Crowdclass: Designing Classification-Based Citizen Science Learning Modules

Abstract

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.

Lessons from an Online Massive Genomics Computer Game

“…Overall, our observations (Lesson 4) follow previous conclusions made on other platforms suggesting that (i) recurrent users outperforms rookies on complex tasks only (Papoutsaki et al 2015), and (ii) a prior training does not provide an significant long-term advantage (Andersen et al 2012;Horowitz et al 2016). However, recent studies suggest that customized training mechanisms could take advantage of this situation (Lee et al 2016).…”

Schema and Metadata Guide the Collective Generation of Relevant and Diverse Work

“…Workers can get inspiration from prior examples of creative work (Kulkarni, Dow, and Klemmer 2014;Yu and Nickerson 2011). Workers can also enhance prior creative work by assessing, comparing, or combining ideas (Goucher-Lambert and Cagan 2019; Girotto, Walker, and Burleson 2017; Lee et al 2016;Chiang, Kasunic, and Savage 2018), effectively building on the examples by producing additional data around the creative work. Similarly, crowd workers can build on prior sensemaking by adding, filtering, and interpreting data such that produces an overall synthesis (Zhang, Verou, and Karger 2017;Chilton et al 2013;André, Kittur, and Dow 2014).…”