By Wenda Sheard.
For the past six months I’ve been working full time as a research specialist exploring the educational and workforce lives of people with disabilities. Some people might question how a person deeply committed to the unique social and emotional needs of gifted children and adults could also be deeply concerned about the needs of people with disabilities, including intellectual disabilities. For me, the answer is simple: I believe in the inherent worth and dignity of every person.
I believe that educational advocates, to be effective, must understand how education policies affect all children, and must be willing to advocate for the best possible education for all children. The key to successful education policy lies not in arguing for one group of children at the expense of another; the key lies in arguing for the best interests of all children.
The best interests of all children lie in treating each child as a unique individual. The best schools resist attempts to fit children into molds. The best schools refrain from moving children in lockstep, headed to identical diplomas in identical timeframes. The best schools decline to fly banners proclaiming “we strive for excellence” under circumstances where the same definition of “excellence” is applied to all children regardless of differences in body, mind, and spirit. The best schools embrace diversity in all senses of the word, including diversity of learning styles and learning speeds.
The rest of this article offers a tour of the brain research articles and education policy statistics that compel me to advocate for all children. On the tour I’ll point out what scientists are discovering about learning diversity, and what students and educators are experiencing in diverse classrooms across our country and beyond. Because so many students aspire but fail to graduate from college, I’ve extended the tour into education policy on the college level.
I hope the information will inspire people to learn more, and thus become better advocates for the social, emotional, and academic needs of all students.
Stop One: Learning Diversity at Birth
Some learning diversity is present at birth. Molfese and Molfese (1997) tested newborns and found that brain information recorded in response to auditory events within 36 hours after birth can be used to predict the reading performance of children eight years later. Subsequent work by Molfese and Molfese exploring the brain responses and abilities of young children is equally fascinating.
Begley (2008) reports on research finding that nearly 30 percent of children are born with genes that result in their brains having fewer dopamine receptors than normal. Having few dopamine receptors is linked to an inability to learn from mistakes, and to less activity in the brain’s frontal cortex, the site of higher-order thinking. In her article, Begley quotes Jack Shonkoff, director of the Center on the Developing Child at Harvard University as saying, “individual genetic differences are the 800-pound gorilla of child development.” In the future will schools know which students have a genetic propensity to have less activity in the frontal cortex? Will schools be able to provide experiences to meet the unique needs of those students?
Williams and O’Donovan (2006) and other researchers have found evidence that dyslexia has genetic components. Lepkowska (2008) reported that some scientists who have researched the genetics of dyslexia contend that early intervention with children of parents with dyslexia can combat the development of literacy problems. What would happen if we provided early interventions to children whose parents have dyslexia? Could early interventions help prevent literacy and other problems associated with dyslexia?
Stop Two: Learning Diversity during the Preschool Years
Other aspects of learning diversity originate during the preschool years. Hart and Risley (1995, 2003) found that preschool children from professional families hear more than three times the number of words per hour than do preschool children from families living on welfare. They also found that by the time the children were four years old, the vocabularies of the children from professional families were larger than the vocabularies of the parents of the children living on welfare. How much do vocabulary size differences affect the futures of young children? What, if anything should we do in response to this astonishing finding that four year olds in professional families have larger vocabularies than the parents of families living in poverty?
Scientists are beginning to discover early signs of learning disabilities. Campbell and von Stauffenberg (2009) found that children’s performances between 36 months of age and first grade on measures of resistance to temptation, delay of gratification, response inhibition, attention, and planning predicted whether the children would have symptoms of ADD or ADHD in third grade. Does impulsivity at 36 months of age cause ADD or ADHD in third grade? If we teach impulsive young children to resist temptation and to delay gratification, will that reduce their incidence of ADD or ADHD?
Stop Three: Experiences during the School Years
A child’s experiences during the school years can vary dramatically depending on where the child attends school. Although as a nation we strive to provide children with equal educational opportunity, statistics show that educational experiences vary greatly from one school district to the next. Sadly, those variations have almost nothing to do with the learning needs of individual children. To the contrary, those variations can exacerbate the need for individualized learning for many students.
