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Expert Audience

English

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Kaiserslautern

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 talk, I 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 own 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.

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