Learning About Learning Disabilities [4th ed] 9780123884091, 9780123884145, 0123884144

Table of contents :
Front Cover --
Learning About Learning Disabilities --
Copyright Page --
Dedication --
Contents --
List of Contributors --
Preface --
Acknowledgments --
1 Classification and Identification of Learning Disabilities --
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 --
2 Learning Disabilities and Memory --
Introduction --
A Historical Perspective --
Understanding Memory Differences for Students with LD --
Parallels to Normal Memory Development. Mapping Memory Components that Might Be Deficient for Students with LD --
Short-Term Memory (STM) --
Working Memory --
Executive System --
Phonological Loop --
Visual-Spatial Sketch Pad --
Everyday Memory --
Memory Intervention --
Memory Strategies Serve Different Purposes --
Good Memory Strategies for NLD Students are not Necessarily Good Strategies for Students with LD and Vice Versa --
Effective Memory Strategies do not Necessarily Eliminate Processing Differences --
The Strategies Taught are not Necessarily the Ones Used. Memory Strategies in Relation to a Student's Knowledge Base and Capacity --
Comparable Memory Strategy May not Eliminate Performance Differences --
Memory Strategies Taught do not Necessarily become Transformed into Expert Strategies --
Strategy Instruction Must Operate on the Law of Parsimony --
Training WM Directly --
Testing-the-Limits Studies --
Summary and Conclusions --
References --
3 Brain and Behavioral Response to Intervention for Specific Reading, Writing, and Math Disabilities: What Works for Whom? --
Learning about the Brain --
Defining Brain --
Brain Geography. Brain Imaging Technologies --
Systems Approach --
Working Memory --
Controlled and Automatic Processing --
Nature-Nurture Interactions --
Comparing Reading, Writing, and Math Brains --
Brain Differences of Individuals with and without SLDS --
Reading --
Writing --
Math --
Behavioral and Brain Response to Intervention (RTI) --
RTI as a Nature-Nurture Perspective --
Behavioral RTI --
Behavioral Reading RTI --
Behavioral Writing RTI --
Behavioral Math RTI --
Brain Response to Intervention (RTI) --
Brain Reading RTI --
Brain Writing RTI --
Brain Math RTI.

Citation preview

LEARNING ABOUT LEARNING DISABILITIES EDITION

4

LEARNING ABOUT LEARNING DISABILITIES EDITION

4 BERNICE WONG & DEBORAH BUTLER

Amsterdam • Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 225 Wyman Street, Waltham, MA 02451, USA 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA First edition 1991 Second edition 1998 Third edition 2004 Fourth edition 2012 Copyright © 2012 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively, visit the Science and Technology Books website at www.elsevierdirect.com/rights for further information Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-388409-1 For information on all Academic Press publications visit our website at elsevierdirect.com Typeset by MPS Limited, Chennai, India www.adi-mps.com Printed and bound in United States of America 12 13 14 15 16 10 9 8 7 6 5 4 3 2 1

DEDICATION This book is dedicated to Teresa, Rod, Kristi, Jason, and Jaden.

LIST OF CONTRIBUTORS Numbers in brackets indicate the pages on which the authors’ contributions begin Dannette Allen-Bronaugh (217) James Madison University, Harrisonburg,VA 22807, USA Virginia W. Berninger (59) University of Washington, Seattle, WA 98195-3600, USA Jean B. Crockett (405) School of Special Education, School Psychology and Early Childhood Studies, University of Florida, Gainesville, FL 32611, USA Todd Cunningham (355) Department of Psychology,The Hospital for Sick Children,Toronto, Ontario, Canada M5G 1X8 Susan De La Paz (325) University of Maryland, Department of Special Education, College Park, MD 20742, USA Donald D. Deshler (299) University of Kansas, Center for Research on Learning, Lawrence, KS 66045, USA Michael Dunn (59) Washington State University, Vancouver, WA 98686-9600, USA Elizabeth A. Filippi (405) School of Special Education, School Psychology and Early Childhood Studies, University of Florida, Gainesville, FL 32611, USA Jack M. Fletcher (1) Department of Psychology, University of Houston, Houston, TX 77204-5053, USA John G. Freeman (355) Queen’s University, Faculty of Education, Kingston, Ontario, Canada K7M 5R7 Esther Geva (271) Ontario Institute for Studies in Education of the University of Toronto, Toronto, Ontario, Canada, M5S 1V6 Steve Graham (141), (243) Arizona State University, Tempe, AZ 85069-3151, USA Patricia Sampson Graner (299) University of Kansas, Center for Research on Learning, Lawrence, KS 66045, USA Jessica L. Hagaman (191) University of Wisconsin-Whitewater, WI 53190, USA Karen R. Harris (141), (243) Arizona State University, Tempe, AZ 85069-3151, USA