In the Hartford, Connecticut public schools, where over 95% of the students are eligible for free or reduced price lunches, less than 17% of fourth graders meet the state goal in reading and only 11% of tenth graders meet the state goal in reading. The average SAT score in Hartford is under 400 in reading, math, and writing. By contrast, some school districts in wealthy Connecticut towns have average SAT scores nearly two standard deviations higher. If we take a set of identical twins and place one in Hartford schools for twelve years and other in the schools of a wealthy Connecticut town for twelve years, would we see any differences that occurred, not due to any funding differences or teacher quality differences, but as a result of their exposure to significantly different sets of classmates? If we take a different set of identical twins, would we see different results? Would any of these four children need individualized help in one school system, but not the other?
Loveless (2008) studied the results of policy initiatives to increase the numbers of eighth graders taking algebra in the United States. He found that in a typical classroom of 26 students, one or two of those students are functioning at approximately a second grade level. Loveless noted that “any teacher who stops to teach misplaced students fractions shortchanges the well prepared students who sit in that algebra class.” He also noted that most of the 120,000 misplaced algebra students in our nation attend large urban schools in high poverty areas, and nearly all waste the year unable to learn algebra because they haven’t yet mastered whole number and fraction arithmetic. Should the algebra policy initiative be changed to require students to pass an algebra readiness test before taking the class?
The average scores of children in some school districts can vary significantly from the average scores of children in other school districts. Variation of academic achievement within a classroom can affect the learning experiences of the children in the classroom. The fact that variations exist supports the notion that children must be treated as individuals. What individual treatment a particular child needs might vary depending on the abilities of other children in the class, and might vary depending on the particular child’s learning speed, styles, and needs.
Stop Four: Different Brains, Changing Brains
During the past two decades scientists have been discovering correlations between intelligence and certain anatomical brain characteristics. Alexander, O’Boyle, and Benbow (1996) used EEG technology to study the brains of 30 gifted adolescents (mean age 13.3, SAT averages 1100), 30 average ability adolescents, and 30 college-age subjects. They discovered “that gifted adolescents may have a developmentally enhanced state of brain activity, one that more closely resembles that of college-age adults to whom they also resemble in terms of cognitive ability.” Other research by O’Boyle and Benbow (1990) found that the degree of right hemisphere involvement in a cognitive task correlated with intellectual ability as measured by SAT scores. Is there a way for educators to increase right hemisphere involvement in cognitive tasks, and will increased right hemisphere involvement result in academic gains and “college-aged” brains?
Haier, White, and Alkire (2003) found that the individual differences in general intelligence correlate with brain function not only during reasoning tasks, but also during tasks that do not require reasoning. Their findings support the idea that differences in intelligence may be distributed among specific systems throughout the brain rather than localized within the frontal lobe. Would it make sense for educators to plan lessons that force students to exercise many systems inside their brains, and would multi-system exercise improve academic achievement?
Over the past decade many scientists have found that certain brain characteristics are correlated with a diversity of learning disabilities, including autism, ADD, and dyslexia. For an interesting article on autism see Baron-Cohen’s (2009) article, Talent in autism: Hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Does the fact that a learning disability is correlated with a brain characteristic mean that we might be able to change the brain characteristic, and thus change the learning disability?
Does learning change brain characteristics? Maguire and Gadian (2000) and Woollett (2009) discovered that the brains of London taxi drivers differ from the brains of non-taxi drivers. Schwenkreis, El Tom, Ragert, Plege, Tegenthoff, & Dinse (2007) similarly discovered that the brains of professional violin players differ from the brains of people who don’t play the violin. These discoveries suggest that brains can and do change, even in adulthood, and can and do change in response to learning. Might exercising different parts of the brain improve cognitive abilities?
Do great strengths in one area result in weakness in another area? Is there a cost to expert-level brain development? There could be a cost. Woollett and Maguire (2009) noted that the London taxi drivers were significantly more knowledgeable than others about London streets, landmarks, and their spatial relationships. Despite their advanced knowledge in those respects, the taxi drivers were significantly worse at forming and retaining new associations involving visual information.
