Understanding both the depth and breadth of scholarship on learning is central to addressing the committee’s charge of investigating how citizen science can be poised to support science learning. In this chapter, we review the complex landscape of scholarship on learning in a way that highlights concepts relevant to the design of citizen science for learning. The concepts lay the groundwork for Chapter 5, which delves into how citizen science can advance specific science learning outcomes. We begin with an explanation of the committee’s perspective on learning in the context of the history and evolution of learning theories. This discussion will set the stage for a description of some of the central cognitive processes involved in learning generally. We conclude the chapter with descriptions of some of the specific kinds of learning that happen in science content domains.
Although we describe the different theoretical perspectives on how learning occurs, contemporary scholars of learning generally recognize that learning is a complicated, interactive phenomenon. Individuals are nested within communities that are nested within societies, and these contexts matter for how knowledge is acquired and engaged. Different theories of learning are not mutually exclusive and can be used in complementary ways to attend to the multifaceted nature of learning, even in a single environment such as a citizen science project. Moreover, participants in citizen science project are also learning in a wide variety of other contexts and may even participate in multiple citizen science projects. It is helpful in both design of citizen science projects and in research about learning to
remember that all learning is happening with a larger ecosystem of citizen science opportunities and other science education experiences, both formal and informal.
This chapter is not intended as a comprehensive review of scholarship on learning; rather, we attempt to lay out central principles of learning, particularly with respect to science, for readers new to the field of science learning.
The committee has elected to take an expansive view of learning in general and science learning more specifically: Both what the learning is and the many contextual factors that influence it. Historically, most learning research focuses on individuals, and as we discussed in our section on community science literacy in Chapter 3, many research literatures and theoretical perspectives (including developmental, social, organizational, and cultural psychology; cognitive science, neuroscience, and the learning sciences; and education) have endeavored to construct frameworks for understanding and facilitating learning in individuals. As we discuss the processes of learning (both in general and in science) later in this chapter, the committee recognizes that these processes are aimed at characterizing what the individual learner knows and is able to do.
Over the past few decades, the study of human learning and development has moved beyond the examination of individual characteristics to understand learning as dependent on sociocultural contexts, even when examining a single individual’s learning. In order to explain why and how people think and act in the world the way they do, scholars employing sociocultural perspectives often study and characterize how people in places interact with each other toward goals and use materials to mediate and support their interactions and goals.
From a sociocultural perspective, culture, learning, and development are seen as dynamic, contested, and variably distributed and transformed within and across groups, and involve a reciprocal and evolving relationship between individuals’ goals, perspectives, values, and their environment (Cole, 2000; Gutiérrez and Rogoff, 2003; Hirschfeld, 2002; Lave, 1988; Lave and Wenger, 1991; Nasir and Hand, 2006; Rogoff, 2003). Culture, in this sense, is both historically constituted and dynamically changing through participation in social practices and making sense of life. More simply put, all people explore, narrate, and build knowledge about their worlds, but they do so in varied ways that are dynamically linked to particular contexts and depend on interaction with others (e.g., Bang et al., 2012; National Research Council, 2009; Rogoff, 2003).
While there remain important distinctions between individual and sociocultural perspectives, it is increasingly accepted that what and how
people think are interdependent, and that both are sculpted by the daily activities, discursive practices, participation structures, and interactional processes over the course of a person’s life. Sociocultural perspectives have expanded our foundational knowledge of human learning as well as led to important practice-based innovations in learning environments. While we acknowledge that much of the research on specific processes of learning mentioned in this chapter are concerned with individual learners, the committee believes that given the explicitly social nature of many citizen science projects, it is critically important to consider learning in citizen science through a sociocultural lens.
Given this perspective, the committee wishes to highlight three major principles of learning that undergird our discussion of how learning happens—both in science and in general. First, as we discussed in Chapter 3: Learners come to their learning experiences with prior knowledge experiences that shape what they know, their skills, their interests, and their motivation. Constructivist frameworks explain how this prior knowledge and experience matter for learning, positing that learning involves an interplay of the learner’s prior knowledge and current ways of thinking with new ideas introduced by instruction or through interactions in the world (e.g., Piaget, Carey, Vosniadou, Chi, Posner, et al.) Second, learners actively construct their own understanding of the world; they are not passive recipients of knowledge, and transmitting knowledge is not equivalent to learning. Later in this chapter, we will discuss this principle in relationship to conceptual development, and how educators must actively engage learners in the process of developing conceptual understandings of science. Finally, some learning objectives in science are more challenging to achieve than others, so more intentional supports for learning are necessary. We will discuss this in the context of citizen science in Chapter 5, as we review how the existing literature describes different learning outcomes in citizen science.
In summary, the committee recognizes that learning is inherently social. It is situated in, and dependent upon, social interactions among people as well as their social and cultural tools and practices. In the following discussions of learning processes and kinds of learning in science, the committee emphasizes this sociocultural perspective on learning while also considering the insights gained from many decades of research from other theoretical perspectives.
We begin our discussion of learning by considering the processes of learning in individuals; specifically, the processes of memory, activity, and developing expertise. Then, the chapter narrows in on the specifics of science learning, including learning disciplinary content; using scientific tools; understanding and working with data; developing motivation, interest, and identity; and developing scientific reasoning, epistemological thinking, and the nature of science.
This section considers the dominant cognitive processes that contribute to learning—that is, those processes that can be understood at the level of the individual and relate to content knowledge and reasoning. Because the charge of this study is specific to science learning, wherever possible the committee elects to discuss how these learning processes happen in the context of the domain of science. It is critical to note that these processes are not unique to science learning. Indeed, much of the general scholarship on learning has emerged in relationship to other academic disciplines, each with their own scholarly research traditions.
Learning depends fundamentally on memory. Well over a century of research has delved into the properties of human memory in action, detailing the remarkable role memory plays in both developing and sustaining learning over time. From this research, there are several themes that are helpful to keep in mind.
Durable, long-term learning is best accomplished by repeated experience with the material one seeks to remember. Many researchers of memory and learning would caution against relying on a training program that involves a one-time introduction and immediate assessment of proficiency, which tends to result in short-term performance that predictably deteriorates over time, rather than long-term learning (Soderstrom and Bjork, 2015). Further, learning episodes are most efficient when they are spread out over multiple sessions rather than crammed together—a phenomenon known as the spacing effect (Cepeda et al., 2006; Rawson and Dunlosky, 2011). That is, the same amount of time invested in studying material one wants to remember will generally result in longer-lasting learning if it is distributed over time rather than performed all at once.
Learning can be enhanced by strategies that promote cognitive engagement with and elaboration of the material one is attempting to learn. Knowledge and skills that are densely interconnected to other information have better storage strength in long-term memory and also have links to more potential retrieval cues. Examples of beneficial strategies include such activities as concept mapping, note-taking, self-explanation, and representing material in multiple formats (e.g., text and graphics). Learning researchers Michelene Chi and Ruth Wylie (2014) have proposed a framework that differentiates cognitive engagement during learning into four modes: interactive, constructive, active, and passive (presented in decreasing order of the intensity of engagement), with interactive and constructive modes having the greatest impact on learning and conceptual development.
Constructive engagement is defined as activities where learners generate some kind of additional externalized product beyond the information they were originally provided with, such as generating inferences and explanations or constructing a new representational format (e.g., a diagram). Interactive engagement goes one step further and occurs when two or more partners (peers, teacher and learner, or intelligent computer agent and learner) together contribute to a mutual dialogue in a constructive mode.
Learning is improved when people are asked to actively apply or construct material from long-term memory, as opposed to passively restudying or being re-told the content, a phenomenon known as the “testing effect” (Karpicke and Blunt, 2011; Karpicke and Roediger, 2008; Rowland, 2014). Providing regular opportunities to generate active responses, such as through informal assessments or practice in the field, helps learners reinforce their learning while at the same time providing information about current states of proficiency. As these examples suggest, corrective feedback is another tool that can help to promote accurate learning and reinforce retention over time (Lyster and Ranta, 1997).
