Lights, camera…citizen science: assessing the effectiveness of smartphone-based video training in invasive plant identification.
Lights, camera…citizen science: assessing the effectiveness of smartphone-based video training in invasive plant identification.
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DOI：
10.1371/journal.pone.0111433
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Bargeron CT
中科院分区：
文献类型：
--
作者：
Starr J;Schweik CM;Bush N;Fletcher L;Finn J;Fish J;Bargeron CT
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.
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