Having discussed in depth the processes of learning and the specific kinds of learning that occur in science in the previous chapter, we now turn to a discussion of how citizen science can be an opportunity for supporting, facilitating, and extending science learning. Each section in this chapter represents a learning outcome in science; for each, we will discuss how citizen science can address the outcome, with examples from citizen science projects of the strategies and practices used to advance those outcomes. As mentioned in Chapter 4 and illustrated here, learning outcomes in citizen science are intertwined: learning related to one outcome can reinforce, build on, or set the stage for learning related to other outcomes. Application of a single practice or strategy in citizen science may advance learning across several outcomes; and a single learning outcome may be advanced through the interplay of several elements of citizen science.
Given these observations, the committee chose to organize this chapter around learning outcomes: Because the framework of the strands has utility and widespread use beyond citizen science, it allowed the committee an opportunity to consider how the field of citizen science can fit into an established scholarly landscape. The sections presented here are organized by how “proximal” citizen science is to each learning outcome, that is, how easily participation in citizen science can be leveraged toward achieving the outcome described. Where achieving an outcome through citizen science is more challenging, we aim to offer examples of how those challenges can be mitigated.
In order to identify the examples of learning in citizen science highlighted below, the committee first conducted an ad hoc review of 28 typical
citizen science projects (see Appendix D of this report). In executing this review, the committee was able to identify trends in how citizen science projects provide supports for learning, as well as what evidence for learning these projects cite when describing their work. This review is also critical to our discussion of project design in Chapter 6; so that we may be useful to the field in offering assistance related to how to leverage design for learning, we wanted to first ensure that we fully understood the existing landscape of what projects are currently doing to support learning.
As we describe below, there is evidence that many kinds of science learning can occur through participation in citizen science. This chapter represents the science learning outcomes where research in citizen science currently exists: As a note, the committee did not find enough research to effectively discuss the role of citizen science in supporting the development of the understanding of the nature of science, and as a result we have not discussed that learning outcome below. More research on this and other learning outcomes is certainly needed, an issue discussed in depth in the final chapter of this report.
One final note: it is important to note that not all citizen science projects are poised to support all kinds of science learning outcomes. In fact, participation in a project may not necessarily lead to learning on any front. With that said, some projects may be well-suited to pursue one particular outcome or another, while other projects may need some adaptation in order to get at the more challenging, less proximal learning outcomes. Where we use examples below, they are intended to serve as case examples of how pursuing one learning outcome might look in action. As we will describe in the following chapter, context and design choices are critically important factors in determining the extent to which a project supports science learning. These factors and others must be considered in the design of citizen science intended to achieve science learning.
In the following sections, we will discuss science learning outcomes that are proximal to citizen science; that is, outcomes that are relatively easy to achieve through participation in a citizen science project. In each section, we will discuss how these outcomes manifest in existing citizen science projects, and unpack how specific project activities support participants’ development in each outcome. Where appropriate, we also attempt to identify how mastery of specific outcomes improves individual performance in the citizen science project, thereby improving the quality of participation.
In Chapter 4, we discussed the role of identity, interest, and motivation as mediators for learning science. Given these different but interrelated constructs, we turn now to how citizen science can support the development of these competencies as learning outcomes. Though these constructs may act similarly when serving as mediators in the learning process, fostering interest and motivation as learning outcomes through participation in science is very different from cultivating identity. For this reason, we turn to the outcome of identity later in this chapter, as the committee views identity development as more distal to the work of citizen science.
As discussed in Chapter 4, when people are interested in a subject area, they are more likely to attack challenges, use effective learning strategies, and make appropriate use of feedback (Csikszentmihalyi, Rathunde, and Whalen, 1993; Lipstein and Renninger, 2006; Renninger and Hidi, 2002). The EyesOnALZ citizen science project leverages interest in order to motivate participants and improve participant performance in the project (EyesOnALZ, 2018). This project relies on crowdsourcing data analysis to accelerate the pace of research into the links between blocked blood vessels in the brain (stalls) and Alzheimer’s disease. In an online game-like environment, participants learn to use a virtual microscope to analyze blood flow to identify stalls in mouse blood vessels. Participants are also offered support and encouragement, which are included with all training materials in the form of a 1-minute video, online help guides, instant feedback from experts, and other “catchers” who are available to support learning.
Other citizen science projects have also found competition and gamification to contribute to motivation for participants, for example, Old Weather (Eveleigh et al., 2013) and Zooniverse (Greenhill et al., 2014). An analysis of gamification in Biotracker suggested that this was a particularly important attractor for millennials (Bowser et al., 2013), while in-depth interviews with participants in Foldit and EyeWire suggested gamification was more effective at sustaining the interest of participants than attracting new participants (Iacovides et al., 2013).
Citizen science projects may also choose to focus more on performance-oriented goals, especially if there is an emphasis on externally determined and validated data collection. For example, the Vital Signs Experience presents multiple “missions” through which participants can investigate the presence of native and invasive species in different habitat types throughout Maine (Vital Signs Experience, 2018). It is essential that documentation of invasive species is accurate and precise for this investigation, so the online data submission process includes an evaluation of evidence, with quality control and peer review steps. Online profiles of the program’s professionals include detailed explanations on how citizen data are utilized, which
serve to remind participants of the need for quality control. In this case, the outcome for the project (high-quality data) hinges on the interest of participants: Project designers are able to capitalize on participants’ commitment to the project content in pursuit of accurate, high-quality project data.
It is also important to consider how participation that occurs in a non-voluntary context (such as a formal education setting where a grade might hinge on participation) might influence learning outcomes. The resources and structure available in a formal education setting may positively support the development of motivation and interest, but negative consequences associated with lack of participation or “incorrect” participation could also have deleterious impacts on participant motivation. To the extent that free choice is one important mechanism in developing interest in science, it is important to consider the extent to which that free choice may be eroded for potential participants.
