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Keywords:

  • evolution;
  • knowledge;
  • acceptance;
  • belief;
  • feeling of certainty;
  • teachers

Abstract

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

We propose a new model of the factors influencing acceptance of evolutionary theory that highlights a novel variable unexplored in previous studies: the feeling of certainty (FOC). The model is grounded in an emerging understanding of brain function that acknowledges the contributions of intuitive cognitions in making decisions, such as whether or not to accept a particular theoretical explanation of events. Specifically, we examine the relationships among religious identity, level of education, level of knowledge, FOC, and level of evolutionary acceptance to test whether our proposed model accurately predicts hypothesized pathways. We employ widely used measures—the CINS, MATE, and ORI—in addition to new variables in multiple regression and path analyses in order to test the interrelationships among FOC and acceptance of evolutionary theory. We explore these relationships using a sample of 124 pre-service biology teachers found to display comparable knowledge and belief levels as reported in previous studies on this topic. All of our hypothesis tests corroborated the idea that FOC plays a moderating role in relationships among evolutionary knowledge and beliefs. Educational research into acceptance of evolutionary theory will likely benefit from increased attention to non-conscious intuitive cognitions that give rise to feeling of knowing or certainty. © 2011 Wiley Periodicals, Inc. J Res Sci Teach 49: 95–121, 2012

Evolutionary theory is considered “the most powerful theory within the field of biology” (Rutledge & Warden, 2000), and without the “light of evolution,” biology “becomes a pile of sundry facts—some of them interesting or curious but making no meaningful picture as a whole” (Dobzhansky, 1973). The theory of evolution is considered so central to biology that the National Academy of Sciences (1998) has recommended that teachers “use evolution as the organizing theme in teaching biology,” and major reform documents in science education identify evolutionary theory as a central organizing principle of the biological sciences (National Research Council, 1996, 2011; Rutherford & Ahlgren, 1990). Evolution is listed first in the Core Concepts for Biological Literacy for college undergraduates (Brewer & Smith, 2011), and the “findings of evolutionary biology are deeply integrated into our culture … [informing] agriculture, medicine, public health, environmental health, natural resource management, human understanding, and even the pursuit of justice within the legal system” (Mindell, 2006, p. iv). Yet, many, including biology teachers (Berkman & Plutzer, 2011), do not accept evolutionary theory as an adequate explanation for the diversity and unity of life on Earth (Miller, Scott, & Okamoto, 2006). This circumstance is a serious concern to science educators in that it is both a rejection of the central theoretical framework of modern biology and a denial of the fundamental science underlying many applications and practices on which we have come to depend for advancing human well-being and sustaining environmental health.

There have been many studies focusing on acceptance of evolutionary theory, from surveys documenting levels of acceptance among members of various groups (Cavallo & McCall, 2008; Donnelly, Kazempour, & Amirshokoohi, 2009; Miller et al., 2006) to studies of the influence of religious views on acceptance (Dagher & BouJaoude, 1997; Lawson & Worsnop, 1992; McKeachie, Lin, & Strayer, 2002) and the relationships among beliefs, knowledge level, and rate of acceptance (Deniz, Donnelly, & Yilmaz, 2008; Sinatra, Southerland, McConaughy, & Demastes, 2003; Southerland & Sinatra, 2003). These studies have employed a variety of instruments to measure knowledge level and level of acceptance, and a range of populations have been sampled, from high school students to college students and practicing teachers. Despite the variety of studies that have been reported, there are no convincingly clear findings about the relationships among knowledge level, beliefs, and acceptance level regarding the theory of evolution. While some studies have provided evidence for a robust relationship between knowledge level and level of acceptance (Paz-y-Miño & Espinosa, 2009; Rutledge & Warden, 1999), others found no evidence of a straightforward relationship (Sinatra et al., 2003), and little evidence that instructional treatments affect acceptance levels (Chinsamy & Plagányi, 2007), even when learning gains have been substantiated (Nehm & Schonfeld, 2007). It has also been suggested that the nature of relationships change when acceptance of evolutionary theory is framed in the context of macroevolution rather than microevolution (Nadelson & Southerland, 2010).

Investigations into the factors influencing or related to acceptance of evolutionary theory have given rise to two conceptual models of proposed relationships. One model (Southerland & Sinatra, 2003) focuses on the influence of Intentional Level Constructs (epistemological beliefs, dispositions, and goals) on both understanding and acceptance of “controversial” topics such as evolutionary theory. We use quotation marks here to indicate that the topic of evolution, while not controversial within the scientific community, appears to be so among students and the general public. A competing conceptual model (Deniz et al., 2008) represents acceptance of evolutionary theory as being related to understanding in the cognitive domain and two factors in the affective domain: learning/thinking dispositions, and an unknown mediator between parents' education level (contextual domain) and acceptance. It is also noted that this model includes the number of years in a biology education program (contextual domain) as a modulator of understanding. Both studies used a multiple-choice instrument as test of evolution knowledge, both studies engaged undergraduate students as participants, and both studies used regression analysis to determine how much variance in acceptance of evolution could be explained by the measured variables.

In testing their model, Southerland and Sinatra (2003) found that level of acceptance of evolution could not be predicted at a statistically meaningful level by any of the measured variables, though there were statistically significant correlations between acceptance of human evolution and both epistemological sophistication and dispositions. In testing their model, Deniz et al. (2008) found that a modest 8.3% of the variance in acceptance of evolution could be explained by the combined contributions of knowledge of evolution and thinking dispositions. An additional 2.2% of the variance was explained by Parents' Education Level, but it was acknowledged that this portion of the explained variance may not be generalizable beyond the population being studied. In short, testing of these two models has provided little evidence of a robust relationship between acceptance of evolution and any other variable, including understanding (knowledge) of evolution.

In this study, we propose a revised model of the factors influencing acceptance of evolution, a model that is derived in part from the models described above with modifications informed by a broader theoretical framework and a reinterpretation of previously reported findings. Our findings—based on a test of selected components of the revised model—will be presented along with implications for further research. First, we discuss the potentially confounding language that has become associated with this line of research, and we offer working definitions of the terms used to represent the core concepts of our model.

Beliefs, Knowledge, and Acceptance

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Research on factors associated with acceptance of evolution has been confounded by lack of agreement on what is meant by words such as beliefs, knowledge, and acceptance. Even policy decisions regarding what is to be reported regarding public acceptance of evolution have been questioned on the basis of distinctions being made between beliefs, knowledge, and acceptance (Bhattacharjee, 2010). Some insist that believing is distinct from knowing, and that use of the term belief should be avoided in the science classroom (Smith, Siegel, & McInerney, 1995) while others insist that consideration of what is believable is central to understanding and accepting evolution (Cobern, 1994). This particular disagreement seems at odds with the epistemological view that knowing is a particular form of believing; that is, believing something counts as knowing when: (a) what is believed is in fact true, and (b) one has firm grounds for the belief (Quine & Ullian, 1978). It is further noted that one can believe without knowing (i.e., what one believes is not true, or there are no firm grounds for believing, or both), but knowing requires belief. Wolpert (2006, p. 25) has identified reliability of evidence as the key feature distinguishing unfounded belief from factual knowledge, so the criterion of evidence seems to determine the extent to which believing is equivalent to knowing.

Further, “acceptance of a scientific theory involves the belief that it is true” according to van Fraassen (1980, p. 8), and conversely, “to believe something is to accept it as true” (Haack, 2007, p. 63). So, in our view, believing, knowing, and accepting are intimately related terms, all having a form of belief as a component. The concept of belief ramifies even farther into our language, to include causal beliefs, religious beliefs (convictions), epistemological beliefs, cultural beliefs, and more. Belief is a word that takes on meaning from context and the nature of supporting evidence, even within science when biologists assert that they believe evolution to be a true accounting of how life forms emerge and change over time.

Those who have suggested that we avoid using the words believe and belief in science classrooms, particularly when discussing evolution (Smith et al., 1995), warn of the prospect of fostering semantic confusions between scientific and religious uses of language. While we agree with the need to use language carefully and deliberately in science classrooms, it seems to us that we have a broader obligation to help students understand the multiple meanings and applications of words, and the particular meanings that many words take on in the context of science.

This view is further supported by current models of brain functioning, presented in the following section, that couple knowing and the feeling of knowing. To that end, we will assume that beliefs of a particular sort are embedded in scientific knowledge and in acceptance of scientific theories; we cannot escape our webs of belief. From this perspective, “scientists would be better served by saying, ‘I believe that evolution is correct because of the overwhelming evidence”’ (Burton, 2008, p. 219).

We accept as self-evident that there must be relationships among level of knowledge about evolution, understanding, and level of acceptance of evolutionary theory, but research findings to date lead us to believe that the relationships are not straightforward, and are not adequately captured by epistemological considerations alone, nor by disputes over semantics. It seems to us that we must also consider what is coming to light about how the brain actually functions, and how our intellectual dispositions are both constrained and enabled by webs of belief. As a first step along the path to developing a theoretically grounded model that includes consideration of cognitive function, we have explored what it means to know and understand from a neurological point of view.

Neuroscience Perspectives on Evolutionary Belief and Knowledge

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

From a neuroscience perspective, there are two components to understanding: having the knowledge that enables comprehension, and having the feeling of knowing (Burton, 2008, p. 4). One component—knowing—is the result of conscious thought processes, while the other component—the feeling of knowing—results from involuntary, unconscious processes, intuitive cognitions, produced by neural networks within the brain's interface between incoming sensory data and the construction of a final perception. The feeling of knowing is a form of metaknowledge from an internal monitoring system that “qualifies or colors our thoughts” (Burton, 2008, p. 3). The feeling of knowing, because of its universality, relatively specific site of origination within the brain, and capability of being activated without conscious thought, is considered a primary mental state, an emotion, akin to fear and anger, “not dependent on any underlying state of knowledge” (p. 41).

