Overview of the project so far and how it links to Jisc's Learning Analytics Service.

1. Digital apprenticeships
Using data to enhance and improve the apprenticeship journey

2. Aim
We’re working on ways to improve
the apprentice experience by
capturing and analysing the many
kinds of data that can be collected
through the apprenticeship journey.
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3. Background
The digital apprenticeship project is
one of five new ideas to emerge from
our co-design consultations with
members and other stakeholders.
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4. Background
Our members and stakeholders have
asked us to research how we can use
technology to enhance and improve
the apprenticeship journey in order
to meet the needs of employers and
apprentices in the 21st century.
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5. What we’re doing
This work is developing alongside our
effective learning analytics project
and our work to build a learning
analytics service.
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6. Learning data hub
At the core of the learning analytics
service is the learning data hub.
We’ll extend the learning data hub to
enable data to be gathered from all
aspects of the apprenticeship
journey.
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7. Jisc learning analytics service12/07/2018

8. Outline
»Learning analytics
› Service
› Toolkit, community, consultancy
»Learning analytics – service development
› Student success
› Curriculum enhancement
› Employability
› Digital apprenticeships
› Intelligent campus
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9. Effective Learning Analytics Challenge
»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
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10. Take up
»30 institutions signed-up
»8 institutions institution wide roll-out Sept
»14 HEIs in data integration/pilot stage
»8 Colleges in service development
»Starting to explore apprenticeship data
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11. Community
»Blog: http://analytics.jiscinvolve.org
»Docs: http://docs.analytics.alpha.jisc.ac.uk/
»Mailing List: analytics@jiscmail.ac.uk
»Networks: pathfinder/implementation
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12. 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
› https://analytics.jiscinvolve.org/wp/on-boarding/
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13. Data
Collection
Data
Storage
and Analysis
Presentation
and Action Alert and Intervention
system
Staff Dashboards Consent Student App
Learning
Analytics Processor
Learning
Data Hub
Student Records VLE Library
DataExplorer
Self Declared Data Attendance, Presence, Equipment use etc….
Data Aggregator
UDDTransformationToolkit Plugins and/or Universal xAPITranslator
Employer
Dashboards
Learning Analytics Open Architecture

14. Alpha phase one
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15. Products and dashboards
» Data Explorer: Learning Analytics dashboards for staff, role appropriate views
» 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.
» 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.
» Learning Data Hub: the core of Jisc's learning analytics service, holds data about
students, works in conjunction with an institutions data warehouse (where
present), to share data between applications in a standard way, a collection point
for semi-structured learning data such as student activity.
» Apprenticeship Dashboard: “Data Explorer for Employers” – see apprentices
progress, attendance, attainment, etc., in a common view for multiple providers (In
development)
LearningAnalytics Service

16. Analytics
LearningAnalytics 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

17. 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.u
k/docs/data-explorer/Home
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18. Data Explorer
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19. 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.jis
c.ac.uk/docs/study-goal/Home
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20. LearningAnalytics 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. Contacts
LearningAnalytics Service
Rob Bristow – rob.bristow@jisc.ac.uk
Further Information:
http://www.analytics.jiscinvolve.org
https://digitalapprenticeships.jiscinvolve.
org/wp/
Join: analytics@jiscmail.ac.uk