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Changing spaces of education: new perspectives on the nature of learning
Authors:Alan Tuckett
Institution:1. Information Office Rm003, Vaughan Harley, Open University, Milton Keynes, UKCarol.calvert@open.ac.uk
Abstract:This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at different milestones identified. The explanatory variables, milestones and reference points when the model is run will be different at other institutions but the approach may be generalised to distance learning institutions and, more broadly, to any HE institution. Institutions, and especially distance education institutions which do not have the advantages of frequently seeing students, need to make full use of any recorded information they hold to try and identify students who are, or become, at potential risk of leaving. The importance of different factors, at different milestones, may help tailor student support to individual students and therefore improve low retention in open access distance education.
Keywords:predictive analytics  student success  retention  probabilities of success  logistic regression  distance education  learning analytics
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