Learning About Learning Disabilities

Bernice Wong,Deborah L. Butler

• Publisher: Academic Press
• Year: 2012
• ISBN:
  9780123884145
• Edition: 4
• Topic: Psychologie
• Subtopic: Psychologie du développement

Learning about Learning Disabilities, Fourth Edition continues to provide equal attention to the intellectual, conceptual, and practical aspects of learning disabilities. The Fourth Edition of this popular title presents 80% new material, keeping the chapters up to date in this fast-moving field. With new contributors, and 11 new chapters, coverage is both comprehensive and thorough, encompassing the classification and identification of learning disabilities, learning disabilities in reading, writing, math, and social studies, interventions, and the issues germane to different age ranges of the learning disabled: children, adolescents, and adults. Readers will find Learning About Learning Disabilities, Fourth Edition suitable for use as a reference source for researchers or as a graduate level text.

Reviews of previous editions:

"This text provides a balanced focus on both the conceptual and practical aspects of learning disabilities. Its research coverage is more comprehensive and of greater depth than any other LD textbook, and it is distinctive in its treatment of such important areas as consultation skills and service delivery." -CHILD ASSESSMENT NEWS "... provides a broad overview of some important issues in relation to the education and development of pupils with learning disabilities... Wong has succeeded in providing detailed descriptions and comments within a book which covers a broad range of topics. Without exception the chapters are clearly written and accessible, and many provide the reader with challenging ideas and practical suggestions." -BRITISH JOURNAL OF SPECIAL EDUCATION

• Learning Disabilities occur in 20% of the population. Three million children in the US have a learning disability and receive special education in school
• 30% of children with learning disabilities drop out of high school, and 48% of those with learning disabilities are out of the workforce or unemployed
• Discusses different types of learning disabilities including problems with attention, memory, language, math, reading, and writing
• Encompasses the impact of LD on learning as well as social competence and self-regulation
• Provides research summaries on most effective ways to teach children with LD
• Encompasses a lifespan perspective on LD, discussing the impact on children, adolescents, and adults

Chapter 1

Classification and Identification of Learning Disabilities

Jack M. Fletcher

Department of Psychology, University of Houston, Houston, TX 77204-5053, USA

Chapter Contents

Introduction

What Is a Learning Disability?

Exclusionary Criteria

Inclusionary Criteria

LD Is an Unobservable Construct

Classification Issues in LD

Categorical versus Dimensional Classifications

Neurological Models of LD

Behavior Disorders Due to Intrinsic Factors

Dyslexia

Demise of the Concept of MBD

Cognitive Models

Emergence of the Concept of LD

LD and US Public Policy

IQ-Achievement Discrepancy

Other Cognitive Discrepancy Approaches

Psychometric Issues Underlying Cognitive Discrepancy Methods

Instructional Models

Low Achievement Methods

Response to Intervention Methods

A Hybrid Approach to LD Identification

Conclusions

Acknowledgment

References

Introduction

What Is a Learning Disability?

Few terms used to identify people with a set of problems producing major difficulties with adaptation to life and society generate as much contention and confusion as the term “learning disability” (LD). Children with the attributes of a LD in reading have been identified since before the start of the previous century as children with severe reading problems who seemed “bright” and “otherwise normal” (Hinshelwood, 1895; Morgan, 1896). As was the situation then, there is consensus among contemporary researchers and practitioners that LDs are brain-based and heritable. However, measuring brain dysfunction and heritability have proven elusive, although people with LDs clearly differ in brain function compared to typically developing people and people with different types of LD (e.g., reading versus math LD) (Fletcher, Lyon, Fuchs, & Barnes, 2007; Gabrieli, 2009). Similarly, there is strong evidence that LDs have partial genetic origins (Pennington, 2009; Plomin & Kovas, 2005), but the effects of individual genes are small and the mode of inheritance fits a multifactorial model similar to that seen in other developmental disorders, such as Attention-Deficit/Hyperactivity Disorder (ADHD) (Willcutt, Pennington et al., 2010).

