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Literacy and Learning

ISBN : 978-1-84950-776-9 , eISBN : 978-1-84950-777-6

Publication date: 22 February 2010

This chapter describes a number of research experiences of the authors, directed to increasing our understanding of exceptional individuals, most typically those with learning or behavioral disabilities. A number of examples is presented, to demonstrate how a research emphasis on exceptional persons can help to advance our understanding of human learning and cognition, and how such findings can contribute to the development of an overall, adequate theory of learning and instruction. Several general points from these experiences are presented, generally that the study of human exceptionality (a) can help to clarify our understanding of what we learn, and why we should learn it, (b) can enhance our understanding of what is “possible,” (c) demonstrate that what we do is more important than who we are, (d) demonstrate that we learn best by doing, and that our experience informs our understanding, and (e) demonstrate that we are all exceptional cases.

Mastropieri, M.A. and Scruggs, T.E. (2010), "The study of human exceptionality: how it informs our knowledge of learning and cognition", Scruggs, T.E. and Mastropieri, M.A. (Ed.) Literacy and Learning ( Advances in Learning and Behavioral Disabilities, Vol. 23 ), Emerald Group Publishing Limited, Leeds, pp. 303-319. https://doi.org/10.1108/S0735-004X(2010)0000023014

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• Published: 12 January 2017

Individual differences in the learning potential of human beings

• Elsbeth Stern 1  

npj Science of Learning volume  2 , Article number:  2 ( 2017 ) Cite this article

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To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50,000 years. However, because of their cognitive potential, humans have changed the world tremendously in the past centuries. They have invented technical devices, institutions that regulate cooperation and competition, and symbol systems, such as script and mathematics, that serve as reasoning tools. The exceptional learning ability of humans allows newborns to adapt to the world they are born into; however, there are tremendous individual differences in learning ability among humans that become obvious in school at the latest. Cognitive psychology has developed models of memory and information processing that attempt to explain how humans learn (general perspective), while the variation among individuals (differential perspective) has been the focus of psychometric intelligence research. Although both lines of research have been proceeding independently, they increasingly converge, as both investigate the concepts of working memory and knowledge construction. This review begins with presenting state-of-the-art research on human information processing and its potential in academic learning. Then, a brief overview of the history of psychometric intelligence research is combined with presenting recent work on the role of intelligence in modern societies and on the nature-nurture debate. Finally, promising approaches to integrating the general and differential perspective will be discussed in the conclusion of this review.

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Uniquely human intelligence arose from expanded information capacity

Genetic variation, brain, and intelligence differences

Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

Human learning and information processing.

In psychology textbooks, learning is commonly understood as the long-term change in mental representations and behavior as a result of experience. 1 As shown by the four criteria, learning is more than just a temporary use of information or a singular adaption to a particular situation. Rather, learning is associated with changes in mental representations that can manifest themselves in behavioral changes. Mental and behavioral changes that result from learning must be differentiated from changes that originate from internal processes, such as maturation or illness. Learning rather occurs as an interaction with the environment and is initiated to adapt personal needs to the external world.

From an evolutionary perspective, 2 living beings are born into a world in which they are continuously expected to accomplish tasks (e.g., getting food, avoiding threats, mating) to survive as individuals and as species. The brains of all types of living beings are equipped with instincts that facilitate coping with the demands of the environment to which their species has been adapted. However, because environments are variable, brains have to be flexible enough to optimize their adaptation by building new associations between various stimuli or between stimuli and responses. In the case of classical conditioning, one stimulus signals the occurrence of another stimulus and thereby allows for the anticipation of a positive or negative consequence. In the case of operant conditioning, behavior is modified by its consequence. Human beings constantly react and adapt to their environment by learning through conditioning, frequently unconsciously. 1

However, there is more to human learning than conditioning, which to the best of our knowledge, makes us different from other species. All living beings must learn how to obtain access to food in their environment, but only human beings cook and have invented numerous ways to store and conserve their food. While many animals run faster than humans and are better climbers, the construction and use of vehicles or ladders is unique to humans. There is occasional evidence of tool use among non-human species passed on to the next generation, 3 , 4 but this does not compare to the tools humans have developed that have helped them to change the world. The transition from using stonewedges for hunting to inventing wheels, cars, and iPhones within a time period of a few thousand years is a testament to the unique mental flexibility of human beings given that, to the best of our knowledge, the genes that guide human brain development have not undergone remarkable changes during the last 50,000 years. 5 This means that as a species, humans are genetically adapted to accomplish requirements of the world as it existed at approximately 48,000 BC. What is so special about human information processing? Answers to this question are usually related to the unique resource of consciousness and symbolic reasoning abilities that are, first and foremost, practiced in language. Working from here, a remarkable number of insights on human cognition have been compiled in the past decades, which now allow for a more comprehensive view of human learning.

Human learning from a general cognitive perspective

Learning manifests itself in knowledge representations processed in memory. The encoding, storage, and retrieval of information have been modeled in the multi-store model of human memory depicted in Fig.  1 . 6 Sensory memory is the earliest stage of processing the large amount of continuously incoming information from sight, hearing, and other senses. To allow goal-directed behavior and selective attention, only a fractional amount of this information passes into the working memory, which is responsible for temporarily maintaining and manipulating information during cognitive activity. 7 , 8 Working memory allows for the control of attention and thereby enables goal-directed and conscious information processing. It is the gatekeeper to long-term memory, which is assumed to have an unlimited capacity. Here, information acquired through experience and learning can be stored in different modalities as well as in symbol systems (e.g., language, script, mathematical notation systems, pictorials, music prints).

A model of human information processing, developed together with Dr. Lennart Schalk

The multi-store model of human information processing is not a one-way street, and long-term memory is not to be considered a storage room or a hard-disk where information remains unaltered once it has been deposited. A more appropriate model of long-term memory is a self-organizing network, in which verbal terms, images, or procedures are represented as interlinked nodes with varying associative strength. 9 Working memory regulates the interaction between incoming information from sensory memory and knowledge activated from long-term memory. Very strong incoming stimuli (e.g., a loud noise or a harsh light), which may signal danger, can interrupt working memory activities. For the most part, however, working memory filters out irrelevant and distracting information to ensure that the necessary goals will be achieved undisturbed. This means that working memory is continuously selecting incoming information, aligning it with knowledge retrieved from long-term memory, and preparing responses to accomplishing requirements demanded by the environment or self-set goals. Inappropriate and unsuitable information intruding from sensory as well as from long-term memory has to be inhibited, while appropriate and suitable information from both sources has to be updated. 8 The strength with which a person pursues a particular goal has an impact on the degree of inhibitory control. In case of intentional learning, working memory guards more against irrelevant information than in the case of mind wandering. Less inhibitory control makes unplanned and unintended learning possible (i.e., incidental learning).

These working memory activities are permanently changing the knowledge represented in long-term memory by adding new nodes and by altering the associative strength between them. The different formats knowledge can be represented in are listed in Fig.  1 ; some of them are more closely related to sensory input and others to abstract symbolic representations. In cognitive psychology, learning is associated with modifications of knowledge representations that allow for better use of available working memory resources. Procedural knowledge (knowing how) enables actions and is based on a production-rule system. As a consequence of repeated practice, the associations between these production rules are strengthened and will eventually result in a coordinated series of actions that can activate each other automatically with a minimum or no amount of working memory resources. This learning process not only allows for carrying out the tasks that the procedural knowledge is tailored to perform more efficiently, but also frees working memory resources that can be used for processing additional information in parallel. 10 , 11 , 12

Meaningful learning requires the construction of declarative knowledge (knowing that), which is represented in symbol systems (language, script, mathematical, or visual-spatial representations). Learning leads to the regrouping of declarative knowledge, for instance by chunking multiple unrelated pieces of knowledge into a few meaningful units. Reproducing the orally presented number series “91119893101990” is beyond working memory capacity, unless one detects two important dates of German history: the day of the fall of the Berlin Wall: 9 November 1989 and the day of reunification: 3 October 1990. Individuals who have stored both dates and can retrieve them from long-term memory are able to chunk 14 single units into two units, thereby freeing working memory resources. Memory artists, who can reproduce dozens of orally presented numbers have built a very complex knowledge base that allows for the chunking of incoming information. 13

Learning also manifests itself in the extension of declarative knowledge using concept formation and inferential reasoning. Connecting the three concepts of “animal, produce, milk” forms a basic concept of cow. Often, concepts are hierarchically related with superordinate (e.g., animal) and subordinate (e.g., cow, wombat) ordering. This provides the basis for creating meaningful knowledge by deductive reasoning. If the only thing a person knows about a wombat is that it is an animal, she can nonetheless infer that it needs food and oxygen. Depending on individual learning histories, conceptual representations can contain great variations. A farmer’s or a veterinarian’s concept of a cow is connected to many more concepts than “animal, produce, milk” and is integrated into a broader network of animals. In most farmers’ long-term memory, “cow” might be strongly connected to “pig”, while veterinarians should have particularly strong links to other ruminants. A person’s conceptual network decisively determines the selection and representation of incoming information, and it determines the profile of expertise. For many academic fields, first and foremost in the STEM area (Science, Technology, Engineering, Mathematics), it has been demonstrated that experts and novices who use the same words may have entirely different representations of their meaning. This has been convincingly demonstrated for physics and particularly in the area of mechanics. 14 Children can be considered universal novices; 15 therefore, their everyday concepts are predominantly based on characteristic features while educated adults usually consider defining features, 16 , 17 , 18 as the example of “island” demonstrates. For younger children, it primarily refers to a warm place where one can spend ones’ holidays. In contrast, adults’ concept of island does refer to a tract of land that is completely surrounded by water but not large enough to be considered a continent.

