  Student procrastination and cramming for deadlines are major challenges in
online learning environments, with negative educational and well-being side
effects. Modeling student activities in continuous time and predicting their
next study time are important problems that can help in creating personalized
timely interventions to mitigate these challenges. However, previous attempts
on dynamic modeling of student procrastination suffer from major issues: they
are unable to predict the next activity times, cannot deal with missing
activity history, are not personalized, and disregard important course
properties, such as assignment deadlines, that are essential in explaining the
cramming behavior. To resolve these problems, we introduce a new personalized
stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all
student-assignment pairs and utilizing their similarities, to predict students'
next activity times even when there are no historical observations. Unlike
regular point processes that assume a constant external triggering effect from
the environment, we model three dynamic types of external stimuli, according to
assignment availabilities, assignment deadlines, and each student's time
management habits. Our experiments on two synthetic datasets and two real-world
datasets show a superior performance of future activity prediction, comparing
with state-of-the-art models. Moreover, we show that our model achieves a
flexible and accurate parameterization of activity intensities in students.
