This paper describes a proposal of relevant clustering features and the results of experiments using them in the context of determining students' learning behaviors by mining Moodle log data. Our clustering experiments tried to show whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. They were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced.