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Modelling student online behaviour in a virtual learning environment

Hlosta, Martin; Herrmannova, Drahomira; Vachova, Lucie; Kuzilek, Jakub; Zdrahal, Zdenek and Wolff, Annika (2014). Modelling student online behaviour in a virtual learning environment. 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, 24-28 Mar 2014, Indianapolis, Indiana, USA.

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In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) - General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis - and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities. Drahomira Herrmannova,Lucie Vachova,Jakub Kuzilek,Zdenek Zdrahal,Annika Wolff

Item Type: Conference or Workshop Item
Copyright Holders: 2014 The Authors
Keywords: student data; distance learning; predictive models; machine learning; information visualisation
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Research Group: Centre for Research in Computing (CRC)
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Item ID: 40670
Depositing User: Kay Dave
Date Deposited: 06 Aug 2014 09:07
Last Modified: 15 Feb 2019 09:21
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