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List of Contributors

Allyson Harrison (355) Regional Assessment and Resource Centre, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Clara Hauth (217) George Mason University, Fairfax,VA 22030-4444, USA Katherine Herbert (271) Ministry of Children and Family Development of British Columbia,Victoria Child and Youth Mental Health,Victoria, BC, Canada,V8W 9S3 Ena Holtermann (355) Queen’s University, Faculty of Education, Kingston, Ontario, Canada K7M 5R7 Ingrid Jackson (355) Queens University, Nepean, Ottawa, Ontario, Canada K2G 5Z3 Sylvia Linan-Thompson (175) The University of Texas at Austin, TX 78745, USA Charles A. MacArthur (243) University of Delaware, Newark, DE 19716, USA Linda H. Mason (191) The Pennsylvania State University, University Park, PA 16802, USA Margo A. Mastropieri (217) George Mason University, Fairfax,VA 22030-4444, USA Jeremy Miciak (175) The University of Texas at Austin, TX 78745, USA Cheryl L. Morgan (405) School of Special Education, School Psychology and Early Childhood Studies, University of Florida, Gainesville, FL 32611, USA Zoi Philippakos (243) University of Delaware, Newark, DE 19716, USA Robert R. Reid (141) University of Nebraska-Lincoln, NE 68583-0732, USA Marcia Rock (141) University of North Carolina-Greensboro, NC 27402-6170, USA Thomas E. Scruggs (217) George Mason University, Fairfax,VA 22030-4444, USA Danielle Stomel (27) Graduate School of Education, University of California, Riverside, CA 92521, USA H. Lee Swanson (27) Graduate School of Education, University of California, Riverside, CA 92521, USA

List of Contributors

Victoria Timmermanis (89) School and Clinical Child Psychology, Department of Applied Psychology and Human Development, OISE/University of Toronto, Toronto, ON M5S 1V6, Canada Judith Wiener (89) School and Clinical Child Psychology, Department of Applied Psychology and Human Development, OISE/University of Toronto, Toronto, ON M5S 1V6, Canada John Woodward (377) School of Education, University of Puget Sound, Tacoma, WA 98416, USA.

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PREFACE As we embarked on imagining a fourth edition of Learning about Learning Disabilities, we recognized that the need for a graduate level text in this area remains as acute as it was in 1991 when the first edition was produced. Research on learning disabilities has advanced considerably in the past 20 years. For example, as a field we have come to understand better that the challenges students with learning disabilities face are not contained to the school years, but are apparent across the lifespan, and that those challenges are experienced not only in academic work, but also across social and emotional domains. On a more hopeful note, research has also documented exciting and productive ways forward, for example, by identifying factors associated with the resilience and tenacity of individuals with learning disabilities who are experiencing great success, and by evaluating the qualities of academic, social, and emotional supports with potential to foster more positive outcomes. Nonetheless, as the chapters in this text uncover very insightfully, important questions remain, in relation to understanding learning disabilities and their impact, and to advancing practice so as to ensure individuals with learning disabilities have opportunities to achieve their full potential. Thus, in this fourth edition we once again offer to advanced undergraduate and graduate students a collection of chapters designed to describe the “state of the art” in research in learning disabilities. Our primary goals remain the same as in the previous editions. The first of these is to enable students to acquire a solid knowledge base in the research literature on learning disabilities. The second is to fuel students’ interests, inspiring them to the extent that they might even select a critical issue described herein to take up in their own thesis research. To achieve these aims, we invited leading scholars in some of the “hottest” and most productive areas of inquiry to help us in constructing this text. We asked authors to describe for readers the most important issues facing us as a community (of researchers and educators) in their respective areas of expertise, provide an overview and analysis of what we know from research, and identify critical implications and important future directions. The text is bookended by chapters designed to push thinking about how we understand, identify, and construct services to support learners with learning disabilities (see Chapters 1 and 15). In between is a rich set of xiii

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chapters that combine to consider academic, social, emotional and behavioral dimensions of learning disabilities, and how curriculum, instruction, and other forms of support might be conceptualized and marshaled in ways that are most supportive to students. Across chapters, readers will learn how research is now focusing on learning at all ages, from early childhood (for example, in Chapter 6), through adolescence (for example, in Chapter 11), and into the early adult years (see Chapter 13), and across multiple settings. As they engage with this text, we expect students to bring to bear a sharply critical eye and a vigorously questioning mind, working on their own and together to distill key issues and tease out important implications for research and practice. But we also hope they might start to consider how they might contribute to the “discussion” our authors launch in this book, imagining how they too might push our field forward through their own practice and research.

