This special issue of the Journal of Research in Science Teaching seeks to explore conceptualizations of culture that address contemporary challenges in science education. Toward this end, we unite two theoretical perspectives to advance a conceptualization of culture as a complex system, emerging from iterative processes of cultural bricolage, that is, a cyclic (re)application, and adaptation via approximation, of cultural tools to contexts as they uniquely arise. Our goal here is to imbricate two distinct theoretical frameworks, Balkin's (1998) theory of cultural software and complexity thinking (e.g., Bar-Yam, 2004; Davis & Sumara, 2006; Doll, Fleener, Truit, & St. Julien, 2005; Gleick, 1987; Mason, 2008), to provide a novel way to think about persistent issues in education. In so doing, we are engaging in exactly the process of cultural bricolage we describe: we are (re)applying existing ideas in new ways and contexts and thereby constructing new tools for understanding. The frameworks we draw from are each highly developed in their own right and cannot be fully described or explored in the space available in the present venue. While we attempt to represent these frameworks as faithfully as possible, we acknowledge that each is much more substantial and nuanced than our presentation here. Our goal is to articulate a view of culture as an emergent system, which we believe provides a powerful way of understanding the development and evolution of culture as well as its dynamical1 nature. Thus the novelty of the perspective we present here is not so much in the ideas themselves—most of which have been described previously—but in our recasting of them.
As recently as a decade ago, Eisenhart (2001) indicated that, in science education, culture was rarely conceptualized and ideas about culture or cultural issues had apparently not influenced directions for reform. While considerations of culture were not altogether absent from science education prior to that time (see, e.g., Cobern and Aikenhead, 1997), Eisenhart's assertion highlights the reality that “culture” has historically been on the margins in science education. A growing body of literature now addresses issues of culture, from diverse theoretical perspectives. However, such considerations remain peripheral to the dominant discourse in the science education community. For example, the National Association for Research in Science Teaching (NARST), which purports to be a leading, worldwide organization committed to the improvement of science teaching and learning through research, is presently organized around 15 “strands”—only one of which is focused on culture (Strand 11: Cultural, Social, and Gender Issues). While the other 14 strands by no means exclude culture or cultural considerations, the explicit reference to culture within only a single strand communicates that culture is a separable element of science education and that it is merely one among many distinguishable elements of teaching and learning. An argument we advance here is that, as a community, we will not understand the teaching or learning of science until we attend to culture.
While no single definition of culture is supported by the literature (Mulcahy, 2006), Buxton (2005) and Eisenhart (2001) describe a historical progression, in the broader work of educational anthropologists and sociologists, from perspectives based on cultural deficit models (i.e., attempts to explain gaps such as those seen in achievement on standardized tests in terms of inherent inferiorities of girls, racial minorities, lower SES students, etc.) to cultural difference models (i.e., recognition that different groups' cultures have been shaped by historical and contextual influences, but that none is inherently superior). A recognition that these largely homogenizing ways of conceptualizing culture do not adequately account for porous boundaries between cultural groups or substantial individual differences within them has led to an emergence of critical perspectives that separate conceptualizations of culture from social groups and look instead to ways individuals' values, beliefs, and actions are shaped by cultural experiences (see also Carlone, 2004). We see this progression as representing a shift from a top-down understanding of culture—as a supra-individual set of shared norms, values, actions, etc., that is, conveyed to, and acquired by, new members of a social group—toward a recognition of ideological influences on the development of culture and emphases on processes of cultural (re)production by individuals acting within cultural contexts.
This shift can be seen in science education in the growing body of literature taking up socio-cultural issues. Science has been recognized as a culture—or rather a sub-culture, within Western traditions (Cobern & Aikenhead, 1997). One thrust in recent research has been to expose ways that systems of power and privilege work to limit access of some groups, whose cultural backgrounds and practices differ from those of school science (Barton and Yang, 2000; Brown, 2004; Solomon, 2003). Another thrust has been to recognize the diverse cultural perspectives learners bring to the science classroom as assets to learning (Chinn, 2006; Richmond & Kurth, 1999; Warren, Ballenger, Ogonowski, Roseberry, & Hudicourt-Barnes, 2001) and alter curricular and instructional approaches to be more commensurate with cultures of diverse student groups (Buxton, 2005; Parsons, 2008; Tobin, 2006). While many such attempts have shown positive outcomes for groups historically marginalized in science and science education, another line of research points to more complex, underlying socio-cultural issues that have yet to be addressed. Even in cases where attempts at more equitable science instruction seem to have led to narrowing or elimination of achievement gaps, students' negotiation of school/science norms and values has been shown to reproduce barriers to authentic engagement of some individuals in science (Carlone, 2004; Carlone, Haun-Frank, & Webb, 2011). A need exists for a framework to draw together findings from studies such as these, which have employed diverse perspectives on culture as it applies to science education and in so doing have uncovered additional layers of complexity within science teaching and learning. Our framework meets this need, as it understands culture to be simultaneously an individual, group, and system phenomenon.
