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Learning Through Citizen Science: Enhancing Opportunities by Design (2018)

Chapter: Appendix A: Demographic Analyses of Citizen Science

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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

Appendix A

Demographic Analyses of Citizen Science

Pandya (2012) points out that participation in citizen science, at least in the United States, does not reflect the demographics of the population, and that this schism hurts both citizen science and unrepresented groups. There is a generalized assumption that participants in citizen science are generally white, older/retired females with above average education. However, there are no analyses across the citizen science community exploring this assertion. To address that gap in service of this report, we cite three different analyses of participation data. The first is a simple analysis of reported participant data on online citizen science aggregator platform SciStarter 2.0 (SciStarter, 2018), the second is a published analyses of participant data, and the third is an original meta-analysis of published literature on citizen science projects reporting participant data.

SCISTARTER 2.0

SciStarter is a Web platform for individuals looking to “find, join, and contribute to science.” The platform offers access to more than 2,700 searchable citizen science projects and events, as well as helping interested parties access tools that facilitate project participation (SciStarter, 2018). As of September 2018, SciStarter 2.0 has offered a profile feature, allowing individuals to record demographics and other attributes to a profile. Once a profile has been completed, information about the individual can be attached to projects listed on SciStarter as a “bookmark,” which indicates potential interest in the project, or because the individual elects to join the project. The committee reviewed sex and age information about individu-

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

als electing to fill out a profile on SciStarter (N = 653) as a function of the type of project (hands-on versus online), based on SciStarter profile data.

Of the 653 SciStarter profiles completed by the end of 2017, the majority of individuals were female (64%) and in the 35–44 age range (female median = 41; male = 47). Individuals with profiles have the option to join projects through SciStarter and/or bookmark them, allowing some determination of preference as a function of project type. Females represented the vast majority of bookmarkers (80%), and appeared to bookmark both online and hands-on projects equally. Other statistics were slightly more revealing: whereas females comprised 68 percent of SciStarter users joining hands-on projects, this value dropped to 57 percent for online projects.

Dimensions of Biodiversity Meta-Analysis

Theobald and colleagues (2015) and Burgess and colleagues (2017) surveyed biodiversity citizen science projects as part of a large, multiuniversity project funded by the Dimensions of Biodiversity Program within the Division of Environmental Biology at the National Science Foundation (hereafter the Dimensions meta-analysis). In these studies, biodiversity was defined as explicit inclusion of the presence and/or abundance of identified taxonomic, genetic, or functional groups, and citizen science was defined as projects engaging volunteers not otherwise paid or receiving college credit for their participation, and collecting and/or processing quantifiable information related to an issue or question. Of the original 388 projects harvested from a comprehensive Internet search for English-version project Websites, contact information for extant projects was available for 329. Surveys sent to this set resulted in 125 responses. Questions included information on the demographics of participants, including sex and qualitative exclusive categorization (all, most, some, few, or none) of age, education, and race/ethnicity. Although the vast majority of projects were centered in the United States, the demographic analysis included projects from a range of countries; therefore, race/ethnicity categories were simultaneously general and specific to U.S. census categories (e.g., black or African American). Age and education data were visualized in Figure 4 (Burgess et al., 2017); sex and race/ethnicity data have not appeared elsewhere.1

Of the 125 projects where managers/directors responded to the survey, a subset (44–69%) were able to provide some amount of demographic information, most often age-education and least often race/ethnicity (see Table A-1). This sampling of hands-on, outdoors, ecologically focused citizen science projects indicates that participants are principally white, well-educated adults with no gender bias. Almost all project managers

___________________

1 See Theobald et al. (2015) and Burgess et al. (2017) for complete methods.

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

TABLE A-1 Percentage of the Responding Population Indicating the Abundance of a Particular Demographic Class of Project Participants (e.g., retirees)