ACKNOWLEDGMENTS We would like to offer our heartfelt thanks to the contributors to this fourth edition of Learning about Learning Disabilities. We are greatly in debt to our friends and colleagues who have given so generously of their time and energy in writing these excellent chapters, each of which is not only brimming with important and interesting research information, but also raises thought-provoking issues and implications. This book could not exist without you. We are also very grateful to Nikki Levy and Barbara Makinster, from Elsevier. The former has provided us with invaluable inspiration, encouragement, and support throughout. The latter has consistently been understanding and reliable in problem-solving with the glitches that inevitably accompany the production process of a book. We also wish to thank the production department staff, particularly Caroline Johnson and Carolyn Holleyman, for their careful assistance in producing this book. With gratitude, Bernice Wong and Deborah Butler

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CHAPTER

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Classification and Identification of Learning Disabilities Jack M. Fletcher Department of Psychology, University of Houston, Houston, TX 77204-5053, USA

Chapter Contents Introduction 1 What Is a Learning Disability? 1 Exclusionary Criteria 2 Inclusionary Criteria 3 LD Is an Unobservable Construct 3 Classification Issues in LD 4 Categorical versus Dimensional Classifications 5 Neurological Models of LD 8 Behavior Disorders Due to Intrinsic Factors 8 Dyslexia 9 Demise of the Concept of MBD 10 Cognitive Models 10 Emergence of the Concept of LD 10 LD and US Public Policy 11 IQ-Achievement Discrepancy 12 Other Cognitive Discrepancy Approaches 13 Psychometric Issues Underlying Cognitive Discrepancy Methods 14 Instructional Models 15 Low Achievement Methods 15 Response to Intervention Methods 17 A Hybrid Approach to LD Identification 19 Conclusions 20 Acknowledgment 21 References 21

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

Learning about Learning Disabilities

© 2012 Elsevier Inc. All rights reserved.

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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

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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

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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,

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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).

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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 measure the same ability constructs individuals will fluctuate around the cut-off point because of measurement error (Francis et al., 2005; Macmann et al., 1989). This fluctuation is most serious for individual diagnostic decisions and is why defining LD should never rest solely on a single indicator or a battery of test scores. For research comparing groups, this kind of unreliability does not have much effect on the patterns of group differences because individuals around the cut-off point are more similar than different. However, if the attributes are dimensional and a category is introduced, the difference between the groups (effect size) will be smaller and more participants will be needed in the contrasted groups to detect the difference (i.e., the power of the study is reduced) (Cohen, 1983; Markon et al., 2011). Contrasting groups in studies of LD may be an inefficient way of understanding the relations of different attributes of LD (Doehring, 1978), especially because the methods that emerge for testing of group differences are based on analysis of variance (ANOVA). In a dimensional approach, knowing the correlation of the dependent measures with the independent

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measures used to form the groups, and the intercorrelations of the dependent measures, would lead to an estimate of the relations (effect sizes) among the independent and dependent variables (e.g., achievement and cognition) that would likely be more reliable across studies and require fewer participants to evaluate (Stuebing et al., 2002). Why has research and practice on LD relied upon categorical classifications? This has occurred because of policy considerations and the need to identify individuals who might be eligible for services. However, epidemiological and behavior genetic studies of the achievement attributes of LD have supported a dimensional view (Plomin & Kovas, 2005). Although a considerable body of research has examined possible subtypes of LD using some type of theory to create groups or through the application of empirical, exploratory methods like cluster analysis to search for subgroups (see review in Fletcher et  al., 2007), these efforts have not been strongly related to treatment outcomes or other external validity indicators except along the broader dimensions of achievement (e.g., reading vs. math disability). Here it is clearer that the dimensions, while correlated, are differentially related to treatment outcomes and other external indices. Nonetheless, researchers for many years have argued that the achievement attributes of LD are normally distributed (Ellis, 1984; Stanovich, 1988) and that differences relate to severity. More recently, Snowling and Hulme (2012) have argued that reading disabilities involving word recognition and comprehension, while correlated, represent distinct dimensions of a broader classification of LD. In our book (Fletcher et  al., 2007), we suggested that the evidence supported six subgroups of LD involving reading (word recognition, fluency, and comprehension), math (calculations and problem solving), and probably written expression. The latter could involve either the generation of text (handwriting, spelling) or composition, but research was not clear on distinctions among these written expression components or overlap with other forms of LD. However, treatment needs were clearly different depending on the affected component. Although the language refers to groups, these attributes may be correlated dimensions with no explicit group structure. Dimensionality also helps us understand how people with LD may also have low achievement in more than one of these domains (e.g., both reading and math LD) and may also meet criteria for other neurodevelopmental disorders, especially ADHD. These are considered co-occurring or comorbid associations in which the person has more than a single problem and are usually not explained as the presence of one problem causing

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another (Willcutt, Betjemann et al., 2010). Research suggests that people meeting criteria for comorbid disorders show similar cognitive performance to individuals with a single disability (i.e., show characteristics of both a reading disability and ADHD). Behavior genetic studies suggest that there are “generalist genes” that appear to be involved in reading and math LD, and in ADHD, as well as specific genetic factors related to each disorder in isolation (Plomin & Kovas, 1985; Willcutt, Pennington et  al., 2010). Understanding these attributes as correlated dimensions and not as independent categories helps us understand comorbidity. There is good evidence from cognitive, neurobiological, and treatment studies for the six component classification we proposed (Fletcher et  al., 2007), but more research needs to be completed. In particular, the field of LD needs to look at categorical versus dimensional distinctions on other attributes of LD. Instructional response, for example, may lie on a continuum of severity (Fletcher et  al., 2011; Vellutino et  al., 2006). Understanding these classification issues would also be facilitated by the application of more recent statistical methods for identifying discrete groups, such as latent mixture modeling and other methods commonly utilized in classification research on psychopathology (Ruscio, Haslam, & Ruscio, 2006). I will return to these issues as I review different models for conceptualizing LD.

NEUROLOGICAL MODELS OF LD Behavior Disorders Due to Intrinsic Factors Although a discussion of earlier concepts of LD as a form of brain dysfunction may seem outdated, the conceptualizations that motivated and emerged from these concepts have strongly influenced contemporary concepts of LD. The fundamental issue was the identification of children whose difficulties seemed to be of “constitutional origin” and not attributable to environmental factors. Early neurological models initially focused on children with behavioral problems we would now recognize as ADHD and emerged because these children had behavior problems that were unexpected, along with poor school performance. One early paper described children with a “disorder of morbid control” to represent children with a behavioral pattern characterized by hyperactivity, impulsivity and difficulty with abstraction (Still, 1902). Because this behavioral pattern seemed to be associated with birth complications, other physical anomalies, and occurred more

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frequently in boys than girls, Still (1902) concluded that the origin was intrinsic to the child. Continuing the focus on behavioral patterns associated with brain dysfunction, concepts emerged with terms like organic driveness (Kahn & Cohen 1934), minimal brain injury (Strauss & Lehtinen, 1947), and minimal brain dysfunction (Clements, 1962). As the concept of MBD emerged, it was recognized that many of the children had academic problems and the concept was expanded to include reading, math, and writing difficulties. The concept of MBD was formally defined in 1964 as “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. These deviations may manifest themselves by various combinations of impairment in perception, conceptualization, language, memory, and control of attention, impulse, or motor function” (Clements, 1966, pp. 9–10). This definition of MBD incorporated (almost verbatim) the exclusionary criteria in the subsequent first US statutory definition of LD: “The term does not include children who have learning disabilities, which are primarily the result of visual, hearing, or motor handicaps, or mental retardation, or emotional disturbance, or of environmental, cultural, or economic disadvantage” (US Office of Education, 1968, p. 34). These exclusionary criteria have been part of every statutory and regulatory definition of LD in the US since 1968.

Dyslexia A somewhat separate strand represented efforts to understand children with severe reading disabilities. Described initially as “word blindness” by ophthalmologists (Morgan, 1894; Hinshelwood, 1895), Orton (1928) developed a neurological theory of dyslexia in which problems with reading were part of a broader failure to establish hemispheric dominance for language, such that people with dyslexia saw mirror images of letters and words. Thus, letter reversals and related signs became evidence of dyslexia in much the same way that behavioral patterns and perceptual and motor difficulties became signs of MBD. Subsequent research increasingly focused on characteristics that could be assessed through neuropsychological evaluations, such as right-left confusion, finger agnosia (difficulty appreciating numbers and letters written on the fingers), language and perceptual problems, and motor coordination problems. As we can see, conceptualizations of MBD and dyslexia reflected a neurological classification. The purpose was to determine the cause of the

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brain-related disorder in order to identify treatments that would correct the brain dysfunction (Benton, 1975). However, for both dyslexia and MBD, the pool of children was very heterogeneous and there was no agreement on how to define people with the disorders. Theories based on single deficits proliferated and training models for correcting problems seen as direct evidence of MBD or LD (e.g., motor or perceptual skills training) were clearly not efficacious (Doehring, 1978). For example, to diagnose MBD, people were taught to use a checklist of 37 behaviors. If the person had nine of the symptoms, treatment for MBD was indicated almost regardless of whether the problem was academic, cognitive, or behavioral (Peters, Davis, & Goolsby, 1973).

