Webinar: Training design, data type, and data reliability in citizen science

Dr. Maggie Gaddis

Publication Date:

The work of citizen scientists expands the data collection possibilities in natural resource management.  The problem is that some scientists and land managers view the data collected by citizen scientists as unreliable. To investigate the potential correlation between training and data reliability in citizen science, the researcher assessed 22 citizen science programs around the world. These data indicated alignment between citizen science training, andragogy, and social learning theory. Also revealed was a bimodal distribution of citizen science programs that related data collection type and training design across the general categorizations of citizen science engagement. Quantitative data analyses supported the assessment of data reliability when citizen scientists collected water quality or photographic data. Terrestrial data collected lacked quantitative assessment and was therefore more difficult to validate. Few citizen science programs illustrated principles of backwards design. The implementation of training assessment to validate citizen scientist learning gains may promote data reliability in citizen science. Presenter Bio: Dr. Maggie Gaddis teaches biology at the University of Colorado – Colorado Springs. She is also a member of the Bard College Citizen Science faculty.  Her research involves ecological restoration monitoring in southern Colorado and citizen science. In the education realm, Maggie investigates the efficacy of training for citizen scientists. In the science realm, she investigates the ecological success of restoration efforts in public lands.

Resource Type:

Pre-approved for CECs under SER's CERP program