There seems to be a cluster of allied mental states, such as feeling of knowing, feeling of certainty (FOC), feeling of correctness, feeling of conviction, and feeling of rightness that “arise out of involuntary mental sensory systems that are integral and inseparable components of the thoughts they qualify” (p. 139). It must be noted that these intuitive cognitions, or feelings, are to be differentiated from levels of certainty or a disposition of confidence arrived at through conscious reasoning. Whereas reasoned confidence or certainty arise from consciously evaluating situations or evidence and making judgments, an intuitive FOC emerges involuntarily without prior, conscious cognition. It is also noted that: “the emotional signal is not a substitute for proper reasoning. It has an auxiliary role, increasing the efficiency of the reasoning process and making it speedier” (Damasio, 2003, p. 148). The increased speed and efficiency result from the automatic nature of intuitive cognitions that are described more fully below. Further, “the emotional signal can operate entirely under the radar of consciousness. It can produce alterations in working memory, attention, and reasoning so that the decision-making process is biased toward selecting the action most likely to lead to the best possible outcome” (p. 148). For simplicity and ease of discussion, we have chosen to use feeling of certainty (FOC) in this study as being representative of this cluster of mental states that arise unconsciously in association with conscious thoughts. We acknowledge that a comprehensive model accounting for acceptance of evolution would likely include interactions within a cluster of discrete mental states, but focusing on FOC allows us to test for a specific intuitive cognition and examine its influence on acceptance of evolution.

Some have characterized these two components of understanding—the conscious process of knowing and the intuitive feeling of knowing, or FOC—as being the products of two separate cognitive systems that have distinctly different evolutionary histories (Evans, 2003, 2008; Gigerenzer, 2007; Smith & DeCoster, 2000). Different theorists give different names to these two systems (Evans & Over, 1996; Sloman, 1996; Stanovich & West, 2000), but all seem to agree that the two systems give rise to dual processing of information, with one system (System 1) tending to provide rapid, automatic responses that enter consciousness only in final form. The parallel system (System 2) is slower, sequential, and enables conscious, hypothetical, abstract thinking. System 1 gives rise to what have been termed “gut feelings” or intuitions (Burton, 2008; Gigerenzer, 2007), including FOC, while System 2 gives rise to logical reasoning. Others (Evans, 2010, p. 5) have characterized the two systems as giving rise to what are called the Intuitive Mind (System 1) and the Reflective Mind (System 2). An alternative to this dualist approach is the single-system framework of Osman (2004) that unifies forms of reasoning within one system, but acknowledges the roles of implicit and automatic cognitive processing in concert with conscious reasoning.

While many of the biases and limitations of the system giving rise to human reasoning are well known (Sinatra, Brem, & Evans, 2008), both in terms of perceptual biases and reasoning fallacies, the factors affecting intuitions generally or FOC in particular are not well known. It does seem evident, however, that when a cognitive task leads to conflict between the intuitive system and the reflective system, there tends to be a belief bias, with the validity of the reflective, reasoning processes being evaluated on the basis of the feeling of believability of the conclusions (intuitive cognition) (De Neys, Moyers, & Vansteenwegen, 2010).

This dual-processing account of thinking has some rather specific implications for one's level of acceptance of evolutionary theory. One implication is that the intuitive system can give rise to a FOC that is independent of conscious reasoning, so acceptance of evolutionary theory may be more strongly associated with FOC than level of knowledge. The range of factors affecting FOC is unclear other than to say that they seem to be part of an associative processing system that operates pre-consciously (Smith & DeCoster, 2000). Another implication of the dual-processing account is that FOC arrived at unconsciously may have a greater influence on final decisions, dispositions, or actions than conclusions arrived at through principled reasoning. As Burton (2008) notes, “Once firmly established, a neural network that links a thought and the feeling of correctness is not easily undone. An idea known to be wrong continues to feel correct” (pp. 97–98).

It is notable that several researchers have alluded to an intuitive factor in acceptance of evolutionary theory, but no one has included testing for such a factor in attempts to account for level of acceptance. For instance, Williams (2009) suggested that not believing evolution “begins with the natural, intuitive development of ‘creationist’ ideas as a very young child,” and these ideas are “reinforced by friends, family, or social attachments.” Evidence that children strongly hold essentialist beliefs and generate intuitive creationist beliefs about origins has been provided by Evans (2001).

The implication for acceptance of evolutionary theory is that constrained levels of certainty resulting from intuitive feelings may lead to a lowered level of acceptance, even when conceptual knowledge of evolutionary theory is high, and the level of acceptance will likely be resistant to change. To illustrate how the parallel cognitive systems may interact to determine levels of acceptance of evolution, we will here use our model (Figure 1) to describe the two cognitive pathways. The general form of our proposed model is adapted from a more general decision-making model developed by Damasio (2003, p. 149) that depicts decision-making using two complementary cognitive pathways. We have modified the Damasio model, however, to reflect the findings from research in science education regarding the cognitive factors associated with constructing knowledge, beliefs, and dispositions related to evolution and acceptance of evolution.

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Figure 1. Model of factors contributing to level of acceptance of evolutionary theory factors included in Path A are those of the cognitive domain, affective domain, contextual background, and reasoning processes that are considered elements of the conscious, reflective mind. Path B represents a separate, non-conscious pathway that gives rise to intuitive cognitions, including feeling of certainty.

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In the upper left portion of our model, we represent acceptance as a question, or decision, to be processed by the cognitive systems. The need for a response activates two cognitive pathways simultaneously: Path A is the conscious, reflective pathway that involves consideration of one's knowledge, understandings, memories of experiences, beliefs, conscious dispositions, and assumptions; and Path B is the non-conscious, intuitive pathway that involves the production of FOC based on associations developed over time as a result of previous outcomes, decisions, and cognitive reward patterns. Along Path A is a complex interplay of contextual factors, knowledge, and dispositions, including presuppositions associated with personal worldviews (Cobern, 1996), that lead to a conscious cognitive stance regarding evolution. This innate tendency to cognitively organize both the external world and the inner mental world has been called a “belief engine” (Wolpert, 2006), and we depict this component of the pathway as a compartment within which factors of the affective and cognitive domains interact with each other and with contextual factors to produce a response. The dashed lines within this compartment in the model are used only to illustrate the conventional grouping of these factors in educational research, not any presumed neurological compartmentalizations.

The conscious cognitive processing of Path A is facilitated by one's capacities to reason and understand, and various reasoning strategies and understandings are called into play as knowledge is assessed and alternative responses are considered. In our view, the interplay of factors within this “belief engine” has been the focus of previous research and models of acceptance of evolution. Our model expands previous conceptualizations of the factors leading to acceptance of evolution by proposing that this conscious cognitive processing pathway is complemented by a second cognitive pathway that operates outside the network of intentional, conscious mental activity.

Path B, the complementary, non-conscious cognitive pathway, “prompts activation of prior emotional experiences in comparable situations” (Damasio, 2003, p. 149). Unlike Path A, which begins with a decision to be made, and ends with a decision being made, Path B is a continuous loop, with feelings associated with the outcomes of past decisions influencing the reasoning process through interference, or influencing the conscious decision-making process through forcing attention on particular factors. When intuitive feelings are strong, processing of Path B can lead to an immediate final decision without conscious input. Indeed, it has been shown that when a limited amount of time is allotted to completing a cognitive task, the outcomes tend to be the result of intuitive cognitions (Evans & Curtis-Holmes, 2005).

In our conceptual model, a final decision, such as acceptance of evolution, is a synthesis of two cognitive systems; the two systems could be in agreement, and thereby be mutually reinforcing, or they could be in conflict, leading to the possibility of a low level of acceptance despite a high level of knowledge and logical reasoning. The extent to which each path influences the final outcome, or the extent to which the two systems are in conflict or are mutually reinforcing, depends on the situation, the individual's prior experiences and dispositions, and the individual's cognitive development.

Our model implies, then, that FOC influences the level of acceptance of evolution through two separate, but interrelated, mechanisms: through modulation of processing within the conscious “belief engine,” and by presenting a competing, intuitive acceptance signal. When the dual-processing system is activated by the question of whether or not to accept evolution, intuitive cognitions (Path B) have three points of influence as indicated by arrows in our model: (a) on the interplay of conscious beliefs, (b) on the processes of understanding and reasoning, and (c) on the final decision regarding level of acceptance. The influence at points (a) and (b) would take the form of modulating conscious thought, and the influence at point (c) would take the form of a competing signal that could be either reinforcing or contradictory to the output of conscious thought. All of these influences occur subconsciously, with only FOC emerging to consciousness at the conclusion of the intuitive cognitions.

The dashed line in Figure 1 from “Understanding & Reasoning” to “Level of Acceptance” reflects our uncertainty about the nature of the relationship between understanding and acceptance. Though several researchers have posited a positive relationship, they have also equated knowledge and understanding and used instruments measuring knowledge to represent level of understanding. We, however, differentiate knowledge and understanding, and view understanding as a transient state of comprehending a particular utterance or message during the course of communication. From this perspective, it seems likely that the primary influence of understanding on acceptance would be indirect, through modulation of knowledge construction or application within the “belief engine” compartment (represented by the rectangle in Figure 1).