Demise of the Concept of MBD The neurological model eventually collapsed with the demise of the concept of MBD in the 1980s, reflecting the failure of training programs addressing special signs to generalize to important areas of adaptation (e.g., better reading performance). In addition, medication treatment using stimulants, which are clearly efficacious for problems with impulsivity and hyperactivity, were often recommended because a person showed multiple attributes of the group, but not those for which stimulants appeared particularly useful. With the rise of the formal concept of learning disabilities and federally led efforts to define them in the 1960s, the Diagnostic and Statistical Manual (DSM) III (American Psychiatric Association, 1980) formally separated academic skills disorders involving reading, math, and writing from ADHD, which was a set of problems in the behavioral domain involving inattention, hyperactivity, and impulsivity. The issue now is the comorbidity of different disorders and few would lump together children with these diverse difficulties into a single group (for an exception, see Gilger & Kaplan, 2001). Moreover, neurobiological research into brain function and genetics has flourished in part because criteria for different kinds of LD are specific about the area of academic impairment and separate LD from ADHD.

COGNITIVE MODELS Emergence of the Concept of LD As the influence of older neurological models began to subside, the role of cognitive factors in LD became increasingly prominent. Instead of conceptualizing LD as a form of brain dysfunction with a set of special signs that might represent qualitative distinctions separating those with

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and without LD, behavioral scientists and speech and language specialists like William Cruickshank, Helmer Myklebust, Doris Johnson, and Samuel Kirk viewed language and cognitive functions as central to the concept of LD. This early work on the concept of LD (as opposed to MBD or dyslexia, the latter a common and specific form of LD) emphasized unevenness in cognitive functions and a need for cognitive and educational interventions, along with an absence of other conditions associated with low achievement (i.e., the exclusionary criteria). Thus, Cruickshank, Bice, and Wallin (1957) recommended modifications in classroom environments to reduce distractions for children with academic and behavioral problems even as they evaluated perceptual and motor training programs. At Northwestern University, Myklebust and Johnson evaluated language and perceptual deficits on academic and social functioning of children suggesting both verbal and nonverbal factors in LD. They also introduced scripted interventions remediating these deficient skills (Johnson & Myklebust, 1967). Samuel Kirk developed assessment methods for detecting unevenness in language abilities and as I discussed above, proposed the term “learning disabilities” at a 1963 conference.

LD and US Public Policy As this research and advocacy continued, the idea of LD rapidly emerged and made its way into public policy. Based on the work of Kirk and others, it was recognized that children with LD: (a) had learning characteristics that were different from children identified with intellectual or emotional difficulties; (b) demonstrated unexpected problems with achievement given strengths in other areas; and (c) required specialized educational interventions that were not needed for typically achieving children. In addition, reflecting the influence of neurological conceptualizations, it was argued that learning difficulties in this population resulted from neurobiological factors intrinsic to the child rather than environmental factors. Not surprisingly, the formal definition of MBD in 1962 led to reactions by educators and other professionals interested in moving away from etiological concepts. At a meeting convened in 1966 by the US Office of Education, Kirk’s (1963) concept of “learning disability” was formally defined: 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 may manifest itself in an imperfect ability to listen, speak, read, write, spell, or to do mathematical calculations. The term includes such conditions

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as perceptual handicaps, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. The term does not include children who have learning problems, which are primarily the result of visual, hearing, or motor handicaps, or mental retardation, or emotional disturbance, or of environmental, cultural, or economic disadvantage. (US Office of Education, 1968, p. 34).

The importance of this definition is easily seen by the fact that it continues to serve as the federal statutory definition of LDs in US policy for special education. It has persisted since Public Law 94–142 was adopted in 1975, which subsequently became known as the Individuals with Disabilities in Education Act (IDEA; U.S. Department of Education, 2004) last revised by the US Congress in 2004. This definition has endured despite the fact that all it really says is that LDs are heterogeneous, associated with problems involving cognitive processing, and are not to be mixed with other disorders and conditions that represent exclusionary criteria. The 1966 definition of LD and the 1962 definition of MBD are similar, reflecting their common roots in neurological models of LD (Satz & Fletcher, 1980). Both are represented as “unexpected” disorders not attributable to intellectual difficulties, sensory disorders, emotional disturbance, or economic and cultural diversity. The definitions acknowledged intrinsic factors within a child. However, there are no clearly specified and measurable inclusionary criteria, which became a problem when schools were expected to identify and serve children with LDs. Thus, in 1977, the US Office of Education (1977) published a regulatory definition of LD that included a discrepancy between IQ and achievement as an inclusionary criterion: … a severe discrepancy between achievement and intellectual ability in one or more of the areas: (1) oral expression; (2) listening comprehension; (3) written expression; (4) basic reading skill; (5) reading comprehension; (6) mathematics calculation; or (7) mathematic reasoning. The child may not be identified as having a specific learning disability if the discrepancy between ability and achievement is primarily the result of: (1) a visual, hearing, or motor handicap; (2) mental retardation; (3) emotional disturbance, or (4) environmental, cultural, or economic disadvantage (p. G1082).

