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The Science of Education
Informing Teaching and Learning through the Brain Sciences
The new field of neuroeducation connects neuroscientists who study learning and educators who hope to make use of the research. But building a bridge between these groups will require overcoming some high hurdles: a method needs to be established for translating research findings into educational practices.
As Mariale Hardiman and Martha Bridge Denckla emphasize here, the next generation of educators will need to broaden their approach—focusing not just on teaching math, for example, but also on how math reasoning develops in the brain. Meanwhile, scientists should take the needs and concerns of educators into account as they continue to investigate how we learn. Such crosstalk is already occurring in collaborative efforts focusing on learning, arts and the brain.
Research shows that learning changes the brain. The brain is “plastic”—it makes new cellular connections and strengthens existing ones as we gain and integrate information and skills. In the past decade, the enormous growth in understanding brain plasticity has created an entirely new way to consider how learning and achievement take place in the education of children.
As this knowledge has grown, teachers have increasingly sought to apply it in the classroom. But the link between research lab and school need not be a one-way street—the experiences of educators and students also can suggest questions about learning that neuroscientists should be exploring. Collaboration among educators and cognitive scientists will enrich both fields: Educators can design instructional methods based on research results, and researchers can assess whether these new methods enhance student learning. Such translational research collaborations have the potential to improve teaching and learning and to influence both the practices of school administrators and the policies of boards of education. Neuroeducation—a field establishing how neuroscience research can inform educational practice and vice versa—is taking root.
Yet as educators seek new insights from cognitive research about how people think and learn, they must reconcile extreme opinions in their own field on how—and whether—to apply these findings to the science of teaching. On one end of the spectrum are skeptics who believe that neuroscientific findings have little relevance in the classroom; on the other are people who overstate or misinterpret the results by making claims that certain “brain-based” materials or techniques are sure to improve children’s IQs. Our charge to cognitive neuroscientists and educators is to work together to apply compelling, evidence-based findings to teaching and learning, but also to identify misplaced exuberance.
The Science of Learning
Whether or not a teacher understands fundamental concepts derived from basic brain science, such as plasticity, can have a profound effect on how he or she views the learner. Many classroom teachers today, for example, were trained at a time when scientists thought the brain was fixed at birth and changeable only in one direction: degeneration due to aging, injury or disease. Such a misunderstanding of brain anatomy and physiology would limit a teacher’s view of the learning capacity of children, especially those who enter the classroom lagging behind their peers. For example, a teacher may think that a fifth-grader who has failed to master basic mathematics skills will always struggle with math because of limited cognitive capacities.
Contrast this view with contemporary knowledge that the brain constantly changes with experience, makes new brain cell connections (synapses), strengthens connections through repeated use and practice, and even produces new cells in certain regions. Imagine how differently a teacher armed with this information would view students’ capacity for learning. Knowing that experiences change the brain might encourage this teacher to design targeted remedial lessons. Engaging the student in multiple, creative math-oriented tasks might do more than increase achievement scores: It might actually change brain circuitry.
Cognitive neuroscientists also are providing new insights into the brain’s executive functions. For example, we are learning more about the brain’s capacity to retain new information in working memory until the tasks that depend on this information are completed. We are also discovering the importance of the cognitive and emotional control people use to arrive at judgments and to make decisions. Valuable contributions to our understanding of these abilities in healthy children have been provided by imaging brain anatomy and connectivity in children with attention-deficit/hyperactivity disorder (ADHD). Findings suggest that ADHD symptoms may represent developmental delay rather than damage in the brain, and that any neural circuitry with such protracted development may be exquisitely sensitive to environmental and experiential influences, which may even alter brain structures.1 Careful, mindful child-rearing and education are crucial for the development of the brain regions and connections that underlie executive functions.
Research demonstrating the effects of emotions on learning2 provides another example of how teaching involves not only transmitting information but also crafting classroom climates that promote learning. Teachers may know intuitively that an atmosphere of stress and anxiety impedes children’s learning, yet many common practices in classrooms, such as embarrassing a child or making sarcastic rather than constructive comments, can create stressful environments. Teachers who understand that the brain’s emotional wiring connects with the prefrontal cortex—the center for higher-order thought—would appreciate the need to provide their students with a positive emotional connection to learning.
Other research highlights the role of motivation in learning and cognition. Studies by Michael Posner, Ph.D., professor emeritus of psychology at the University of Oregon, indicate that children who receive training in a subject that interests them, such as the visual arts, become highly motivated. This motivation sustains their attention, and the result is an improvement in cognition3 (see “How Arts Training Improves Attention and Cognition,” Cerebrum, September 2009).
