messing with their minds or making connections ... · describing cognitive functions than affective...

Messing with their Minds or Making Connections: Educational Neuroscience

Abstract Educators have been slow to engage with the field of brain research, neuroscience, which has brought new insights to the development of cognitive functions. Theories of learning, such as constructivism and cognitive load theory, have generally avoided any direct reference to brain function. We are now many years on from the ‘bridge too far’scenario by which Bruer (1997) meant that there was an unbridgeable gap between contemporary understandings from brain imaging, and the mind models derived from cognitive psychology. This paper documents some of the recent findings in neuroscience, which is richer in describing cognitive functions than affective aspects of learning, with three purposes: 1. To explore the current state of knowledge with respect to educational neuroscience with implications for policy makers, teachers and researchers in education and neuroscience. 2. To bring attention to the range of neuroscience myths which pervade education. Claims of brain-based learning support a wide range of products for parents and teachers. Some of the myths are explored here. 3. Finally, we offer suggestions for educators to embrace recent findings from neuroscience about brain development and function. Neuroscience ‘investigates the processes by which the brain learns and remembers, from the molecular and cellular levels right through to brain systems (e.g., the system of neural areas and pathways underpinning our ability to speak and comprehend language)’(Goswami, 2004, p. 1) and has shed light on ‘attention, stress, memory, exercise, sleep and music’(Carew & Magsamen 2010, p. 686) as well as learning difficulties such as dyslexia and dyscalculia. New understandings of working memory and the hippocampus-mediated establishment of long term memories have some resonance with existing theories of learning, including constructivist ideas of how conceptual understandings are developed. We suggest that neuroscience and education together can foster the development of evidenced-based theories, to draw on what is known about genetics, imaging, child development and pedagogy. There are implications for considering the impact of neuroscience at all levels of education – from the classroom teacher and practitioner to policy. This relatively new cross-disciplinary area of research implies a need for educators and scientists to engage with each other. What questions are emerging through such dialogues between educators and scientists are likely to shed light on, for example, reward, motivation, working memory, bilingualism and child development. The sciences of learning are entering a new paradigm. Key Words Neuroscience, working memory, learning


Research in the field of educational neuroscience is being used to understand how the brain develops and functions; for example, in the diagnosis of neurological conditions that then allows for early remediation, shedding light on the reward system in the brain, the neural impact of ostracism as well the evaluation of intervention programs. Goswami (2006) called for the gulf between educators and neuroscientists to be bridged more effectively in the interest of children’s education. Developmental psychology has long informed educational theory, and constitutes required preparation for pre service teachers, equipping us with insight into, for example, stages of child development, strategies for managing children’s behaviour and language acquisition. All educators deliberate on students’ misconceptions, conceptual change, cognitive and practical skills, and curriculum development. Education and neuroscience come together when we consider learning: for what greater impact does learning have but upon the brain? Educational neuroscientists include educators, physiologists, anatomists, cognitive psychologists, imaging specialists and those with interest in learning and development. With the growth in research in education and neuroscience (Howard, Jones, 2007; OECD, 2007; PMSEIC, 2009), it is timely to reflect on the impact and possibilities neuroscience can bring to education (Oliver, 2011). First, we consider the techniques used in neuroscience, and what sort of information is yielded using different methods. We then discuss conceptual complexity, how this is a term used in education with implications for learning, teaching and understanding. We examine the mis-use of neuroscientific findings in ‘brain-based’ learning programs and suggest that (particularly) teachers of science bring a sense of rigour to examining the merit and efficacy of such programs. Finally, we consider the potential for future research in educational neuroscience. Neuroimaging techniques Neuroimaging uses a range of tools to measure activity of the brain. Some of these are non-invasive, such as electroencephalographic (EEG) recordings, which use electrodes on the scalp to measure the electrical potentials over different parts of the head, thus generating an image of neuronal activity. More usefully, event related potentials (ERPs), extracted from EEG recordings, are the small changes in voltages measured in response to sensory or cognitive events. ERPs are variations in voltage that occur when someone is thinking or processing information. Using a hair net embedded with electrodes, the activity of brains can be observed as individuals undertake particular activities or tasks. ERPs are most useful in studying both the timing and sequence of response during a task. Other imaging tools include magnetic resonance imaging (MRI) and positron emission tomography (PET). Both of these methods can be used to measure changes in blood flow in the brain and ‘map’ the brain. Since PET depends on using radioactively labelled isotopes, we will not consider its use further in this paper as it is unlikely that productive neuroscience educational research will depend on this technique. Functional magnetic resonance imaging (fMRI) is a technique that highlights changes in region-specific brain metabolic activity via the blood oxygenated level dependant (BOLD) signal due to synaptic activity. Spatial resolution of fMRI offers the opportunity to establish networks in the brain associated with specific cognitive functions and ‘identify a specific part of the brain which plays a key role in a given taskʼ’ (Sżűcs & Goswami, 2007, p. 121). Cognitive demand results in increased neural activity and greater metabolic activity. We can


