skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Cross-Project Analysis of Volunteers’ Scientific Observation Skills
This paper explores the assumptions that citizen science (CS) project leaders had about their volunteers’ science inquiry skill–proficiency overall, and then examines volunteers’ actual proficiency in one specific skill, scientific observation, because it is fundamental to and shared by many projects. This work shares findings from interviews with 10 project leaders related to two common assumptions leaders have about their volunteers’ skill proficiency: one, that volunteers can perform the necessary skills to participate at the start of a CS project, and therefore may not need training; and two, volunteer skill proficiency improves over time through involvement in the CS project. In order to answer questions about the degree of accuracy to which volunteers can perform the necessary skills and about differences in their skill proficiency based on experience and data collection procedures, we analyzed data from seven CS projects that used two shared embedded assessment tools, each focused on skills within the context of scientific observation in natural settings: Notice relevant features for taxonomic identification and record standard observations. This across-project and cross-sectional study found that the majority of citizen science volunteers (n = 176) had the necessary skill proficiency to collect accurate scientific observations but proficiency varied based on volunteer experience and project data collection procedures.  more » « less
Award ID(s):
1713424
PAR ID:
10476231
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Ubiquity Press
Date Published:
Journal Name:
Citizen Science: Theory and Practice
Volume:
8
Issue:
1
ISSN:
2057-4991
Page Range / eLocation ID:
54
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper describes the collaborative process for how a group of citizen science project leaders, evaluators, and researchers worked together to develop, validate, and test embedded assessments of two different volunteer science inquiry skills. The development process for creating these embedded assessments (activities integrated into the learning experience, allowing learners to demonstrate competencies) is articulated, as well as challenges encountered in assessing two science inquiry skills common in citizen science projects: notice relevant features and record standard observations. The authors investigate the extent to which the assessments were successful at achieving four criteria identified as ideal for shared embedded assessments of volunteers’ skills, namely: broadly applicable, authentic, performance-based, and integrated. 
    more » « less
  2. This paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers’ accuracy and skills. CS teams identified a wide range of skill variables that were “hiding in plain sight” in their data records, and that could be explored as part of a secondary analysis, which we define here as analyses based on data already possessed by the project. Each team identified a small number of evaluation questions to explore with their existing data. Analyses focused on accurate data collection and all teams chose to add complementary records that documented volunteers’ project engagement or the data collection context to their analysis. Most analyses were conducted as planned, and included a range of approaches from correlation analyses to general additive models. Importantly, the results from these analyses were then used to inform the design of both existing and new CS projects, and to inform the field more broadly through a range of dissemination strategies. We conclude by sharing ways that others might consider pursuing their own secondary analysis to help fill gaps in our current understanding related to volunteer skills. 
    more » « less
  3. Abstract The bulk of research on citizen science participants is project centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n = 3894) and digital trace data (n = 3649) from volunteers, who collectively engaged in 1126 unique projects, revealed that multiproject participation was the norm. Only 23% of volunteers were singletons (who participated in only one project). The remaining multiproject participants were split evenly between discipline specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics) and unevenly between mode specialists (52%) and mode spanners (25% participated in online and offline projects). Public engagement was narrow: The multiproject participants were eight times more likely to be White and five times more likely to hold advanced degrees than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. 
    more » « less
  4. Citizen science projects face a dilemma in relying on contributions from volunteers to achieve their scientific goals: providing volunteers with explicit training might increase the quality of contributions, but at the cost of losing the work done by newcomers during the training period, which for many is the only work they will contribute to the project. Based on research in cognitive science on how humans learn to classify images, we have designed an approach to use machine learning to guide the presentation of tasks to newcomers that help them more quickly learn how to do the image classification task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning is presented. 
    more » « less
  5. Fortson, Lucy; Crowston, Kevin; Kloetzer, Laure; Ponti, Marisa (Ed.)
    In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data. 
    more » « less