Using memes and memetic processes to explain social and conceptual influences on student understanding about complex socio-scientific issues

JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 45, NO. 8, PP. 900–921 (2008)

Using Memes and Memetic Processes to Explain Social and Conceptual Influenceson Student Understanding about Complex Socio-Scientific Issues

Susan Yoon

Graduate School of Education, University of Pennsylvania, 3700 Walnut Street,

Philadelphia, Pennsylvania 19104

Received 13 December 2005; Accepted 19 November 2007

Abstract: This study investigated seventh grade learners’ decision making about genetic engineering concepts and

applications. A social network analyses supported by technology tracked changes in student understanding with a focus

on social and conceptual influences. Results indicated that several social and conceptual mechanisms potentially affected

how and why ideas were taken up in the learning system of the classroom. Mechanisms included copying or memetic

processes such as ‘‘do as the smart students do’’ and friendship selection. Study outcomes are compared with the broader

literature on memes and memetic processes to reveal general evolutionary ideas such as the development of prestige,

identity versus problem-solving strategies, extended phenotypes, and memeplexes. Educational implications for this

research are also addressed. � 2008 Wiley Periodicals, Inc. J Res Sci Teach 45: 900–921, 2008

Keywords: general science; student beliefs; values; ethics; socio-scientific issues; middle school science

In October of 1990, the US Department of Energy and the National Institutes of Health launched a

research program of arguably unequaled magnitude in human evolutionary history. Over the next 13 years,

the Human Genome project set out to identify the approximately 30,000 genes and the sequences of 3 billion

chemical base pairs that make up human DNA. The historical importance of the Human Genome project has

been compared to that of the Cambrian explosion, a period that spanned 40 million years in geological time

during which most of the major groups of animals first appeared in the fossil records. Humans now possess

the capabilities to select, construct, and fashion their own evolutionary path. In true Lamarckian form,

information can now flow from the extended phenotype (societal or cultural norms) to the genotype (Gardner,

1999). Furthermore, the mass proliferation of genetic engineering (GE), techniques such as germline

manipulation, xenotransplantation, cloning, and stem cell research, has sparked an ethical debate on the

extent to which cultural influences will alter the current trajectories of both human and non-human biological

evolution (Grace, 1997; Somerville, 2000).

However, it appears that the debate remains largely academic. Despite its enormous contemporary

saliency, a 2002 National Science Foundation (NSF) Science and Engineering Indicators report (NSF, 2002)

stated that only 16% of the general public followed the human genome story. Furthermore, in the same report,

85% of Americans indicated that they felt less than well informed about new scientific discoveries and the use

of new inventions and technologies. Studies in Europe, the UK, and Canada have also shown similar results in

terms of lack of interest and understanding of GE and other important biotechnology research (Canadian

Press, 2001; Gaskell & Durant, 1997; Gunter, Kinderlerer, & Beyleveld, 1998; Zimmerman, Kendall, Stone,

& Hoban, 1994).

These results illustrate the challenges in improving societal levels of scientific and technological

literacy. With ever increasing scientific and technological innovations that can produce potentially helpful

and harmful effects, people need to move beyond disinterest and ambivalence to acquire the knowledge and

Correspondence to: S. Yoon; E-mail: [email protected]

DOI 10.1002/tea.20256

Published online 29 August 2008 in Wiley InterScience (www.interscience.wiley.com).

� 2008 Wiley Periodicals, Inc.

evaluative skills that will enable thoughtful and informed decision making about GE and other socio-

scientific issues (Osborne, Erduran, & Simon, 2004; Patronis, 1999).

Argumentation, Socio-Scientific Issues, and Complexity

Within science education, researchers have suggested the inability to engage with contemporary socio-

scientific issues has resulted from the depiction of science as an uncontested and unproblematic body of

knowledge (Driver, Leach, Millar, & Scott, 1996) that does not require or invite critique. Consequently school

science activities have taken the well-known forms of traditional methods including too heavy a reliance on

textbooks, exclusive representation of final form theories, an emphasis on memorization and regurgitation,

transmissive modes of delivery, and the lack of meaningful collaboration between students (Duschl, 1990;

Linn, 1992; Loving, 1997; Tobin, 1997). As a challenge to such depictions of science and practices of school

science, a growing body of research has advocated for educational experiences to simulate the discursive

practices of scientists and the scientific community that are predicated on language, communication, and

argumentation (Driver, Asoko, Leach, Mortimer, & Scott, 1994, Driver, Newton, & Osborne, 2000; Newton,

Driver, & Osborne, 1999; Osborne et al., 2004). As a core scientific process, argumentation enables the

critical evaluation of scientific and technological claims (Driver et al., 2000), develops logical reasoning

skills and conceptual capacities (Osborne et al., 2004; Zohar & Nemet, 2002), encourages the use of evidence

to challenge and support theories (Sandoval & Millwood, 2005), creates classroom environments that allow

students to reflect on multiple and diverse perspectives (Driver et al., 1994), requires active participation and

co-construction of knowledge (Newton et al., 1999), and engages students in a process that foregrounds the

importance of participation in decision-making about scientific and technological issues (Jimenez-

Aleixandre & Pereiro-Munoz, 2002; Patronis, 1999).

Recently science education researchers have sought to document student attitudes and beliefs about

socio-scientific issues and have also investigated the efficacy of curricular interventions on student learning

that incorporates many of the necessary reasoning, argumentation, and decision-making skills listed above.

Results have shown that students hold a wide range of beliefs about what is the acceptable use of certain socio-

scientific research (Dawson & Schibeci, 2003). They also lack an understanding of essential processes and

often display widespread uncertainty about socio-scientific knowledge (Lewis & Wood-Robinson, 2000).

Moreover, despite direct teaching and extensive curricular interventions, some studies have found that

student attitudes largely remain unchanged (Dawson & Schibeci, 2003; Dawson & Soames, 2006; Olsher

& Dreyfus, 1999). With respect to the efficacy of curricular interventions, in a comprehensive and

critical review of the educational socio-scientific field, Sadler (2004) suggests that there is inclusive evidence

that shows such programs aid in the development of argumentation skills, increase student’s abilities to

evaluate socio-scientific information, or improve conceptual understanding.

While the Sadler (2004) article presents detailed and plausible recommendations for researchers to

pursue, much of the literature reviewed focused on interventions that involved students interacting with

concepts and issues through teacher or researcher selected texts and problem scenarios or their teachers.

However, the same article acknowledges that socio-scientific research often involves cutting edge or frontier

forms scientific activity, thus people need to rely on multiple sources when forming opinions about such

research. This situation suggests that the manner in which students acquire and evaluate these information

sources must be studied in addition to studying their reasoning abilities or understanding through text

analyses. Several researchers have additionally suggested that some of the difficulties in understanding socio-

scientific concepts lie in the inherent complexities of these issues and provide insights into constructing

programs that both encourage access to and evaluation of these multiple decision influencing sources which

may include peers. For example, as written in Levinson (2006), including communicative virtues—a set of

dispositions that promote dialogue across differences, encourages the belief that there is something to learn

from everyone including peers, promotes freedom to state varying perspectives, and enables openness to

being convinced by other points of view. Kolstoe (2000) likewise advocates for a process of consensus-

building in projects premised on the presentation and defense of data with the expectation that ideas are

debated, opposed, and negotiated by fellow classmates (there are other robust and long-standing research

programs, e.g., Bereiter, 2002; Scardamalia, 2002 that do not focus exclusively on socio-scientific issues but

nonetheless promote similar knowledge-building peer-to-peer activities—a point that will be further

MEMES AND MEMETIC PROCESSES 901

Journal of Research in Science Teaching

addressed in the ‘‘Discussion’’ section). Hmelo-Silver and Azevedo (2006) suggest that learning how to

design appropriate and effective curricular contexts when dealing with complex scientific phenomena

requires efforts by researchers to investigate the cognitive, metacognitive, and motivational skills and

strategies that affect not only the individual, but also collaborative and situational outcomes.

