Pre-service Science Teachers’ Neuroscience Literacy: Neuromyths and a Professional Understanding of Learning and Memory

Transferring current research findings on the topic of learning and memory to “brain-based” learning in schools is of great interest among teachers. However, numerous international studies demonstrate that both pre-service and in-service teachers do not always succeed. Instead, they transfer numerous misconceptions about neuroscience, known as neuromyths, into pedagogical practice. As a result, researchers call for more neuroscience in teacher education in order to create a professional understanding of learning and memory. German pre-service science teachers specializing in biology complete neuroscientific modules (human biology/animal physiology) during their studies because they are expected to teach these topics to their students. Thus, they are required to demonstrate a certain degree of neuroscience literacy. In the present study, 550 pre-service science teachers were surveyed on neuromyths and scientific concepts about learning and memory. Pre-service science teachers’ scientific concepts increased over the course of their training. However, beliefs in neuromyths were independent of participants’ status within teacher education (first-year students, advanced students, and post-graduate trainees). The results showed that 10 neuromyths were endorsed by more than 50% of prospective science teachers. Beliefs in the existence of learning styles (93%) and the effectiveness of Brain Gym (92%) were most widespread. Many myths were endorsed even though a large share of respondents had thematically similar scientific concepts; endorsement of neuromyths was found to be largely independent of professional knowledge as well as theory-based and biography-based learning beliefs about neuroscience and learning. Our results suggest that neuromyths can exist in parallel to scientific concepts, professional knowledge and beliefs and are resistant to formal education. From the perspective of conceptual change theory, they thus exhibit characteristic traits of misconceptions that cannot simply be counteracted with increased neuroscientific knowledge. On the basis of our study’s findings, it can be concluded that new teacher programs considering neuromyths as change-resistant misconceptions are needed to professionalize pre-service science teachers’ neuroscience literacy. For this, an intensive web of exchange between the education field and neuroscientists is required, not just to deploy the latest scientific insights to refute neuromyths on learning and memory, but also to identify further neuromyths.

Introduction

Findings from brain research have unleashed a veritable “neuro-boom” in recent years, which has taken the form of numerous publications for teachers as well as learning guides for students (e.g., Doyle and Zakrajsek, 2013). Teachers in all subjects have expressed great interest in neuroscience research findings and find it useful to incorporate them into their instruction (Dekker et al., 2012). Even incorrectly interpreted research findings have great appeal once images of the brain and/or neuroscientific explanations are added (McCabe and Castel, 2008; Lindell and Kidd, 2013). Media and even educational programs make use of this effect; they are filled with bold and eye-catching yet empty promises like ‘learn while you sleep’ or ‘innate intelligence through Brain Gym’. Money, time, and effort are expended integrating so-called “neuromyths” into the school system (Pasquinelli, 2012). It is understandable that people who lack knowledge in the field of neuroscience might struggle to distinguish facts from myths (Wallace, 1993; Beck, 2010). However, even teachers, the alleged experts on learning, endorse misconceptions¹ about neuroscience and base their pedagogical practice on neuromyths (e.g., Dekker et al., 2012).

The term “neuromyth” was coined in the 1980s by the neurosurgeon Alan Crockard, who used it to refer to unscientific understandings of the brain in medical culture (Howard-Jones, 2010). The Organization for Economic Co-operation and Development [OECD] (2002) defines neuromyths as “misconception[s] generated by a misunderstanding, a misreading, or a misquoting of facts scientifically established (by brain research) to make a case for use of brain research in education and other contexts” (p. 111). Neuromyths are thus falsely or overly interpreted neuroscientific research findings that are transferred to applied contexts such as teaching, learning and instruction. Neuromyths are often seen as originating in simplistic language in the reporting of neuroscientific research findings. These research findings are often published at a challenging reading level (Yeung et al., 2018) and tend to be very complex and difficult for non-neurobiologists to understand, meaning that simplistic formulations are often resorted to. These ‘pop-science’ statements are then falsely interpreted and quickly lose their kernel of truth. They are packed into “low-cost and easily implemented classroom approaches” (Howard-Jones, 2014, p. 819) that claim to promote learning. While fun, these approaches also result in the rapid propagation of neuromyths among students, parents, and teachers. This process is strengthened by media, whose simplified and/or overly interpreted portrayals of research findings reach a wide audience (Wallace, 1993; Beck, 2010), and companies looking to offer learning programs that claim to be “brain-based” but usually provide consumers with little hope of learning success (Goswami, 2006; Pasquinelli, 2012). The OECD has been calling attention to the problem of neuromyths almost as long as it has been calling for teaching and learning to be based on neuroscience.

Neuromyths often originate from overgeneralizations of empirical research (Macdonald et al., 2017). Today, neuromyths have emerged for many aspects of neuroscience, including specific learning difficulties such as dyslexia (Macdonald et al., 2017) and the influence of nutrition (Dekker et al., 2012) or music (Düvel et al., 2017) on the brain. This study focuses on neuromyths related to learning and memory. Table 1 illustrates with three examples how these neuromyths arise from errors in transferring neuroscientific information (the kernel of truth). The depicted transfer steps (left) as well as their relationships to neuroscientific findings (right) are based on a summary of the current state of theory on neuromyths as well as supplementary literature research.

TABLE 1

Table 1. Errors in argumentation from the neuroscientific kernel of truth to erroneous implications for school instruction compared to the neuroscientific evidence.

The existing research on neuromyths primarily focuses on teachers. Studies investigating the endorsement of neuromyths among teachers of various subjects have been conducted in the Netherlands, England (Dekker et al., 2012; Simmonds, 2014), Latin America (Bartoszeck and Bartoszeck, 2012; Gleichgerrcht et al., 2015; Hermida et al., 2016), Portugal (Rato et al., 2013), Australia (Bellert and Graham, 2013; Horvath et al., 2018), Greece (Deligiannidi and Howard-Jones, 2015), China (Pei et al., 2015), Turkey (e.g., Karakus et al., 2015), Switzerland (Tardif et al., 2015), Spain (Ferrero et al., 2016), the United States (Lethaby and Harries, 2016), and Canada (Macdonald et al., 2017). All of these studies have found that teachers believe in a large number of neuromyths, although only a few of these, such as Brain Gym, are related to the topic of learning and memory and there are country-specific differences in the endorsement of specific myths.² Cultural differences between countries seem to have an influence on which neuromyths spread (Pei et al., 2015; Ferrero et al., 2016; Hermida et al., 2016).

One study of post-graduate teacher trainees found that 56–83% of respondents encountered educational programs based on neuromyths in their first year working in schools, which was associated with high levels of acceptance of those myths (Howard-Jones et al., 2009). Studies by Fuentes and Risso (2015, Spain), Dündar and Gündüz (2016, Turkey), Canbulat and Kiriktas (2017, Turkey), Düvel et al. (2017, Germany), Kim and Sankey (2017, Australia), Papadatou-Pastou et al. (2017, Greece), and Im et al. (2018, South Korea) indicate that neuromyths are already present during the academic stage of teacher education. However, no studies focusing on neuromyths related to learning and memory have been conducted with pre-service samples either. Nevertheless, it can generally be concluded on the basis of these results that the neuroscience content knowledge necessary to critically evaluate neuromyths does not seem to be integrated into teacher education to a sufficient degree (Howard-Jones, 2014).

Most studies have not found personal characteristics like age, professional experience, teaching subject, school type, school location (urban/rural), and participation in professional development trainings to be associated with endorsement of neuromyths or with scientific concepts about the brain (Dekker et al., 2012; Rato et al., 2013; Gleichgerrcht et al., 2015). Only Ferrero et al. (2016) found a correlation with gender, with female teachers more likely to endorse neuromyths. Macdonald et al. (2017) found evidence that age (being younger), training (having a university degree), and enrollment in neuroscience courses predict reduced endorsement of neuromyths. The topic of neuroscience is of great interest to teachers internationally (e.g., Dekker et al., 2012), but there seems to be a large gap between teachers’ interest and their ability to actually deal with neuroscientific findings in a professional way (Rato et al., 2013). Teachers with high levels of scientific concepts about the brain have proven to be more susceptible to neuromyths (Dekker et al., 2012; Rato et al., 2013; Gleichgerrcht et al., 2015; Ferrero et al., 2016; Düvel et al., 2017; Canbulat and Kiriktas, 2017) in almost all studies (except Howard-Jones et al., 2009). Horvath et al. (2018) found the acceptance of neuromyths to be nearly identical between populations of award-winning and non-award-winning teachers. There seems to be a general tendency to agree with neuroscientific statements, but a lack of ability to separate myths from facts (Ferrero et al., 2016). While reading scientific articles can reduce endorsement of neuromyths, teachers tend to use pop-science sources like TV and the Internet as their main sources of information for neuroscientific facts (Rato et al., 2013).

Im et al. (2018) demonstrated that taking an educational psychology course only improves neuroscience literacy; it does not reduce beliefs in neuromyths. Macdonald et al. (2017) found differences in endorsement of neuromyths between the general public, teachers, and people with high levels of neuroscientific knowledge, and Papadatou-Pastou et al. (2017) indicate that general knowledge about the brain is the best “safeguard against believing in neuromyths” (p. 1). German pre-service science teachers specializing in biology receive such knowledge during their university education so that they will later be able to pass it on to their students in their classroom instruction. It can thus be assumed that they develop the theory-based learning beliefs and professional knowledge about neuroscience needed to critically evaluate neuromyths about learning and memory during their university studies. According to Kunter et al. (2013), these two factors, along with motivational orientations and self-regulative skills, are prerequisites for reflective instruction, or in other words, the teaching profession. Kunter et al. (2013) subdivides beliefs into epistemological beliefs and beliefs about learning content and instructional practice. Applying this to the topic of neuroscience and learning, pre-service teachers might have theory-based learning beliefs on the nature of science and teaching and learning. However, they also bring with them beliefs about the definition of learning and learning strategies rooted in their own learning biographies (biography-based learning beliefs). The professional knowledge pre-service science teachers in Germany specializing in biology are expected to acquire during their studies can be subdivided into, inter alia, psychological-pedagogical knowledge (PPK), content knowledge (CK), and pedagogical content knowledge (PCK) (Shulman, 1987; Kunter et al., 2013). Applying this to the topic of neuroscience and learning, pre-service science teachers need to acquire PPK about the psychology of human learning, CK about curricular content related to neuroscience, and PCK about instructional strategies for sustainable learning (Meier et al., 2018). According to Dündar and Gündüz (2016), pre-service science teachers perform significantly better in terms of neuromyths than pre-service teachers in other subjects. As of yet, there are no studies specifically investigating neuromyths about learning and memory or on their prevalence among pre-service science teachers (specializing in biology) depending on their status within teacher education.

As mentioned above, Dekker et al. (2012) and other existing studies on neuromyths interpret the frequently found association between endorsement of neuromyths and scientific concepts as a general tendency to agree with neuroscientific statements among teachers. After more intensive theoretical work on neuromyths, we also see these correlations as rooted in the fact that the test instrument asks in some cases about both the neuromyth and the corresponding kernel of truth as scientific concepts. For example: neuromyth = “Individuals learn better when they receive information in their preferred learning style (e.g., auditory, visual, kinesthetic)“ and scientific concept (kernel of truth) = “Individual learners show preferences for the mode in which they receive information (e.g., visual, auditory, kinesthetic).“ Dekker et al.’s (2012) instrument for scientific concepts, which was applied in many of the previous studies on neuromyths, cannot be seen as an appropriate knowledge test for pre-service science teachers in light of our theoretical perspective on professional knowledge among science teachers. In order to more effectively design professional development offerings, it will be necessary to further clarify the causal relations between misconceptions and aspects of professional competency (beliefs and professional knowledge). The present study is the first to do this.

Methods

Building upon the aforementioned theoretical work, this study addresses three research questions: (1) How are pre-service science teachers’ misconceptions and scientific concepts about learning and memory associated with their status within teacher education? (2) What misconceptions and scientific concepts do pre-service science teachers have on the topic of learning and memory? (3) How are their misconceptions associated with their beliefs and professional knowledge about the topic of learning and memory?

Participants

The study was conducted among pre-service science teachers specializing in biology at two German universities (University of Kassel and University of Kiel) as well as several institutes for post-graduate teacher trainees (Studienseminare) in the federal state of Hesse (N = 550). The total sample consisted of 152 first-year students, 260 advanced students (second year and above), and 138 post-graduate teacher trainees (Referendare, who were an average of 9 months into their training, SD = 4.47). Respondents were 24.8% male and 75.2% female, and were between 18 and 38 years of age (M = 24 years old, SD = 3.79). 69.4% of respondents were studying to be teachers at college-preparatory secondary schools (Gymnasium), and 30.6% were studying to be teachers in lower-track secondary schools (Lehramt fr Haupt- und Realschulen). Research Question 3 was investigated with a subsample of 79 advanced students, who had the opportunity to participate in a more extensive testing for organizational reasons. Advanced means that they had already completed a human biology course with neuroscience content during their studies. 21.5% of the respondents in this sample were male, while 78.5% were female. The average age was 25 years (SD = 2.70) and the respondents were in their eighth semester of studies on average (SD = 2.56).

Procedure

The data was collected in 19 courses in the field of instructional methods for science (biology) education. The post-graduate teacher trainees were recruited and surveyed via their supervisors at the teacher training institutes. Participation took the form of a paper-and-pencil test lasting approximately 15 min. The testing time was expanded to 1 h for a subsample of participants (n = 79) in order to apply further instruments (see Materials). In both cases, the project was introduced as a study on the topic of neuroscience and learning; the term “neuromyths” was not used. Participation in the evaluation was voluntary and the students provided informed written consent to use the data for research purposes. They were informed that the goal of the study was to collect information on their current state of knowledge and attitudes toward the topic of neuroscience and learning, and that the anonymity of their data would be ensured via a coding system. They were further notified that they could withdraw from participation at any time without consequences. The authors strictly handled student anonymity and ethical issues.

Instruments

The test instrument for Research Questions 1 and 2 consisted of 11 items on scientific concepts and 11 items on misconceptions/neuromyths (α = 0.66 and 0.76, respectively³). 13 of these 22 items (8 items on scientific concepts and 5 on neuromyths) were taken from Dekker et al. (2012) and translated into German. One item (“Memory is stored in the brain much like as in a computer. That is, each memory goes into a tiny piece of the brain”) was taken from Howard-Jones et al. (2009) and put into more concrete terms: “The brain works like a hard drive; information is stored in specific locations.” To guarantee the fidelity of the translation, the resultant version was back-translated into English by a native speaker and both English versions were compared by a third person. Five neuromyths-items on development (Myth: most receptive to learning before age 3), hemispheric asymmetry (Myth: logic in the left hemisphere, creativity in the right), memory (Myth: Genetically determined number of cells determines learning), learning while you sleep (Myth: You can learn while you sleep, e.g., via audio recordings) and evidence-based learning techniques (i.e., desirable difficulties, Bjork and Bjork, 2011; Myth: Blocked learning is better than interleaved learning) were newly constructed for this study, as they have been widely publicized in German media and learning guides. We followed Macdonald et al. (2017)’s methodological recommendations and replaced the three-option answer format Correct/Incorrect/I don’t know used by Dekker et al. (2012) and other studies of neuromyths with a 4-point Likert scale in order to force respondents to take a position and allow them to specify how sure they were of their answer (4 = Strongly agree/1 = Strongly disagree) or how torn (3 = Somewhat agree/2 = Somewhat disagree). Because the German versions of some items from Dekker et al. (2012) were refined in terms of content and several new items were created, an English version of the instrument has been provided along with this article (see Appendix). Future studies should note that the German version of the instrument was employed in this study (published in Grospietsch and Mayer, 2018a).

Six instruments on professional knowledge, biography-based learning beliefs, and theory-based learning beliefs on neuroscience and learning were used to answer Research Question 3. Table 2 provides an overview of the instruments and corresponding scales, numbers of items, and reliability coefficients. Example items are provided here; a complete overview of all items can be found in the Supplementary Material. Biography-based learning beliefs were measured via 6-point Likert scales and theory-based learning beliefs via 4-point Likert scales. Professional knowledge was measured via three self-constructed knowledge tests. CK about curricular content in neuroscience was measured via six multiple-choice items with four distractors each. PCK about instructional strategies for sustainable learning (including how to deal with students’ misconceptions about the structure and function of the brain) was measured via 12 open-ended and closed-ended questions, and PPK about the psychology of human learning via 17 open-ended and closed-ended items. The differences in test construction are rooted in the project’s research focus. The interrater reliability for all open-ended items was found to be Cohen’s κ = 0.91 (p < 0.001). This indicates almost perfect agreement (Landis and Koch, 1977).

