  Hawkes processes have been shown to be efficient in modeling bursty sequences
in a variety of applications, such as finance and social network activity
analysis. Traditionally, these models parameterize each process independently
and assume that the history of each point process can be fully observed. Such
models could however be inefficient or even prohibited in certain real-world
applications, such as in the field of education, where such assumptions are
violated. Motivated by the problem of detecting and predicting student
procrastination in students Massive Open Online Courses (MOOCs) with missing
and partially observed data, in this work, we propose a novel personalized
Hawkes process model (RCHawkes-Gamma) that discovers meaningful student
behavior clusters by jointly learning all partially observed processes
simultaneously, without relying on auxiliary features. Our experiments on both
synthetic and real-world education datasets show that RCHawkes-Gamma can
effectively recover student clusters and their temporal procrastination
dynamics, resulting in better predictive performance of future student
activities. Our further analyses of the learned parameters and their
association with student delays show that the discovered student clusters
unveil meaningful representations of various procrastination behaviors in
students.
