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Hawkes processes have been shown to be efficient in modeling bursty sequencesin a variety of applications, such as finance and social network activityanalysis. Traditionally, these models parameterize each process independentlyand assume that the history of each point process can be fully observed. Suchmodels could however be inefficient or even prohibited in certain real-worldapplications, such as in the field of education, where such assumptions areviolated. Motivated by the problem of detecting and predicting studentprocrastination in students Massive Open Online Courses (MOOCs) with missingand partially observed data, in this work, we propose a novel personalizedHawkes process model (RCHawkes-Gamma) that discovers meaningful studentbehavior clusters by jointly learning all partially observed processessimultaneously, without relying on auxiliary features. Our experiments on bothsynthetic and real-world education datasets show that RCHawkes-Gamma caneffectively recover student clusters and their temporal procrastinationdynamics, resulting in better predictive performance of future studentactivities. Our further analyses of the learned parameters and theirassociation with student delays show that the discovered student clustersunveil meaningful representations of various procrastination behaviors instudents.

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs