Lights, camera…citizen science: assessing the effectiveness of smartphone-based video training in invasive plant identification

• Published in: PloS one 2014-11, Vol.9 (11), p.e111433-e111433
• Main Authors: Starr, Jared, Schweik, Charles M, Bush, Nathan, Fletcher, Lena, Finn, Jack, Fish, Jennifer, Bargeron, Charles T
• Format: Article
• Language: English
• Subjects: Cell Phone; Crowdsourcing; Data collection; Digital cameras; Ecology; Ecology and Environmental Sciences; Ecosystems; Engineering and Technology; Evolution; Humans; Introduced Species; Invasive plants; Light; Multimedia; Nonnative species; Online instruction; Plants - classification; Science; Science Policy; Scientists; Smartphones; Studies; Training
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0111433

The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional "in-person" training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical.