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.

提出了基于概率建模的众包学习框架，通过利用其他用户对贡献的评估，揭示了用户专业知识随时间的演变。还开发了一个可扩展的估计方法来拟合模型参数，并在 Stack Overflow 上跟踪了约 25,000 个用户的活动，发现高知识价值的答案很少，初学者和专家获得的知识比处于中等水平的用户少，励志经常学习者倾向于是高质量答案的熟练贡献者。