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Learning Disabilities: Assessment, Identification, and Treatment

Abstract and Keywords

This chapter provides an overview of definitions, assessment procedures, and instructional treatments for children with learning disabilities. A focus is placed on assessment issues related to reading and math disabilities. Also reviewed are potential causes of learning disabilities, as well as some of the controversies (e.g., role of IQ testing, discrepancy models, RTI) related to assessment practices. An operational definition of LD is discussed that focuses on using cutoff scores from standardized measures related to IQ and specific academic domains (e.g., reading, math).

Keywords: learning disabilities, assessment, treatment, subtypes, reading disabilities, math disabilities

Overview of Definitions

The term learning disabilities (LD) was first coined in a speech that Samuel Kirk delivered in 1963 at the Chicago Conference on Children with Perceptual Handicaps. Clinical studies prior to his 1963 presentation identified a group of children who suffered perceptual, memory, and attention difficulties related to their poor academic performance, but who were not intellectually retarded (see Hallahan & Cruickshank, 1973, for a review). Currently, children (as well as adults) classified with LD are individuals who are of normal intelligence, but suffer mental information processing difficulties that underlie poor academic achievement. Several definitions across the last four decades have referred to children with LD as reflecting a heterogeneous group of individuals with “intrinsic” disorders that are manifested by specific difficulties in the acquisition and use of listening, speaking, reading, writing, reasoning, or mathematical abilities. These definitions (see Hammill, 1990, for a review) assume that the learning difficulties of such individuals are:

  1. (1) Not due to inadequate opportunity to learn, to general intelligence, or to significant physical or emotional disorders, but to basic disorders in specific psychological processes (such as remembering the association between sounds and letters).

  2. (2) Not due to poor instruction, but to specific psychological processing problems that have a neurological, constitutional, and/or biological base.

  3. (3) Not manifested in all aspects of learning. Such individuals’ psychological processing deficits depress only a limited aspect of academic behavior. For example, such individuals may suffer problems in reading, but not arithmetic.

To assess LD at the cognitive and behavioral level, school psychologists must employ systematic efforts to detect: (a) normal psychometric intelligence, (b) below normal achievement on standardized measures of achievement (e.g., word recognition), (c) below normal performance on measures of specific cognitive processes (e.g., phonological awareness, working memory), (d) that evidence-based instruction has been presented under optimal conditions, and (e) that academic and/or cognitive processing deficits are not directly caused by environmental factors or contingencies (e.g., socioeconomic status [SES]). In essence, the identification of children with LD requires the documentation of (p. 335) normal intelligence (i.e., individuals do not suffer from mental retardation) and deficient academic performance that persists after best instructional practices have been systematically provided.

Overview of Common Testing Measures

There are several common measurement instruments commonly used to help the school psychologist define LD. These instruments are assumed to evaluate the cognitive and academic weaknesses that may contribute to children’s academic difficulties. Tests such as the Wechsler Intelligence Scale for Children, 4th ed. (WISC IV; Wechsler,2003), the Woodcock-Johnson Tests of Cognitive Abilities, 3rd ed. (W-III COG; Woodcock, McGrew, & Mather, 2001), the Das-Naglieri Cognitive Assessment System (CAS; Naglieri & Das, 1997), the Kaufman Assessment Battery for Children, 2nd ed. (KABC-II; Kaufman & Kaufman, 2004), and the Stanford-Binet Intelligence Scales, 5th ed. (SB5; Roid, 2003) are useful for evaluating children’s cognitive skills. Academic achievement tests such as the Wechsler Individual Achievement Test, 2nd ed. (WIAT II; Psychological Corporation, 2001), and the Woodcock-Johnson Test of Achievement, 3rd ed. (WJ-III ACH; Woodcock, McGrew, & Mather, 2001) provide standardized methods of evaluating and documenting academic skills. These tools allow for the measurement of specific academic skills and acquired knowledge. If a child is found to have a deficit in these areas, exclusionary factors are evaluated (i.e., limited English proficiency, mild mental retardation, behavioral or emotional disturbance). If these exclusionary factors are not found to be the primary cause of the academic skill deficit, then measurement of cognitive abilities, processes, and aptitudes for learning are indicated. The school psychologist attempts to establish the consistencies between a child’s cognitive processing abilities and the academic performance profile that would account for the child’s academic underachievement. For example, a child’s demonstrated difficulties with phonological awareness, speed of information processing, and working memory, may provide a likely explanation for a child’s low basic reading skill level.


Depending on the definition, the incidence of children with LD is conservatively estimated to reflect 2%–5% of the public school population. It is the largest category of children served in special education. There is, of course, state to state variability in the prevalence of learning disabilities. Some states report students identified as LD as high as 8% (Rhode Island), whereas other states report lower incidence rates of 3% (Kentucky; see Hallahan, Keller, Martinez, Byrd, Gelman, & Fan, 2007, for a review); however, the high incidence of LD does not appear to be any more variable than that of low-incidence categories. For example, Hallahan, et al. (2007) showed that the incidence of LD was no more variable than those of low incidence disabilities (e.g., hearing impaired), suggesting that as a group, children with LD are as well defined as those with other handicapping conditions.

Reading disabilities, or dyslexia, are the most common forms of LD; some studies have reported that approximately 80% to 90% of the children served in special education have problems in reading (Lerner, 1989; Kavale & Reese, 1992). Epidemiological data for children suggests that reading disabilities (RD) fits a dimensional model in which proficient reading and RD occurs along a continuum, with RD representing the lower tail of a normal distribution of reading ability (e.g., Gilger, Borki, Smith, DeFries, & Pennington, 1996). Further, longitudinal studies—both prospective (Francis, Shaywitz, Stuebing, Shaywitz, & Fletcher, 1996) and retrospective (Bruck, 1990)—indicate that RD is a persistent chronic condition from childhood to adulthood. For example, in the Connecticut Longitudinal Project approximately 70% of children identified with RD in grade 3 had RD as adults (e.g., Shaywitz, et al, 1999). Thus, over time and age, proficient readers and those with RD maintain their relative position along the spectrum of reading ability (Shaywitz & Shaywitz, 2003; 2005).


A great deal of heterogeneity exists within the groups defined as having LD. For example, the 2004 reauthorization of the Individuals with Disabilities Education Act (IDEA) defines “specific learning disability” in a similar manner to previous versions of the law in capturing this heterogeneity, as follows:

“ (A) The term ‘specific learning disability’ means a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, which a disorder may manifest itself in the imperfect ability to listen, think, speak, read, write, spell, or do mathematical calculations.