measure this either directly (PET) or indirectly (fMRI) as blood flow through the brain is inferred to determine cognitive demand. A very ‘efficient’ brain might have a lower blood flow in response to a particular task. Growth in neuroimaging studies has led to increased understandings of the brain. As a result, brain functions have been identified with specific areas or regions of the brain. Work with individuals with brain lesions or damage has helped establish just how closely or not cognitive functions and brain structures are related. Whist areas of the brain have long been associated with specific functions, such as language acquisition, imaging has revealed that response to visual stimuli is complex and relies on a network of neurons across the brain. Not all brain functions are so disparately distributed in the brain; for example, circadian rhythms are maintained by localised neurons in the brain. So the picture of the brain dominated by either an area or network focus can be over simple and conclusions drawn from them misleading for teachers (Varma & Schwartz, 2008). Conceptual Complexity The term ‘concept’is widely, but loosely, used in discussions of science education. Although the literature contains many studies focussed on teaching for conceptual understanding and conceptual change (Hubber & Tytler, 2004), few of these deal with the nature of scientific concepts. It tends to be assumed that everyone knows what a concept is, and which concepts are important to teach. That is to say, the issue of what concepts should be taught is not generally regarded to be problematic. Our experience with pre-service teachers, as well as informal discussions with practiced teachers, suggests that they are far from clear about how to identify key concepts, or even to describe the key concepts in areas they are teaching. Teaching for conceptual depth is hardly likely to occur under these circumstances. Yet, the complexity of concepts as they pertain to human learning has long been recognized. A century ago Peirce (1965-1966) developed what has been referred to as a science of signs, which was called semiotic. Morris (1939) stated that ‘signs are simply the objects studied by the biological and physical sciences related in certain complex functional ways. A ‘sign’ encompasses the complex relationships between the real world and the mental models we make to describe and account it. In this case they are probably also what science teachers teach, that is the conceptual basis of science teaching. The value of semiotic analysis is that it provides some framework for developing links between concepts, scientific ideas, and the real world; a link that is crucially important from the perspective of exposing students to experiential learning opportunities and constructivist teaching approaches. Kankkunen (2001) conducted a study, which used Peirce’s semiotic paradigm to help comprehend students’ conceptual learning and world of meaning through the use of concept maps. This study provided a link between Peirce’s semiotic and Novak (1998) and Novak and Canas’ (2008) definition of a concept as an ‘event or object, designated by a label’. In this case the semiotic provides the complex link between the real world phenomenon, the label we assign to this, and the meanings that we assign to this label. Novak’s idea then shows how concepts can be linked through propositions to form scientific ideas. Whether or not a semiotic approach provides a suitable way of analysing concepts there is certainly a case to be made for increasing our focus on the nature of scientific concepts and how these should interplay with teaching and learning strategies. The nature of ‘conceptual representations’ is also significant in the field of cognition. An entire issue of Language and Cognitive Processes in 2003 was devoted to just this topic.