The study seeks to advance the science education and socio-scientific issues knowledge base by

addressing the complexity of socio-scientific issues and employs methodological and analytical tools that

reveals information about collaborative and situational outcomes. The study of complexity or complex

systems has been a focus of research in several academic disciplines such as biology (Kauffman, 1995),

physics and chemistry (Bak, 1996; Prigogine & Stengers, 1984), psychology (Arrow, McGrath, & Berhahl,

2000), and economics (Stacey, 1996). Complex systems have also been the subject of more recent popular

mainstream books (Johnson, 2001) but has only recently garnered attention in educational research (Jacobson

& Wilensky, 2006). As part of a larger research program investigating the efficacy of a complex systems

approach in science and technology education (see Yoon, 2007) this smaller study investigates the subfield of

memetics (e.g., the study of how information moves across cultural systems as well as individuals) that

attempts to address the aforementioned problem of understanding how students acquire and evaluate

information sources. The question under investigation is: What educational insights do the study of memes

and memetic processes within a complex systems paradigm provide in terms of understanding what and how

students learn about socio-scientific issues?

Complex Systems Research in Education

Complex systems can be generally defined as existing when any given number of interconnected

elements, parts or individuals, communicate in non-linear ways. The patterns of interactions form a collective

network of relationships that exhibit emergent properties that are not observable at subsystem levels. When

perturbations occur, the network self-organizes in often unpredictable ways, where new properties can

emerge. The manner in which complex systems communicate, respond to perturbations, and self-organize is

understood by studying the dynamical processes through which they evolve over time. In the case of GE as a

socio-scientific issue, interacting variables might include amongst other things scientists, pharmaceutical

companies, plants and animals, human recipients of genetically engineered products, ethics councils, and

ecological systems. As scientific decisions are made, for example, to insert genes that encode the Bt toxin

(commonly used to promote the manufacture of self-produced insecticides) into mass-produced crops, other

system variables will be affected such as more people being fed on cheaper grains. The impact of such

decisions are observed and evaluated as responses or effects (often unintended) emerge such as the

development of a human allergy to genetically modified corn that prompted a ban on US exports of the crop

putting economic strain on US farmers (Pollack, 2002).

Several science associations have already emphasized the need to construct programs focusing on

systems thinking. For example, in the Science for All Americans report (AAAS, 1993), the American

Association for the Advancement of Science recommend that classroom curricula in the K-12 learning years

should be organized around the following four scientific inquiry themes: systems, models, constancy and

change, and scale. However, this report appears to have had minimal impact on classroom practice. Studies

have shown that students lack basic understanding of central complex systems ideas such as self-organization

and evolution by natural selection despite their relative importance in standard high school curricula

(Jacobson, 2001).

At the moment, complex systems applications in education are new. As a first step toward constructing

educational interventions, some current educational research in this area has explored variables that make

learning about complex systems difficult. Researchers in the fields of cognitive science and educational

technology have speculated that difficulties, in part, lie in students’ inabilities to understand mechanisms that

drive the emergence of global phenomenon from lower levels of interacting agents (Chi, 2001). The

confusion of levels is thought to be a main source of misunderstandings or misconceptions not only in the

formal study of science but in everyday life experiences (Wilensky & Resnick, 1999). Other studies have

shown that students struggle with important complex systems concepts such as decentralization (Resnick &

Wilensky, 1998), emergence (Penner, 2000), and complex causal explanations (Grotzer, 2005). Constructing

educational interventions to improve student understanding of complex systems have also been the subject of

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several progressive research programs in the last decade. For example, computational 2D modeling tools for

creating complex systems and investigating properties and processes such as StarLogo (Colella, Klopfer,

& Resnick, 2001; Klopfer, Yoon, & Um, 2005; Resnick, 1994), and NetLogo (Steiff & Wilensky, 2003;

Wilensky & Reisman, 2006) are continually evolving and being tested in educational settings. Others have

investigated wearable technologies in which students themselves become embedded agents in the simulation.

For example, an innovative technology called the Thinking Tag has been used to study how students gain first

person, immediate knowledge of how their individual behaviors and interactions affect whole-group

dynamics (Borovoy, McDonald, Martin, & Resnick, 1996). These microcomputers communicate with each

other through infrared and can be programmed to represent simulated characteristics of the user. They have

been used successfully to explore complex scientific concepts such as epidemiological factors in the spread of

viruses (Colella, 2000) as well as other complex socio-scientific issues (Yoon, 2007). As this technology has

been shown to reveal important interactional dynamics and requires students to share, discuss, and negotiate

ideas, it is used as a primary methodological activity and data collection tool in the present study. Further

description about the technology and its use can be found in the section ‘‘Data Sources and Analyses.’’

Memes and Memetic Processes

In the previous literature reviewed, much of the research has been focused on revealing the quality of

participant argument and reasoning abilities as well as evaluating the efficacy of interventions that are aimed

at improving those abilities. For example, Hogan and Maglienti (2001) found among other things that

scientists and non-scientists (including middle school students) differed in their reasoning skills in that the

former group drew on epistemological standards constructed by the scientific community such as the use of

empirical evidence to make conclusions while the latter group used personal opinions to make judgments

about knowledge claims. Kuhn (1997) discusses the importance of dyadic interactions on improving a host of

argumentation skills such as consideration of alternative perspectives and greater differentiation of

justifications. What appears to be missing from the literature are studies that reveal why students have great

difficulties reasoning in the first place. Recent literature in the social and psychological sciences on copying

mechanisms suggests that there may be robust influences that stand in the way of effective reasoning skills in

much the same manner as misconceptions have been shown to prevent correct conceptual understanding

(Sadler, 1998).

These copying mechanisms, also known as memetic processes, are thought to exert powerful control

over decision making through informational units called memes that get passed on unintentionally from

person to person through interactions. First introduced by Richard Dawkins (1976) in his book The Selfish

Gene, over 25 years ago, the concept of the meme is generally understood to be a self-propagating unit of

cultural transmission. Stanovich (2004) suggests that a meme ‘‘is a brain control (or informational) state that

can potentially cause fundamentally new behaviors and/or thoughts when replicated in another brain’’

(p. 175). Dennett (1999) states that a meme ‘‘is an information-packet with attitude—with some phenotypic

clothing that has differential effects in the world that thereby influence its chances of getting replicated (What

is a meme made of? It is made of information, which can be carried in any physical medium. . .)’’ (p. 3). The

Oxford English Dictionary offers perhaps the simplest definition, that is, ‘‘an element of a culture that may be

considered to be passed on by non-genetic means, esp. imitation.’’

There appears to be some contention in the literature about how to define a meme due to the lack of

empirical studies in the field of memetics (Blackmore, 1999). However, a commonly cited characteristic of

memes is that they are not always beneficial to the host or carrier and, as true replicators, often act only in the

service of their own ends. Thus, as Dennett (1999) states, they can be thought of as hitchhikers or symbionts

that have more or less beneficial effects on the host—a notion which may explain why some human behaviors

such as smoking continue to exist in society. The mechanisms by which memes propagate—memetic

processes—can therefore be explained in intentional versus non-intentional terms or by reflective or non-

reflective selection (Stanovich, 2004). Similar to the ‘‘virus’’ or ‘‘contagion’’ metaphor and popularized by

several recent books such as Gladwell’s (2000) The Tipping Point, Lynch’s (1996) Thought Contagion,

Brodie’s (1996) Virus of the Mind, and Godin’s (2000) Unleashing the Ideavirus, non-reflective selection of

memes are thought to be ‘‘caught’’ by the host irrespective of their utility or degree of benefit. Non-reflective

selection may also be thought of as roughly parallel to such concepts in memetic literature as selection bias



not based on the content of the idea (Gil-White, 2004), socially based mechanisms of transmission

(Castelfranci, 2001), or unconscious selective forces (Dennett, 1999). Reflectively acquired memes

conversely are ones that have been scrutinized against selection criteria, ultimately serve the ends of the host

rather than the meme itself and are intentionally admitted to our corpus of understanding. They are roughly

equivalent to memetic selection through content selection bias (Gil-White, 2004), cognitively based

transmission mechanisms (Castelfranci, 2001) or methodical selection forces (Dennett, 1999).