IQ-Achievement Discrepancy From this regulatory definition, the idea of a cognitive discrepancy between higher IQ and lower achievement as a marker has become instantiated in policy and societal concepts of LD. There was research at the time supporting an IQ-achievement discrepancy model (Rutter & Yule, 1975) that has

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not held up over time (Fletcher et  al., 1998). The Isle of Wight studies showed a qualitative break in the distribution of reading scores suggesting a categorical distinction between children with reading achievement well below IQ (specific reading disability) and reading consistent with IQ (general backwards readers). However, the Isle of Wight study was epidemiological and asked the question of whether there was a specific form of reading failure that stood out against all other forms of reading failure (Rutter & Yule, 1975). This study did not exclude children with brain injury and intellectual disabilities, many of whom emerged as backwards readers (Fletcher et  al., 1998). The qualitative break in the distribution was due to the inclusion of children with low IQ scores. To reiterate, most studies find that the achievement attributes of LD are dimensional with no qualitative breaks (Rodgers, 1983; Shalev, Manor, Auerbach, & Gross-Tsur, 2000; Shaywitz et al., 1992; Silva, McGee & Williams, 1985). At this point in time, it is well-established that cognitive discrepancies based on differences in aptitude and achievement measures do not have strong validity based on two meta-analyses of the literature (Hoskyn & Swanson, 2000; Stuebing et al., 2002). These studies did not identify major differences in the behavioral, cognitive, and achievement characteristics of children who met IQ-achievement discrepancy criteria in reading versus children with reading difficulties whose achievement was consistent with IQ (low achievers, excluding those with intellectual deficiencies). It has also been found that these two subgroups do not differ in the long term development of reading skills (Shaywitz et al., 1999) and that IQ and IQ-achievement discrepancies are at best weak predictors of treatment outcomes (Stuebing et al., 2009;Vellutino et al., 2000). Most recently, Tanaka et al. (2011) found no differences in the brain activation patterns of two different samples of children identified as IQ-achievement discrepant and low achieving when reading real words and pseudowords in a functional brain imaging study (functional magnetic resonance imaging). Fletcher et al. (2007) reviewed evidence questioning the validity of the IQ-achievement classification method using other methods for assessing aptitude (e.g., listening comprehension), other domains of achievement, and for children with language problems.

Other Cognitive Discrepancy Approaches Given the difficulties with classifications based on IQ-achievement discrepancies, other approaches to operationalizing a cognitive discrepancy method have been proposed. The most prominent uses a pattern of processing strengths and weaknesses across a battery of cognitive tasks

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(Hale et al., 2008). Depending on the model, a child may be identified as LD because of a strength in cognitive processing, a weakness in achievement, and an achievement weakness related to the processing weaknesses. Thus, children with a word level reading disability may have a strength in nonverbal processing (e.g., matrix reasoning), a weakness in vocabulary, and a weakness in reading comprehension (for which vocabulary weakness are common). Children who show achievement deficits but no processing strength (or processing weaknesses not related to the achievement domain) would not be identified as LD (Hale et al., 2008). There are multiple methods for operationalizing “patterns of strengths and weaknesses (PSW)” methods. In contrast to the research on aptitude achievement discrepancy models, there is little evidence for their validity. Given the absence of validity studies, Stuebing et al. (2012) simulated three PSW methods that have been proposed for the identification of LD. Based on the assumptions of each of the three PSW methods, latent data that replicated the reliabilities, intercorrelations, and cut-off points were generated. Observed data were then created so that the agreement of decisions about LD and not LD could be compared. All three methods were stronger for decisions concerning not LD. However, the methods did not identify many as LD and when those with LD were identified, many of the children were not LD at the latent level, suggesting high false positive rates. Cognitive skills are related to LD, but this relation does not mean that assessment of these skills helps identify or plan treatment. A method with a high false positive rate will systematically mismatch children with instruction that might not be beneficial. In thinking about this issue, consider that LD is related to how the brain functions, which we can assess with functional brain imaging studies. However, a brain imaging study is not recommended for every person evaluated for possible LD. It is simpler to assess people with measures like those used to activate the brain because the changes in brain activity (by definition) are neurophysiological correlates of task performance. In addition, at this point in time, brain imaging studies help us understand the neural mechanisms underlying LD, but don’t have specific implications for intervention (Goswami, 2008).

