Are advances in brain sciences useful to the field of education? Dr Jodi Tommerdahl looks at whether breakthroughs in our knowledge of how the human brain works can provide insight into how children learn, particularly in the area of SEN, and, if so, what’s taking so long?
The rise of educational neuroscience
The last 20 years have repeatedly brought to our attention the narrowing of the gap between the brain-sciences and the field of education. Academic journals and the popular press have been rife with reports of advances being made in our knowledge about how the brain works; advances which often appear promising to the area of education. The interest in the growing connection between the physical aspects of the human brain and its learning function is also evident in the workings of major organisations. Examples of this include the development of major projects by the OECD (Organisation for Economic Co-operation and Development) in the field of education in the context of brain-based studies, Harvard University’s establishment of a Master’s programme entitled ‘Mind, Brain and Education’ and the opening of the Centre for Neuroscience in Education at Cambridge University. Although brain science researchers have been asking questions related to how humans learn for decades, there is a clear movement at hand to formalise this connection.
This new field, sometimes referred to as ‘neuro-learning’ or ‘educational neuroscience’ has been met with varying degrees of warmth. While Davis (2004) states that medical models of cognition ‘have only a very limited role in the broader field of education and learning’ mainly because learning-related intentional states are not internal to individuals in a way which can be examined by brain activity, Pettito and Dunbar (2004) are at the other end of the spectrum as they find that this new discipline ‘provides the most relevant level of analysis for resolving today’s core problems in education.’ Opinions of other academics addressing the question fall throughout this range (Goswami, 2004; Willingham and Lloyd, 2007). Teaching professionals who were surveyed on the matter (Pickering and Howard-Jones, 2007) replied that they were generally enthusiastic concerning the use of neuroscientific findings in the field of education, and that they felt these findings would be more likely to influence their teaching methodology than curriculum content. Whether or not this will turn out to be the case, it is clear that the topic is one that is currently attracting a great deal of attention and debate.
The popularity of neuromyths
Despite the gathering momentum of this new field of education, classroom teaching has not yet been radically altered by the introduction of proven and reliable methodologies built on the brain sciences. To the contrary, educators are instead being warned against educational programmes based upon a host of neuromyths. These neuromyths are ideas which are becoming popular in certain areas of education, but which have no scientific foundation. Some of the more popular include teaching methods based on the ideas of being a right brain or left brain learner, having a particular learning style such as visual, auditory or kinaesthetic, or programmes based on the concept of neuroplasticity (Goswami 2006). Opponents of these ideas may go beyond the argument that these theories lack scientific founding to add that labelling and pigeon-holing students into certain categories actually limits their learning opportunities and may reinforce their beliefs of what they think they cannot do. Given the amount of contradictory information that today’s educators face, it would not be surprising if they were asking the question of how far a genuine relationship between the brain sciences and education has actually developed and what role this can and should play in their current practice.
Obstacles
The answer to the question of why findings from the neurosciences are not playing a larger role in current educational practices is not an easy one. Perhaps the best place to start answering the question is to first look at the three major difficulties standing in the way of the success of this new field.
Complexity
The first difficulty relates to complexity. Not only is the human brain with all of its neurons and synapses an intricate object of study; learning is also a multifaceted area, possibly much more than is evident at first glance. After all, we all experience learning every day of our lives, sometimes even when expending very little effort in doing so. However, this activity that comes so naturally to our species is not without its complexities. We can take an example of what might be considered to be a relatively simple classroom activity such responding to a request from the teacher to open a notebook and to write a sentence dictated by the teacher onto the page. Putting aside the complex motor control required for this task, the cognitive demands include the linguistic and pragmatic processing of the question, recognition of words in the dictated sentence, the transfer of the spoken units to written signs, and the integration of our world knowledge about how writing is carried out, for example, using a capital letter at the beginning of a sentence and using the correct punctuation and spelling. This list could be made even more complex if we added on tasks such as the use of working memory and attention systems.
We can then take any one of these subtasks and break it down further. For discussion’s sake, we can choose to take a closer look at the required language processing. Linguistic processing of the teacher’s instructions includes the separation of linguistic and non-linguistic sounds, access to phonological rules, semantic and syntactic rules and the ability to integrate all of these levels into
the seamless experience of language comprehension. Each of these tasks could be broken down again into several constituents. Suddenly, analysing the reasons that a particular child doesn’t follow the instructions from the example above becomes very difficult. The number of possibilities becomes overwhelming.
Neurological experiments are likely to be carried out at the most basic levels of cognition, assuming a need to build the foundations before being able to understand more complex acts of learning. Illustrative of this is an fMRI (functional magnetic resonance imaging) experiment which found differences between dyslexics and normal readers in the V5/MT area of the visual system in response to moving stimuli (Eden et al 1996). Although no direct educational strategies can be drawn from this, it points research in the direction of further investigating precise aspects of visual processing.
At the same time, it is easy to imagine the inexperienced educational researcher jumping to the conclusion that dyslexia is a result of visual difficulties, while ignoring the fact that the study requires replication, and, if found to be factual, could still be a very small piece of the puzzle of what dyslexia is.
Equipment limitations
A second difficulty stems from limitations inherent to brain imaging equipment. Although educators and educational theorists will be familiar with the idea of neuroimaging, they are less likely to be aware of the tools used and the difficulties met by researchers in examining the brain. The goal of these tools is to ‘see’ the brain in action, but different tools have different capabilities which allow for different possibilities.
Machines that monitor brain activity can be divided into two main groups; one giving primarily spatial information that shows where activities are happening in the brain and those giving primarily temporal information providing precise timings of when things in the brain are happening. Both types of information are vital. Unfortunately, machines tend to have either high spatial or high temporal capabilities but not both, thereby making it difficult to capture the whole picture of what’s going on with a single machine. The tools providing strong spatial resolution include the fMRI and PET (positron emission tomography). PET, however, suffers from the necessity of injecting a radioactive isotope into the person being examined and the fMRI has the drawback of being very noisy. Both of these facts limit the types of experiments that can be carried out with these tools, either due to ethical reasons or difficulties in experimental design.
The EEG (electroencephalogram) and the MEG (magnetoencephalogram) are popular methods with high-quality temporal resolution. Neither gives images of the brain, but instead detailed information about the time course of neural activity. This information is vital to our knowledge of how the brain works given the fact that complex brain activities related to learning and thought are complicated sequences of interrelated activities at the neural level. One positive force in the improvement of brain monitoring is the attempt to pair tools to combine spatial and temporal information, an action known as ‘multimodality data fusion’ (Horwitz and Poeppel, 2002). If successful, this will allow researchers to observe brain activity in real time with excellent spatial resolution as well.
Complex road from the laboratory to the classroom
The third difficulty revolves around the fact that findings from the laboratory cannot be immediately applied to the classroom. Several levels of research are in fact required before this transition can be made. I propose a model of this path below. Five basic levels are offered in the model , the levels of neuroscience, cognitive neuroscience, psychology, educational theory and testing, and finally the classroom. For effective and proven teaching methods based on the brain sciences to be developed, the levels of research shown in this model are likely to be required.
Model of levels of development
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CLASSROOM V EDUCATIONAL THEORY AND TESTING V PSYCHOLOGY V CONGITIVE NEUROSCIENCES V NEUROSCIENCES |
It is useful to make the distinction between neuroscience and cognitive neuroscience. While the former concentrates on the cellular level of the brain, cognitive neuroscience focuses on collections of cells that function together as mechanisms responsible for activities such as speech perception, word retrieval or working memory. The next level of analysis is the psychological. At this level, the findings from the previous levels are examined and proposed structures are studied in regard to how they might function. At this level, questions could be raised that get re-examined at the cognitive level before returning to the psychological. Eventually, in ideal situations, research would progress to the educational theory and testing level where educational theorists would develop possible teaching and learning theories based upon work stemming from the neurological level and their knowledge of teaching and learning.