TABLE 2

Table 2. Overview of the instruments for learning beliefs and professional knowledge.

In addition, information on sociodemographic data (age, gender, field of study, years of study/training, enrolled in university courses on neuroscience and learning) were collected for all participants. Except for years of study/training and enrollment in a human biology course, these demographic data were requested for descriptive purposes only and were not explored further in the subsequent analyses.

Data Analysis

We used multifactorial analyses of variance to test whether first-year students, advanced students, and post-graduate teacher trainees differed in their endorsement of scientific concepts and neuromyths (Research Question 1). These three groups were considered to be in different stages of teacher education due to differences in educational content: Group 1 was enrolled in introductory courses in instructional methods in science (biology) education, disciplinary content, and education science; Group 2 was enrolled in or had completed more advanced education science and subject-specific instructional methods courses on teaching and learning (in science) as well as modules that covered neuroscience (human biology and/or animal physiology); and Group 3 was in the process of completing a practical training phase after university graduation. One-way analyses of variance and Bonferroni-adjusted post hoc analyses were used to determine the extent to which the groups differed in their endorsement of individual neuromyths. One-way analyses of variance with Welch corrections and Games-Howell post hoc analyses were applied in the case of heterogeneous variance. To this end and to answer Research Question 2, the 4-point Likert scale was recoded into a dummy format (agree/disagree) for better comparability with the previously cited studies. First, the percentage of respondents who agreed with each item (both neuromyths and scientific concepts) was calculated. Then, the neuromyths/scientific concepts were grouped by content into different neuroscientific topics (categories) on the basis of theory. If a category had more than one item, the mean of the percentages was taken. Correlation analyses (Pearson) were conducted to determine the associations between endorsement of neuromyths and beliefs about learning and memory (biography-based learning beliefs about the definition of learning at university and use of learning strategies as well as theory-based beliefs about the nature of science and teaching and learning), professional knowledge (CK about curricular content related to neuroscience, PCK about instructional strategies for sustainable learning, and PPK about the psychology of human learning). The significance level for all analyses was p ≤ 0.05.

Results

Scientific Concepts and Misconceptions by Status Within Teacher Education

A multifactorial analysis of variance revealed a significant main effect for conception type (misconceptions vs. scientific concepts: F(1,1076) = 311.70, p ≤ 0.001, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.225) but not for stage of teacher education [first-year students, advanced students, and post-graduate teacher trainees: F(2,1076) = 1.95, p = 0.143, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.004]. There was a statistically significant interaction between stage of teacher education and conception type: F(2,1076) = 8.13, p ≤ 0.001, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.015. The mean levels in Figure 1 show that respondents in different stages of teacher education differed from one another in their endorsement of scientific concepts (left) but not in their endorsement of neuromyths (right).

FIGURE 1

Figure 1. Group comparison on endorsement of scientific concepts (left) and misconceptions (right) (mean and standard deviations are presented; 4 = strongly agree, 3 = somewhat agree, 2 = somewhat disagree, 1 = strongly disagree).

Turning to the percentage agreeing with individual neuromyths (dichotomous answer format), one-way analyses of variance only uncovered differences between the three groups of subjects with respect to the neuromyths on critical periods of childhood development [Welch’s F(2,297) = 11.84, p ≤ 0.001], blocked learning is better than interleaved [Welch’s F(2,305) = 4.80, p = 0.009], the existence of learning styles [Welch’s F(2,325) = 3.58, p = 0.029] and a genetically determined number of cells determines learning [F(2,533) = 24.29, p ≤ 0.001]. Games-Howell post hoc analyses revealed significantly greater agreement with the myth of critical periods of childhood development among advanced students compared to first-year-students and post-graduate trainees (68 vs. 56%, p = 0.049, 0.12, 95%-CI[0.00, 0.24] and 43%, p ≤ 0.001, 0.25, 95%-CI[0.13, 0.37]). The neuromyth that blocked learning is better than interleaved was more frequently rejected by advanced students and post-graduate trainees than by first-year students (49% p = 0.021, -0.14, 95%-CI[-0.27, -0.02] and 46% p = 0.017, -0.16, 95%-CI[-0.3, -0.02] vs. 62%). The neuromyth on the existence of learning styles was significantly less frequently endorsed by advanced students than by first-year students (90 vs. 97%, p = 0.023, -0.06, 95%-CI[-0.12, -0.01]). A Bonferroni-adjusted post hoc analysis revealed that the neuromyth that a person’s genetically determined number of cells forms an upper limit for learning success was actually endorsed significantly (p ≤ 0.001) more by post-graduate trainees (64 vs. 33% of first-year students 0.32, 95%-CI[0.18, 0.45], and 31% of advanced students, 0.33, 95%-CI[0.21, 0.45]).

Endorsement of Misconceptions and Scientific Concepts

Pre-service science teachers’ neuroscience literacy was to a large extent rooted in neuromyths (Figure 2). 10 of 11 misconceptions on the topic of learning and memory were endorsed by more than half of respondents. The existence of learning styles, the effectiveness of Brain Gym, and the notion that information is stored in specific locations (hard drive) were endorsed most frequently (with 93, 92, and 85% of respondents agreeing with these items, respectively). The only neuromyth to be endorsed by fewer than half of the pre-service teachers in the sample was the notion that a person’s genetically determined number of cells determines learning success (with 40% of respondents agreeing).

FIGURE 2

Figure 2. Agreement with misconceptions (neuromyths) among all participants.

Neuromyths were sometimes endorsed even when respondents had thematically similar scientific concepts (Table 3). This was seen in the categories of development, memory, learning techniques, brain activity, and sensory modalities. On the other hand, high levels of agreement with neuromyths were found in categories in which fewer respondents had thematically similar scientific concepts. This was the case for the categories of neuroplasticity and hemispheric asymmetry.

TABLE 3

Table 3. Comparing endorsement of scientific concepts and misconceptions.

Correlations With Beliefs and Professional Knowledge

As can be seen in Table 4, the advanced students for whom individual aspects of professional competency were investigated tended toward agreement with respect to biography- and theory-based learning beliefs. Transmissive beliefs were endorsed to a lesser extent than constructivist beliefs. The latter received the highest average agreement alongside nature of science beliefs. On the knowledge tests, the advanced students were able to correctly answer about 50% of the CK, 20% of the PCK, and 30% of the PPK questions. The standard deviations here varied more widely than they did for beliefs.

TABLE 4

Table 4. Correlations of misconceptions with learning beliefs and professional knowledge.

Correlational analyses revealed only a small positive correlation between neuromyths und constructivist beliefs about teaching and learning (r = 0.313, p = 0.006). This correlation within the area of theory-based learning beliefs means that pre-service science teachers who endorse misconceptions also exhibit a constructivist view of teaching and learning and think of learning as an active, self-directed, constructive process in which knowledge cannot simply be transferred to the learner (Staub and Stern, 2002). No correlations were found between misconceptions and the other theory-based learning beliefs (transmissive and nature of science beliefs) or the three areas of professional knowledge on neuroscience and learning (CK, PCK, PPK). Nor were there any significant correlations between misconceptions and biography-based learning beliefs related to respondents’ subjective definition of learning or inventory of learning strategies (Table 4).

Discussion

Pre-service Science Teachers’ Scientific Concepts

The results of our study demonstrate that pre-service science teachers’ scientific concepts on learning and memory increase over the course of their training. This finding is in accordance with expectations, because German science teachers specializing in biology complete modules containing neuroscientific content (human biology and animal physiology) during their university studies. It should be noted that average endorsement of scientific concepts did not increase dramatically and was quite high even among first-year students. Based on these findings, one could conclude that many pre-service biology teachers have already acquired scientific concepts during school (e.g., in advanced high school biology courses) and bring them with them to university. However, from a critical perspective, it should be noted that our instrument did not allow us to measure what the students actually know and when they simply took a position despite a lack of knowledge (intuiting/guessing). This problem was strengthened by our use of a Likert scale, which was however recommended by Macdonald et al. (2017). Despite differences to Dekker et al.’s (2012) instrument, our survey was able to confirm their finding that there is a general tendency to agree with neuroscientific statements. Our results indicate that this tendency persists despite academic and practical training.