(B) Disorders included.—Such term includes such conditions as perceptual disabilities, brain injury, (p. 336) minimal brain dysfunction, dyslexia, and developmental aphasia.

(C) Disorders not included.—Such term does not include a learning problem that is primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage.” (P.L. 108-446, Section 602(30)

As can be determined from this definition, there are several areas in which LD may manifest itself. Most researchers attempt to handle this heterogeneity by subtyping or subgrouping children along various dimensions. Although several types of LD have been discussed in the literature, few of these subtypes have been considered valid, because (a) these particular subtypes do not respond differently to instructional programs when compared to other subtypes, and/or (b) the skills deficient in a particular subtype are not relevant to the academic areas important in the school context.

However, there are two subtypes that have been extensively researched and are relevant to the school context: reading disabilities and mathematical disabilities. These subtypes are usually defined by standardized (norm-referenced) and reliable measures of intelligence and achievement (as described above). The most commonly used intelligence tests, however, are from the Wechsler series and common achievement tests that include measures of word recognition or identification (Woodcock-Johnson Psychoeducational Battery, Wide Range Achievement Test, Woodcock Reading Mastery Test, Kaufman Test of Educational Achievement, Peabody Individual Achievement Test) and arithmetic calculation (all the aforementioned tests and the Key Math Diagnostic Test). In general, individuals with intelligence quotient (IQ) scores equal to or more than a full-scale IQ score of 85, reading subtest scores equal to or less than the 25th percentile, and/or arithmetic subtest scores equal to or less than the 25th percentile captures two high incidence disorders within LD: reading (word recognition) and arithmetic (computation, written work).

Reading Disabilities

Several researchers argue that fundamental to evaluating RD is a focus on word recognition measures, because they capture more basic processes and responses than reading comprehension (e.g., Siegel, 2003a). Reading problems are best conceptualized as a continuum with varying degrees of severity because, as several studies show, children with RD show a remarkable homogeneity in cognitive profiles (e.g., Stanovich & Siegel, 1994). Several authors (e.g., Francis et al., 1996; Grigorenko, 2001; Mann, 2003; Morris, Stuebing, Fletcher, Shaywitz, Lyon, Shankwieler, et al., 1998; Swanson & Siegel, 2001) find in these profiles deficiencies in three critical processes: phonological processing (ability to segment sounds), syntactical processing (ability to understand grammatical structure), and working memory (combination of transient memory and long-term memory) (also see Siegel, 2003a, for a review).

Because not all problems in reading are at the word recognition level, some other types of distinct reading problems have been suggested (e.g., Morris et al., 1998). In their recent examination of the current state and perspectives on learning disabilities, Fletcher, Lyon, Fuchs, and Barnes (2007) proposed the existence of three types of RD. The first type is associated with problems in word recognition and spelling. The second form refers to difficulties in reading comprehension. Finally, the third type includes individuals who experience difficulty in reading fluency and poor automaticity of word reading. The largest numbers of students with reading disabilities demonstrate problems on word level recognition; however, a subset of students with RD who have intact word recognition skills shows deficits in reading comprehension. This type of disability is related to problems in oral language and working memory—the capacity to integrate new information with old information when high demands are made on attention. Finally, a group of students with average word decoding skills differ in reading fluency: these individuals have either slow reading rates, or average rates but low reading accuracy.

Morris, et al (1998) studied the variability of RD subtypes in a large group of 7- to 9-year-old children with reading problems. Based on the results of their study, several reading subtypes emerged: two subtypes without reading disability, five subtypes with specific reading disability, and two categorized as “globally deficient” in the sense that performance across all measures was very low. Five specific reading disability subgroups varied with regard to phonological (working) memory and rapid naming. Six of the reading disability groups exhibited deficits in phonological awareness skills. The authors concluded that children with RD could be differentiated from the “garden variety” poor readers on the basis of their vocabulary level, which was (p. 337) in the average range for children with specific RD. However, Morris and colleagues’ work on subtyping reading disabilities is consistent with a phonological processing hypothesis, which postulates that problems in the phonological domain account for reading difficulties. These phonological problems either occur in isolation or co-occur with problems in other cognitive domains.

Other subtype studies tested whether good and poor readers could be differentiated on their performance on memory related measures (e.g., Swanson, 1993a; Katzir, Kim, Wolf, Morris, & Lovett, 2008). For instance, Swanson (1993a) examined individual differences in several forms of memory of students with and without RD. Although several subgroups with different profiles emerged, the results indicated that children with RD had low performance on memory tasks not because of reading, but rather due to inefficient working memory (WM). Swanson concluded, that “the connection between reading and WM operates on a continuum of independence to dependence as reading becomes more skilled.” (p.327)

Math Disabilities

In terms of math disabilities, several studies (Badian, 1983; Shalev, Manor, & Gross-Tsur, 1997; Kosc, 1974) estimated that approximately 6% to 7% of the school age population have mathematical disabilities. Although this figure may be inflated because of variations in definition (e.g., Desoete, Roeyers, & De Clercq, 2003, suggest the figure varies between 3% and 8%), a significant number of children demonstrate poor achievement in mathematics. Some studies suggest that math disabilities (MD) are as common as RD and that a similar cognitive deficit may contribute to the co-occurrence of MD and RD in some children (Geary, 1993, 2003). Interestingly, although MD is a common disorder, the majority of research has been directed toward children with RD.

One of the most comprehensive syntheses of the cognitive literature on MD was provided by Geary (1993; also see Geary, 2003, for a review). His review indicated that children with MD are a heterogeneous group and show one of three types of cognitive disorders. One disorder characterizes children with MD as deficient in semantic memory. These children are characterized as having weak fact retrieval and high error rates in recall. Disruptions in ability to retrieve basic facts from long-term memory (LTM), due to inhibition, may be a defining feature of MD (Geary, 1993). Further, Geary’s review indicated that the characteristics of these retrieval deficits, such as slow solution times, suggest that children with MD do not experience a simple developmental delay but rather have a more persistent cognitive disorder across a broad age span. However, other studies (e.g., Goldman, Pellegrino, & Merz, 1988) have suggested that some children with MD have a developmental delay related to immature counting knowledge (e.g., use of fingers to count).