Psychometric Issues Underlying Cognitive Discrepancy Methods The problems with the reliability and validity of cognitive discrepancy models of LD are not surprising given what we have learned in this chapter

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about categorical and dimensional classifications, the reliability of decisions based on cut-off points, and the small amount of measurement error in even the best psychological and achievement tests. Such approaches are inherently low in reliability in identifying individual children as LD or not LD in real and simulated data (Frances et  al., 2005; Macmann & Barnett, 1989). This unreliability occurs across different attributes of LD, including assessments of achievement (Francis et al., 2005; Macmann & Barnett, 1989), IQ-achievement discrepancies (Francis et al., 2005; Macmann et al., 1985), patterns of strengths and weaknesses across cognitive tests (Kramer et  al., 1987; Stuebing et al., 2012), and even assessments of instructional response (Barth et  al., 2008). In the math area, it has been proposed that children who perform below the 10th percentile have a specific learning disability and those between the 10th and 25th percentile are low achieving (Geary, Hoard, Byrd-Craven, Nugent, & Chattavee, 2007). However, because of the imposition of rigid cut-off points, the measurement error of the tests, and the possibility that math skills are an unbroken continuum of severity, individual decisions may not be reliable. A problem with any cognitive model involves the psychometric issues we have described throughout this chapter. The other major problem with cognitive models is that they are predicated on the assumption that there are treatment implications signaled by the presence of a cognitive deficit or a pattern of strengths and weaknesses. There is little evidence that directly training deficient cognitive skills out of the context of an intervention that involves reading, math, or writing produces achievement gains (Mann, 1979; Torgesen, 2002). Researchers have for many years searched for aptitude by treatment interactions, which have largely not emerged for cognitive patterns, learning styles, and similar efforts to identify child traits that interact with specific forms of treatment (Pashler et al., 2009). The exceptions, as we shall see below, are interactions of specific academic strengths and weaknesses (Connor et  al., 2009). None of these concerns should be taken to indicate that cognitive skills are not related to LD because the manifestations of LD in achievement and other functional limitations are clearly associated with specific cognitive difficulties. Using this information for identification and treatment has proven elusive.

INSTRUCTIONAL MODELS Low Achievement Methods One alternative to the difficulties presented by cognitive discrepancy models is to focus just on the achievement domain and identify children

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Learning about Learning Disabilities

as potentially LD if they read, write, or do arithmetic below a certain level (Siegel, 1992). This approach simplifies the assessment and identification approach and eliminates any complications from the need to compare two scores, with differences scores known to be less reliable than single indicators (Bereiter, 1967). In addition, variations in different domains of reading, math, and/or writing are clearly related to variations in cognitive processing, neurobiological correlates, and intervention response whether categorical or dimensional methods are used (Fletcher et al., 2007). The presence of child attribute by treatment interactions is clearly apparent in studies where a group of poor readers receives only schoolbased reading instruction and extra tutorial math instruction and does not improve in reading compared to children who receive reading tutoring (Morris et  al., 2012). In reading, Connor et  al. (2009) evaluated reading decoding and comprehension and used a computer algorithm to adjust the amount of instruction in each domain. Helping teachers evaluate strengths and weaknesses in decoding versus comprehension skills and adjust instruction based the assessment led to better outcomes in classrooms receiving this assistance than in classrooms that did not receive this assistance. Low achievement methods are associated with good external validity. There are also problems with low achievement methods, especially because the group identified with LD solely on the basis of low achievement would include students with other disorders and conditions typically considered exclusionary. Exclusionary criteria could be included in the definition. With this modification, the primary inclusionary criterion would be low achievement and unexpectedness would be indicated by absence of exclusionary criteria. However, this definition is still based primarily on exclusion because there is no inclusionary criterion indicating “unexpectedness”. Low achievement methods do not resolve the psychometric issues involved in identifying individual people with LD and make even plainer the issues with cut-off points. What is the threshold for defining low achievement? Policy and different studies vary considerably in the cutoff points used to indicate the possibility of LD. The selection of a cutoff point makes assumptions about prevalence. If the 20th percentile is selected, and we exclude 2% for intellectual disability, and some unknown proportion because of exclusionary criteria (say 3%), the resulting estimate of 15% on a single type of achievement measure seems high for the number of children with LD, especially if the same cut-off point is used across six achievement domains. Certainly, there would be overlap because some

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would have problems in multiple domains and the measures of achievement are correlated, but the overall prevalence will be much higher than 15%. The cut-off point could be lowered, but because achievement distributions do not have natural breaks, any decision is potentially arbitrary. These issues are fundamental to the concept of LD and reflect social, political, and economic decisions that have not been adequately addressed.

