The potential for scientific outreach and learning in mechanical turk experiments

Abstract

The global reach of online experiments and their wide adoption in fields ranging from political science to computer science poses an underexplored opportunity for learning at scale: the possibility of participants learning about the research to which they contribute data. We conducted three experiments on Amazon's Mechanical Turk to evaluate whether participants of paid online experiments are interested in learning about research, what information they find most interesting, and whether providing them with such information actually leads to learning gains. Our findings show that 40% of our participants on Mechanical Turk actively sought out post-experiment learning opportunities despite having already received their financial compensation. Participants expressed high interest in a range of research topics, including previous research and experimental design. Finally, we find that participants comprehend and accurately recall facts from post-experiment learning opportunities. Our findings suggest that Mechanical Turk can be a valuable platform for learning at scale and scientific outreach.