Another type of MD is procedural. Children in this category generally use developmentally immature procedures in numerical calculations, and therefore have difficulties in sequencing multiple steps in complex procedures. For example, Gross-Tsur et al. (1996) indicated that children with MD have a basic understanding of numbers and small quantities. However, children with MD have difficulties keeping information in working memory and monitoring the counting process (Geary, Hamson, & Hoard, 2000; Hitch & McAuley, 1991), which creates errors in their counting. Other studies (e.g., Jordan & Montani, 1997) indicate that children with MD have difficulties in solving simple and complex arithmetic problems. These differences are assumed to involve both procedural and memory based deficits. Procedural deficits relate to miscounting or losing track of the counting process.

The third type is a visual/spatial math disorder. These children have difficulties representing numerical information spatially. For example, they may have difficulties representing the alignment of numerals in multicolumn arithmetic problems and rotation of numbers. Further, they have difficulties in areas that require spatial ability, such as geometry and place values. Recent work by Geary, Hoard, Byrd-Craven and DeSoto (2004) suggests that these deficits are not due to poor spatial abilities, but rather to poor monitoring of the sequence of steps of an algorithm, and from poor skills in detecting and then self-correcting errors.

Regardless of the type of disorder for MD, however, the majority of these studies suggest that children with MD have memory deficits. The ability to utilize memory resources to temporarily store numbers when attempting to reach an answer is of significant importance in learning arithmetic. Poor recall of arithmetic facts, of course, leads to difficulties executing calculation procedures and immature problem-solving strategies (Geary, 1993).

Causes of LD

The most researched causes of LD have been in the area of reading. This is because most of the (p. 338) interventions and referral in the assessment of LD in the schools are related to reading problems. Vellutino, Fletcher, Snowling, and Scanlon (2004) provide an overview of research on specific RD and effective interventions across four decades. They conclude that inadequate facility in word identification, due to basic deficits in phonological coding (converting written letters and words into sounds, skills of segmenting and blending sounds associated with letters—what is generally referred to as phonics knowledge) underlie RD. These deficits in phonological coding are defined as an inability to use speech codes to represent information in the form of words and parts of words. In short, these individuals have an inability to represent sound units in one’s mind. There are some studies that have suggested that there may be some general language deficits in this population (e.g., Mann, 2003; Scarborough, 1990). Some of these general problems have been related to difficulties in attention, naming speed, making association between sounds and visual shapes, processing verbal to auditory information or transfer, and working memory. We will briefly review some of the other research programs that seek to determine the causes of RD.

Naming Speed

Wolf, Bowers, and Biddle (2000) have found a connection between rapid naming of letters, numbers, and objects, and reading disabilities. Slow naming speed marks a core deficit associated with reading disabilities. Rapid naming is associated not only with initial reading fluency, but also whether there are any fluency gains after practice. Some research has investigated the role of rapid naming and reading achievement in languages other than English. The research suggests that slow naming speed is somewhat distinct from phonemic awareness. Some of Wolf’s work has focused on subtyping by strengths and weaknesses in rapid naming, as well as phonological awareness. That research suggests a double deficit hypothesis, in which children can vary in terms of difficulty on phonological skills, rapid naming skills, or both of those skills.

Working Memory

Swanson and his colleagues (1993b, 1999; Swanson, Howard, Sáez, 2006) have researched RD by primarily focusing on short-term memory, working memory, and their distinction. Deficits in reading comprehension and problem solving experienced by children with RD are related to memory problems in a speech-based storage system, and/or memory problems related to specific aspects of a general executive system of working memory. The executive system focuses on the monitoring of information, focusing and switching attention, and activating representations from long-term memory. Problems in the executive system of children with RD are related to the inefficient mental allocation of attention, and the poor inhibition of irrelevant information. Problems in executive processing are described in terms of limitations in attentional capacity rather than processing strategies. Because short-term memory has minimal application to complex academic tasks, the majority of his research on RD focuses on the relationship between working memory and complex cognition (reading comprehension, word problems).

Neurological Correlates

Several Studies have focused on the neurological correlates of reading disabilities (e.g., see Miller, Sanchez, & Hynd, 2003; Pugh, Mencl, Jenner, Lee, Katz, Frost, et al., 2001; Shaywitz, Mody, & Shaywitz, 2006, for review). Neurobiological evidence for reading disabilities is primarily done through postmortem, electrophysiological, family, and functional imaging studies. Evidence from this neurological data suggests the disruption of the neurological system for language in individuals with RD. Brain-based research in RD has focused on the planum temporale, gyral morphology of the perisylvian region, corpus colossum, as well as cortical abnormalities of the tempoparietal region. Although at this point in time it is difficult to summarize this research, the neural biological codes believed to underlie cognitive deficits in the reading disabled are centered on the left tempoparietal region. Differences in the asymmetry of the planum temporale have consistently been found in association with RD (see Miller et al., 2003, for a review). Specifically, asymmetry of the planum temporale is due to a larger right plana. A reversal of normal pattern of left greater than right asymmetry has been found in individuals with developmental dyslexia.

Recent studies by Shaywitz, Shaywitz, Blachman, Pugh, Fulbright, and Skudlarski et al. (2004) have found differences in the tempo-parieto-occipital brain regions between reading disabled and non-impaired readers. The converging evidence using functional brain imaging in adult reading disabled readers shows a failure in the left hemisphere posterior brain system to function properly during reading. Some brain imaging studies show differences in brain activation in frontal regions in reading (p. 339) disabled compared to non-impaired readers. The majority of this research has focused on the brain regions where previous research has implicated reading and language. The research shows clear activation patterns related to phonological analysis. For example, on nonword rhyming tasks, reading disabled readers experience a disruption of the posterior system that involves activation of the posterior superior temporal gyrus (also known as Wernicke’s area, the angular gyrus, and the striate cortex.) The research demonstrates a persistent functional disruption in the left hemispheric neural systems, and indicates that this disorder is lifelong.

Genetic Factors

Several studies have addressed genetic influences on RD. This research suggests that phonological coding abilities have a genetic etiology. The Colorado twin studies support the existence of major gene effects on reading disabilities, although the precise information about the mode of inheritance is less clear (Olson, Forsberg, Gayen, & Defries, 1999). Some of the literature (see Grigorenko, 2001) has found the localization of dyslexic gene sites (some of the gene sites have been attributed to chromosome 1, 2, 6, 15, and 18). Some research suggests that genes contributing to nonword repetition also account for the genetic basis of memory span score. Recent developments have focused on genotype/phenotype correlations, biological consequences of specific genetic changes, and intentional intervention strategy guided by genetic profiles.