For participants who choose sustained participation in a project, there can be multiple, repeating opportunities to work on a progression of goals, during which regular performance feedback can serve to nurture the participant interest. Even for people who choose less frequent participation, activities can be presented with incremental performance goals to align with participant abilities and desires. Projects can capitalize on individuals perceiving some inherent worth in the focal phenomena or questions, where those individuals believe that participating in answering these questions are likely to yield successful outcomes. In EyesOnALZ, for example, participants are often reminded that they are contributing to finding a cure for Alzheimer’s disease, which may speak to and serve to extend any manner of personal motivation for participating.
Projects can also support individuals who have some degree of mastery-oriented goals. Avid birdwatchers may be attracted to citizen science programs such as Project FeederWatch, which relies on observations from birdwatchers to track broad-scale movements of winter bird populations and long-term trends in bird distribution and abundance (Project FeederWatch, 2018). Participants can choose to take their bird knowledge to the next level by accessing online tools that describe how to identify bird species, how to recognize bird diseases, and how participant data are utilized across the study.
Citizen science can also provide a useful context for learning about the use of scientific tools and practices. Indeed, the “real-life” feature of many citizen science projects facilitates a space in which participants are often able to immerse themselves directly in the use of project-specific tools,
protocols, and methods, enabling an up-close experience with “doing” science. The discrete skills that may be gained through participation in citizen science map directly onto this particular learning outcome.
As discussed in Chapter 2, although activity in citizen science may involve any number of scientific activities, data collection and observation are the most common kinds of citizen science activities available for participation. The skills necessary to perform these functions are not unsophisticated: they rely on sufficient knowledge to distinguish significant features of a data set or object from less significant features, as well as mastery of the procedural knowledge necessary to perform the tasks at hand. Participation in citizen science can build in an opportunity to extend these skills through initial training, practice, and regular feedback.
One example of how citizen science can provide a context for learning related to scientific tools and practices (along with science content knowledge) is a project known as the Coastal Observation and Seabird Survey Team (COASST, 2018), which has focused on monitoring beached birds since the year 2000. Currently, there are approximately 800 participants involved in data collection at hundreds of beaches on the West Coast of the United States. COASST staff lead training workshops for volunteers to learn the skills they need to collect rigorous data for coastal monitoring and management, with some training materials available on the project website as well. Volunteers learn how to safely and accurately take measurements of beached seabirds, use keys and field guides to identify bird species, and then tag carcasses so that future survey efforts will not recount them. Volunteers are guided through proper data entry on standardized forms, and then instructed to upload all data and photos to the data entry portal following the survey. After initial training, participants are actively engaged in their recently acquired project skills during monthly beach surveys. COASST participants are also asked to engage in the same scientific practices as experts in order to classify specimens at the species level. After a single 5-hour training, participants can correctly identify species 85 percent of the time (Parrish, 2013). With extended participation, volunteers can see how their individual contributions are aggregated and used to establish baseline temporal patterns of carcass occurrence and then investigate whether and how systems are changing, both locally and at larger scales (Jones et al., 2017).
Another project where citizens learn how to collect data to answer research questions is the West Oakland Environmental Indicators Project (WOEIP, 2018). This project, which has been ongoing for more than 17 years, was developed after West Oakland residents identified diesel traffic as an issue of concern in their neighborhoods. In this community-based participatory research project, local residents collaborate with academic partners from the Pacific Institute in order to evaluate the air quality
of residential areas in West Oakland, California. Residents learn how to use professional air monitoring equipment and Global Positioning System (GPS) devices to collect data as they walk around their houses and schools. In addition to learning about how to use project equipment, the project’s formal training program offers 12 hours of leadership training on topics of the development of the Port of Oakland, the impact of the freight transportation industry on local development, the health impacts of diesel exhaust and air pollution, technological solutions to air pollution, how the air quality regulation works, and how to advocate successfully for social justice and community health. In learning how to use the equipment, participants can collect enough firsthand data to support their own community leadership: In the past, residents have also surveyed streets to estimate traffic volume as well as the routes and speeds of heavy-duty trucks along the surface streets and freeways in West Oakland, which led to strategic partnerships between diverse stakeholders and ultimately policy-level changes in truck route ordinances (Gonzalez et al., 2011). In this case, mastery of project tools is a precursor to engaging in other community activities.
The Acadia Learning Project is an example of a citizen science project developed exclusively to form partnerships among teachers, students, and scientists (Acadia Learning Project, 2018). There are multiple projects within the overarching program (e.g., investigating snowpack, mercury in watersheds, and nitrogen cycling in watersheds) with data collection and analysis activities designed to align with many different educational standards. For example, to learn more about the prevalence of mercury in the environment, students and teachers collect samples of invertebrates, fish, plants, and soil. They may take measurements of mass or weight, size, or species abundance, and collect samples to send to a lab where mercury concentrations will be analyzed. The identified requirements of scientists, teachers, and students were notably different, which created some conflict in the program initially (Zoellick, Nelson, and Schauffler, 2012). For example, teachers and students needed assistance developing skills to create and interpret graphs of data from the project, and the learning outcomes specified in state educational standards did not mesh with the research questions of interest to the scientists. The Acadia Learning Project and their teacher partners navigated these tensions by implementing professional development for teachers focused on helping students develop good research questions and facilitating opportunities for the students to be of service to scientists by carefully following field protocols related to the scientists’ research. Zoellick, Nelson, and Schauffler (2012) suggest that it can be helpful to have a third party, such as a university-based project team, who understands the needs of all of the participants to take responsibility for the overall success of the project and to manage both parallel and intersecting efforts.