Within this special issue Hampton & Moss (2003) referred to conceptual representations as ‘arguably the most important cognitive function in humans’ due to their central role in a range of cognitive processes including memory and inductive reasoning. Although little is known about neural processes underpinning the emergence of conceptual knowledge and its application recent work supports the central role of the hippocampus and related systems in this process (Kumaran et al., 2009). There is even the possibility that neurogenesis in the adult hippocampus is involved in learning (Kitamura et al., 2009). The fact that this study was conducted in rats is somewhat disturbing if only because it does seem to provide real insights into human learning at a fundamental neurophysiologic level. According to the interleaved learning hypothesis, for example, supported by LeDoux (2002), memory is initially stored via synaptic changes that take place in the hippocampus. When some aspect of the stimulus situation recurs, the hippocampus participates in the reinstatement of the pattern of cortical activation that occurred during the original experience. Each reinstatement changes cortical synapses a little…….The slow rate of change of the cortex prevents the acquisition of new knowledge from interfering with old cortical memories. Eventually, the cortical representation comes to be self-sufficient. At that time, the memory becomes independent of the hippocampus. (LeDoux, 2002, p. 106-107) Although brains lose neurons throughout adulthood, the hippocampus, long associated with long term memory and learning, continues to generate neurons throughout life. Maguire et al. (2000) showed the result of ‘learning the knowledge’ on the brains of would-be-taxi drivers in London, in which adult brains were shown to respond to particular environmental demands. Draganski (2004, 2006) found that as a result of learning to juggle over a three month period, there were measurable changes to grey matter although ceasing to juggle saw a reversion to ‘normal’ brain anatomy, suggesting that anatomical changes and plasticity is dependent on stimulation. Plasticity comes at a cost, though, as there is not unlimited capacity: brains of the London taxi drivers showed an increase in grey matter volume in the posterior hippocampus (which was positively correlated with the number of years spent as experienced taxi drivers), and reduced grey matter elsewhere in the hippocampus (Maguire, Woollett, & Spiers, 2006). Nevertheless, the suggestion from these studies that learning necessarily involves the development of new neuronal pathways and the erasing of old ones resonates somewhat with one of the fundamental tenets of constructivist theory (see Tytler, 2004), that students have a strong resistance to overturning existing concepts to develop new ones, and that learning must take into account existing conceptual frameworks. It would seem that ‘concepts’ relate closely to what the brain uses to think with and are, hence, what teachers teach. However, the use of this term in science education often conflates what cognitive scientists might regard as concepts with larger and more complex representations. In this case concepts are labels, generally identifiable by single words (eg acceleration, gene, cell, acid, electron). These are usually terms that relate to specific classes of phenomena that scientists typically study. Also, they probably relate to distinct thoughts that are processed in working memory when our students, and we, think about science. Scientific ideas relate more closely to actual scientific theories that link particular concepts in particular ways. For example, acceleration is proportional to force and inversely proportional to mass is a (big) scientific idea (Newtonʼ’s Second Law), which links the concepts of ‘acceleration’, ‘force’ and ‘mass’ in a particular way (F = ma). Another example would be Genes are carried within DNA and are the basis of inheritance, a scientific theory, which relates the concepts of ‘gene’ ‘DNA’ and ‘inheritance’.