Stop Five: Learning Diversity in College
Let’s think back to those Hartford, Connecticut public school students, the ones with the average SAT scores under 400. From the London taxi driver studies we can assume that after the Hartford students graduate from high school, it’s not too late for their brains to change in response to learning that happens during adulthood. Might we also assume that their low SAT scores evidence a need for individualized attention extending beyond high school and into college?
Some of the Hartford high school graduates might choose to attend a community college near their home, and might want to earn a degree from that community college. If the graduates poke around on www.ct.gov, they might find what the federal Student Right-to-Know Act of 1990 requires the states to publish—the graduation rate for first-time, full-time, degree-seeking students. For Connecticut community colleges, that graduation rate is below 11% for the most recent cohort listed on the website. Another 22% of those students transferred to other colleges, and another 17% were still enrolled after three years. But what happened to the remaining 50% of the first-time, full-time, degree-seeking students? Did they leave for social, emotional, academic, or financial reasons?
McClenney (2009) notes that close to 60% of entering community-college students need at least one remedial course. In some community colleges, over 95% of entering students need a remedial math, reading, or writing course. The best community colleges recognize the importance of individualization, and pay attention to the social and emotional needs of students. McClenney notes that the Community College of Denver has improved student success by using a highly personalized case-management approach and “proceed at your own pace” open-entry and open-exit courses. McClenney also notes that Kingsborough Community College in New York and Skagit Valley College in Washington have had success with learning communities and increased student support services including counseling for students needing remedial courses. Paying attention to the learning needs of individual students is best practice, whether we’re talking about children in schools or adults in community colleges.
What about students who attend four year colleges? The National Center for Education Statistics (2009) reports that the graduation rate of all bachelor’s-seeking students in the latest cohort studied is 36% after four years, 53% after five years, and 57% after six years. What has happened to the 43% of students who sought a bachelor’s degree but had not obtained one after six years? To what extent did the four year colleges meet the learning needs of the students who failed to earn a degree? Who are the students who failed to earn a four year degree within six years?
Many of those students are low income students. The U.S. Department of Education (2006) notes that only 36% of college-qualified low-income students complete bachelor’s degrees within eight and a half years, compared with 81% of high-income students. See, also, Adelman (2006). A student’s class rank during high school also appears to play a role in whether the student will graduate from college. Nemko (2008) notes that fewer than 34% of students who graduate in the bottom 40% of their high school class and matriculate into four year colleges graduate from college within eight and one-half years. The U.S. Department of Education (2006) noted: “The consequences of these problems are most severe for students from low-income families and for racial and ethnic minorities. But they affect us all.”
Final Stop: Passion & Hope
My passion has always been the bigger picture, the picture that encompasses all children regardless of ability or achievement, regardless of wealth or poverty, and regardless of appearance or belief. As Saul Alinsky (1971) wrote, “The spirit of democracy is the idea of importance and worth in the individual, and faith in the kind of world where the individual can achieve as much of his potential as possible”(p. xxiv).
Let’s hope we become better advocates for the unique social and emotional needs of gifted children and adults by better understanding the diversity of learning speeds, learning styles, and learning needs of all students.
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, D.C.: U.S. Department of Education. Retrieved April 26, 2009, from http://www.ed.gov/rschstat/research/pubs/toolboxrevisit/toolbox.pdf
Alexander, J. E., O’Boyle, M. W., & Benbow, C. P. (1996). Developmentally advanced EEG alpha power in gifted male and female adolescents. International Journal of Psychophysiology, 23(1-2), 25-31.
Alinsky, S. D. (1971). Rules for radicals: A pragmatic primer for realistic radicals. New York: Vintage Books.
Baron-Cohen, S. (2009). Talent in autism: Hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions: Biological Sciences, 364(1522), 1377-1383.
Begley, S. (2008, August 9). But I did everything right. Newsweek. Retrieved April 26, 2009, from http://www.newsweek.com/id/151758
Campbell, S. B., & von Stauffenberg, C. (2009). Delay and inhibition as early predictors of ADHD symptoms in third grade. Journal of Abnormal Child Psychology, 37(1), 1-15.
