This paper introduces a novel method that utilises graph-based 3D convolutional neural networks to predict dropout in online courses. The methodology allows for the inclusion of the sequence of events, along with their irregular time steps, as input variables. By considering this additional information, we can analyse the time intervals between events to determine whether a student is likely to dropout based on their clickstream actions and the time intervals between clicks.

The full paper is available here.