Hypotheses formed at the educational level then need to undergo rigorous testing in order to judge the efficacy of the proposed new methods in comparison to teaching methods already being used. Only when shown to be successful, and it is not easy to know how success would be appropriately measured, might the methods be found appropriate for the classroom level. It is not in any way being suggested that this is the route that all classroom teaching should follow; instead it is a rough estimation of the long and complex road that is likely to exist between findings from brain observation and the use of these findings in classrooms.
The final step represented in the model is that of the classroom where the new teaching methodologies are implemented. At this stage, close monitoring should be implemented in order to ensure that teaching shown to work at the earlier level is successful on a larger scale. This level is much more than a recipient of the knowledge stemming from other levels of the model but is instead an active component of the research. One example of this model is provided in the panel overleaf.
Even this model of the complex route bridging the neurosciences and the classroom is a simplified version of what is required in reality. Information must flow both upwards and downwards in the model and great overlap exists between categories. What the model highlights is the fact that the research being carried out at the neurological levels must be met with equally rigorous research at the psychological and educational levels. In order to translate recent advances in science to student learning, much work remains to be done.
Conclusion
Despite the difficulties faced by the emerging field of brain-based learning, advances in the neurosciences hold an enormous potential to education, and particularly to the education of students with special educational needs.
It is almost certain that aggregations of findings from several studies, mediated through higher levels culminating in the behavioural and educational levels will eventually spur the development of new teaching methodologies. However, these proposed methodologies are not the final step in the journey, but only the beginning of a new one as new methodologies undergo rigorous testing in the classroom in order to allow for their efficacy to be judged.
Although dyslexia is a prime area of neurologically based research, it is far from being the only one. Brain-based research has also looked at areas as diverse as the cognitive mechanisms that underlie arithmetic abilities, social communication in autism, brain activation during facial processing in children with Williams Syndrome, MRI findings for children exposed to maternal alcohol intake before birth, and the list goes on. Given the wide range of difficulties that children in schools can be exposed to along with the enormous complexities of human brain function, no quick fix awaits us around the corner; unfortunately, much work remains to be done.
Perhaps one of the most important points to be made is that the elements of brain-based education that teachers may eventually want to use in their classrooms will not come directly from neurological and biological research. Instead, to move forward as effectively as possible, it is necessary for educational researchers to work alongside scholars at the cognitive and psychological levels to develop and test hypotheses about the functioning of mechanisms underlying learning. The road from the laboratory to the classroom may be a longer one than many realise, but given the possible benefits, it is certainly one worth travelling.
References
- Bosch, X (2006) ‘European Researchers Team up to Probe Genetic, Environmental Links in Dyslexia’, Journal of the American Medical Association, 296, 2664.
- Davis, A (2004) ‘The Credentials of Brain-based Learning’, Journal of Philosophy of Education, 38, 21-36.
- Eden, GF, Vanmeter, JW, Rumsey, JM, Maisog, JMA, Woods, RP and Zeffiro, TA (1996) ‘Abnormal Processing of Visual Motion in Dyslexia Revealed by Functional Brain Imaging’, Nature, 382, 66-69.
- Goswami, U (2004) ‘Neuroscience, Education and Special Education’, British Journal of Special Education, 31, 175-183.
- Goswami, U (2006) ‘Neuroscience and Education: From Research to Practice?’, Nature Reviews Neuroscience 7, 406-413.
- Horwitz, B and Poeppel, D (2002) ‘How Can EEG/MEG and fMRI/PET Data be Combined?’, Human Brain Mapping, 17, 1-3.
- Petitto, LA and Dunbar, K (2004) New Findings from Educational Neuroscience on Bilingual Brains, Scientific Brains and the Educated Mind. MBE/Harvard Conference, October.
- Pickering, SJ 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, and Education, 1, 109-113.
- Willingham, DT and Lloyd, JW (2007) ‘How Educational Theories Can Use Neuroscientific Data’, Mind, Brain, and Education, 1, 140-149.
Jodi Tommerdahl currently directs the Master’s degree in Speech and Language Difficulties at the University of Birmingham and continues to work with both adults and children with speech and language difficulties