Most adherents of human memetics would also agree that memes arise from social learning (Aunger,

2002), of which the principal medium of transmission is language (Dennett, 1995; Plotkin, 1998), creating the

‘‘infosphere’’ in which cultural development occurs (Dennett, 1995). From this description, there are obvious

similarities to the argumentation in science literature in that language and communication are understood to

be the primary sources through which arguments and decisions are made. This study seeks to establish an

alternative but not mutually exclusive account of why understanding of complex socio-scientific issues might

be difficult through revealing what memes and memetic processes are at play in the science classroom.

MethodologyParticipants

The research reported is a case study of one grade 7 classroom in which a complex systems intervention

was employed. There were 18 student participants, 10 males and 8 females, with varying cognitive levels,

social abilities and ethnic/cultural backgrounds from a junior high school in Toronto, Ontario. One-third of

the students were, either formally or informally, identified as special education students and worked under

modified individualized education programs while being fully integrated. Another four students were

designated second language learners. The teacher participant, Ms. Saunders was an enthusiastic and energetic

teacher with the 6 years experience. At the time of the study she had been teaching the class for 7 months and

had a solid understanding of each student’s social and cognitive history. She was involved in all the planning

phases of the study and provided insight into the nature of group dynamics through observation notes, formal

interviews, and informal discussions with the researcher.

Although data was collected and analyzed from all participants, due to the complex nature of both

individual level and group level patterns of interaction, and to highlight the notion that complex systems

dynamics exert influence both at the individual and group levels, a subgroup of six students in the class are

profiled in the analysis. While population-based statistical analyses are normally used in cultural evolution

and anthropological studies to demonstrate changes at the group level, an individual agent-embeddedness

approach is used due to the belief that memetic processes can be most accurately understood at the individual

level in educational settings. Moreover, given the current state of confusion and the lack of sound

methodologies and strong empirical evidence in the field of memetics, it is thought that the best evidence can

be collected by indirect means (Aunger, 2000) in that one can presume the existence of memes from the

behaviors (phenotypes) that they inform and these behaviors, in turn, are best understood by observing and

analyzing the students who perform them. These six students detail cases of social and conceptual behaviors

that best illustrate themes generated in the data and provide a rich context through which group level results

are embedded. They also represent categories of students.

Cognitive and Social Profile of Six Students in the Subgroup

The following information about students in the subgroup was gleaned from questionnaires, school

records, teacher interviews, and observations from researcher field notes.

Ben and Natalie. Ben and Natalie were highly respected members of the class in terms of their

academic abilities. Both had a sophisticated understanding of current events and advanced verbal and written

reasoning skills, relative to other students in the class. Each indicated on their preliminary questionnaires and

in informal conversations with the researcher that they had heard of the term GE before from media sources as

well as their family members. Natalie was one of only three students in the class who, when asked on the

questionnaire what they knew about GE mentioned anything about genes and the transferring of genes

between different organisms. Ben commented on a number of occasions that his father was a former scientist

904 YOON


and that they frequently discussed new ideas in scientific research. Socially, both students appeared to have

a good deal of influence on others in the class, although Natalie more than Ben took on a leadership role in

the class.

Thomas and Yasmin. Thomas was one of the six students in the class identified with a learning disability

and had been designated as such for several years. Although Yasmin had not been formally identified as an

English language learner, English was her second language. She frequently experienced difficulties

comprehending written materials and verbal instruction. She was specifically placed in this smaller class with

Ms. Saunders in order to receive more individualized support. Both students had a great deal of pressure put

on them from their family to raise their academic achievement and as a result were determined diligent

workers. What set them apart from other similar students in the class was that they had developed fairly

advanced coping strategies. For example, over the course of the study, whenever the opportunity and need

arose, they publicly asked questions for clarification and frequently took advantage of after-class review

sessions. While maintaining a few close personal friendships in the class, both socially and academically they

did not hold a lot of status in the class.

Greg and Marshall. Like Ben and Natalie, Greg was highly regarded academically. He received

excellent grades, was an active participant in all school-related activities, and was the most popular student in

the class. Marshall was exceptionally bright and had the most highly developed cognitive skills. He was not

challenged by the standard grade 7 curricula and often appeared bored, distracted, or inattentive. Despite his

enormous potential, he was an underachiever. Greg and Marshall were best friends, although each occupied a

very different social niche in their peer group. As is often the case with extreme forms of exceptionality,

Marshall was not well understood by other students in the class.

Context

In an effort to ensure that mutual participant and researcher curricular and study goals were met, prior to

the intervention, several meetings were held to discuss the purpose of the study. Ms. Saunders identified a

focus in the grade 7 Science curriculum that she was particularly interested in pursuing ‘‘Relating of science

and technology to each other and to the world outside the school’’ (Ministry of Education and Training, 1998,

p. 13). Ms. Saunders noted in one of the planning sessions that this theme and related concepts of cause and

effect, human and natural patterns, and ecology were difficult to address in standard curricular activities due

to the complex relational understanding that needed to be cultivated with students which took a great deal of

time in an otherwise dense and fact-acquisition-oriented curriculum.

The study took place over 17 days within a 4-week time span. Each session lasted between one and two

hours per day for a total of 24 hours of instructional time. Students explored a number of teacher-selected and

student-selected multimedia and print materials that presented information on xenotransplantation, cloning

techniques, ethical and practical issues in both the GE of animals and GE applications in crop farming. These

materials were carefully chosen to represent a variety of critical arguments both for and against GE research.

The pedagogical strategies used to promote the learning of concepts were designed to develop complex

systems thinking. These strategies included the following: constructing risks/benefits charts examining

tensions between environmental and societal goals; developing concept maps of relevant social, political,

economic, and environmental stakeholders; participating in several whole class cocktail party (described

below) discursive events; creating and performing a data play, and debating the position of a special interest

group linked to GE in a town hall meeting simulation. For each strategy, students were asked to move between

cycles of individual-level metacognitive processing and group-level metacognitive processing. A sample of

2-hour session of study activities can be found in Figure 1.

Data Sources and Analyses

The majority of analyses were completed using data generated from three Thinking Tags Cocktail Party

activities (Activity 4 in Figure 1). A labeled graphic of the Thinking Tag technology can be found in Figure 2

(a description of the technology and previous research studies is found under the section ‘‘Complex Systems



Research in Education’’)1. The cocktail party activity was designed to simulate a discursive environment in

which argumentation processes would spontaneously emerge.

In the cocktail party activity, students were required to rate the question, ‘‘Human use of the natural

world for GE purposes is acceptable,’’ on a number line ranging between �5 (unacceptable) to þ5

(acceptable) and provide rationales for their rating. Students were then asked to share their ratings and

rationales in paired discussions with every student in the class. Each student was required to wear a Thinking

Tag, which displayed how many people they talked to and the number of students who agreed with their

statement. After each discussion, students were asked to register one of three votes on their partner’s Thinking

Tag: Yes, I agree with the rationale; No, I do not agree with the rationale; or I am undecided. Each Thinking

Tag was programmed to keep a record of which students had met and at what time, each partner’s respective

ratings and what each student had voted after their paired discussions. This cocktail party activity was

Figure 1. Sample two-hour study session illustrating curricular activities.