Learning opportunities that are deliberately designed with these principles of learning and memory in mind often show significant learning gains over traditional instructional practices such as lecture and rote memorization or self-organized learning (Bjork and Bjork, 2011; Bjork, Dunlosky, and Kornell, 2013). Although it was developed primarily to improve studying and instructional practices in school learning, the IES Practice Guide on Organizing Instruction and Study to Improve Student Learning (Pashler et al., 2007) provides a concise summary of these and several other principles of learning that are supported by substantial bodies of research and are relevant across learning contexts (see Box 4-1).
In Chapter 6, we will discuss the choices that project designers need to make in order to support science learning in citizen science. As with the all the processes of learning described below, designers of citizen science projects can leverage the role of memory in learning to support specific science learning outcomes.
As noted above, human thinking, learning, and behavior is fundamentally shaped by the need to engage in purposeful activity within social systems involving other people. As active agents, humans engage with the objective world in ways that infuse it with meaning. Activity theory (e.g., Engestrom, Miettinen, and Punamaki, 1999) takes a systems approach, treating as the unit of analysis a community of interacting individuals, such as a team or an organization, who have a common object of their activity.
For example, members of a team of health care providers in a hospital are the individual subjects in a community and their patients are the objects.
Activity systems are characterized by rules and conventions, which evolve historically and culturally, as well as divisions of labor and participation structures, which may include social strata or a hierarchical structure to the activity, with different actors taking on distinctive roles. A key insight of activity theory is that “tools,” which may be culturally created artifacts
or concepts (e.g., machines, software interfaces, information systems, protocols, etc.) that evolve over time, mediate behavior in the system, including learning and transmitting knowledge (Jonassen and Rohrer-Murphy, 1999).
Individuals may participate in multiple activity systems, and more recent work on activity theory has brought out the importance of considering interactions among multiple activity systems, which raises issues of individual and cultural identity, power, motivation, and difference (Bakhurst, 2009; Gutiérrez and Rogoff, 2003) and also points back to the need to consider citizen science learning in the context of a larger ecosystem of learning experiences. Activity systems are often used as a way of modeling practice in various contexts, including educational practice, in such a way that systems-level relations and dynamics are highlighted. In the context of citizen science, activity theory offers ways to think about the complex set of roles, objectives, values, and activities that can emerge when volunteer participants are simultaneously members of other communities, such as master naturalists and conservationists, community activists, hobbyists, students or teachers in formal or informal education, or workers engaged in related economic activity (e.g., fishing or harvesting). Actors may come from distinctly different groups, each with its own set of objectives, tools, customs, discourse patterns, role structures, and ways of doing things. Activity theory suggests that participants and organizers may advance collaborative goals by paying deliberate attention to recognizing or designing appropriate role structures, shared tools, and systems of communication to take advantage of the resources that different activity systems can potentially contribute while promoting common action and understanding.
Another example that lends itself to an activity systems analysis comes from Ottinger (2016), who presents the case of a multisite study and report completed by a coalition of environmental and community groups working in parallel with credentialed scientists (Coming Clean and Global Community Monitor, 2014). The study entails the development and deployment of modified instruments and protocols for sampling air quality in ways that were scientifically credible but more affordable and responsive to the concerns and questions of community groups. They allowed project participants to collect data at time intervals and in locations associated with community health concerns, and they provided data that pushed beyond prior standards that focused primarily on long-term averages. Ottinger’s account also illustrates the tensions and interplay among the roles taken by community activists, scientists, and regulatory authorities around issues such as authorship and dissemination of reports, setting standards, and critiquing standard scientific practices vs. aligning with them for the sake of credibility. In summary, activity theory provides a way of identifying, analyzing, and modifying the elements—such as communities, actors and roles, objects of activity, tools, and practices—that both mediate and represent learning.
Competence in any domain, and specifically in science, requires the ability to recognize relevance and potential applications of knowledge in varying contexts. While individuals new to the field (known as novices) tend to focus on superficial aspects of a situation and may have correspondingly shallow problem solving methods, experts quickly and accurately perceive higher-order relations, deep structure, and meaningful patterns (Chi, Feltovich and Glaser, 1981; Kellman and Massey, 2013). Experts tend to be fast and accurate, in large part because they process available information selectively—ignoring information that is irrelevant and registering information that is not noticed by novices. They are also better able to make fine discriminations and to apply their knowledge to novel cases. Experts are particularly good at recognizing conditions of application of knowledge—that is, knowing which principles and concepts are relevant in a particular situation (Chi, Feltovich and Glaser, 1981; Kellman and Garrigan, 2009).
In this subsection, we discuss the role of conceptual change and perceptual learning in the development of expertise. It is important to note that in science, development of expertise hinges on the ability to utilize scientific tools and practices. We discuss this particular aspect of developing expertise—using scientific tools and participating in science practices—later in this chapter, where we discuss specific kinds of learning in science.
One way of understanding how people develop expertise in content areas—specifically in the domain of science—explores the evolution of foundational ideas from the perspective of conceptual development over time. Theorists of conceptual development have noted repeatedly that mature concepts are often qualitatively different from concepts held by children or by uninstructed adults (Duit and Treagust, 2003; National Research Council, 2007). Acquiring sophisticated understanding of concepts is not merely a matter of accumulating more factual knowledge.
A common idea in theories of conceptual development is that concept learning varies in the degree to which knowledge must be restructured to move from naïve to more expert understanding. Some early understandings can be readily nurtured in thoughtful learning settings (Gelman et al., 2010). On the other hand, strong restructuring is required when novice and expert conceptual structures are fundamentally incompatible or incommensurate (Carey, 1988). In this case, rather than refining individual concepts or adding new concepts to existing ones, the nature of the concepts themselves and the explanatory structures in which they are embedded undergo change. Chi and her colleagues (Chi, Slotta, and de Leeuw, 1994) argue
that some science learning is particularly difficult because learners’ initial conceptions belong to a different ontological category than corresponding scientific conceptions. For example, many novices think of heat, gravity, and force as types of material substances, or properties of matter, rather than interactive processes. This can lead learners to misconstrue instruction, as happens when a learner who thinks of electrical current as similar to flowing water draws on matter-based conceptions, like volume or mass, to try to understand electrical phenomena.
The degree to which scientific concepts displace naïve knowledge during the process of strong restructuring is a subject of much debate. Strike and Posner (1982) show how conceptual change can occur when a learner begins to be sufficiently dissatisfied with a prior conception (e.g., by being confronted with anomalous information) and comes to see a new alternative conception as intelligible, plausible, and fruitful in its ability to explain and understand other problems. However, a number of studies indicate that intuitive ideas are also persistent and learners may ignore, reject or distort anomalous information. Even experts do this, as is illustrated by the history of science (Chinn and Brewer, 1993). Further, intuitive beliefs and alternative frameworks can continue to be activated in particular contexts even after an individual shows evidence of understanding and using a scientific concept.
Importantly, people can hold multiple conceptions about phenomena as they engage in rapid reorganization of knowledge and respond to the demands of a particular context. Even experts will shift their reasoning and understanding about a phenomenon depending upon the context (e.g., Hogan and Maglienti, 2001). When confronted with novel activities or practices, learners may need to create their own alternative pathways to reconcile conflicting cultural, ethnic, and academic identities (Nasir and Saxe, 2003).
Learning environments that only see learners’ alternative conceptions as wrong can produce conflicts between learners’ cultural, ethnic, and academic identities (Nasir and Saxe, 2003), and this approach can also leave narrow the possibilities of generative engagements between community ways of knowing and scientific ways of knowing (e.g., Bang and Medin, 2010). Instead, research shows that many phenomena of interest in scientific study are intimately related to people’s everyday experiences and knowledge systems of cultural communities historically underrepresented in science can, and should, be regarded as assets for learning (Cajate, 1999; National Research Council, 2007). Educators can do this in a variety of ways. The use of culturally relevant examples, analogies, artifacts, and community resources that are familiar to learners can make science more relevant and understandable (Barba, 1993), and integrated approaches that rely on the input of community member participation (e.g., input
from elders, use of traditional language, respect of cultural values) help learners navigate between Western modern scientific thinking and other ways of knowing (Bang and Medin, 2010). Sconiers and Rosiek (2000) point out that science inquiry demands patience, skepticism, and a willingness to embrace uncertainty and ambiguity—which demands trust between teachers and students. Accordingly, the development of trust and caring relationships between teachers and students may be necessary in order to develop deep understandings of science content and practices. In short, research demonstrates that conceptual learning is advanced in contexts and with instructors that recognize learners are simultaneously developing expertise in multiple knowledge systems (Bang and Medin, 2010; Levine Rose and Calabrese Barton, 2012).