The shift from characteristic to defining features is termed “conceptual change”, 16 and promoting this kind of learning is a major challenge for school education. Students’ understanding of central concepts in an academic subject can undergo fundamental changes (e.g., the concept of weight in physics). Younger elementary school children often agree that a pile of rice has weight, but they may also deny that an individual grain of rice has weight at all. This apparently implausible answer is understandable given that younger children consider the concepts of “weight” and “being heavy” as equivalent. As such, children tend to agree that a grain of rice has weight if it is put on an ant’s back. 16 As a consequence of their education, students usually understand that an object’s weight is determined with the assistance of scales and not necessarily by personal sensation. However, representing weight as the property of an object is still not compatible with scientific physics in the Newtonian sense by which weight is conceptualized as a relation between objects. Understanding weight in this sense requires an interrelated network of knowledge, including the concepts of force, gravity, and mass (among others).

As a result of classroom instruction, students are expected to acquire procedural and conceptual knowledge of the subjects they were taught. While procedures emerge as a function of repetition and practice, the acquisition of advanced concepts, which are consistent with state of the art science, is less straightforward. 14 , 19 To support this kind of conceptual learning, insights from cognitive learning research have been integrated into educational research and are increasingly informing classroom practice. Several instructional methods have been developed and evaluated that support students in restructuring and refining their knowledge and thereby promote appropriate conceptual understanding, including self-explanations, 20 contrasting cases, 21 , 22 and metacognitive questions. 23 Cognitive research has also informed the development of the “taxonomy of learning objects”. 24 This instrument is widely employed for curriculum development and in teacher training programs to support the alignment of content-specific learning goals, means of classroom practice, and assessment. The taxonomy acknowledges the distinction between procedural and conceptual knowledge and includes six cognitive processes (listed in Fig.  1 ) that describe how knowledge can be transformed into observable achievement.

How core knowledge innate to humans can meet with academic learning

What makes humans efficient learners, however, goes beyond general memory functions discussed so far. Similar to other living beings, humans do not enter the world as empty slates 2 but are equipped with so-called core knowledge (Fig.  1 ). Evidence for core knowledge comes from preferential looking experiments with infants who are first habituated to a particular stimulus or scenario. Then, the infant is shown a second scenario that differs from the first in a specific manner. If the time he or she looks at this stimulus exceeds the looking-time at the end of the habituation phase of the first stimulus, this suggests that the infant can discriminate between the stimuli. This paradigm helps to determine whether infants detect violations of principles that underlie the physical world, such as the solidity of objects, where an object cannot occupy the same space as another object. 25 , 26 Core knowledge, which allows privileged learning and behavioral functioning with little effort, also guides the unique human ability of symbolic communication and reasoning, first and foremost, langue learning. 27 , 28 It is uncontested that humans are born with capacities for language learning, which includes the awareness of phonological, grammatical, and social aspects of language. 4 , 29 , 30

Core knowledge can serve as a starting point for the acquisition of content knowledge that has emerged as a result of cultural development. This has been examined in detail for numerical and mathematical reasoning. Two core systems have been detected in infants. As early as at 6 months of age, infants show an ability for the approximate representations of numerical magnitude, which allow them to discriminate two magnitudes depending on their ratio. 31 At the same age, the system of precise representations of distinct individuals allows infants to keep track of changes in small sets of up to three elements. 32 Mathematical competencies emerge as a result of combining both core systems and linking them to number words provided by the respective culture. 33 The Arabic place value number system, which is now common in most parts of the world, was only developed a few 100 years ago. Only after the number “0” had made its way from India via the Arabic countries to Europe were the preconditions for developing our decimal system available. 34 The Arabic number system opened up the pathway to academic mathematics. Cultural transformations based on invented symbol systems were the key to advanced mathematics. Today’s children are expected to understand concepts within a few years of schooling that took mankind centennials to develop. Central content areas in mathematics curricula of high schools, such as calculus, were only developed less than three centuries ago. 35 Given the differences between the Arabic and the Roman number systems, children born 2000 years ago could not make use of their numerical core knowledge in the same way today’s children can.

Core knowledge about navigation is meant to guide the acquisition of geometry, an area involved in numerous academic fields. 36 , 37 The cornerstone of cultural development was the invention of writing, in which language is expressed by letters or other marks. Script is a rather recent cultural invention, going back approximately 5,000 years, whereas the human genome emerged approximately 50,000 years ago. 38 Clearly, unlike oral language, humans are not directly prepared for writing and reading. Nonetheless, today, most 6-year-old children become literate during their 1st years of schooling without experiencing major obstacles. Human beings are endowed with the many skills that contribute to the ability to write and read, such as, first and foremost, language as well as auditory and visual perception and drawing. These initially independent working resources were coopted when script was invented, and teaching children to write and read at school predominantly means supporting the development of associations among these resources. 39

Part of the core knowledge innate to humans has also been found in animals, for instance numerical knowledge and geometry, but to the best of our knowledge, no other animals have invented mathematics. 40 Only humans have been able to use core knowledge for developing higher order cognition, which serves as a precondition for culture, technology, and civilization. Additionally, the unique function of human working memory is the precondition for the integration of initially independent representational systems. However, the full potential of working memory is not in place at birth, but rather matures during childhood and undergoes changes until puberty. 41 Children under the age of two are unable to switch goals 42 and memorize symbol representations appropriately. 43

To summarize what has been discussed so far, there are two sources for the exceptional learning capacity of humans. The first is the function of working memory as a general-purpose resource that allows for holding several mental representations simultaneously for further manipulation. The second is the ancient corpus of the modularized core knowledge of space, quantities, and the physical and social world. Working memory allows for the connection of this knowledge to language, numerals, and other symbol systems, which provides the basis for reasoning and the acquisition of knowledge in academic domains, if appropriate learning opportunities are provided. Both resources are innate to human beings, but they are also sources of individual differences, as will be discussed in the following sections.

Learning potentials are not alike among humans: the differential perspective

In the early twentieth century, a pragmatic need for predicting the learning potential of individuals initiated the development of standardized tests. The Frenchman Alfred Binet, who held a degree in law, constructed problems designed to determine whether children who did not meet certain school requirements suffered from mental retardation or from behavioral disturbances. 44 He asked questions that still resemble items in today’s intelligence tests; children had to repeat simple sentences and series of digits forwards and backwards as well as define words such as “house” or “money”. They were asked in what respect a fly, an ant, a butterfly and a flea are alike, and they had to reproduce drawings from memory. William Stern, an early professor of psychology at the newly founded University of Hamburg/Germany, intended to quantify individual differences in intelligence during childhood and adolescence by developing the first formula for the intelligence quotient (IQ): 45 IQ = Mental age/chronological age*100. Mental age refers to the average test score for a particular age group; this means that a 6-year-old child would have an IQ = 133 if their test score was equivalent to the mean score achieved in the group of 8-year-olds. From adolescence on, however, the average mental age scores increasingly converge, and because of the linear increase in chronological age, the IQ would decline—a trend that obviously does not match reality.

Psychologists from the United States, specifically headed by the Harvard and later Yale professor Robert Yerkes, decided to look at a person’s score relative to other people of the same age group. The average test score was assigned to an IQ = 100 by convention, and an individual’s actual score is compared to this value in terms of a standard deviation, an approach that has been retained to this day. World War I pushed the development of non-verbal intelligence tests, which were used to select young male immigrants with poor English language skills for military service. 46 In the UK, the educational psychologist Cyril Burt promoted the use of intelligence tests for assigning students to the higher academic school tracks. 47 Charles Spearman from the University College London was among the first to focus on the correlations between test items based on verbal, numerical, or visual-spatial content. 48 The substantial correlations he found provided evidence for a general intelligence model (factor-g), which has been confirmed in the following decades by numerous studies performed throughout the world. 49

The high psychometric quality of the intelligence tests constructed in different parts of the world by scientists in the early decades of the twentieth century have influenced research ever since. In 1923, Edward Boring, a leading experimental psychologist concluded, “Intelligence is what the tests test. This is a narrow definition, but it is the only point of departure for a rigorous discussion of the tests. It would be better if the psychologists could have used some other and more technical term, since the ordinary connotation of intelligence is much broader. The damage is done, however, and no harm need result if we but remember that measurable intelligence is simply what the tests of intelligence test, until further scientific observation allows us to extend the definition.”(ref. 50 , p. 37). More than 70 years later, psychologists widely agreed on a definition for intelligence originally offered by Linda Gottfredsonin 1997: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do” (ref. 51 , p. 13). This definition is in line with the substantial correlations between intelligence test scores and academic success, 52 whereas correlations with measures of outside-school success, such as income or professional status, are lower but still significant. 53 , 54 Numerous longitudinal studies have revealed that IQ is a fairly stable measure across the lifespan, which has been most convincingly demonstrated in the Lothian Birth Cohorts run in Scotland. Two groups of people born in 1921 and 1936 took a test of mental ability at school when they were 11 years old. The correlation with IQ tests taken more than 60 years later was highly significant and approached r  = .70 (ref. 55 ). The same data set also demonstrated a substantial long-term impact of intelligence on various factors of life success, among them career aspects, health, and longevity. 56

