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2. Can predictive analytics be used to support students in HE?
Kevin Mayles, Alison Gilmour, Avinash Boroowa
The Open University

4. Predictive Data Analytics

5. Predictive Data Analytics

7. Group discussion
What is predictive analytics?

10. Predictive learning analytics
Subhead

11. Creation of actionable insight Impact the student experience
Data science, data analysis, visualisation and storytelling
Data literacy and culture
Data governance and compliance
Productionised output and MI
Strategic analysis
Modelling
Data collection
Data storage and access
Technology architecture
Learning design and delivery
Student lifecycle management
Continuous improvement and innovation
Creation of actionable insight
Availability of data
Impact the student experience

12. Predictive Modelling: A Pilot at The Open University in Scotland
From previous retention initiatives there was:
1. Strong staff support for proactive student support
2. Interest in how we make decisions about targeting students and making best use of limited resource.
This led to our objective:
To improve the retention rate of ‘at risk’ students as defined by the Strategy and Information Office Predictive Model
Explored in a collaboratively designed project involving various staff from different units
[Parallel session 3.7.1]

13. Evaluating a predictive learning analytics intervention: OU Analyse

14. Group discussion Applications of predictive learning analytics
Opportunities and challenges

15. What are the potential opportunities for predictive analytics to impact the student experience at your institution – what are the problems that predictive analytics could be applied to?
What might be some of the challenges that you might face?
Data availability – who might you need to talk to – does this represent an opportunity or a challenge?
Creation of the predictive models – what opportunities might arise? What challenges might be faced?

16. In your group pick your top three opportunities and top three challenges

19. Thank you