Another process by which people develop domain expertise is perceptual learning, defined as an increase in the ability to extract relevant information from the environment as a result of experience (Adolph and Kretch, 2015; Gibson, 1969). Perceptual learning happens at all ages from infancy through mature adulthood, and has been studied in many professional and academic domains, including medical learning, aviation, mathematics, and chemistry, as well as in everyday learning (Kellman and Massey, 2013). Perceptual learning is often implicit and can be seen as a fundamental complement to more familiar ways of knowing, such as factual and procedural knowledge. Common instructional techniques emphasizing explicit didactic instruction or procedural practice typically do not advance perceptual learning very effectively (Kellman and Massey, 2013). Instead, perceptual learning often results from extended experiences with many examples as individuals participate in a meaningful activity. Recent research demonstrates that perceptual learning can be accelerated by providing systematic opportunities for learners to practice making relevant discriminations and classifications with feedback (Kellman, Massey, and Son, 2010). Learning software is an efficient and cost-effective way to do this. However, it is important for learners to experience a full range of variation in the examples they work with, so that the critical features, patterns, and structures involved in the activity are observed repeatedly across many different situations. Deliberate training tutorials can also ensure that participants have sufficient exposure to unusual or rare cases or difficult discriminations that they might not otherwise encounter often enough to gain proficiency. This kind of repeated classification activity across a range of examples is a central feature of many citizen science projects, like Zooniverse or COASST, suggesting that citizen science projects may be a particularly rich venue for perceptual learning.
Although the term “perceptual” may give the impression that it applies only to simple sensory tasks and discriminations, recent work drawing on modern theories of perception emphasizes that perceptual learning is abstract and adaptive, working synergistically with other cognitive processes (Kellman and Massey, 2013). Rather than conceiving of learning as the acquisition of discrete mental contents, the focus is on how human minds attune themselves to meaningful patterns, relations, and structures in the environment, typically in the context of a purposeful task or activity (Bereiter and Scardamalia, 1996; Goldstone, Landy, and Son, 2010). In addition to enabling the selective pick up of information in natural settings, as when a geologist effortlessly sees complex structure and patterns in natural rock formations, it also applies to processing of image representations, such as medical images read by a radiologist, and to symbolic representations, such as equations perceived by a mathematician or chemical formula notations read by a chemist. (Indeed, fluent reading in everyday life relies heavily on automatic information pick up obtained through perceptual learning).
Other approaches to the development of expertise have also emphasized how gaining experience in a domain or sphere of activity changes how one “sees.” Working from an anthropological perspective and drawing on activity theory, Goodwin (1994) introduced the term “professional vision” to describe how members of a professional community engage in discursive practices that shape how they perceive relevant entities and phenomena. Goodwin’s concept of professional vision focuses on practices within professions that create and operate on highly mediated representations of experience. For example, professional practices may highlight specific phenomena in a complex scene to make them salient, and they may apply verbal codes to classify phenomena and relate them to each other in an articulated framework. Professionals also produce shared material representations, such as graphs, charts, images, and annotated records. For example, teams of archeologists excavating a site use shared procedures to create profile maps of dirt that capture spatial relations among distinctive layers. Novices typically gain experience with these practices and tools as apprentices and, over time, develop the professional vision characteristic of their profession.
Similarly, Stevens and Hall (1998), has introduced the term “disciplined perception” to describe forms of visual interaction that develop among people as they engage in practice or in teaching and learning in a discipline such as mathematics. People create, coordinate, and behaviorally interact with aspects of visual displays to make objects or conditions of interest visible to themselves and to each other. For example, a student working with a tutor on graphs of linear functions develops a set of visual practices specific to the graphing of points and lines on grids representing the Cartesian plane. In Stevens’ analysis, embodied action (e.g., gesture), visual perception, and
talk work together in specific and coordinated ways throughout the teaching and learning process, both enabling and constraining the understanding that the student develops.
This section focuses on the kinds of learning in science: learning disciplinary content; using scientific tools; understanding and working with data; developing motivation, interest, and identity; and developing scientific reasoning, epistemological thinking, and an understanding of the nature of science. Throughout this section, we refer back to the strands of informal science learning outlined in Chapter 3 to provide a framework for understanding the outcomes that result from these different kinds of learning in science. As emphasized in that chapter, we note that focusing on strands in insolation is an analytic convenience to help understand science learning; in practice strands are inextricably interwoven and projects that effectively advance science learning outcomes often advance and connect multiple strands. In the next chapter, we see examples of these kinds of learning in the context of citizen science.
Learning science content and developing expertise in a scientific discipline involve several types of knowledge, which are acquired through multiple learning processes. Following standard practice, we refer to this kind of learning as “developing expertise in a scientific content area” or “science content learning.” Science content learning may be a stand-alone goal of the project and/or it may be part of achieving other scientific or community goals. With respect to the Learning Science in Informal Environments: People, Places, and Pursuits (LSIE; National Research Council, 2009) strands, science content learning is most closely related to understanding scientific content and knowledge (Strand 2) and using the tools and language of science (Strand 5).
The learning processes that help develop specific disciplinary knowledge and associated competencies, which can be quite sophisticated, go well beyond simple rote memorization of facts. Although the acquisition of specific knowledge is sometimes contrasted with conceptual understanding and the two are treated as if they are competing learning priorities, evidence shows that they play complementary and mutually supportive roles in learning. Specific knowledge and skills that are not incorporated into coherent conceptual organizations tend to exist as isolated “factoids”—difficult to remember, recognize in context, or apply in a productive way. At the same time, a rich foundation of specific knowledge animates abstract
concepts and provides accessible, meaningful instantiations of important relations and patterns.
Expertise in specific disciplinary content requires declarative knowledge—concepts that can be verbalized. This kind of learning is sometimes described as “knowing that.” Declarative knowledge can be thought of as facts that can be reliably and accurately retrieved and applied. A budding geologist, for instance, must learn the names and composition of different types of rocks and minerals and the processes by which they are formed. A volunteer monitoring invasive or endangered species must learn their typical habitats and the properties by which each type is identified. However, as described above in the section on conceptual change, a rich body of factual knowledge is not simply an accumulation of independent facts.
To be functional, science content knowledge must be organized and integrated through conceptual frameworks that provide coherence and explanatory power. Facility in this arena supports the evolution of learners’ relationships to foundational ideas that have broad importance for conceptual development over time. As discussed above, theorists of conceptual development in science learning have noted repeatedly that mature science concepts are often qualitatively different from concepts held by children or by uninstructed adults.
One strong example of how this conceptual change can play out in science domains can be observed through the implementation of A Framework for K–12 Science Education’s core disciplinary ideas, which aim to focus science learning around fewer science topics but to develop them in more depth across multiple years while simultaneously integrating them with science practices, described in the following sections (National Research Council, 2012). The NGSS Framework lays out a small, focused set of core disciplinary ideas in the physical sciences, life sciences, earth and space sciences, engineering, technology, and applications of science. Box 4-2 presents an example of how core disciplinary ideas in life sciences can set the stage for learners’ conceptual change over time.
Not only are specific knowledge and conceptual understanding mutually supportive but also they are both situated in existing knowledge and understanding that learners bring into their experience in citizen science. It can be tempting to think of developing conceptual understanding and specific knowledge as an almost remedial process, where learners enter projects with a deficit and project activities fill that deficit. It is important to note that this approach can undermine other sources of knowledge and other ways of knowing, alienate learners, and impede learning. Learners enter projects with a variety of relevant prior knowledge and experience, some of it cultural, and the research shows that providing opportunities to connect new knowledge and emerging understandings with previous knowledge and experience advances learning.