Participation in citizen science regularly requires some facility with the science content at hand, and often asks participants to engage with new or unfamiliar content. The committee found evidence that scientific domain content learning occurs in citizen science. Many ecologically focused scientific and community projects, such as projects engaged in species observation or air and water quality monitoring, require participants to develop expertise in identifying and documenting species or other natural phenomena and gathering and organizing related data. This can range from making simple visual observations or routine measurements at fixed times or locations to more complex activities such as identifying and providing scientific evidence of observed instances; to discerning and predicting patterns to optimize the likelihood that irregular or unusual cases will be sampled; to solving nonroutine problems that may arise under changing conditions in the field. In order to conduct these activities, participants require sufficient relevant disciplinary content knowledge. In the examples below, we identify how projects supported the development of relevant disciplinary content knowledge.
In the Wildcam Gorongosa project, a network of motion-sensitive trail cameras snap photos of animals throughout the Gorongosa National Park in Mozambique (Wildcam Gorongosa, 2018). Participants contribute to the massive species identification efforts by identifying and classifying animals that appear in the images. For unfamiliar species, the online identification tool allows users to develop enough content knowledge to make decisions based on body shape, pattern, color, and the presence of horns. Users are encouraged to make their best guess with the reassurance that many people review the same photo and experts will verify any cases of substantial disagreement.
While there may be a tendency to dismiss or undervalue species identification tasks as involving “simple fact learning,” such an attitude often belies the nature of the learning that needs to occur and sheds little light on how to improve performance and outcomes. For example, accurate species identification is typically more complex than merely checking off whether a specimen has a couple of distinguishing features. Considerable within-species variation (e.g., juvenile versus adult forms, gender differences, and seasonal and individual variation) is not uncommon. At the same time, instances from other categories may share many similarities. Identification may also involve degraded samples (e.g., carcasses, partial or blurred photographs, instrument calibration issues) or nonvisual evidence, such as auditory calls, such that experienced participants must develop enough content knowledge to be able to recognize and discriminate species. In Wildcam Gorongosa, successful participation hinges on mastery of these
content details, and the project is specifically designed to help participants get to that level of mastery through participation.
An example of a project that developed content knowledge in the field of biochemistry is Foldit (2018). Foldit1 is a multiplayer online game in which players work in the computationally challenging domain of protein structure prediction. Experienced human players—most of whom do not have prior experience in molecular biology—have been able to match or outperform state-of-the-art automated computational methods in both their ability to remodel complex protein structures and their ability (both individual and collective) to generate and refine creative strategies for exploring this very large and complex problem space (Cooper et al., 2010). Foldit players have independently discovered new algorithms that parallel those developed by professional scientists (Khatib et al., 2011a), and they have generated successful models for structures that had eluded prior attempts in research labs, such as the crystal structure of a monomeric retroviral protease, which is now providing insights for the design of antiretroviral drugs (Khatib et al., 2011b). Foldit players who come to perceive the organization of complex protein structures are demonstrating the often-impressive results of successful learning processes, such as perceptual learning described in Chapter 4. With extended experience, Foldit players fine-tune their three-dimensional spatial reasoning skills and their problem-solving strategies to the particular requirements of this domain and the entities they encounter in it.
It is clear from these examples that sophisticated learning occurs in citizen science projects. However, research exploring learning processes in citizen science, such as how participants acquire the expertise needed for a particular project, has been noticeably absent. Attending more deliberately to learning is a promising strategy for improving the consistency and quality of learning in citizen science, and for contributing to other project outcomes that depend on learning, such as collecting high-quality data.
1 The committee notes that Foldit is on the boundary of what the committee considered as citizen science, as it is possible to participate in Foldit without any awareness of the underlying scientific content or the project’s larger-scale scientific goals. Nevertheless, the committee includes this example here as it provides a well-documented example of how one might learn content through citizen science practices.
In considering all of the science learning outcomes discussed in Chapter 4, the committee noted several learning outcomes that were distal to citizen science in that they are possible to achieve, but need more conscious planning or effort on the part of project designers. In the following sections, we discuss these distal outcomes. As noted in Chapter 4, these outcomes are distal in any learning context, not just citizen science: achieving mastery in these arenas requires intentionality on the part of educators. As with the sections above, we will discuss how specific project activities can be leveraged in support of participants’ development in each outcome.
Chapter 4 discusses the import of conceptual change in learning generally, and specifically in science. As mentioned in our discussion in Chapter 1 about what kinds of citizen science projects we considered in our investigation, the committee looked at projects beyond those solely focused on achieving scientific goals. As conceptual change is not necessarily one of the easiest learning outcomes for citizen science, much of the evidence reviewed in this section is from approaches inspired by citizen science that have been successfully applied in more focused educational contexts. Learning goals involving conceptual change and development typically depend on more active facilitation, structuring, and sequencing of learning materials and opportunities over substantial periods of time. As such, learning outcomes involving conceptual change and development may be easier to achieve in formal educational settings because of the opportunities for more extensive and sustained support for learning.
Designers should not be surprised if participants bring intuitive or naïve knowledge that is not consistent with scientific explanations of the natural world. The conceptual change literature has documented common misconceptions in the physical sciences related to matter, force, and energy (Chi, Feltovich, and Glaser, 1981; Chi, Slotta, and deLeeuw, 1994; Clark, 2006; McCloskey et al., 1980); in the life sciences related to variability and natural selection in evolution, ecology (Munson, 1994), the operation of the circulatory system, and processes such as photosynthesis and respiration (Anderson, Sheldon, and Dubay, 1990); and in earth and space sciences related to explanations of day/night cycles, seasons, the solar system, and planetary rotation and orbits (Borun, Massey, and Lutter, 1993; Vosniadou and Brewer, 1992, 1994). Scientific phenomena that involve extreme scales of time and space are also challenging for naïve individuals to process (Jones et al., 2007; Libarkin, Kurdziel, and Anderson, 2007).