Our results further indicate that the pre-service science teachers in our study have the weakest scientific concepts with respect to neuroplasticity and hemispheric asymmetry (60 and 40%). This could be because these topics tend to be covered only marginally or as an aside in neuroscience courses and textbooks. From a critical perspective, it should be noted that these values are location-specific and could be different at other German universities. There is currently no curriculum stipulating which scientific concepts must be covered as part of science teachers’ training in the fundamentals of neuroscience. One item from each of the two aforementioned categories adopted from Dekker et al. (2012) have also been employed in other studies of neuromyths. Putting aside the differences in answer format (our 4-point Likert Scale vs. correct/incorrect/I don’t know) and the slightly more concrete items in our translation, comparing our results to previous studies indicates that German pre-service science teachers have stronger scientific concepts than Turkish and British pre-service teachers with respect to the items “When one brain region is damaged due to injury, other parts of the brain can take up its function” and “The left and right hemispheres of the brain always work together in processing information” (Dündar and Gündüz, 2016: 20 and 15% correct answers, Papadatou-Pastou et al., 2017: 12 and 14% correct answers, although around 30% of respondents in both studies selected I don’t know). The pre-service science teachers in this study endorsed the topics of development, memory, learning techniques, brain activity, and sensory modalities at very high rates (98-92%). The values of all matching items were higher than in the study by Papadatou-Pastou et al. (2017), although the instruments’ differences in language and answer format must be taken into account. The presented tendencies concerning overlapping items indicate that pre-service science teachers seem to have stronger scientific concepts related to neuroscience than other pre-service teachers. This should be tested in a study employing the same instruments for both groups of participants.

Pre-service Science Teachers’ Misconceptions (Neuromyths)

Given that this study’s quasi-longitudinal design found no differences in endorsement of neuromyths between first-year students, advanced students, and post-graduate trainees, teacher education does not seem to be able to successfully professionalize students’ misconceptions about learning and memory. Only two neuromyths about learning and memory (critical periods and blocked learning) were endorsed less by post-graduate teacher trainees, who have already completed their university training in neuroscience and learning, than by students still in university. In fact, the neuromyth that a person’s genetically determined number of cells forms an upper limit for learning success was endorsed more frequently among the group of post-graduate trainees. This increase is alarming, as belief in this type of myth bestows upon or denies learners a pre-determined, non-malleable aptitude for learning. This could have consequences for teachers’ interactions with students and thus also for students’ self-efficacy beliefs. Whether and to what extent these individual neuromyths find their way into pre-service science teachers’ later pedagogical practice is not clear on the basis of our study. We join Horvath et al. (2018) in arguing that future studies must test the extent to which the endorsement of neuromyths influences teachers’ effectiveness. However, other studies show that teachers’ beliefs and attitudes guide their actions (Wahl, 1979). We assume that university education represents the most significant opportunity for German science teachers to acquire neuroscientific knowledge in a guided way. In light of the previously cited studies of pre-service and in-service teachers in all school subjects (e.g., Dekker et al., 2012) revealing comparatively high levels of endorsement of neuromyths, we do not assume that in-service science teachers endorse misconceptions to a lesser extent than the pre-service teachers in our study. It might even be the case that committed efforts among in-service teachers to optimally guide students’ learning lead to greater use of practical approaches based on neuromyths, such as Brain Gym or learning styles. In this way, neuromyths might be even more widespread among German in-service science teachers than pre-service teachers. However, further comparative and longitudinal studies are necessary to investigate these hypotheses. In any event, teacher education as it currently exists in Germany does not seem sufficient to dismantle misconceptions about learning and memory or replace them with scientific concepts. New, more effective professional development opportunities and learning programs must be created.

The results presented in this study confirm previous findings that it is not just in-service teachers who believe in neuromyths – a large share of pre-service teachers endorse them as well (Howard-Jones et al., 2009; Fuentes and Risso, 2015; Dündar and Gündüz, 2016; Canbulat and Kiriktas, 2017; Düvel et al., 2017; Kim and Sankey, 2017; Papadatou-Pastou et al., 2017; Im et al., 2018). Out of a total of 11 misconceptions (neuromyths) about learning and memory, the existence of learning styles (93%), the effectiveness of Brain Gym (92%), and the assumption that information is stored in specific locations (hard drive) (85%) were endorsed most frequently. Comparing our results to those of other studies (despite the difference in answer format and the slightly more concrete items in our translation), these myths were also quite frequently endorsed by pre-service teachers in Turkish (Dündar and Gündüz, 2016: 97, 67, and 79%) and British studies (Howard-Jones et al., 2009: 82, 62, und 36%, Papadatou-Pastou et al., 2017: 94, 37% und -%)⁴. There thus appears to be a core group of neuromyths whose prevalence is independent of culture.

Furthermore, German pre-service teachers believe more frequently than Turkish (Dündar and Gündüz, 2016) or British pre-service teachers (Howard-Jones et al., 2009; Papadatou-Pastou et al., 2017) in the neuromyths of only using 10% of our brain (57% compared to 42, 52 and 47%) and critical periods of childhood development (item = If the brain is not sufficiently supported in early childhood, learning problems that can no longer be remediated by education can occur: 59% compared to 59, 9, and 24%)⁵. Comparing our results to those of Papadatou-Pastou et al. (2017), it appears that German pre-service teachers believe more strongly in the myths of learning differences due to the use of different hemispheres (82% vs. 55%, although I don’t know was selected quite frequently in Papadatou-Pastou et al., 2017)⁵ and learning while you sleep (56 vs. 38%)⁵. This points to cultural differences in levels of agreement with individual neuromyths among pre-service teachers, just as among in-service teachers. Moreover, it indicates that the characterization of neuromyths as ‘inadequate scientific concepts’ is insufficient. Instead, these cultural differences suggest that neuromyths to a large degree feed off of a socio-cultural discourse that finds its specific expression – cultural differences included – in these neuromyths. Thus, these misconceptions might be better described as scientific myths (cf. Papadatou-Pastou et al., 2017). Empirical inquiries and interventions should take socio-cultural discourses about teaching and learning into consideration as the relevant context of neuromyths. This study also found evidence for endorsement of the myth that blocked learning is more effective than interleaved learning (i.e., desirable difficulties, Bjork and Bjork, 2011) for the first time. Thus, there is a need for action in teacher education with respect to the aforementioned neuromyths, particularly in light of the importance of a professional understanding of learning and memory for instructional content and instructional methods in science.

Relations Between Neuromyths and Aspects of Professional Competency

Turning to the aspects of professional competency, firstly, our results with respect to theory-based learning beliefs were in accordance with expectations. Our hypothesis that constructivist and nature of science beliefs would be stronger among advanced students were confirmed. In accordance with the existing research literature (e.g., Brauer et al., 2014), transmissive beliefs about teaching and learning were in turn less strong among these students. Thus, for these students, learning is more of an active than a passive process in which knowledge can be generated independently. This can be interpreted together with professional beliefs on the origins of knowledge in biology (nature of science) as evidence in favor of a professional understanding of learning and memory. The students’ biography-based learning beliefs can also be positively interpreted in light of the rather strong deployment of learning strategies and the definition of learning as transformation (professional definition of learning). However, the existing research literature (Drechsel, 2001) also indicates that advanced students maintain less professional definitions of learning (learning as reproduction). In fact, in our study, these were even more widespread than the professional ones. Whether and to what extent these beliefs influence the students’ later actions in schools remains an open question. Our results further indicate that students have some knowledge of PCK about instructional strategies for sustainable learning and PPK about the psychology of human learning, but not a great deal. We see this as primarily rooted in our methodological decision to select advanced students who had completed human biology courses with neuroscientific content. We have no information on the extent to which this sample is also at an advanced level with respect to educational science, psychology, and instructional methods course. However, the CK about curricular content in neuroscience we selected for was present to a stronger extent, although this could also be rooted in the closed answer format. The larger standard deviations for the knowledge results point to differences in the students‘ performance.

Our results show that all of the aspects of professional competency we investigated were present among the students. However, there were few correlations with endorsement of neuromyths. Building upon studies that call for more neuroscience in teacher education (e.g., Papadatou-Pastou et al., 2017), we expected negative correlations with students’ professional knowledge, which would mean that students with greater knowledge endorse neuromyths to a lesser extent. However, no such correlations were found for CK about curricular content related to neuroscience, PCK about instructional strategies for sustainable learning or PPK about the psychology of human learning. For PCK and PPK, this might be rooted in the students’ low levels of knowledge or methodologically in the difficulty level of the tests, which was too high. We reject this possibility with respect to CK about curricular content related to neuroscience, as the students had more knowledge here. Our results indicate that neuromyths are independent of CK. However, it must be emphasized that our instrument only asked about curricular content in neuroscience with respect to the topics of brain structure, memory, and long-term potentiation commonly found in school textbooks. General knowledge of neuroscience and the latest research findings were not considered in this study and could still be a predictor of endorsement of neuromyths.

