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

Starr J, Schweik CM, Bush N, Fletcher L, Finn J, Fish J, Bargeron CT - PLoS ONE (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, United States of America.

ABSTRACT
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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Sample screenshot images from the Outsmart App.
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pone-0111433-g001: Sample screenshot images from the Outsmart App.

Mentions: We conducted our experimental study in the context of the Outsmart Invasive Species Project (Outsmart). Outsmart is a collaboration between the University of Massachusetts Amherst, the Massachusetts Department of Conservation and Recreation (MA DCR) and the Center for Invasive Species and Ecosystem Health at the University of Georgia. The project aims to strengthen ongoing invasive species monitoring efforts by enlisting help from citizens across New England (Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine), with a particular focus on Massachusetts. Volunteers are asked to identify and report data on invasive plants and insects in their own time and submit data via a free account through the Early Detection and Distribution Mapping System (EDDMapS) website (www.eddmaps.org) or through our smartphone app called “Outsmart Invasive Species” (“Outsmart” for short; http://masswoods.net/outsmart) (Figure 1). The project leverages the increasing number of people equipped with smartphones or digital camera/web technology and aims to expand the scope of invasive monitoring with a particular focus on early detection of new or emergent threats.


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

Starr J, Schweik CM, Bush N, Fletcher L, Finn J, Fish J, Bargeron CT - PLoS ONE (2014)

Sample screenshot images from the Outsmart App.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4221027&req=5

pone-0111433-g001: Sample screenshot images from the Outsmart App.
Mentions: We conducted our experimental study in the context of the Outsmart Invasive Species Project (Outsmart). Outsmart is a collaboration between the University of Massachusetts Amherst, the Massachusetts Department of Conservation and Recreation (MA DCR) and the Center for Invasive Species and Ecosystem Health at the University of Georgia. The project aims to strengthen ongoing invasive species monitoring efforts by enlisting help from citizens across New England (Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine), with a particular focus on Massachusetts. Volunteers are asked to identify and report data on invasive plants and insects in their own time and submit data via a free account through the Early Detection and Distribution Mapping System (EDDMapS) website (www.eddmaps.org) or through our smartphone app called “Outsmart Invasive Species” (“Outsmart” for short; http://masswoods.net/outsmart) (Figure 1). The project leverages the increasing number of people equipped with smartphones or digital camera/web technology and aims to expand the scope of invasive monitoring with a particular focus on early detection of new or emergent threats.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus