Evaluating Citizen Scientists’ User Experience and Engagement Using a Mobile Watershed Data Management App

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Abstract

Environmental problems, such as climate change are global in scope and multivariate in complexity. Today, citizen scientists can make important contributions by providing observations to further understand environmental related issues and by playing an important role in data collection. Citizen scientists now have the ability to use EPA certified testing tools that increase their ability to collect accurate and precise environmental data. Smart phones allow citizens to efficiently submit timely data. Given that citizen scientists now have the capacity to be involved in scientifically meaningful data acquisition campaigns, it is important to evaluate the citizen scientists’ experience and engagement using technology to fully maximize their potential contribution. For this study we developed an interactive mobile app, called mWatershed, which enabled citizen scientists to be involved in all aspects of the campaign: environmental monitoring, information gathering, data collection and analysis, and reporting. Specifically, the citizen scientists were given EPA tools to test their drinking water or neighboring surface waters for: toxic metals, certain pesticides, nutrients and certain bacteria. Citizen scientists had the option to use the mWatershed app. At the end of the campaign they were invited to take a survey assessing their experience and engagement. The survey utilized the MUX instrument, which provides a holistic assessment of mobile software-device use. In addition, we included tailored engagement questions. We report on the results from our study and discuss implications for future citizen scientists’ efforts. We also examine the MUX instrument in the context of this research.

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APA

Fruhling, A., Bartelt-Hunt, S. L., & Kolok, A. (2019). Evaluating Citizen Scientists’ User Experience and Engagement Using a Mobile Watershed Data Management App. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11786 LNCS, pp. 541–554). Springer Verlag. https://doi.org/10.1007/978-3-030-30033-3_42

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