In addition, the results of our study revealed no correlations with respect to a person’s learning at university (definition of learning and learning strategies), despite our theoretical assumptions indicating that less professional biography-based learning beliefs could facilitate the endorsement of neuromyths. Perhaps our scale on learning as reproduction, with α = 0.58, could not measure the theoretical construct sufficiently and accurately enough. Future studies could also ask about concrete learning experiences that could promote neuromyths (e.g., if they conducted learning style tests during their school years). However, this study found a small positive correlation between misconceptions and constructivist beliefs about teaching and learning despite the fact that some neuromyths (e.g., that the brain works like a hard drive) are not theoretically compatible with such beliefs. People with constructivist views of teaching and learning actually view learning as an active, self-directed, constructive process in which knowledge cannot simply be transferred to the learner (Staub and Stern, 2002). Consequently, neuromyths seem to be integrated into the semantic network of theory-based learning beliefs despite their scientific inconsistencies, which can make them more difficult to change. The co-existence or even synthesis of misconceptions and theory-based beliefs are well-established in theories of misconceptions and conceptual change (Vosniadou, 2013). Studies by Petitto and Dunbar (2004) describe how university students can stubbornly hold onto their original concepts despite empirical demonstrations and theoretical explanations. Newton and Miah (2017) demonstrated this specifically for the neuromyth on the existence of learning styles. In addition, the authors warn of a “backfire effect,” a phenomenon in which attempts to address myths and misunderstandings can lead to a strengthening of beliefs in these myths. Thus, the results of this study indicate that interventions against the endorsement in neuromyths must begin deep in participants’ belief systems.

Implications for Intervention Approaches With Respect to Neuroscience Literacy

Overall, our results for pre-service science teachers show that Papadatou-Pastou et al.’s (2017) call to integrate neuroscientific content into teacher education is not in itself sufficient to limit the spread of neuromyths. Even these students, who are taught such content in their courses, must be trained to become critical consumers of neuroscientific research findings (a kind of preventative focus; Macdonald et al., 2017).

In line with the previously cited neuromyth studies, our findings confirm the need for action with respect to neuromyths. Few intervention approaches have been proposed. Papadatou-Pastou et al. (2017) stresses the importance of developing an understanding of how neuroscience research is conducted and presented (e.g., imaging techniques using differential images). This might not be occurring to a sufficient extent in German pre-service science teacher education, which is primarily set up to enhance professional knowledge. Neuroscience will always be in a continuous state of development and progress. Pre-service science teachers should be put in a position in which they are able to follow the latest developments by effectively reading and critically evaluating the information they obtain from various sources.

According to Guzzetti et al. (1993) and Kowalski and Taylor (2009, 2011), one of the most effective evidence-based methods of addressing scientific myths consists of directly refuting misunderstandings. Grospietsch and Mayer (2018b) have confirmed this for neuromyths. Neuromyths arise and persist from an entire line of argumentation consisting of misinterpretations and exaggerations that can only be refuted with a multitude of neuroscientific facts (examples provided in Table 1 of this study). This speaks in favor of examining each neuromyth individually, determining its “kernel of truth,” uncovering its argumentation structure, and comparing it to the corresponding scientific concepts. Only by investigating each neuromyth individually and in more detail than previously will we be able to determine why neuromyths have been spreading and develop appropriate interventions to stop them. This requires strong cooperation between education, neuroscience, and cognitive psychology. MacNabb et al. (2006) demonstrate the positive effects of such cooperation. Materials and courses counteracting the false transfer of scientific concepts to classroom teaching and learning need to be jointly developed. A web of exchange between the field of education and neuroscientists is required to put neuromyths into a more scientifically accurate light. Neuroscientists’ assistance is particularly necessary when it comes to incorporating the latest research.

Several neuromyths found among pre-service science teachers in this study have also been frequently demonstrated in international studies of in-service teachers (cf. e.g., Dekker et al., 2012; Ferrero et al., 2016). Given that even first-year students exhibit beliefs in neuromyths, it is likely that pre-service teachers encounter these misconceptions even before beginning their university studies, i.e., during school. It is well-known that neuromyths such as the theory of learning styles and Brain Gym exercises are found in a large number of learning guides and educational programs (Pasquinelli, 2012). Students can also encounter neuromyths through their teachers. Studies by Schletter and Bayrhuber (1998) provide empirical indications that misconceptions (e.g., that the brain works like a hard drive) are present among school students. To the best of our knowledge, there are not yet any studies systematically investigating the spread of neuromyths about learning and memory among school students. What we do know is that the misconceptions that develop over a person’s school years are difficult to change via formal education at university (Pajares, 1992). Starting intervention during students’ school years or at the beginning of university education seems advantageous.

Recommendations for Future Studies

As previously discussed, the results of our study show that pre-service science teachers have weak scientific concepts on neuroplasticity and hemispheric asymmetry. Our results further show that the low levels of scientific concepts for these topics were accompanied by high levels of endorsement of thematically similar neuromyths. Based on our findings, one might conclude that these topics need to be more strongly integrated into teacher education and associated neuroscience teaching materials. Even though pre-service science teachers endorsed scientifically accurate statements (scientific concepts) about the topics of development, memory, learning techniques, brain activity, and sensory modalities, the thematically similar neuromyth items were widely endorsed as well (50–93%). In these cases, we concur with Dekker et al. (2012) and follow-up studies by other authors that there is a lack of ability to differentiate scientific concepts from misconceptions. Endorsement of misconceptions (neuromyths) was not lower for topics in which there was less knowledge of scientific concepts (93% for learning styles despite high endorsement of scientific concepts; 92% for Brain Gym amid low endorsement of scientific concepts). Consequently, the results obtained with our survey suggest two different causes for the emergence of neuromyths: a lack of scientific concepts, but also false transfer of accurate scientific concepts to teaching and learning. Future studies should further clarify these different explanations for the emergence of neuromyths. In doing so, it would be advantageous to not only contrast thematically similar items on scientific concepts and neuromyths, as we did in the questionnaire for this study, but rather to consistently identify and inquire about the kernel of truth and unique argumentation errors for each neuromyth (see Table 1). Given the current state of theoretical work on neuromyths, we were only able to do this for a few categories (e.g., sensory modalities). Much more theoretical work on neuromyths needs to be completed with respect to this issue. With regard to intervention approaches, future studies must also test whether the theoretical argumentation in favor of neuromyths previously described actually conform to those held by pre-service and in-service teachers. Thus, we see a need for more intensive empirical and theoretical research on neuromyths.

In this study, we were able to show that pre-service science teachers endorse a variety number of neuromyths. We did not collect data on the sources of the students’ neuroscientific information or their perceptions of the origin of their misconceptions on the topic of learning and memory. Biography-based learning experiences in the everyday world, independent learning by reading scientific/leisure magazines, or even the structure of university trainings could be potential sources of neuromyths. Following Yeung et al. (2018), the readability of neuroimaging articles and their abstracts could be particularly problematic, especially for first-year students. All of these aspects should be more thoroughly investigated in future studies. In our opinion, qualitative studies in which students are asked to describe their arguments in favor of neuromyths seem more worthwhile than surveys of various sources of information.

Summary and Outlook

In summary, our results indicate that neuromyths can exist in parallel to scientific concepts, professional knowledge and beliefs about neuroscience and learning and are resistant to conventional German teacher education, which promotes many aspects of professional competency on this topic. From the perspective of conceptual change theory, neuromyths thus exhibit characteristic traits of misconceptions that cannot simply be counteracted with increased neuroscientific knowledge. Both neuroscientific knowledge and didactic interventions will be required to effectively and sustainably banish neuromyths from the education system. For this reason, we call for stronger cooperation between neuroscientists and didactics experts. Creating links between education/didactics on the one hand and neuroscience and cognitive psychology on the other seems to be essential for confronting neuromyths’ lines of argumentation with scientific knowledge as well as improving science teachers’ neuroscience literacy. According to the Organization for Economic Co-operation and Development [OECD] (2008), each discipline has its own specific methods and language, which makes it particularly difficult for experts in one area to apply knowledge from the other. Joint publications and training programs for pre-service and in-service teachers on the cognitive errors involved in neuromyths would be an important first step toward eliminating ‘language barriers’ (Pickering and Howard-Jones, 2007) and closing the gap between neuroscience and the practice of education (Edelenbosch et al., 2015), at least with respect to neuromyths. Teachers train the neuroscientists of tomorrow. It is therefore important to take teachers seriously, to investigate how neuroscience can help them better understand learning, and to invest in their ability to optimally use neuroscience in their practice.

