Atelier

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 crowdsourcing task, finding that Atelier helped interns maintain forward progress and absorb best practices.

Crowdsourcing as a Tool for Research

“…Recent research in crowdsourcing has introduced a variety of new interaction paradigms that enable non-experts to learn and perform complex, expert-level tasks [57,67,79]. The ideas and lessons learned have not yet made their way into citizen science or research-oriented crowdsourcing, suggesting many opportunities for future work.…”

Flash Organizations

“…Instead, we hope that flash organizations will offer crowd workers, who are currently predominantly piecework employees, the opportunity to join longer-term and more fulfilling projects. More broadly, flash organizations help envision a world that enables crowd workers to pursue long-term careers [42], including skill growth [77], access to labor collectives [73], and guarantees of stable income.…”

“…Recent work instead sought to achieve complex goals by moving from microtask workers to expert workers. Such systems now support user interface prototyping [70], questionanswering and debugging for software engineers [11,22,50], worker management [28,45], remote writing tasks [61], and skill training [77]. For example, flash teams demonstrated that expert workflows can achieve far more complex goals than can be accomplished using microtask workflows [70].…”

Complex task allocation for crowdsourcing in social network context

“…The results are shown in Now we test the influence of skill number of tasks on the six types of performances of the five approaches. The scopes of skill number of tasks in the experiments are set as [5,10], [10][11][12][13][14][15], [15][16][17][18][19][20], [20][21][22][23][24][25]. The parameter of the relative importance factor α is set to 0.75.…”

“…Compared with traditional task allocation, the advantages of crowdsourcing include faster completion speed [8], lower costs [9], higher accuracy [10], and completion of tasks that computers cannot perform [11]. There are many crowdsourcing platforms have been developed, such as Amazon's Mechanical Turk [12], UPwork [13], and MicroWorkERs [14].…”