Another way that science learning occurs is by using scientific tools and methods to engage in scientific reasoning (Strand 3) and to engage in scientific practices and discourse (Strand 5). Gaining competence with the scientific tools and practices related to a given content domain is known as procedural knowledge, sometimes described as “knowing how.” In science, “knowing how” enables one to perform procedures and tasks in the service of scientific protocols. This competency might involve developing laboratory skills, measurement techniques, field methods, or analytic skills, such as how to organize, analyze, and present data. While procedural knowledge is sometimes condensed into a fixed set of rote behaviors—and there is certainly scientific value in maintaining consistent methods and protocols—functional competence and active problem solving in science typically require adaptability and flexibility in application, which in turn requires a deeper understanding of why procedures and practices take the form that they do and what the implications of contextual variations might be. It is important to note that the use of tools and scientific practices is strongly influenced by cultural and social norms (e.g., what is a valid practice, how tools are judged) and the interaction of groups. Indeed, learning is mediated through the tools, artifacts, and discourse structures that are used
to frame, create, and convey knowledge. The cultural construction of tools1 profoundly influences how people learn and how knowledge is organized and communicated, but more local and individualized tools play similar roles in particular contexts. For example, data collection protocols, maps, databases, online interfaces, and computer simulations may all shape how knowledge is produced and how learning occurs in a given setting. Social norms and conventions—whether at a scientific conference, in a classroom, or among a self-organized community group—may also serve as tools that mediate learning and knowledge sharing.
Along those same lines, it can take time for learners who are new to science to understand that measures and the evidence that they provide are developed according to community norms, rather than being direct, self-evident representations of the world (Manz, 2016). It can take even longer for learners to feel like they can contribute to those norms, especially if those norms are presented as the exclusive providence of professional scientists or are grounded in cultural norms from dominant communities. For example, the vigorous questioning that is a norm in discourse among practicing scientists can be discouraging when it is extended, often without thinking about it, to people new to science (Pandya et al., 2007). It is particularly dissonant compared to values of welcoming people to a field and affirming their identity as valued contributors.
Many of the tools and practices of science are linked to bodies of data and the associated practices for collecting, organizing, representing, modeling, and interpreting data. The power of data to enhance our understanding of the natural world and to address meaningful problems in our local and global communities is one of the factors that inspires people to participate in science. Though understanding and working with data is technically a subset of participating in scientific practices, the committee chooses to highlight these particular practices because of their centrality to citizen science.
Opportunities to learn to understand science and do science through active engagement with data are rich, plentiful, and multifaceted. In everyday thinking, most people are accustomed to interacting with whole objects embedded in naturalistic contexts. In contrast, framing scientific questions and designing methods to investigate them typically requires a more precise focus on the specific attributes of the objects or phenomena that
1 The committee wishes to clarify that, in this case, “tools” is defined broadly. Written language, for example, is a tool constructed to transmit ideas. In science, tools are the apparatuses that facilitate the work and process of science: a tool might be a methodological protocol or a mechanism for measuring data.
are relevant to the question and the intentional development of a method for measuring or classifying those attributes. Most people have practical experience with measures of spatial dimensions, such as length, volume, area, and weight, but many measured attributes in science may take less familiar forms, such as rates and ratios (e.g., parts per million, radioactive decay rates) or involve magnitudes—either very large or very small—that fall outside everyday experience (e.g., geologic time, light years, microns, nanometers). Science may also involve developing ways of measuring or classifying behavioral phenomena (e.g., aggressive behavior), which must be operationally defined in the context of a scientific investigation—that is, the investigators and participants have to share a definition of what counts as an occurrence of the behavior of interest in the context of the study and specify how to reliably rate its intensity or frequency.
Data collection also provides a gateway for learning about issues related to measurement and variability, especially when learners have opportunities to reflect on and reason about what they are doing. Repeated measurement often creates conditions for noticing variability and for beginning to think about the sources of that variability. Representing and visualizing variability in a variety of ways can help people see data in the aggregate and to recognize distributions that have central tendencies (e.g., mean, mode, median) and variability or spread, as well as shapes of various sorts (Lehrer and Schauble, 2004). Repeated experience representing variability in data and thinking about different possible explanations for observed variability can help people better explore what drives good practice in designing and implementing data collection. They may become more responsive to or even spontaneously suggest procedures such as improving conditions of observation, using reliable instruments, training multiple data collectors to be consistent, and using multiple samples to reduce error variation in data being collected.
Lehrer and English (2018) wrote a comprehensive overview of methods for introducing young learners to central ideas related to measurement, sampling, variability, and distributions through data modeling activities. In this review, they propose a framework for organizing key concepts and the practices through which they are expressed and understood. Although this framework is aimed at younger learners in classrooms, such an approach could be applied to learners of all ages in various settings. The learning-focused road map starts with forming questions, and then moves into making decisions about relevant attributes and how they will be measured, organizing data and representing variability in distributions of data, and ultimately making inferences, which will in turn stimulate new questions (see Figure 4-1). Similar to other inquiry-driven approaches to science education that emphasize doing science as engaging in interrelated practices (e.g., Manz, 2016; National Research Council, 2007, 2012; Schwartz et
al., 2009), data collection and data modeling can be connected in iterative cycles. This cycle begins with forming questions, and then moves into making decisions about relevant attributes and how they will be measured, organizing data and representing variability in distributions of data, and ultimately making inferences, which will in turn stimulate new questions.
Several projects have looked more closely at how students learn to engage in practices related to scientific modeling; these projects offer field-tested strategies and curricular resources for supporting this learning with topics such as genetics, Darwinian evolution, plant growth, light and shadows, and evaporation and condensation (Lehrer and Schauble, 2004; Schwarz et al., 2009; Stewart, Cartier, and Passmore, 2005). Some common features have appeared across these various projects. One feature is that learners generally need some prior knowledge in a topic or domain to ground their thinking. As has been demonstrated in many studies of cognition and learning, it is difficult for people to engage in sophisticated, productive thinking and problem solving without a sufficient knowledge base to think with. For example, in scientific modeling, students working in the domain of genetics should already have some background in topics such as
meiosis. Modeling activities would be aimed at deepening this knowledge further, integrating it with new concepts, and using it to develop specific models. This background knowledge may come from a variety of sources—provided by instructors and curricular materials, gathered through online or library research, and so forth. At the same time, it is important to set up the learning situation to encourage learners to be able to probe their own understanding of established knowledge, to raise questions about it, and to evaluate the credibility of their sources rather than passively accepting everything on authority.
A second common feature across a variety of projects is providing sufficient time for repeated cycles of data collection, modeling, and revision. Many of the projects reported in the literature played out over multiple months or even entire academic years. A third common feature is that teachers or teams of teachers and researchers provided systematic facilitation to help guide students toward more and more sophisticated ways of thinking about and engaging in modeling. They did this through the types of assignments they made and how they sequenced them, how they modeled and managed classroom discourse, and the physical and representational resources they provided for conducting investigations and for organizing and representing data and models.
Motivation, interest, and identity can be thought of as inputs to, mediators for, and outcomes of participation in science. For example, interest in a science topic can motivate people to seek out information; people whose whole identities are welcomed and appreciated are more likely to participate in science learning activities (Rahm et al., 2003); and building identity as someone with something to contribute to science (Ballard, Harris, and Dixon, 2017) can deepen an individual’s interest in science (Bonney et al., 2009).2
Learning research suggests that motivation, interest, and identity are important touchstones for learning. An individual’s identity plays an important role in learning—both through shaping what is of interest, as well as what people find motivating. A spark of curiosity can develop into an interest, but to support long-term learning and eventual identification with the scientific enterprise, learners must demonstrate sustained and persistent motivation (Hidi and Renninger, 2006). Underdevelopment of these compe-
2 As a note, the committee wishes to acknowledge issues around motivation, interest, and identity are not specific to science, and are important to learning in any disciplinary context. For the purposes of this report, however, the committee is interested in how to support these outcomes in science and is discussing research with that specific focus.
tencies present substantial obstacles to learning, while support for the development of these competencies can lead to achievement of science learning outcomes. In the “free-choice” contexts of citizen science, these constructs are particularly important as they are integral to the drive to participate, as well as the choice to stay engaged in the work. The committee finds it particularly important to call out this interplay of identity, motivation, and interest, as it is critical to support learning in citizen science.
Learning experiences can be purposefully designed in ways that support or constrain development in these arenas. In this chapter, we discuss these competencies as mediators for learning and their subsequent role(s) in learning processes. In the following chapter, we consider how citizen science can support their development as outcomes in science learning.
