Modelling student online behaviour in a virtual learning environment
This work addresses retention issues for students and tutors in distance education, but it is incremental as it applies existing methods to a specific educational context.
The paper tackled the problem of improving student retention in distance education by analyzing online behavior in a virtual learning environment, showing that both GUHA and Markov chain-based methods are valid for modeling student activities, with the Markov approach offering advantages in graphical output and time dependency modeling.
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.