Jisc analytics slides HESPA workshop Nov 2018

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

2. Sources of data….
Jisc learning analytics service

3. Learning Analytics 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

4. Data
Collection
Data
Storage
and Analysis
Presentation
and Action
Jisc Learning Analytics open architecture: core
Alert and Intervention
system
Other Staff
Dashboards
Consent Service
(tbc)
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

5. Intelligent Campus
Learning Analytics Service

6. Health and well-being
• Can we use activity data to support health and well-being?
• Timely interventions identify students earlier
• Patterns of behaviour
• Improved student support processes
• Developing coping strategies
• Additional data
• Student sentiment analysis
• Long term data study
• Sensitive data
• Build AI models to predict at risk students, also beyond
graduation
Learning Analytics Service

7. Student Success
• Behavioural patterns that lead to success (attendance, engagement, attainment, submission
date/time of assignments)
• Predictive models that look at success i.e. first or 2:1 – that will model the behaviours
• Grouping of behaviours that lead to success (e.g. accessing a wider range of resources, time on
task, linking intended with actual behaviours)
Learning Analytics Service

8. Employability
• Analyse data to find indicators that
lead to employability
Baseline data on employability
Activity data e.g.
• Careers entry profiles
• Careers engagement activity
• Employability skills in modules
• Work experience
Learning Analytics Service
HEPI Employability: Degrees of
Value

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

10. Panel Session
Questions – panel
How are planners engaged in what you do? How important is it for you to work with
planners?
What skills sets do you need to make effective use of this data?
To the planners
How many of you inform learning and teaching through your work?
Who do you connect with in institutions?
What skills sets do you feel are required and do L&T staff have them?
Learning Analytics Service

11. User Stories Template
Title:
As a
I want
so that I

12. Activity
What challenges may you need to overcome?
What additional data may be required?
What else maybe required to realise these
opportunities?
What will it look like?
Learning Analytics Service
Questions