Two primary theories support contemporary understandings of motivation. Expectancy value theory posits that people are goal oriented and that behavior is driven by the relationship between an individual’s expectations or perceptions and the value they place on the goal they are working toward. Such an approach predicts that when more than one behavior is possible, the behavior chosen will be the one with the largest combination of expected success and value (Palmgreen, 1984). An alternative theory, achievement goal theory, was developed in order to understand the unfolding or development of engagement in a task. Achievement goals generally refer to the purposes or reasons an individual is pursuing a task as well as the standards or criteria used to judge successful performance (Pintrich, 2000; Pintrich and Schunk, 1996). This theory identifies two types of co-mingled achievement goals: mastery, sometimes called competence, and performance. Mastery goals have been labeled task-goals (Nicholls, 1984) and learning goals (Dweck and Leggett, 1988; Elliott and Dweck, 1988), whereas performance goals have been labeled ego-goals (Nicholls, 1984) and ability goals (Ames and Ames, 1984). However, mastery and performance goals may also comingle.
An individual who adopts a performance goal toward learning is generally more concerned with the outcome and demonstrating his or her competence to others. A person who adopts mastery goals toward learning is often more focused on the process of learning rather than the outcome and often experiences learning to be a rewarding in and of itself. In the domain of education, mastery goals have been articulated to focus on what learners should know, understand, and be able to do. Thus, mastery requires that individuals understand concepts, have background knowledge (content), and can address tasks that require critical thinking, inference, induction, deduction, and application of knowledge—to solve problems and address
issues in novel situations. In schools, students with mastery orientations show consistent, positive learning outcomes, engage in deeper cognitive strategies, and are intrinsically motivated to learn (Anderman and Young, 1994; Lee and Brophy, 1996; Meece, Blumenfeld, and Hoyle, 1988).
An important development in the field of motivation has been focused on the ways in which goals and forms of motivation are variable and context dependent—that is, how the social context impacts motivation, goals, and participation (Nolen and Ward, 2008). Part of this social context is the ways in which tasks and forms of participation are intertwined. For individuals that have mastery-oriented goals, a task that does not afford continual mastery goals can lead to disengagement—if something is too easy, a mastery-oriented person may lose interest and seek other opportunities.
Another important finding in the field of science education has been the interlocking of motivation and learning with opportunities to participate in the full range of scientific practices and sense-making (e.g., Chin and Brown, 2000). That is, motivation and learning increase when individuals have opportunities to develop explanations, carry out investigations, and evaluate knowledge claims (Blumenfeld et al., 1991). Importantly, the different forms of practice and activity tend to mutually reinforce each other—learning in one area tends to promote learning and engagement in another (Eveleigh et al., 2014). Furthermore, scholarship has demonstrated the need to carefully attend to the variation in factors that motivate or fail to motivate students from particular demographic groups when designing instruction.
Motivation is a central component of the ability to develop self-efficacy (i.e., feelings of “I can do this”). There is considerable evidence that people will work harder, perform better, and persist in the face of challenges—all central components in learning—if they have some sense of control and believe that they are capable of success (Atkinson, 1964; Eccles et al., 1983; Hidi and Ainley, 2008; Sansone, 2009; Wigfield et al., 2006). People generally develop feelings of self-efficacy from past experiences, observations of others, performance feedback, emotional or physiological states, and social influences. As such, feelings of self-efficacy can evolve with new experiences.
When people are interested in a topic or task, they are more likely to be attracted to challenges, use effective learning strategies, and make appropriate use of feedback (Csikszentmihalyi, Rathunde, and Whalen, 1993; Lipstein and Renninger, 2006; Renninger and Hidi, 2002). With increased interest, participants will begin to develop and seek out answers to questions as they work on a project (Renninger, 2000), and they are also
more likely to use systematic approaches to answer these questions (Engle and Conant, 2002; Kuhn and Franklin, 2006; Renninger, 2000). Having an interest in a subject helps individuals to pay attention, learn, and retain more information for longer periods of time (Beier and Ackerman, 2003; Hidi and Renninger, 2006; National Research Council, 2000; Renninger and Hidi, 2011). Learning contexts that engage participants’ personal interests have demonstrated increased participation, particularly by people from underrepresented groups (Barton and Tan, 2018).
A person’s interest in a topic may be an enduring connection to a domain (e.g., they have a concern about water quality and public health) or connection to specific features of a task (e.g., they enjoy hiking and being outdoors with their family). Interest is not fixed but rather develops over time. Interest begins with sparks of curiosity, extends to voluntary re-engagement, and if supported, can develop into a part of a person’s identity (Hidi and Renninger, 2006; Renninger and Hidi, 2011). Vocational interests in children often change with age and seem to be particularly aligned with one’s social class at ages 9–13 (Cook et al., 1996), whereas beyond age 13, children develop differentiated and individualized career interests based on their internal, unique selves (Schoon, 2001). Learners of all ages can be supported to develop specific interests (Renninger, 2010). Beyond changes associated with getting older, interests are also influenced by other mutable factors, such as gender, race, ethnicity, and social class, all of which are discussed in the identity section of this chapter, below.
Part of learning involves the construction of identities, including viewing one’s self as a member or part of an enterprise. We discuss two primary ways of understanding issues of identity and science learning including: (1) disciplinary identities—who develops, and how, an identity as someone who does science and contributes to science learning, and (2) social and cultural identities—how socially and culturally constructed identities such as racial and gendered identities intersect with learning, as well as how power dynamics and processes such as racialization impact learning and engagement.
Disciplinary identity. In science, one particularly important aspect of learning is developing a disciplinary identity as someone who actually does science and can contribute to science more broadly. Developing an identity as someone who does and can contribute to science is shaped by an individual’s long-standing perceptions and experiences with science (Atwater et al., 2013), some of which may not be very positive. For example, more than 60 years of research has demonstrated that young people, as well
adults, tend to think about science as a body of facts or as a rigid, largely laboratory-based process that white males engage in (Finson, 2002; Mead and Metraux, 1957). However, this perception is changing; a recent meta-analysis of more than 50 years of “draw-a-scientist” surveys collected from more than 20,000 children in the United States shows drawings depicted more female scientists in later decades, especially among younger children (Miller et al., 2018).
Social and cultural identities. This research also highlights the ways in which individuals develop, even if implicitly, gendered and racialized perspectives about who does science; thus, social identities and disciplinary identities are intertwined, which we explore in the following section. It is important to note the ways in which these issues exclude people and influence the progress and relevance of science. For example, the increased participation of women and scientists from nondominant backgrounds has led to important new foundational knowledge in several fields of science. The environmental justice community draws a link between the historical exclusion of certain communities from science and the prevalence of toxic areas within communities of color.
The ways in which researchers have investigated the construction, reinforcement, and interaction of social and cultural identities with learning has shifted over time. An individual’s social and cultural identity shapes how he or she will engage with science and what each will learn from these experiences. Similarly, these identities will influence the extent to which they come to identify with science or as someone who can contribute to science. The next chapter will explore the ways in which these identities intersect with, influence, and are influenced by science learning outcomes in citizen science.
The concepts covered in this subsection—scientific reasoning and epistemological thinking3—correspond to Strand 2 (using arguments and fact related to science) and Strand 4 (reflecting on science as a way of knowing). Critical thinking and reasoning in science involve a number of factors that must be coordinated in complex ways. Learners need to develop an understanding of how to differentiate among facts, hypotheses, theories, and evidence, and how data can gain meaning as they are used to evaluate potential explanations (King and Kitchener, 1994; Kuhn, 1999; Smith et al., 2000). Further learning objectives involve knowledge of how research
3 Epistemological thinking understands the nature of building knowledge in science and the use of the methods of science to develop knowledge through scientific inquiry and argumentation.
designs, sampling, and measurement methodologies provide frameworks by which research questions and hypotheses are related to data, and how these methodologies can enable or limit the strength of the inferences that can be drawn from data. A central example of this is distinguishing when patterns of evidence do and do not warrant conclusions about causality (Kuhn et al., 1995; Schauble, 1996). Closely related to these abilities is the process of scientific argumentation, whereby people construct knowledge claims, justify them with evidence, consider and critique alternative claims, and revise claims (Berland and McNeill, 2010). There is general consensus among learning scholars that acquiring competence in scientific reasoning, argumentation, and discourse requires rich and extended opportunities to engage actively in these as practices (National Research Council, 2007, 2012).
