Training citizen scientists: A qualitative, comparative, multiple case study to identify theoretical and instructional design themes in current citizen science training initiatives

Margaret Gaddis, Department of Biological Sciences, University of Colorado - Colorado Springs, Colorado Springs, CO

Background/Question/Methods
This dissertation inquiry investigated citizen science training and data reliability. Citizen scientists are increasingly important to the on-going assessment of ecological restoration, species identification, and monitoring of natural lands. The general problem is data collected by citizen scientists is often viewed as unreliable by the scientists and land managers who might use it. The specific problem is the absence of educational training measurement in citizen science program design and analysis with which to ascertain the learning gains of trained citizen scientists. The following research questions guided this research. What are the characteristics of training designed to teach citizen scientists to collect ecological data in natural land settings? How do organizational leaders describe their perception of the efficacy of the training to produce reliable data collection?
For this qualitative multiple case study research design, a sequential design included case identification, training document analysis, a survey, and semi-structured interviews with training leaders to identify theoretical and instructional design themes and perceptions of data reliability in current citizen science training initiatives.
Results/Conclusions
Citizen scientists facilitated data collection involving flora, fauna, and water in marine, terrestrial, and riparian environments. Training analysis revealed an abundance of instructional materials (86%) but many fewer evaluative documents (16%) from which data reliability could be measured. The theory of backwards design was not well-supported. The training analysis indicated strong alignment with andragogy and social learning theory. Training design followed a bimodal distribution related to the type of data collected. When simple data was collected, little training existed. Citizen scientists brought prior skills to the task, but new procedural learning needs were minimal to complete the data collection task. Photo data reliability was perceived to be high, in part due to the technological capabilities of modern cameras and software. Trainers' confidence in water quality data often was supported by quality control analyses that are not published in the literature. Publishing these data would promote citizen science data reliability in the peer-reviewed literature. When more complex measurements were collected by citizen scientists, classroom and field mentoring facilitated learning. Training leaders' confidence in complex measurement data reliability was lower. It was also not supported by quality control analyses.
A greater focus on principles of backwards design would facilitate data reliability in citizen science programs. By leading training curriculum design with a focus on evaluation, citizen science programs may increase their ability to provide high quality data for scientific research.