  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.
