2016/02/22 by Ryo Suzuki, Suzuki, Ryo, Niloufar Salehi +7 · 2 citations
Computer Science · #Expert finding and Q&A systems #FOS: Computer and information sciences #H.5.3 #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Privacy-Preserving Technologies in Data #cs.HC
paper · pdf · doi:10.48550/arxiv.1602.06634
CHI 2016
arxiv created 2016/02/22 · openalex publication_date 2016/02/22 · arxiv updated 2016/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.