Research Questions

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

In this study we initiate testing of our model by exploring the relationships among FOC and selected factors identified through previous research in science education as being related to acceptance of evolutionary theory. Specifically, we examine the relationships among religious identity, level of education, level of knowledge, FOC, and level of acceptance to determine if the proposed model accurately predicts the hypothesized relationships. We do so using a sample of 124 pre-service biology teachers. Following are the research questions that guided this study:

  • (1)
    To what extent does the hypothesized feeling of certainty (FOC) explain variance in the acceptance of evolutionary theory?
  • (2)
    To what extent are the predicted relationships among religious identity, level of education, level of knowledge, FOC, and level of acceptance of evolutionary theory confirmed?
  • (3)
    To what extent does FOC change as participants progress through a biology teacher preparation program?

On the basis of prior findings and the implications of the proposed conceptual model, we expect to find level of education, level of knowledge, and religious identity to be interrelated and for each factor to account for some variance in the level of acceptance. We further expect to find FOC separately accounting for a significant portion of the variance in level of acceptance. Finally, given the presumed interplay between the two cognitive systems, we anticipate that there may be discernable influences on FOC by factors associated with the reflective mind (conscious cognitions of Path A). Findings supporting this expected pattern of relationships will be considered evidence for the contributions of FOC in the proposed conceptual model.

Sample

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Although evolution and natural selection are widely recognized as core ideas in the life sciences (NRC, 1996), numerous studies have revealed that many biology teachers are partial to non-scientific or antievolutionary worldviews, despite significant coursework in both biology and evolution (Kim & Nehm, 2011; Moore, 2002; Nehm, Kim, & Sheppard, 2009). For this reason, a better understanding of biology teachers' intuitive cognitions holds promise for developing fresh perspectives for addressing such widespread ambivalence toward evolution (Nehm & Reilly, 2007). Our sample of biology teachers was carefully chosen in order to minimize the effects of exogenous variables common to many other studies of evolutionary knowledge and belief. In most prior studies of teacher cohorts, participants differed dramatically in coursework completion, content preparation, and age, potentially masking subtle relationships among explanatory variables (e.g., Rutledge & Mitchell, 2002). In our sample of pre-service biology teachers, in contrast, all students were generally of the same age; completed the same coursework requirements; displayed comparable intellectual abilities as measured by a standardized exam (the National college enrollment exam); and completed the same program (no students dropped out). Thus, when attempting to measure very complex constructs as we attempt here, standardization of the participant sample may minimize measurement “noise” and reveal more subtle signals.

We studied 124 pre-service biology teachers enrolled in two universities in South Korea. Participants were affiliated with the department of biology education at both institutions, and all ranked within the top 10% in the national college enrollment examination. One of the universities specializes in teacher education, whereas the other is classified as a Research University. It is important to note that the teacher qualification system in Korea presents teacher licenses after graduation from specific undergraduate programs, and so our sample is most closely comparable to American undergraduate students.

We did not collect exact age data because of the consistency of student ages in Korean undergraduate programs, which range from 19 to 23 years. Participation rate was 86.1%, with 28 first-year, 26 second-year, 32 third-year, and 38 fourth-year students completing the study. In terms of religious affiliation, participants self-identified as Buddhists (16.1%), Protestants (18.5%), Roman Catholics (11.3%), and No-religious affiliation (54.0%), closely mirroring the Korean population at large (Buddhists 22.8% of the population, Protestants 18.3%, Roman Catholics 10.9%, and no religious affiliation 46.5%) (The Statistics Korea, 2005).

The biology/biology education curriculum was very similar at the two universities because the curriculum is required to cover topics in the high-stakes Korean Biology Teacher Employment examination. First-year students enroll in a 1-year introductory biology course; second-year students enroll in animal and plant taxonomy classes, morphology, and biochemistry; third-year students enroll in cell biology, genetics, and microbiology; and fourth-year students enroll in ecology and molecular biology classes. Thus, biology coursework is part of the curriculum each year. It is important to note that neither institution offers classes specifically focusing on evolution, though many of the courses include evolution content; evolution is explicitly covered in introductory biology during the first year, in taxonomy during the second year, in genetics during the third year, and in ecology during the fourth year.

Instruments and Measures

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Level of Education

The academic calendars at Korean and most American universities are similar in that both comprise two 15-week semesters. In Korea, the first semester starts in March and the second semester starts in September. Data collection for our study was completed in early March at the beginning of the school year for each of the four cohorts; participants' level of education was simply noted as the academic year in which each participant was enrolled (1, 2, 3, or 4).

Religion

Korean society is very diverse in terms of religious identity. Unlike in the United States, a majority of Koreans self-report not being affiliated with any particular religion. For those citizens who do report religious affiliations, Buddhist, Protestant, and Catholic are the most common. As previously indicated (see the Sample Section), our sample of pre-service biology teachers mirrors Korean society at large and represents a diverse array of religious affiliations. We chose not to measure “religiosity” as others have done (e.g., Lombrozo, Thanukos, & Weisberg, 2008; see also Neumann, Neumann, & Nehm, 2011) because in Korea religiosity may be less relevant than religious affiliation. Recently, a Korean broadcast poll noted that 83.0% of Catholics “believed” in evolutionary theory, in contrast to 39.6% of Protestants. Differences were much less pronounced between those Koreans with no religious affiliation (69.9%) and Buddhists (68.0%) (Educational Broadcasting System, 2009). Thus, it appears that capturing religious affiliation may be more meaningful than measuring participant “religiosity.” For this reason, in our quantitative analyses (see below) we coded religious affiliations as no-religion, Buddhist, Protestant, or Catholic, or in some cases collapsed religious identity into Protestant or non-Protestant to increase statistical power.

Evolutionary Knowledge Measures

Measuring evolutionary knowledge is a challenging task (Nehm & Schonfeld, 2008, 2010). Unfortunately, the vast majority of studies conducted thus far to examine the relationship between evolutionary knowledge and belief have relied upon forced-choice instruments of unknown psychometric quality (Deniz et al., 2008; Korte, 2003; Lawson, 1983; Lawson & Worsnop, 1992; Peker, Comert, & Kence, 2009; Rutledge & Warden, 2000; Sinatra et al., 2003). Given this potential problem, our study used two different assessment tools for measuring evolutionary knowledge that have published reliability and validity data. The first instrument is the forced-choice (multiple-choice) Conceptual Inventory of Natural Selection (hereafter: CINS), which was developed to measure 10 evolutionary concepts using 20 items (Anderson, Fisher, & Norman, 2002). Despite displaying some psychometric problems (Battisti, Hanegan, Sudweeks, & Cates, 2010; Nehm & Schonfeld, 2008), the CINS is generally recognized as a valid tool for measuring evolutionary knowledge. Each item of the CINS has one correct response option for each question; therefore, the total score of the CINS instrument ranged from 0 to 20. While the original CINS paper suggests that it is a test only of natural selection knowledge, in fact it includes questions about speciation, which is widely recognized as a macroevolutionary concept (Futuyma, 2009). Thus, the CINS is a test of both microevolutionary and macroevolutionary content. For our sample, the reliability of CINS scores (measured with Cronbach's alpha) was 0.737.

The second instrument that we used to measure evolutionary knowledge was the Open Response Instrument (ORI), modified from Bishop and Anderson's (1990) widely used test. We used three of the five items from Nehm and Reilly's (2007) instrument, which included: (1) Explain why some bacteria have evolved a resistance to antibiotics; (2) Cheetahs (large African cats) are able to run faster than 60 miles per hour when chasing prey. How would a biologist explain how the ability to run fast evolved in cheetahs, assuming their ancestors could run only 20 miles per hour? And (3) Cave salamanders (amphibian animals) are blind (they have eyes that are not functional). How would a biologist explain how blind cave salamanders evolved from ancestors that could see? The ORI is a test of both microevolutionary and macroevolutionary knowledge because it prompts students to explain the mechanisms that account for between-species (i.e., macroevolutionary) change. To score students' ORI responses, we utilized the rubrics of Nehm et al. (2010). These scoring rubrics consist of seven key conceptions and six misconceptions. Key Concept (KC) scores for each item ranged from 0 to 7, and misconception scores for each item ranged from 0 to 6; therefore, the maximum score for KCs across the three items was 21 and the maximum score for misconceptions was 18. The ORI responses were scored independently by two raters: a Ph.D. student in biology education and an in-service biology teacher. In an initial comparison of score agreement, Kappa values were >0.8 for all KCs and misconceptions (n = 60). Consensus scores were subsequently established for all responses. ORI reliabilities (measured using Cronbach's alpha) were 0.73 for KCs and 0.48 for misconceptions. The relatively low consistency of misconceptions use has been noted in several other studies (Nehm & Ha, 2011). Given the low consistency of misconception scores, we only included ORI KC scores in our measures of participant knowledge.

Acceptance of Evolution Theory

Acceptance of evolutionary theory was measured using the MATE instrument (Rutledge & Warden, 1999). This instrument consists of 20 five-point Likert scale items. This measure is composed of six evolution concepts, including the process of evolution, the scientific validity of evolutionary theory, and related nature of science topics. The original MATE instrument utilized a five-point Likert scale; however, we employed a seven-step Likert scale because we were concerned that Korean participants might tend to avoid selecting extreme values (Chen, Lee, & Stevenson, 1995; Lee, Jones, Mineyama, & Zhang, 2002). MATE scores were subsequently transformed into a 100-point scale in order to compare to the results from our work with previous studies that also employed the MATE. The reliability (Cronbach's alpha) of the modified MATE was 0.940, which is quite similar to the value reported in Rutledge and Sadler's (2007) study (α = 0.941).