Scientific reasoning entails learning to coordinate knowledge claims with evidence, but this, in turn, depends on understanding that there is a difference between claims and evidence or between facts and beliefs. Researchers who study epistemological development in children and adults in Western cultures typically propose that there is a general progression in the development of epistemological understanding (Hofer and Pintrich, 1997; King and Kitchener, 1994; Kuhn, 1999; Perry, 1970). An early view takes a dualistic stance toward knowledge, believing that all knowledge is unproblematically true or false and can be known with certainty by authorities. Facts are seen as a direct representation of reality, and experts should not disagree unless one knows less, has made a mistake, or is intentionally lying. Further in the progression, some uncertainty may be admitted, but it is seen as temporary. Eventually, an individual may recognize that knowledge is uncertain and that different people can have different subjective views, but he or she may still not fully distinguish between theory and evidence and may not feel that how well a belief is justified by evidence can or should be adjudicated, because it is a matter of personal opinion. Evidence may be seen more as an illustration of a belief than a justification for it. At more advanced levels, knowledge is viewed as something that is actively constructed and must be supported and justified by evidence. Differing interpretations of evidence vary in how well-grounded they are, and even experts’ judgments can be productively questioned. One’s own beliefs and conclusions are also open to revision based on new evidence or new interpretations of evidence. Individuals with this stance see knowledge as constructed and view themselves as active meaning-makers.
Both longitudinal and cross-sectional studies indicate that the most advanced levels are uncommon even among graduating college seniors (King and Kitchener, 1994), and are most often seen among advanced graduate students. However, older adults, noncollege-educated adults, and non-Western populations have not been well-represented in research sam-
ples (for an exception, see Belenky et al., 1986). It is possible that older individuals may bring more sophisticated critical thinking skills and more advanced beliefs about what they think knowledge is and how it is generated as a result of work and life experience.
While there are clear developmental progressions in epistemological thinking, current theories generally do not conceive of them in terms of fixed all-or-nothing stages, and the same individual may show somewhat more or less sophisticated reasoning or may draw on alternative views of knowledge and knowing as a function of the situation and the types of supports available in the environment. There is also evidence of the importance of structured learning opportunities: younger learners are capable of advancing in their epistemological reasoning and their use of evidence to support arguments in appropriate science contexts (Berland and McNeill, 2010; Smith et al., 2000); at the same time, adults may not commonly achieve higher levels of sophistication spontaneously without such learning opportunities (King and Kitchener, 1994). Kuhn (1999) argues that, in addition to epistemological knowledge, critical thinking also involves metacognitive knowledge—that is, an individual becomes more aware of his or her own thinking and is able to intentionally reflect on it and also control it by monitoring it and selecting strategies to manage critical thinking. Constructing a rebuttal in science, for example, requires this kind of complex, controlled thinking to evaluate the strengths and weaknesses of counterclaims and to generate and evaluate support for one’s own claims.
Sociocultural perspectives are an important additional lens for understanding how epistemologies and scientific reasoning develop. They also call attention to variations in how people from different cultural backgrounds think about knowledge and the sources and processes that create and validate knowledge (e.g., Bang and Medin, 2010). Globally, many different cultures have developed sophisticated epistemologies based in systematic observations of nature. The traditional ecological knowledge of Indigenous communities is one example. The interplay of indigenous epistemologies and more mainstream scientific disciplines has been productive for a range of topics including, but not limited to, ecosystem management, fisheries, agroforestry, animal behavior, medicine, and pharmacology. Traditional knowledge not only brings diverse ideas to these areas of study, but also is associated with a cultural framework of respect, reciprocity, and responsibility (Kimmerer, 1998; Pierotti and Wildcat, 2000). Although traditional ecological knowledge has recently been formally recognized as having an equal status with Western scientific knowledge (United Nations Environment Programme, 1998), it has historically been marginalized or ignored in the scientific community (Salmon, 1996).
While European and Western scientific epistemologies have been productive in many contexts, history is rife with examples in which it has
been used to oppress certain peoples. For example, colonists have utilized biased, ethnocentric tests to support racist ideals and assert their cultural superiority over colonized people, resulting in a legacy of persistent distrust and alienation of some cultures or communities from scientific research. Sociocultural analyses emphasize that the ways of knowing associated with Western science are not culturally neutral, and they have been privileged in part because they have been associated with power and dominant culture (Agrawal, 1995).
Some recent projects have attempted to develop new approaches to community participation in and support for science and science education by taking an explicitly integrative approach toward epistemological differences. In formal education contexts, for learners who recognize differences in the orientations of their home culture and that of western science, effective instructors can help students negotiate “border crossings” between the different ways of thinking (Aikenhead and Jegede, 1999; Costa, 1995). For example, Bang and Medin (2010) describe how a large project collaborating with urban and rural Native American communities blends the practice of science with elements of culturally based epistemological orientations, such as the stance that humans are an interconnected part of the natural world rather than independent and external from it. An integrated approach that relies on the participation of community members (e.g., elder input, use of traditional language, community participation in the research agenda, respect of cultural value, informed consent) may be useful to remove the implicit privileging of Western scientific thinking and recognize the importance of different cultural values and orientations. Place-based educational programs that are co-created and implemented with members of indigenous communities have demonstrated success in helping Native American learners to navigate multiple epistemologies and deepen their understanding of science related to plants, animals, and ecology while also appreciating the historic legacy and contemporary relevance of their own communities’ knowledge and experience of the natural world.
Fluency in science also includes an understanding of the nature of science, which includes an in-depth understanding of the histories, philosophies, and sociologies of the institution of science. This metacognition also requires an awareness of the values implicit in scientific endeavors that shape the products of science, and an awareness of the ways in which science is not neutral and subject to constant review. It also includes an understanding of how science knowledge is built and the notion that there is a community of scientists working together to build knowledge through the use of scientific practices. Mastery of these concepts is embodied in Strand 4, reflecting on science as a way of knowing.
Council, 2007; Osborne, Simons, and Collins, 2003). First and foremost, understanding the nature of science recognizes that science is an empirical way of knowing about the world that utilizes transparent methods to make evidence-based claims. Science is an ongoing enterprise: Knowledge acquired scientifically is subject to continued review and revision. It is also important to understand that scientific knowledge is partially based on human inference, human imagination and creativity, and the social and cultural contexts in which it is formed. Data are collected and interpreted in context: current scientific perspectives, cultural influences, and the experiences and values of individual scientists all matter in the building of scientific knowledge. Third, there is no unitary scientific method. Instead, science is built on a number of methods, which like scientific knowledge in general, are subject to constant innovation, creativity, and revision. Finally, science can be understood as an epistemological framework, and even that framework is subject to revision as new ideas. In fact, thinking about the way in which learners approach science can yield insight into how the nature of science itself evolves over time.
It has been argued that engaging students in authentic science experiences contributes to their understanding of the nature of science (Schwartz et al., 2004), but evidence suggests that it is important to explicitly teach students about the nature of science (Abd-El-Khalick and Lederman, 2000). Because citizen science engages directly in scientific activity, it has the potential—though largely unrealized and not without significant supports—to provide opportunities to learn about the nature of science.
This chapter outlines some of the most current understandings of how people learn, and how people learn science. As we explain throughout this chapter, individuals learn, they learn through interaction with others, and their learning occurs in a broad landscape that is influenced by culture, practice, and history. Historically, inequities in society have affected people’s opportunity to learn by discounting or neglecting cultural knowledge and prior experience. Attending to those prior experiences and providing learning opportunities that welcome the individual, social, and sociocultural aspects of learning are especially effective for addressing these inequities and provide enriched opportunities for all learners.
As we will see in the next chapter, awareness of the multiple factors that influence learning provide opportunities to build rich learning experiences that leverage and build out from citizen science. At the same time, research on learning reveals that any learning, including learning is citizen science, occurs in a larger ecosystem of learning opportunities and experiences. That means design and practice of citizen science for learning should be
considered within a broader landscape of learning experiences, which can inform, enrich, and extend learning opportunities in citizen science. The next chapter will discuss these learning processes in the specific contexts of citizen science projects.