Intelligence tests scores have proven to be objective, reliable, and valid measures for predicting learning outcome and more general life success. At the same time, the numerous data sets on intelligence tests that were created all over the world also contributed to a better understanding of the underlying structure of cognitive abilities. Although a factor g could be extracted in almost all data sets, correlations between subtests varied considerably, suggesting individual differences beyond general cognitive capabilities. Modality factors (verbal, numerical, or visual spatial) have been observed, showing increased correlations between tests based on the same modality, but requiring different mental operations. On the other hand, increased correlations were also observed between tests based on different modalities, but similar mental operations (e.g., either memorizing or reasoning). The hierarchical structure of intelligence, with factor g on the top and specific factors beneath, was quite obvious from the very beginning of running statistical analyses with intelligence items. Nonetheless, it appeared a major challenge for intelligence researchers to agree on a taxonomy of abilities on the second and subsequent levels. In 1993, John Carroll published his synthesis of hundreds of published data sets on the structure of intelligence after decades of research. 57 In his suggested three-stratum model, factor g is the top layer, with the middle layer encompassing broader abilities such as comprehension knowledge, reasoning, quantitative knowledge, reading and writing, and visual and auditory processing. Eighty narrower abilities, such as spatial scanning, oral production fluency, and sound discrimination, are located in the bottom layer. To date, Carroll’s work is considered the most comprehensive view of the structure of individual variations in cognitive abilities. 58 However, the interpretation of factor g is still under discussion among scientists. Factor g could be a comprehensive characteristic of the brain that makes information processing generally more or less efficient (top-down-approach). Existing data sets, however, are also compatible with a model of intelligence according to which the human brain is comprised of a large number of single abilities that have to be sampled for mental work (bottom-up approach). In this case, factor g can be considered a statistical correlate that is an emerging synergy of narrow abilities. 59

Genetic sources of individual differences in intelligence

From studies with identical and fraternal twins, we know that genetic differences can explain a considerable amount of variance in IQ. The correlation between test scores of identical twins raised together approaches r  = .80 and thereby is almost equal to the reliability coefficient of the respective test. On the other hand, IQ-correlations between raised-together same-sex fraternal twins are rarely higher than .50, a value also found for regular siblings. Given that the shared environment for regular siblings is lower than for fraternal twins, this result qualifies the impact of environmental factors on intelligence. The amount of genetic variance is judged in statistical analyses based on the difference between the intra-pair correlations for identical and fraternal twins. 60 High rates of heritability, however, do not mean that we can gauge a person’s cognitive capabilities from his or her DNA. The search for the genes responsible for the expression of cognitive capabilities has not yet had much success, despite the money and effort invested in human genome projects. It is entirely plausible that intelligence is formed by a very large number of genes, each with a small effect, spread out across the entire genome. Moreover, these genes seem to interact in very complicated ways with each other as well as with environmental cues. 61

An entirely false but nonetheless still widespread misunderstanding is to equate “genetic sources” with “inevitability” because people fail to recognize the existence of reaction norms, a concept invented in 1909 by the German biologist, Richard Woltereck. Reaction norms depict the range of phenotypes a genotype can produce depending on the environment. 62 For some few physiological individual characteristics (e.g., the color of eyes) the reaction norm is quite narrow, which means gene expression will rarely be affected by varying environments. Other physiological characteristics, such as height, have a high degree of heritability and a large reaction norm. Whether an individual reaches the height made possible by the genome depends on the nutrition during childhood and adolescence. In a wealthy country with uniform access to food, average height will be larger than in a poor country with many malnourished inhabitants. However, within both countries, people vary in height. The heritability in the wealthy country can be expected to approach 100% because everybody enjoyed sufficient nutrition. In contrast, in the poor country, some were sufficiently nourished and, therefore, reached the height expressed by their genome, while others were malnourished and, therefore, remained smaller than their genes would have allowed under more favorable conditions. For height, the reaction norm is quite large because gene expression depends on nutrition during childhood and adolescence. This explains the well-documented tendency for people who have grown up in developed countries to become progressively taller in the past decades.

The environment regulates gene expression, which means that instead of “nature vs. nurture”, a more accurate phrase is “nature via nurture”. 63 The complex interaction between genes and environment can also explain the fact that heritability of intelligence increases during the lifespan. 61 This well-established finding is a result of societies in which a broad variety of cognitive activities available in professional and private life enable adults more than children to actively select special environments that fit their genes. People who have found their niche can perfect their competencies by deliberate learning.

In the first decades of developing intelligence tests, researchers were naive to the validity of non-verbal intelligence; so-called culture-free or culture-fair tests, based on visual-spatial material such as mirror images, mazes or series and matrices of geometric figures, were supposed to be suitable for studying people of different social and cultural levels. 64 This is now considered incorrect because in the meantime, there has been overwhelming evidence for the impact of schooling on the development of intelligence and the establishment and stabilization of individual differences. Approximately 10 years of institutionalized education is necessary for the intelligence of individuals to approach its maximum potential. 65 , 66 , 67

Altogether, twin and adoption studies suggest that 50–80% of IQ variation is due to genetic differences. 61 This relatively large range in the percentage across different studies is due to the heritability of intelligence in the population studied, specifically, the large reaction norm of the genes giving rise to the development of intelligence. Generally, the amount of variance in intelligence test scores explained by genes is higher the more society members have access to school education, health care, and sufficient nutrition. There is strong evidence for a decrease in the heritability of intelligence for children from families with lower socioeconomic status (SES). For example, lower SES fraternal twins resembled each other more than higher SES ones, indicating a stronger impact of shared environment under the former condition. 68 In other words, because of the less stimulating environment in lower SES families, the expression of genes involved in the development of intelligence is likely to be hampered. Although it may be counterintuitive at first, this suggests that a high heritability rate of intelligence in a society is an indicator of economic and educational equity. Additionally, this means that countries that ensure access to nutrition, health care, and high quality education independent of social background enable their members to develop their intelligence according to their genetic potential. This was confirmed by a meta-analysis on interactions between SES and heritability rate. While studies run in the United States showed a positive correlation between SES and heritability rate, studies from Western Europe countries and Australia with a higher degree of economic and social equality did not. 69 , 70

Cognitive processes behind intelligence test scores: how individuals differ in information processing

In the first part of this paper, cognitive processes were discussed that, in principle, enable human beings to develop the academic competencies that are particularly advantageous in our world today. In the second part, intelligence test scores were shown to be valid indicators of academic and professional success, and differences in IQ were shown to have sound genetic sources. Over many decades, research on cognitive processes and psychometric intelligence has been developing largely independently of one another, but in the meantime, they have converged. Tests that were developed to provide evidence for the different components of human cognition revealed large individual differences and were substantially correlated with intelligence tests. Tests of memory function were correlated with tests of factor g. Sensory memory tests have shown that the exposure duration required for reliably identifying a simple stimulus (inspection time) is negatively correlatedwith intelligence. 71 For working memory, there is a large body of research indicating substantial relationships between all types of working memory functions and IQ, with average correlations >.50 (refs 72 , 73 , 74 ). In these studies, working memory functions are measured by speed tasks that require goal-oriented active monitoring of incoming information or reactions under interfering and distracting conditions. Neural efficiency has been identified as a major neural characteristic of intelligence; more intelligent individuals show less brain activation (measured by electroencephalogram or functional magnetic resonance imaging) when completing intelligence test items 75 , 76 as well as working memory items. 77 Differences in information-processing efficiency were already found in 4-month-old children. Most importantly, they could predict psychometric intelligence in 8-year-old children. 78

These results clearly suggest that a portion of individual differences can be traced back to differences in domain-general cognitive competencies. However, psychometric research also shows that individual differences do exist beyond factor g on a more specific level. Differences in numerical, language, and spatial abilities are well established. Longitudinal studies starting in infancy suggest that sources of these differences may be traced back to variations in core knowledge. Non-symbolic numerical competencies in infancy have an impact on mathematical achievement. 79 Similar long-term effects were found for other areas of core knowledge, 80 particularly language. 81

Endowed with general and specific cognitive resources, human beings growing up in modern societies are exposed to informal and formal learning environments that foster the acquisition of procedural as well as declarative knowledge in areas that are part of the school curriculum. Being endowed with genes that support efficient working memory functions and that provide the basis for usable core knowledge allows for the exploitation of learning opportunities provided by the environment. This facilitates the acquisition of knowledge that is broad as well as deep enough to be prepared for mastering the, as of yet, unknown demands of the future. 18 Regression analyses based on longitudinal studies have revealed that the confounded variance of prior knowledge and intelligence predicts learning outcome and expertise better than each single variable. 82 , 83 , 84 Importantly, no matter how intelligent a person is, gaining expertise in a complex and sophisticated field requires deliberate practice and an immense investment of time. 85 However, intelligence differences will come into play in the amount of time that has to be invested to reach a certain degree of expertise. 86 Moreover, intelligence builds a barrier to content areas in which a person can excel. As discussed in the first part of this paper, some content areas—first and foremost from STEM fields—are characterized by abstract concepts mainly based on defining features, which are themselves integrated into a broader network of other abstract concepts and procedures. Only individuals who clearly score above average on intelligence tests can excel in these areas. 84 , 87 For individuals who were fortunate enough to attend schools that offered high-quality education, intelligence and measures of deep and broad knowledge are highly correlated. 88 , 89 A strong impact of general intelligence has also been shown for university entrance tests such as the SAT, which mainly ask for the application of knowledge in new fields. 90 , 91 Societies that provide uniform access to cognitively stimulating environments help individuals to achieve their potential but also bring to bear differences in intelligence. Education is not the great equalizer, but rather generates individual differences rooted in genes.