Feeling of Certainty

Although studies of FOC have not been conducted in educational settings, FOC measures have been implemented in several medical research projects (e.g., Bruttomesso et al., 2003, 2006; El Saadawi et al., 2010; Reach, Zerrouki, Leclercq, & d'Ivernois, 2005). Our FOC items were nearly identical to those used in this prior medical work. It is important to note that in English, the words “certain,” “sure,” and “confident” have slightly different meanings, whereas all three words are equivalent to only one word in Korean (“hwak-sin”). Our FOC items asked participants, after each knowledge question (“How certain are you that your answer is correct? After thinking carefully about your feelings please indicate your degree of certainty on the 11-point scale below”). Although some studies of FOC have used Likert-scale items or dichotomous-scale items (e.g., Bruttomesso et al., 2006; El Saadawi et al., 2010; Reach et al., 2005), Hodge and Gillespie (2007) suggested that phrase completion scales (11-point scales) are capable of measuring psychological states much more accurately than traditional Likert-scale items. Moreover, Hodge and Gillespie's study indicated that phrase completion scales are more suited for studies employing structure equation modeling statistics than traditional Likert-scale items. Because the path analysis that we used (see below) utilizes structure equation modeling statistics, we chose to use 11-point phrase completion scales to measure FOC. FOC phrase completion items were employed after all 20 CINS items and the 3 ORI items. FOC item reliabilities (measured using Cronbach's alpha) for the CINS items were 0.98 and those of the ORI were 0.89.

Statistical Methods

We used analyses of variance (ANOVA) to explore possible differences between knowledge measures, FOC measures, and acceptance of evolution measures in relation to participants' education level (1–4) and religion. Correlation coefficients were also calculated to explore putative relationships among these variables. In particular, we employed partial correlation analysis, path analysis, and step-wise multiple regression analysis to test for possible mediating effects of FOC on evolutionary knowledge and acceptance relationships (as suggested in our theoretical model, Figure 1). Partial correlations were used to measure the degree of association between two variables while controlling for the effect of a third variable (Field, 2005). Often, when both X and Y variables are strongly correlated to Z variables, X and Y variables seem to be correlated even though they are not correlated. Suppose, for instance, that we want to find the associations among grades earned in a science course, vocabulary knowledge, and performance on a measure of scientific literacy. If results included a statistically significant correlation between grades earned in science class and scores on a measure of scientific literacy, we might infer a strong relationship between achievement in class and level of scientific literacy. However, if we find that vocabulary is strongly correlated to both course grades and scientific literacy scores and remove its effects, we may find there is no remaining (partial) correlation of significance between grades and scientific literacy scores. Likewise, if FOC is correlated to both knowledge and acceptance of evolution, the knowledge and acceptance may seem to be correlated to each other when they are not. If the correlation between knowledge and acceptance becomes lower under the condition of removing the effect of using partial correlation, FOC is likely to be a mediating factor.

We also used path analysis to examine different possible causal interrelationships among measured variables. A major strength of path analysis is that it can measure and compare the robustness (or fitness) of alternative explanatory models, thereby empirically testing components of our theoretical model (Figure 1). While both Pearson correlations and partial correlations provide clear and easy to understand quantitative tests of variable relationships, they are not capable of testing the validity of larger causal pathways, or the directionality of causal pathways. We used AMOS version 18.0 to generate and analyze model fit, and produce several widely used quantitative estimates of path model robustness (Blunch, 2008): (1) p-value of chi-square, (2) adjusted goodness of fit index (AGFI), (3) normed fit index (NFI), (4) Tucker–Lewis index (TLI), (5) comparative fit index (CFI), and (6) the root-mean-square error of approximation (RMSEA). These statistics help inform us as to the reliability of the models that the AMOS program generates.

The last statistical method that we employed was a step-wise multiple regression analysis. This method is useful for determining the level of explanatory power produced by particular variables. Based upon our theoretical model, the acceptance of evolution was considered the dependent variable, and FOC, knowledge, religion, and the level of education were considered to be independent variables. Specifically, we used multiple regression analyses to compare the explanatory power of two different models—one that included FOC and one that did not. All of these statistical methods were performed using PASW version 18.0.

Findings

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Knowledge of and Misconceptions About Evolution by Level of Education and Religion

We employed the CINS and ORI to assess pre-service biology teachers' evolutionary knowledge. Overall, while both CINS and ORI scores increased at each level of education, changes in ORI scores mirrored program progression more closely than CINS scores (Figure 2). An ANOVA of CINS scores indicated a significant difference in knowledge among cohorts (F = 3.23, p < 0.05). In contrast, across religions, the four cohorts did not display significant differences in CINS scores (F = 0.86, p > 0.05).

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Figure 2. CINS scores for our sample compared to previous studies (error bars equal one standard deviation). 1US non-biology majors (Anderson et al., 2002), 2US biology majors (Nehm & Schonfeld, 2008).

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KC scores derived from ORI responses displayed a significant knowledge progression across levels of education, but no differences in knowledge among religions (Figure 3). Additionally, participants' knowledge, as measured by the ORI, was more closely tied to program progression than was observed using CINS scores (see above). Specifically, an ANOVA revealed significant differences in ORI scores across levels of education (F = 13.37, p < 0.01) and, as was observed with CINS scores, non-significant differences among religions (F = 1.43, p > 0.05). Measures of participants' naïve ideas about evolution (e.g., teleology, use and disuse) were also captured in ORI responses (Figure 3). Here we found similar patterns of knowledge change as discussed previously. Specifically, an ANOVA demonstrated significant differences in misconceptions among levels of education (F = 5.04, p < 0.01), but no significant differences among religions (F = 1.23, p > 0.05).

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Figure 3. ORI key concepts and misconception scores across levels of education and religion (error bars represent standard errors).

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Acceptance of Evolution in Relation to the Level of Education and Religion

Acceptance of evolution was measured using the MATE instrument. As mentioned in the Methods Section, we converted our 7-point, Likert-scale items to a 100-point scale so that our findings would be readily comparable to prior work using the MATE (Figure 4). MATE scores displayed significant but weak differences among levels of education. Specifically, the ANOVA revealed that: (a) third-year teachers exhibited greater MATE scores than did fourth-year teachers, (b) first, second, and fourth-year teachers did not exhibit significant differences in MATE scores, and (c) the first, second, and third-year teachers did not exhibit significant differences in MATE scores (F = 2.86, p < 0.05). Teachers' MATE scores across religious groups indicated that third year teachers tended to exhibit greater MATE scores regardless of religious identity. An ANOVA of MATE scores among religious identities revealed that Protestant participants exhibited the lowest MATE scores (F = 5.86, p < 0.01), while Buddhists, Catholics, and those who self identified as not having a religious affiliation displayed non-significant differences in MATE scores.

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Figure 4. MATE scores for our sample compared to previous studies (error bars equal one standard deviation). 1Turkish pre-service biology teachers (Deniz et al., 2008), 1US Oregon biology teachers (Trani, 2004), 2US Ohio biology teachers (Korte, 2003), 3US Indiana biology teachers (Rutledge & Warden, 2000), 4US non-biology majors (Rutledge & Sadler, 2007).

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Feeling of Certainly in Relation to Evolution Knowledge, Level of Education and Religion

FOC scores were obtained using 11-point scales for each of the 20 CINS items and the 3 ORI items, and FOC scores were averaged for each participant (Figure 5). No significant differences in FOC scores were found across religious groups (F = 0.35, p > 0.05). FOC scores for CINS items displayed modest increases with program completion, although the ANOVA result showed significant differences among education levels (F = 4.99, p < 0.01). Post hoc tests showed no significant differences between second-year, third-year, and fourth-year students but showed significant differences between first-year and third-year, and first-year and fourth-year teachers. FOC scores for ORI items showed generally similar patterns as those for the CINS items (Figure 5). An ANOVA for the ORI FOC scores was significant among groups (F = 5.07, p < 0.01). Post hoc tests showed no significant differences between second-year, third-year and fourth-year students, but did reveal significant differences between first-year and third-year, and first-year and fourth-year teachers. FOC scores for ORI items did not differ among religious groups (F = 1.16, p > 0.05). Overall, unlike knowledge and misconception measures, FOC displayed modest but significant changes with program completion.

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Figure 5. Feeling of Certainty (FOC) for evolution knowledge across levels of education and religious affiliation (error bars represent standard errors).

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Associations Among Knowledge, Feelings of Certainty, and Acceptance of Evolution

We used Pearson correlation analyses to explore putative relationships among knowledge, FOC, and acceptance of evolution in our sample. Variables included total CINS score, ORI key concept score, CINS FOC score, ORI FOC score, and total MATE score (see the Methods Section); therefore, associations among five variables were investigated. We found that all five variables displayed statistically significant associations of varying magnitude with one another (Table 1). The two different knowledge measures (CINS and ORI key concept scores) displayed significant associations with one another; likewise, FOC scores for the CINS and ORI key concept scores were also very highly and significantly correlated. MATE scores were significantly but weakly associated with both CINS and ORI key concept scores. Interestingly, MATE scores were more highly correlated to FOC scores than they were to knowledge scores.