Abd-El-Khalick, F., and Lederman, N.G. (2000). Improving science teachers’ conceptions of nature of science: A critical review of the literature. International Journal of Science Education, 22(7), 665-701.
Adolph, K.E., and Kretch, K.S. (2015). Gibson’s theory of perceptual learning. In J.D. Wright (Ed.), International Encyclopedia of the Social and Behavioral Sciences (pp. 127-134). New York: Elsevier.
Agrawal, A. (1995). Dismantling the divide between indigenous and scientific knowledge. Development and Change, 26(3), 413-439.
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.
Ames, C., and Ames, R. (1984). Systems of student and teacher motivation: Toward a qualitative definition. Journal of Educational Psychology, 76(4), 535.
Anderman, E.M., and Young, A. (1994). Motivation and strategy use in science: Individual differences and classroom effects. Journal of Research in Science Teaching, 31, 811-831.
Atkinson, J.W. (1964). An Introduction to Motivation. Princeton, NJ: Van Nostrand.
Atwater, M.M., Lance, J., Woodard, U., and Johnson, N.H. (2013). Race and ethnicity: Powerful cultural forecasters of science learning and performance. Theory into Practice, 52(1), 6-13.
Bakhurst, D. (2009). Reflections on activity theory. Educational Review, 61(2), 197-210.
Ballard, H.L., Harris, E.M., and Dixon, C.G. (2017). Science Identity and Agency in Community and Citizen Science: Evidence and Potential. Paper commissioned by the Committee on Designing Citizen Science to Support Science Learning at the National Academies of Sciences, Engineering, and Medicine.
Bang, M., and Medin, D. (2010). Cultural processes in science education: Supporting the navigation of multiple epistemologies. Science Education, 94(6), 1008-1026.
Bang, M., Warren, B., Rosebery, A.S., and Medin, D. (2012). Desettling expectations in science education. Human Development, 55(5-6), 302-318.
Barba, R.H. (1993). A study of culturally syntonic variables in the bilingual/bicultural science classroom. Journal of Research in Science Teaching, 30, 1053-1070.
Barton, A.C., and Tan, E. (2018). A longitudinal study of equity-oriented STEM-rich making among youth from historically marginalized communities. American Educational Research Journal, 55(4), 761-800.
Beier, M.E., and Ackerman, P.L. (2003). Determinants of health knowledge: An investigation of age, gender, abilities, personality, and interests. Journal of Personality and Social Psychology, 84(2), 439-448.
Belenky, M.R, Clinchy, B.M., Goldberger, N.R., and Tarule, J.M. (1986). Women’s Ways of Knowing: The Development of Self, Voice, and Mind. New York: Basic Books.
Bereiter, C., and Scardamalia, M. (1996). Rethinking learning. In D.R. Olson and N. Torrance (Eds.), The Handbook of Education and Human Development: New Models of Learning, Teaching and Schooling (pp. 485-513). Oxford, England: Blackwell.
Berland, L.K., and McNeill, K.L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94, 765-793.
Bjork E.L., Bjork R.A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M.A. Gernsbacher, R.W. Pew, L.M. Hough, and J.R. Pomerantz (Eds.), Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society. New York: Worth.
Bjork, R.A., Dunlosky, J. and Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417-444.
Blumenfeld, P.C., Soloway, E., Marx, R.W., Krajcik, J.S., Guzdial, M., and Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3-4), 369-398.
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.
Cajete, G.A. (1999). Igniting the Sparkle: An Indigenous Science Education Model. Skyland, NC: Kivaki Press.
Carey, S. (1988). Conceptual differences between children and adults. Mind & Language, 3(3), 167-181.
Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T., and Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132, 354-380.
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.
Chi, M.T., and Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219-243.
Chin, C., and Brown, D.E. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching, 37(2), 109-138.
Chinn, C.A., and Brewer, W.F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1-49.
Cole, M. (2000). Struggling with complexity: The Handbook of Child Psychology at the millenium. Essay Review of The Handbook of Child Psychology by W. Damon. Human Development, 6, 369-375.
Coming Clean and Global Community Monitor. (2014). Warning Signs: Toxic Air Pollution Identified at Oil and Gas Development Sites. Results from Community Air Monitoring Reveal Chemicals Linked to Health Hazards. Available: https://comingcleaninc.org/assets/media/images/Reports/Warning%20Signs%20Report.pdf [December 2018].
Cook, T.D., Church, M.B., Ajanaku, S., Shadish,W.R., Jr., Kim, J., and Cohen, R. (1996). The development of occupational aspirations and expectations of inner-city boys. Child Development, 67(6), 3368-3385.
Costa, V.B. (1995). When science is “another world”: Relationships between worlds of family, friends, school, and science. Science Education, 79(3), 313-333.
Csikszentmihalyi, M., Rathunde, K., and Whalen, S. (1993). Talented Teenagers: The Roots of Success and Failure. Cambridge, UK: Cambridge University Press.
Duit, R., and Treagust, D.F. (2003). Conceptual change: A powerful framework for improving science teaching and learning. International Journal of Science Education, 25(6), 671-688.
Dweck, C.S., and Leggett, E.L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256.
Eccles, J., Adler, T., Futterman, R., Goff, S., Kaczala, C., Meece, J., and Midgley, C. (1983). Expectancies, values, and academic behaviors. In J.T. Spence (Ed.), Achievement and Achievement Motivation (pp. 75-146). San Francisco, CA: W.H. Freeman.
Elliott, E.S., and Dweck, C.S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54(1), 5.
Engeström, Y., Miettinen, R., and Punamäki, R.L. (Eds.). (1999). Perspectives on Activity Theory. Cambridge, UK: Cambridge University Press.
Engle, R.A., and Conant, F.R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20(4), 399-483.
Eveleigh, A., Jennett, C., Blandford, A., Brohan, P., and Cox, A.L. (2014, April). Designing for dabblers and deterring drop-outs in citizen science. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2985-2994). New York: Association for Computing Machinery.
Finson, K.D. (2002). Drawing a scientist: What we do and do not know after fifty years of drawings. School Science and Mathematics, 102(7), 335-345.
Gelman, R., Brenneman, K., MacDonald, G., and Román, M. (2010). Preschool Pathways to Science: Facilitating Ways of Doing, Thinking, Communicating, and Knowing About Science. Baltimore, MD: Brookes.
Gibson, E.J. (1969). Principles of Perceptual Learning and Development. New York: Appleton-Century-Crofts.
Goldstone, R.L., Landy, D.H., and Son, J.Y. (2010). The education of perception. Topics in Cognitive Science, 2(2), 265-284.
Goodwin, C. (1994). Professional vision. American Anthropologist, 96(3), 606-633.
Gutiérrez, K.D., and Rogoff, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher, 32(5), 19-25.
Hidi, S., and Ainley. M. (2008). Interest and self-regulation: Relationships between two variables that influence learning. In D.H. Schunk and B.J. Zimmerman (Eds.), Motivation and Self-Regulated Learning: Theory, Research, and Application (pp. 77-109). Mahwah, NJ: Erlbaum.
Hidi, S., and Renninger, K.A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127.
Hirschfeld, L.A. (2002). Why don’t anthropologists like children? American Anthropologist, 104(2), 611-627.
Hofer, B.K., and Pintrich, P.R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67(1), 88-140.
Hogan, K., and Maglienti, M. (2001). Comparing the epistemological underpinnings of students’ and scientists’ reasoning about conclusions. Journal of Research in Science Teaching, 38(6), 663-687.
Karpicke, J.D., and Roediger, H.L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968.
Karpicke, J.D., and Blunt, J.R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331(6018), 772-775.
Kellman, P.J., and Garrigan, P. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53-84.
Kellman, P.J., and Massey, C.M. (2013). Perceptual learning, cognition and expertise. Psychology of Learning and Motivation, 58, 117-165.
Kellman, P.J., Massey, C.M., and Son, J.Y. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction and fluency. Topics in Cognitive Science, 2(2), 285-305.
Kimmerer, R. (1998). Intellectual diversity: Bringing the native perspective into natural resources education. Winds of Change, 13(3), 14-18.
King, P.M., and Kitchener, K.S. (1994). Developing Reflective Judgment. San Francisco: Jossey-Bass.
Kuhn, D. (1999). A developmental model of critical thinking. Educational Researcher, 28(2), 16-26.