Math Disabilities and Causes

As indicated above, more is known about potential causes of RD than MD. Some of the difficulty in the literature is that the primary cognitive mechanisms that separate MD from RD are unclear. Although there is some agreement that children with MD are slower and less efficient at recognizing numerosities, some authors have suggested that memory representations for arithmetic facts are supported in part by the same phonological and semantic memory systems that support decoding words and reading comprehension (see Geary, 1993; Hecht, Torgesen, Wagner, & Rashotte, 2001, for a review). Others challenge this assumption (Jordan, Hanich & Kaplan, 2003).

Unfortunately, there are several reasons why it is difficult to determine from existing literature whether the cognitive processing of children with MD is distinct from other children. One reason is that the operational criteria for measures used in the selection of children with MD and RD vary across studies (Swanson & Jerman, 2006). For instance, measures used to establish MD vary from the 48th percentile to the 8th percentile across studies. Previous studies that have examined math difficulties in groups around the 30th percentile have been described as investigations of learning “difficulties,” whereas studies that have examined groups in about the lower 10th percentile have been described as investigations of MD (Mazzocco, 2007). Children with MD who perform in the lower 10th percentile on math tests, and have been exposed to evidence-based math instruction, are likely to have MD with a biological origin, and represent developmental deviance (Mazzocco, 2007).

Another reason is that most studies on math disabilities in terms of brain involvement are done with adults. Cognitive development in children and neurological findings in adults may lead to different conclusions. A casual review of the literature shows that MD in adults has been associated with the left basal ganglia, thalamus, and the left parieto-occipito-temporal areas (e.g., Dehaene & Cohen, 1995; 1997). Damage to these regions may be associated with difficulties in accessing number facts. With adult samples, there is some neurological evidence that the ability to understand numbers is dissociable from language (Cohen, Dehaene, Cochon, Lehericy, & Naccache, 2000), semantic memory, and short-term memory (Cappeletti, Butterworth & Kopelman, 2001). However, when the focus is on children who demonstrate normal cognitive development (e.g., Geary et al., 2000; Hecht, Close, & Sabatisi, 2003; Jordon & Montani, 1997), a different theoretical account of various types of math difficulties emerges. For example, Geary et al. indicated that the mechanisms that underlie this have been related to poor WM, either related to the phonological system or the executive system, such as the inability to inhibit irrelevant information. Jordan indicated that the mechanisms that may underlie MD are related to deficits in fact retrieval, and by extension, calculation and fluency. In contrast, Hecht et al. focus on the conceptual knowledge of children with MD. They find that conceptual understanding is a consistent and important source of variability in estimation, word problem solving, and computation in children with MD.

Swanson and Jerman (2006) completed a quantitative synthesis of the literature that compared children with MD to average achievers, children with RD, and children with comorbid disabilities (RD+MD). Average achievers clearly outperformed (p. 340) children with MD on measures of verbal problem solving, naming speed, verbal and visual-spatial working memory, and long-term memory. The results further indicated that children with MD outperformed children with combined reading and math disabilities on measures of literacy, visual-spatial problem solving, LTM, short-term memory for words, and verbal working memory. Children with MD could only be clearly differentiated from children with RD on measures of naming speed and visual-spatial working memory. These problems were persistent across age and severity of math disability.


The causes of RD and MD are assumed to have a biological base. In the area of reading, there is some converging evidence that deficits in specific language and memory processes in children with RD have a biological and genetic origin.

Scientifically Based Treatments

As indicated earlier, children with LD are a heterogeneous group; therefore, no general instructional model can be recommended for all of them. However, some common general principles for teaching students with LD have been identified. Although these principles often operate in different ways with different students, in different content areas and different settings, nevertheless they can be used in designing effective remediation programs for such students. We summarize these principles from a comprehensive educational intervention research synthesis for students with LD (Swanson, Hoskyn, & Lee, 1999). Before reviewing these underlying principles, a historical context is necessary.

Trends Related to Instructional Interventions

Wiederholt (1974), in reviewing the history of the LD field, indicated that its unique focus was on identifying and remediating specific psychological processing difficulties. Popular intervention approaches during the 1960s and 1970s focused on visual-motor, auditory sequencing, or visual perception training exercises. Several criticisms were directed at these particular interventions on methodological and theoretical grounds.

By the late 1970s, dissatisfaction with a processing orientation to remediation of learning disabilities, as well as the influence of federal regulations in the United States (Public Law 94-142), remediation programs focused on basic skills such as reading and mathematics. The focus on basic skills rather than psychological processes was referred to as direct instruction. The mid 1980s witnessed a shift from the more remedial/academic approach of teaching, to instruction that included both basic skills and cognitive strategies (ways to better learn new information and efficiently access information from long-term memory). Children with LD were viewed as experiencing difficulty in “regulating” their learning performance. An instructional emphasis was placed on teaching students to check, plan, monitor, test, revise, and evaluate their learning during their attempts to learn or solve problems.

The early 1990s witnessed a resurgence of direct instruction intervention studies, primarily influenced by reading research, which suggested that a primary focus of intervention should be directed to phonological skills. The rationale was that because a large majority of children with LD suffer problems in reading, some of these children’s reading problems were exacerbated because of lack of systematic instruction in processes related to phonological awareness (the ability to hear and manipulate sounds in words and understand the sound structure of language). This view gave rise to several interventions that focused heavily on phonics instruction, and intense individual one-to-one tutoring to improve children’s phonological awareness of word structures and sequences.

From the turn of the twenty-first century to the present, some interventions have been linked to assessment. A method of identifying school-aged students with LD known as Response to Intervention (RTI, to be discussed later) first establishes low academic performance, and then determines if a disability is present. The RTI model is partially based on intervention programs that have distinguished children experiencing academic difficulty due to instructional deficits, from those with disability-related deficits (Al Otaiba & Fuchs, 2002; Vellutino, Scanlon, Sipay, Small, Pratt, Chen et al., 1996). Federal regulations in the United States regarding the Individuals with Disabilities Education Improvement Act of 2004 have influenced the use of RTI by supporting a child’s response to scientific, research based intervention as a process for LD identification. In general, the RTI model identifies whether a student’s current skill level is substantially lower than instructional level (based on predetermined criteria: e.g., below the 25th percentile). Low academic performance is established using standardized, norm-referenced and/or curriculum (p. 341) based measurements (Compton, Fuchs, Fuchs & Bryant, 2006; Fuchs, Fuchs, & Compton, 2004). After establishing low performance, empirically based interventions are implemented to determine if a disability is present. Student progress is monitored during the intervention. When a student does not respond to high quality intervention, the student may have a learning disability.