Table 1. Correlations among scores from the Conceptual Inventory of Natural Selection (CINS), Open Response Instrument (ORI), Feeling of Certainty (FOC) instrument for the CINS and ORI, and the Measure of Acceptance of the Theory of Evolution (MATE)
 CINS ScoreORI Key ConceptCINS FOCORI FOCMATE
  • *

    p < 0.05.

  • **

    p < 0.01.

Conceptual Inventory of Natural Selection1.0000.518**0.497**0.431**0.313**
Open Response Instrument key concepts 1.0000.421**0.459**0.214*
Feeling of Certainty on CINS  1.0000.830**0.451**
Feeling of Certainty on ORI   1.0000.309**
Measure of Acceptance of the Theory of Evolution    1.000

We also employed partial correlation analyses to explore putative mediating effects of FOC, as hypothesized in our model (see Table 2). For both knowledge measures (i.e., CINS and ORI key concept scores), the partial correlation values between knowledge and acceptance of evolution (controlling for FOC) were not significant. In contrast, relationships between FOC and knowledge, and between FOC and acceptance, were significantly correlated in the condition in which the other variable was controlled (Table 2). This indicates that both knowledge and acceptance measures do not display mediating effects. In addition, Pearson correlation values between knowledge and acceptance of evolution indicated that they were indirectly associated through FOC. Overall, partial correlation analyses revealed the mediating effects of FOC.

Table 2. Partial correlations among measures of acceptance, feeling of certainty, and knowledge (for both the CINS and ORI)
MeasuresControl VariableCorrelated Variablesr
  • *

    p < 0.01.

  • **

    p < 0.001.

Conceptual Inventory of Natural Selection(1) Acceptance(2)–(3)0.420**
 (2) Feeling of Certainty(1)–(3)0.115
 (3) Knowledge(1)–(2)0.358**
Open Response Instrument(1) Acceptance(2)–(3)0.423**
 (2) Feeling of Certainty(1)–(3)0.085
 (3) Knowledge(1)–(2)0.243*

Testing Model Fit

The ANOVA and partial correlation results provide strong evidence for the mediating effects of FOC in knowledge–belief relationships. Building upon these findings, we expanded our analyses to encompass more variables—religion and level of education (i.e., cohort year)—that are also likely to influence these associations. Methodologically, we used Path Analysis to explore these more complex relationships. In order to execute a Path Analysis, several data transformations needed to be performed, and assumption tests needed to be checked.

First, in order to increase statistical power in our analysis, we combined the two knowledge variables (CINS and ORI key concept scores) and the two associated FOC scores (derived from the CINS and ORI items). Specifically, we converted raw scores into z-scores, and subsequently combined them into one variable. Second, because religious identity was measured on a nominal scale, it needed to be converted into a dummy variable. We coded the dummy variable as Protestant or Non-Protestant, since Protestant was the only religious group in our sample shown to display significant patterns of association with acceptance of evolution. Third, distribution normality was tested for our analysis variables (acceptance of evolution, FOC, and knowledge of evolution) using Kolmogorov–Smirnov tests. These tests revealed that all three variables were distributed normally (see Table S2). Fourth, we examined the homogeneity of variances and confirmed homogeneity for these three variables (Table S2). All of these steps were taken to ensure accurate and powerful Path Analysis results.

Prior to interpreting our Path Analysis results, it was important to examine the fitness of the model. Benchmark fitness values have been established in the statistics literature to evaluate the robustness of Path Analysis models. Our model demonstrated good fit with our data, as indicated by the cutoff values for the six fitness variables (Table S3 in Supplementary Material). Specifically, fitness values for our model were: chi-square: 7.366, p = 0.20; AGFI: 0.928; NFI: 0.942; TLI: 0.960; CFI: 0.980; and RMSEA: 0.062. Therefore, our Path Analysis model demonstrates acceptable support.

Given the standardized path values, we used Keith's (1993) recommended criteria to judge how variance in the dependent variable is influenced by variance in the independent and intermediary variables. Keith (1993) considered path values ranging from 0.05 to 0.10 to be of “small” influence, path values ranging from 0.11 to 0.25 to be of “moderate” influence, and path values above 0.25 to be of “large” influence. Following these benchmarks, the individual path values in our model all display “large” influences (Figure 6), with both FOC (0.48) and religious affiliation (−0.40) having large influences on acceptance of evolution, and with identity as a Protestant being inversely related to acceptance. Likewise, level of education is shown to have a large influence (0.40) on knowledge of evolution, and knowledge of evolution is shown to have a large influence (0.54) on FOC. Indirect effects of factors can be calculated by multiplying path values for compound paths. Using this procedure, the indirect effect of evolutionary knowledge on acceptance of evolution is determined by the product of (0.54)(0.48) which is 0.26, a “large” influence. Likewise, the indirect effect of level of education on acceptance of evolution is determined by the product of (0.40)(0.54)(0.48) which is 0.10, a “small” influence. Readers interested in support for alternative path models are encouraged to consult the Supplementary Materials (Figures S1, S2, and S3).

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Figure 6. Path analysis model. Fitness index of the model: Chi-square: 7.366, p = 0.20; AGFI: 0.928; NFI: 0.942; TLI: 0.960; CFI: 0.980; RMSEA: 0.062.

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Our final statistical test examined how strongly FOC scores predict acceptance of evolution scores (as measured by the MATE). Methodologically, we employed a pair of step-wise multiple-regression analyses, one including FOC as a predictor variable, and one without FOC. The first analysis excluded FOC and generated an adjusted R2 value of 0.23 (Table 3), which indicates that 23% of the variance in acceptance of evolution is explained by the regression equation. In this equation, knowledge is the strongest predictor of acceptance of evolution. In the second analysis, we included the FOC variable, and the regression equation generated a larger adjusted R2 value of 0.33, which indicates that 33% of the variance in acceptance of evolution is explained by the regression equation including FOC. In the second analysis, Beta weights indicate that knowledge of evolution accounted for less of the variance in acceptance of evolution than did FOC, corroborating our previous findings.

Table 3. Multiple regression results for acceptance of evolution with and without FOC values
ModelUnstandardized CoefficientsStandardized CoefficientstAdjusted R2
BStd. ErrorBeta
  • *

    p < 0.05.

  • **

    p < 0.001.

Regression without Feeling of Certainty
 Constant53.585.14 10.42**0.23
 Knowledge0.240.060.384.38** 
 Level of education−0.730.87−0.07−0.85 
 Religion−10.922.34−0.38−4.66** 
Regression with Feeling of Certainty
 Constant44.805.26 8.52**0.33
 Knowledge0.120.060.192.10* 
 Feeling of Certainty0.220.050.374.18** 
 Level of education−1.140.82−0.12−1.40 
 Religion−11.132.20−0.39−5.07** 

Discussion

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Our central aim was to test a newly proposed theoretical model of factors influencing acceptance of evolutionary theory. The new model builds on previous work of others by adding a new factor, FOC, to factors previously found to be associated with levels of acceptance: knowledge of evolution, religious identity, and level of education. The new model reflects an integration of research findings from studies in science education on acceptance of evolution and findings from neuroscience research on dual-process theories of cognition. Since knowledge of concepts associated with evolution has been a key factor in most previous studies of acceptance of evolution, we begin by determining whether our sample of participants from Korea exhibited patterns of evolutionary knowledge and acceptance comparable to those reported in previous studies.

Level of Evolutionary Knowledge Among Undergraduates

Several studies of evolutionary knowledge have employed single measures of knowledge derived from multiple-choice instruments. Nehm and Schonfeld (2008), in a study of first-year underrepresented biology majors, documented average CINS scores most similar to our sample of second-year pre-service teachers in Korea. Anderson's et al. (2002) CINS scores from a sample of American non-science majors were lower than those of the Korean biology majors in our sample. Thus, CINS scores among our Korean participants were very similar to levels exhibited by comparable American samples. Our sample also displayed evolutionary knowledge patterns similar to those reported by Nehm and Schonfeld (2007) study of American second-semester biology undergraduates. They reported mean key concept scores for ORI items comparable to those of our third-year students. While it is not possible to directly compare the misconception scores of our sample with those from Nehm and Schonfeld (2007) because different rubric coding categories were used in the two studies, in both cases significant decreases in misconceptions with increasing education were noted. Overall, we found generally consistent patterns among the different knowledge measures (CINS, ORI KC, and ORI MIS): increases of knowledge with the level of education, slight decreases in misconceptions, and no significant differences among religious groups. We also found that these patterns were in line with prior studies of American samples.

The MATE instrument has been widely used in assessing student and teacher acceptance of evolution (Deniz et al., 2008; Korte, 2003; Rutledge & Sadler, 2007; Rutledge & Warden, 2000; Trani, 2004). The MATE encompasses both micro and macroevolutionary change (as do our knowledge measures). We compared our results from Korean pre-service teachers with those from American students and teachers. American in-service biology teachers from Indiana (Rutledge & Warden, 2000), Ohio (Korte, 2003), and Oregon (Trani, 2004) harbored slightly greater MATE scores than our sample of Korean pre-service biology teachers. We also noted that pre-service biology teachers in Turkey (Deniz et al., 2008) had similar MATE scores to the Korean Protestants in our study. Another notable point is that standard deviation values for MATE scores for our Korean participants were much smaller than those typically found in samples of American teachers. Thus, while the Korean acceptance scores were slightly lower than those of American teachers, they were in the general range of “moderate” acceptance levels.