Figure 2. Thinking tag technology worn by students in the ‘‘cocktail party’’ activity.

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performed three times; at the beginning (Day 2, T1), in the middle (Day 10, T2) and at the end (Day 16, T3) of

the study. A content analysis of 51 student rationales was performed in which 105 memes were identified

(further description of the meme analyses follows in the section below).

On Day 10 (T2) and Day 16 (T3), students were also asked to rank each student in the class from 1 to 18 in

terms of the degree of like-mindedness. An in-degree score for each student was calculated from the average

of students’ like-mindedness rankings for each time sample (see Table 4). In social network analysis the in-

degree score is a measure of the total number of connections that is directed toward an actor (Scott, 1991). In-

degree scores can be used to represent an actor’s prestige or status in a system if the relation is directional

(Wasserman & Faust, 1994). In the case of prestige or status, in-degree measures attempt to quantify the rank

that an actor has within a given set of actors (Wasserman & Faust, 1994). In this study, the in-degree score was

used to determine the status that either the students themselves or their ideas had within the larger learning

system at different points of the study.

Other data sources (for the larger study) included pre- and post-questionnaires, materials generated from

in-class activities, transcriptions of audio-taped paired and group discussions, video-taped footage, teacher

participant journal notes, transcriptions of teacher interviews, and researcher field notes of classroom

observations. Although the primary data sources were students’ written rationales at each of the three time

samples and the in-degree scores, some of the other data sources collected for the larger study were used for

interpretation and triangulation. Chief among those were the sources generated from our teacher participant.

Meme Analyses

The selection of the units to be analyzed was based on ideas found in the rationales that could have a

differential effect on how many students would agree with another student’s rationale. Accordingly, reasons

or evidence used to justify a position or opinion were deemed the unit of analysis. For example, the statement,

‘‘I believe GE is wrong’’ does not qualify as a meme because evidence to justify why GE is ‘‘wrong’’ is not

given. However the statement, ‘‘I believe that GE has good points like finding a cure for Parkinson’s’’

provides evidence to support the claim that GE has ‘‘good points’’ and therefore would count as a meme. The

following is an example of a complete student rationale:

The reason why I gave a rating of þ1 is because I think genetic engineering is a great advancement for

human knowledge but by destroying the Earth, we are killing animals and their environment, which is

not right.

In this example there are two separate memes: (a) GE is a great advancement for human knowledge and

(b) by destroying the Earth, we are killing animals and their environment.

Meme clusters and meme types were superordinate constructs that emerged from the identification and

labeling of the meme units. Meme clusters and types were first negotiated between the researcher and a

graduate assistant. A categorization manual was constructed around 11 meme types within four meme

clusters (the meme unit of analysis description is found below). Meme clusters emerged around: (a) ideas for

or against GE research for anthropocentric purposes, that is, the natural world exists only to serve human

ends; and (b) ideas for or against GE research for biocentric purposes, that is, the natural world has intrinsic

value. A final category of meme type was added for ones that were not applicable or were unable to be coded

due to ambiguity. Two raters with previous meme coding experience in a pilot study with a different set of

student rationales were trained using this categorization manual. Due to their previous experience, sufficient

understanding of the coding process was obtained using only 20 sample memes in one 1-hour training session.

Ninety-two percent inter-rater reliability was obtained on the entire data set of 105 memes with respect to

meme types. Codes for the eight memes in which discrepancies occurred were negotiated until a consensus

was reached on the specific code to be assigned.

Results

Results from the meme analyses are organized into three tables for population or group and individual

level perspectives on the data. Table 1 shows the 12 meme types that were combined and subsumed under the

four superordinate meme cluster categories. Table 2 shows aggregate frequencies of meme types occurring in



each category obtained from student rationales during each of the three cocktail party activities (T1–T3).

Table 3 shows the frequency of meme types as they occurred in the individual student rationales in each of the

cocktail party activities (T1–T3).

Based on the memes analyses, data collected from the like-mindedness rankings and other data sources

such as teacher participant observations, the sections below present both social and conceptual memetic

processes operating in the classroom.

Social Memetic Processes

Under social memetic processes, the data are organized with respect to individual and group behaviors

hypothesized to have emerged as a result of social influence dynamics. Described below are three processes

thought to be influenced by memetic mechanisms: (i) ‘‘Do as the smart students do’’: The Influence of Status;

(ii) Identity Influences; and (iii) Feedback Signals: Thinking Tags as a Memetic Vehicle.

‘‘Do as the Smart Students Do’’: The Influence of Status. As previously described, social network in-

degree scores were calculated from students’ like-mindedness rankings. This data and analysis are presented

in Table 4. A ranking of number 1 indicates that this student, Lisa for example on Day 10, held the highest

status in terms of the aggregate student rankings on the like-mindedness criteria. By contrast, Mark on Day 10

occupied the lowest ranking. From Table 4, on the measure of like-mindedness, at T2, Natalie, Ben, and Greg

had the second, third, and fourth highest in-degree scores. From all data accounts, the top four students in this

category were considered the smartest students in the class. However, at T3, while Natalie occupied the top

position, Ben’s ranking moved to 11th and Greg’s to 18th. Thomas and Yasmin moved up in their positions

from 8th and 11th to 2nd and 3rd, respectively. What could account for such a seemingly unusual shift in

ordinals? When these data were presented to Ms. Saunders, her response shed some interesting insight. She

believed that the pattern was not unusual but rather exactly how the dynamics should have unfolded. Based on

Table 1

Meme clusters and meme types

Meme clusters Meme types

For genetic engineering for anthropocentricpurposes

1. Represents human progress, knowledge, or technolo-gical advancement

2. Helps to improve world hunger crisis or aids thepopulation increase

3. Improves human life, enhances human health4. No other reliable alternatives to using

non-human organismsAgainst genetic engineering for anthropocentric purposes 5. Processes are not natural

6. Economics (e.g., GE is too expensive)7. Safety concerns, uncertainty of future effects, processes

are riskyFor genetic engineering for biocentric purposes 8. Environmental/non-human species improvementAgainst genetic engineering for biocentric purposes 9. Cruelty to animals

10. Tampering with natural processes11. Not necessary, waste of life, other alternatives

Other 12. N/A or answer cannot be coded due to ambiguity

Table 2

Frequency of memes occurring in individual meme types at each of three time samples

Meme type/time period 1 2 3 4 5 6 7 8 9 10 11 12 Totals

Day 2 (T1) 3 0 7 1 0 1 5 2 9 2 1 1 32Day 10 (T2) 6 1 7 1 1 1 2 1 10 3 1 1 35Day 16 (T3) 3 5 6 0 1 1 7 1 6 2 5 1 38

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her professional experience, she felt that the topic of GE was cognitively advanced for a grade 7 integrated

special education class albeit important to address given the aforementioned potential for cultivating the

curricular goal of complex relational understanding of scientific issues. In her classroom observations prior to

T2, she felt strongly that the majority of the students didn’t understand the concepts being addressed in the

curriculum. This in addition to the method of instruction being so strikingly different from their normal

classroom activities, for students who typically needed more time to settle into routines, it was likely that they

chose a strategy of ‘‘do as the smart students do.’’ In other words, in the face of learning challenges many of

the students consciously or unconsciously identified themselves with the smartest students in the class

suggesting that a selection force based on the social influence of status was operating at the group level.