Sample Demographic Category All Most Some Few None
83 Retirees 1.2 37.3 41.0 15.7 4.8
Adult Nonscientists
86 w/ a college degree 2.3 48.8 39.5 9.3 0.0
84 w/out a college degree 0.0 11.9 54.8 25.0 8.3
Students
82 College 0.0 3.7 54.9 35.4 6.1
83 High School 3.6 14.5 34.9 36.1 10.8
79 Middle School 1.3 6.3 32.9 38.0 21.5
77 Primary School 1.3 5.2 16.9 41.6 35.1
53 Caucasian, Non-Hispanic 7.5 81.1 9.4 0.0 1.9
49 Hispanic 0.0 6.1 22.4 51.0 20.4
44 Asian 2.3 2.3 27.3 50.0 18.2
46 Black, African American 0.0 0.0 15.2 54.3 30.4
46 Indigenous, Native American 0.0 0.0 10.9 43.5 45.7
41 Hawaiian Native or Pacific Islander 0.0 0.0 0.0 31.7 68.3
62 Male 1.6 21.0 77.4 0.0 0.0
61 Female 0.0 27.9 70.5 1.6 0.0

NOTE: Majority response categories are highlighted in bold, and where all responses less than 3 percentage points apart are highlighted. Sample indicates number of project managers responding.

SOURCE: Data from Theobald et al. (2015) and Burgess et al. (2017).

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

who reported race/ethnicity demographics indicated that “all” or “most” of their participants were white (88.6%), while only 6.1 percent indicated this same level of participation for Hispanics, with slightly lower levels (4.6%) for Asians, including Asian Americans. No projects reported overwhelming participation among blacks or African Americans, indigenous peoples including Native Americans, or Hawaiian/Pacific Islanders. Projects with a higher than average participation of one or more minority groups were either outside of the United States (e.g., Migrant Watch and Citizen Sparrow are two bird-focused citizen science projects in India, with a majority of Asian participants), or geographically local and linked to a site and/or taxon with high cultural importance (e.g., the Camas Citizen Science Monitoring Program, centered on the Nez Perce National Historical Park’s Weippe Prairie Site, is a project of the National Park Service in which high school students monitor camas flowering and incorporate aspects of the cultural and ecological values of this native prairie plant). These projects may have been tied directly to local schools; the Bosque Ecosystem Monitoring Program is a collaboration between the Bosque School and the University of New Mexico to engage students and volunteers in riparian forest–bosque–monitoring along the middle Rio Grande).

Youth were clearly not the focus of projects reporting demographics. Adult nonscientists with a college degree made up just over one-half (51.2%) of the combined “all” and “most” categories, versus only 11.9 percent for adult nonscientists without a degree. Finally, while most student categories had relatively low representation at the higher participation levels, 18.1 percent of high school students fell into the combined “all” “most” category, or almost five times the rate of college student participation.

There were no clear trends in participation of females versus males. Although females were slightly overrepresented in the combined “all” or “most” category (27.9% vs. 22.6%), only males were cited as having total representation in some projects (1.6%).

Literature Search and Meta-Analysis

Using Web of Science (2018), we performed searches of the topic fields (title, keywords, abstract) for literature from the year 2000 to present, language = English and peer-reviewed articles only, using combinations of the search terms:

  1. crowd sourc* or crowd-sourc* or crowd-sourc*
  2. online or on-line or online
  3. citizen science or public participation in scien*
  4. assessment or evaluation
Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
  1. demographic
  2. survey or interview

We augmented this set with articles not previously captured from all issues of the journal Citizen Science: Theory and Practice, as well as volumes dedicated to citizen science of the journals Conservation Biology (30:3), Biological Conservation (208:SI), and Maine Policy Review, 26(2). To this set we added the following:

  1. Online Citizen Science Projects: An Exploration of Motivation, Contribution and Participation, a dissertation awarded by The Open University, focused on three citizen science projects (Curtis, 2015)
  2. Engagement and Learning in Environmentally-based Citizen Science: A Mixed Methods Comparative Case Study, a dissertation awarded by Cornell University, focused on participant learning in six citizen science projects (Phillips, 2017)
  3. Eleven primary research chapters from the book Citizen Science for Coastal and Marine Conservation (Cigliano and Ballard, 2017)
  4. Eleven primary research chapters from the book Citizen Inquiry: Synthesizing Science and Inquiry Learning (Herodotou, Sharples, and Scanlon, 2018)

Our initial source count, including duplications and nonrelevant sources (defined as those without a focus on citizen science) was 735. Excluding duplications and nonrelevant sources to include a corpus of primary research articles, case studies, and meta-analyses pertaining to citizen science resulted in 303 sources. Of these, 32 included numerically or graphically extractable information on at least one of the following demographics, such that the data could be included in subsequent central tendency (mean, median, range, variation) measure. Because several sources were meta-analyses, the final “project” count (including composite samples of unnamed project volunteers, N = 7) was 68.