Testing the Model

Given the evidence that our participant sample is generally comparable to those of previous studies in levels of evolutionary knowledge and acceptance, we tested our theoretical model (see Figure 1) by examining the relationships among evolutionary knowledge, level of education, religious identity, FOC, and acceptance of evolutionary theory. Our model predicts a number of relationships among knowledge, level of education, religious identity, religiosity, dispositions, and acceptance of evolution. Some of the predicted relationships were tested in this study, and others were not. We chose to focus on a smaller subset of relationships that have been repeatedly studied by others in the past, along with the addition of intuitive FOC. On the basis of the variables we included in our study, our theoretical model gives rise to two specific predictions that were tested: (a) more variance in acceptance of evolutionary theory will be explained with the addition of FOC as a factor, and (b) the pattern of relationships among measured factors will be in alignment with pathways of the proposed model.

Based on results from multiple regression analysis (see Table 3), inclusion of FOC significantly increases the amount of explained variance in acceptance of evolutionary theory. This finding, in response to our first research question, is in accord with predictions based on our proposed theoretical model and provides strong evidence that intuitive cognitions, such as FOC, contribute significantly to acceptance of evolutionary theory.

Our second research question focused on a pattern of relationships among variables. The model predicts a number of relationships among knowledge, level of education, religious identity, religiosity, dispositions, and acceptance of evolution. We predicted that the pattern of relationships among measured variables would be in alignment with pathways of the proposed model. As shown by Figure 6, relationships predicted by our theoretical model are supported by confirmatory path analysis. Indeed, it is notable that path analysis confirms the contribution of intuitive cognition (FOC, Path B), to level of acceptance, but not the previously assumed direct contribution of knowledge level to level of acceptance. The overall pattern of relationships confirmed by path analysis, however, is in accord with what was predicted by the model.

Our third research question focused on the effects of education on FOC. Specifically, we sought to determine the extent of change in FOC as participants progress through a biology teacher preparation program. Though we were unable to measure changes in FOC as individual participants progressed through the program, we did compare education-level cohorts representing all 4 years of the program. FOC has been found through clinical trials in psychology to be a relatively stable factor that changes very gradually over time, becoming very difficult to alter once established (Burton, 2008, pp. 97–98). Our expectation, then, was that there would be little change in FOC across education-level cohorts. In our sample of participants, FOC did trend gradually upward across education-level cohorts over the 4 years of undergraduate education, but differences were not statistically significant in most cases. This finding is what one would expect if FOC is a relatively stable factor that changes gradually over time.

Beyond gaining evidence of construct validity for the model, the findings reported here may provide insight into the high degree of variability previously reported in the relationship between knowledge level and level of acceptance of evolutionary theory in the science education literature. As the proposed model illustrates (see Figure 1), FOC can moderate outcomes in level of acceptance at three points in the decision making process: by moderating the processes of understanding and reasoning, by moderating the interplay of knowledge, dispositions, and context, or by presenting an acceptance outcome that competes with the outcome of conscious cognitions (Path A) associated with the reflective mind.

In this study, multiple regression analysis shows that intuitive cognitions (FOC of Path B) had an additive effect to conscious cognitions (knowledge level of Path A). There are presumed to be situations, however, such as those described by Burton (2008, pp. 97–98) where there is a conflict between what is consciously known and the FOC. In a case where there is a high level of knowledge about evolution, but an intuitive feeling of uncertainty, the level of acceptance would be reduced because the output of the two cognition systems would be opposite in polarity. As mentioned previously, in cases of conflict there tends to be a “belief bias” where the output of intuitive cognition draws into question the outcome of the reasoning process (De Neys et al., 2010). As Burton (2008, p. 17) characterizes it, “The conflict between logic and a contrary feeling of knowing tends to be resolved in favor of feeling.”

According to our model, not acknowledging the moderating effects of intuitive cognitions will likely lead to inconsistent results in the relationship between knowledge and acceptance of evolution from study to study, and that is in accord with what has been previously reported (e.g., Paz-y-Miño & Espinosa, 2009; Rutledge & Warden, 1999; Sinatra et al., 2003; see Table S1 in the Supplementary Materials).

Measuring Feeling of Certainty

Quantifying the complex interactions and mediators between evolutionary knowledge and acceptance using paper and pencil tests is dependent upon using quality measures. Our FOC scale, while displaying robust reliability and based upon similar measures from medical research (e.g., Bruttomesso et al., 2003, 2006; El Saadawi et al., 2010; Reach et al., 2005), is admittedly simple, and future work must focus on developing a more expansive and robust measure of FOC. Interviews with participants, in concert with paper and pencil tests, are needed to better define the construct of FOC and explicate constituent dimensions. While the findings presented in this study from confirmatory path analysis and multiple regression analysis are in alignment with relationships predicted by the proposed model, the predicted feedback effects of FOC on understanding, reasoning, and dispositions have not been tested. Additionally, the methods used to analyze the contribution of FOC in this study did not enable us to investigate the predicted moderating effects of FOC on conscious thought in addition to the tested direct effect on level of acceptance. Nevertheless, the simplicity of our current measure, and its ease of implementation, makes it well suited for inclusion with other paper and pencil tests (such as the CINS, MATE, and ORI).

Science Learning: Can FOC Be Addressed?

Results of this study provide evidence that intuitive cognitions play a significant moderating role in acceptance of evolutionary theory, so the question emerges, “What are the implications for learning?” Very little is known about the mechanisms of intuitive cognitions, or how they are moderated by experiences, knowledge, and beliefs, so prescribing educational interventions at this point would be premature. It does seem, however, that intuitive cognitions activate “prior emotional experiences in comparable situations” (Damasio, 2003, p. 149) and are molded over long periods of time (Burton, 2008, p. 98). Burton goes on to suggest, “If the fundamental thrust of education is ‘being correct’ rather than acquiring a thoughtful awareness of ambiguities, inconsistencies, and underlying paradoxes, it is easy to see how the brain reward systems might be molded to prefer certainty over open-mindedness” (p. 99). When it comes to accepting evolution, the situation is likely complicated further by reward systems associated with the ecologies of ideas reinforced by various worldviews (Cobern, 1996). Until more is known, it seems prudent to at least acknowledge that resistance to accepting evolution will likely not be completely neutralized through increased attention to reasoning, understanding, or knowledge gains (cf. Nehm & Schonfeld, 2007).

Implications for Science Teaching

While notions of “reflective” and “intuitive” cognitions are core components of many psychological sub-disciplines (Evans, 2010), their relative contributions to learning in science remain to be explicitly integrated into theories and practices of science teaching. If intuitive cognitions, such as those represented by FOC, contribute to acceptance of evolutionary theory, then intuitive cognitions likely influence acceptance of other ideas in science, particularly ideas that are perceived as being “controversial” by the general public. For example, the same dynamic may account for some level of resistance to the idea of climate change being caused by human impacts on the environment. In general, students may benefit from being made aware that their thoughts and feelings about scientific and “everyday” phenomena are not restricted to conscious, deliberate, reflection. For instance, there is a broad consensus that an ability to think critically about scientific claims and findings is a central element of scientific literacy (e.g., Norris & Phillips, 2003), and recent conceptualizations of scientific literacy (e.g., Choi, Lee, Shin, Kim, & Krajcik, 2011) encompass metacognition. Choi et al. (2011) specifically include, as an element of literacy, “Explicit understanding one's own cognition and cognitive ability in order to reflect upon one's level of knowledge to know if one understands, or if more information is necessary…” Choi et al.'s notion of scientific literacy also includes competencies such as “self-directed monitoring.” If, as we argue, intuitively generated feelings of certainty have significant moderating effects on scientific reasoning (see Figure 1), bringing such feelings to the fore—into conscious thought—is necessary for deliberate, metacognitive evaluation and critique. Pedagogical and curricular interventions that recognize and highlight such cognitive processes have the potential to make the outcomes of intuitive cognitions about scientific phenomena (e.g., FOC) accessible to conscious attention, reflection, and evaluation. Students must be made aware of the reality of intuitive cognitions in order to understand their own reasoning, and in turn to become scientifically literate citizens. Overall, then, we view the inclusion of FOC and other intuitive cognitions as consonant with contemporary notions of scientific literacy.

As noted by Evans (2010: 107), the intuitive mind always has an influence upon our reasoning processes, and yet this influence on thinking may remain hidden to science students and teachers. Curricular examples could be employed that demonstrate to students that their intuitive feelings of “rightness” may in some cases be misleading and contradict their own intentional reasoning efforts (Evans, 2010). While we view the development of curricular interventions based upon intuitive cognition to be premature, we speculate on some possible examples. Several inexpensive and easily implemented card-sort tasks may be employed in the classroom to illustrate to students how FOC may in some cases contradict effortful, rational deliberation. One example activity is the Wason card sort task (for details, see Evans, 2008, 2010). In most cases, participants become convinced and “feel” that they know a particular (hidden) card sort rule, yet in fact they do not. Changing the context and structure of the card sort task from abstract to familiar/concrete may also assist students in realizing that their intuitive reasoning may in some cases produce a confluence of intuitive feelings of certainty and consciously derived conclusions (Evans, 2010, pp. 122–131). Thus, activities that focus students' attention on intuitive feelings and deliberate reasoning may assist in promoting a richer understanding of scientific thinking.

