Appraising Human Impact on Watersheds: The Feasibility of Training Citizen Scientists to make Qualitative Judgments

TL;DR

This paper explores training citizen scientists to perform qualitative water quality assessments, emphasizing experiential learning, discussion, and narrative construction to enhance their observational skills and motivation.

Contribution

It introduces a framework for training volunteers in qualitative watershed assessments and identifies key factors for effective citizen science participation.

Findings

Diverse stream experiences improve assessment accuracy.

Discussion among monitors enhances judgment quality.

Constructing internal narratives supports learning and motivation.

Abstract

Citizen science often requires volunteers to perform low-skill tasks such as counting and documenting en- vironmental features. In this work, we contend that these tasks do not adequately meet the needs of citizen scientists motivated by scientific learning. We propose to provide intrinsic motivation by asking them to no- tice, compare, and synthesize qualitative observations. We describe the process of learning and performing qualitative assessments in the domain of water quality monitoring, which appraises the impact of land use on habitat quality and biological diversity. We use the example of water monitoring because qualitative wa- tershed assessments are exclusively performed by professionals, yet do not require specialized tools, making it an excellent fit for volunteers. Within this domain, we observe and report on differences in background and training between professional and volunteer monitors, using these experiences to synthesize findings about volunteer training needs. Our findings reveal that to successfully make qualitative stream assessments, vol- unteers need to: (1) experience a diverse range of streams, (2) discuss judgments with other monitors, and (3) construct internal narratives about water quality. We use our findings to describe how different technologies may support these needs and generalize our findings to the larger citizen science community.