1. Data visualisation with
predictive learning analytics
Chris Ballard
Innovation Consultant (Analytics)

2.  Background
 Predictive analytics
 Visualisation goals and issues
 Examples
 Guidelines
Agenda

3. R&D
Partnership
Objective
• Predictive
models for
student
success
• Map to
retention
themes
• Visualisation
Data
• VLE Activity
• Library
Activity
• Student MIS
• Open data
Background

4. • Retrospective
• What
happened?
Historical
• Reactive
• Why?
Present
• Proactive
• What next?
Predictive
Use of data in Learning Analytics
When used
together
enables
improved
insight into
student learning
Understand
student learning
based on what
we know now
and what might
happen

5. Adaptive
Learning
Platforms
Predicting
student success
and at risk
students
Course
recommendation
Using predictive analytics in education

6. Goals
 Identify earlier students who are at risk of
failure or dropping out
 Understand the factors which influence
student success
 Simple data visualisations to help staff to
support students
 Actionable insights
 Interventions
 Monitoring
Predicting student success

7. Issues with predictive models
 They tell us what might happen, not what will
happen
 They are not infallible
 Cannot always generate predictions
 Need careful interpretation
Predicting student success
Appropriate
visualisation is
critical to its
successful
interpretation
Predictions
need to be
combined with
experience and
knowledge of
the student

8. Data visualisation examples

9. Analytics that adapts to the user

10. Monitoring courses and modules

11. Identifying students at risk for a course

12. Identifying students at risk for a module

13.  Using “traffic lights” to highlight risk:
 Colours can be emotive
 Accessibility issues
 Displaying probabilities
 More vs Less granular information
 Does this aid interpretation?
Design considerations

14. Understand the factors which influence success

15. Visualisations which are easy to interpret

16. Overlaying predictive and historical analytics

17. 1. Visualisations should be simple to interpret
2. Adapt content to the user
3. Indicate how prediction is built up
4. Bridge the gap between predictive and
historic data
5. Enable users to respond and take action
6. Allow users to monitor the effectiveness of
their actions
Design Guidelines

18.  Cross browser
 Responsive user
interface
 Support for different
devices
(mobile, tablet, PC)
 Touch friendly
Technology Guidelines

19. Thank you
@chrisaballard
chris.ballard@tribalgroup.com
www.triballabs.net
www.tribalgroup.com