Science Gamers, Citizen Scientists, and Dabblers: Characterizing Player Engagement in Two Citizen Science Games

Abstract

Our understanding of volunteers’ engagement in citizen science games is limited, and the impact of gamefulness on volunteer engagement requires further investigation. In this study, we adopted a data-driven approach, exploring volunteers’ psychological and behavioral engagement in two citizen science games: Forgotten Island and Happy Match. We performed a cluster analysis based on the quantity and accuracy of volunteers’ contributed data and conducted a qualitative content analysis of player survey responses to open-ended survey questions. We combined the results of the clustering analysis and the content analysis to identify and characterize three player groups based on their engagement patterns: “science gamers,” “citizen scientists,” and “dabblers.” Our identification of this three-group typology of citizen science players enhances our understanding of volunteers’ contribution and engagement patterns, and we provide design recommendations that may help scientists and designers to refine their own citizen science game initiatives.

Acknowledgements

The authors would like to thank Citizen Sort’s principal investigators, Kevin Crowston and Jun Wang, for their continued support and guidance, as well as the research and development team for their efforts on this project: Nathan Brown, Chris Duarte, Susan Furest, Jiayan Guo, Yang Liu, Supriya Mane, Nitin Mule, Gongying Pu, Sixian Qi, Trupti Rane, Jimit Shah, Sheila Sicilia, Jessica Smith, Dania Souid, Peiyuan Sun, Rui Wei, Xueqing Xuan, Meihua Yu, Shu Zhang, and Zhiruo Zhao, Xuhong Zhu. The authors would also like to thank the following for their advice and assistance: Jennifer Hammock, Nancy Lowe, John Pickering, and Andrea Wiggins.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes

1 https://eyewire.org

2 https://fold.it

3 http://www.citizensort.org (Note: the citizen sort system is no longer actively recruiting players for all games)

4 When players registered in the Citizen Sort system, some players chose not to report their accurate ages, so we report both the mean and median of players’ ages.

5 One participant responded to both surveys because this participant played both games. Since our data analysis results were reported by game, we treated this participant’s answers to each survey as separate responses in the following analysis and report.

Additional information

Funding

The Citizen Sort system development, implementation, maintenance, and research were supported by the US National Science Foundation [grant number SOCS 09-68470]. This current research study and collaboration were supported by the National Natural Science Foundation of China [grant number 71904215] and the Ministry of Education, Humanities and Social Sciences Council in China [grant number 18YJCZH160].

Notes on contributors

Jian Tang

Jian Tang is an Associate Professor in the School of Information at Central University of Finance and Economics, China. She received the PhD Degree at Syracuse University. She is interested in crowdsourcing, citizen science, and gamification design, and studies issues related to user experience, motivation, and behaviors in various contexts.

Nathan R. Prestopnik

Nathan R. Prestopnik is a professor of virtual reality design at Shenandoah University. He studies game design, interactive storytelling, mixed reality systems, citizen science and serious games, and the intersection of VR and the digital humanities.