1. Beneath the Bonnet of Education in the Post-Digital Age DR RAY STONEHAM UNIVERSITY OF GREENWICH

2. Learning Analytics and Big Data Analysis of the "digital exhaust" all students leave behind in the post-digital age could possibly be used to ◦identify failing students early enough to be able to intervene (either automatically, or manually) in order to prevent failure ◦provide personalised learning ◦enable all students to reach their full potential ◦reduce costs

3. The promise of Learning Analytics Developing systems and using data "beneath the bonnet" to meet these requirements is the "Holy Grail" of Learning Analytics and Education o Big data is being collected o Learning Analytics are being developed o Algorithms are being tested o The "Internet of Things" is developing o Systems are being implemented

4. Where are we now? The Gartner Hype Cycle July 2014

5. Beneath the bonnet with "small data" Currently "Small data" tells us a lot. Analysis of data relating student outcomes to various criteria based on historical data will be presented  Time of Coursework submission  Use of the VLE  Attendance at classes  Viewing of videos and screencasts But does it tell us anything useful?

6. Beneath the bonnet with Big Data It is a long way to the "Plateau of Productivity" in the Gartner Cycle for Big Data and Content Analytics Will there be a "Trough of Disillusionment"? How will we know when we reach the "Slope of Enlightenment"?

7. Some ethical issues What data should be used? Informed consent is essential Interpretation of the data must be objective Danger of potential bias and oversimplification Automated pattern recognition and data mining can keep individuals prisoner to past choices Ineffective and misdirected interventions resulting from faulty learning diagnoses might result in resentment, and broken trust What about data about staff?

8. Risks Whose fault is it if a student fails? Suggestions from the audience....

9. Next steps Students can be helped if "beneath the bonnet" data is used Before implementation of any Learning Analytics system Ethical issues need to be discussed and agreed at institutional level Criteria to identify "failing students" need to be validated Systems must be designed for the benefit of students