The research in cognitive science, combined with enhanced imaging techniques, has begun to provide us with a window into the brain at work as new ideas are processed. Baddeley (1986) introduced the concept of working memory, a high-speed part of short-term memory, that is used to store programs or data currently in use and which is concerned with immediate conscious perceptual and linguistic processing. Many people are familiar with the old idea that short term memory can handle only a limited number of elements at one time (Miller, 1956), and evidence suggests that this is a real phenomenon that places severe limitations in our capacity to handle new information and which has real relevance to teaching and learning (Gathercole, 2008), although the number of elements may be fewer than seven (Cowan, 2000). These limitations appear to apply to a wide range of learning tasks and contribute to a phenomenon more widely recognized by educators as cognitive load (Paas, Renkl & Sweller, 2003). Functional MRI imaging has, as long as ten years ago, revealed that working memory can be associated with identifiable regions of the frontal cortex, which are active across a wide range of learning tasks (Na et al., 2000). Breaking down ideas into component concepts provides a link with cognitive processes involving the working of new ideas through working memory and helps identify the cognitive load that students often have to bear (Paas, Renkl & Sweller, 2003). Physics Nobel Laureate Carl Wieman has suggested that reducing the cognitive load (see Wieman, 2007 for a clear illustration) by slowing down, minimising jargon and explicit structuring and ‘chunking’ of material reduces the cognitive load and helps students learn more deeply. Many people are familiar with the idea of a working memory, or short-term memory, which places limitations on our capacity to remember such things as sequences of numbers, but the development of cognitive load theory appears to relate directly to the limited capacity of working memory. The authors suggest that the style of instruction used should be tailored to different stages of learning as working memory is gradually freed up. The current pedagogical imperative to expose students to problem-based learning, for example, may be a more productive approach in the final stages of learning, when more working memory capacity has become available, than in earlier stages. Mirror, mirror Constructivist Learning Theory has already been mentioned in this paper. One of its fundamental tenets originates with Glasersfeld (1989), who emphasized that learners construct their own understanding and that they do not simply mirror what they have read or have been told. This theoretical perspective became a driving force for pedagogical change, particularly in science education, towards a more student-centred approach to teaching and learning. It has become a self-evident truth that knowledge cannot be transferred directly from teacher to student, that ideas cannot be transferred directly from one brain to another. But what if they can? An area of cognitive research with potential relevance to teaching and learning is the nature of the interactions that occur between individuals; potentially between teacher and learner. Investigations involving predominantly motor skills suggest that individuals have a particular capacity, through mirror neuron mechanisms, to respond directly to the actions of others, almost as if actions are directly transferred from one brain to another (Iacoboni et al, 2005). MRI scanning techniques revealed that motor neurones, which would fire when an individual carried out certain actions, would also fire when they simply observed another individual carry out the same actions, seemingly in preparation for when they needed to fire in the real


situation. While this level of evidence is currently restricted to the learning of motor skills, it is quite clearly counter to the idea that learning only involves the learner’s own actions leading to the development of new skills. The possible extension of this phenomenon to learning in other areas is tentative, but exciting. In studies involving young children it has become apparent that learning is supported by brain circuits linking perception and action that connect people to one another, through shared attention and brain systems (Meltzoff et al, 2009). In a recent study Stephens, Silbert and Hasson (2010), in a study involving storytelling, found that ‘during successful communication, speakers’ and listeners’ brains exhibit joint, temporally coupled, response patterns’. This phenomenon of neural coupling, detected using fMRI scanning, was widespread and extended well beyond low-level auditory areas of the brain. Research in cognition and neuroscience might also influence how we structure the school learning environment. A recent report, following on from studies in non-human species, suggests that learning in humans is enhanced during awake rest periods that follow the initial learning experience. Tambini, Ketz & Davachi (2010) used functional magnetic resonance imaging (fMRI) to gauge activity in the hippocampus and linked cortical regions during a learning task and an ensuing awake rest period. They found that there was a significant correlation between brain activity in the subjects’ hippocampus and cortical regions during rest in those individuals who successfully remembered the original learned association. These results provide strong evidence that resting brain correlations contribute to long-term memory and suggest that they may be pivotal in facilitating memory consolidation. Taking new evidence such as this into account could transform the day-to-day school experience, which currently very rarely includes periods of rest designed to enhance student learning. Many of our classrooms resemble those of many years ago, with content-driven curriculum and practice (Dillon, 2009). Capitalising on what is known about research-based pedagogies to improve learning needs to be central to both pre-service and in-service professional learning. The Hattie report published in 2009 comprised a meta-analysis of more 800 studies to determine the effects of different educational practices. Using effect size as an indicator of educational interventions, diet (d=0.12), out of school curricula experiences (d=0.09) and perceptual motor programs (d=0.06) fare rather poorly; on the other hand, self-reported grades (d=1.44), Piagetian programs (d=1.28, direct instruction (d=0.82) and formative assessment (d=0.9) have demonstrated efficacy and impact. Clearly, the ‘evidence for how to teach better and how to help students to learn better is available in the academic literature and the gap between the literature and the preparation of teachers needs to be closed’ (Oliver & Anderson, 2011 in press). Disappointingly, ‘much classroom practice appears to neglect what has been shown to be effective’ (Royal Society, 2010, p. 83) in the teaching of science and mathematics in UK schools and there is no reason to suppose that this represents an isolated example. Why are teachers not using methods that have demonstrated best practice? Bringing about teacher change in pedagogy seems to be very challenging at an individual level and has policy implications for educational practice. Neurophysiologic studies may provide support for the place of direct instruction in teaching, in situations where shared attention and shared cognitive systems are successfully established between teachers and students. Meltzoff et al. (2009) note that in formal school settings, research shows that individual face-to-face tutoring is the most effective form of instruction. Expert teachers who can establish close links with individual students, even in whole class settings, may have a powerful capacity to connect directly with her students in a way that produces significant learning outcomes through direct instruction. Of course, the process of setting up learning environments that facilitate the establishment of effective shared attention