Scientifically Based Intervention Programs

The term intervention is defined as the direct manipulation (usually assigned at will by the experimenter) of variables (e.g., instruction) for the purposes of assessing learning (1) efficiency, (2) accuracy, (3) and understanding. Swanson et al. (1999) have provided the most comprehensive analysis of the experimental intervention literature on LD to date. Interventions were analyzed at three levels: general models of instruction, tactics used to convey information, and components that were most important to the instructional success.

In terms of general models, methodologically sound studies (those studies with well-defined control groups and clearly identified samples) found that positive outcomes in remediation were directly related to a combination of direct and strategy instructional models. Components of direct instruction emphasize fast paced, well-sequenced, highly focused lessons (e.g., Adams & Carnine, 2003 Kame’enui, Jitendra, & Darch, 1995). The lessons are delivered in small groups to students, who are given several opportunities to respond and receive feedback about accuracy and responses. Components related to effective strategy include: advance organizers (provide students with a type of mental scaffolding on which to build new understanding, i.e., consisting of information already in the students’ minds, and the new concepts that can organize this information); organization (information or questions directed to students to stop from time to time, to assess their understanding); elaboration (thinking about the material to be learned in a way that connects the material to information or ideas already in the student’s mind); generative learning (learners must work to make sense out of what they are learning by summarizing the information); and general study strategies (e.g., underlining, note taking, summarizing, having students generate questions, outlining, and working in pairs to summarize sections of materials (see Wong, Harris, Graham & Butler, 2003, for a review).

The two models included a graduated sequence of steps with multiple opportunities for overlearning the content and skills, cumulative review routines, mass practice, and teaching of all component skills to a level that showed mastery. The interventions involved (a) teaching a few concepts and strategies in depth rather than superficially, (b) teaching students to monitor their performance, (c) teaching students when and where to use the strategy in order to enhance generalization, (d) teaching strategies as an integrated part of an existing curriculum, and (e) providing teaching that includes a great deal of supervised student feedback and practice.

In terms of tactics, Swanson (2000) divided studies into eight models based on key instruction tactics: direct instruction (a focus on sequencing and segmentation of skills), explicit strategy training, monitoring (teaching children strategies), individualized and remedial tutoring, small interactive group instruction, teacher indirect instruction (teacher makes use of homework and peers’ help for instruction), verbal questioning/attribution instruction (asking children key questions during the learning phase, and whether they thought what they were learning would transfer), and technology (using computers to present concepts). The results indicated that explicit strategy instruction (explicit practice, elaboration, strategy cuing) and small group interactive settings best improved the magnitude of treatment outcomes.

In terms of components, Swanson (1999b) found that all effective instructional models follow a sequence of events:

  1. 1. State the learning objectives and orient the students to what they will be learning, and what performance will be expected of them.

  2. 2. Review the skills necessary to understand the concept.

  3. 3. Present the information, give examples, and demonstrate the concepts/materials.

  4. 4. Pose questions (probes) to students and assess their level of understanding and correct misconceptions.

  5. 5. Provide group instruction and independent practice. Give students an opportunity to demonstrate new skills and learn the new information on their own.

  6. 6. Assess performance and provide feedback. Review the independent work and give a quiz. Give feedback for correct answers and reteach skills if answers are incorrect.

  7. 7. Provide distributed practice and review.

They also found that some instructional components were far more important than others. (p. 342) For example, in the domain of reading comprehension, key instructional components that contributed significantly to improving the magnitude of outcomes were:

  1. 1. Directed Response/Questioning. Treatments related to dialectic or Socratic teaching, the teacher directing students to ask questions, the teacher and a student or students engaging in reciprocal dialogue.

  2. 2. Control Difficulty or Processing Demands of Task. Treatments that included short activities, level of difficulty controlled, teacher providing necessary assistance, teacher providing simplified demonstration, tasks sequenced from easy to difficult, and/or task analysis.

  3. 3. Elaboration. Treatments that included additional information, or explanation provided about concepts, procedures or steps, and/or redundant text or repetition within text.

  4. 4. Modeling by the Teacher of Steps. Treatments that included modeling by the teacher in terms of demonstration of processes and/or steps the students are to follow to solve the problem.

  5. 5. Small Group Instruction. Treatments that included descriptions about instruction in a small group, and/or verbal interaction occurring in a small group with students and/or teacher.

  6. 6. Strategy Cues. Treatments that included reminders to use strategies or multi-steps, use of “think aloud” models, and/or teacher presenting the benefits of strategy use or procedures.

In contrast, the important instructional components that increased the effect sizes for word recognition were:

  1. 1. Sequencing. Treatments included a focus on breaking down the task, fading of prompts or cues, sequencing short activities, and/or using step by step prompts.

  2. 2. Segmentation. Treatments included a focus on breaking down the targeted skill into smaller units, breaking into component parts, segmenting and/or synthesizing components parts.

  3. 3. Advanced Organizers. Treatments included a focus on directing children to look over material prior to instruction, directing children to focus on particular information, providing prior information about task, and/or the teacher stating objectives of instruction prior to commencing.

The importance of these findings is that only a few components from a broad array of activities were found to enhance treatment outcomes. Regardless of the instructional focus (math, writing, reading), two instructional components emerged in Swanson et al.’s analysis of treatments for children with LD. One component was explicit practice, which included activities related to distributed review and practice, repeated practice, sequenced reviews, daily feedback, and/or weekly reviews. The other component was advanced organizers, which included: (a) directing children to focus on specific material or information prior to instruction; (b) directing children about task concepts or events before beginning; and/or (c) the teacher stating objectives of the instruction.

Controversies within the Field of LD

Before discussing some of the controversies in the field, as well as recent trends, areas of some consensus are reviewed. Some consensus statements were developed at a federally funded Learning Disabilities Summit (Bradley, Danielson, & Hallahan, 2002) regarding the nature of specific learning disabilities (SLD). Areas of consensus included the following:

  • * The concept of SLD is valid, supported by strong converging evidence.

  • * SLD are neurologically based and intrinsic to the individual.

  • * Individuals with SLD show intra-individual differences in skills and abilities.

  • * SLD persist across the life span, though manifestations and intensity may vary as a function of developmental stage and environmental demands.

  • * SLD may occur in combination with other disabling conditions, but are not due to other conditions, such as mental retardation, behavioral disturbance, lack of opportunities to learn, primary sensory deficits, or multilingualism.

  • * SLD are evident across ethnic, cultural, language, and economic groups.

Given these consensus statements, we will now address some of the issues within the field.

