Incorporating Community Science in Improving Environmental Data Quality Through Model Based Reasoning Techniques

John Gilbert Olgin, University of Texas at El Paso


This dissertation will address the questions: In what ways can MBR theory be applied to design training that will improve the capacity of Citizen scientists to collect and analyze high quality scientific data? To what degree does this training impact their ability to formulate complex understandings of environmental problems in their Citizen? The project will be comprised of three research objectives focused in three sections, carried out with NASA’s GLOBE Observer Citizen Science Program: 1 Section 1: An analysis of existing GLOBE data will be conducted, identifying where errors in data collection originate. At least three principal errors will be selected from this analysis; 2 Section 2: An online training regimen for participants will be developed that targets the selected errors; one based in traditional training techniques, the other rooted in MBR theory. The training will be implemented in an existing El Paso Citizen College (EPCC) service-learning course. Participants will be evaluated prior and after training to measure changes in knowledge, skills, and data quality compared with traditional training mechanisms; 3 Section 3: The training will then be evaluated and determine additional action to further develop the application of MBR to improve data quality. The expected outcome is for those participants trained to record cloud and dust event data using MBR to show increased data collection accuracy and more complex formulations of scientific problem-solving experience in environmental processes. This will improve our understanding of how to more effectively train Citizen scientists. This knowledge can be applied in other Citizen science contexts.

Subject Area

Geology|Education|Educational technology

Recommended Citation

Olgin, John Gilbert, "Incorporating Community Science in Improving Environmental Data Quality Through Model Based Reasoning Techniques" (2023). ETD Collection for University of Texas, El Paso. AAI30521853.