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In recent years, distance education has enjoyed a major boom. Much work atThe Open University (OU) has focused on improving retention rates in thesemodules by providing timely support to students who are at risk of failing themodule. In this paper we explore methods for analysing student activity inonline virtual learning environment (VLE) -- General Unary Hypotheses Automaton(GUHA) and Markov chain-based analysis -- and we explain how this analysis canbe relevant for module tutors and other student support staff. We show thatboth methods are a valid approach to modelling student activities. An advantageof the Markov chain-based approach is in its graphical output and in thepossibility to model time dependencies of the student activities.

Modelling student online behaviour in a virtual learning environment