• cultural software;
  • complexity;
  • emergence;
  • science education


  1. Top of page
  2. Abstract
  3. Cultural Emergence
  4. Implications: Further Theoretical Development, Policy, and Research in Science Education
  5. Notes
  6. References

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. This conception of culture provides a means for transmission of culture from one individual to another, but it also allows for substantial diversity of individual perspectives within a cultural group in ways that supra-individual conceptions of culture do not. What is more, this diversity of individuals provides a mechanism for cultural evolution and simultaneously allows for an individual to be shaped by culture and culture to be shaped by the individual. Culture, in this conception, is understood to be an emergent phenomenon, built up from iterative application of cultural software. Implications are considered with respect to further development toward a theoretical framework; educational research agenda and methodology; and education policy—particularly in the context of science education. © 2012 Wiley Periodicals, Inc. J Res Sci Teach 50:122–136, 2013

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.

Cultural Software

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 Thinking

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

thumbnail image

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.

Cultural Emergence

  1. Top of page
  2. Abstract
  3. Cultural Emergence
  4. Implications: Further Theoretical Development, Policy, and Research in Science Education
  5. Notes
  6. References

The perspective we have been constructing understands culture as an emergent system arising from the iterative instantiation of cultural software. While Balkin's (1998) theory of cultural software and complexity thinking are each powerful frameworks in their own right, neither is new. The novelty in the perspective we are proposing here is in the imbrication of ideas drawn from those two existing frameworks.

Others have conceptualized culture, in science education, as processes involving individual (agentic) activity enabled and constrained by social structures and forms (e.g., Buxton, 2005 and Carlone, 2004). In particular, Balkin's conceptualization of cultural software is in many ways like Bourdieu's (1977, 1985, 1987) notion of habitus. The concept of habitus enunciates a “second sense,” generated through the social position in which a person was born and lives. For Bourdieu, individuals are embedded in the social world, and the social world has some influence over the individual and this can be reflected in the choices individuals make. In a similar way, our proposition of cultural emergence aims to show the ways in which the social world is literally incorporated into the self: what/who individuals become is the result of their social settings (the field). However, with its emphasis on ways the individual is shaped by the field, Bourdieau's habitus still implies a top-down vision of culture. Balkin's presentation of cultural software, on the other hand, better facilitates an understanding of culture as a bottom-up process, which is easily imagined in our world of ubiquitous media influence. Consequently, cultural software more naturally connects to ideas of emergence.

Emergence is not a new concept in science education either. Others have theorized aspects of human behavior as emergent processes (e.g., Roth & Duit, 2003). However, to understand culture as an emergent process requires a mechanism whereby the behaviors of independent and interdependent agents give rise to stable cultural expressions at the group level. Cultural software theory provides such a mechanism in the process of cultural bricolage as it is potentiated by communicative interactions among individuals.

In short, viewing cultural software theory through the lens of complexity thinking, we see it as describing a complex system. Viewing complexity thinking from a cultural software lens, we see emergence as a key concept in understanding the bottom-up process of constructing cultural software. Thus, imbricating ideas from these two frameworks gives us a powerful perspective on culture that simultaneously understands it as an individual, group, and system phenomenon.

Revisiting Extant Literature With a Cultural Emergence Lens

We began by suggesting that the novelty in our framework was not so much in the ideas we have presented as it is in our recasting of them: that we have been engaging in cultural bricolage by (re)applying existing ideas to constructing new tools for understanding. The result, we believe, is a new perspective for viewing culture that harmonizes with and potentiates existing ideas relating to culture in science education. For example, we see conceptualizations such as repertoires of practice (Parsons, 2008) and everyday sense-making practices (Warren et al., 2001) as being essentially types of cultural software. At the same time, we see van Elijck and Roth's (2011) account of the epicization of science as a shared narrative that scripts scientists in a heroic role—one not attainable to the vast majority of students. Said another way, epic narratives of science script students' unique sets of cultural software as irrelevant in science education. Thus we see the call for novelization of science education as being akin to calls from others to recognize the diverse cultural perspectives learners bring to the science classroom as assets to learning (Chinn, 2006; Richmond & Kurth, 1999; Warren et al., 2001), foregrounding a shared narrative that scripts all students as potential contributors in science. The power in such a shift derives from the emergent nature of culture because seemingly small changes at the individual level can have radical consequences for complex systems. Thus the cultural emergence framework provides a way to draw together findings from previous studies in science education, which have employed diverse perspectives on culture.

