  The increasing availability of learning activity data in Massive Open Online
Courses (MOOCs) enables us to conduct a large-scale analysis of learners'
learning behavior. In this paper, we analyze a dataset of 351 million learning
activities from 0.8 million unique learners enrolled in over 1.6 thousand
courses within two years. Specifically, we mine and identify the learning
patterns of the crowd from both temporal and course enrollment perspectives
leveraging mutual information theory and sequential pattern mining methods.
From the temporal perspective, we find that the time intervals between
consecutive learning activities of learners exhibit a mix of power-law and
periodic cosine function distribution. By qualifying the relationship between
course pairs, we observe that the most frequently co-enrolled courses usually
fall in the same category or the same university. We demonstrate these findings
can facilitate manifold applications including recommendation tasks on courses.
A simple recommendation model utilizing the course enrollment patterns is
competitive to the baselines with 200$\times$ faster training time.