While the aforementioned studies point to injustices in science education that clearly need to be addressed, van Elijck and Wolff-Roth (2011), suggest that looking only at the experiences of marginalized groups may distract from the underlying sources of problems. They suggest that it is the mundane science education practices that give rise to school science and create barriers to science learning for so many students. Our position here is very similar. However, where they employ a Bakhtinian perspective to theorize the epicizing effect of language and discourse in science, we present a perspective that incorporates additional, essential tools of cultural understanding. Furthermore, in characterizing culture as an example of emergence, we believe our perspective allows for highly similar local cultures to arise in disparate contexts. This last point is important in understanding similarities in student culture across widely varying contexts (Wood, Lawrenz, & Haroldson, 2009). Thus we see the perspective we present here as building on the recognition of the many ways that school science tends to marginalize those groups historically underserved in science and science education to advance an argument that school science tends to marginalize all students.
Balkin (1998) invokes the metaphor of software to describe his theory of culture. Rather than presenting culture as a unitary, supra-individual, or even coherent entity, he argues that, cultural groups are populations of people who possess relatively similar collections of cultural information or know-how. We, as individuals, are constituted by our cultural information, which grows and evolves through interactions with other people. This cultural information can be thought of as cultural software. While we do not claim that the human brain can be adequately understood to be like any existing computer (Balkin is clear on this point as well), the ubiquity of personal computing devices in contemporary society makes the metaphor particularly salient and accessible. In fact, we would argue that, given the exponential growth of networking capabilities, the software metaphor is even more apropos now than when it was advanced in 1998. It illuminates how cultural software enables and constrains the ways individuals can think and behave, much like the software loaded onto a computer determines how, and how effectively, that computer can operate: a computer with the most powerful processer available, if loaded only with a card game, can only be used to play that card game. However, like any metaphor, it is imperfect, so in the following paragraphs, we hope to delimit the ways and extent to which it can be applied to culture.
Four supporting concepts help to explicate the theory of cultural software. First, cultural software comprises a set of tools of understanding; tools we use to negotiate and make sense of our experiences and to communicate with other people. Second, these tools are heuristics. None is perfectly designed and different tools are better suited to certain tasks than others. Third, these are tool-making-tools, which evolve through use. Fourth, cultural know-how is symbiotic with individuals and is passed from person to person, sometimes deliberately, but often without conscious intention or choice. As Balkin puts it: “ideas have us as much as we have them” (p. x). There are profound ideological implications in this quote, which Balkin discusses at length, but we are only able to touch on here (in our discussion of implications for science education), due to limitation of space.
To describe cultural software as tools of understanding invokes a broader definition than the term “tool” often connotes. Cultural software is not the typical tool that exists separately from the user and can be taken up or discarded at will. To be human is to embody cultural know-how within our current historicity. Tools of understanding are not mere technologies or instruments apart from ourselves that we can apply to our world. Their use is not merely a rational or technical exercise. Rather, tools of understanding are the most basic functions with which we can make meaning and generate understanding within our ever-changing world—and without which we have no understanding, no cultural know-how, no perspective. While conventional conceptions of culture might position the clay pot as a prototypical human tool, our perspective understands language as the quintessential cultural tool (not unlike the position advocated by van Elijck & Wolff-Roth, 2011).
Heuristics are cognitive functions serving as aids to or involving learning, discovery, or problem-solving. They both enable and constrain our thinking capacities. The heuristic nature of cultural software allows for adaptation and use in new and unforeseen ways. The metaphor of bricolage is central here: just as the bricoleur uses whatever tools lay to hand (e.g., if a hammer is unavailable, a wrench can be used to pound a small nail into a wall to hang a picture), individuals can only apply tools of understanding they have at their disposal. When faced with novel contexts, individuals are able to apply existing cultural tools in new and unexpected ways. However, cultural bricolage necessarily results in cultural software being (re)applied in contexts in which it is imperfectly suited. Thus, we do not simply acquire our culture, we become cultural entrepreneurs who produce new ideas, knowledge, and know-how within changing contexts which will help constitute future generations of human beings.
A consequence of bricolage is that cultural software is an expanding collage of heuristics: we continue to build the capacities of our cultural tools with the tools we presently have. These tools are adapted through a series of more and less successful heuristic (re)application and, as in evolutionary processes, are comprised of the vestigial developments of cultural emergence. We use and adapt the tools we currently have as best as we can to solve our current problems, but since we can only use those cultural tools that already lay to hand, the new can only arise from the old. Gould (1992) points to vestigial structures, such as the panda's thumb as evidence of the designoid nature of biological evolution rather than a priori design. That is, while a snapshot of a (biological) species at any given point in time shows highly ordered characteristics and relationships that appear to have been thoughtfully designed, longitudinal examination of the broader ecology points to underlying (bottom-up) processes being drivers of selection rather than top-down design. Likewise, human culture is necessarily imperfect exactly because it is an evolutionary process: the stability and behaviors of cultural groups are markedly designoid. It is adaptable to changing environments precisely because it is imperfect.
