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We study user behavior in the courses offered by a major Massive Online OpenCourse (MOOC) provider during the summer of 2013. Since social learning is akey element of scalable education in MOOCs and is done via online discussionforums, our main focus is in understanding forum activities. Two salientfeatures of MOOC forum activities drive our research: 1. High decline rate: forall courses studied, the volume of discussions in the forum declinescontinuously throughout the duration of the course. 2. High-volume, noisydiscussions: at least 30% of the courses produce new discussion threads atrates that are infeasible for students or teaching staff to read through.Furthermore, a substantial portion of the discussions are not directlycourse-related.  We investigate factors that correlate with the decline of activity in theonline discussion forums and find effective strategies to classify threads andrank their relevance. Specifically, we use linear regression models to analyzethe time series of the count data for the forum activities and make a number ofobservations, e.g., the teaching staff's active participation in the discussionincreases the discussion volume but does not slow down the decline rate. Wethen propose a unified generative model for the discussion threads, whichallows us both to choose efficient thread classifiers and design an effectivealgorithm for ranking thread relevance. Our ranking algorithm is furthercompared against two baseline algorithms, using human evaluation from AmazonMechanical Turk.  The authors on this paper are listed in alphabetical order. For media andpress coverage, please refer to us collectively, as "researchers from the EDGELab at Princeton University, together with collaborators at Boston Universityand Microsoft Corporation."

Learning about social learning in MOOCs: From statistical analysis to generative model