Training citizen scientists through an online game developed for data quality control

Abstract

Some form of training is often necessary in citizen science projects. While in many citizen science projects it is possible to keep tasks simple so that training requirements are minimal, some projects include more challenging tasks and, thus, require more extensive training. Training can hinder joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated 10 if an online game that was originally developed for data quality control in a citizen science project, can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to use the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the 15 placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project related tasks intuitively by observing actual gauge placements by other citizen scientists and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or performance increase.These results demonstrate the value of an online game for training, particularly when compared to other information materials, 20 which are often not used extensively by citizen scientist. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods.

Mainstreaming civic tech and citizen sensing: a research agenda on co-creation methods, data interfaces, and impact pathways

“…Given the fair recency of many of these approaches, though, D'Ignazio and Zuckerman caution that "the world of science, journalism and communities using environmental data and sensors is a messy one" (D'Ignazio and Zuckerman, 2017: 201). Recurrent concerns about the impact of citizen science and civic tech projects relate to the quality of data, citizens' skills and competences to work together, to political biases in project designs, and missing opportunities for trainings (Callaghan et al, 2019;Strobl et al, 2019;Stylinski et al, 2020). Balancing civic, journalistic, or scientific goals often results in collecting only "good enough data" (Gabrys et al, 2016).…”