There is also strong consensus that the core attribute of any conceptual model of LD is “unexpected underachievement” (Kirk, 1963), largely because people with LD do not learn to read, write, and/or do arithmetic despite the absence of conditions frequently associated with low achievement. Samuel Kirk, often credited with coining the term LD, stated that “It is clear that people with LD do not learn to read, write, or do arithmetic despite the absence of conditions that are known correlates of low achievement, such as an intellectual, sensory, or motor disability, emotional and behavioral difficulties, economic disadvantage, and lack of instructional language proficiency.” (Kirk, 1963, pp. 2–3). These conditions, which are present in most definitions of LD, are commonly referred to as “exclusionary” because they represent factors in which low achievement is expected.

Exclusionary Criteria

Defining LD according to the absence of conditions that cause other forms of low achievement has never been satisfactory (Rutter, 1978), with some arguing that definition by exclusion makes efforts to identify LD circular: “Stripped of clauses which specify what a learning disability is not, this definition is circular, for it states, in essence, that a learning disability is an inability to learn. It is a reflection of the rudimentary state of knowledge in this field that every definition in current use has its focus on what the condition is not, leaving what it is unspecified and thus ambiguous” (Ross, 1976, p. 11). Thus, the classification issue with which researchers and practitioners have wrestled is what makes low achievement unexpected. To address this issue, efforts have been made to identify attributes other than achievement that can be used to operationalize the concept of unexpected underachievement and represent “inclusionary” criteria.

Inclusionary Criteria

In considering additional criteria for classifying and identifying LD, three overarching models have emerged over the past 125 or so years: Neurological, cognitive, and instructional. The earliest models were neurological because they attempted to identify special signs of brain dysfunction that indicated the presence of LD. These models began to recede in the 1970s and models based on some form of cognitive discrepancy gained prominence. More recently, instructional models based on the idea of using intervention response as an indicator of unexpected underachievement have emerged. The methods are tied to “Response to Intervention (RTI)” service delivery frameworks used by schools to accelerate academic and behavioral outcomes in all children. Thus, attributes of LD variously considered as indicators of unexpected underachievement include neurological markers and signs, unevenness in cognitive functions, and an inability to respond to instruction that benefits most children.

LD Is an Unobservable Construct

Altogether, classifying LDs, which leads to definitions for identification, involves the application of criteria that include and exclude specific attributes of people hypothesized to represent the construct of LD. As a construct, LD is unobservable, which means that at a latent level, the concept is pure and untarnished by our imperfect efforts to measure it. We can propose key features of the construct, especially the concept of unexpected underachievement, and propose attributes of LD, like low achievement, cognitive discrepancies, and poor instructional response. However, these attributes are hypotheses and must be validated through research (Morris, 1988). We can measure them, but our efforts at measurement will always be imperfect because of measurement error. Thus, no single indicator is likely to be adequately reliable for measuring the different hypothetical attributes of LD.

In itself, the construct indicates that low achievement is a necessary but not sufficient condition for identification of LD because there must be criteria that indicate unexpectedness as well as low achievement. As I discussed above, many would agree that LD should not be invoked when there are other attributes that explain low achievement. As such, LD is one of several factors that produce low achievement in children; it is the unexpected kind of low achievement. Nonetheless, researchers and practitioners disagree on inclusionary criteria and the precise role of different exclusionary criteria. These disagreements are less about the construct of LD at a latent level, but more about how the essential attributes (e.g., unexpected underachievement) are measured.

Because efforts at measurement always have error, there will be imprecision in efforts to measure and indicate any latent variable. The situation is no different than attempts to measure intelligence. Few doubt that there is a latent construct of intelligence. The problem is that there are competing theories and multiple IQ tests that don’t always provide the same conclusion about a person’s IQ. But these are differences in IQ test scores that reflect in part differences in the underlying theory of intelligence that leads to differences in how the tests are constructed and the measurement error of the tests. The construct of IQ is untarnished by our efforts to measure and operationalize it.