There is some evidence to substantiate this claim. In the meme analysis data (Table 3), the rationales

used to identify memes at all three time samples were constructed by students individually prior to any paired

Table 3

Categories of meme types found in each student’s rationale at three time samples

Time/student Day 2 (T1) Day 10 (T2) Day 16 (T3)

Mark 10 6 9Lisa — 1, 3, 4 3, 11Miranda 3, 7, 12 5 3, 6Ebby 3, 6, 7, 8 10 2, 7Patrick — — 9Marshal 4 12 12Ben 3, 9 3, 9 9, 11Natalie 8, 9 1, 2, 9, 10 1, 2, 3, 9, 10, 10, 11Greg 3, 3, 9 1, 1, 3, 11 1, 3, 11Sandy 3 3 1, 2Norah 1, 3, 9 9, 9 2, 3, 7, 7Yasmin 9 3, 3, 7, 9 3, 9Annie 7, 7, 7, 9 3, 9, 10 7, 8Janice 9, 10 7, 9 5, 9Joel 1 1, 8, 9 7Avery 1, 9 10 7, 11Thomas 9 1, 9 2, 7Saul 11 — —

Table 4

Student’s in-degree scores based on a ranking of like-mindedness

RankingStudent

(Day 10)In-degree

scoreStudent

(Day 16)In-degree

score

1 Lisa 6.1 Natalie 5.82 Natalie 6.6 Thomas 6.43 Ben 7.6 Yasmin 6.74 Greg 8.0 Ebby 7.15 Janice 8.5 Norah 7.26 Ebby 8.5 Mark 8.17 Joel 9.1 Miranda 8.48 Thomas 9.1 Annie 8.59 Marshall 9.2 Lisa 8.5

10 Norah 9.4 Janice 8.611 Yasmin 9.8 Ben 9.612 Sandy 11 Joel 9.613 Annie 11 Patrick 9.814 Miranda 11 Saul 1015 Patrick 12 Avery 1116 Saul 13 Sandy 1317 Avery 13 Marshall 1318 Mark 13 Greg 13



discussions occurring in the cocktail party activity. At T1, Ben’s, Natalie’s, and Greg’s rationales presented

the most balanced and reasoned justifications to their ratings. For example, Ben wrote the following:

I think this because sometimes we are helping the animals or we are hurting them. If we clone animals,

those that fail will hurt the animal, bit if it does work we could help many people or things.

Xenotransplantation is just hurting the animals because we have to kill them to complete the operation

and we are not sure that it will always work, but if it does many problems would be solved for people

who need organ donations.

This was Thomas’s rationale for the same time sample:

Why I think my answer [is] at 0 was because I think it’s [w]rong to do that to animals and it’s right

because[there] will be more of them.

Comparing the two responses, we can see clearly that Ben presents two sides of the issue, uses

terminology accurately and provides several pieces of evidence to justify his rating. Thomas however, does

not provide substantive evidence. It is also not entirely clear what he means by the statement it’s right because

[there] will be more of them, suggesting that there is a lack of understanding.

The fact that Yasmin and Thomas moved into second and third position at T3 further substantiates the

claim of ‘‘do as the smart students do’’ and suggests that a qualitatively different dynamic had emerged

somewhere between T2 and T3. At T3, Thomas writes:

I chose 0 because I think it’s good and bad. I’ll start off with good. Well, I think it’s good because we

are gaining more crops for use to survive. Now I think it’s bad because when they do that process, we

don’t know if it is safe for us to eat because we don’t know what is in it and we don’t know what can

happen. Say like if somebody gets ill or some one can die that’s why I chose neutral.

A closer look at the curriculum concepts being addressed during that time sheds some light on the new

dynamic. Just after the rationales were recorded at T2 the topic shifted from GE applications involving

animals to GE involving farming and crop manipulation. Thomas’s response reflects the growing concern of

uncertainty of future effects and safety issues surrounding genetically modified crops which was salient in the

class’s general conceptual system at T3. Table 2 shows the largest shift in meme frequencies in meme type 7

from T2 to T3. While Ben continued to provide reasoned arguments throughout the study, his rationale at T3

became entirely concerned with a ‘‘cruelty to animals’’ idea, the meme type (9) that took the most substantial

frequency drop from T2 to T3. He writes:

I am against it because I don’t think this is fair to kill off the animal species. They shouldn’t do this

because in the end it might not even be helpful so there really is no point.

The significance of these findings in terms of conceptual memetic processes is addressed in greater detail

in the section on meme coupling. It is suggested here that students in this class were no longer operating under

the ‘‘do as the smart students do’’ mechanism but were now making decisions about like-mindedness based

on conceptually informed decisions.

Influences of Friendship. Data analyzed on which students selected as the most like-minded with

themselves also provide evidence of social mechanisms influencing group dynamics. This data is

summarized in Table 5—on Day 10 (T2), the top line of the table shows that Natalie who has a rating of 0

selects Lisa and Ben who also have a rating of 0 (the selection denoted by an arrow). Through researcher

observations and informal discussions with students and corroborated by Ms. Saunders in interviews, student

friendship clusters within the class were determined. At T2, Table 5 shows that 62% of students selected their

first choice for like-mindedness as someone in their friendship cluster. In roughly half of the cases, student

ratings were dissimilar and 3 out of the 13 students had one meme in common. At T3 that percentage dropped

to 22% where three out of the four students had the same ratings and three out of the four students had one

meme in common.

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Thus in addition to the ‘‘do as the smart students do’’ social dynamic occurring at T2, it is hypothesized

that another social bias mechanism, that is, selecting on the basis of friendship, contributed to decision

making at the group level. There is other evidence to support this hypothesis. For example, during

small group collaborative activities in the first half of the study, students were allowed to exercise free

choice as to whom they wanted to work with. At T2, generally, students chose to work within friendship

clusters. However, during the period prior to T3, a number of students indicated a preference to be assigned to

groups. There are several plausible explanations as to why the dynamic shifted. One reason may have been

that similar to the first social mechanism, just until after T2, students had difficulties grasping the concepts

and felt more comfortable discussing ideas with students with whom they had an established social

connection or positive identity, thereby mitigating the possible negative judgments they may have perceived

resulting from having cognitive deficiencies revealed. As students gained more confidence in their

understanding of the concepts, this identity bias, further elaborated on in the ‘‘Discussion’’ section, was

displaced by a more evidence-based content-specific bias that we believe was a direct impact of the learning

events embedded in the complex systems heuristic. As greater volumes of information entered into the

cognitive system over time, where students cycled through both individual and group level metacognitive

processing in conjunction with discursive activities such as the cocktail party in which students were required

to publicly display their knowledge or understanding, an important conceptual feedback loop was

established. This feedback loop may have served as a selection mechanism that, in turn, influenced greater

variability in decision-making both by individual students and within the classroom conceptual system as

a whole.

Feedback Signals: Thinking Tags as a Memetic Vehicle. The notion of feedback has also been useful in

identifying possible Thinking Tag technology influences on social processes. In a questionnaire aimed at

understanding student perspectives on the value afforded to this novel learning tool, the majority of students

both high achieving and low achieving, indicated that they thought the Thinking Tags enhanced the

enjoyment of the cocktail party activity. Greg writes:

Thinking tags were great. They made me more interested in hearing other people’s opinions. Also, the

thinking tags made everyone more energetic about expressing their view.

Table 5

Student selections of most like-minded others who are part of their friendship cluster

Day 10 (T2) Day 16 (T3)

Natalie (0)!Lisa (0), Ben (0) Greg (þ2)!Marshall (þ4)

Sandy (þ5)! Janice (0) Yasmin (0)!Miranda (0)

Ebby (�2)! Janice (0) Miranda (0)!Yasmin (0)

Greg (þ1)!Marshall (þ3) Patrick (�3)!Ben (�3)

Lisa (0)!Natalie (0)

Marshall (þ3)!Greg (þ1)

Janice (0)!Ebby (0)

Norah (0)!Lisa (0)

Yasmin (0)!Miranda (0)

Miranda (0)!Yasmin (0)

Sandy (0)!Ben (0)

Ben (0)!Natalie (0)

Totals 12/18¼ 67% Totals 4/18¼ 22%



Yasmin echos Greg’s thoughts. She writes:

I thought the thinking tags were cool. It made me happy, excited. I enjoyed working with the thinking

tags and communicating with my classmates.