On the basis of this study’s results, the University of Kassel has developed a learning environment in accordance with the conceptual change model through interdisciplinary cooperation. This learning environment gives students reasons and opportunities to more closely interlink their professional knowledge in neuroscience, cognitive psychology, and instructional methods in science and to critically question incomplete or incorrect misconceptions and beliefs about learning and memory. An accompanying study demonstrated positive results with respect to pre-service science teachers’ neuroscience literacy (Grospietsch and Mayer, 2018b).

Data Availability Statement

The raw data supporting the conclusions of this manuscript are available on Open Science Framework. The link will be provided by the authors upon request.

Ethics Statement

No ethics approval was required for the reported study as per the guidelines of the University of Kassel or national guidelines. We conducted the study in line with the recommendations of the University of Kassel’s ethics committee. All participants gave written informed consent in accordance with the Helsinki Declaration.

Author Contributions

JM was responsible for the project administration and funding acquisition. Both authors developed the study concept and the methodology. FG developed the study materials, conducted the data collection, and analyzed the data. This article is part of the Ph.D. thesis of FG supervised by JM. FG drafted the manuscript. JM provided critical revisions. Both authors approved the final version of the manuscript for submission.

Funding

This project is part of the “Qualitätsoffensive Lehrerbildung,” a joint initiative of the Federal Government and the Federal States aiming to improve the quality of teacher training. The program is funded by the Federal Ministry of Education and Research under the funding code 01JA1505. The authors are responsible for the content of this publication. The study was also supported by the LOEWE Excellence Program: “Desirable Difficulties in Learning” from the Hessian Ministry for Science and the Arts, of which the authors are associated (FG) and full members (JM).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank everyone who contributed to having the pre-service science teachers fill out the survey (supervisors of the post-graduate trainees, instructors at the University of Kassel and the University of Kiel) as well as the students and post-graduate trainees themselves.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum.2019.00020/full#supplementary-material

Footnotes

1. ^ The term “misconceptions” was selected in this article in order to express that the theoretical assumptions, lines of argumentation, and conclusions drawn from neuromyths violate the rules of scientific thought and argumentation. Thus, we consider misconceptions to be inadequate scientific concepts that stand in clear contrast to scientific concepts.
2. ^ Howard-Jones (2014) provides a comparative overview of some of these studies.
3. ^ The Cronbach’s alpha values for these scales refer to a subsample of advanced students (n = 75) who filled out the questionnaire as a post-test after participating in a learning environment on the topic of neuroscience and learning.
4. ^ Agreement with the neuromyths on the brain as a hard drive and Brain Gym was not quite as high in the English studies because the answer option I don’t know was selected very often.
5. ^ The items were formulated slightly differently in the cited studies.

References

Arzi, A., Shedlesky, L., Ben-Shaul, M., Nasser, K., Oksenberg, A., Hairston, I. S., et al. (2012). Humans can learn new information during sleep. Nat. Neurosci. 15, 1460–1465. doi: 10.1038/nn.3193

PubMed Abstract | CrossRef Full Text | Google Scholar

Baer, M. F., Connors, B. W., and Paradiso, M. A. (2018). in Neurowissenschaften: Ein grundlegendes Lehrbuch für Biologie, Medizin und Psychologie [Neuroscience: A foundational textbook for biology, medicine, and psychology], 4th Edn, ed. A. K. Engel, trans. A. Held, and M. Niehaus-Osterloh (Berlin: Springer).

Bartoszeck, A. B., and Bartoszeck, F. K. (2012). How in-service teachers perceive neuroscience as connected to education: an exploratory study. Eur. J. Educ. Res. 1, 301–319. doi: 10.12973/eu-jer.1.4.301

CrossRef Full Text | Google Scholar

Beck, D. M. (2010). The appeal of the brain in the popular press. Perspect. Psychol. Sci. 5, 762–766. doi: 10.1177/1745691610388779

PubMed Abstract | CrossRef Full Text

Bellert, A., and Graham, L. (2013). Neuromyths and neurofacts: information from cognitive neuroscience for classroom and learning support teachers. Special Educ. Perspect. 22, 7–20.

Google Scholar

Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., et al. (2010). Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A. 107, 4734–4739. doi: 10.1073/pnas.0911855107

PubMed Abstract | CrossRef Full Text | Google Scholar

Bjork, E. L., and Bjork, R. A. (2011). “Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning,” in Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society, eds M. A. Gernsbacher, R. W. Pew, L. M. Hough, and J. R. Pomerantz (New York, NY: Worth Publishers), 56–64.

Google Scholar

Bloom, J. S., and Hynd, G. W. (2005). The role of the corpus callosum in interhemispheric transfer of information: excitation or inhibition? Neuropsychol. Rev. 15, 59–71. doi: 10.1007/s11065-005-6252-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Brauer, H., Balster, S., and Wilde, M. (2014). Lehr- und Lernvorstellungen künftig Lehrender zum Lernen von Schülerinnen und Schülern im Fach Biologie [Future teachers‘ understandings of teaching and learning with respect to students‘ learning in the subject of biology]. ZfDN 20, 191–200. doi: 10.1007/s40573-014-0019-z

CrossRef Full Text | Google Scholar

Canbulat, T., and Kiriktas, H. (2017). Assessment of educational neuromyths among teachers and teacher candidates. J. Educ. Learn. 6, 326–333. doi: 10.5539/jel.v6n2p326

CrossRef Full Text | Google Scholar

Centre for Educational Research and Innovation and Organization for Economic Co-operation and Development [OECD] (2007). Understanding the Brain: The Birth of a Learning Science. Paris: OECD.

Google Scholar

Darvas, F., Pantazis, D., Kucukaltun-Yildirim, E., and Leahy, R. M. (2004). Mapping human brain function with MEG and EEG: methods and validation. Neuroimage 23, 289–299. doi: 10.1016/j.neuroimage.2004.07.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Dekker, S., Lee, N. C., Howard-Jones, P., and Jolles, J. (2012). Neuromyths in education. Prevalence and predictors of misconceptions among teachers. Front. Psychol. 3:429. doi: 10.3389/fpsyg.2012.00429

PubMed Abstract | CrossRef Full Text | Google Scholar

Deligiannidi, K., and Howard-Jones, P. A. (2015). The neuroscience literacy of teachers in Greece. Proc. Soc. Behav. Sci. 174, 3909–3915. doi: 10.1016/j.sbspro.2015.01.1133

CrossRef Full Text | Google Scholar

Doyle, T., and Zakrajsek, T. (2013). The New Science of Learning: How to Learn in Harmony with Your Brain. Sterling, VA: Stylus Pub.

Google Scholar

Drechsel, B. (2001). Subjektive Lernbegriffe und Interesse am Thema Lernen bei angehenden Lehrpersonen. [Pre-service teachers’ subjective definitions of learning and interest on the topic of learning]. Münster: Waxmann.