and brain systems probably already feature in the pedagogic repertoire of many skilled and experienced teachers, who have been strangely reluctant to give up direct instruction as a potent pedagogic tool. To find that college students in the US and China have very similar reasoning abilities, with very different performances on knowledge based tests, suggests that the current state of assessment in many of the STEM subjects rely on content and recall at the expense of improved reasoning (Bao, Cai, Koenig, Fang, Han, Wang, et al., 2009). Education is traditionally thought of as a product of disciplines in social sciences as disparate as philosophy, sociology and psychology. This includes teaching and learning, applying both to what occurs inside and outside of classrooms settings. We educators tend to think of learning as something that students ‘do’ whilst engaged in the acquisition of knowledge, skills and behaviours to help inform and produce useful citizens. Learning or developing mastery is ‘slow and hard’ (Schwartz, 2009, p. 199) with effort having to be expended over years to become expert or develop mastery. In educational contexts, learning is computational, social, and contextually driven. Few educational settings currently attend to the biological processes involved in learning, with one teacher recently asking, ‘why should I be interested in the brain? I teach Physics’. Across the globe, there are few teacher preparation programs in universities that include in their courses, the biological basis of learning and we argue that this deficit be made good to include both teacher preparation and ongoing professional learning opportunities. Understanding the different philosophical bases of neuroscience and education needs to be reconciled or at least clarified so that discussion between educators and neuroscientists can be purposeful from classroom to educational policy level. Teachers are necessarily interested in the cognitive and other developments of the students they teach. Lack of knowledge about the biological basis of learning can be remedied through professional learning and teacher preparation. With the unparalleled growth in neuroscience research, the practical application of neuroscience findings in education seems to be currently occupied by highly marketed instead of evidence-based programs (Stephenson, 2009) directed at improving cognitive performance. As well as playing a role in judicial matters, health management and policy, neuroscience can be ‘a tool for science-based education policy, which can help assess the performance and impact of different educational approaches’ (Royal Society, 2011, p. 9). We therefore suggest that educators at all levels consider how the findings from neuroscience can shed light on our practice. Rather than wholeheartedly embracing or refuting claims made about the benefits of brain based learning programs, we suggest that all professionals, teachers and educators, engage with neuroscience findings critically, scientifically and professionally (Oliver, 2011). Developments in neuroscience have paved the way for myths especially in education: it seems that a little learning can be a dangerous thing! Educational neuromyths include but are not limited to: 1. There are critical periods of learning (OECD, 2007). a. This may have its origins in studies of rats maintained in low stimulus environments whose brains showed low synaptic density, or imprinting studies in young birds. The application to human from animal studies has been to suggest that enriched environments in the early years were essential to intellectual growth. Imaging studies have confirmed the plasticity of the brain with growth and pruning of synapses throughout life.