Partial change or changes in thinking that are confined to some contexts are also not uncommon.
Although there is virtually no direct research on conceptual change as an outcome of citizen science learning experiences, we speculate that aspects of some citizen science activities may support conceptual change. For example, some participants engage in citizen science over a long period of time and have successive opportunities to broaden and deepen their involvement (e.g., by seeing patterns of data over time, by participating in intensive workshops with scientists and scientifically trained facilitators, by becoming a mentor or trainer, or by engaging in more phases of a project) (Bonney et al., 2009). Also, many science educators see potential opportunities to enhance existing citizen science projects with additional learning activities and curricular resources or incorporate citizen science–style activities within a curricular sequence.
For projects interested in positioning citizen science activities as part of an intentional effort to promote conceptual change, a more recent approach termed “learning progressions” may be of interest (Corcoran, Mosher, and Rogat, 2009; National Research Council, 2012). Learning progressions focus on core concepts, such as the core disciplinary ideas and associated science practices outlined in A Framework for K–12 Science Education (National Research Council, 2012) and use empirical research on students’ learning to pose testable hypotheses about how learning progresses over multiple years. Learning progressions describe coherent pathways and sequences so that learners’ ideas can be developed and reconceptualized over time to achieve mature, scientific understanding (Wiser, Smith, and Doubler, 2012). Several learning progressions have already been systematically developed for key science topics, such as water, energy, and carbon cycling in socioecological systems (Gunckel et al., 2012; Jin and Anderson, 2012; Mohan, Chen, and Anderson, 2009); genetics (Duncan, Rogat, and Yarden, 2009); the nature of matter and atomic molecular theory (Smith et al., 2006; Stevens, Delgado, and Krajcik, 2010); force and motion (Alonzo and Steedle, 2009); and evolution (Catley, Lehrer, and Reiser, 2005). Active efforts are under way in other scientific domains.
The “iEvolve with STEM” program (iEvolve, 2018) presents an example of how citizen science activities can support learning progressions and the related conceptual change. In this program, two school districts present science teachers with the option of participating in a variety of citizen science projects for students in grades 3–5 and grades 6–8. To support science learning, curriculum is developed by a team of lead teachers: Curriculum development experts who generate structured templates, curriculum maps, and cross-curricular lesson planners. A 3-year program of teacher professional development, involving summer workshops and monthly meetings, begins by ensuring that teachers are comfortable and confident leading
hands-on inquiry-based learning, and knowledgeable in specific science content knowledge. The second year focuses on understanding the true nature of scientific research by refining skills and methodologies related to data collection, analysis, and reporting. The third year focuses on optimizing teaching methods, improving assessment, and ensuring the sustainability of projects in subsequent years. This intensive approach may not be necessary to support all learning outcomes, but programs that can bring together researchers, educators, and curriculum professionals are much more likely to be able to support longer term learning outcomes associated with conceptual change.
Citizen science may also appeal to educators who are interested in supporting conceptual change and development of a deep understanding of core disciplinary ideas in science (e.g., those ideas described in the National Research Council’s 2012 report) within a formal education setting. Because some citizen science activities can be productively sustained over longer periods of time (which is important when the goals of science learning involve conceptual change and development), it can be leveraged in support of gradual processes requiring extended learning opportunities. While a concern with developing deeper conceptual understanding of foundational ideas in science is typically thought of as a goal for school students, it is worth noting that citizen science could potentially provide unique and uncommon opportunities for adults who wish to do this but who do not have access to or who are not involved in formal education settings. Moreover, because citizen science–style projects and activities can provide a natural way to infuse the learning of core concepts with science practices (as is strongly advocated in the NGSS Framework and Standards), tools, and resources provided by a citizen science project can support an enriched set of practices, such as data analysis, modeling, and interpretation. Mastery of these outcomes can in turn spur deeper understanding of science concepts and how they are related.
Chapter 4 discusses the complexity of identity as both a mediator and an outcome in science learning. These constructs are tricky to untangle: An identity as someone whose ideas are welcome in science and as someone who has the ability to contribute to science mediates participation, and those identities can be reinforced by positive experiences participating in science.
Participation in citizen science is poised to support the development of both disciplinary identities (someone who actually does science and can contribute to science) and social and cultural identities (the extent to which participants are able to integrate their cultural selves into the culture of sci-
ence). This is particularly true for participants who come to science from nondominant communities that are not always widely represented or visible within the institution of science. As discussed in Chapter 4, researchers have documented how recognizing and honoring different identities in learning, for example by inviting elders to share indigenous knowledge in the course of the project, can open new learning opportunities for learners from nondominant backgrounds (Aikenhead and Jegede, 1999; Bang et al., 2010; Morris, Chiu, and Liu, 2015). Indeed, this becomes particularly important with respect to developing disciplinary identities for learners from underrepresented groups because of the historical trends with respect to who does science, and to what extent their contributions are recognized—or not.
The contributors to the broad range of scientific knowledge are diverse, with important innovations and insights coming from all over the world, and reflecting many cultures, but the culture of modern science is dominated by Euro-American norms and emphasizes Western contributions. Learning environments concerned with equity need to include deliberate interventions in dominant narratives and perspectives by including multiple, diverse forms of relevance and contributions as part of peoples’ experiences. Further, while this is especially important to successfully engaging more underrepresented groups, highlighting a diversity of perspectives can lead to better social interactions for all learners (Rosenthal and Levy, 2010). Additionally, intentionally showing respect for and engagement with multiple perspectives can lead to more rigorous learning and problem solving (Rosenthal and Levy, 2010).