As an example of the subtly radical shift we envision for science education, we point to the results of a recent study by Carlone et al. (2011). They examined two, U.S., 4th-grade classes, situated in similar contexts and taught by teachers similarly committed to reform-based teaching practices, whose students ultimately performed similarly in terms of developing understanding of science ideas. However, marked differences developed, between the two classes, in students' perceptions about who could be a “smart science student.” In one class, African American and Latina girls, in particular, expressed outright rejection of science identities for themselves, while in the other class, science identities were more equitably available to all members of the class. The authors of the study attributed this disparity to subtle differences in the instructional behaviors of the teachers. During cooperative problem-solving activities, students in the first class deferred to the member of the group recognized to most often know answers to science questions and the teacher essentially accepted that person spoke for the group. In contrast, the teacher of the second class routinely insisted that all members of a given group must understand and agree to it before she would accept an answer from any member of that group. Students in the first class came to identify a “smart science person” as one who knew answers to science questions, whereas students in the second class came to identify a “smart science person” as one who was able to communicate ideas effectively. From the perspective we have advanced herein, we would argue that the second teacher communicated subtly, yet profoundly different cultural software to her students that radically altered their understanding of a “smart science student” and, in so doing, radically altered the science identities available to many of them. As is typical in the evolution of cultural software, students did not seem to be consciously aware of the new narrative their teacher was shaping—nor did this micro-scale change seem particularly revolutionary. However, as is typical of emergent systems, the change had enormous implications beyond the individual students (i.e., across other scales), altering the cultural behavior of the entire group of students and dynamically altering individuals' science identities. Such change, while seemingly small, has the potential to have much broader impacts as it compounds over time as these students will take this new cultural software to subsequent science experiences and continue to shape their culture and identities. This study epitomizes the subtly radical paradigm shift we are advocating: in recognizing the interplay of individuals' cultural software and shared cultural expressions of the group, we see hope for meaningful cultural change at individual, group, and systemic levels in science education.

Ideological Effects

Conspicuously missing from our discussion so far is overt consideration of how systems of power and privilege operate within our conception of culture to produce ideological effects. Our choice not to foreground such issues in the present work should not be interpreted as discounting the centrality of ideological effects in culture. On the contrary, we believe consideration of such is essential for further development of our conceptualization toward a robust theoretical framework for understanding culture—and this is a primary reason we have languaged our presentation as a “perspective” thus far. Balkin (1998) argues that cultural software is neither inherently oppressive nor empowering. Rather, he takes an ambivalent view, arguing that cultural software and circumstance work together to produce ideological effects. In other words, ideological effects are in the instantiation of cultural software, not in the software itself. In the space available in this venue, we cannot fully unpack the implications of this view. However, we point to one example we see as an ideological effect in extant science education literature that we see as marginalizing all students: the culture of “dealing.”

Wood et al. (2009) have described student culture as one of “dealing.” In a study involving nearly 7,000 middle and high school science students, spread across 271 teachers' classrooms, situated within 135 schools, in 39 U.S. states, they demonstrated that components of student culture, such as values and ways of seeing schooling and the classroom, are consistent and stable across schools, throughout the U.S. What is more, they found substantially greater within-school variation, on all measures, than between-school variation, which they interpreted to suggest that the full range of diversity represented within this cultural group is present in any given school. These same patterns held for teachers as well, but the nature of teacher culture was clearly very different from the nature of student culture. Whereas teacher culture was apparently about preparation (i.e., of students for the future), student culture was about jumping through hoops: school is just something with which one has to “deal.” We see this as profoundly dehumanizing for students and thus evidence of cultural software being instantiated in such a way as to produce ideological effects.

We speculate that a factory narrative comprises part of the cultural software that gives rise to this culture of “dealing.” Others have discussed the ubiquity of the factory model in U.S. education (see for example, Pinar, Reynolds, Slattery, & Taubman, 2008). However, instead of seeing the factory model as an ideology in and of itself, imposed on students (and teachers) from the top down, we see it as arising from shared cultural software. In particular, Balkin (1998) emphasizes, narratives are powerful sources of cultural know how, because they are scripts that provide roles for the various actors and the factory narrative is a quintessential example of such. This narrative scripts students as the developing products and teachers as production workers in an educational formation process. Each teacher has a place on the line and a designated set of parts (i.e., knowledge and skills) to add to each student as he/she passes on the line. Thus a teacher's job is then to contribute to preparing students to be finished products when they reach the end of the assembly line. Graduation is akin to the factory rolling out its newest model—the expectation being that students will be fully assembled when they reach that point. Importantly, this educational formation process treats students as essentially inert and incomplete beings, constantly in need of monitoring to ensure efficiency in continued formation. In short, students must deal with life on the assembly line until they either reach its end or are discarded from it. As students (and teachers) construct their roles in light of the factory narrative, “dealing” is (re)produced as a shared cultural expression. Furthermore, we would argue that the reproduction of “dealing” is uncontested largely because, as indicated above, culture is rarely considered in the mainstream of science education.