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A Model of Twice-Exceptionality

• Michelle Ronksley-Pavia
• Published 1 July 2015
• Education, Sociology
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CATEGORIES AND DEFINITIONS OF EXCEPTIONALITIES CATEGORIES AND DEFINITIONS OF EXCEPTIONALITIES

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Special Education: From Disability to Exceptionality

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Additional needs ; Exceptionality ; Inclusive education ; Learning difficulties/disabilities ; Special needs ; Students with special needs/disabilities

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Special education refers to the educating of students with special needs by specialist teachers in special schools or mainstream schools. The difference between the two educational settings is that special schools offer segregated settings and students are educated solely by specialist teachers, whereas mainstream schools offer segregated, integrated, or inclusive settings and students are educated by regular or specialist teachers. Special education is typically associated with educating students who have disabilities ranging from mild to severe and may include students with learning difficulties also known as learning disabilities. Special education also covers the area of gifted and talented education and students who are identified as twice-exceptional (i.e., disability and gifted).

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Winter, S. (2020). Special Education: From Disability to Exceptionality. In: Leal Filho, W., Azul, A.M., Brandli, L., Özuyar, P.G., Wall, T. (eds) Quality Education. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-95870-5_32

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Initial Thoughts

Perspectives & resources, is it important to acknowledge students’ diversity in classroom settings why or why not.

• Page 1: Introduction to Diversity
• Page 2: Influence of Teacher Perceptions

What should teachers understand in order to address student diversity in their classrooms?

• Page 3: Cultural Diversity
• Page 4: Linguistic Diversity

Page 5: Exceptionalities

• Page 6: Socioeconomic Factors
• Page 7: References & Additional Resources
• Page 8: Credits

The term exceptionalities in K–12 schooling refers to both disabilities and giftedness. The Individuals with Disabilities Education Act ’04 (IDEA ’04), the national law that guarantees an appropriate education to students with disabilities, recognizes fourteen disability categories. These are:

• Deaf-blindness
• Developmental delay
• Emotional disturbance
• Hearing impairment
• Intellectual disability
• Multiple disabilities
• Orthopedic impairment
• Other health impairment
• Specific learning disability
• Speech or language impairment
• Traumatic brain injury
• Visual impairment, including blindness

intellectual disability

Replaces the term mental retardation and is the currently accepted term.

Special Education Jargon at a Glance

Students with disabilities have a right to a free appropriate public education (FAPE) in the (LRE). A student’s special education services and supports, which might include related services , accommodations , and modifications , are outlined in his or her individualized education program (IEP).

free appropriate public education (FAPE)

A provision of IDEA ensuring that students with disabilities receive necessary education and services without cost to the child or family.

least-restrictive environment (LRE)

One of the principles outlined in the Individuals with Disabilities Education Act requiring that students with disabilities be educated with their non-disabled peers to the greatest appropriate extent.

related services

A part of special education that includes services from professionals (e.g., occupational therapist [OT], physical therapist [PT], Speech-Language Pathologist [SLP]) from a wide range of disciplines typically outside of education, all designed to meet the learning needs of individual children with disabilities.

accommodations

A service or support that allows a student to access the general education curriculum without changing the content or curricular expectations (e.g., audio books for students who have difficulty reading).

modification

A service or support that allows a student to access the general education curriculum but that fundamentally alters the content or curricular expectations (e.g., a sixth-grade student is given a third-grade science text about the solar system that covers the same content but not at the same depth).

individualized education program (IEP)

A written plan used to delineate an individual student’s current level of development and his or her learning goals, as well as to specify any accommodations, modifications, and related services that a student might need to attend school and maximize his or her learning.

Almost every general education classroom includes students with exceptionalities. Students with disabilities (ages 6–17) make up 11% of the total school population. Of these students, three out of four spend all or part of their day in the general education classroom.

Why Exceptionalities Matter

Some of Ms. Christie’s students appeared bored and uninterested; however, some of her students have disabilities which might contribute to their disengagement. Without the appropriate instructional adjustments or supports, these students are unable to fully participate.

What Teachers Can Do

Teachers are not alone in making specific instructional decisions for students with disabilities. A multidisciplinary team develops an IEP for every student who receives special education services. These IEPs outline needed supports and services. The teacher can turn to members of this team, many of whom have specific expertise (e.g., special education, occupational therapy, assistive technology), to help her implement appropriate instructional techniques, interventions, and supports.

Accommodations or Modifications

Differentiated Instruction

Universal Design for Learning

Definitions

For additional information about these areas view the following IRIS Modules:

• Universal Design for Learning: Designing Learning Experiences That Engage and Challenge All Students
• Differentiated Instruction: Maximizing the Learning of All Students
• Accommodations: Instructional and Testing Supports for Students with Disabilities
• Assistive Technology: An Overview

Ginger Blalock discusses some key considerations for students with disabilities.

Individualized instruction

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Accessing the general education curriculum

(time: 2:07) /wp-content/uploads/module_media/div_media/audio/div_05_02_audio_blalock.mp3

Transcript: Ginger Blalock, PhD – Individualized instruction

Individualized instruction is taking the goals and objectives that the team has identified as critical for a particular student and then putting them into play in the classroom. It may mean that a student has and needs certain modifications in either the materials or the content or the sequence of presentation, the way that the instruction is delivered, or the way that he or she demonstrates knowledge or competence. Individualized instruction may also mean changing some goals and objectives so that the student only learns part of what other peers are learning, or it may mean in some instances that students participate in a different curriculum that may parallel the general curriculum but that will get them closer to achieving the goals and objectives identified as critical for that particular student. So it means making changes to ensure that the kid doesn’t have a cookie-cutter approach. It means designing instruction, carrying it out, and assessing it all along the way to make sure that students are progressing and learning what is most essential for him or her to learn. And you always want to key that back to the general content standards and benchmarks that all peers are learning, but sometimes students need to also acquire additional or other skills.

Transcript: Ginger Blalock, PhD – Accessing the general education curriculum

Regarding the education of students with disabilities, their individual education program includes a statement of how the student will be supported in obtaining the annual goals that the team decides is important. Every individual education program has to also include a statement about how the child will be involved in the general curriculum and actually progress in that general curriculum, and also, related to LRE, how much the student will be educated and participate with students with and without disabilities. And this access to the general education curriculum is intended to be with appropriate modifications or supports or services that allow the student to be able to access the curriculum, to be able to learn from it, to be able to demonstrate what they know, and to be able to be a part of that curriculum with their peers.

The reason why this provision is so important is because historically many students with disabilities who were in the general ed. settings, classroom or school were still denied access to that general ed. curriculum. There was a tendency for educators to say, “The student cannot learn this, and therefore we’re not even going to bother. We’ll just provide them with their own curriculum, or we’ll unfortunately just kind of let them bide [their time] and not really progress.” And what this does is compel all the planners, all the folks on the team, to make sure that this student is participating as much as possible in what every other kid is learning. And so one of the greatest ways in which you see that facilitated is that now all planning that goes on for these students with disabilities must address the regular content standards and benchmarks that every child is learning at that grade level. And so it just forces us all to think about how can we help this kid at least achieve as much as possible, in the same content, and the same skills that his or her peers are learning.

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Understanding, Educating, and Supporting Children with Specific Learning Disabilities: 50 Years of Science and Practice

Elena l. grigorenko.

1 University of Houston, Houston, USA

2 Baylor College of Medicine, Houston, USA

Donald Compton

3 Florida State University, Tallahassee, USA

4 Vanderbilt University, Nashville, USA

Richard Wagner

Erik willcutt.

5 University of Colorado Boulder, Boulder, USA

Jack M. Fletcher

Specific learning disabilities (SLD) are highly relevant to the science and practice of psychology, both historically and currently, exemplifying the integration of interdisciplinary approaches to human conditions. They can be manifested as primary conditions—as difficulties in acquiring specific academic skills—or as secondary conditions, comorbid to other developmental disorders such as Attention Deficit Hyperactivity Disorder. In this synthesis of historical and contemporary trends in research and practice, we mark the 50th anniversary of the recognition of SLD as a disability in the US. Specifically, we address the manifestations, occurrence, identification, comorbidity, etiology, and treatment of SLD, emphasizing the integration of information from the interdisciplinary fields of psychology, education, psychiatry, genetics, and cognitive neuroscience. SLD, exemplified here by Specific Word Reading, Reading Comprehension, Mathematics, and Written Expression Disabilities, represent spectrum disorders each occurring in approximately 5–15% of the school-aged population. In addition to risk for academic deficiencies and related functional social, emotional, and behavioral difficulties, those with SLD often have poorer long-term social and vocational outcomes. Given the high rate of occurrence of SLD and their lifelong negative impact on functioning if not treated, it is important to establish and maintain effective prevention, surveillance, and treatment systems involving professionals from various disciplines trained to minimize the risk and maximize the protective factors for SLD.