Through participating in citizen science, individuals as well as communities can be empowered to make decisions about what to study, how research should be conducted, and who should be involved in scientific matters. Informal science experiences, such as those offered through citizen science projects can provide people, especially those from underrepresented backgrounds, valuable opportunities to practice and develop their connections to science (Farland-Smith, 2012; Rahm and Ash, 2008). These opportunities may be especially valuable for learners who are navigating conflicting identities from their home culture, as they look for activities that align with the values and practices of their home communities and the scientific community. It is important to recognize that individuals first need to be made aware of opportunities to engage in science, including citizen science projects. To this end, SciStarter embeds its project database on the Websites of partners including the National Science Teachers Association, PBS, Discover magazine, libraries, museums, and more (SciStarter, 2018). The contexts surrounding citizen science projects are filled with opportunities to engage with families and diverse communities.
As discussed in Chapter 3, considering who is learning through participation in citizen science and adopting an asset-based approach to sup-
porting that learning can ultimately facilitate mastery of desired learning outcomes: the multiple ways of knowing within a citizen science project should be considered a source of the creative perspectives and approaches necessary for progress in the scientific endeavor. One example of a program using a foundation of traditional indigenous knowledge is the Urban Explorers Program designed by the American Indian Center in Chicago; this program helps the community cultivate the land in alignment with indigenous land management practices (American Indian Center, 2018). Indigenous Science Days encourage participants of all backgrounds to gather in different outdoor locations throughout Chicago to learn about culturally relevant seasonal activities (e.g., harvesting, land restoration and management, invasive species removal, planting) through traditional indigenous practices. Although the program was not designed or facilitated by professional scientists, it provided an opportunity for community members of all backgrounds to become familiar with Indigenous ways of knowing.
A different community-based summer science education program in Wisconsin and Illinois focused on supporting student’s navigation among multiple epistemologies, with the participation of community members (Bang and Medin, 2010). While not a typical citizen science project, the program used an integrated approach to increase autonomy of community members, including the use of traditional knowledge, elder involvement, community participation in the research agenda, and respect of cultural value and informed consent. Through participation in the program, students became more engaged in school science as they learned to view it as more relevant and useful to their communities. Pre- and post-interviews showed a consistent increase in the ways that students identified with science, through their willingness to endorse the statement, “My tribe has been doing science for a long time.”
These examples are useful because they capitalize on a series of evidence-based strategies for developing identity in science. In order to do this work, these programs seek to attend to different ways of knowing from different backgrounds by ensuring appropriate learning scaffolds that do not assume participant limits based on background. Moreover, participant contributions are not limited to data: participants are invited to bring previous knowledge into the work, which honors the cultural identity that participants bring into the project and allows participants to integrate identities rather than reject aspects of their cultural identity that might not “fit” in science. These strategies and others are highlighted in the following chapter on designing citizen science experiences to support science learning.
Described in Chapter 4, engaging in scientific reasoning is a central part of doing science. However, it is a challenging outcome to pursue through citizen science and requires significant investment from both designers and participants. A small number of research and evaluation studies of citizen science projects have attempted to measure whether participants show gains in their understanding of the nature of science and their ability to engage in various aspects of scientific reasoning (see Strands 3 and 4 and the scientific practices described in Chapter 3). Reasoning and critical thinking are often difficult to measure reliably (National Research Council, 2011, 2014), and they may look different depending on the context of a specific project. As a result, measures of reasoning may vary across projects and may involve self-report (interview or survey), case studies, and observation methodologies (Bonney et al., 2009; Jordan et al., 2011).
Allowing participants an opportunity to understand the reasoning involved in making project decisions in different phases and aspects of a citizen science project has been shown to help them engage in more scientific thinking over the course of their participation. This can be as simple as regular or occasional updates from the project leads that discuss the scientific reasoning involved in project design and analysis, or as involved as a joint effort to design, implement, and evaluate new scientific approaches within a project. To give one example: the Cornell Laboratory of Ornithology analyzed unsolicited letters from more than 700 participants in a successful citizen science project focused on investigating seed preferences in ground feeders for common bird species to examine the degree to which participants (mostly older and well-educated) spontaneously indicated that they were engaging in scientific thinking related to the project (Trumbull et al., 2000). In this project, the experimental questions and research design were already given. Participants were provided with a research kit, including data forms, a full-color poster of common feeder birds, and step-by-step instructions to set up the experiment and gather and submit their tallies. They also received a subscription to a newsletter, which reported results from the project and included articles about how to analyze their own data if they chose to do so. While the data from participants’ letters do not enable causal conclusions about whether people improved their scientific reasoning as a result of the project, they do provide evidence that, for some participants, the project provided occasions to engage in scientific thinking. Some letter writers provided detailed observations; others proposed their own hypotheses for the data they were observing. A few proposed more than one hypothesis or suggested other ways to test a hypothesis. The letters also revealed areas in which improvements could be made to help participants better understand scientific processes and reasoning. For
example, multiple writers did not appreciate the power of a large nationwide sample or what role their own data collection played in the larger project. Some also did not appreciate the value of a consistent protocol across many different sites.