b. No critical periods of learning have yet been found in humans. There may be sensitive rather than critical periods for learning and studies on very young children have shown that they are responsive to sounds produced by a variety of different language groups. This responsiveness is ‘lost’ unless the child hears the sounds regularly as part of his linguistic environment. Plasticity also means loss in response to lack of environmental cues. 2. Individual students have specific learning styles such as visual, spatial, kinaesthetic (VAK). Multiple intelligences (MI), ‘Brain Gymʼ’® (Stephenson, 2009) and learning styles (Crossland, 2008; Howard-Jones, Franey, Mashmoushi, & Liao, 2009, Scott, 2010). a. The idea that ‘multiple intelligences’ theory is the basis for different learning styles is not grounded in empirical evidence: the claim that individual students have different forms of intelligence such as visual, spatial, kinaesthetic (VAK) has currency in educational settings. There are a great number of advocates of ‘learning styles’, from early childhood, parenting through to post graduate education. Clearly, the ‘learning styles’ appeals to audiences, with more than 14 million ‘hits’ on the internet. A survey showed that 82% of pre-service teachers agree with the statement that, ‘individuals learn better when they receive information in their preferred learning style’ (Howard-Jones et al. 2009, p. 23). b. What is the evidence? There is good evidence that there is no evidence in support of the claim of a preferred learning style and even when the preferred learning style is used, no evidence of educational improvement. Indeed, the evidence is that ‘individualising instruction is inefficient’ (Scott, 2010, p. 5) and has very low impact on student achievement (see Hattie, 2009). Data from such meta analyses compel us to speculate on the adoption by schools of these sorts strategies to support student learning instead of a much more powerful strategy: student feedback. c. The prevalence of Brain Gym ®, MI and VAK in schools calls into question the use of evidence in practice: are school administrators persuaded by a ‘feel good’ factor, anecdotal recommendations or the use of evidence-based research to determine educational programs? It is true that ‘we used to think that the brain never changed … it was useful to categorize children according to their learning styles’ (Hardiman et al., 2011) but while our understanding of the brain has changed, it seems that practice of disseminating evidence has not. 3. Only about 10% of our brains are used at any time. a. This myth is often attributed to Einstein but more likely have found fame with the neurologists in the nineteenth century who found that only about 10% of neurons were active at any one time. b. Imaging studies have shown that brain activity is disparate and can be very ‘precisely described’ (OECD, 2007, p. 113). Localised areas of the brain damaged through injury, disease, strokes, can result in considerable reduction in function. c. The brain is disproportionately demanding of the body’s resources and when food is scarce, the impact on brain development is evident and optimal function is compromised. (Eppig, Fincher, & Thornhill, 2010). 4. We are either right-brain or left-brain learners. a. Lateralisation of the brain into two hemispheres with identifiable functions has given rise to the popular myth that learners can be classified into right or left brain individuals. This is likely to have developed as a result of the work of Roger Sperry, awarded the 1981 Nobel Prize for the discoveries of functional specialization of the cerebral hemispheres. He reported that a split-brain individual behaved ‘as if it had two separate brains – each with a mind of its


