Quality control is one of the biggest challenges facing data management firms, especially those that rely on crowdsourced resources for some or all of their production. In an effort improve worker skills and accuracy for crowdsourced projects, I conducted a study under Dr. Matt Lease at the University of Texas at Austin’s Crowdsourcing Lab earlier this year and found that workers who completed an online course in advance of starting work produced measurably more accurate results on data refinement tasks. Expanding on these results, I built a Crowdlearning Center for Information Evolution, Inc. (IEI) to train and certify workers in IEI research protocols.
IEI’s Crowdlearning Center hosts Small Private Online Courses (or SPOCs) that teach data refinement skills to contractors worldwide and internal teams as well. Based on the Moodle open-source learning management system, each self-paced course features a guided learning path of resources and activities. The Crowdlearning Center enables IEI to train an agile workforce that produces high-quality output and adapts quickly to changing market demands.
Clients are not the only ones to benefit from a Crowdlearning Center. Workers who complete the SPOC lessons and earn at least 90% on the final unit test are awarded a badge that certifies their skills. The badges are built on the Mozilla Open Badges framework and registered with Cred.ly.
Each badge links back to the issuer, the issuing criteria, and the evidence verifying the credential. Workers can display their badges on the web and share them for employment, education, and lifelong learning. The Mozilla Open Badges standard enables individuals to combine badges from any of the 1,000+ issuers to tell the story of their achievements.
In the Wild West of online labor markets, badged workers are better able to gain employer trust, earn accuracy bonuses, and see continued opportunity. Allowing workers to build their skill sets and demonstrate that they have done so makes credentialed projects more attractive to high-performing resources and build a stable foundation for data teams and the firms that employ them to grow and prosper.
