Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
Complex Tasks and Learning in Crowdsourcing
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Suzuki, Ryo
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Salehi, Niloufar
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Lam, Michelle S.
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Marroquin, Juan C.
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Bernstein, Michael S.
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.2645-2656
© Copyright 2016 ACM
Summary: Expert crowdsourcing marketplaces have untapped potential to empower
workers' career and skill development. Currently, many workers cannot afford to
invest the time and sacrifice the earnings required to learn a new skill, and a
lack of experience makes it difficult to get job offers even if they do. In
this paper, we seek to lower the threshold to skill development by repurposing
existing tasks on the marketplace as mentored, paid, real-world work
experiences, which we refer to as micro-internships. We instantiate this idea
in Atelier, a micro-internship platform that connects crowd interns with crowd
mentors. Atelier guides mentor-intern pairs to break down expert crowdsourcing
tasks into milestones, review intermediate output, and problem-solve together.
We conducted a field experiment comparing Atelier's mentorship model to a
non-mentored alternative on a real-world programming crowdsourcing task,
finding that Atelier helped interns maintain forward progress and absorb best
practices.