Using Discrepancy Criteria to Define LD

One impediment to making advances in the field of LD revolves around issues of definition. Traditionally, assessment practices have relied primarily on uncovering a significant discrepancy between achievement in a particular academic domain, and general intellectual ability. The implicit assumption for using discrepancy scores is that individuals who experience reading, writing, and/or (p. 343) math difficulties, unaccompanied by a low IQ, are distinct in cognitive processing from slow or low achievers (e.g., Fletcher, Francis, Rourke, Shaywitz, & Shaywitz, 1992). This assumption is equivocal (e.g., Stuebing, Fletcher, LeDoux, Lyon, Shaywitz, & Shaywitz, 2002). A plethora of studies have compared children with reading disabilities (RD; i.e., children with discrepancies between IQ and reading) with nondiscrepancy defined poor readers (i.e., children whose IQ scores are in the same low range as their reading scores), and found that these groups are more similar in processing difficulties than different (Hoskyn & Swanson, 2000; Stuebing et al., 2002). As a result, some researchers have suggested dropping the requirement of average intelligence, in favor of a view where children with reading problems are best conceptualized as existing at the extreme end of a continuum from poor to good readers (Siegel, 2003b; Stanovich & Siegel, 1994). Although there are operational definitions of individuals with LD provided by researchers that do not rely on discrepancy criteria (e.g., cutoff scores for determining RD on various measures, e.g., Siegel & Ryan, 1989, Swanson, 1993b), there are two issues that limit consensus on defining individuals with LD. One is related to the role of IQ in the assessment process, and the other to whether the cognitive processes that underlie LD are distinct from other poor reading groups. These issues and related research are briefly reviewed.

determining the role intelligence plays in the assessment of ld

Several authors have argued that variations in IQ tell us little about differences in processing when groups are defined at low levels of reading (e.g., Francis, Fletcher, Stuebing, Lyon, Shaywitz & Shaywitz, 2005). In response to these concerns, current legislation (IDEIA, 2004) has allowed alternative procedures to using IQ as a measure of aptitude for determining RD. Thus, the issue on the importance of IQ scores is not clear from current legislation. However, are variations in IQ and reading (former indicators of LD) really irrelevant? The literature on this issue is equivocal when considered in reference to three major meta-analyses (quantitative synthesis of the literature) on the topic.

Three meta-analyses were completed prior to the passing of IDEIA (2004; Fuchs, Fuchs Mathes & Lipsey, 2000; Hoskyn & Swanson, 2000; Stuebing et al., 2002) that addressed the role of IQ in defining RD. The contradictions in the three meta-analyses are reviewed in Stuebing et al. (2002). Stuebing et al. considered the Hoskyn and Swanson (2000) selection process of studies to be the more conservative of the three, and these findings are highlighted related to the relevance of IQ. Hoskyn and Swanson (2000) analyzed published literature comparing children who are poor readers but either had higher IQ scores than their reading scores, or had IQ scores commensurate with their reading scores. The findings of the synthesis were consistent with previous studies, outside the domain of reading, that report on the weak discriminative power of discrepancy scores. Although the outcomes of Hoskyn and Swanson’s synthesis generally supported current notions about comparable outcomes on various measures among the discrepancy and nondiscrepancy groups, verbal IQ significantly moderated effect sizes between the two groups. That is, although the degree of discrepancy between IQ and reading was irrelevant in predicting effect sizes, the magnitude of differences in performance (effect sizes) between the two groups were related to verbal IQ. They found that the effect size differences between discrepancy (reading disabled group) and nondiscrepancy groups (low achievers, in this case) were significantly moderated (influenced the strength and direction of effect sizes) by verbal IQ measures. When the mean verbal IQ of the reading disabled (RD) group was greater than 100, the differences between the low achieving (LA) groups were significantly greater. In contrast, when verbal IQ was less than 100, estimates of effect size between the two groups on various cognitive measures were close to 0. Thus, the farther the RD group moved from IQs in the 80 range to above the 100 range (the cutoff score used to select RD samples), the greater the chances their overall performance on cognitive measures would differ from the low achievers.

Stuebing et al. (2002) in their meta-analysis concluded that IQ was irrelevant to reading. However, their analysis showed (see Table 6) that IQ accounted for a substantial amount of the explainable variance in reading (explainable variance ranges from approximately .47 to .58). Moreover, robust differences on measures between the two groups were found by Fuchs et al. (2000). For example, Fuchs et al. (2000), comparing low achieving students with and without LD, found moderate effect sizes (ES=.61, see p. 94) in favor of low achievers without LD. In general, the three meta-analyses suggest that removing IQ as an aptitude measure in classifying children as LD, especially verbal IQ, is not uniformly supported by the assessment literature.

(p. 344) treatment outcomes as a function of definition

One test of the validity of using IQ as part of the identification criteria is whether IQ is related to treatment outcomes. Although some studies have found very little relevance of IQ to treatment outcomes (e.g. Vellutino, Scanlon & Lyon, 2000), the literature on the issue of whether IQ has relevance across an array of intervention studies needs to be considered.

One means of evaluating whether aptitude variations in the LD sample interact with treatment is to compare the relationship between treatment outcomes (treatment vs. control) with multivariate data that include different configurations of how samples with LD are defined. This can be accomplished by placing studies on the same metric (e.g., effect size), and comparing the magnitude of these outcomes as a function of variations in the sample definition (e.g., on measures of intelligence and reading). An analysis of treatment outcomes (as measured by effect size) and IQ has been reported (Swanson & Hoskyn, 1998; Swanson et al., 1999). The analysis shows significant LD definitions x treatment interactions exist across evidence based studies (see Swanson & Hoskyn, 1999, for a review). Variations in IQ and reading level were important moderators of instructional outcomes in both group design (Swanson & Hoskyn, 1998, 1999) and single subject design studies (Swanson & Sachse-Lee, 2000). The general pattern across these results was that studies that failed to report psychometric information on participants with LD yielded significantly higher effect sizes than studies reporting psychometric information. Thus, a poorly defined sample inflated treatment outcomes, by introducing greater heterogeneity into the sample when compared to studies that selected samples based on psychometric criteria. Significant effects related to the magnitude of treatment outcomes were isolated to the severity of reading x intelligence interaction. The influence of IQ scores on the magnitude of the treatment outcomes was relevant when reading scores were below the 25th percentile. The effect sizes were moderate (0.52) when intelligence was above 90, but substantial (.95) when IQs were below 90. Thus, the implication of these findings is that variations in IQ and reading cannot be ignored when predicting treatment outcomes and, therefore, are a critical ingredient to the identification process.

