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National learning analytics
Rob Wyn Jones
Head of data and analytics services, Jisc
What is learning analytics?
“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
Descriptive or predictive models
identify students at risk
Rich data on student
activity and attainment
Timely intervention by
teaching or support staff
Data shared with
student prompting
them to change own
behaviour
Data can be
explored to
understand patterns
of behaviour
Increased
retention
Better understanding
of the effectiveness of
interventions
Better student
outcomes
Better understanding
of the behaviours
linked to differential
outcomes
Learning analytics service
Learning analytics service
A more efficient campus
Buildings data
+
Learning space data
+
Location data
Improved teaching and curricula
Teaching quality data
+
Assessment data
+
Curriculum design data
Personalised and adaptive
learning
Content data
+
Learning pathways data
Improved teaching and
curricula
Better retention
and attainment
VLE data
+
Student record
system
+
Attendance data
+
Library data
Efficient campus
Retention and attainment
+
+
+
Learning
analytics
Institutional
analytics
Educational
analytics
Cognitive
Analytics andAI
Now Future
Rob Wyn Jones – Head of
data and analytics services
Jisc – UK learning analytics national service
https://docs.analytics.alpha.jisc.ac.uk/docs/learning-analytics/Home
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
Effective Learning Analytics Challenge
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
Toolkit: Code of practice
» Code of practice
jisc.ac.uk/guides/code-of-practice-for-learning-
analytics
» Literature review
http://repository.jisc.ac.uk/5661/1/Learning_Ana
lytics_A-_Literature_Review.pdf
» Template learning analytics policy
https://analytics.jiscinvolve.org/wp/2016/11/29/d
eveloping-an-institutional-learning-analytics-
policy/
» Guidance on consent for learning analytics
https://analytics.jiscinvolve.org/wp/2017/02/16/c
onsent-for-learning-analytics-some-practical-
guidance-for-institutions/
Legal and ethical: consent and GDPR
» Make sure your collection notice covers the
use of data to support the student learning and
wellbeing
» Not ask for consent for the use of non-
sensitive data for analytics (our current
understanding is that this can be considered as
of legitimate interest or public interest)
» Ask for consent for use of sensitive data
(which, under the GDPR, is called “special
category data”)
» Ask for consent to take interventions directly
with students on the basis of the analytics
https://analytics.jiscinvolve.org/wp
Advice is:
Jisc learning analytics open architecture: core
Data
Collection
Data
Storage
and Analysis
Presentation
and Action Alert and
Intervention system
Other Staff
Dashboards
Student Consent
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
Data collection
Learning
Data Hub
Attendance Study Goal
ETL
About the student Activity data
TinCan
(xAPI)
Student information
system
VLE Library
» 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. Includes hardware-independent Attendance
Tracking functionality, as standard
» 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 from TLC
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
Products and dashboards
Data Explorer
» Data Explorer Release 1.0
› Site Overview – overview of all data
› My Students and My Modules
› RAG Status and predictive models
» User Guide and videos
https://docs.analytics.alpha.jisc.ac.uk/do
cs/data-explorer/Home
Study Goal
»Study Goal aims
› Social learning app with gamification
› Setting targets and logging self-
declared activity (fitbit model)
› View activity and attainment data
› Exclusive – Student Attendance
Check-in
»Guides and videos
https://docs.analytics.alpha.jisc.ac.uk/
docs/study-goal/Home
Attendance check-in (via Study Goal)
» Cheap and simple to implement
» No hardware installation or maintenance required
» Attendance tracking only – no need to integrate with
timetabling systems
» Attendance monitoring can be done using our Data
Explorer tool – or extract yourself!
» Raw attendance data can be accessed directly from
our LDH, using our APIs
» Easy for staff and students to use
» Can be used alongside other attendance systems
eg to record attendance in locations where other
solutions are not installed
» Allows staff to monitor which students are attending
and see students who are not
» Identify students whose attendance pattern is poor
Who we are working with….
