  We study user behavior in the courses offered by a major Massive Online Open
Course (MOOC) provider during the summer of 2013. Since social learning is a
key element of scalable education in MOOCs and is done via online discussion
forums, our main focus is in understanding forum activities. Two salient
features of MOOC forum activities drive our research: 1. High decline rate: for
all courses studied, the volume of discussions in the forum declines
continuously throughout the duration of the course. 2. High-volume, noisy
discussions: at least 30% of the courses produce new discussion threads at
rates that are infeasible for students or teaching staff to read through.
Furthermore, a substantial portion of the discussions are not directly
course-related.
  We investigate factors that correlate with the decline of activity in the
online discussion forums and find effective strategies to classify threads and
rank their relevance. Specifically, we use linear regression models to analyze
the time series of the count data for the forum activities and make a number of
observations, e.g., the teaching staff's active participation in the discussion
increases the discussion volume but does not slow down the decline rate. We
then propose a unified generative model for the discussion threads, which
allows us both to choose efficient thread classifiers and design an effective
algorithm for ranking thread relevance. Our ranking algorithm is further
compared against two baseline algorithms, using human evaluation from Amazon
Mechanical Turk.
  The authors on this paper are listed in alphabetical order. For media and
press coverage, please refer to us collectively, as "researchers from the EDGE
Lab at Princeton University, together with collaborators at Boston University
and Microsoft Corporation."