Going a step further, there is strong body of research on the learning outcomes from actively engaging in scientific reasoning activities, such as in hypothesis formulation and testing; research design; data modeling and interpretation; and the development, critique, and communication of evidence-based arguments. Opportunities to do this are available in some citizen science projects—typically those in which nonscientist participants have had significant collaborative roles and have participated in shared decision making in creating or implementing projects and activities (Bonney et al., 2009). For example, the Shermans Creek Conservation Association, started by a group of residents in south-central Pennsylvania in 1998, has run a long-term project with support from the Alliance for Aquatic Resources Monitoring (ALLARM, 2018) to monitor the health of the Shermans Creek Watershed. Volunteers have been trained by ALLARM to collect and analyze monthly samples and conduct seasonal assessments and have then used their data to make recommendations to target critical areas for restoration and protection, to engage in public education, and to empower community decision making related to land development and watershed management. While many participants are satisfied with participating primarily in data collection activities, some core organizers and volunteers have also been engaged in data analysis workshops aimed at teaching them to interpret project data and to evaluate the strength of evidence for drawing conclusions and framing recommendations. Working in conjunction with ALLARM, they have compiled detailed scientific reports covering multiple chemical and biological indicators and have developed and advocated for specific recommendations. In addition to cultivating strong data analysis skills, these more engaged participants developed a deeper understanding of scientific methodology through active participation in developing questions that could be successfully answered through scientific investigation, redesigning studies to improve their scientific quality, and matching data collection methods to the intended uses of data (Bonney et al., 2009; Wilderman, 2005).
The Virginia Master Naturalists is a citizen science program that provides significant supports to facilitate learning and the use of participatory modeling for environmental decision making (Virginia Master Naturalists, 2018). The volunteers participate in 40 hours of classroom and field work to become trained as Master Naturalists, and then participate in 8 hours of specialized training in citizen science. Their training is further enhanced by additional continuing education and annual recertification. They volunteer on a variety of environmental conservation projects involving citizen
science, education, and stewardship of natural resources in Virginia. Using both online resources and facilitated in-person sessions, participants, scientists, and environmental managers are trained to do collaborative modeling using an online system known as Mental Modeler (Gray et al., 2013). This system allows collaborating teams to define issues; model and represent assumptions, existing information, and evidence; run scenarios to inform potential research or management options; and co-develop plans. Online tools allow teams to upload, view, and share data and also provide discussion forums and collaboration spaces. The Mental Modeler software supports the construction of models in the form of concept maps that specify variables and relationships among them, with associated evidence and confidence ratings. Concept maps are converted into a matrix structure that can be analyzed using matrix algebra calculations in which the relationships among variables are examined, classified, and assigned weights, and then used to run and compare scenarios. Once a team of Master Naturalist volunteers and professionals has collaboratively created a model, field data collection can be used to validate or reject aspects of a given model. Gray and colleagues (2013) provide case study evidence based on changes in concept maps over time that show how both individuals and collective groups engaged in new learning during the process of collaborative modeling. Further, the development of a shared conceptual model played a central role in the development of specific management plans and conservation action. In one case, a group focusing on the quality of woodpecker habitat ultimately developed and implemented an experimental design to evaluate different methods of controlling an invasive grass. In contrast to their early conceptual models, later models represented alternative hypotheses involving variables that either were not present originally or did not play a driving role in early models. Another group, which was focused on implementing best practices for agricultural water quality improvement, found in their modeling that cost was a central driving variable influencing the overall systems dynamics. As this emerged, this group’s planning process led volunteers to seek out new funding sources.
Teaching people to evaluate evidence and understand the scientific process requires significant investments in terms of time, pedagogy, and instructional design in both formal and informal settings. Doing it in a way that does not privilege certain cultural values and marginalize others requires careful attention to sociocultural understandings of learning. It is essential for designers and implementers to be aware of some of the beliefs and patterns of reasoning that they may encounter in participants, and to treat those beliefs and patterns of reasoning with respect. The committee noted that key aspects of citizen science activities—namely their connection to scientific or community questions—provide potential inputs for learning and development of scientific reasoning. Active science problems or
science-related community questions, by their very nature, are open-ended and ill-structured, and the practices and discourse processes of science focus squarely on generating reliable, relevant evidence and evaluating how it does or does not support explicit claims. For this reason, the committee sees distinct possibility in engaging in citizen science–inspired learning opportunities in order to achieve learning outcomes related to scientific reasoning.
The examples listed above offer insight into how learning outcomes that might be more distal to citizen science are, in fact, possible to achieve through participation in citizen science projects with proper supports. Because structured learning settings such as K–12 classrooms and afterschool programs may have access to specific resources (sustained meeting times, educators with experience supporting science learning, access to tools and resources, etc.), they may be poised to leverage citizen science in order to address some of these more challenging learning outcomes. In the following chapter, we discuss how project designers can make choices in project design and implementation that can specifically support science learning.
In summary, our investigation revealed that citizen science projects support a variety of learning outcomes. Some of these outcomes, such as developing motivation and learning new scientific skills, are relatively common within the activities and practices that are common across all citizen science projects. Others, such as encouraging the development of scientific reasoning, come only with significant supports and scaffolds that are less ubiquitous. The committee identified an affinity between citizen science practices and best practices for supporting learning. However, there are few investigations into the unique learning opportunities associated with citizen science, though the work around identity development in citizen science heads in this direction (as outlined in the paper contributed by Ballard). Similarly, there are few methods that have been consistently applied across a range of citizen science projects—indeed, there are relatively few common tools for analyzing learning within the community of people who study learning in citizen science. This is not surprising for a nascent field.
As a note, because citizen science invites nonscientists into science, it provides an opportunity to welcome and explore differing epistemologies and cultural traditions and how they enrich learning. This has the potential to shed light on the persistent underrepresentation and under-participation of many communities and their members in science, and these insights are likely to be useful well beyond citizen science. Further, this investigation of
how epistemologies of science interact with other epistemologies may be particularly salient to learning outcomes related to the nature of science. We encourage the citizen science community to investigate how learning outcomes related to the nature of science are advanced by citizen science practices and participation. In fact, citizen science may provide a novel laboratory for education researchers to explore how people develop and refine their understanding of the nature of science.
