Predictive analytics based student intervention

The field of Learning Analytics is beginning to gain traction in educational institutions, with perhaps the main driver being the prediction of student outcomes in order to identify students at risk. This is not just key to improving the student experience, but critical to institutional budgets, where student withdrawals can cause significant damage to revenue streams. However, the identification of students at risk is only useful when positive and successful interventions are made. Currently, institutional interventions are ad-hoc and usually based upon the experience and style/approach of the individual tutor or course leader, with supporting institutional processes arguably focussed upon protecting the establishment. An emerging area is that of using intelligent techniques to provide evidential input and specific intelligent support to the intervention process itself. In addition to describing the relevant issues and methods I will describe the approach taken by the Open University, a leader in the field.

Date: 07/04/2017
Time: 16:00
Location: LB252

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