Obstacles to Uniting Science and Education
Even as the field of neuroeducation grows, educators will continue to face hurdles. Howard Gardner, the professor of cognition and education at the Harvard Graduate School of Education who developed the theory of multiple intelligences, points out that it will be challenging to align the wide-ranging interests of neuroeducators (whom he defines as bench scientists, clinicians, teachers and policy-makers) with the public’s notion of effective educational policies and practices.4 Gardner asserts that with no tradition of applying neuroeducation findings to the practice of teaching, it is difficult to provide benchmarks for good work. We must begin to establish that tradition.
Coherent translation of cognitive neuroscience to education is sparse. We need to translate knowledge about how children learn in ways that are relevant to teachers’ work in school settings. Educational policy-makers and administrators focus on the external structures of education, such as standards, data analysis, scheduling, curriculum, school governance and accountability, yet they pay little attention to the learners themselves. And few teacher preparation programs include courses on cognition and learning.
One source of this apparent disconnect is the human tendency to view research findings through the lens of a specific discipline. Neuroeducation, on the other hand, involves examining and synthesizing findings across disciplines. Michèle Mazzocco, director of the Math Skills Development Project at the Kennedy Krieger Institute, emphasizes the need to attend to “both the forest and the trees” when analyzing research relevant to academic performance. Mazzocco, who trained as both an elementary school educator and an experimental psychologist, stresses that the importance of basic cognitive processes sometimes gets lost amid a strong focus only on the result of those processes—student achievement.5
For instance, despite their relationship with one another, mathematical ability, performance and achievement involve different cognitive processes, and the study of each facet makes a unique contribution to enhancing student learning. According to Mazzocco, our knowledge of basic cognitive processes, while informative, is not yet sufficiently advanced to provide a solid basis for a specific method of teaching or curriculum. However, teachers are seeking to improve their students’ learning and performance right now, and neuroeducators therefore need to determine how best to apply current research findings to improving classroom learning skills.
Setting a New Research Agenda
As neuroeducators seek to address the practical needs of teachers and administrators, they need to conduct more interdisciplinary research to bridge the differences among the methods that scientific and education communities use. Bringing scientists and educators together allows for such intellectual exchange and offers the opportunity to formulate questions that neither group could answer alone.
An example from dyslexia research early in my (Denckla’s) career illustrates this point and raises a new one: Input from students about how they learn best, as well as what hinders their learning, can also help direct our investigation. While evaluating children who were having difficulty speaking and reading, I viewed the possible brain basis of their difficulties in terms of a popular notion at that time: that dyslexia is caused, in part, by seeing twisted or reversed symbols (such as confusing the letters b and d). But one child with dyslexia volunteered that the experiment was “easy and stupid.” His explanation indicated that it was the similar sounds and names of specific letters, not how they looked, that was confusing. As he explained, his difficulty was in assigning a name or sound to each letter seen alone; b and d sound too much alike. In contrast, p and q, another visually similar pair, do not have confusable sounds or names.
Testing a conventional theory, coming up empty and receiving a child’s insight steered my research in new a direction: developing an entire line of testing, called the Rapid Automatized Naming Test, which is one of the best predictors of biologically based reading disability. Results from this test, in turn, led neuroimaging researchers to determine where to look in the brain to identify the circuit, or neural connections, normally involved in making automatic the naming of colors, letters and numbers. All of this resulted from a child describing why it was hard for him to learn to read.
To encourage cooperation today, Mary Brabeck, dean of the Steinhardt School of Culture, Education, and Human Development at New York University, suggests establishing a web of collaboration among scientists—including neuroscientists conducting work in medical schools, applied researchers and cognitive scientists working in schools of arts and sciences—and teacher-educators from schools of education.6 As Brabeck and others have noted, researchers must consider the real needs of educators by visiting schools, engaging in meaningful dialogue and then testing their hypotheses in authentic school settings.
The Learning, Arts, and the Brain educational summit in May 2009, sponsored by the Johns Hopkins University School of Education in collaboration with the Dana Foundation, was an example of such an effort. More than 300 researchers, educators and policy-makers gathered in roundtable groupings to discuss current findings on arts and cognition and to brainstorm ideas for translational research based on educators’ questions.
The research reported was preliminary but intriguing, especially the suggestion that skills learned via arts training could carry over to learning in other domains. In addition to Posner’s work, Ellen Winner, Ph.D., a professor of psychology at Boston College, and Gottfried Schlaug, M.D., a neurology professor at Beth Israel Deaconess Medical Center and Harvard Medical School, found evidence from brain-imaging studies that music training transfers to the highly related cognitive abilities of sound discrimination and fine motor tasks, a process termed near transfer.7 Brian Wandell, Ph.D., professor and chair of psychology at Stanford University, described results showing that music training is tightly correlated with phonological awareness—the ability to manipulate speech sounds—a strong predictor of reading fluency, which represents far transfer of cognitive skills.8
The research presented at the forum builds on previous studies, including the work of the seven groups of scientists involved in the Dana Arts and Cognition consortium, that show close correlations between arts training and several cognitive abilities. A report from the summit, published in October 2009, reveals rich conversations among scientists and educators that will help shape a research agenda to examine the influence of arts training on creativity and learning.