Science instruction may also benefit from consideration of the fact that intuition may compete with the outputs of students' rational, reflective, and intentional reasoning. Intuitive cognitive processing may have particular relevance to the rich and expansive literature concerning students' naïve science conceptions (Driver, Asoko, Leach, Scott, & Mortimer, 1994). A bird's eye view of this work reveals an emphasis on cognitive and developmental factors (Strike & Posner, 1992), although more recent work is moving away from purely rational and logical perspectives (Pintrich, Marx, & Boyle, 1993). Nevertheless, much of the prior work on naïve science ideas implies that intuitive cognition is accessible to conscious reflection and evaluation; in contrast, the neuroscience literature on intuitive cognition and cognitive pathways suggests that this may not be the case (Evans, 2010). Thus, rational and emotional efforts directed at conceptual change (Pintrich et al., 1993) may not be penetrating to the level of intuitive cognitions, and may be hijacked or suppressed by opposing feelings of “rightness.” Overall, then, a central research question raised by our present study is whether intuitive cognitions have explanatory utility in the domain of students' naïve science ideas, and whether they may contribute to instructional failure in cases of innovative interventions (Nehm & Schonfeld, 2007).

Limitations of the Study

With this study we have introduced a new factor, FOC, to the science education research community, and we have proposed and tested a new theoretical model to account for levels of acceptance of evolutionary theory. Though we have taken care not to violate the assumptions of procedures used, to employ widely used measures of knowledge and acceptance of evolution, and to engage participants representative of pre-service biology teachers generally, the findings of this study must be interpreted with caution. In developing our measure of FOC, we drew on previous work in the medical sciences, and have assumed that the means of measuring FOC with patient populations can be used with confidence with pre-service teacher populations. Though the results obtained exhibit a high level of reliability, considerable additional work is needed to establish the construct validity of FOC. At this early stage in examining the role of FOC and intuitive cognitions in general on acceptance of evolution there is much yet to learn about the most valid and reliable ways to detect and measure the outcomes of non-conscious cognitions. It is also critical that we gain confidence in clearly distinguishing intuitive feelings, such as feelings of certainty, from consciously formed feelings that reflect dispositions arising from webs of belief.

The theoretical model we have proposed to account for levels of acceptance of evolutionary theory is grounded both in previous research findings in science education and in the research and theoretical frameworks of neuroscience. Our study emphasized the mediating relationships between FOC, knowledge of evolution, and acceptance, so additional research is required to elucidate the ways in which FOC mediates or is influenced by dispositions and related factors. Indeed, future work is required to explore the ways in which these proposed points of mediation interact to generate model outputs. In this study we tested only a portion of the full model, so care must be taken in using the full model to account for findings beyond those of this study. The pattern of relationships among variables in this study is well aligned with predictions that one would make based on the model, but we have not tested for the presumed influence of conscious dispositions on acceptance of evolution, nor have we tested for the presumed influence of FOC feedback on reasoning and understanding. Future research on these aspects of the model is needed before the full model can be used to characterize the complex interplay of FOC, knowledge, dispositions, reasoning, understanding, religiosity, and level of education on acceptance of evolution.

Finally, caution must be taken in generalizing the results of this study beyond the population sampled. Though we took care in seeking a sample that provided a broad continuum of religious orientations and a representative sample of pre-service science teachers, too little is known about the range of factors that modulate intuitive cognitions to make broad claims. The study was situated in a particular culture, Korea, and was conducted within the context of a particular educational environment. We are confident in saying that we have gained evidence for the potentially significant influence of intuitive cognitions on acceptance of evolution and that our initial findings support the proposed conceptual model of the interplay between conscious and intuitive cognitions, but we have yet to uncover the many factors that mediate the interactions of our two cognitive systems.

Conclusions

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

Findings of this study provide evidence that a previously unexplored variable, FOC, explains a significant proportion of the variance in acceptance of evolution among pre-service biology teachers. Further, the pattern of relationships found among FOC, knowledge of evolution, level of education, and acceptance of evolution provide support for a newly proposed theoretical model of factors that influence acceptance of evolution. Though additional components of the model remain to be tested, results reported here indicate that a product of intuitive cognitions, FOC, strongly moderates acceptance of evolution. This finding is both encouraging and sobering. On one hand, we have identified a previously unexamined factor that seems strongly predictive of level of acceptance of evolution. On the other hand, we seem to have uncovered a factor that represents a category of cognitions, intuitive cognitions, for which we have little to guide us in designing educational interventions. Since it is likely that intuitive cognitions play a role in acceptance of other ideas (or all ideas) in science, the findings reported here have implications for a broad range of issues and challenges in science education. For those engaged in studies of evolution education, explication of factors arising from intuitive cognitions and their interrelationships may enable us to move beyond a pre-occupation with discrete convictions associated with particular religious traditions or worldviews and focus more intently on general principles associated with intuitive cognitions. For those engaged in studies of other key ideas in science, or issues such as climate change, genetically modified organisms, or alternative energy sources, exploration of possible influences of intuitive cognitions seems warranted.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

We thank Young-Shin Kim for help with data collection and Gye-Soo Kim for help with statistical analyses. We thank the National Science Foundation (REESE 090999 to Nehm) for partial support of this study.