Jisc learning analytics service
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 planning
On-boarding process
https://analytics.jiscinvolve.org/wp/on-boarding
Topic ID Question Commentary Response Score
Leadership 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
Leadership 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
Leadership 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
Discovery readiness
A supported review of institutional readiness
»2017-18 - Currently working with 20+ institutions (HE and FE)
on beta service
»Deadline for beta service implementation is April 2018
(12 slots)
»Target 40 institutions signed up to the learning analytics
service by Aug 2018
Engaging institutions
Institutional engagement (pathfinders)
» University of South Wales
» University of Brighton
» University of Abertay, Dundee
» Glasgow Caledonian University
» City, University of London
» Regents University
» Bath Spa University
» Milton Keynes College
» Kings College London
» University of Stirling
» Edinburgh Napier
» Plymouth University
» Aberystwyth University
» University of East Anglia
» Cardiff Metropolitan University
» University of Greenwich
» University of Gloucestershire
» Oxford Brookes University
» City of Wolverhampton College
» Newman University
» University of Chester
» USPs for Institutions:
› Marketplace for LA product and services compatible with the core Jisc service
› Procurement Framework – mini competitions can be easily initiated
› Mandatory clauses included – ensures a consistent and 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
Learning analytics purchasing service –
How we are working with suppliers of LA solutions
» Learning analytics solution and service providers
› Altis, HT2, Phoenix Software, SolutionPath, Tribal, Unicon-Marist,
Kortex
» Data sources including
› Tribal Education, Agresso (UNIT4), HESA, Turnitin, Blackboard,
Canvas, ExLibris, OCLC (Online Computer Library Service), Capita,
Thales, TDS Student, Kortex
Vendor engagement
Examples
»Discovery- helps you assess readiness for implementing
learning analytics. Culture, Data, technology and strategy
»Legal and ethical issues – explores data protection,
consent, GDPR
»Intervention planning to review data to plan interventions with
students and usingdata to enhance the curriculum
Learning analytics workshops/consultancy
Except where otherwise noted, this work is licensed under CC-BY-NC-ND.
Rob Wyn Jones
Head of data and analytics services
rob.jones@jisc.ac.uk
Thankyou

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National learning analytics service overview

  • 1. National learning analytics Rob Wyn Jones Head of data and analytics services, Jisc
  • 2. What is learning analytics?
  • 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
  • 4. Descriptive or predictive models identify students at risk Rich data on student activity and attainment Timely intervention by teaching or support staff Data shared with student prompting them to change own behaviour Data can be explored to understand patterns of behaviour Increased retention Better understanding of the effectiveness of interventions Better student outcomes Better understanding of the behaviours linked to differential outcomes Learning analytics service
  • 5. Learning analytics service A more efficient campus Buildings data + Learning space data + Location data Improved teaching and curricula Teaching quality data + Assessment data + Curriculum design data Personalised and adaptive learning Content data + Learning pathways data Improved teaching and curricula Better retention and attainment VLE data + Student record system + Attendance data + Library data Efficient campus Retention and attainment + + + Learning analytics Institutional analytics Educational analytics Cognitive Analytics andAI Now Future
  • 6. Rob Wyn Jones – Head of data and analytics services Jisc – UK learning analytics national service https://docs.analytics.alpha.jisc.ac.uk/docs/learning-analytics/Home
  • 7. 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 Effective Learning Analytics Challenge
  • 8. 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
  • 9. Toolkit: Code of practice » Code of practice jisc.ac.uk/guides/code-of-practice-for-learning- analytics » Literature review http://repository.jisc.ac.uk/5661/1/Learning_Ana lytics_A-_Literature_Review.pdf » Template learning analytics policy https://analytics.jiscinvolve.org/wp/2016/11/29/d eveloping-an-institutional-learning-analytics- policy/ » Guidance on consent for learning analytics https://analytics.jiscinvolve.org/wp/2017/02/16/c onsent-for-learning-analytics-some-practical- guidance-for-institutions/
  • 10. Legal and ethical: consent and GDPR » Make sure your collection notice covers the use of data to support the student learning and wellbeing » Not ask for consent for the use of non- sensitive data for analytics (our current understanding is that this can be considered as of legitimate interest or public interest) » Ask for consent for use of sensitive data (which, under the GDPR, is called “special category data”) » Ask for consent to take interventions directly with students on the basis of the analytics https://analytics.jiscinvolve.org/wp Advice is:
  • 11. Jisc learning analytics open architecture: core Data Collection Data Storage and Analysis Presentation and Action Alert and Intervention system Other Staff Dashboards Student Consent 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
  • 12. Data collection Learning Data Hub Attendance Study Goal ETL About the student Activity data TinCan (xAPI) Student information system VLE Library
  • 13. » 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. Includes hardware-independent Attendance Tracking functionality, as standard » 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 from TLC 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 Products and dashboards
  • 14. Data Explorer » Data Explorer Release 1.0 › Site Overview – overview of all data › My Students and My Modules › RAG Status and predictive models » User Guide and videos https://docs.analytics.alpha.jisc.ac.uk/do cs/data-explorer/Home
  • 15.
