Barriers to Expertise in Citizen Science Games


Expertise-centric citizen science games (ECCSGs) can be powerful tools for crowdsourcing scientific knowledge production. However, to be effective these games must train their players on how to become experts, which is difficult in practice. In this study, we investigated the path to expertise and the barriers involved by interviewing players of three ECCSGs: Foldit, Eterna, and Eyewire. We then applied reflexive thematic analysis to generate themes of their experiences and produce a model of expertise and its barriers. We found expertise is constructed through a cycle of exploratory and social learning but prevented by instructional design issues. Moreover, exploration is slowed by a lack of polish to the game artifact, and social learning is disrupted by a lack of clear communication. Based on our analysis we make several recommendations for CSG developers, including: collaborating with professionals of required skill sets; providing social features and feedback systems; and improving scientific communication.

Josh Aaron Miller
Northeastern University, Boston, Massachusetts, United States
Seth Cooper
Northeastern University, Boston, Massachusetts, United States


会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Games in the World

4 件の発表
2022-05-02 20:00:00
2022-05-02 21:15:00