In the following chapter, we will turn to in-depth descriptions of how the citizen science project design can support the learning outcomes highlighted above, as well as outline how the knowledge and experience of participants can be used to support desired learning outcomes. In order to do this work, we rely on our discussion of the common and divergent elements of citizen science described in Chapter 2 to frame an analysis of how project designers may leverage specific design choices in pursuit of particular learning outcomes. Using the cases highlighted above as concrete examples of the kinds of learning in citizen science, we are now poised to offer guidance to project designers, educators, and others interested in learning about how to support science learning.
Acadia Learning Project. (2018). Available: http://participatoryscience.org [May 2018].
Aikenhead, G.S., and Jegede, O.J. (1999). Cross-cultural science education: A cognitive explanation of a cultural phenomenon. Journal of Research in Science Teaching, 36(3), 269-287.
ALLARM (Alliance for Aquatic Resource Monitoring). (2018). Available: https://www.dickinson.edu/allarm [May 2018].
Alonzo, A.C., and Steedle, J.T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389-421.
American Indian Center. (2018). Available: https://www.aicchicago.org [May 2018].
Anderson, C.W., Sheldon, T.H., and Dubay, J. (1990). The effects of instruction on college nonmajors’ conceptions of respiration and photosynthesis. Journal of Research in Science Teaching, 27(8), 761-776.
Bang, M., and Medin, D. (2010). Cultural processes in science education: Supporting the navigation of multiple epistemologies. Science Education, 94(6), 1008-1026.
Bonney, R., Ballard, H., Jordan, R., McCallie, E., Phillips, T., Shirk, J., and Wilderman, C.C. (2009). Public Participation in Scientific Research: Defining the Field and Assessing Its Potential for Informal Science Education. A CAISE Inquiry Group Report. Washington, DC: Center for Advancement of Informal Science Education.
Borun, M., Massey, C., and Lutter, T. (1993). Naive knowledge and the design of science museum exhibits. Curator, 36, 210-219.
Bowser, A., Hansen, D., He, Y., Boston, C., Reid, M., Gunnell, L., and Preece, J. (2013, October). Using gamification to inspire new citizen science volunteers. In Proceedings of the First International Conference on Gameful Design, Research, and Applications (pp. 18-25). New York: Association for Computing Machinery.
Catley, K., Lehrer, R., and Reiser, B. (2005). Tracing a Prospective Learning Progression for Developing Understanding of Evolution. Paper commissioned by the National Academies Committee on Test Design for K–12 Science Achievement.
Chi, M.T.H., Feltovich, P.J., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
Chi, M.T.H., Slotta, J.D., and de Leeuw N. (1994). From things to processes: A theory of conceptual change for learning science concepts. Learning and Instruction 4, 27-43.
Clark, D.B. (2006). Longitudinal conceptual change in students’ understanding of thermal equilibrium: An examination of the process of conceptual restructuring. Cognition and Instruction, 24(6), 467-563.
COASST (Coastal Observation and Seabird Survey Team). (2018). C Available: https://depts.washington.edu/coasst [September 2018].
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A, Baker, D., and Popovic, Z., and >57,000 Foldit players. (2010). Predicting protein structures with a multiplayer online game. Nature, 466(7307), 756-760.
Corcoran, T., Mosher, F.A., and Rogat, A. (May, 2009). Learning Progressions in Science: An Evidence-Based Approach to Reform. Consortium for Policy Research in Education, CPRE Research Report #RR-63.
Csikszentmihalyi, M., Rathunde, K., and Whalen S. (1993). Talented Teenagers: The Roots of Success and Failure. Cambridge, UK: Cambridge University Press.
Duncan, R.G., Rogat, A.D., and Yarden, A. (2009). A learning progression for deepening students’ understandings of modern genetics across the 5th-10th grades. JRST, 46(6), 655-674.
Eveleigh, A., Jennett, C., Lynn, S., and Cox, A.L. (2013). “I want to be a Captain! I want to be a Captain!”: Gamification in the Old Weather Citizen Science Project. In Proceedings from the First International Conference on Gameful Design, Research, and Applications (pp. 79-82). New York: Association for Computing Machinery.
EyesonAlz. (2018). Available: http://eyesonalz.com [May 2018].
Farland-Smith, D. (2012). Personal and social interactions between young girls and scientists: Examining critical aspects for identity construction. Journal of Science Teacher Education, 23(1), 1-18.
Foldit. (2018). Available: https://fold.it/portal [September 2018].
Gonzalez, P. A., Minkler, M., Garcia, A. P., Gordon, M., Garzón, C., Palaniappan, M., Prakash, S. and Beveridge, B. (2011). Community-based participatory research and policy advocacy to reduce diesel exposure in West Oakland, California. American Journal of Public Health, 101(S1), S166-S175.
Gray, S.A., Gray, S., Cox, L.J., and Henly-Shepard, S. (2013). Mental modeler: A fuzzy-logic cognitive mapping modeling tool for adaptive environmental management. In Proceedings from the 46th Hawaii International Conference on System Sciences (pp. 965-973). IEEE.
Greenhill, A., Holmes, K., Lintott, C., Simmons, B., Masters, K., Cox, J., and Graham, G. (2014). Playing with Science: Gamised Aspects of Gamification Found on the Online Citizen Science Project-Zooniverse. Paper presented at GAMEON 2014.
Gunckel, K.L., Covitt, B.A., Salinas, I., and Anderson, C.W. (2012). A learning progression for water in socio-ecological systems. JRST, 49(7), 843-868.
Iacovides, I., Jennett, C., Cornish-Trestrail, C., and Cox, A.L. (2013, April). Do games attract or sustain engagement in citizen science?: A study of volunteer motivations. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems (pp. 1101–1106). New York: Association for Computing Machinery.
iEvolve. (2018). Bowling Green State University. Available: http://www.bgsu.edu/nwo/currentgrant-projects/ievolve.html [May 2018]
Jin, H., and Anderson, C.W. (2012). A learning progression for energy in socio-ecological systems. JRST, 49(9), 1149-1180.