To be clear, we do not assume the factory metaphor encompasses all types and aspects of cultural software that give rise to “dealing.” In describing this cultural expression as arising from instantiation of shared cultural software, we believe we can account for the empirical evidence that points to its existence in a way that top-down conceptions of culture cannot. However, this characterization begs questions about how such cultural software comes to be shared among students. To suggest that cultural software evolves via communicative interactions, often virally, and to suggest that cultural expressions emerge from a confluence of shared cultural software highlights a potentially insidious mechanism for hegemonic infiltration into seemingly mundane, human interactions. Is the factory narrative central to the emergence of “dealing” as we have speculated? We believe it is plausible, but empirical evidence is needed in order to confirm or disconfirm this claim. If the factory narrative is widely shared cultural software, from whence does it evolve? How is it communicated to students (by peers, teachers, parents, media, etc. …)? How do students construct their roles? Why do some students (e.g., white males) appear to be more successful at “dealing?”

Implications: Further Theoretical Development, Policy, and Research in Science Education

  1. Top of page
  2. Abstract
  3. Cultural Emergence
  4. Implications: Further Theoretical Development, Policy, and Research in Science Education
  5. Notes
  6. References

We have focused on emergence as a critical concept in our understanding of culture. However, complexity thinking offers a number of other ideas with important implications for culture and education (Doll et al., 2005; Page, 2011; Morrison, 2008). For example, understanding complex systems often requires a holistic perspective including multi-scalar considerations. That is, features of such systems often manifest or connect across multiple levels. While we have chosen to focus on the interplay between constructions of cultural software at the individual level and emergent cultural expressions at the level of social groups, we expect culture, as a complex system, to demonstrate a multi-scalar nature, thus we expect there are connections to be made to other levels as well. For example, we see the factory metaphor scripting roles for educators and constructions of educational ideas at multiple levels we have not considered here. It scripts policy-makers as executives who must reduce and organize the process to simpler steps (i.e., scope and sequence). Process managers (administrators) are then needed to further reduce complication (personnel assignments) and monitor phases of the process. The magnitude of the educational formation process requires an enormous investment of resources. Therefore, in the interest of decreasing waste, quality control measures (achievement testing) provide a means for ongoing monitoring of development. Failing quality control measures are indicators of defects in either processes (poor schools), the production workers (poor teacher(s)), or materials (poor students) requiring either repair/modification or rejection. Thus, the factory narrative is built of the confluence of ingrained scripts for dealing with and assigning blame to failing schools, teachers, and students—so the present accountability movement can be seen as a natural outgrowth. This also plays well into the framing of the national level policy narrative of global market competition and the demand for better assembled students, workers, schools, and product delivery systems. In these terms, the search for so-called “best practices” (optimization) makes perfect sense, as does emphases on accountability (quality control), and competition-inducing policies such as school voucher programs (incentive strategies) as means of increasing productivity and better assembled products through more efficient processes.

With respect to implications for science education policy and reform, the recognition of culture as an example of emergence has important implications. Complex systems are robust and adapt to resist perturbation precisely because they emerge from the bottom-up. Thus systemic change cannot be affected solely by top-down policies or practices. We attribute the failures of so many past efforts at reform to a focus on essentially superficial factors without attention to underlying issues (Wood et al., 2009), because most have been based on research operating from a paradigm that aims to render complex educational systems simple through atomistic reductionism. While it is worthwhile to investigate individual components of educational systems, those systems cannot be understood without simultaneously considering the whole. Educational systems must be understood differently: complexly. Just like the seemingly minor practices of one teacher can give rise to radical cultural shifts in a whole classroom (Carlone et al., 2011), science education policy makers must attend to the cultural software that gives rise to systemic issues. Meaningful changes emerge from the bottom-up.