These feelings from students were certainly evident from classroom observations in that, apart from the

initial awe and confusion, once the class understood how the Thinking Tags worked, there was a higher degree

of motivation to participate in the activity. It should also be noted that this high level of interest was evident

during all three ‘‘cocktail-party’’ events.

One additional finding that merits some consideration with respect to the development of social

processes is what information students believed was gleaned from this use of technology. In their responses to

the question, ‘‘Did the Thinking Tags present you with information that you thought was important?’’, both

Greg and Yasmin respond in the following way:

(Greg)

It was neat to see other people’s opinion about your statement displayed electronically. The thinking

tags helped me understand other people’s ideas because when I asked for their opinion, the tags

notified if it was a match, or not a match to that person’s idea.

(Yasmin)

It showed how many people agreed with me and disagreed with me. I first saw that everyone’s answer

was different from mine, but before it was too late, I changed my answer and mine [was] the same as

everyone’s [which I] decided was great.

In the above comments, we see some evidence of how the Thinking Tags may have served as an

important signal as to how students own opinions measured against the collective opinions of the larger group.

For Yasmin in particular, this feedback provided information that forced her to adopt a different stance. It is

hypothesized that this public display of understanding (which normally remains hidden) initiated a new

selection pressure that potentially served as a trigger for a new cognitive configuration. In this way, the

Thinking Tags became a memetic vehicle.

Conceptual Memetic Processes

In this section developments within the cognitive system through a content analysis of memes presented

in student rationales are discussed. Two memetic processes are advanced: (i) Meme Coupling Influences and

(ii) Meme Outlier Influences.

Meme-Coupling Influences. Table 3 shows that at T1, in their written rationales, students most often

selected memes in the meme type category of ‘‘Cruelty to Animals’’ (9). Where there is more than one meme

represented, this meme type was most often coupled (44% of the time) with memes from the categories of

‘‘Improves human life, enhances human health’’ (3) and ‘‘Represents human progress, knowledge, or

technological advancement’’ (1). The curricular materials during this phase of the study, in fact, focused on

GE technologies such as cloning and xenotransplantation that required students to examine their beliefs about

the value of non-human animal species relative to human life. This result can perhaps be viewed as the

emergence of feelings of ambivalence, which were also evident anecdotally in verbal discussions between

students. This ambivalence may have been manifested through equal consideration of both sides of the issue

that forced students to take a neutral position. Furthermore, at T2, this coupling effect increased in frequency

to 66%. While statistical claims or predictions based strictly on the numbers cannot be made, potentially

significant cognitive memetic mechanisms at play can be explored through examination of student subgroup

rationales.

In a previous section, data that illustrated a social mechanism of ‘‘do as the smart students do’’ was

suggested to account for differences in like-mindedness selections between T2 and T3. If at T2 the class was

generally imitating the high academic status students then it is reasonable to conclude that reasoning inherent

in explanations of the smart students would be mimicked. At T1 three out of the four rationales that

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represented the meme coupling in the categories of 1/3 and 9 were posited by Ben, Natalie, and Greg. And

either coincidentally or not, the number of meme coupling instances of this type increased at T2. Both Yasmin

and Thomas are among the group that employed this potential mimicking strategy. At T2, the high status

students continue to take neutral stances on the issue. However at T3, there appears to be an interesting shift.

Natalie maintains her neutral position as evidenced in her final rationale:

I have chosen 0 because I am still not really for genetic engineering, nor against it. This is because

genetic engineering has its pros and cons. For everything new and exciting that scientists discover, it

always has negative effects even though it will be extremely helpful in the future. We should try and

look into each discovery a little more before we change the natural world. It would be helpful because

it will help us with the shortage of organs, cloning, food and health, milk from BGH, more organs from

pigs, and more population of endangered species from gene changing. It is unacceptable because by

using BGH in cows, it endangers them as well as hurts them. By using organs from pigs, we create

more and then kill them, what do we do with the rest of the pig? Plus, it could hurt the animals if [their

genes are changed].

However, Ben and Greg change their neutral stances to negative (against GE) and positive (for GE),

respectively. Ben’s rationale at T3 has already been presented in another section. Greg’s is as follows:

I gave the question a þ2 for a number of reasons. One reason is because most, if not all of the

environment will be given back by new forms of technology such as cloning (if research is successful).

If research isn’t successful though, a piece of our natural world will be lost. Another reason why I think

this is semi-acceptable is because lots of research will be needed to make our world better, safer,

healthier, and a piece of natural land is just a small contribution to this study.

It is suggested that the meme coupling effect resulting in the evolution of the neutral stance created a

strong selection force that actively selected against more parochial views that may have influenced the drastic

drop in rankings for Ben and Greg at T3. Furthermore, as previously discussed, at T3, Thomas moves to the

second position in the like-mindedness ranking where his rationale reflected concerns in the curricular focus

at the time (crop farming). His selections included memes in the meme-type categories of ‘‘Helps to improve

world hunger crisis or aids the population increase’’ (2) and ‘‘Safety concerns, uncertainty of future effects,

processes are risky’’ (7) resulting in a neutral stance. Tables 2 and 3 show increases in the frequencies of

memes occurring in these categories at T3 and also demonstrate that when they occurred they were most often

(38% of the time) coupled with each other. Ebby and Norah, ranked 4 and 5, respectively, also presented

rationales that included this meme-coupling. This result provides additional evidence supporting the meme-

coupling claim advanced here.

Meme Outliers Influences. In this section data that suggest another kind of conceptual selection force

may have been operating in the study are explored.

One individual in the student subgroup who has yet to be discussed in any detail is Marshall. Recall in his

social and cognitive profile, Marshall is characterized as a different kind of thinker. Although according to

Ms. Saunders, he possesses the greatest intellectual capacity, he does not experience the same academic

respect as the other bright students in the class. In the like-mindedness data (Table 4), at T2 and T3 he ranks

9th and 17th, respectively. When his meme selections in Table 3 are analyzed, he again stands apart from the

larger group. His rationale at T1 contained one of only two memes coded for the meme type ‘‘No other

reliable alternatives to using non-human organisms’’ (4), and at T2 and T3 he provided two of the three

memes in the ‘‘N/A or answer cannot be coded due to ambiguity’’ (12). This is what he wrote in his final

rationale:

I think that use of the natural world for genetic engineering is okay because if we use domestic

animals, it would be irrelevant whether you genetically modify them then use them for something like

food or you use them for something like food without modifying them. This is because either way the

animals would be slaughtered and use for something afterward.



He is presenting the argument here that implies that domesticated animals are being used already for

human consumption, therefore any notions put forth against GE for biocentric purposes such as ‘‘cruelty to

animals’’ is hypocritical. This is rather more a sophisticated argument as it requires the investigation of

current societal norms and values afforded to non-human species. Based on the collective cognitive level of

the class a case could be made that neither Marshall nor his ideas would be selected because they were beyond

comprehension at the level of his peers.

A similar rationale can be used for categorizing ‘‘winner’’ and ‘‘loser’’ memes in Tables 1 and 2. The

meme types with the lowest frequency counts happened to be ones that are the most difficult to

grasp conceptually. For example, selecting for the meme types ‘‘Processes are not natural’’ (5) and

‘‘Tampering with natural processes’’ requires knowledge of the concept of biodiversity, which is advanced

biological or ecological content. Both Marshall’s and the winner/loser memes examples exhibit a kind of

narrowing or normalizing effect that could be interpreted as an evolution of a system of thinking closely

guarded against outside sources that have the potential to alter the trajectory of foundational conceptual

stabilization.

