Uncovering student temporal learning patterns

Because of the flexibility of online learning courses, students organise and manage their own learning time by choosing where, what, how, and for how long they study. Each individual has their unique learning habits that characterise their behaviours and distinguish them from others. Nonetheless, to the best of our knowledge, the temporal dimension of student learning has received little attention on its own. Typically, when modelling trends, a chosen configuration is set to capture various habits, and a cluster analysis is undertaken. However, the selection of variables to observe and the algorithm used to conduct the analysis is a subjective process that reflects the researcher's thoughts and ideas. To explore how students behave over time, we present alternative ways of modelling student temporal behaviour. Our real-world data experiments reveal that the generated clusters may or may not differ based on the selected profile and unveil different student learning patterns.