Dündar, S., and Gündüz, N. (2016). Misconceptions regarding the brain: the neuromyths of preserve teachers. Mind Brain Educ. 10, 212–232. doi: 10.1111/mbe.12119

CrossRef Full Text | Google Scholar

Düvel, N., Wolf, A., and Kopiez, R. (2017). Neuromyths in music education: prevalence and predictors of misconceptions among teachers and students. Front. Psychol. 8:629. doi: 10.3389/fpsyg.2017.00629

PubMed Abstract | CrossRef Full Text | Google Scholar

Edelenbosch, R., Kupper, F., Krabbendam, L., and Broerse, J. E. W. (2015). Brain-based learning and educational neuroscience: boundary Work. Mind Brain Educ. 9, 40–48. doi: 10.1111/mbe.12066

CrossRef Full Text | Google Scholar

Ferrero, M., Garaizar, P., and Vadillo, M. A. (2016). Neuromyths in education: prevalence among spanish teachers and an exploration of cross-cultural variation. Front. Hum. Neurosci. 10:469. doi: 10.3389/fnhum.2016.00496

PubMed Abstract | CrossRef Full Text | Google Scholar

Fuentes, A., and Risso, A. (2015). Evaluación de conocimientos y actitudes sobre neuromitos en futuros/as maestros/as. [Evaluation of knowledge and attitudes towards neuromyths in future teachers]. Revista de Estudios e Investigación en Psicología y Educación 6, 193–198. doi: 10.17979/reipe.2015.0.06.530

CrossRef Full Text

Gais, S., and Born, J. (2004). Declarative memory consolidation: mechanisms acting during human sleep. Learn. Mem. 11, 679–685. doi: 10.1101/lm.80504

PubMed Abstract | CrossRef Full Text | Google Scholar

Geake, J. (2008). Neuromythologies in education. Educ. Res. 50, 123–133. doi: 10.1080/00131880802082518

CrossRef Full Text | Google Scholar

Gimbel, K., Ziepprecht, K., and Mayer, J. (2018). “Überzeugungen angehender Lehrkräfte fachspezifisch und inhaltsspezifisch operationalisieren und erfassen“ [Operationalizing and measuring pre-service teachers‘ beliefs in a subject-specific and content-specific way],” in Kohärenz in der universitären Lehrerbildung: Vernetzung von Fachwissenschaft, Fachdidaktik und Bildungswissenschaften, eds I. Glowinski, A. Borowski, J. Gillen, S. Schanze, and J. von Meien (Potsdam: Universität Potsdam), 179–198.

Gleichgerrcht, E., Lira Luttges, B., Salvarezza, F., and Campos, A. L. (2015). Educational neuromyths among teachers in Latin America. Mind Brain Educ. 9, 170–178. doi: 10.1111/mbe.12086

CrossRef Full Text | Google Scholar

Goswami, U. (2006). Neuroscience and education: from research to practice? Nat. Rev. Neurosci. 7, 406–413. doi: 10.1038/nrn1907

PubMed Abstract | CrossRef Full Text | Google Scholar

Grospietsch, F., and Mayer, J. (2018a). “Professioneller Konzeptwechsel zum Thema Neuromythen in der universitären Lehramtsausbildung Biologie” [Professional conceptual change on the topic of neuroymths in unviersity teacher education in biology],” in Lehrerausbildung in vernetzten Lernumgebungen, eds M. von, K. Meier, Ziepprecht, and J. Mayer (Münster: Waxmann), 179–198.

Grospietsch, F., and Mayer, J. (2018b). Professionalizing pre-service biology teachers’ misconceptions about learning and the brain through conceptual change. Educ. Sci. 8:120. doi: 10.3390/educsci8030120

CrossRef Full Text | Google Scholar

Guzzetti, B. J., Snyder, T. E., Glass, G. V., and Gamas, W. S. (1993). Promoting conceptual change in science: a comparative meta-analysis of instructional interventions from reading education and science education. Read. Res. Q 28, 116–159. doi: 10.2307/747886

CrossRef Full Text | Google Scholar

Hermida, M. J., Segretin, M. S., Soni Garcia, A., and Lipina, S. J. (2016). Conceptions and misconceptions about neuroscience in preschool teachers: a study from Argentina. Educ. Res. 58, 547–472. doi: 10.1080/00131881.2016.1238585

CrossRef Full Text | Google Scholar

Hilgetag, C. C., and Barbas, H. (2009). Are there ten times more glia than neurons in the brain? Brain Struct. Funct. 213, 365–366. doi: 10.1007/s00429-009-0202-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Horvath, J. C., Donoghue, G. M., Horton, A. J., Lodge, J. M., and Hattie, J. A. C. (2018). On the irrelevance of neuromyths to teacher effectiveness: comparing neuro-literacy levels amongst award-winning and non-award winning teachers. Front. Psychol. 9:1666. doi: 10.3389/fpsyg.2018.01666

PubMed Abstract | CrossRef Full Text | Google Scholar

Howard-Jones, P. (2010). Introducing Neuroeducational Research – Neuroscience, Education and the Brain from Contexts to Practice. New York, NY: Routledge.

Google Scholar

Howard-Jones, P. A. (2014). Neuroscience and education: myths and messages. Nat. Rev. Neurosci. 15, 817–824. doi: 10.1038/nrn3817

PubMed Abstract | CrossRef Full Text | Google Scholar

Howard-Jones, P. A., Franey, L., Mashmoushi, R., and Liau, Y.-C. (2009). “The neuroscience literacy of trainee teachers,” in Paper Presented at the British Educational Research Association Annual Conference, Manchester.

PubMed Abstract | Google Scholar

Im, S., Cho, J.-Y., Dubinsky, J. M., and Varma, S. (2018). Taking an educational psychology course improves neuroscience literacy but does not reduce belief in neuromyths. PLoS One 13:e0192163. doi: 10.1371/journal.pone.0192163

PubMed Abstract | CrossRef Full Text | Google Scholar

Jäncke, L. (2013). Lehrbuch kognitive Neurowissenschaften [Textbook on cognitive neuroscience]. Milwaukee, WI: Hans Huber.

Karakus, O., Howard-Jones, P. A., and Jay, T. (2015). Primary and secondary school teachers’ knowledge and misconceptions about the brain in Turkey. Proc. Soc. Behav. Sci. 174, 1933–1940. doi: 10.1016/j.sbspro.2015.01.858

CrossRef Full Text | Google Scholar

Kim, M., and Sankey, D. (2017). Philosophy, neuroscience and pre-service teachers’ beliefs in neuromyths: a call for remedial action. Educ. Philos. Theory 50, 1214–1227. doi: 10.1080/00131857.2017.1395736

CrossRef Full Text | Google Scholar

Kowalski, P., and Taylor, A. K. (2009). The effect of refuting misconceptions in the introductory psychology class. Teach. Psychol. 36, 153–159. doi: 10.1080/00986280902959986

CrossRef Full Text | Google Scholar

Kowalski, P., and Taylor, A. K. (2011). Effectiveness of refutational teaching for high-and low-achieving students. J. Scholar. Teach. Learn. 11, 79–90.

Google Scholar

Kunter, M., Klusmann, U., Baumert, J., Richer, D., Voss, T., and Hachfeld, A. (2013). Professional competence of teachers: effects on instructional quality and student development. J. Educ. Psychol. 105, 805–820. doi: 10.1037/a0032583

CrossRef Full Text | Google Scholar

Lai, V., van Dam, W., Conant, L. L., Binder, J. R., and Desai, R. H. (2015). Familiarity differentially affects right hemisphere contributions to processing metaphors and literals. Front. Hum. Neurosci. 9:44. doi: 10.3389/fnhum.2015.00044

PubMed Abstract | CrossRef Full Text | Google Scholar

Landis, J. R., and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics 33, 159–174. doi: 10.2307/2529310

CrossRef Full Text | Google Scholar

Lethaby, C., and Harries, P. (2016). Learning styles and teacher training: are we perpetuating neuromyths? ELT J. 70, 16–27. doi: 10.1093/elt/ccv051

CrossRef Full Text | Google Scholar

Lilienfeld, S. O., Lynn, S. J., Ruscio, J., and Beyerstein, B. L. (2010). 50 Great Myths of Popular Psychology: Shattering Widespread Misconceptions About Human Behavior. Hoboken, NJ: John Wiley & Sons.

Google Scholar

Lindell, A. K., and Kidd, E. (2013). Consumers favor “right brain” training: the dangerous lure of neuromarketing. Mind Brain Educ. 7, 35–39. doi: 10.1111/mbe.12005

CrossRef Full Text | Google Scholar

Macdonald, K., Germine, L., Anderson, A., Christodoulou, J., and McGrath, L. M. (2017). Dispelling the myth: training in education or neuroscience decreases but does not eliminate beliefs in neuromyths. Front. Psychol. 8:1314. doi: 10.3389/fpsyg.2017.01314

PubMed Abstract | CrossRef Full Text | Google Scholar

MacNabb, C., Schmitt, L., Michlin, M., Harris, I., Thomas, L., Chittendon, D., et al. (2006). Neuroscience in middle schools: a professional development and resource program that models inquiry-based strategies and engages teachers in classroom implementation. CBE Life Sci. Educ. 5, 144–157. doi: 10.1187/cbe.05-08-0109

PubMed Abstract | CrossRef Full Text | Google Scholar

Maquet, P. (2001). The role of sleep in learning and memory. Science 294, 1048–1052. doi: 10.1126/science.1062856

PubMed Abstract | CrossRef Full Text | Google Scholar

McCabe, D. P., and Castel, A. D. (2008). Seeing is believing: the effect of brain images on judgement of scientific reasoning. Cognition 107, 343–352. doi: 10.1016/j.cognition.2007.07.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Meier, M., Grospietsch, F., and Mayer, J. (2018). “Vernetzung von Wissensfacetten professioneller Handlungskompetenz in hochschuldidaktischen Lehr-Lernsettings“ [Linking facets of knowledge regarding professional action competence in university teaching and learning settings],” in Kohärenz in der universitären Lehrerbildung: Vernetzung von Fachwissenschaft, Fachdidaktik und Bildungswissenschaften, eds I. Glowinski, A. Borowski, J. Gillen, S. Schanze, and J. von Meien (Potsdam: Universitätsverlag Potsdam), 143–178.