Fifty years ago, the US federal government, following an advisory committee recommendation ( United States Office of Education, 1968 ), first recognized specific learning disabilities (SLD) as a potentially disabling condition that interferes with adaptation at school and in society. Over these 50 years, a significant research base has emerged on the identification and treatment of SLD, with greater understanding of the cognitive, neurobiological, and environmental causes of these disorders. The original 1968 definition of SLD remains statutory through different reauthorizations of the 1975 special education legislation that provided free and appropriate public education for all children with disabilities, now referred to as the Individuals with Disabilities Education Act (IDEA, 2004). SLD are recognized worldwide as a heterogeneous set of academic skill disorders represented in all major diagnostic nomenclatures, including the Diagnostic and Statistical Manual-5 (DSM-5, American Psychiatric Association, 2013) and the International Statistical Classification of Diseases and Related Health Problems (ICD-11, World Health Organization, 2018).

In the US, the SLD category is the largest for individuals who receive federally legislated support through special education. Children are identified as SLD through IDEA when a child does not meet state-approved age- or grade-level standards in one or more of the following areas: oral expression, listening comprehension, written expression, basic reading skills, reading fluency, reading comprehension, mathematics calculation, and mathematics problem solving. Although children with SLD historically represented about 50% of the children aged 3–21 served under IDEA, percentages have fluctuated across reauthorizations of the special education law, with some decline over the past 10 years ( Figure 1 ).

The Individuals with Disabilities Education Act (IDEA), enacted in 1975 as Public Law 94–142, mandates that children and youth ages 3–21 with disabilities be provided a free and appropriate public school education in the least restricted environment. The percentage of children served by federally mandated special education programs, out of total public school enrollment, increased from 8.3 percent to 13.8 percent between 1976–77 and 2004–05. Much of this overall increase can be attributed to a rise in the percentage of students identified as having SLD from 1976–77 (1.8 percent) to 2004–05 (5.7 percent). The overall percentage of students being served in programs for those with disabilities decreased between 2004–05 (13.8 percent) and 2013–14 (12.9 percent). However, there were different patterns of change in the percentages served with some specific conditions between 2004–05 and 2013–14. The percentage of children identified with SLD declined from 5.7 percent to 4.5 percent of the total public school enrollment during this period. This number is highly variable by state: for example, in 2011 it ranged from 2.3% in Kentucky to 13.8% in Puerto Rico, as there is much variability in the procedures used to identify SLD, and disproportional demographic representation. Figure by Janet Croog.

This review is a consensus statement developed by researchers currently leading the National Institute of Child Health and Human Development (NICHD) supported Consortia of Learning Disabilities Research Centers and Innovation Hubs. This consensus is based on the primary studies we cite, as well as the meta-analytic reviews (*), systematic reviews (**), and first-authored books (***) that provide an overview of the science underlying research and practice in SLD (see references). The hope is that this succinct overview of the current state of knowledge on SLD will help guide an agenda of future research by identifying knowledge gaps, especially as the NICHD embarks on a new strategic plan. The research programs on SLD from which this review is derived represent the integration of diverse, interdisciplinary approaches to behavioral science and human conditions. We start with a brief description of the historical roots of the current view of SLD, then provide definitions as well as prevalence and incidence rates, discuss comorbidity between SLD themselves and SLD and other developmental disorders, comment on methods for SLD identification, present current knowledge on the etiology of SLD, and conclude with evidence-based principles for SLD intervention.

Three Historical Strands of Inquiry that Shaped the Current Field of SLD

Three strands of phenomenological inquiry culminated in the 1968 definition and have continued to shape current terminology and conventions in the field of SLD ( Figure 2 ). The first, a medical strand, originated in 1676, when Johannes Schmidt described an adult who had lost his ability to read (but with preserved ability to write and spell) because of a stroke. Interest in this strand reemerged in the 1870s with the publication of a string of adult cases who had lived through a stroke or traumatic brain injury. Subsequent cases involved children who were unable to learn to read despite success in mathematics and an absence of brain injury, which was termed “word blindness” ( W. P. Morgan, 1896 ). These case studies laid the foundation for targeted investigations into the presentation of specific unexpected difficulties related to reading printed words despite typical intelligence, motivation, and opportunity to learn.

A schematic timeline of the three stands of science and practice in the field of SLD. The colors represent the strands (blue—first, yellow—second, and green—third). Blue: provided phenomenological descriptions and generated hypotheses about the gene-brain bases of SLD (specifically, dyslexia or SRD); it also provided the first evidence that the most effective treatment approaches are skill-based and reflect cognitive models of the conditions. Yellow: differentiated SLD from other comorbid conditions. Green: stressed the importance of focusing on SLD in academic settings and developing both preventive and remediational evidence-based approaches to managing these conditions. Due to space constraints, the names of many highly influential scientists (e.g., Marilyn Adams, Joseph Torgesen, Isabelle Liberman, Keith Stanovich, among others) who shaped the field of SLD have been omitted. Figure by Janet Croog.

The second strand is directly related to the formalization of the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM). Rooted in the work of biologically oriented physicians, the 1952 first edition (DSM-I) referenced a category of chronic brain syndromes of unknown cause that focused largely on behavioral presentations we now recognize as hyperkinesis and Attention Deficit Hyperactivity Disorder (ADHD). The 1968 DSM-II defined “mild brain damage” in children as a chronic brain syndrome manifested by hyperactive and impulsive behavior with reference to a new category, “hyperkinetic reaction of childhood” if the origin is not considered “organic.” As these categories evolved, they expanded to encompass the academic difficulties experienced by many of these children.

After almost 30 years of research into this general category of “minimal brain dysfunction,” representing “... children of near average, average, or above average general intelligence with certain learning or behavioral disabilities ... associated with deviations of function of the central nervous system.” ( Clements, 1966 , pp. 9–10), the field acknowledged the heterogeneity of these children and the failure of general “one size fits all” interventions. As a result, the 1980 DSM-III formally separated academic skill disorders from ADHD. The 1994 DSM-IV differentiated reading, mathematics, and written expression SLD. The DSM-5 reversed that, merging these categories into one overarching category of SLD (nosologically distinct from although comorbid with ADHD), keeping the notion of specificity by stating that SLD can manifest in three major academic domains (reading, mathematics, and writing).

The third strand originated from the development of effective interventions based on cognitive and linguistic models of observed academic difficulties. This strand, endorsed in the 1960s by Samuel Kirk and associates, viewed SLD as an overarching category of spoken and written language difficulties that manifested as disabilities in reading (dyslexia), mathematics (dyscalculia), and writing (dysgraphia). Advances have been made in understanding the psychological and cognitive texture of SLD, developing interventions aimed at overcoming or managing them, and differentiating these disorders from each other, from other developmental disorders, and from other forms of disadvantage. This work became the foundation of the 1968 advisory committee definition of SLD, which linked this definition with that of minimal brain dysfunction via the same “unexpected” exclusionary criteria (i.e., not attributable primarily to intellectual difficulties, sensory disorders, emotional disturbance, or economic/cultural diversity).

Although its exclusionary criteria were well specified, the definition of SLD did not provide clear inclusionary criteria. Thus, the US Department of Education’s 1977 regulatory definition of SLD included a cognitive discrepancy between higher IQ and lower achievement as an inclusionary criterion. This discrepancy was viewed as a marker for unexpected underachievement and penetrated the policy and practice of SLD in the US and abroad. In many settings, the measurement of such a discrepancy is still considered key to identification. Yet, IDEA 2004 and the DSM-5 moved away from this requirement due to a lack of evidence that SLD varies with IQ and numerous philosophical and technical challenges to the notion of discrepancy (Fletcher, Lyon, Fuchs, & Barnes, 2019). IDEA 2004 also permitted an alternative inclusion criterion based on Response-to-Intervention (RTI), in which SLD reflects inadequate response to effective instruction, while the DSM-5 focuses on evidence of persistence of learning difficulties despite treatment efforts.

These three stands of inquiry into SLD use a variety of concepts (e.g., word blindness, strephosymbolia, dyslexia and alexia, dyscalculia and acalculia, dysgraphia and agraphia), which are sometimes differentiated and sometimes used synonymously, generating confusion in the literature. Given the heterogeneity of their manifestation and these diverse historical influences, it has been difficult to agree on the best way to identify SLD, although there is consensus that their core is unexpected underachievement. A source of active research and controversy is whether “unexpectedness” is best identified by applying solely exclusionary criteria (i.e., simple low achievement), inclusionary criteria based on uneven cognitive development (e.g., academic skills lower than IQ or another aptitude measure, such as listening comprehension), or evidence of persisting difficulties (DSM-5) despite effective instruction (IDEA 2004).

Manifestation, Definition, and Etiology

That the academic deficits in SLD relate to other cognitive skills has always been recognized, but the diagnostic and treatment relevance of this connection has remained unclear. A rich literature on cognitive models of SLD ( Elliott & Grigorenko, 2014 ; Fletcher et al., 2019) provides the basis for five central ideas. First, SLD are componential ( Melby-Lervåg, Lyster, & Hulme, 2012 ; Peng & Fuchs, 2016 ): Their academic manifestations arise on a landscape of peaks, valleys, and canyons in various cognitive processes, such that individuals with SLD have weaknesses in specific processes, rather than global intellectual disability ( Morris et al., 1998 ). Second, the cognitive components associated with SLD, just like academic skills and instructional response, are dimensional and normally distributed in the general population ( Ellis, 1984 ), such that understanding typical acquisition should provide insight into SLD and vice versa ( Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2001 ). Third, each academic and cognitive component may have a distinct signature in the brain ( Figure 3 ) and genome ( Figure 4 ). These signatures and etiologies likely overlap because they are correlated, but are not interchangeable, as their unique features substantiate the distinctness of various SLD ( Vandermosten, Hoeft, & Norton, 2016 ). Fourth, the overlap at least partially explains their rates of comorbidity ( Berninger & Abbott, 2010 ; Szucs, 2016 ; Willcutt et al., 2013 ). Fifth, deficiencies in these cognitive and academic processes appear to last throughout the lifespan, especially in the absence of intervention ( Klassen, Tze, & Hannok, 2013 ).

