Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
Ryo Suzuki, Niloufar Salehi, Michelle S. Lam, Juan C. Marroquin,, Michael S. Bernstein

TL;DR
This paper introduces Atelier, a platform that transforms expert crowdsourcing tasks into mentored micro-internships to facilitate skill development and improve intern outcomes.
Contribution
It presents a novel approach to repurposing crowdsourcing tasks as mentored internships, connecting interns with mentors for real-world learning experiences.
Findings
Mentored interns maintained progress better than non-mentored ones.
Mentorship helped interns absorb best practices.
The platform facilitated skill development in a real-world setting.
Abstract
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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Expert finding and Q&A systems · Privacy-Preserving Technologies in Data
