This document discusses Jisc's learning analytics service, which aims to help higher education institutions in the UK implement learning analytics through three core strands: a toolkit, community, and service. The service will provide dashboards, apps, and tools to collect, analyze, and report on student data in order to improve retention, attainment, and personalize learning. It outlines the onboarding process, technical architecture, and implementation approach for institutions to get started with learning analytics.

1. Paul Bailey, Senior Codesign Manager, Research and Development
Jisc learning analytics service
http://www.slideshare.net/paul.bailey/

2. Learning Analytics
What is learning analytics?
Learning Analytics Service

3. “learning analytics is the measurement,
collection, analysis and reporting of data
about learners and their contexts, for
purposes of understanding and
optimising learning and the
environments in which it occurs”
SoLAR – Society for Learning Analytics Research
Learning Analytics Service

4. Learning Analytics Sophistication ModelLearning Analytics Service

5. Analytics categories by intervention
Learning Analytics Service
Improve individual student performance - interventions aimed directly
at learners
Improve teaching and learning quality - interventions aimed at
curriculum design
Improve support systems and process - interventions aimed at support
staff and the process around support staff and students.
Develop strategy - interventions required to improve the performance
of the institution

6. 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 basic learning analytics service with 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)
»Sep 2017—launch, measure impact: retention and achievement

7. 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 Service

8. Learning Analytics Service
Sector
Transformation
Awareness
Experimentation
Organisation
support
Organisational
transformation
Descriptive Analytics
what happened? How do I compare?
Predictive Analytics
what will happen?
Prescriptive Analytics
what should I do?
Automated
it’s done
Data
Diagnostic Analytics
why did it happen?
Ordered Data
Standardised Data
Adaptive learning etc.
Recommendation engines
etc.
Predictive models,
Intervention management etc
Data exploration tools,
processes etc
Dashboards,
Benchmarking etc.
Data Warehouse, data
stores
Data connectors
Analytics with a national approach

9. Descriptive
Analytics
Predictive
Analytics
Prescriptive
Analytics
AutomatedDiagnostic
Analytics
Standardised
Data
Learning
Records
Warehouse
xAPI Plugins
Data
transformation
tools
Data and API
Standards
Jisc
Services
Other
Provider
Services
Basic
dashboards
Student App
Analytics Labs
Benchmarking
services
College
Analytics
Basic predictive
modelling and
intervention
management
Procurement
frameworks
Integration
tools
Services for
researchers
Pilot projects
Services for
researchers
Pilot projects
Institutional
Dashboards
Data
visualisation
tools
Data
exploration
tools
Advanced
predictive
modelling
Integrated
intervention
management
??? ???

10. AI and analytics
Learning Analytics Service

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

12. Community: Project Blog,
mailing list and network events
Blog: http://analytics.jiscinvolve.org
Mailing: analytics@jiscmail.ac.uk
Learning Analytics Service

13. Toolkit: Code of Practice
Learning Analytics Service
 Code of Practice
http://www.jisc.ac.uk/guides/code-of-practice-for-learning-
analytics
 Literature Review
http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-
_Literature_Review.pdf
 Template Learning Analytics Policy
https://analytics.jiscinvolve.org/wp/2016/11/29/developing-
an-institutional-learning-analytics-policy/
 Guidance on consent for learning analytics
https://analytics.jiscinvolve.org/wp/2017/02/16/consent-for-
learning-analytics-some-practical-guidance-for-institutions/

14. Learning Analytics Service Architecture
Learning Analytics Service

15. Dashboards
Dashboards for different users of the
analytics
 Administrators to see over all activity
 Course tutors to view and compare
students
 Student view to see engagement activity
Based on either commercial tools from Tribal
(Student Insight) or open source tools from
Unicon/Marist or other providers of learning
analytics products
Learning Analytics Service

16. Learning Analytics Service

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24. First version will include:
» Overall engagement
» Comparisons
» Self declared data
» Consent management
Bespoke development by Therapy Box
Student App
Learning Analytics Service

25. Learning Analytics Service
Stats – Provides an engagement
and attainment overview and
drilling down to gives
comparative activity graphs.
Log – Allows you to log time
spent on specified activities e.g.
reading for an assignment
Target – Allows you set personal
targets to improve your
engagement e.g. study for 10
hours this week

26. Alert and Intervention System
Tools to allow management of interactions with students
once risk has been identified:
» Case management
» Intervention management
» Data fed back into model
» etc…
Based on open source tools from Unicon/Marist
(Student Success Plan)
Learning Analytics Service

27. Learning Analytics Service

28. On-boarding Process
Stage 1: Orientation
Stage 2: Discovery
Stage 3: Culture and Organisation Setup
Stage 4: Data Integration
Stage 5: Implementation Planning
Learning Analytics Service
https://analytics.jiscinvolve.org/wp/on-boarding/

29. 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

30. Learning Analytics Service
Data collection
About the student Activity data
TinCan
(xAPI)ETL

31. Jisc Learning Analytics 2017
Initial Implementation Approach
Historic
Data
Model
Live
Data
Analyse
Results
Analysis
and Pilot
Choose
full
product
Suppliers
Institution
Team
End Users
Deploy
Study
Goal

32. Jisc Learning Analytics 2017
Current Implementation Approach
Analyse
and Pilot
Choose
full
Product
Historic
Data
Analyse
Results
Live
Data
Deploy dashboards/other
tools
Suppliers
Institution
Team
End Users
Model
Build
Deploy
Study
Goal
Deploy
DataX
Analyse
and Pilot

33. Learning Analytics Service
On-boarding Process
Data Explorer
Visualisation Tools
Ready to
implement
Ready to
implement

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