  Learning from the crowd has become increasingly popular in the Web and social
media. There is a wide variety of crowdlearning sites in which, on the one
hand, users learn from the knowledge that other users contribute to the site,
and, on the other hand, knowledge is reviewed and curated by the same users
using assessment measures such as upvotes or likes.
  In this paper, we present a probabilistic modeling framework of
crowdlearning, which uncovers the evolution of a user's expertise over time by
leveraging other users' assessments of her contributions. The model allows for
both off-site and on-site learning and captures forgetting of knowledge. We
then develop a scalable estimation method to fit the model parameters from
millions of recorded learning and contributing events. We show the
effectiveness of our model by tracing activity of ~25 thousand users in Stack
Overflow over a 4.5 year period. We find that answers with high knowledge value
are rare. Newbies and experts tend to acquire less knowledge than users in the
middle range. Prolific learners tend to be also proficient contributors that
post answers with high knowledge value.