Discussion

In the science education literature and national science surveys, it has been shown that understanding of

important contemporary socio-scientific issues amongst students and the general public is lacking both in

depth and in engagement. Recent research that has addressed this problem has focused on students’ inabilities

to reason using argumentation practices commonly found in scientific investigation and the culture of science

itself. While this research has been invaluable in illustrating the lack of argumentation skills and in supporting

educational programs that promote argumentation skills for effective decision-making, little is still

understood about the processes of reasoning and influences that may affect such decision-making. Due to the

complexity often associated with socio-scientific issues, curricular interventions and pedagogical practices

need to reveal and address this inherent complexity which include creating discursive environments that

enable students to share their ideas, negotiate multiple perspectives and claims, and evaluate new ideas as they

enter the learning system. Within such environments, this study presents some evidence to suggest that there

may be social and cognitive copying mechanisms at play. The following section speculates on how such

social and cognitive copying mechanisms align with the extant memes and memetic processes literature and

provides further explanation for how these processes may exert influences of students’ decision-making

processes.

It is widely understood that the field of memetics as a viable research program has hit a critical point.

Aunger (2000) writes, ‘‘The question of whether memetics has an empirical future remains open. Among

partisans and detractors alike, a major disappointment with the current status of the field is the lack of studies

in what might be called ‘applied memetics’’’ (p. 230). Classrooms seem a likely venue for just such an

undertaking. Students, teachers, curricula, and communities all coalesce into functional working systems

every day where it is assumed that learning and ideas evolve. A crucial challenge remains however, in

developing methodologies that would allow researchers to observe memetic processes in operation and

provide evidence that reveals what these processes are and how and why they work. Thus, an important

motivation underpinning this study was to search for evidence of memetic selection forces operating in the

classroom. However, a measure of success and validity still rests on whether or not theoretical constructs can

be explained by the evidence produced. In this section the hypothesized mechanisms are compared to

theoretical mechanisms already advanced in the field of memetics.

One of the major findings of this study is that memetic processes can be described in at least two ways—

social and conceptual and both must be taken into consideration in order to understand how and why ideas

change. Gil-White (2004) discusses social and conceptual mechanisms in terms of content and non-content

biases. He suggests that several of our leading meme theorists such as Blackmore (1999) and Dennett (1995)

place an unwarranted primacy on transmission and selection forces based on content biases which refer to the

properties of a meme, that is, the idea itself. He argues that non-content biases such as prestige and

conformism (pioneered by Boyd & Richerson, 1985 and most thoroughly addressed in Henrich & Gil-White,

2001 and Henrich & Boyd, 1998) are equally important cultural selection forces.

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. . .if we believe psychological biases are the main source of selective forces acting on memes, then the

discovery and implications of non-content biases should be taken seriously. This detracts nothing from

the importance of content biases, it merely adds to the repertoire of forces that must be considered.

(Gil-White, 2004: p. 14).

Castelfranci (2001) also draws a distinction between social and conceptual influences. In support of his

main claim that autonomy of cognitive agents militates against social influence and that cognitive constraints

mediate the adoption of a given meme, he proposes three socio-cognitive micro-mechanisms: a ‘‘practical

problem-solving’’ mechanism as a cognitively based memetic process; and norm adoption and identity/

membership mechanisms as socially based memetic processes. He suggests that these different socio-

cognitive micro-mechanisms make different predictions and have different macro-results in meme

propagation. If the most probable environment for the development and transmission of memes is the mind, in

order to understand cultural evolution it is necessary to identify the cognitive principles affecting the success

of memes within minds.

The main goal here is not to fashion any claims that would lend more importance to either social or

conceptual memetic processes, rather, it is to merely highlight the fact that the results of the study indeed

consider both. But exactly how closely do the hypothesized memetic processes mirror the specific

mechanisms found within each theoretical lens of social and conceptual? It appears that the results

corroborate a number of theoretical arguments in the field, which can in turn lend some validity to the

applications of memes and memetic processes in educational settings where empirical studies in educational

research are lacking. The following section discusses the three social and two cognitive mechanisms

hypothesized to have been operating in the classroom and compares these mechanisms to theoretical

constructs established in the memes and memetic processes literature.

Social Mechanisms

‘‘Do as the Smart Students Do’’: The Influence of Status¼Prestige. In this study, a status selection

strategy called ‘‘do as the smart students do’’ is hypothesized to have occurred during times when students

experienced conceptual difficulties. As summarized in Laland and Odling-Smee (2000), studies of social

learning in a variety of species show that some animals adopt a similar strategy of ‘‘do-what-the-successful-

individuals do’’ in order to improve survival success. For example, bats that cannot find food on their own

follow other successful bats to find food. In the social learning of food preferences, redwing blackbirds watch

to see whether the leader bird becomes sick or survives. In primate species, the adoption of a novel behavior is

dependent on the identity of the exhibitor of the behavior. In all of these cases, the evolution of certain

behaviors within the species is strongly linked to status.

In humans, studies of opinion leaders on the quality of practice in health care professions present similar

results. Soumerai et al. (1998) found that working with opinion leaders accelerated the adoption of beneficial

medical therapies. Likewise, O’Brien, Oxman, Haynes, Davis, and Freemantle, (2000) showed that who

delivers an educational intervention is strongly correlated with whether the intervention is successfully

implemented.

Finally, Henrich and Gil-White (2001) describe a theory that explains the evolution of prestige. They

state that copying the behaviors of those who are likely to have better-than-average information saves learners

the costs of individual learning. Natural selection will favor improved learning efficiencies that include

increased frequency and greater quality of interaction with the person being copied. Therefore, certain

behaviors such as deference and by virtue of this, prestige or status will be selected for.

Influences of Friendship¼ Identity Versus Problem-Solving Strategies. In the second social memetic

process identified, again where learning difficulties appeared to be present, students selected a person from

their friendship cluster in order to mitigate the possibility of negative judgment. This occurred both during

group activities and when ranking for like-mindedness implying that identity played an important role.

Castelfranci (2001) writes that a likely explanation for such behavior stems from a motivation for ‘‘not

propagating in some direction, not revealing our ‘knowledge’ or feature, in order to protect our difference...’’

(p. 6). In this study, it was hypothesized that as students gained greater confidence in their conceptual abilities,



this selection strategy changed into one based on the quality or content of rationales. Castelfranci (2001) also

provides an explanation for this behavior as adopting a problem-solving mechanism where ‘‘individuals

accept new behaviors, plans, or tools as better solutions for their own problems, as good means for their goals;

the group diffuse and preserve (memorize) and transmit the best (discovered solutions)’’ (p. 4).

Feedback Signals: Thinking Tags as a Memetic Vehicle¼Extended Phenotypes. With Thinking Tags

activity, it was hypothesized that the technology sent an important signal to others about the degree of

acceptance or rejection of individuals’ ideas (information which is normally hidden). Once it was revealed,

this feedback may have forced the adoption of new stances or opinions in some cases like with the example of

Yasmin. Therefore, it is hypothesized that the technology became a pivotal memetic vehicle. Dennett (1995)

and a number of other theorists propose the analogy of meme as gene. Just as genes are invisible and carried by

gene vehicles (biological organisms), memes are also invisible and carried by meme vehicles such as pictures,

books, and computers. Both gene and meme produce phenotypic effects that ultimately become the objects or

characteristics acted on by selection.

The fate of memes is. . .determined by whether copies and copies of copies of them persist and

multiply, and this depends on the selective forces that act directly on the various physical vehicles that

embody them. (Dennett, 1995: p. 348).

