc service
30 institutions signed-up
8 institutions institution wide roll-out Sept
14 HEIs in data integration/pilot stage
8 Colleges in service development
Learning Analytics Service

5. Jisc’s Learning Analytics Project
Three core strands:
Learning
Analytics Service
Toolkit Community
Jisc Learning Analytics
Learning Analytics Service

6. Toolkits and community
Jisc learning analytics service

7. Community: Project Blog,
mailing list and network events
Blog: http://analytics.jiscinvolve.org
Docs: http://docs.analytics.alpha.jisc.ac.uk/
Mailing: analytics@jiscmail.ac.uk
Networks: pathfinder/implementation
Learning Analytics Service

8. On-boarding Process
Stage 1: Orientation – get more info
Stage 2: Discovery – DIY and/or paid for consultancy
Stage 3: Culture and Organisation Setup – sign up for
Jisc service and/or supplier products
Stage 4: Data Integration - push data to learning data
hub
Stage 5: Implementation and Scaling
Learning Analytics Service
https://analytics.jiscinvolve.org/wp/on-boarding/

9. Discovery readiness
Topic ID Question Commentary Response Score
Leadersh
ip
1 The institutional senior management
team is committed to using data to
make decisions
Please provide a commentary on you
response to each question where
appropriate
0 - Hardly or not at
all
1 - To some extent
2 - To a great
extent
Leadersh
ip
2 Our vice-chancellor / principal has
encouraged the institution to
investigate the potential of learning
analytics
0 - Hardly or not at
all
1 - To some extent
2 - To a great
extent
Leadersh
ip
3 There is a named institutional
champion / lead for learning analytics
0 - No
2 - Yes
Vision 4 We have identified the key
performance indicators that we wish to
improve with the use of data
0 - Hardly or not at
all
1 - To some extent
2 - To a great
extent
Learning Analytics Service
A supported review of institutional readiness

10. Intervention planning workshops
Learning Analytics Service

11. Learning Analytics Purchasing Service –
How we are working with suppliers of LA solutions
 USPs for Institutions:
 Marketplace for LA product & services compatible with the core Jisc service
 Procurement Framework – mini competitions can be easily initiated
 Mandatory clauses included – ensures a consistent & safe approach to data protection
 Institutions will control and own the contracts directly
 Framework will available to institutions from 18th September 2017
 Three categories of supplier services will be offered:
1. Learning Analytics Solutions
2. Learning Analytics Services
3. Learning Analytics Infrastructure
 https://docs.analytics.alpha.jisc.ac.uk/docs/learning-analytics/Learning-Analytics-Purchasing-
Service
Jisc Learning Analytics 2017

12. Paul Bailey, Senior Codesign Manager, Research and Development
Jisc learning analytics service
https://docs.analytics.alpha.jisc.ac.uk/docs/learning-analytics/Home

13. Data
Collection
Data
Storage
and Analysis
Presentation
and Action
Jisc Learning Analytics open architecture: core
Alert and Intervention
system
Other Staff
Dashboards
Consent Service
(tbc)
Student App:
Study Goal
Jisc Learning
Analytics Predictor
Learning
Data Hub
Student Records VLE Library
Staff dashboards in
Data Explorer
Self Declared Data Attendance, Presence, Equipment use etc….
Data Aggregator
UDD Transformation Toolkit Plugins and/or Universal xAPI Translator

14. Products and dashboards
Data Explorer: Learning Analytics dashboards for staff, focussing on showing learning analytics
data to staff based on their role.
Study Goal: An app for students - allowing them to view their learning analytics data, and set
measurable actions to support their success.
Learning Analytics Predictor: A predictive model designed to do one thing well - predict
success at course level. Output can be viewed in Data Explorer or any other system that can
integrated in the Learning Data Hub.
Traffic Lights Calculator: A straightforward rules based engine, allowing RAG status to be
calculated for online activity, attendance and achievement, at module level. Output fromTLC
can viewed in data explorer or any other system that can integrated in the learning data hub.
Learning Data Hub: the core of Jisc's learning analytics service, holds data about students,
works in conjunction with an institutions data warehouse, rather than replace it, to share data
between applications in a standard way, a collection point for semi-structured learning data
such as student activity.
Learning Analytics Service

15. Agenda
Learning Analytics Service
Predictive models
identify students at risk
Timely intervention by teaching or support
staff
Increased retention
Better understanding
of the effectiveness
of interventions
Rich data on student
activity and attainment
Data shared with
student prompting
them to change
own behaviour
Better student
outcomes
Data can be
explored to
understand patterns
of behaviour
Better understanding
of the behaviours
linked to differential
outcomes

16. Data Explorer
 Data Explorer Release 2.0 - Aug 18
 View data in learning records warehouse
 Site Overview – overview of all data
 My Students and My Modules
 Notes (interventions) on students
 RAG Status and predictive models
 User Guide and videos
 https://docs.analytics.alpha.jisc.ac.uk/docs/d
ata-explorer/Home
Jisc Learning Analytics 2017

18. Study Goal
 Study Goal aims
 Social learning app with gamification
 Setting targets and logging self-declared activity
(fitbit model)
 View activity and attainment data
 Attendance check-in
 Guides and videos
https://docs.analytics.alpha.jisc.ac.uk/docs/study-
goal/Home
Jisc Learning Analytics 2017

19. Service development
Jisc learning analytics service

20. Learning Analytics Service
VLE data
+
Student record system
+
Attendance data
+
Library data
Buildings data
+
Learning space data
+
Location data
Teaching quality data
+
Assessment data
+
Curriculum design data
Content data
+
Learning pathways data
Better retention
and attainment
Retention and
attainment
A more efficient
campus
Improved teaching
& curricula
Personalised and
adaptive learning
Efficient campus
Improving teaching
& curricula
Now
Learning
analytics
Institutional
analytics
Educational
analytics
Cognitive
Analytics and AI
Future

21. Student Success
• Behavioural patterns that lead to success (attendance, engagement, attainment, submission
date/time of assignments)
• Predictive models that look at success i.e. first or 2:1 – that will model the behaviours
• Grouping of behaviours that lead to success (e.g. accessing a wider range of resources, time on
task, linking intended with actual behaviours)
Learning Analytics Service

22. Health and well-being
• Can we use activity data to support health and well-being?
• Timely interventions identify students earlier
• Patterns of behaviour
• Improved student support processes
• Developing coping strategies
• Additional data
• Student sentiment analysis
• Long term data study
• Sensitive data
• Build AI models to predict at risk students, also beyond
graduation
Learning Analytics Service

23. Curriculum enhancement
Learning Analytics Service

24. Employability
• Analyse data to find indicators that
lead to employability
Baseline data on employability
Activity data e.g.
• Careers entry profiles
• Careers engagement activity
• Employability skills in modules
• Work experience
Learning Analytics Service
HEPI Employability: Degrees of
Value

25. Digital Apprenticeships
• DigitalApprenticeToolkit
https://www.jisc.ac.uk/guides/apprenticeship-toolkit
• DigitalApprenticeship Community Event – 10th July 2018 –
AMRC, Sheffield
• Integration with tools and services used for apprenticeships
• Allow employers to view apprentices progress across multiple
suppliers
• Beta product available 2019
Learning Analytics Service

26. Contacts
Paul Bailey paul.bailey@jisc.ac.uk
Cristina
Further Information:
http://www.analytics.jiscinvolve.org
Join: analytics@jiscmail.ac.uk
Learning Analytics Service