Extracted data included any of the following, and no source provided all fields:

  1. Gender (recorded as % female)
  2. Age (recorded as any of the following: mean or median, standard deviation, range, or % distribution)
  3. Retirees (recorded as % of total)
  4. Race/Ethnicity (recorded as % distribution across all reported categories)
Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
  1. Education (recorded as % completing college, and % with a graduate degree)
  2. Income (recorded as % within all reported categories)
  3. Previous participation in a citizen science or relevant (e.g., conservation, restoration, community, etc.) project based on the context of the surveyed population (recorded as % of total)

Metadata included the following:

  1. Citation
  2. Project/program name or description of research audience in the case of multiproject research (e.g., individuals involved in conservation or environmental projects in Colorado)
  3. Whether the project/program involved out-of-doors activities (Y/N)
  4. Project location
  5. Whether the project/program involved only computer-based (e.g., crowdsourcing) work (Y/N)
  6. The research vehicle used to collect the information (e.g., survey, interview)
  7. The sample size (e.g., number of participants for which information were available)
  8. Whether the information was part of a meta-analysis, defined as research on a suite of projects/programs versus targeting a single project (Y/N). In the case of meta-analyses, if data were reported at the project level, individual data lines were created

Because age information was variously reported as mean, median, range, and/or percent distribution across age classes (e.g., 55% 18–29), we created a central tendency super-category as follows. If mean and median were reported, we used the mean. Because studies reporting both resulted in only a small difference, we included median age if that was the only central tendency measure reported. For studies reporting percent distribution across age classes, we created a median value by multiplying each proportion by the median of the class (e.g., 23 is the median value of the age class 18–29) and then summing across all classes. For lowest and highest classes, which were often reported as open-ended (e.g., 28% 60+), we assumed a minimum age of 18 (unless otherwise specified) and a maximum age of 75.

Our publication meta-analysis resulted in 32 sources detailing 68 projects broadly representative of citizen science from entirely online gaming projects (e.g., Foldit) through to long-term environmental data collection projects (e.g., COASST). Literature included multiproject case studies (e.g., Curtis, 2015; Phillips, 2017), studies specific to some aspect of the demographics of participation (e.g., Cooper and Smith, 2010), studies focused

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

on a range of participants involved in similar activities (e.g., volunteers in outdoor conservation projects; Bruyere and Rappe 2007), and an array of publications evaluating a project, or reporting on project findings. However, this sample represents only 10 percent of the articles examined, and only 3.7 percent of the hands-on (N = 1,014) plus online (N = 297) projects listed on SciStarter, suggesting that the results should be interpreted with caution.

Most studies described projects where participants were outdoors doing hands-on work (N = 39; 80%); and projects situated wholly (N = 25) or partially (N = 13) in the United States (N = 38 in total; 74%). A minority of projects were entirely online (N = 11; 22%), and the majority of these (N = 8 of 11) garnered a worldwide participant audience, albeit mostly from developed countries. Almost all studies gathered data via survey (N = 36 projects) although one meta-analysis used participant lists maintained by individual projects to assess gender (N = 11 projects). Respondent sample sizes ranged widely (mean = 1,281; range 12-13, 649) with a total person count of 65, 336.

There were striking patterns in the reported participant demographics, which generally described a slightly male-biased, overwhelmingly white, and well-educated population with somewhat of a tendency to have previously participated in other projects (see Table A-2).