While our discussion has, thus far, focused mainly at the individual-level, culture becomes a social phenomenon through communicative interactions. Learning is a process that is necessarily symbiotic between the individual and their collective memberships. Communication is necessary for social understanding to occur, and understanding occurs only through the symbolic tropes and tools for social understanding that are readily available. The most basic building blocks of social understanding, and thus learning, are language and heuristics (homologies/dialectics, metaphors, narratives, etc.) that individuals share within their cultural groups. Thus, these heuristics are more than individual tools of understanding: they are shared cultural heuristics. These are constitutive of both individuals and cultures: they are co-evolutionary. The power of such heuristics is that they are transmitted through social learning or communication; they are capable of being easily stored in the memory of many individuals; and they are commonly used to reason about the social world. Balkin highlights this symbiosis, saying:
“To exist as a historical being is to have a set of tools available to hand that are the legacy of the past. Existence in human history (as opposed to the natural existence of a mountain or a glacier) is existence in culture. It means one is composed of cultural heuristics shared by others who are similarly constituted by them” (p. 183).
Widely shared cultural heuristics are necessary for cultural groups to function, because they enable efficient communication. For example, scholars commonly share scripts for acknowledging intellectual contributions of colleagues by explicitly referring to them via citations in manuscripts such as this one. The meaning and implications of in-text citations are widely understood by members of our cultural group and it is therefore unnecessary for us to explicitly explain what we mean by them. The somewhat counter-intuitive implication here is that, to make explicit those elements of cultural software that are widely shared by groups is often an indication of a lack of cultural know-how. For example, if we were to include explicit discussion explaining why we include citations in various places throughout this text, the reader is likely to infer that we are unfamiliar with scholarly writing (or that we are being obnoxious). Therefore, cultural software often enables communication by comprising those things that literally go unsaid.
However, the unspoken nature of shared cultural heuristics bears negative consequences as well. Just as cultural software enables and empowers understanding, it also exercises power over individuals. While communicative interactions sometimes lead to changes in cultural software that are conscious and consensual, they frequently occur without intent or awareness. Thus cultural software can, in some cases, be thought of as a virus, which goes undetected as it infiltrates a host and proceeds to reproduce itself through subsequent bricolage and communicative interactions. As Balkin puts it:
“To risk understanding is to risk change through understanding, and there is no guarantee that the change will not in some cases be for the worse.” (p. 275).
While this understanding of communicative interactions helps to explain how individuals come to share cultural software, the process of cultural bricolage must be highly idiosyncratic owing to unique individual histories. Therefore, as cultural software is communicated from one individual to another, it is understood and modified in highly unique ways such that individuals construct unique sets of cultural skills, tools, and “know-how.” As Balkin points out, “there are no identical cultural twins” (p. 49). If this is so, what accounts for observations of widely shared cultural expressions such as ways of being, thinking, doing, particular skill sets, norms, beliefs, attitudes, behaviors, values, knowledge, perennial wisdom, common metaphors, stories, narratives, histories, artifacts, rituals, procedures, etc. among social groups large and small (Cole, 1996; Ferraro, 2008; Mulcahy, 2006; Storck, 2009; Taras, Rowney, & Steel, 2009)? Balkin appeals to the notion of memes (Dawkins, 1976), as a parallel idea to his notion of cultural software, in order to build on the metaphor of biological evolution to explain the spread and development of culture. Such a “memetic evolution” perspective posits that those memes (cultural software) which provide an individual or group with a competitive advantage in its present environment are most likely to survive and propagate. While this metaphor has some explanatory purchase, we believe ideas drawn from complexity thinking (Davis and Sumara, 2006) better explain cultural expressions in social groups. In particular, the concept of emergence provides a powerful means for understanding how such expressions come to be shared among diverse members of a cultural group through a highly individualized, bottom-up process of cultural bricolage.
Complexity has been hailed as a new science (Doll et al., 2005; Gleick, 1987/2008; Sanders, 1998). However, its core presuppositions predate modern science (Meadows, 2008) and its implications span numerous disciplines. Therefore, following the lead of Davis and Sumara (2006) we see complexity as a theoretical and philosophical mindset or paradigmatic perspective and therefore discuss it as “complexity thinking” about systems and their parts.