Two other important findings emerged, as subsets of the Swanson and Hoskyn (1998) data were analyzed. First, LD adolescent samples with discrepancies in intelligence and reading were more likely to yield lower treatment outcomes (effect sizes) than studies that reported aggregated IQ and reading scores in the same low range (e.g., Swanson, 2001). Second, treatment measures related to reading recognition and comprehension vary as a function of IQ. Effect sizes for word recognition studies were significantly related to samples defined by cutoff scores (IQ > 85 and reading < 25th percentile), whereas the magnitude of effect size for reading comprehension studies were sensitive to discrepancies between IQ and reading, when compared to competing definitional criteria.

summary and implications of iq testing

IQ has relevance to definitions of LD. Groups of students with LD who have aptitude profiles similar to generally poor achievers or slow learners (low IQ and low reading), produced higher effect sizes in treatment outcomes (in relation to control situations) than those samples with a discrepancy between IQ and reading. Given that there has been very little research on why these discrepancies occur, it is important to recognize that IQ, especially verbal, has a moderating role in assessment and treatment outcomes.

Alternatives to the Discrepancy Model

One currently popular alternative to defining LD based on the IQ–Achievement discrepancy model is referred to as Response to Intervention (RTI). The goal of RTI is to monitor the intensity of instruction, and make systematic changes in the instructional context as a function of a student’s overt performance. This is done by considering various tiers of instructional intensity. This approach is compatible with those that attempt to identify the cognitive and neuropsychological (i.e., psychometric) aspects of LD. RTI focuses on a systematic manipulation of the environmental context (i.e., instruction, classroom, school) to determine procedures that maximize learning, whereas cognitive and neurological approaches focus on mapping the internal dynamics of learning. The unique application of cognitive and neurological approaches to the field of LD is (1) to explain “why” and predict “how” individual differences emerge in children at risk for LD after intense intervention and (2) to document whether functional brain anatomy changes emerge as a function of intervention.

Historically, the concept of RTI as a means to further refine the definition of LD has been (p. 345) discussed since the inception of the field (see Haring & Bateman, 1977, and Wiederholt, 1974, for a review). For example, the term itself, “learning disabilities,” originated to replace a focus on neurological mechanisms such as minimal brain dysfunction (e.g., Clements, 1966), and thereby place an emphasis on “instruction.” In addition, several earlier writings made distinctions between children who fall by the wayside because of poor instruction, and those who are truly “learning disabled.” For example, Haring and Bateman (1977), in a text entitled Teaching the Learning Disabled Child, differentiated between “instruction disabled” and “learning disabled.” As they stated, “although a child may have no clinically observable signs of neurological disorders, he or she certainly may have some learning disabilities that cannot be readily accounted for by poor instruction. These learning problems seem to persist in a very small number of children, even though their curriculum has been individualized and they have received systematic instruction.” (p. 4). The authors further call for the use of precision teaching via continuous monitoring and recording of student performance on basic academic tasks. Various forms of RTI that we see today, such as curriculum based measurement and progress monitoring, can be traced to refinements or reformatting of precision teaching procedures, and the provision of instruction in various tiers or intensities reflects a reconfiguration of Deno’s (1980) cascade model.

Unfortunately, RTI as an assessment approach to define LD has a weak experimental base. At the time of this writing, there have been no controlled studies randomly assigning children seriously at risk for LD to assessment and/or delivery models (e.g., tiered instruction vs. special education, orresource room placement) to measure outcomes on key variables such as over-identification, stability of classification, and academic and cognitive growth in response to treatment. The few studies that compare RTI with other assessment models (e.g., discrepancy based or low-achievement based models) involve post hoc assessments of children divided at post-test within the same sample. In addition, different states and school districts vary in their interpretations of how RTI should be implemented, thereby weakening any uniformity that could link the science of instruction to assessing children at risk for LD.

Although there is enthusiasm for RTI as a means to provide a contextual (or more ecologically valid) assessment of children at risk for LD when compared to other models (e.g., models based on inferences from behavioral data about internal processing), the use of RTI as a scientific means to identify children at risk for LD has several obstacles to overcome. The first obstacle is that in contrast to standardized formats of testing and assessment, there are no standardized applications of evidence-based instruction. A second obstacle is that teacher effects cannot always be controlled. The teacher variable plays a key role in mediating treatment outcomes for children. Further, this variance cannot be accounted for by merely increasing treatment fidelity. Procedures that control for treatment fidelity in applying evidence-based treatments account for a very small amount of variance in student outcomes (see Simmerman & Swanson, 2001, for discussion). Although the role of teacher effects can be controlled to some degree, there is no “expert teaching model” that has been operationalized and implemented for instructional delivery in evidence-based practices. Another obstacle is that even under the best instructional conditions, individual differences in achievement in some cases will increase. There will be some instructional conditions that vastly improve achievement in both average achievers and children at risk for LD, but these robust instructional procedures will increase the performance gaps between some children. Thus, significant performance differences will remain for some children with LD when compared to their counterparts, even under the most intensive treatment conditions. Perhaps even more fundamental than these three major obstacles, is the lack of consensus about what “nonresponsiveness” entails, and how it should be uniformly measured.

One of the major issues for RTI is the translation of best evidence-based practices to assessment. That is, implementing RTI procedures is predicated on administering best evidence practices. There are two issues related to this translation.

First, individual differences will emerge even under the best intervention conditions. Swanson and several colleagues (e.g., Swanson & Deshler, 2003; Swanson & Hoskyn, 1998; Swanson & Sachse-Lee, 2000) condensed over 3000 effect sizes related to LD treatment versus LD control conditions for group design studies, and found a mean effect size (ES) of .79 for group design studies (Swanson & Hoskyn, 1998) and 1.03 for single subject design studies (Swanson & Sachse-Lee, 2000). According to Cohen’s (1988) classification system, the magnitude of the ES is small when the absolute value is at .20 or below, moderate when the ES is .60 and large when the ES is .80 or above. (p. 346) Thus, on the surface, the results are consistent with the notion that children with LD are highly responsive to intense instruction. However, when children with LD were compared to nondisabled children of the same grade or age, who also were receiving the same best-evidence intervention procedure, effect sizes (M= .97) were substantially in favor of nondisabled children (see Swanson et al., 1999, see p. 162 to 169). More importantly, the mean effect size difference increased in favor of children without LD (ES=1.44; Swanson et al., p. 168) when psychometric scores related to IQ and reading were not included as part of sample reporting. Thus, evidence-based instructional procedures did little to bridge the gap related to performance differences between children with and without LD, with instruction treatments found to be highly effective in samples of children with and without LD. Further, it is important to note that in the regression modeling, the best instructional programs (explicit practice, elaboration, strategy cuing, and small group interactive settings) accounted for less than 15% of the variance in predicting outcomes (Swanson, 1999a). This finding held when controls were made in the analysis for methodology, age, type of research design, and type of academic domain (e.g., reading, math, writing). The finding is consistent with the National Reading Panel’s report (2000) that provided an analysis of best practice in reading (teaching of phonics). This procedure accounted for approximately only 10% of the variance in reading treatment outcomes (Hammill & Swanson, 2006). Thus, a tremendous amount of variance is unaccounted for in studies considered the “best” of evidence-based practices.