Results of meta-analyses of functional neuroimaging studies that exemplify the distribution of activation patterns in different reading- ( A ) and mathematics- ( B ) related networks, corresponding to componential models of the skills. A (Left panel, light blue): A lexical network in the basal occipito-temporal regions and in the left inferior parietal cortex. A (Middle panel, dark blue): A sublexical network, primarily involving regions of the left temporo-parietal lobe extending from the left anterior fusiform region. A (Right panel): Activation likelihood estimation map of foci from the word>pseudowords (light blue) and pseudowords>words (dark blue) contrasts. The semantic processing cluster is shown in green. B (Left panel): A number-processing network, primarily involving a region of the parietal lobe. B (Middle panel): An arithmetic-processing network, primarily involving regions of the frontal and parietal lobes. B (Right panel): Children (red) and adult (pink) meta-analyses of brain areas associated with numbers and calculations. Figure by Janet Croog.

A schematic representation of the genetic regions and gene-candidates linked to or associated with SRD and reading-related processes (shown in blue), and SMD and mathematics-related processes (shown in red). Dark blue signifies more studied loci and genes. Blue highlighted in red indicate the genes implicated in both SRD and SMD. Figure by Janet Croog.

The DSM-5 and IDEA 2004 reflect agreement that SLD can occur in word reading and spelling (Specific Word Reading Disability; SWRD) and in specific reading comprehension disability (SRCD). SWRD represents difficulties with beginning reading skills due at least in part to phonological processing deficits, while other language indicators (e.g., vocabulary) may be preserved ( Pennington, 2009 ). In contrast, SRCD ( Cutting et al., 2013 ), which is more apparent later in development, is associated with non-phonological language weaknesses ( Scarborough, 2005 ). The magnitude of SRCD is greater than that of vocabulary or language comprehension difficulties, suggesting that other problems, such as weaknesses in executive function or background knowledge, also contribute to SRCD ( Spencer, Wagner, & Petscher, 2018 ).

Math SLDs are differentiated as calculations (SMD) versus problem solving (word problems) SLD, which are associated with distinct cognitive deficits ( L. S. Fuchs et al., 2010 ) and require different forms of intervention ( L. S. Fuchs et al., 2014 ). Calculation is more linked to attention and phonological processing, while problem solving is more linked to language comprehension and reasoning; working memory has been associated with both. Specific written expression disability, SWED ( Berninger, 2004 ; Graham, Collins, & Rigby-Wills, 2017 ) occurs in the mechanical act of writing (i.e., handwriting, keyboarding, spelling), associated with fine motor-perceptual skills, or in composing text (i.e., planning and revising, understanding genre), associated with oral language skills, executive functions, and the automaticity of transcription skills. Although each domain varies in its cognitive correlates, treatment, and neurobiology, there is overlap. By carefully specifying the domain of academic impairment, considerable progress has been made in the treatment and understanding of the factors that lead to SLD.

Identification methods have searched for other markers of unexpected underachievement beyond low achievement, but always include exclusionary factors. Diagnosis solely by exclusion has been criticized due to the heterogeneity of the resultant groups ( Rutter, 1982 ); thus, the introduction of a discrepancy paradigm. One approach relies on the aptitude-achievement discrepancy, commonly operationalized as a discrepancy between measures of IQ and achievement in a specific academic domain. IQ-discrepancy was the central feature of federal regulations for identification from 1977 until 2004, although the approaches used to qualify and quantify the discrepancy varied in the 50 states. Lack of validity evidence ( Stuebing et al., 2015 ; Stuebing et al., 2002 ) resulted in its de-emphasis in IDEA 2004 and elimination from DSM-5.

A second approach focuses on identifying uneven patterns of strengths and weaknesses (PSW) profiles of cognitive functioning to explain observed unevenness in achievement across academic domains ( Flanagan, Alfonso, & Mascolo, 2011 ; Hale et al., 2008 ; Naglieri & Das, 1997 ). According to these methods, a student with SLD demonstrates a weakness in achievement (e.g., word reading), which correlates with an uneven profile of cognitive weaknesses and strengths (e.g., phonological processing deficits with advanced visual-spatial skills). Proponents suggest that understanding these patterns is informative for individualizing interventions that capitalize on student strengths (i.e., maintain and enhance academic motivation) and compensate for weaknesses (i.e., enhance the phonological processing needed for the acquisition and automatization of reading), but little supporting empirical evidence is available ( Miciak, Fletcher, Stuebing, Vaughn, & Tolar, 2014 ; Taylor, Miciak, Fletcher, & Francis, 2017 ). Meta-analytic research suggests an absence of cognitive aptitude by treatment interactions ( Burns et al., 2016 ), and limited improvement in academic skills based on training cognitive deficits such as working memory ( Melby-Lervåg, Redick, & Hulme, 2016 ).

Newer methods of SLD identification are linked to the development of the third historical strand, based on RTI. With RTI, schools screen for early indicators of academic and behavior problems and then progress monitor potentially at-risk children using brief, frequent probes of academic performance. When data indicate inadequate progress in response to adequate classroom instruction (Tier 1), the school delivers supplemental intervention (Tier 2), usually in the form of small-group instruction.

A child who continues to struggle requires more intensive, individualized intervention (Tier 3), which may include special education. An advantage of RTI is that intervention is provided prior to the determination of eligibility for special education placement. RTI juxtaposes the core concept of underachievement with the concept of inadequate response to instruction, that is, intractability to intervention. It prioritizes the presence of functional difficulty and only then considers SLD as a possible source of this difficulty ( Grigorenko, 2009 ). Still, concerns about the RTI approach to identification remain. One concern is that RTI approaches may not identify “high-potential” children who struggle to develop appropriate academic skills ( Reynolds & Shaywitz, 2009 ). Other concerns involve low agreement across different methods for defining inadequate RTI ( D. Fuchs, Compton, Fuchs, Bryant, & Davis, 2008 ; L. S. Fuchs, 2003 ) and challenges schools face in adequately implementing RTI frameworks ( Balu et al., 2015 ; D. Fuchs & Fuchs, 2017 ; Schatschneider, Wagner, Hart, & Tighe, 2016 ).

Prevalence and Incidence

Because the attributes of SLD are dimensional and depend on the thresholds used to subdivide normal distributions ( Hulme & Snowling, 2013 ), estimates of prevalence and incidence vary. SWRD’s prevalence estimates range from 5 to 17% ( Katusic, Colligan, Barbaresi, Schaid, & Jacobsen, 2001 ; Moll, Kunze, Neuhoff, Bruder, & Schulte-Körne, 2014 ). SRCD is less frequent ( Etmanskie, Partanen, & Siegel, 2016 ), but still represents about 42% of all children ever identified with SLD in reading at any grade ( Catts, Compton, Tomblin, & Bridges, 2012 ). Estimates of incidence and prevalence of SMD vary as well: from 4 to 8% ( Moll et al., 2014 ). Cumulative incidence rates by the age of 19 years range from 5.9% to 13.8%. Similar to SWRD, SMD can be differentiated in terms of lower- and higher-order skills and by time of onset. Computation-based SMD manifests earlier; problem-solving SMD later, sometimes in the absence of computation-based SMD ( L. S. Fuchs, D. Fuchs, C. L. Hamlett, et al., 2008 ). SWED is the least studied SLD. Its prevalence estimates range from 6% to 22% ( P. L. Morgan, Farkas, Hillemeier, & Maczuga, 2016 ) and cumulative incidence ranges from 6.9% to 14.7% ( Katusic, Colligan, Weaver, & Barbaresi, 2009 ).

Comorbidity and Co-Occurrence

One reason SLD can be difficult to define and identify is that different SLDs often co-occur in the same child. Comorbidity involving SWRD ranges from 30% ( National Center for Learning Disabilities, 2014 ) to 60% ( Willcutt et al., 2007 ). The most frequently observed co-occurrences are between (1) SWRD and SMD ( Moll et al., 2014 ; Willcutt et al., 2013 ), with 30–50% of children who experience a deficit in one academic domain demonstrating a deficit in the other ( Moll et al., 2014 ); (2) SWRD and early language impairments ( Dickinson, Golinkoff, & Hirsh-Pasek, 2010 ; Hulme & Snowling, 2013 ; Pennington, 2009 ) with 55% of individuals with SWRD exhibiting significant speech and language impairment ( McArthur, Hogben, Edwards, Heath, & Mengler, 2000 ); and (3) SWRD and internalizing and externalizing behavior problems, with 25–50% of children with SWRD meeting criteria for ADHD ( Pennington, 2009 ) and for generalized anxiety disorder and specific test anxiety, depression, and conduct problems ( Cederlof, Maughan, Larsson, D’Onofrio, & Plomin, 2017 ), although comorbid conduct problems are largely restricted to the subset of individuals with both SWRD and ADHD ( Willcutt et al., 2007 ).