  • 16. Study Goal »Study Goal aims › Social learning app with gamification › Setting targets and logging self- declared activity (fitbit model) › View activity and attainment data › Exclusive – Student Attendance Check-in »Guides and videos https://docs.analytics.alpha.jisc.ac.uk/ docs/study-goal/Home
  • 17. Attendance check-in (via Study Goal) » Cheap and simple to implement » No hardware installation or maintenance required » Attendance tracking only – no need to integrate with timetabling systems » Attendance monitoring can be done using our Data Explorer tool – or extract yourself! » Raw attendance data can be accessed directly from our LDH, using our APIs » Easy for staff and students to use » Can be used alongside other attendance systems eg to record attendance in locations where other solutions are not installed » Allows staff to monitor which students are attending and see students who are not » Identify students whose attendance pattern is poor
  • 18. Who we are working with…. Jisc learning analytics service
  • 19. 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 planning On-boarding process https://analytics.jiscinvolve.org/wp/on-boarding
  • 20. Topic ID Question Commentary Response Score Leadership 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 Leadership 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 Leadership 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 Discovery readiness A supported review of institutional readiness
  • 21. »2017-18 - Currently working with 20+ institutions (HE and FE) on beta service »Deadline for beta service implementation is April 2018 (12 slots) »Target 40 institutions signed up to the learning analytics service by Aug 2018 Engaging institutions
  • 22. Institutional engagement (pathfinders) » University of South Wales » University of Brighton » University of Abertay, Dundee » Glasgow Caledonian University » City, University of London » Regents University » Bath Spa University » Milton Keynes College » Kings College London » University of Stirling » Edinburgh Napier » Plymouth University » Aberystwyth University » University of East Anglia » Cardiff Metropolitan University » University of Greenwich » University of Gloucestershire » Oxford Brookes University » City of Wolverhampton College » Newman University » University of Chester
  • 23. » USPs for Institutions: › Marketplace for LA product and services compatible with the core Jisc service › Procurement Framework – mini competitions can be easily initiated › Mandatory clauses included – ensures a consistent and 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 Learning analytics purchasing service – How we are working with suppliers of LA solutions
  • 24. » Learning analytics solution and service providers › Altis, HT2, Phoenix Software, SolutionPath, Tribal, Unicon-Marist, Kortex » Data sources including › Tribal Education, Agresso (UNIT4), HESA, Turnitin, Blackboard, Canvas, ExLibris, OCLC (Online Computer Library Service), Capita, Thales, TDS Student, Kortex Vendor engagement
  • 25. Examples »Discovery- helps you assess readiness for implementing learning analytics. Culture, Data, technology and strategy »Legal and ethical issues – explores data protection, consent, GDPR »Intervention planning to review data to plan interventions with students and usingdata to enhance the curriculum Learning analytics workshops/consultancy
  • 26. Except where otherwise noted, this work is licensed under CC-BY-NC-ND. Rob Wyn Jones Head of data and analytics services rob.jones@jisc.ac.uk Thankyou