For Yasmin, we might say that the Thinking Tag was an extended phenotype that provided information

about herself and her memes that could be viewed and evaluated by others and potentially changed (which

actually did happen in her case).

Similarly, Aunger (2002) calls meme vehicles interactors and like Dennett ascribes this role to many

different kinds of artifacts like wagons and rockets. He states that interactor-artifacts can be thought of as

templates for signals. When artifacts come into contact with other artifacts, ‘‘a signal can start to reflect a new

pattern, which changes its amplitude and frequency, for example, to ‘reflect’ the fact that it is now carrying

information about the nature of the artifact it bumped into’’ (p. 286).

Conceptual Mechanisms

Meme-Coupling Influences¼ Linked Loci. In this category of conceptual selection forces, the data

show that meme-coupling is used to protect the existence of certain evolved views while actively selecting out

views that are not part of the accepted corpus of understanding. Four meme types appeared to be coupled in

this study, and within each both meme types occupied different sides of the GE continuum which may have

led in some cases to a neutral position. More renegade ideas were not allowed to gain access to the conceptual

system. Dennett (1995) again offers a plausible explanation for this phenomenon. He compares it to a parallel

mechanism in population genetics called linked-loci, that is, the idea that when two memes happen to be

physically tied together, they tend to replicate together improving the evolutionary advantage of both memes.

Referring to memeplexes, a conceptual mechanism expanded on in the following section, Bloch (2000) states

that there is a recognition by memeticists that different aspects of culture are linked, an effect that affords each

unit a selective advantage. In both cases this advantage comes at the expense of other memes. Filters are

constructed to sanction certain forms of information.

We all have filters of the following sort: Ignore everything that appears in X. For some people, X is the

National Geographic or Pravada; for others, it is The New York review of Books; we all take our

chances, counting on the ‘‘good’’ ideas to make it eventually through the stacks of filters of others into

the limelight of our attention. (Dennett, 1995: p. 350).

Meme Outliers Influences¼ The Development of Memeplexes and Cultural Norms. The meme-

coupling or linked-loci effect is intimately tied to the notion of memeplexes, however the distinction made

here is on the scale of influence. The two meme outlier examples (Marshall and winner/loser memes) could

possibly represent the most important selection mechanism operating in the service of establishing cultural

norms. Once understanding coalesces into paradigmatic thinking, it is extremely difficult to attempt any

conceptual change. Plotkin (1994) explains that memes that group together as ‘‘bundles’’ of ideas form

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higher-order-knowledge structures that allow individuals access to and the ability to function within a culture.

Once access is gained, these meme bundles proffer their own survival advantage by becoming associated with

a person’s self-concept (Blackmore, 1999) and identity which are significant factors influencing decision-

making and learning outcomes.

‘‘Bundles’’ of ideas as memeplexes have been extensively theorized by prominent memeticists like

Blackmore (1999). Again the meme as gene analogy is used to reveal the possible evolutionary force at play.

Genes. . .clump together into chromosomes, and chromosomes are packed together inside cells.

Perhaps more importantly, the whole gene pool of a species can be seen as a group of mutually

cooperating genes. The reason is simple: a free-floating piece of DNA could not effectively get itself

replicated. After billions of years of biological evolution, most of the DNA on the planet is very well

packaged indeed, as genes inside organisms that are their survival machines. . .We could simply draw

the analogy and say that memes should behave the same way. . .Imagine two memes, one ‘‘send a

scratchcard to x’’ and another ‘‘win lots of money.’’ The former instruction is unlikely to be obeyed

just on its own. The latter is tempting but includes no instruction on how to. Together and with

some other suitable co-memes, the two can apparently get people to obey—and copy the whole

package on again. The essence of any memeplex is that the memes inside it can replicate better as part

of the group than they can on their own. (Blackmore, 1999: p. 19–20).

Memeplexes have been described as powerful forces that influence people to adopt beliefs that

seemingly have no adaptive evolutionary advantage or do not make sense to the rational mind such as

memeplexes about religion.

Collectively there appears to be evidence illustrating the kinds of memes and memetic processes

students construct and undergo when reasoning about a complex scientific issue like GE through a complex

systems methodology. The following section outlines the potential contributions these results make to the

field of education.

Implications For Education

Within the science education literature, argumentation has been identified as a pedagogical strategy that

could improve practices and understanding about socio-scientific issues. While the argumentation research

has been focused on demonstrating how students reason with or without the practice of argumentation, no

studies (to the author’s knowledge) have been undertaken to reveal why difficulties in reasoning exist. It has

been shown that the study of memes and memetic processes can provide potential insights about several

social and conceptual influences that appear to exert differential affects on what ideas are salient in the

learning system of a classroom when studying a complex socio-scientific issue.

When the classroom is viewed as a group of interacting agents, the complex network of relationships

formed give rise to behaviors that can evolve over time. Within this complex system, this study provides some

evidence to show that memes and memetic processes can have the potential to provide information about how

the system is performing. The study of memetic processes, both social and conceptual, may allow teachers to

gain a better understanding about why students make decisions before and during an intervention (e.g.,

influences of friendship). After the intervention, it may also potentially allow teachers to understand how

decisions are constructed and what ideas are selected or not (e.g., meme-coupling and meme outlier effects).

Furthermore, the selection forces described and substantiated in the memetics literature may provide

educators with alternative methods to view the learning landscape. For example, the mechanisms influencing

the development of memeplexes may be a central contributing factor to results of the NSF survey discussed in

the introduction to this article and one that educators may need to contend with. Even acknowledging the very

existence of memeplexes gives us a robust foundation with which to begin programming effective and

sustained learning activities where important knowledge can be applied. An additional advantage along this

line of thinking is that it attends to the social and intellectual factors that impact our ideas. These are the

components that need coordinated attention if students are to develop their ideas about socio-scientific issues

that are of central importance in our society. This perspective also helps us to further understand the role

argumentation, evidence and opinion in the development of scientific and technological understanding, and



define mechanisms that help or hinder the ability for people to engage in scientific and technological

knowledge advancements. It is somewhat related to research on how to achieve knowledge-building, a

seminal, long-standing, and highly regarded educational research tradition (Bereiter, 2002; Scardamalia,

2002) in that peer-to-peer interactions can be investigated to determine how to scaffold activities that allow

the ideas or concepts to become the focus of learning and information exchange rather than being usurped by

hidden social copying mechanisms that potentially stand in the way of conceptual understanding. Finally, the

study of memes and memetic processes can challenge our understanding of what understanding might be.

What is it that emerges or coalesces and how and why does this emerge? The final section in the discussion

proposes several interesting insights into how the study of memetics could reconceptualize social and

conceptual processes occurring in the learning systems of classrooms with respect to the emergence of

prestige, identity, the tools and signals that carry information, and how rationales coalesce into decisions that

underpin cultural norms and practices.

Study Limitations and Future Research

As applications of complex systems and memes and memetic processes are at the moment new to

science education research, more empirical and experimental studies using a larger sample size are required

to compare the relatively tenuous albeit insightful claims being made in this exploratory study.

This research was supported in part by a grant from Dr. Derek Hodson and the Imperial Oil Center

for Studies in Science, Mathematics, and Technology Education.

Note

1The Thinking Tag technology is no longer manufactured due to cost and other difficulties in maintaining the

platform. Shortly after this study was completed the Virus game described in Colella (2000), the Discussion game (Yoon,

2007) and various other similar applications collectively called Participatory Simulations were ported to the Palm OS

handheld platform—a more ubiquitous and stable technology. See Klopfer, Yoon, & Perry (2005).

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Using memes and memetic processes to explain social and conceptual influences on student understanding about complex socio-scientific issues