The co-occurrence of SMD is less studied, but there are some consistently replicated observations: (1) individuals with SMD exhibit higher rates of ADHD, and math difficulties are observed in individuals with ADHD more frequently than in the general population ( Willcutt et al., 2013 ); (2) math difficulties are associated with elevated anxiety and depression even after reading difficulties are controlled ( Willcutt et al., 2013 ); and (3) SMD are associated with other developmental conditions such as epilepsy ( Fastenau, Shen, Dunn, & Austin, 2008 ) and schizophrenia ( Crow, Done, & Sacker, 1995 ).

SLD is clearly associated with difficulties in adaptation, in school and in larger spheres of life associated with work and overall adjustment. Longitudinal research reports poorer vocational outcomes, lower graduation rates, higher rates of psychiatric difficulties, and more involvement with the justice system for individuals with SWRD ( Willcutt et al., 2007 ). Importantly, there is evidence of increased comorbidity across forms of SLD with age, with accumulated cognitive burden ( Costa, Edwards, & Hooper, 2016 ). Individuals with comorbid SLDs have poorer emotional adjustment and school functioning than those identified with a single impairment ( Martinez & Semrud-Clikeman, 2004 ).

Identification (Diagnosis)

Comorbidity indicates that approaches to assessment should be broad and comprehensive. For SLD, the choice of a classification model directly influences the selection of assessments for diagnostic purposes. Although all three models are used, the literature (Fletcher et al., 2019) demonstrates that a single indicator model, based either on cut-off scores, other formulae, or assessment of instructional response, does not lead to reliable identification regardless of the method employed. SLD can be identified reliably only in the context of multiple indicators. A step in this direction is a hybrid method that includes three sets of criteria, two inclusionary and one exclusionary, recommended by a consensus group of researchers (Bradley, Danielson, & Hallahan, 2002). The two inclusionary criteria are evidence of low achievement (captured by standardized tests of academic achievement) and evidence of inadequate RTI (captured by curriculum-based progress-monitoring measures or other education records). The exclusionary criterion should demonstrate that the documented low achievement is not primarily attributable to “other” (than SLD) putative causes such as (a) other disorders (e.g., intellectual disability, sensory or motor disorders) or (b) contextual factors (e.g., disadvantaged social, religious, economic, linguistic, or family environment). In the future, it is likely that multi-indicator methods will be extended, with improved identification accuracy, by the addition of other indicators, neurobiological, genetic, or behavioral. It is also possible that assessment of specific cognitive processes beyond academic achievement will improve identification, but presently there is little evidence that such testing adds value to identification ( Elliott & Grigorenko, 2014 ; Fletcher et al., 2019). All identification methods for SLD assume that children referred for assessment are in good health or are being treated and that their physical health, including hearing and vision, is monitored. Currently, there are no laboratory tests (i.e., DNA or brain structure/activity) for SLD. There are also no tests that can be administered by an optometrist, audiologist, or physical therapist to diagnose or treat SLD.

Etiological Factors

Neural structure and function.

Since the earliest reports of reading difficulties, it has been assumed that the loss of function (i.e., acquired reading disability) or challenges in the acquisition of function (i.e., congenital reading disability) are associated with the brain. Functional patterns of activation in response to cognitive stimuli show reliable differences in degrees of activation between typically developing children and those identified with SWRD, and reveal different spatial distributions in relation to children identified with SMD and ADHD ( Dehaene, 2009 ; Seidenberg, 2017 ). In SWRD, there are reduced gray matter volumes, reduced integrity of white matter pathways, and atypical sulcal patterns/curvatures in the left-hemispheric frontal, occipito-temporal, and temporo-parietal regions that overlap with areas of reduced brain activation during reading.

These findings together indicate the presence of atypicalities in the structures (i.e., grey matter) that form the neural system for reading and their connecting pathways (i.e., white matter). These structural atypicalities challenge the emergence of the cognitive—phonological, orthographic, and semantic—representations required for the assembly and automatization of the reading system. Although some have interpreted the atypicalities as a product of reading instruction ( Krafnick, Flowers, Luetje, Napoliello, & Eden, 2014 ), there is also evidence that atypicalities can be observed in pre-reading children at risk for SWRD due to family history or speech and language difficulties ( Raschle et al., 2015 ), sometimes as early as a few days after birth with electrophysiological measures ( Molfese, 2000 ). What emerges in a beginning reader, if not properly instructed at developmentally important periods, is a suboptimal brain system that is inefficient in acquiring and practicing reading. This system is complex, representing multiple networks aligned with different reading-related processes ( Figure 3 ). The system engages cooperative and competitive brain mechanisms at the sublexical (phonological) and lexical levels, in which the phonological, orthographic, and semantic representations are utilized to rapidly form representations of a written stimulus. Proficient readers process words on sight with immediate access to meaning ( Dehaene, 2009 ). In addition to malleability in development, there is strong evidence of malleability through instruction in SWRD, such that the neural processes largely normalize if the intervention is successful ( Barquero, Davis, & Cutting, 2014 ).

The functional neural networks for SMD also vary depending on the mathematical operation being performed, just as the neural correlates of SWRD and SRCD do ( Cutting et al., 2013 ). Neuroimaging studies on the a(typical) acquisition of numeracy posit SMD ( Arsalidou, Pawliw-Levac, Sadeghi, & Pascual-Leone, 2017 ) as a brain disorder engaging multiple functional systems that together substantiate numeracy and its componential processes ( Figure 3 ). First, the intraparietal sulcus, the posterior parietal cortex, and regions in the prefrontal cortex are important for representing and processing quantitative information. Second, mnemonic regions anchored in the medial temporal lobe and hippocampus are involved in the retrieval of math facts. Third, additional relevant regions include visual areas implicated in visual form judgement and symbolic processing. Fourth, prefrontal areas are involved in higher-level processes such as error monitoring, and maintaining and manipulating information. As mathematical processes become more automatic, reliance on the parietal network decreases and reliance on the frontal network increases. All these networks, assembled in a complex functional brain system, appear necessary for the acquisition and maintenance of numeracy, and various aberrations in the functional interactions between networks have been described. Thus, SMD can arise as a result of disturbances in one or multiple relevant networks, or interactions among them ( Arsalidou et al., 2017 ; Ashkenazi, Black, Abrams, Hoeft, & Menon, 2013 ). There is also evidence of malleability and the normalization of neural networks with successful intervention in SMD ( Iuculano et al., 2015 ).

Genetic and environmental factors

Early case studies of reading difficulties identified their familial nature, which has been confirmed in numerous studies utilizing genetically-sensitive designs with various combinations of relatives—identical and fraternal twins, non-twin siblings, parent-offspring pairs and trios, and nuclear and extended families. The relative risk of having SWRD if at least one family member has SWRD is higher for relatives of individuals with the condition, compared to the risk to unrelated individuals; higher for children in families where at least one relative has SWRD; even higher for families where a first-degree relative (i.e., a parent or a sibling) has SWRD; and higher still for children in families where both parents have SWRD ( Snowling & Melby-Lervåg, 2016 ). Quantitative-genetic studies estimate that 30–80% of the variance in reading, math or spelling outcomes is explained by heritable factors ( Willcutt et al., 2010 ).

Since the 1980s, there have been systematic efforts to identify the sources of structural variation in the genome, i.e., genetic susceptibility loci that can account for the strong heritability and familiality of SWRD ( Figure 4 ). These efforts have yielded the identification of nine regions of the genome thought to harbor genes, or other genetic material, whose variation is associated with the presence of SWRD and individual differences in reading-related processes. Within these regions, a number of candidate genes have been tapped, but no single candidate has been unequivocally replicated as a causal gene for SWRD, and observed effects are small. In addition, multiple other genes located outside of the nine linked regions have been observed to be relevant to the manifestation of SWRD and related difficulties. Currently there are ongoing efforts to interrogate candidate genes for SWRD and connect their structural variation to individual differences in the brain system underlying the acquisition and practice of reading.

There are only a few molecular-genetic studies of SMD and its related processes ( Figure 4 ). Unlike SWRD, no “regions of interest” have been identified. Only one study investigated the associations between known single-nuclear polymorphisms (SNP) and a composite measure of mathematics performance derived from various assessments of SMD-related componential processes and teacher ratings. The study generated a set of SNPs that, when combined, accounted for 2.9% of the phenotypic variance ( Figure 4 shows the genes in which the three most statistically significant SNPs from this set are located). Importantly, when this SNP set was used to study whether the association between the 10-SNP set and mathematical ability differs as a function of characteristics of the home and school, the association was stronger for indicators of mathematical performance in chaotic homes and in the context of negative parenting.

Finally, studies have investigated the pleiotropic (i.e., impacting multiple phenotypes) effects of SWRD candidate genes on SMD, ADHD, and related processes. These effects are seemingly in line with the “generalist genes” hypothesis, asserting the pleiotropic influences of some genes to multiple SLD ( Plomin & Kovas, 2005 ).

Environmental factors are strong predictors of SLD. These factors penetrate all levels of a child’s ecosystem: culture, demonstrated in different literacy and numeracy rates around the world; social strata, captured by social-economic indicators across different cultures; characteristics of schooling, reflected by pedagogies and instructional practices; family literacy environments through the availability of printed materials and the importance ascribed to reading at home; and neighborhood and peer influences. Interactive effects suggest that reading difficulties are magnified when certain genetic and environmental factors co-occur, but there is evidence of neural malleability even in SWDE ( Overvelde & Hulstijn, 2011 ). Neural and genetic factors are best understood as risk factors that variably manifest depending on the home and school environment and child attributes like motivation.

