Data visualisation with predictive learning analytics

Data visualisation with predictive learning analyticsChris BallardInnovation Consultant (Analytics)

Background Predictive analytics Visualisation goals and issues Examples Guidelines

Agenda

R&D Partnership Objective

• Predictive models for student success

• Map to retention themes

• Visualisation

Data

• VLE Activity• Library

Activity• Student MIS• Open data

Background

• 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

Adaptive Learning Platforms

Predicting student success and at risk students

Course recommendation

Using predictive analytics in education

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

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

Data visualisation examples

Analytics that adapts to the user

Monitoring courses and modules

Identifying students at risk for a course

Identifying students at risk for a module

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

Understand the factors which influence success

Visualisations which are easy to interpret

Overlaying predictive and historical analytics

1. Visualisations should be simple to interpret

2. Adapt content to the user3. Indicate how prediction is built up4. Bridge the gap between predictive and

historic data5. Enable users to respond and take

action6. Allow users to monitor the

effectiveness of their actions

Design Guidelines

Cross browser Responsive user

interface Support for

different devices (mobile, tablet, PC)

Touch friendly

Technology Guidelines

Thank you@chrisaballardchris.ballard@tribalgroup.comwww.triballabs.netwww.tribalgroup.com