Women were only slightly underrepresented across all studies (42%) although gender statistics were biased by the type of project. For online

TABLE A-2 Demographics of Surveyed or Interviewed Participants in 48 Citizen Science Projects and/or Meta-Populations

Gender (% F) Age (years) Race/ Ethnicity (% white) Education (% college degree) Previous Experience
Sample Size 43 22 9 23 11
Grand Mean 42% 44 94% 73% 56%
Standard Deviation 14
Grand Minimum 2% 21 87% 52% 25%
Grand Maximum 80% 62 100% 96% 81%
Absolute Minimum 3
Absolute Maximum 100

NOTE: Sample is number of projects. Minima and maxima are divided into grand statistics (assessed over all project average or median values) and absolute statistics (assessed at the participant level over all range-reporting projects).

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

TABLE A-3 Amalgamated Gender Demographics of Bird-Oriented Organizations Inviting Public Involvement, Organized by the Degree of Expected Participation and Type(s) of Interaction

Project Category Description Total Count % Female
Supportive Paid membership 1,095,346 54
Participatory Submit bird observations to a database 83,112 45
Competitive Evaluate the quantity/quality of birds reported on lists 6,933 2
Authoritative Acknowledged as experienced or expert in bird-related activities; may lead such activities and/or train others 256 10

NOTE: Data are from 21 U.S. and UK birding organizations where gender was assessed from membership lists.

SOURCE: Adapted from Cooper and Smith (2010).

projects, average female participation dropped to 27 percent (range 2–67%; N = 11). For outdoors projects, female participation was higher (43%, N = 32). However, Phillips (2017), one of the sources used in this meta-analysis, found that even within outdoors projects gender skew toward male was apparent in physical versus biological science projects (e.g., CoCoRHaS, a citizen science project focused on collecting daily precipitation data was 80% male). Across the four projects in this meta-analysis classified as outdoors and entirely physical science, female participation was 37 percent. Cooper and Smith (2010), one of the sources used in this meta-analysis, point out the extreme bias of gender in outdoor environmental projects focused on birds as a function of the structure and expected role of the participants (see Table A-3). Although women were more often likely to be members in organizations focused on birds and bird protection, they were increasingly less likely to participate as the expected role moved through participatory (analogous to hands-on citizen science) to projects where a degree of competition or acknowledged expertise and authority was required.

Participant age (N = 22 projects) was extremely broad across our literature meta-analysis, an indication of the life-long, life-wide, life-deep learning inherent in citizen science. Average age tended toward middle age; however, the central tendency range across all projects was large (21–62) and the absolute range over all projects was essentially birth to death (3–100). The reported number of retirees was also extremely broad (range 5–75%; N = 8). Online projects were only slightly younger in average age (grand mean = 43; N = 6) relative to those performed outdoors (grand

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

mean = 45; N = 17), although the former had a greatly curtailed range (of project means: 37–51) suggesting that online, crowdsourcing projects may have limited appeal to all age classes. A serious caveat to participant age findings is that demographic information was only available for projects focused on adults and/or the entire participant population. We were not able to find published demographics for a single youth-focused project, perhaps due to the combination of age-gating (i.e., a third-grade project would, by definition focus on students primarily ages 7–9) and privacy requirements (i.e., Family Educational Rights and Privacy Act [FERPA]).

By far the most extreme trends were in race and education. Of the nine projects reporting race and/or ethnicity, only one reported statistics on non-white participants, an indication of the overwhelming trend of white participation (94%). Although the Casler, Bickel, and Hackett (2013) study is not citizen science per se, shifts in the race/ethnography patterns in populations recruited to their study as a function of recruitment vehicle are informative: social media postings resulted in 93 perent white study participants whereas Amazon MTurk recruitment resulted in only 37 percent of this demographic, with the gap replaced by Asian Americans (40%), Hispanics (6%), and African Americans (6%).

Education trends were similarly extreme, with a large proportion of the surveyed population (and here it should be noted that all studies focused on nonyouth projects) completing a college degree (73%) and a substantial proportion also completing a graduate or professional degree (mean 34%; range 20–52%; N = 10). Income statistics were rare (N = 6) and not comparatively reported, making specific generalizable conclusions difficult. Of the four U.S. projects with any income information, all reported median incomes above $50,000. Compared to the 2016 median household income of ~$59,000 (U.S. Census Bureau, 2017), this figure does not necessarily indicate wealth, although it certainly suggests a minority of those in the bottom half of the U.S. income strata participate in citizen science activities.