Kuhn, D., and Franklin, S. (2006). The Second Decade: What Develops (and How). New York: John Wiley and Sons.
Kuhn, D., Garcia-Mila, M., Zohar, A., and Andersen, C. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, 245(60), 4.
Lave, J. (1988). Cognition in Practice: Mind, Mathematics and Culture in Everyday Life. Cambridge, UK: Cambridge University Press.
Lave, J., and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.
Lee, O., and Brophy, J. (1996). Motivational patterns observed in sixth-grade science classrooms. Journal of Research in Science Teaching, 33(3), 303-318.
Lehrer, R., and Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41(3), 635-679.
Lehrer R., and English L. (2018) Introducing children to modeling variability. In D. Ben-Zvi, K. Makar, and J. Garfield (Eds.), International Handbook of Research in Statistics Education (pp. 229-260). New York: Springer.
Levine Rose, S., and Calabrese Barton, A. (2012). Should Great Lakes City build a new power plant? How youth navigate socioscientific issues. Journal of Research in Science Teaching, 49(5), 541-567.
Lipstein, R., and Renninger, K.A. (2007). “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.
Lyster, R., and Ranta, L. (1997). Corrective feedback and learner uptake: Negotiation of form in communicative classrooms. Studies in Second Language Acquisition, 19(1), 37-66.
Jonassen, D.H., and Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61-79.
Manz, E. (2016). Examining evidence construction as the transformation of the material world into community knowledge. Journal of Research in Science Teaching, 53(7), 1113-1140.
McComas, W.F., and Olson, J.K. (1998). The nature of science in international science education standards documents. In W.F. McComas (Ed.), The Nature of Science in Science Education: Rationales and Strategies (pp. 41-52). Dordrect, The Netherlands: Springer.
Mead, M., and Metraux, R. (1957). Image of the scientist among high-school students. Science, 126(3270), 384-390.
Meece, J.L., Blumenfeld, P.C., and Hoyle, R.H. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514-523.
Miller, D.I., Nolla, K.M., Eagly, A.H., and Uttal, D.H. (2018). The development of children’s gender–science stereotypes: A meta-analysis of 5 decades of US Draw-A-Scientist studies. Child development. [Epublication ahead of print.] doi: 10.1111/cdev.13039.
Nasir, N.I.S., and Hand, V.M. (2006). Exploring sociocultural perspectives on race, culture, and learning. Review of Educational Research, 76(4), 449-475.
Nasir, N.I.S., and Saxe, G.B. (2003). Ethnic and academic identities: A cultural practice perspective on emerging tensions and their management in the lives of minority students. Educational Researcher, 32(5), 14-18.
National Research Council. (2007). Taking Science to School: Learning and Teaching Science in Grades K–8. Washington, DC: The National Academies Press.
National Research Council. (2009). Learning Science in Informal Environments: People, Places, and Pursuits. 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.
Nicholls, J.G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91(3), 328.
Nolen, S.B., and Ward, C.J. (2008). Sociocultural and situative approaches to studying motivation. Advances in Motivation and Achievement, 15, 425-460.
Osborne, J., Simon, S., and Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079.
Ottinger, G. (2016). Social movement-based citizen science. In D. Cavalier and E.G. Kennedy (Eds.), The Rightful Place of Science: Citizen Science (pp. 89-104). Tempe, AZ: Consortium for Science, Policy and Outcomes.
Palmgreen, P. (1984). Uses and gratifications: A theoretical perspective. Annals of the International Communication Association, 8(1), 20-55.
Pandya, R.E., Henderson, S., Henderson, R.A. Anthes, and Johnson, R.M. (2007). BEST Practices for Broadening Participation in the Geosciences: Strategies from the UCAR Significant Opportunities in Atmospheric Research and Science (SOARS) Program. Journal of Geoscience Education, 55(6), 500-506.
Perry, W.G. (1970). Forms of Intellectual and Ethical Development in the College Years: A Scheme. New York: Holt, Rinehart and Winston.
Pierotti, R., and Wildcat, D. (2000). Traditional ecological knowledge: the third alternative. Ecological Applications, 10(5), 1333-1340.
Pintrich, P.R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92(3), 544.
Pintrich, P., and Schunk, D. (1996). Motivation in Education: Theory, Research and Applications. Englewood Cliffs, NJ: Prentice-Hall.
Rahm, J., Miller, H.C., Hartley, L., and Moore, J.C. (2003). The value of an emergent notion of authenticity: Examples from two student/teacher–scientist partnership programs. Journal of Research in Science Teaching, 40(8), 737-756. doi: 10.1002/tea.10109.
Rawson, K.A., and Dunlosky, J. (2011). Optimizing schedules of retrieval practice for durable and efficient learning: How much is enough? Journal of Experimental Psychology: General, 140(3), 283-302.
Renninger, K.A. (2000). Individual interest and its implications for understanding intrinsic motivation. In Intrinsic and Extrinsic Motivation: The Search for Optimal Motivation and Performance (pp. 373-404). San Diego, CA: Academic Press.
Renninger, K.A. (2010). Working with and cultivating interest, self-efficacy, and self-regulation. In D. Preiss and R. Sternberg (Eds.), Innovations in Educational Psychology: Perspectives on Learning, Teaching and Human Development (pp. 107–138). New York: Springer.
Renninger, K.A., and Hidi, S. (2002). Student interest and achievement: Developmental issues raised by a case study. In A. Wigfield and J. Eccles (Eds.) Development of Achievement Motivation (pp. 173-195). Cambridge, MA: Academic Press.
Renninger, K.A., and Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168-184.
Rogoff, B. (2003). The Cultural Nature of Human Development. Oxford, UK: Oxford University Press.
Rowland, C.A. (2014). The effect of testing versus restudy on retention: A meta-analytic review of the testing effect. Psychological Bulletin, 140(6), 1432-1463.
Salmon, E. (1996). Decolonizing our voices. Winds of Change. Summer, 70-72.
Sansone, C. (2009). What’s interest got to do with it?: Potential trade-offs in the self-regulation of motivation. In J.P. Forgas, R. Baumeister, and D. Tice (Eds.), Psychology of Self-Regulation: Cognitive, Affective, and Motivational Processes (pp. 35-51). New York: Psychology Press.
Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32(1), 102-119.
Schoon, I. (2001). Teenage job aspirations and career attainment in adulthood: A 17-year follow-up study of teenagers who aspired to become scientists, health professionals, or engineers. International Journal of Behavioral Development, 25(2), 124-132.
Schwarz, R.S., Lederman, N.G., and Crawford, B.A. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science Education, 88(4), 610-645.
Schwartz, C.V., Reiser, B.J., Davis, E.A., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., and Krajcik, J.S. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654.
Sconiers, Z.D., and Rosiek, J.L. (2000). Voices inside schools-historical perspective as an important element of teachers’ knowledge: A sonata-form case study of equity issues in a chemistry classroom. Harvard Educational Review, 70(3), 370-405.
Smith, C.L., Maclin, D., Houghton, C., and Hennessey, M.G. (2000). Sixth-grade students’ epistemologies of science: The impact of school science experiences on epistemological development. Cognition and Instruction, 18(3), 349-422.
Soderstrom, N.C., and Bjork, R.A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176-199.
Stevens, R., and Hall, R. (1998). Disciplined perception: Learning to see in technoscience. Talking Mathematics in School: Studies of Teaching and Learning, 107-149.
Stewart, J., Cartier, J.L., and Passmore, C.M. (2005). Developing understanding through model-based inquiry. In M.S. Donovan and J.D. Bransford (Eds.), How Students Learn: Science in the Classroom (pp. 515-565). Washington, DC: The National Academies Press.
Strike, K.A., and Posner, G.J. (1982). Conceptual change and science teaching. European Journal of Science Education, 4(3), 231-240.
United Nations Environment Programme. (1998). Report on the Fourth Meeting of the Parties to the Convention on Biodiversity, UNEP/CBD/COP/4/27. Nairobi, Kenya.
Wigfield, A., Schiefele, U., Eccles, J., Roeser, R.W., and Davis-Kean, P. (2006). Development of achievement motivation. In W. Damon and N. Eisenberg (Eds.), Handbook of Child Psychology: Social, Emotional, and Personality Development (6th ed., vol. 3). New York: Wiley.