References

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information
  • Anderson, D. L., Fisher, K. M., & Norman, G. J. (2002). Development and evaluation of the conceptual inventory of natural selection. Journal of Research in Science Teaching, 39(10), 952978.
  • Battisti, B. T., Hanegan, N., Sudweeks, R., & Cates, R. (2010). Using item response theory to conduct a distracter analysis on Conceptual Inventory of Natural Selection. International Journal of Science and Mathematics Education, 8, 845868.
  • Berkman, M. B., & Plutzer, E. (2011). Defeating creationism in the courtroom, but not in the classroom. Science, 331(6016), 404.
  • Bhattacharjee, Y. (2010). NSF board draws flak for dropping evolution from indicators. Science, 328, 150151.
  • Bishop, B. A., & Anderson, C. W. (1990). Student conceptions of natural selection and its role in evolution. Journal of Research in Science Teaching, 27(5), 415427.
  • Blunch, N. (2008). Introduction to structural equation modeling using SPSS and Amos. Thousand Oaks, CA: Sage Publications Ltd.
  • Brewer, C., & Smith, D. (2011). Vision and change in undergraduate education: A call to action. Washington, DC: American Association for the Advancement of Science.
  • Bruttomesso, D., Costa, S., Dal Pos, M., Crazzolara, D., Realdi, G., Tiengo, A., … Gagnayre, R. (2006). Educating diabetic patients about insulin use: Changes over time in certainty and correctness of knowledge. Diabetes & Metabolism, 32(3), 256261.
  • Bruttomesso, D., Gagnayre, R., Leclercq, D., Crazzolara, D., Busata, E., d'Ivernois, J. F., … Baritussio, A. (2003). The use of degrees of certainty to evaluate knowledge. Patient Education and Counseling, 51(1), 2937.
  • Burton, R. A. (2008). On being certain: Believing you are right even when you're not (1st ed.). New York: St. Martin's Press.
  • Cavallo, A. M. L., & McCall, D. (2008). Seeing may not mean believing: Examining students' understandings & beliefs in evolution. The American Biology Teacher, 70(9), 522530.
  • Chen, C., Lee, S. Y., & Stevenson, H. W. (1995). Response style and cross-cultural comparisons of rating scales among East Asian and North American students. Psychological Science, 6, 170175.
    Direct Link:
  • Chinsamy, A., & Plagányi, É. (2007). Accepting evolution. Evolution, 62(1), 248254.
  • Choi, K., Lee, H., Shin, N., Kim, S. W., & Krajcik, J. (2011). Re-conceptualization of scientific literacy in South Korea for the 21st century. Journal of Research in Science Teaching, 48(6), 670697.
  • Cobern, W. W. (1994). Point: Belief, understanding, and the teaching of evolution. Journal of Research in Science Teaching, 31(5), 583590.
  • Cobern, W. W. (1996). Worldview theory and conceptual change in science education. Science Education, 80(5), 579610.
  • Dagher, Z. R., & BouJaoude, S. (1997). Scientific views and religious beliefs of college students: The case of biological evolution. Journal of Research in Science Teaching, 34(5), 429445.
  • Damasio, A. R. (2003). Looking for spinoza: Joy, sorrow, and the feeling brain (1st ed.). Orlando, FL: Harcourt.
  • De Neys, W., Moyens, E., & Vansteenwegen, D. (2010). Feeling we're biased: Autonomic arousal and reasoning conflict. Cognitive, Affective, & Behavioral Neuroscience, 10(2), 208216.
  • Deniz, H., Donnelly, L. A., & Yilmaz, I. (2008). Exploring the factors related to acceptance of evolutionary theory among Turkish preservice biology teachers: Toward a more informative conceptual ecology for biological evolution. Journal of Research in Science Teaching, 45(4), 420443.
  • Dobzhansky, T. (1973). Nothing in biology makes sense except in the light of evolution. The American Biology Teacher, 35(3), 125129.
  • Donnelly, L. A., Kazempour, M., & Amirshokoohi, A. (2009). High school students' perceptions of evolution instruction: Acceptance and evolution learning experiences. Research in Science Education, 39, 643660.
  • Driver, R., Asoko, H., Leach, J., Scott, P., & Mortimer, E. (1994). Constructing scientific knowledge in the classroom. Educational researcher, 23(7), 512.
  • Educational Broadcasting System. (2009). The era of God and Darwin. Seoul: Educational Broadcasting System.
  • El Saadawi, G. M., Azevedo, R., Castine, M., Payne, V., Medvedeva, O., Tseytlin, E., … Crowley, R. S. (2010). Factors affecting feeling-of-knowing in a medical intelligent tutoring system: The role of immediate feedback as a metacognitive scaffold. Advances in Health Sciences Education, 15(1), 930.
  • Evans, E. M. (2001). Cognitive and contextual factors in the emergence of diverse belief systems: Creation versus evolution. Cognitive Psychology, 42(3), 217266.
  • Evans, J. S. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10), 454459.
  • Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognitions. Annual Review of Psychology, 59, 255278.
  • Evans, J. S. B. T. (2010). Thinking twice: Two minds in one brain. Oxford: Oxford University Press.
  • Evans, J. S. B. T., & Curtis-Holmes, J. (2005). Rapid responding increases belief bias: Evidence for the dual-process theory of reasoning. Thinking & Reasoning, 11, 382389.
  • Evans, J. S. B. T., & Over, D. E. (1996). Rationality and reasoning. Hove, UK: Psychology Press.
  • Field, A. (2005). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage Publications Ltd.
  • Futuyma, D. (2009). Evolution (2nd ed.). Sunderland: Sinauer Associates.
  • Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. New York: Viking.
  • Haack, S. (2007). Defending science—Within reason: Between scientism and cynicism. Amherst, NY: Prometheus Books.
  • Hodge, D. R., & Gillespie, D. F. (2007). Phrase completion scales. Journal of Social Service Research, 33(4), 112.
  • Keith, T. Z. (1993). Causal influences on school learning. In: H. J. Walberg (Ed.), Analytic methods for educational productivity (pp. 2147). Greenwich, CT: JAI Press.
  • Kim, S. Y., & Nehm, R. H. (2011). A cross-cultural comparison of Korean and American science teachers' views of evolution and the nature of science. International Journal of Science Education, 33(2), 197227.
  • Korte, S. E. (2003). The acceptance and understanding of evolutionary theory among Ohio secondary science teachers (Master's thesis). Ohio University, Lancaster, OH.
  • Lawson, A. E. (1983). Predicting science achievement: The role of developmental level, disembedding ability, mental capacity, prior knowledge, and beliefs. Journal of Research in Science Teaching, 20(2), 117129.
  • Lawson, A. E., & Worsnop, W. A. (1992). Learning about evolution and rejecting a belief in special creation: Effects of reflective reasoning skill, prior knowledge, prior belief, and religious commitment. Journal of Research in Science Teaching, 29(2), 143166.
  • Lee, J. W., Jones, P. S., Mineyama, Y., & Zhang, X. E. (2002). Cultural differences in responses to a Likert scale. Research in Nursing & Health, 25(4), 295306.
  • Lombrozo, T., Thanukos, A., & Weisberg, M. (2008). The importance of understanding the nature of science for accepting evolution. Evolution: Education and Outreach, 1(3), 290298.
  • McKeachie, W. J., Lin, Y. G., & Strayer, J. (2002). Creationist vs. evolutionary beliefs: Effects on learning biology. The American Biology Teacher, 64(3), 189192.
  • Miller, J. D., Scott, E. C., & Okamoto, S. (2006). Public acceptance of evolution. Science, 313(5788), 375376.
  • Mindell, D. P. (2006). The evolving world: Evolution in everyday life. Cambridge MA: Harvard University Press.
  • Moore, R. (2002). Teaching evolution: Do state standards matter? BioScience, 52(4), 378381.
  • Nadelson, L. S., & Southerland, S. A. (2010). Examining the interaction of acceptance and understanding: How does the relationship change with a focus on macroevolution? Evolution Education & Outreach, 3(1), 8288.
  • National Academy of Sciences. (1998). Teaching about evolution and the nature of science. Washington, DC: National Academy Press.
  • National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.
  • National Research Council. (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core Ideas. Washington, DC: The National Academies Press.
  • Nehm, R. H., & Ha, M. (2011). Item feature effects in evolution assessment. Journal of Research in Science Teaching, 48(3), 237256.
  • Nehm, R. H., Ha, M., Rector, M., Opfer, J., Perrin, L., Ridgway, J., & Mollohan, K. (2010). Scoring guide for the open response instrument (ORI) and evolutionary gain and loss test (EGALT). Technical Report of National Science Foundation REESE Project 0909999. Accessed online 10 Jan 2011 at: http://evolutionassessment.org.
  • Nehm, R. H., Kim, S. Y., & Sheppard, K. (2009). Academic preparation in biology and advocacy for teaching evolution: Biology versus non-biology teachers. Science Education, 93, 11221146.
  • Nehm, R. H., & Reilly, L. (2007). Biology majors' knowledge and misconceptions of natural selection. BioScience, 57, 263272.
  • Nehm, R. H., & Schonfeld, I. S. (2007). Does increasing biology teacher knowledge of evolution and the nature of science lead to greater preference for the teaching of evolution in schools? Journal of Science Teacher Education 18, 699723.
  • Nehm, R. H., & Schonfeld, I. S. (2008). Measuring knowledge of natural selection: A comparison of the CINS, an open-response instrument, and an oral interview. Journal of Research in Science Teaching 45, 11311160.
  • Nehm, R. H., & Schonfeld, I. S. (2010). The future of natural selection knowledge measurement: A reply to Anderson et al. (2010). Journal of Research in Science Teaching, 47, 358362.
  • Neumann, I., Neumann, K., & Nehm, R. H. (2011). Evaluating instrument quality in science education: Rasch based analyses of a nature of science test. International Journal of Science Education, 33, 13731405.
  • Norris, S. P., & Phillips, L. M. (2003). How literacy in its fundamental sense is central to scientific literacy. Science Education, 87(2), 224240.
  • Osman, M. (2004). An evaluation of dual-process theories of reasoning. Psychonomic Bulletin & Review, 11(6), 9881010.
  • Paz-y-Miño, C. G., & Espinosa, A. (2009). Acceptance of evolution increases with student academic level: A comparison between a secular and a religious college. Evolution Education & Outreach, 2, 655675.
  • Peker, D., Comert, G. G., & Kence, A. (2009). Three decades of anti-evolution campaign and its results: Turkish undergraduates' acceptance and understanding of the biological evolution theory. Science & Education, 19(6), 739755.
  • Pintrich, P. R., Marx, R. W., & Boyle, R. B. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167199.
  • Quine, W. V., & Ullian, J. S. (1978). The web of belief (2d ed.). New York: Random House.
  • Reach, G., Zerrouki, A., Leclercq, D., & d'Ivernois, J. F. (2005). Adjusting insulin doses: From knowledge to decision. Patient Education and Counseling, 56(1), 98103.
  • Rutherford, E. J., & Ahlgren, A. (1990). Science for all Americans. Washington, DC: American Association for the Advancement of Science.
  • Rutledge, M. L., & Mitchell, M. A. (2002). High school biology teachers' knowledge structure, acceptance & teaching of evolution. The American Biology Teacher, 64(1), 2128.
  • Rutledge, M. L., & Sadler, K. C. (2007). Reliability of the measure of acceptance of the theory of evolution (MATE) instrument with university students. The American Biology Teacher, 51, 275280.
  • Rutledge, M. L., & Warden, M. A. (1999). Development and validation of the measure of acceptance of the theory of evolution instrument. School Science and Mathematics, 99, 1318.
  • Rutledge, M. L., & Warden, M. A. (2000). Science and high school biology teachers: Critical relationships. The American Biology Teacher, 62, 2331.
  • Sinatra, G. M., Brem, S. K., & Evans, E. M. (2008). Changing minds? Implications of conceptual change for teaching and learning about biological evolution. Evolution: Education & Outreach, 1(2), 189195.
  • Sinatra, G. M., Southerland, S. A., McConaughy, F., & Demastes, J. W. (2003). Intentions and beliefs in students' understanding and acceptance of biological evolution. Journal of Research in Science Teaching, 40(5), 510528.
  • Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 322.
  • Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory system. Personality and Social Psychology Review, 4(2), 108131.
  • Smith, M. U., Siegel, H., & McInerney, J. D. (1995). Foundational issues in evolution education. Science & Education, 4, 2346.
  • Southerland, S. A., & Sinatra, G. M. (2003). Learning about biological evolution: A special case of intentional conceptual change. In G. M. Sinatra & P. Pintrich (Eds.), Intentional conceptual change (pp. 317348). Mahwah: Lawrence Erlbaum.
  • Stanovich, K. E., & West, R. F. (2000). Advancing the rationality debate. Behavioral and Brain Sciences, 23, 701726.
  • Strike, K. A., & Posner, G. J. (1992). A revisionist theory of conceptual change. In R. A. Duschl & R. J. Hamilton (Eds.), Philosophy of science, cognitive psychology, and educational theory and practice (pp. 147176). Albany: State University of New York Press.
  • The Statistics Korea, (2005). Composition of population by religion. Retrieved from http://kostat.go.kr/portal/korea/index.action. Accessed 13 Feb 2011.
  • Trani, R. (2004). I won't teach evolution: It's against my religion. The American Biology Teacher, 66, 419427.
  • van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.
  • Williams, J. D. (2009). Belief versus acceptance: Why do people not believe in evolution? BioEssays, 31, 12551262.
  • Wolpert, L. (2006). Six impossible things before breakfast: The evolutionary origins of belief (1st American ed.). New York: W. W. Norton & Co.

Supporting Information

  1. Top of page
  2. Abstract
  3. Beliefs, Knowledge, and Acceptance
  4. Neuroscience Perspectives on Evolutionary Belief and Knowledge
  5. Research Questions
  6. Sample
  7. Instruments and Measures
  8. Findings
  9. Discussion
  10. Conclusions
  11. Acknowledgements
  12. References
  13. Supporting Information

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