REFERENCES

Bruyere, B., and Rappe, S. (2007). Identifying the motivations of environmental volunteers. Journal of Environmental Planning and Management, 50(4), 503-516.

Burgess, H.K., DeBey, L.B., Froehlich, H.E., Schmidt, N., Theobald, E.J., Ettinger, A.K., Hille Ris Lambers, J., Tewksebury, J., and Parrish, J.K. (2017). The science of citizen science: Exploring barriers to use as a primary research tool. Biological Conservation, 208, 113-120.

Casler, K., Bickel, L., and Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 2156-2160.

Cigliano, J.A., and Ballard, H.L. (Eds.). (2017). Citizen Science for Coastal and Marine Conservation. New York: Routledge.

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×

Cooper, C.B., and Smith, J.A. (2010). Gender patterns in bird-related recreation in the USA and UK. Ecology and Society, 15(4), 4.

Curtis, V. (2015). Online Citizen Science Projects: An Exploration of Motivation, Contribution and Participation (Doctoral dissertation). The Open University.

Herodotou, C., Aristeidou, M., Sharples, M., and Scanlon, E. (2018). Designing citizen science tools for learning: Lessons learnt from the iterative development of nQuire. Research and Practice in Technology Enhanced Learning, 13(1), 4.

Pandya, R.E. (2012). A framework for engaging diverse communities in citizen science in the U.S. Frontiers in Ecology and the Environment, 10(6), 314-317.

Phillips, C.B. (2017). Engagement and Learning in Environmentally Based Citizen Science: A Mixed Methods Comparative Case Study (Doctoral dissertation). Cornell University, Ithaca, NY.

SciStarter. (2018). Available: https://scistarter.com [September 2018].

Theobald, E.J., Ettinger, A.K., Burgess, H.K., DeBey, L.B., Schmidt, N.R., Froehlich, H.E., Wagner, C., Hille Ris Lambers, J., Tewksebury, J., Harsch, M.A., and Parrish, J.K. (2015). Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research. Biological Conservation, 181, 236-244.

U.S. Census Bureau. (2017). Report on Household Income: 2016. (ACSBR/16-02). Available: https://www.census.gov/content/dam/Census/library/publications/2017/acs/acsbr16-02.pdf [September 2018].

Web of Science. (2018). Available: https://webofknowledge.com/ [September 2018].

Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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Suggested Citation:"Appendix A: Demographic Analyses of Citizen Science." National Academies of Sciences, Engineering, and Medicine. 2018. Learning Through Citizen Science: Enhancing Opportunities by Design. Washington, DC: The National Academies Press. doi: 10.17226/25183.
×
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In the last twenty years, citizen science has blossomed as a way to engage a broad range of individuals in doing science. Citizen science projects focus on, but are not limited to, nonscientists participating in the processes of scientific research, with the intended goal of advancing and using scientific knowledge. A rich range of projects extend this focus in myriad directions, and the boundaries of citizen science as a field are not clearly delineated. Citizen science involves a growing community of professional practitioners, participants, and stakeholders, and a thriving collection of projects. While citizen science is often recognized for its potential to engage the public in science, it is also uniquely positioned to support and extend participants’ learning in science.

Contemporary understandings of science learning continue to advance. Indeed, modern theories of learning recognize that science learning is complex and multifaceted. Learning is affected by factors that are individual, social, cultural, and institutional, and learning occurs in virtually any context and at every age. Current understandings of science learning also suggest that science learning extends well beyond content knowledge in a domain to include understanding of the nature and methods of science.

Learning Through Citizen Science: Enhancing Opportunities by Design discusses the potential of citizen science to support science learning and identifies promising practices and programs that exemplify the promising practices. This report also lays out a research agenda that can fill gaps in the current understanding of how citizen science can support science learning and enhance science education.

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