This document summarizes Paul Bailey's presentation on Jisc's learning analytics service. It discusses what learning analytics is, how it can be used to improve student performance, teaching quality, and institutional strategy. The service will provide dashboards, a student app, and an alert system to help identify at-risk students. It will initially focus on student engagement and attainment data to improve retention and achievement. An on-boarding process and readiness assessment are also outlined to help institutions implement learning analytics. The goal is to launch the service in 2017 to measure its impact on key performance indicators.

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?
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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
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4. Learning Analytics Sophistication ModelLibrary Analytics Service

5. Analytics categories by intervention
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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
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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)
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8. Jisc’s Learning Analytics Project
Three core strands:
Learning
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Toolkit Community
Jisc Learning Analytics
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9. Community: Project Blog,
mailing list and network events
Blog: http://analytics.jiscinvolve.org
Mailing: analytics@jiscmail.ac.uk
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10. http://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics
Toolkit: Code of Practice
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http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf

11. Learning Analytics Service Architecture
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12. 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
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20. First version will include:
» Overall engagement
» Comparisons
» Self declared data
» Consent management
Bespoke development by Therapy Box
Student App
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21. Library 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

22. 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)
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24. On-boarding Process
Stage 1: Orientation
Stage 2: Discovery
Stage 3: Culture and Organisation Setup
Stage 4: Data Integration
Stage 5: Implementation Planning
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https://analytics.jiscinvolve.org/wp/on-boarding/

25. 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
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A supported review of institutional readiness

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Data collection
About the student Activity data
TinCan
(xAPI)ETL

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On-boarding Process
Data Visualisation
Dashboards
Ready to
implement
Ready to
implement

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