Second, “best evidence studies” cannot be taken at face value. Simmerman and Swanson (2001) analyzed best evidence studies and found that slight variations in the internal and external validity significantly moderated the magnitude of treatment outcomes. Some violations that were significantly related to treatment outcomes included: teacher effects (studies that used the identical experimenter for treatment and control in administrating treatments yield smaller effect sizes than studies using different experimenters in administering treatments—a condition that may be analogous to 3-tiered instruction); reliance on “non” norm referenced measures (studies that did not use standardized measures had much larger effect sizes than those using standardized measures); and heterogeneous sampling (e.g., studies that included both elementary and secondary students yielded larger effect sizes than the other age-level conditions).

More importantly, studies that left out critical information commonly used in most neuropsychological test batteries (e.g., IQ and achievement scores) on individual differences data (or aggregated differences) greatly inflated treatment outcomes. For example, the underreporting of information related to ethnicity (studies that reported ethnicity yielded significantly smaller effect sizes than those that did not report ethnicity) and psychometric data (significantly larger effect sizes occurred when no psychometric information was reported, when compared to the other conditions) positively inflated the magnitude of treatment outcomes. The magnitude of effect sizes was also influenced by whether studies relied on federal definitions (studies that did not report using the federal definition [PL-94-142] yielded a larger weighted effect score than those that did), or reported using multiple definitional criteria in selecting their sample (studies that included multiple criteria in defining their sample yielded smaller effect sizes than those that did not use multiple criteria).

In summary, “best evidence” studies are influenced by a host of environmental and individual difference variables that make a direct translation to assessing children at risk for LD based on an RTI-only model difficult. In addition, although RTI relies on evidence based studies in the various tiers of instruction, especially in the area of reading, it is important to note that even under the most optimal instructional conditions for teaching reading (direction instruction), less than 15% of the variance in outcomes is related to instruction (see Table 5, Swanson, 1999b).

sample of RTI models

Some RTI models are gaining a research base, and these are briefly reviewed. For example, a 3-tiered model (Fuchs, Mock, Morgan, & Young, 2003; Fuchs & Vaughn, 2005) includes, as a first step, continual monitoring by the classroom teacher of all children’s academic performance. Children who are not responding to instruction are moved to the second level, where they are provided with more or different instruction, and progress is monitored. The intervention at this level is administered by the classroom teacher, or in small groups with a learning specialist. Those children who do not demonstrate accelerated growth, despite a well-implemented intervention, move to the third level, where a comprehensive evaluation becomes necessary in order to (p. 347) make the best determination of whether a specific learning disability exists. Differential diagnosis takes into account the cognitive abilities of the individual child, and attempts to rule out mental retardation (as required by the federal definition of specific learning disabilities), behavioral and emotional factors (such as attention deficit/hyperactivity disorder), and language acquisition (e.g., English language learners).

There are a number of strengths to this approach. First, the approach has the advantage of intervening early, with close monitoring of student achievement and adjustment of curriculum and teaching methodologies that may be more helpful. Frequent monitoring of a child’s response to the intervention provided allows the teacher to adjust goals for the child, resulting in improved student achievement (Fuchs, Fuchs, & Hamlett, 1989). Second, monitoring progress in RTI provides the opportunity to assess the rate of development for the child who is at risk. Monitoring of progress may be the domain of the teacher, special educator, or of a psychologist, either inside or outside of the school. It can be done using a variety of methods and at a variety of time intervals. Use of norm-referenced tests, particularly those which provide alternative forms, is optimal when assessing a child’s response to intervention over a longer period of time.

A common strategy for routinely assessing RTI over shorter periods of time is the use of academic probes drawn directly from local curricula, referred to as curriculum based measurement (CBM). These probes are brief, and vary widely in terms of their reliability. Some common tools include the Dynamic Indicators of Basic Early Literacy Skills (DIBELS: University of Oregon Center for Teaching and Learning, 2007), Monitoring Basic Skills Progress (MBSP; Fuchs, Hamlett, & Fuchs, 1990), and Yearly Progress Pro (McGraw-Hill Digital Learning, 2002; also, see National Center for Student Progress Monitoring, 2006).


The assessment of LD (in contrast to other learning problems) focuses primarily on: (a) determining achievement difficulties (e.g., reading, math) that are not due to inadequate opportunity to learn, general intelligence, or to physical or emotional/behavior disorders, but to basic disorders in specific cognitive information processes; (b) determining the link between specific information processing deficits and neurological, constitutional, and/or biological correlates; and (c) determining those specific information processing deficits that are limited to aspect of academic behavior (e.g., phonological processing). There is growing consensus among researchers that it is more appropriate to use an absolute definition of learning disabilities (below some cutoff score on achievement), rather than a discrepancy between achievement and IQ. Although there is controversy over what the absolute cutoff should be to determine LD, the majority of researchers rely on cutoff scores on standardized measures at or above 85 on general intelligence measures, and at or below a standard score of 90 on primary academic domains (e.g., reading and mathematics). Researchers distinguish individuals with LD from those with other general handicapping conditions, such as mental retardation, and visual and/or hearing impairments. Further specification is made that bilingualism, socioeconomic status, and conventional instructional opportunity do not account for depressed achievement scores. Such specification allows the scientist to infer that learning problems are intrinsic to the individual.

The scientific research shows that children with LD can be assessed, and significant gains can be made in academic performance as a function of treatment. However, there is considerable evidence that some children with normal intelligence, when exposed to the best instructional conditions, fail to efficiently master skills in such areas as reading, mathematics, and/or writing. Some literature suggests that individuals with LD are less responsive to intervention than individuals with similar primary academic levels but without LD, and that these academic problems persist into adulthood. Finally, these difficulties in academic mastery reflect fundamental cognitive deficits (e.g., phonological process, working memory).


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