Another model of collaboration, championed by Kurt Fischer of the Harvard Graduate School of Education and others, is the “research school.” Following a medical school model, neuroeducation “residents” would integrate theories of learning and develop practical applications for actual classrooms. Such schools would serve as laboratories for university-based researchers to design and develop studies based on the needs of teachers, test new methods, evaluate interventions and provide teacher-development opportunities.
In this model, a neuroscientist might examine how a specific neurotransmitter, such as dopamine, affects attention; a developmental neurologist might study the delayed structural development of the brain in children with ADHD and compare it with structural abnormalities related to dyslexia; a cognitive scientist might review the neurophysiological correlates involved in self-control; educational researchers might assess whether specific types of enriched environments and experiences improved attention for students with ADHD; and teachers might observe instructional interventions that appeared to improve math or reading skills and propose studies to determine how those interventions might affect brain processes.
Effect on Educational Policy and Practice
These types of collaborations would help us start to better align educational practices with evidence from cognitive development studies. For instance, preschool-age children may not be ready for reading instruction, and young adolescents may not be cognitively prepared for the type of conceptual thinking that algebra requires. Studies suggest that connectivity of the brain’s frontal lobes (which are involved in memory, language, problem-solving, judgment, impulse control, flexibility and social behaviors) with the neural circuits involved in emotion does not fully mature until about age 32.9 Cognitive control of the brain’s executive function—which is involved in regulating behaviors that are necessary to reaching goals and essential for achieving in school, including the abilities to think abstractly and to form concepts—may not reach maturity until about age 25.9 Given that these brain processes mature well after students graduate from public schools, what are the implications for the way we teach the relevant subjects? Educators and parents are asking how this information should influence educational practice and wondering who will translate such knowledge from the brain sciences to the educational community.
To provide such information, university research and academic programs, too, must break free from a narrow focus on specific disciplines (such as teaching mathematics) and instead view education through a wider lens that includes the science of learning (such as the development of mathematic reasoning skills). Programs such as the Johns Hopkins University School of Education’s new graduate certificate in Mind, Brain, and Teaching and Harvard University’s master’s degree in Mind, Brain, and Education will produce generations of researchers who are comfortable with an interdisciplinary approach.
Focusing on the science of learning should be as important as accountability for student achievement. Schools’ policies and practices must reflect a focus on how children learn, and professionals who conduct learning-related research must view educators as consumers and partners in the work. Education in the 21st century requires a new model for preparing children to become more creative and innovative thinkers and learners. Incorporating multiple perspectives in our study of how children learn can lead us to reimagine and re-create children’s learning experiences in our schools.
1. P. Shaw, K. Eckstrand, W. Sharp, J. Blumenthal, J. P. Lerch, D. Greenstein, L. Clasen, A. Evans, J. Giedd, and J. L. Rapoport, “Attention-deficit/Hyperactivity Disorder Is Characterized by a Delay in Cortical Maturation,” Proceedings of the National Academy of Sciences 104 (2007): 19649–19654.
2. M. H. Immordino-Yang and A. Damasio, “We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education,” Mind, Brain, and Education 1, no. 1 (2007): 3–10.
3. J. Fan, J. I. Flombaum, B. D. McCandliss, K. M. Thomas, and M. I. Posner, “Cognitive and Brain Consequences of Conflict,” Neuro Image 18 (2003): 42–57.
4. H. Gardner, “Quandaries for Neuroeducation,” Mind, Brain, and Education 2, no. 4 (2008): 165–169.
5. M. M. Mazzocco, “Introduction: Mathematics Ability, Performance, and Achievement,” Developmental Neuropsychology 33, no. 3 (2008): 197–204.
6. M. Brabeck, “Why We Need ‘Translational’ Research: Putting Clinical Findings to Work in Classrooms,” Education Week 27, no. 38 (2008): 28, 36.
7. K. L. Hyde, J. Lerch, A. Norton, M. Forgeard, E. Winner, A. C. Evans, and G. Schlaug, “Musical Training Shapes Structural Brain Development,” Journal of Neuroscience 29 (2009): 3019–3025.
8. B. Wandell, R. Dougherty, M. Ben-Shachar, G. Deutsch, and J. Tsang, “Training in the Arts, Reading, and Brain Imaging,” Learning, Arts, and the Brain: The Dana Consortium Report (2008): 51–59.
9. P. I. Yakovlev and A. R. Lecours, “The Myelogenetic Cycles of Regional Maturation of the Brain,” in Regional Development of the Brain in Early Life, ed. A. Minkowsky, 3–70 (Oxford, England: Blackwell Scientific, 1967).