Intervention

Although the content of instruction varies depending on whether reading, math, and/or writing are impaired, general principles of effective intervention apply across SLD i . First, intervention for SLD is explicit ( Seidenberg, 2017 ): Teachers formally present new knowledge and concepts with clear explanations, model skills and strategies, and teach to mastery with cumulative practice with ongoing guidance and feedback. Second, intervention is individualized: Instruction is formatively adjusted in response to systematic progress-monitoring data ( Stecker, Fuchs, & Fuchs, 2005 ). Third, intervention is comprehensive and differentiated, addressing the multiple components underlying proficient skill as well as comorbidity. Comprehensive approaches address the multifaceted nature of SLD and provide more complex interventions that are generally more effective than isolated skills training in reading ( Mathes et al., 2005 ) and math ( L. S. Fuchs et al., 2014 ). For example, children with SLD and ADHD may need educational and pharmacological interventions ( Tamm et al., 2017 ). Anxiety can develop early in children who struggle in school, and internalizing problems must be treated ( Grills, Fletcher, Vaughn, Denton, & Taylor, 2013 ). Differentiation through individualization in the context of a comprehensive intervention also permits adjustments of the focus of an intervention on specific weaknesses.

Fourth, intervention adjusts intensity as needed to ensure success, by increasing instructional time, decreasing group size, and increasing individualization ( L. S. Fuchs, Fuchs, & Malone, 2017 ). Such specialized intervention is typically necessary for students with SLD ( L. S. Fuchs et al., 2015 ). Yet, effective instruction for SLD begins with differentiated general education classroom instruction ( Connor & Morrison, 2016 ), in which intervention is coordinated with rather than supplanting core instruction ( L. S. Fuchs, D. Fuchs, C. Craddock, et al., 2008 ).

In addition, intervention is more effective when provided early in development. For example, intervention for SWRD was twice as effective if delivered in grades 1 or 2 than if started in grade 3 ( Lovett et al., 2017 ). This is underscored by neuroimaging research ( Barquero et al., 2014 ) showing that experience with words and numbers is needed to develop the neural systems that mediate reading and math proficiency. A child with or at risk for SWRD who cannot access print because of a phonological processing problem will not get the reading experience needed to develop the lexical system for whole word processing and immediate access to word meanings. This may be why remedial programs are less effective after second grade; with early intervention, the child at risk for SLD develops automaticity because they have gained the experience with print or numbers essential for fluency. Even with high quality intensive intervention, some children with SLD do not respond adequately, and students with persistent SLD may profit from assistive technology (e.g., computer programs that convert text-to-speech; Wood, Moxley, Tighe, & Wagner, 2018 ).

Finally, interventions for SLD must occur in the context of the academic skill itself. Cognitive interventions that do not involve print or numbers, such as isolated phonological awareness training or working memory training without application to mathematical operations do not improve reading or math skill ( Melby-Lervåg et al., 2016 ). Physical exercises (e.g., cerebellar training), optometric training, special lenses or overlays, and other proposed interventions that do not involve teaching reading or math are ineffective ( Pennington, 2009 ). Pharmacological interventions are effective largely due to their impact on comorbid symptoms, with little evidence of a direct effect on the academic skill ( Tamm et al., 2017 ).

No evaluations of recovery rate from SLD have been performed. Intervention success has been evaluated as closing the age-grade discrepancy, placing children with SLD at an age-appropriate grade level, and maintaining their progress at a rate commensurate with typical development. Meta-analytic studies estimate effect sizes of academic interventions at 0.49 for reading ( Scammacca, Roberts, Vaughn, & Stuebing, 2015 ), 0.53 for math ( Dennis et al., 2016 ), and 0.74 for writing ( Gillespie & Graham, 2014 ).

Implications for Practice and Research

Practitioners should recognize that the psychological and educational scientific evidence base supports specific approaches to the identification and treatment of SLD. In designing SLD evaluations, assessments must be timely to avoid delays in intervention; they must consider comorbidities as well as contextual factors, and data collected in the context of previous efforts to instruct the child. Practitioners should use the resulting assessment data to ensure that intervention programs are evidence-based and reflect explicitness, comprehensiveness, individualization, and intensity. There is little evidence that children with SLD benefit from discovery, exposure, or constructivist instructional approaches.

With respect to research, the most pressing issue is understanding individual differences in development and intervention from neurological, genetic, cognitive, and environmental perspectives. This research will ultimately lead to earlier and more precise identification of children with SLD, and to better interventions and long-term accommodations for the 2–6% of the general population who receive but do not respond to early prevention efforts. More generally, other human conditions may benefit from the examples of progress exemplified by the integrated, interdisciplinary approaches that underlie the progress of the past 50 years in the scientific understanding of SLD.

Acknowledgments

The authors are the Principal Investigators of the currently funded Learning Disabilities Research Centers ( https://www.nichd.nih.gov/research/supported/ldrc ) and Innovation Hubs ( https://www.nichd.nih.gov/research/supported/ldhubs ), the two key NICHD programs supporting research on Specific Learning Disabilities. The preparation of this articles was supported by P20 HD090103 (PI: Compton), P50 HD052117 (PI: Fletcher), P20 HD075443 (PI: Fuchs), P20 HD091005 (PI: Grigorenko), P50 HD052120 (PI: Wagner), and P50 HD27802 (PI: Willcutt). Grantees undertaking such projects are encouraged to express their professional judgment. Therefore, this article does not necessarily reflect the position or policies of the abovementioned agencies, and no official endorsement should be inferred.

i For examples of effective evidence-based interventions see www.evidenceforessa.org , intensiveintervention.org , What Works Clearinghouse, www.meadowscenter.org , www.FCRR.org/literacyroadmap , www.understood.org/en/about/our.../national-center-for-learning-disabilities , https://ies.ed.gov/ncee/edlabs/infographics/pdf/REL_SE_Implementing_evidencebased_literacy_practices_roadmap.pdf , among others.

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Exceptional Learners

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Article contents

Exceptional learners.

• Daniel P. Hallahan , Daniel P. Hallahan University of Florida; University of Virginia
• Paige C. Pullen , Paige C. Pullen University of Florida
• James M. Kauffman James M. Kauffman University of Virginia
•  and  Jeanmarie Badar Jeanmarie Badar Independent Scholar
• https://doi.org/10.1093/acrefore/9780190264093.013.926
• Published online: 28 February 2020

Exceptional learners is the term used in the United States to refer to students with disabilities (as well as those who are gifted and talented). The majority of students with disabilities have cognitive and/or behavioral disabilities, that is, specific learning disability (SLD), intellectual disability (ID), emotional disturbance, (ED), attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD). The remaining have primarily sensory and/or physical disabilities (e.g., blindness, deafness, traumatic brain injury, cerebral palsy, muscular dystrophy).

Many of the key research and policy issues pertaining to exceptional learners involve their definitions and identification. For example, prior to SLD being formally recognized by the U.S. Department of Education in the 1970s, its prevalence was estimated at approximately 2% to 3% of the school-age population. However, the prevalence of students identified for special education as SLD grew rapidly until by 1999 it reached 5.68% for ages 6 to 17 years. Since then, the numbers identified as SLD has declined slowly but steadily. One probable explanation for the decrease is that response to intervention has largely replaced IQ-achievement as the method of choice for identifying SLD.

The term intellectual disability has largely replaced the classification of mental retardation . This change originated in the early 2000s because of the unfortunate growing popularity of using retard as a pejorative. Although ID used to be determined by a low IQ-test score, one must also have low adaptive behavior (such as daily living skills) to be diagnosed as ID. That is the likely reason why the prevalence of students with ID at under 1% is well below the estimated prevalence of 2.27% based solely on IQ scores two standard deviations (i.e., 70) below the norm of 100.

There are two behavioral dimensions of ED: externalizing (including conduct disorder) and internalizing (anxiety and withdrawal) behaviors. Research evidence indicates that students with ED are underserved in public schools.

Researchers have now confirmed ADHD as a bona fide neurologically based disability. The American Psychiatric Association recognizes three types of ADHD: (a) ADHD, Predominantly Inattentive Type; (b) ADHD, Predominantly Hyperactive-Impulsive Type; and (c) ADHD, Combined Type.

The American Psychiatric Association recognizes two types of ASD: social communication impairment and repetitive/restricted behaviors. The prevalence of ASD diagnosis has increased dramatically. Researchers point to three probable reasons for this increase: a greater awareness of ASD by the public and professionals; a more liberal set of criteria for diagnosing ASD, especially as it pertains to those who are higher functioning; and “diagnostic substitution”—persons being identified as having ASD who previously would have been diagnosed as mentally retarded or intellectually disabled.

Instruction for exceptional children, referred to as “special education,” differs from what most (typical or average) children require. Research indicates that effective instruction for students with disabilities is individualized, explicit, systematic, and intensive. It differs with respect to size of group taught and amount of corrective feedback and reinforcement used. Also, from the student’s viewpoint, it is more predictable. In addition, each of these elements is on a continuum.

• exceptional children
• special education
• Individuals with Disabilities Education Act
• learning disabilities
• intellectual disabilities
• emotional disturbance
• attention deficit hyperactivity disorder
• response to intervention
• evidence-based instruction

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