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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.