In the next sections, I will expand this discussion of conceptual issues in classifying and defining LD and then discuss evidence for the three models of LD in a historical context. I will provide evidence that supports the reality of the LD construct and then discuss efforts to operationalize it from neurological, cognitive, and instructional models. By way of preface, it is important to recognize that LD has neurological, cognitive, and instructional attributes. Deciding among the models are not black and white issues and all contribute to our understanding of LD. However, the ultimate decisions may be pragmatic and guided by how well different models facilitate outcomes given available resources.

Classification Issues in LD

Any discussion of LD seems to assume that LD is represented by discrete groups that can be operationalized and defined. In a neurological model, people with LD are identified because of special signs presumed to indicate brain dysfunction: motor clumsiness, perceptual difficulties, confusion of right and left, difficulty perceiving symbols written on the finger tips, and even specific language problems (e.g., slow naming speed). Cognitive discrepancy and instructional models use psychometric criteria and look for performance below a specified threshold to indicate the presence of an attribute of LD.

Regardless of the model, the most common approach to identifying people with LD for research is to select an achievement measure, establish a threshold for low achievement (e.g., reading score below the 20th percentile) and then compare children who are low achieving on the achievement measure with another group that achieves above the threshold. Such a method would represent a “low achievement” approach to definition that I have classified as an instructional model of LD. In a cognitive discrepancy method or another instructional method, the attributes might change, but the approach would be the same: contrast groups created by a cut-off point on the measurement tool used to define the attribute of interest. In providing services, similar psychometric approaches are used; children receive services when they score below a specified threshold on a test, show a specified difference in IQ and achievement, or are below the threshold on an assessment of instructional response. When groups are compared on measures not used to define them, such as measures of cognitive function, brain function, or a genetic assessment, the approach taken to define the groups is validated if the groups are significantly different, which in classification research is termed “external validity” (Skinner, 1981). The fact that (a) children defined as LD using these different psychometric methods differ from typically achieving children and (b) that children with different types of LD (reading vs. math LD) differ on measures of cognitive functions, brain function, and heritability, is strong evidence for the validity of the construct of LD. Moreover, it is rather obvious that there are interactions of the type of LD with the treatment approach: children with reading difficulties improve in reading when they receive a reading intervention, but not a math intervention, and vice versa (Morris et al., 2012).

Categorical versus Dimensional Classifications

The problem with these approaches is the assumption that LD represents a discrete group, representing a categorical classification. A categorical classification is usually appropriate when there are subgroups with firm boundaries and whose members are qualitatively different from one another. Alternatively, if the differences across members of an overarching classification like LD are not qualitatively different, the classification may be dimensional. In a dimensional classification, members are quantitatively different and usually represent an unbroken continuum where specific levels of severity lead to problems with adaptation (Morris, 1988). If there are no qualitative breaks, dichotomizing dimensions leads to unreliability in identification of people around the threshold and reduces power in research studies (Cohen, 1983).

The best examples of dimensional disorders in medicine are problems like obesity or hypertension (Ellis, 1984). Weight and blood pressure are continuous attributes of a human population. When decisions are made to treat a person as being overweight or for high blood pressure, it is because the risk of an adverse outcome is triggered at certain levels of the continuum. This threshold is not firmly fixed, but is usually represented by multiple criteria and will vary depending on different risk characteristics. But decisions to treat are related to indices of outcome and may vary across individuals.

Whether the attributes of LD can be represented as a categorical or a dimensional classification is an open empirical question. But it is very important because subdividing a normally distributed dimension to create categories is an arbitrary process that introduces unreliability into decisions about individuals who may or may not be members of a group. To take simple examples, defining a reading LD as a score below the 20th percentile on a reading test or a difference between IQ and achievement of 15 standard score points (at one point the predominant definition in public policy in North America) are unreliable indicators of individuals who need services because the tests used to assess the cut-off point have small degrees of measurement error and are correlated in the case of IQ and achievement. If we assess individuals across multiple occasions with either a single test or an aptitude-achievement discrepancy, or use different tests that meas...