Crowdclass: Designing Classification-Based Citizen Science Learning Modules


  • Doris Jung-Lin Lee University of Illinois Urbana-Champaign
  • Joanne Lo University of California, Berkeley
  • Moonhyok Kim University of California, Berkeley
  • Eric Paulos University of California, Berkeley



citizen science, learning, crowdsourcing


In this paper, we introduce Crowdclass, a novel framework that integrates the learning of advanced scientific concepts with the crowdsourcing microtask of image classification. In Crowdclass, we design questions to serve as both a learning experience and a scientific classification. This is different from conventional citizen science platforms which decompose high level questions into a series of simple microtasks that require no scientific background knowledge to complete. We facilitate learning within the microtask by providing content that is appropriate for the participant’s level of knowledge through scaffolding learning. We conduct a between-group study of 93 participants on Amazon Mechanical Turk comparing Crowdclass to the popular citizen science project Galaxy Zoo. We find that the scaffolding presentation of content enables learning of more challenging concepts. By understanding the relationship between user motivation, learning, and performance, we draw general design principles for learning-as-an-incentive interventions applicable to other crowdsourcing applications.




How to Cite

Lee, D. J.-L., Lo, J., Kim, M., & Paulos, E. (2016). Crowdclass: Designing Classification-Based Citizen Science Learning Modules. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 4(1), 109-118.