Muzet, A. (2007). Environmental noise, sleep and health. Sleep Med. Rev. 11, 135–142. doi: 10.1016/j.smrv.2006.09.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Newton, P. M., and Miah, M. (2017). Evidence-based higher education – Is the learning styles ‘myth’ important? Front. Psychol. 8:444. doi: 10.3389/fpsyg.2017.00444

PubMed Abstract | CrossRef Full Text | Google Scholar

Nielsen, J. A., Zielinski, B. A., Ferguson, M. A., Lainhart, J. E., and Anderson, J. S. (2013). An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. PLoS One 8:e71275. doi: 10.1371/journal.pone.0071275

PubMed Abstract | CrossRef Full Text | Google Scholar

Ocklenburg, S., Hirnstein, M., Beste, C., and Güntürkün, O. (2014). Lateralization and cognitive systems. Front. Psychol. 5:1143. doi: 10.3389/fpsyg.2014.01143

PubMed Abstract | CrossRef Full Text | Google Scholar

Organization for Economic Co-operation and Development [OECD] (2002). Understanding the Brain: Towards a New Learning Science. Paris: OECD.

Organization for Economic Co-operation and Development [OECD] (2008). “Understanding the brain: the birth of a learning science: new insights on learning through cognitive and brain science,” in Paper presented at the OECD/CERI International Conference “Learning in the 21st Century: Research, Innovation and Policy, Paris.

Pajares, M. F. (1992). Teachers’ beliefs and educational research: cleaning up a messy construct. Rev. Educ. Res. 62, 307–332. doi: 10.3102/00346543062003307

CrossRef Full Text | Google Scholar

Papadatou-Pastou, M., Haliou, E., and Vlachos, F. (2017). Brain knowledge and the prevalence of neuromyths among prospective teachers in Greece. Front. Psychol. 8:804. doi: 10.3389/fpsyg.2017.00804

PubMed Abstract | CrossRef Full Text | Google Scholar

Pasquinelli, E. (2012). Neuromyths: why do they exist and persist? Mind Brain Educ. 6, 89–96. doi: 10.1111/j.1751-228X.2012.01141.x

CrossRef Full Text | Google Scholar

Pei, X., Howard-Jones, P. A., Zhang, S., Liu, X., and Jin, Y. (2015). Teachers’ understanding about the brain in East China. Proc. Soc. Behav. Sci. 174, 3681–3688. doi: 10.1016/j.sbspro.2015.01.1091

CrossRef Full Text | Google Scholar

Petitto, L.-A., and Dunbar, K. (2004). “New findings from educational neuroscience on bilingual brains, scientific brains, and the educated mind,” in Paper presented at the Conference on Building Usable Knowledge in Mind, Brain, & Education, Harvard Graduate School of Education, Cambridge, MA.

Google Scholar

Pickering, S. J., and Howard-Jones, P. (2007). Educators’ views on the role of neuroscience in education: findings from a study of UK and international perspectives. Mind Brain Educ. 1, 109–113. doi: 10.1111/j.1751-228X.2007.00011.x

CrossRef Full Text | Google Scholar

Rato, J. R., Abreu, A. M., and Castro-Caldas, A. (2013). Neuromyths in education: what is fact and what is fiction for Portuguese teachers? Educ. Res. 55, 441–453. doi: 10.1080/00131881.2013.844947

CrossRef Full Text | Google Scholar

Riese, J. (2009). Professionelles Wissen und professionelle Handlungskompetenz von (angehenden) Physiklehrkräften [(Pre-service) physics teachers’ professional knowledge and professional competence]. Berlin: Logos.

Ruffo, E. (2010). Das Lernen angehender Lehrpersonen. Eine empirische Untersuchung an der Pädagogischen Hochschule Zürich [Learning among pre-service teachers. An empirical investigation at the University of Education Zurich]. Bern: Peter Lang.

Google Scholar

Schletter, J. C., and Bayrhuber, H. (1998). Lernen und Gedächtnis – Kompartimentalisierung von Schülervorstellungen und wissenschaftlichen Konzepten [Learning and memory – Compartmentalization of students’ conceptions and scientific concepts]. ZfDN 4, 19–34.

Google Scholar

Seidel, T., and Meyer, L. (2003). “Skalendokumentation Lehrerfragebogen,” [Scale documentation for the teacher questionnaire.],” in Technischer Bericht zur Videostudie “Lehr-Lern-Prozesse im Physikunterricht“, eds T. Seidel, M. Prenzel, R. Duit, and M. Lehrke (Kiel: IPN), 241–273.

Shulman, L. (1987). Knowledge and teaching: foundations of the new reform. Harvard Educ. Rev. 57, 1–23. doi: 10.17763/haer.57.1.j463w79r56455411

CrossRef Full Text | Google Scholar

Simmonds, A. (2014). How Neuroscience is Affecting Education: Report of Teacher and Parent Survey. Available at: https://wellcome.ac.uk/sites/default/files/wtp055240.pdf [accessed October 5, 2018].

Singh, H., and O’Boyle, M. (2004). Interhemispheric interaction during global-local processing in mathematically gifted adolescents, average-ability youth, and college students. Neuropsychology 18, 371–377. doi: 10.1037/0894-4105.18.2.371

PubMed Abstract | CrossRef Full Text | Google Scholar

Staub, F. C., and Stern, E. (2002). The nature of teachers’ pedagogical content beliefs matters for students’ achievement gains: quasi-experimental evidence from elementary mathematics. J. Educ. Psychol. 94, 344–355. doi: 10.1037/0022-0663.94.2.344

CrossRef Full Text | Google Scholar

Stickgold, R. (2012). To sleep: perchance to learn. Nat. Neurosci. 15, 1322–1323. doi: 10.1038/nn.3223

PubMed Abstract | CrossRef Full Text | Google Scholar

Tardif, E., Doudin, P.-A., and Meylan, N. (2015). Neuromyths among teachers and student teachers: neuromyths. Mind Brain Educ. 9, 50–59. doi: 10.1111/mbe.12070

CrossRef Full Text | Google Scholar

Urhahne, D., Kremer, K., and Mayer, J. (2008). Welches Verständnis haben Jugendliche von der Natur der Naturwissenschaften? [What are adolescents’ understandings of the nature of science?]. Unterrichtswissenschaft 36, 71–93.

Vosniadou, S. (ed.) (2013). International Handbook of Research on Conceptual Change, 2nd Edn. New York, NY: Routledge.

Google Scholar

Wahl, D. (1979). Methodische Probleme bei der Erfassung handlungsleitender und hand- lungsrechtfertigender subjektiver psychologischer Theorien von Lehrern [Methodological problems in collecting data on subjective psychological theories that guide and justify teachers‘ actions]. Zeitschrift fr Entwicklungspsychologie und pädagogische Psychologie 11, 208–217.

Google Scholar

Wallace, M. (1993). Discourse of derision: the role of the mass media within the education policy process. J. Educ. Pol. 8, 321–337. doi: 10.1080/0268093930080402

CrossRef Full Text | Google Scholar

Whittingstall, K., and Logothetis, N. K. (2009). Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex. Neuron 64, 281–289. doi: 10.1016/j.neuron.2009.08.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeung, A. W. K., Goto, T. K., and Leung, W. K. (2018). Readability of the 100 most-cited neuroimaging papers assessed by common readability formulae. Front. Hum. Neurosci. 12:308. doi: 10.3389/fnhum.2018.00308

PubMed Abstract | CrossRef Full Text | Google Scholar

Appendix

Translation of the German instrument on conceptions of learning and memory.