own’ (Sperry, 1968, p. 296). Although the two hemispheres control different parts of the body and have discrete functions, both are employed and coordinate activities. b. When the two hemispheres become separated, there follows loss of function and capacity. c. Different activities employ different regions of the brain. The over simplification of classifying people into left or right brain thinkers distracts from the reality of brain function: a network across different regions of the brain. Other neuromyths that pervade everyday life are the ‘need to drink 8 glasses of water a day’ as well as the benefits of omega 3 supplements and the harmful effect of sugar. These claims have no evidence to substantiate them and this lack of evidence adds to the confusion for parents, students and all who care for children. There is a need for studies to bring scientific evidence to evaluate educational interventional strategies (Howard-Jones, 2009) as well as bridge the education neuroscience divide that currently exists, in some places more than others. In this sense, the application of evidence-based practice could do for education for what it has achieved in medicine a century ago. Sylvan & Christodoulou (2010) provide an evaluative tool to assess the educational merits of brain based learning products commercially available, cautioning that educational programs need to be evidence-based, bring about measurable behavioural changes (which in usual terms might mean improved student behaviour, well being or achievement) and sustained impact. Neuroplasticity training programs (Goswani, 2006) need to be scrutinised for efficacy and impact. A paradigm shift: a new partnership in educational neuroscience Clearly, the technology to detect, diagnose and evaluate the neural basis for learning is likely to be at an early stage. One such development, cochlear implants has brought the experience of hearing to deaf children, with consequences for learning and social development. Neuroscience may shed light on questions such as ‘how do children learn to associate letters and sounds?’ when learning to read (McCandliss, 2010) but have little to offer the research into student readiness, motivation or autonomy in a science classroom. Clearly, as partners in the emerging cross-disciplinary research, teachers, parents and educators will want to address those sorts of questions. We sense an enthusiasm for a closer partnership between neuroscientists and educators, though some uncertainty as to how this will develop (Pickering & Howard-Jones, 2007). Such collaborations will need to be focused on improving educational practice and understanding the quite different disciplines that each bring to the other. The need for educators and neuroscientists to work closely together has been highlighted in a recent review (Royal Society, 2011). As Kaufmann (2008) has succinctly summarised the argument, that ‘educational experts must share their expertise in pedagogy, and neuroscience researchers must develop ecological paradigms that are capable of investigating cognitive processes and learning mechanisms instead of circumscribed skills’ (Kaufmann, 2008. P. 168) In a recent review of educational neuroscience (Oliver, 2011), a number of issues emerged from the literature for the collaboration between teachers and neuroscientists to be fruitful:

What do educators need to know about the structure and development of human brain, or the neurology of learning?

What insights can neuroscience bring to understanding and improving the learning process?

What is meant by each discipline (education, psychology, neuroscience) when we use the terms reward, motivation, attention, working memory?

What is the role of using ‘reward’ in our teaching and learning programs?


How may the critical evaluation and distillation of research and ‘brain-based’ programs permeate teacher preparation and in-service courses (Summak et al., 2010)?

How may research be best fostered to inform and guide the practice of teaching and the experimental, diagnostic and evaluative work of neuroscience?

How do educators develop a sense of scientific scepticism to assess claims made about educational programs? (OECD, 2007)

What is the most effective way in which cross-disciplinary collaborations can best communicate with each other, conduct research and inform policy?

How do specific materials and environments shape learning? (OECD, 2007, p.6) Given that literacy and numeracy play such important roles in literate cultures, what

role does the brain play in learning? OECD, 2007, p.6) Neuroscience itself is a new science and will inevitably bring new issues to explore. Understanding brain development can inform educational practice: the strongest evidence for sensitive periods is in the development of sensory systems, so that language and music learning ‘skills are likely to be more effectively acquired if learning commences in early schooling’ (Thomas, & Knowland, 2009, p. 19). Learning new skills continues throughout childhood, adolescence and adulthood and is clearly dependent on more than sensitive periods of brain development. The relatively new discipline of educational neuroscience draws from both traditional sciences and educators: we support the establishments of an informed partnership to investigate, to research and to inform interventions and educational practice. Science educators are well placed to champion the cause for being scientific about teaching, embedding findings from neuroscience in pre service teacher courses and continued professional learning. Federal government funding for health and educational research are not comparable in dollar terms, but perhaps a significant contribution to the science of learning will only eventuate with greater financial support. Educators need to be involved with progressing research agendas, engage with the literature, and the research findings. Finally, knowing about synaptic plasticity gives us, as teachers, encouragement that learning is possible for everyone: that every student has the capacity to change their brain (Dubinsky, 2010).


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