Following distinctions originally characterized by Weaver (1948), Davis and Sumara (2006) distinguish among three types of systems: simple, complicated, and complex. Simple systems are those with few interacting variables (such as a ball falling through a vacuum, near the surface of the earth) and are therefore straightforwardly interpretable. The reductionist drive of western2 modern science attempts to understand these and other types of systems by identifying influential components/variables, then isolating and controlling those variables in order to study (linear) causal relationships (often seeking relationships between one cause and one effect). This is often possible with complicated systems as well because, while such systems involve a larger number of variables, those variables interact pairwise, in a causal chain (such as the inner-workings of a mechanical clock). Therefore, one can examine pairs of interacting variables in a complicated system and piece together an understanding of the whole from its parts. In contrast, understanding of complex systems defies reductionism, because its components and variables interact nonlinearly and within/through networks. Thus, it is impossible to understand relationships between two variables or components without also understanding their positioning within broader systems. Complexity typically arises when interconnected, and interdependent components of a system also give rise to adaptation, with patterns and relationships emerging across time. Thus, diversity contributes to the robustness of a complex system (Page, 2011). In short, complexivist thinking recognizes simultaneous transform-ing interplay among multiple components manifesting in often transitory linear and/or nonlinear relationships.
Through the lens of complexity thinking, the theory of cultural software can be understood to describe culture as a complex system. It frames individuals as being, at the same time, independent and interdependent as each uniquely engages in a process of cultural bricolage potentiated by communicative interactions with networks of other individuals. Diversity in the system arises from the idiosyncratic histories of these individuals which, along with the heuristic nature of cultural software, allows for ongoing adaptation of the system. In other words, because individuals in a cultural group mutually influence each other's cultural software through ordinary communicative interaction, it is from the basic human occupation of communication, of instantiating cultural software, that stable cultural groups arise. Yet, at the same time, the robustness of a cultural group depends heavily upon the diversity of the cultural software of its individual members. Culture is a complex, dynamical system.
To suggest that culture is a complex system is certainly not without precedent (see, e.g., Bednar & Page, 2007; Doll et al., 2005). However, from a cultural software perspective we see crucial explanatory power in a phenomenon commonly referred to as emergence or self-organization for understanding the existence and robustness of cultural groups.
A self-organizing system is autocatalytic, that is, has the ability to evolve itself from within. “In this process local circumstances dictate the nature of the emerging self-organization: it is a “bottom-up” process” (Morrison, 2008, p. 17). Systems such as networks of interdependent, but autonomous, individual agents frequently self-organize in such a way that they demonstrate macro-level organization despite not having any central planning, organizing, or control mechanism. This phenomenon is seen in numerous processes in nature such as crystal growth, movements of flocks of birds, or synchronization of fireflies. To illustrate how exquisitely intricate macro-structures can emerge from elegantly simple underlying behavior, we offer the example of the “Chaos Game” (Barnsley, 1988) in Figure 1. This particular version of the “Chaos Game” produces a figure called the Sierpinski triangle, which displays surprising complexity for having been built-up from such a simplistic process. Similar iterative processes, employing different sets of rules can be followed to generate a wide variety of fractal images: ferns, trees, clouds, and even realistic landscapes.3
Figure 1. “The Chaos Game” (Barnsley, 1988): Three points are arranged on a sheet of paper to form a triangle. A small dot is drawn anywhere on the paper and then the following steps are repeated many times: (1) one of the three original points is randomly chosen (e.g., by means of rolling a die); (2) a new dot is added to the sheet of paper half way between the previous dot and the chosen point. Pictured above are the result of the iterating the process 1,000 (a), 10,000 (b), and 100,000 (c) times. As is typical in this iterative process, the first 10 dots have been erased from each image as it takes a few iterations for the “orbit” of the point to approach the attractor visually close enough to reproduce the shape of the Sierpinski triangle.
Download figure to PowerPoint
We suggest that culture is an emergent system: that cultural expressions such as shared norms, values, beliefs, etc. emerge from instantiation of shared cultural software: those mundane, taken-for granted (often viral) tools of understanding shared by interdependent individuals. To be clear, while we see the “Chaos Game” as a useful entry point into thinking about self-organization and how sophisticated macro-structures can emerge from simple and seemingly unrelated behaviors at smaller scales, the heavily deterministic, rule-bound behaviors of this particular iterative process make it a somewhat limited exemplar of emergent systems. We do not claim that culture is a fully deterministic system: that human actors uncritically apply rigid cultural rules. (Nonetheless, it is worth noting that a substantial corpus of research in cognitive psychology suggests that human agents do rely on fundamental heuristics and biases in processing immediate perceptions of environments/events—for a thorough and accessible review see Kahneman, 2011). What we do claim is that, because much communicative interaction occurs unconsciously, individuals are often unaware of many cultural tools they employ. And we argue that it is the unconscious application of this taken-for-granted and seemingly mundane cultural software that enables and constrains individuals' behavior in ways that lead to the emergence of seemingly shared cultural expressions.