We see broader sociological contexts, from outside the immediate context of schooling, as additional levels relevant to the complex system we have been discussing. For example, Willis (1977) has documented ways the context and circumstances of schooling work together with students' acts of resistance, arising from their cultural experiences at home and work, to reproduce working class culture. Similarly, Brown (2004) has documented ways minority students' peer and home cultures can influence language practices, thereby facilitating or inhibiting expressions of science identities. It is clear that cultural software shared among wider U.S. society, such as racism, sexism, and classism, are instantiated in science classrooms. However, in recognizing that culture is a dynamical system, we have a profound sense of responsibility in knowing that the process of cultural bricolage goes both ways: as science educators we are also complicit in the propagation of racist, classist, and sexist (and we would add ableist, homophobic, ageist, etc. …) cultural software.

For sake of clarity, we have delimited our focus here to describing cultural expressions as arising from shared cultural software. In so doing, we have not fully explored the dynamical nature of this relationship. In particular, we have not explored the impact groupwise cultural expressions have on individual constructions of cultural software. We see the instantiation of cultural software to be in dynamical relationship to individual identity development. A thorough unpacking of work around identity in science education (e.g., Brown, 2004, 2006; Carlone, 2003, 2004; Johnson, Brown, Carlone, & Cuevas, 2011) is beyond the scope of our present purpose. However, we acknowledge this connection to illustrate a way we see the multiscalar nature of complex systems extending to finer levels in science education as well.

While we have no interest in rehashing tired methodological debates (Paul & Marfo, 2001), we will note that an implication of seeing culture as a complex process suggests that the agenda and methodologies of science education researchers must attend to multiple levels of educational systems in order to develop robust understanding of them. Much research over the past several decades has focused on determining underlying mechanisms of individual cognition (Bransford, Brown, & Cocking, 1999). The perspective we have been building here suggests that knowledge about individual-level cognitive functioning is needed in thinking about education, but can only be understood to the extent that it is considered within the broader system of cultural evolution. Current policies in the U.S. position experimental research (in particular, designs employing randomized, controlled trials) as a “gold-standard” for research (No Child Left Behind Act, 2001) and we continue to see suggestions that such designs are needed where research findings have been judged to be inconclusive (Kirschner, Sweller, & Clark, 2006). While experimental-type studies can provide worthwhile knowledge about components of educational systems or individual learning, to privilege knowledge so-gained threatens a robust understanding of education and learning. To illustrate this point, we return to an example alluded to above: firefly synchronization. Certain species of fireflies, in certain locations around the world, have long been known to synchronize their light displays (Buck, 1935). That is, groups of hundreds or thousands of individual fireflies “blink” in unison such that they can illuminate entire trees with strobes of light. This groupwise behavior puzzled scientists for decades, as it could not be accounted for in terms of external influences (e.g., environmental triggers) and did not appear to involve leadership by certain members. The behavior of individual fireflies has been widely studied which has provided detailed understandings of reactants and mechanisms of bioluminescent chemical reactions (Hastings, 1996; Skoog et al., 1998). However, it was not until synchronization came to be seen as an example of emergence, arising from confluences among multiple interdependent system parameters including evolutionary factors, mating instincts, and environmental conditions (Camazine, 2003), that any claim could be made of a robust understanding of fireflies. Likewise, a robust understanding of educational systems requires a holistic perspective. Results derived only from experimental studies are far from a “gold standard” as they can only have transient explanatory power if not understood in light of the educational system as a whole.

In closing, imbricating ideas from Balkin's (1998) theory of cultural software and complexity thinking gives us a powerful way to think about culture as a complex system. Complex systems must be considered holistically, on multiple levels, in order for their elegance to be fully understood and appreciated. Culture is a dynamical process that connects individuals, groups, and educational systems. Thus if we hope to better understand science education, we must attend to cultural processes in science education. The study of culture cannot be relegated to the margins in science education. We cannot claim to understand science education until we recognize the centrality of culture.


  1. Top of page
  2. Abstract
  3. Cultural Emergence
  4. Implications: Further Theoretical Development, Policy, and Research in Science Education
  5. Notes
  6. References

1While the term “dynamic” may be more technically appropriate here, its common usage in our field does not convey all of nuances of meaning we intend. Therefore, we use the term “dynamical” here and throughout this manuscript to invoke complexitivist ideas. In particular, we understand dynamical systems to be ones that give rise to complex behavior as the system and the actors and processes that comprise it mutually influence each other.

2Languaging perspectives as “western” is problematic in that it is an arbitrary name for worldviews emerging largely from Greek philosophical traditions. However, we follow the lead of Aikenhead and Ogawa (2007) in adopting it here, because its common usage in the literature carries with it a richly nuanced set of meanings including a history of colonizing Indigenous peoples throughout the world.