Jones, M.G., Taylor, A., Minogue, J., Broadwell, B., Wiebe, E., and Carter, G. (2007). Understanding scale: Powers of ten. Journal of Science Education and Technology 16(2), 191-202.
Jordan, R.C., Gray, S.A., Howe, D.V., Brooks, W.R., and Ehrenfeld, J.G. (2011). Knowledge gain and behavioral change in citizen-science programs. Conservation Biology, 25(6), 1148-1154.
Khatib, F., Cooper, S., Tyka, M.D., Xu K., Makedon, I., Popovic, Z., Baker, D., and Foldit Players. (2011a). Algorithm discovery by protein folding game players. Proceedings of the National Academy of Sciences of the United States of America, 108(47), 18949-18953. doi: 10.1073/pnas.1115898108.
Khatib, F., DiMaio, F., Foldit Contenders Group, Foldit Void Crushers Group, Cooper, S., Kazmierczyk, M., Gilskil, M., Krzywda, S., Zabranska, H., Pichova, I., Thompson, J., Popovic, Jaskolski, M., and Baker, D. (2011b). Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nature Structural and Molecular Biology, 18(10), 1175-1177. doi: http://doi.org/10.1038/nsmb.2119.
Libarkin, J.C., Kurdziel, J.P., and Anderson, S.W. (2007). College student conceptions of geological time and the disconnect between ordering and scale. Journal of Geoscience Education, 55(5), 413-422.
Lipstein, R., and Renninger, K.A. (2006). “Putting things into words”: The development of 12-15-year-old students’ interest for writing. In P. Boscolo and S. Hidi (Eds.), Motivation and Writing: Research and School Practice (pp. 113-140). New York: Elsevier.
McCloskey, M., Caramazza, A., and Green, B. (1980). Curvilinear motion in the absence of external forces: Naive beliefs about the motion of objects. Science, 210(4474), 1139-1141.
Mohan, L., Chen, J., and Anderson, C.W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46(6), 675-698.
Morris, M.W., Chiu, C.Y., and Liu, Z. (2015). Polycultural psychology. Annual Review of Psychology, 66, 631-659.
Munson, B.H. (1994). Ecological misconceptions. Journal of Environmental Education, 25(4), 30-34.
National Research Council. (2011). Assessing 21st Century Skills: Summary of a Workshop. Washington, DC: The National Academies Press.
National Research Council. (2012). A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington, DC: The National Academies Press.
National Research Council. (2014). Developing Assessments for the Next Generation Science Standards. Committee on Developing Assessments of Science Proficiency in K–12. Washington, DC: The National Academies Press.
Parrish, J.K. (2013). Rigor, reliability, and scientific relevance: Citizen science lessons from COASST. In American Geophysical Union Fall Meeting Abstracts.
Project FeederWatch. (2018). Available: http://feederwatch.org [May 2018].
Rahm, J., and Ash, D. (2008). Learning environments at the margin: Case studies of disenfranchised youth doing science in an aquarium and an after-school program. Learning Environments Research, 11(1), 49-62.
Renninger, K.A., and Hidi, S. (2002). Student interest and achievement: Developmental issues raised by a case study. In A. Wigfield and J.S. Eccles (Eds.), Development of Achievement Motivation (pp. 173-195). San Diego, CA: Academic Press.
Rosenthal, L., and Levy, S.R. (2010). The colorblind, multicultural, and polycultural ideological approaches to improving intergroup attitudes and relations. Social Issues and Policy Review, 4(1), 215-246.
Rosenthal, L., and Levy, S.R. (2012). The relation between polyculturalism and intergroup attitudes among racially and ethnically diverse adults. Cultural Diversity and Ethnic Minority Psychology, 18(1), 1.
SciStarter. (2018). Available: https://scistarter.com [September 2018].
Smith, C.L., Wiser, M., Anderson, C.W., and Krajcik, J. (2006). Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic-molecular theory. Measurement: Interdisciplinary Research and Perspective, 4(1-2), 1-98.
Stevens, S.Y., Delgado, C., and Krajcik, J.S. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47(6), 687-715.
Trumbull, D.J., Bonney, R., Bascom, D., and Cabral, A. (2000). Thinking scientifically during participation in a citizen-science project. Science Education, 84, 265-275.
Virginia Master Naturalists. (2018). Available: http://www.virginiamasternaturalist.org [May 2018].
Vital Signs Experience. (2018). Available: http://vitalsigns.gmri.org [May 2018].
Vosniadou, S., and Brewer, W.F. (1992). Mental model of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24, 535-585.
Vosniadou, S., and Brewer, W.F. (1994). Mental models of the day/night cycle. Cognitive Science, 18, 123-183.
WOEIP. West Oakland Environmental Indicators Project. (2018). Available: http://www.woeip.org [May 2018].
Wildcam Gorongosa. (2018). Available: https://www.wildcamgorongosa.org [May 2018].
Wilderman, C.C. (2005). Shermans Creek: A Portrait. Harrisburg: Pennsylvania Department of Environmental Protection.
Wiser, M., Smith, C.L., and Doubler, S. (2012). Learning progressions as tools for curriculum development. In A.C. Alonzo and A.W. Gotwals (Eds.), Learning Progressions in Science: Current Challenges and New Directions (pp. 359-403). Rotterdam, The Netherlands: Sense.
Zoellick, B., Nelson, S.J., and Schauffler, M. (2012). Participatory science and education: bringing both views into focus. Frontiers in Ecology and the Environment, 10(6), 310-313.
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