  People are increasingly relying on the Web and social media to find solutions
to their problems in a wide range of domains. In this online setting, closely
related problems often lead to the same characteristic learning pattern, in
which people sharing these problems visit related pieces of information,
perform almost identical queries or, more generally, take a series of similar
actions. In this paper, we introduce a novel modeling framework for clustering
continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes
process (HDHP), which allows us to automatically uncover a wide variety of
learning patterns from detailed traces of learning activity. Our model allows
for efficient inference, scaling to millions of actions taken by thousands of
users. Experiments on real data gathered from Stack Overflow reveal that our
framework can recover meaningful learning patterns in terms of both content and
temporal dynamics, as well as accurately track users' interests and goals over
time.
