  We profiled three aspects of MOOCs from the perspective of viewing behaviors,
the most prominent and common ones of MOOC learning. They were learner
classification, course attraction, teaching order and learning order. Based on
viewing behavior data, we provided a non-parametric algorithm to categorize
learners, which helped to narrow the scope of finding potential all-rounders,
and a method to measure the correlations between teaching order and learning
order, which helped to assign teaching contents. Using information entropy, we
provided an index to measure course attraction, which integrated the viewing
time invested on courses and the number of viewed course videos. This index
describes the diminishing marginal utility of repeated viewing and the
increasing information of viewing new videos. It has potential to be an
auxiliary method of assessing course achievements.
