NDLI: Uncovering the Dynamics of Crowdlearning and the Value of Knowledge

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Uncovering the Dynamics of Crowdlearning and the Value of Knowledge

Content Provider	ACM Digital Library
Author	Gomez-Rodriguez, Manuel Upadhyay, Utkarsh Valera, Isabel
Abstract	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.
Starting Page	61
Ending Page	70
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018685
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User modelling Education Markets and crowds Social and information networks
Content Type	Text
Resource Type	Article

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