3Numerous computer simulations of the “chaos game” and other fractal generating processes are available on the World Wide Web.


  1. Top of page
  2. Abstract
  3. Cultural Emergence
  4. Implications: Further Theoretical Development, Policy, and Research in Science Education
  5. Notes
  6. References
  • Aikenhead, G., & Ogawa, M. (2007). Indigenous knowledge and science revisited. Cultural Studies of Science Education, 2(3), 539620. DOI: 10.1007/s11422-007-9067-8
  • Balkin, J. M. (1998). Cultural software: A theory of ideology. New Haven, CT: Yale Univeristy Press.
  • Bar-Yam, Y. (2004). Making things work: Solving complex problems in a complex world. Cambridge, MA: NECSI Knowledge Press.
  • Barnsley, M. F. (1988). Fractals everywhere. San Diego, CA: Academic Press.
  • Barton, A., & Yang, K. (2000). The culture of power and science education: Learning from Miguel. Journal of Research in Science Teaching, 37(8), 871889.
  • Bednar, J., & Page, S. (2007). Can game(s) theory explain culture? Rationality and Society, 19(1), 6597. DOI: 10.1177/1043463107075108
  • Bourdieu, P. 1977. Outline of a Theory of Practice, ISBN 9780521291644.
  • Bourdieu, P. (1985). Social space for the genesis of groups. Theory and Society, 14(6), 723744.
  • Bourdieu, P. (1987). Social space and symbolic power. University of California. A French version appeared in P. Bourdieu, Choses Dites, Paris, Editions de Minuit, pp. 147–166.
  • Bransford, J. D. Brown, A. L. & Cocking, R. R. (Eds.), (1999). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
  • Brown, B. A. (2004). Discursive identity: Assimilation into the culture of science and its implications for minority students. Journal of Research in Science Teaching, 41(8), 810834.
  • Brown, B. A. (2006). “ It isn't no slang that can be said about this stuff”: Language, identity, and appropriating science discourse. Journal of Research in Science Teaching, 43, 96126. DOI: 10.1002/tea.20096
  • Buck, J. B. (1935). Synchonous flashing of fireflies experimentally induced. Science, 81(2101), 339340. DOI: 10.1126/science.81.2101.339
  • Buxton, C. A. (2005). Creating a culture of academic success in an urban science and math magnet high school. Science Education, 89(3), 392417.
  • Camazine, S. (2003). Self-organization in biological systems. Princeton, NJ: Princeton University Press.
  • Carlone, H. B. (2003). Innovative science within and against a culture of “achievement”. Science Education, 87, 307328. DOI: 10.1002/sce.10071
  • Carlone, H. B. (2004). The Cultural production of science in reform-based physics: Girls' access, participation, and resistance. Journal of Research in Science Teaching, 41(4), 392414.
  • Carlone, H. B., Haun-Frank, J., & Webb, A. (2011). Assessing equity beyond knowledge- and skills-based outcomes: A comparative ethnography of two fourth-grade reform-based science classrooms. Journal of Research in Science Teaching, 48(5), 459485. DOI: 10.1002/tea.20413
  • Chinn, P. (2006). Preparing science teachers for culturally diverse students: Developing cultural literacy through cultural immersion, cultural translators, and communities of practice. Cultural Studies of Science Education, 1(2), 367402.
  • Cobern, W. W., & Aikenhead, G. S. (1997). Cultural aspects of learning science. In K. Tobin & B. Fraser (Eds.), International handbook of science education (pp. 3952). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge MA: Harvard University Press.
  • Davis, B., & Sumara, D. (2006). Complexity and education: Inquiries into learning, teaching, and research. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
  • Dawkins, R. (1976). The selfish gene. Oxford, UK: Oxford University Press.
  • Doll, W. C. J. Fleener, M. J. Truit, D. & St. Julien, J. (Eds.). (2005). Chaos, complexity, curriculum, and culture: A conversation (Vol. 6). New York, NY: Peter lang Publishing, Inc.
  • Eisenhart, M. (2001). Changing conceptions of culture and ethnographic methodology: Recent thematic shifts and their implications for research on teaching. In V. Richardson (Ed.), Handbook of research on teaching (4th ed., pp. 209225). Washington, DC: American Educational Research Association.
  • Ferraro, G. (2008). Cultural anthropology: An applied perspective EDN (7th ed.). Belmont, CA: Thomson Higher Education.