Haier, R. J., White, N. S., & Alkire, M. T. (2003). Individual differences in general intelligence correlate with brain function during nonreasoning tasks. Intelligence 31, 429–441.
Hart, B., & Risley, T. R. (2003). The early catastrophe: The thirty million word gap by age three. American Educator, 27(1), 4-9.
Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday experiences of young American children. Baltimore, MD: Brookes Publishing Company.
Hartford School District’s Strategic School Profile, 2007-2008. (2008). Retrieved April 25, 2009 from http://www.csde.state.ct.us/public/der/ssp/dist0708/dist041.pdf
Lepkowska, D. (March 28, 2008). Language alert over children of dyslexic parents. Times Educational Supplement, 4781, 19.
Loveless, T. (2008). The misplaced math student: Lost in eighth-grade algebra. Washington, D.C.: Brookings Institution. Retrieved April 26, 2009, from http://www.brookings.edu/reports/2008/~/media/Files/rc/reports/2008/0922_education_loveless/0922_education_loveless.pdf
Maguire, E. A., & Gadian, D. G. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97(8), 4398-4403.
McClenney, K. (2009, April 24). Helping community-college students succeed: A moral imperative [Commentary]. The Chronicle of Higher Education, 55(33), A60. Retrieved April 26, 2009, from http://chronicle.com/free/v55/i33/33a06001.htm
Molfese, D. L., & Molfese, V. J. (1997). Discrimination of language skills at five years of age using event-related potentials recorded at birth. Developmental Neuropsychology, 13(2), 135-156.
Molfese, V. J., Molfese, D. L., Beswick, J. L., Jacobi-Vessels, J., Molfese, P. J., Molnar, A. E., Wagner, M. C., Haines, B. L. (2008). Use of event-related potentials to identify language and reading skills. Topics in Language Disorders, 28(1), 28-45.
National Center for Education Statistics. (2009). Enrollment in postsecondary institutions, fall 2007; graduation rates, 2001 & 2004 cohorts; and financial statistics, fiscal year 2007. Retrieved May 10, 2008, from http://nces.ed.gov/pubs2009/2009155.pdf
Nemko, M. (May 2, 2008). America’s most overrated product: The bachelor’s degree. The Chronicle of Higher Education.
O’Boyle, M. W., & Benbow, C. P. (1990). Enhanced right hemisphere involvement during cognitive processing may relate to intellectual precocity. Neuropsychologia, 28(2), 211-216. Student Right to Know, Retrieved May 9, 2009 from http://www.commnet.edu/planning/Research/SRK/srk.htm and from http://www.commnet.edu/planning/Research/SRK/2004Cohort.html
Schwenkreis, P., El Tom, S., Ragert P., Pleger, B., Tegenthoff, M. & Dinse, H. R. (2007). Assessment of sensorimotor cortical representation asymmetries and motor skills in violin players. European Journal of Neuroscience, 26, 3291–3302.
U.S. Department of Education (2006). A test of leadership: Charting the future of U.S. higher education. Washington, D.C. Retrieved May 10, 2009, from http://www.ed.gov/about/bdscomm/list/hiedfuture/reports/final-report.pdf
Williams J., & O’Donovan, M.C. (2006). The genetics of developmental dyslexia. European Journal of Human Genetics, 14(6), 681–689.
Woollett, K. (2009). Talent in the taxi: a model system for exploring expertise. Philosophical Transactions: Biological Sciences, 364(1522), 1407-1416.
Woollett, K., & Maguire, E. A. (2009). Navigational expertise may compromise anterograde associative memory. Neuropsychologia(47)4, 1088-1095.
Wenda Sheard, Each month a different member of the SENG team describes a personal passion in the realm of social and emotional needs of the gifted. Wenda Sheard, J.D., Ph.D. currently serves as SENG president and lives in Connecticut. She has taught diverse students in many educational settings. Starting this summer, she will enjoy the expertise of London taxi drivers.