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Developing predictive models for early detection of at-risk students on distance learning modules

Wolff, Annika; Zdrahal, Zdenek; Herrmannova, Drahomira; Kuzilek, Jakub and Hlosta, Martin (2014). Developing predictive models for early detection of at-risk students on distance learning modules. In: Machine Learning and Learning Analytics Workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14), 24-28 March 2014, Indianapolis, Indiana, USA.

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Abstract

Not all students who fail or drop out would have done so if they had been offered help at the right time. This is particularly true on distance learning modules where there is no direct tutor/student contact, but where it has been shown that making contact at the right time can improve a student’s chances. This paper explores the latest work conducted at the Open University, one of Europe’s largest distance learning institutions, to identify when is the optimum time to make student interventions and to develop models to identify the at-risk students in this time frame. This work in progress is taking real time data and feeding it back to module teams as the module is running. Module teams will be indicating which of the predicted at-risk students have received an intervention, and the nature of the intervention.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 The Authors
Keywords: predictive models; machine learning; student data; Bayesian models; distance learning
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 40669
Depositing User: Kay Dave
Date Deposited: 06 Aug 2014 08:59
Last Modified: 05 Oct 2016 15:48
URI: http://oro.open.ac.uk/id/eprint/40669
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