  • Gleick, J. (1987). Chaos: Making a new science. New York: The Viking Press.
  • Gould, S. J. (1992). The panda's thumb: More reflections in natural history. New York: W. W. Norton & Company.
  • Hastings, J. W. (1996). Chemistries and colors of bioluminescent reactions: A review. Gene, 173(1), 511. DOI: 10.1016/0378-1119(95)00676-1
  • Johnson, A., Brown, J., Carlone, H., & Cuevas, A. K. (2011). Authoring identity amidst the treacherous terrain of science: A multiracial feminist examination of the journeys of three women of color in science. Journal of Research in Science Teaching, 48, 339366. DOI: 10.1002/tea.20411
  • Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.
  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry based teaching. Educational Psychologist, 41, 7586.
  • Meadows, D. H. (2008). Thinking in systems: A primer. River Junction, VT: Chelsea Green Publishing.
  • Mason, M. B. (Ed.), (2008). Complexity theory and the philosophy of education. Oxford, UK: Wiley-Blackwell.
  • Morrison, K. (2008). Educational philosophy and the challenge of complexity theory. Educational Philosophy and Theory, 40(1), 1934. DOI: 10.1111/j.1469-5812.2007.00394.x
  • Mulcahy, K. V. (2006). Cultural policy: Definitions and theoretical approaches. Journal of Arts Management, Law & Society, 35(4), 319330.
  • No Child Left Behind Act of 2001. (2001). H.R. 1, 107th Cong., 1st Sess., 147 Cong. Rec. H2396–H2446.
  • Page, S. E. (2011). Diversity and complexity. Princeton, NJ: Princeton University Press.
  • Paul, J., & Marfo, K. (2001). Preparation of educational researchers in philosophical foundations of inquiry. Review of Educational Research, 71(4), 525547.
  • Parsons, E. (2008). Learning contexts, black cultural ethos, and the science achievement of African American students in an urban middle school. Journal of Research in Science Teaching, 45(6), 665683.
  • Pinar, W. F., Reynolds, W. M., Slattery, P., & Taubman, P. M. (2008). Understanding curriculum: An introduction to the study of historical and contemporary curriculum discourses. New York, NY: Peter Lang Publishing.
  • Richmond, G., & Kurth, L. (1999). Moving from outside to inside: High school students' use of apprenticeship as vehicles for entering the culture and practice of science. Journal of Research in Science Teaching, 36(6), 677697.
  • Roth, W. M., & Duit, R. (2003). Emergence, flexibility, and stabilization of language in a physics classroom. Journal of Research in Science Teaching, 40, 869897. DOI: 10.1002/tea.1011
  • Sanders, T. I. (1998). Strategic thinking and the new science: Planning in the midst of chaos, complexity and change. New York, NY: The Free Press.
  • Skoog, D. A., Holler, F. J., & Nieman, T. A. (1998). Principles of instrumental analysis (5th ed.). Philadelphia: Saunders College Publishing.
  • Solomon, J. (2003). Home-school learning of science: The culture of homes and pupils' difficult border-crossing. Journal of Research in Science Teaching, 40(2), 219233.
  • Storck, T. (2009). Culture and embodiment of cultural ideals as preliminary to a philosophy of culture. [Article]. Forum Philosophicum: International Journal for Philosophy, 14(1), 6986.
  • Taras, V., Rowney, J., & Steel, P. (2009). Half a century of measuring culture: Review of approaches, challenges, and limitations based on the analysis of 121 instruments for quantifying culture. Journal of International Management, 15(4), 357373. DOI: 10.1016/j.intman.2008.08.005
  • Tobin, K. (2006). Aligning the cultures of teaching and learning science in urban high schools. Cultural Studies of Science Educaiton, 1(2), 219252.
  • van Elijck, M., & Wolff-Roth, M. (2011). Cultural diversity in science education through Novelization: Against the epicization of science and culture centralization. Journal of Research in Science Teaching, 48(7), 824847.
  • Warren, B., Ballenger, C., Ogonowski, M., Rosenbery, A. S., & Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching, 38, 529552.
  • Weaver, W. (1948). Science and complexity. American Scientist, 36, 536544.
  • Willis, P. (1977). Learning to labor: How working class kids get working class jobs. New York: Columbia University Press.
  • Wood, N. B., Lawrenz, F., & Haroldson, R. (2009). A judicial presentation of evidence of a student culture of “dealing”. Journal of Research in Science Teaching, 46(4), 421441.