Digital apprenticeships
Using data to enhance and improve the apprenticeship journey
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.
12/07/2018
Background
The digital apprenticeship project is
one of five new ideas to emerge from
our co-design consultations with
members and other stakeholders.
12/07/2018
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.
12/07/2018
What we’re doing
This work is developing alongside our
effective learning analytics project
and our work to build a learning
analytics service.
12/07/2018
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.
12/07/2018
Jisc learning analytics service12/07/2018
Outline
ÂťLearning analytics
› Service
› Toolkit, community, consultancy
»Learning analytics – service development
› Student success
› Curriculum enhancement
› Employability
› Digital apprenticeships
› Intelligent campus
12/07/2018
Effective Learning Analytics Challenge
ÂťRationale
› Organisations wanted help to get started and have access to
standard tools and technologies to monitor and intervene
ÂťPriorities 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
ÂťTimescale
› 2015-17 Development
› 2017-18 Beta Service
› Aug 2018 Full Service
12/07/2018
Take up
Âť30 institutions signed-up
Âť8 institutions institution wide roll-out Sept
Âť14 HEIs in data integration/pilot stage
Âť8 Colleges in service development
ÂťStarting to explore apprenticeship data
12/07/2018
Community
ÂťBlog: http://analytics.jiscinvolve.org
ÂťDocs: http://docs.analytics.alpha.jisc.ac.uk/
ÂťMailing List: analytics@jiscmail.ac.uk
ÂťNetworks: pathfinder/implementation
12/07/2018
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
ÂťStage 4: Data Integration - push data to learning data hub
ÂťStage 5: Implementation and Scaling
› https://analytics.jiscinvolve.org/wp/on-boarding/
12/07/2018
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
Alpha phase one
12/07/2018
Products and dashboards
Âť Data Explorer: Learning Analytics dashboards for staff, role appropriate views
Âť Study Goal: An app for students - allowing them to view their learning analytics
data, and set measurable actions to support their success.
Âť Learning Analytics Predictor: A predictive model designed to do one thing well -
predict success at course level. Output can be viewed in Data Explorer.
Âť 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.
Âť 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
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
Data Explorer
Âť 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
Âť User Guide and videos
Âť https://docs.analytics.alpha.jisc.ac.u
k/docs/data-explorer/Home
12/07/2018
Data Explorer
12/07/2018
Study Goal
ÂťStudy Goal aims
› Social learning app with
gamification
› Setting targets and logging
self-declared activity (fitbit
model)
› View activity and attainment
data
› Attendance check-in
ÂťGuides and videos
› https://docs.analytics.alpha.jis
c.ac.uk/docs/study-goal/Home
12/07/2018
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
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

Digital Apprenticeships Project Update

  • 1.
    Digital apprenticeships Using datato enhance and improve the apprenticeship journey
  • 2.
    Aim We’re working onways to improve the apprentice experience by capturing and analysing the many kinds of data that can be collected through the apprenticeship journey. 12/07/2018
  • 3.
    Background The digital apprenticeshipproject is one of five new ideas to emerge from our co-design consultations with members and other stakeholders. 12/07/2018
  • 4.
    Background Our members andstakeholders 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. 12/07/2018
  • 5.
    What we’re doing Thiswork is developing alongside our effective learning analytics project and our work to build a learning analytics service. 12/07/2018
  • 6.
    Learning data hub Atthe 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. 12/07/2018
  • 7.
    Jisc learning analyticsservice12/07/2018
  • 8.
    Outline »Learning analytics › Service ›Toolkit, community, consultancy »Learning analytics – service development › Student success › Curriculum enhancement › Employability › Digital apprenticeships › Intelligent campus 12/07/2018
  • 9.
    Effective Learning AnalyticsChallenge »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 12/07/2018
  • 10.
    Take up Âť30 institutionssigned-up Âť8 institutions institution wide roll-out Sept Âť14 HEIs in data integration/pilot stage Âť8 Colleges in service development ÂťStarting to explore apprenticeship data 12/07/2018
  • 11.
    Community ÂťBlog: http://analytics.jiscinvolve.org ÂťDocs: http://docs.analytics.alpha.jisc.ac.uk/ ÂťMailingList: analytics@jiscmail.ac.uk ÂťNetworks: pathfinder/implementation 12/07/2018
  • 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/ 12/07/2018
  • 13.
    Data Collection Data Storage and Analysis Presentation and ActionAlert 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.
  • 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 identifystudents 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 » DataExplorer 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 12/07/2018
  • 18.
  • 19.
    Study Goal »Study Goalaims › 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 12/07/2018
  • 20.
    LearningAnalytics Service VLE data + Studentrecord 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