Response to Intervention Methods Methods for classification, definition, and identification of LD that emanate from response to intervention (RTI) frameworks generate considerable confusion. I find it helpful to separate the RTI service delivery framework from the assessment of intervention response, the latter most often used as an identification criterion. RTI is an approach to delivering services in schools that introduces universal screening for academic and behavioral problems, progress monitoring for students who are at risk, and increasingly intense interventions depending on how well at risk children progress (Fletcher & Vaughn, 2009; Jimerson, VanDerHeyden, & Burns, 2007; VanDerHeyden & Burns, 2010). The interventions are usually organized hierarchically as a set of tiers beginning in the general education classroom through which students pass when progress at less intense intervention levels is not adequate. Some children will continue to struggle despite multiple intense interventions; this intractability to instruction that works with most children may be evidence of a disability. Thus, in identification methods based on RTI frameworks, inadequate response to instruction becomes a key inclusionary criterion and indicates unexpectedness (Fletcher et al., 2007). Decisions about how well a child is progressing are usually made on the basis of short assessments of key academic skills that are predictors of proficiency (Stecker, Fuchs, & Fuchs, 2005). Thus, in reading, it is common to assess oral reading fluency for a short passage and count the number of words read correctly per minute. This type of curriculum-based measurement (CBM) assessment can be repeatedly administered over the course of a school year or intervention and is predictive of multiple proficiencies in reading. Charts showing growth in fluency over time can be created and compared to benchmarks for the end of the intervention or to a normative standard. Similar methods are used in math and writing. Lack of growth or failure to attain key benchmarks may be indicative of LD and certainly support the idea of a need for more intervention. Such an approach to identification is strongly linked to treatment (Fuchs & Fuchs, 1998).

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In the US, the 2004 reauthorization of IDEA (U.S. Department of Education, 2004) led to major changes in identification procedures for LD and introduced requirements for identification that were consistent with an RTI service delivery model. School districts were not required to use a cognitive discrepancy approach, but could adopt an approach based on RTI for identification of LD. However, an assessment of instructional response is required for any student considered for the LD eligibility category in special education. No student may be identified as LD without evidence that instructional programs in reading and math were adequate. Regardless of the identification model adopted by the district, requirements for identification as LD are the same. A comprehensive evaluation of the child is required that uses multiple sources of information. No single criterion is sufficient for designation as LD. For a district that uses a RTI framework, additional requirements are for parental notification that the child has been identified as at risk and a summary of the strategies that the district will use to address instructional needs. Practitioners who use RTI methods for LD generally see little use for cognitive assessments unless there is concern about another disability. In addition, the focus is on factors extrinsic to the child because they are malleable and potential agents of change (VanDerHeyden & Burns, 2010). Concerns have been expressed about the use of RTI methods (i.e., assessments of instructional response) as a “standalone” method (Reynolds & Shaywitz, 2009), but such an approach is clearly not sufficient and inconsistent with IDEA requirements for the use of multiple criteria. Critics have suggested that there is no “gold standard” for LD in a RTI model, but in fact, if a dimensional view is taken, there is no gold standard for any approach to LD identification. The psychometric problems I have identified throughout this chapter clearly apply to assessments of instructional response, with low agreement across measures used to identify inadequate response to instruction (Barth et  al., 2008; Fuchs & Deshler, 2007). However, this lack of agreement extends to any psychometric assessment and is magnified when more tests are used, different constructs are measured, the reliability of the tests is lower, and different normative samples are involved. No psychometric approach based on single indicators will identify the same set of people as LD if a rigid cut-off point is used because of the measurement error of the tests. This problem is not specific to assessments of instructional response. To illustrate this point, Fletcher et  al. (2012) simulated agreement between the two highly reliable norm referenced assessments of decoding and fluency. If the tests were perfectly correlated and perfectly reliable,

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agreement would have been 1.0. If we assume the two measures are perfectly reliable, but with a correlation of .94, agreement is reduced to .76, which is the level where agreement is considered “excellent.” If the measures are perfectly correlated and reliability of each is .90, agreement is reduced to .75. Obviously a correlation of .94 and reliabilities of .90 reduce agreement even further (about .60). At an empirical level, the agreement of the simulated measures was .38 because of differences in the normative samples and sample size. In this example, low agreement is occurring on two highly correlated and highly reliable norm referenced assessments of achievement, so these problems are universal for efforts to identify LD using psychometric tests and cut-off points on what may be dimensional attributes. Given these problems, identification of LD should not be based on a single measure; multiple criteria are needed for determining of instructional response and eligibility for LD. These reliability issues are for individual decisions. As with low achievement methods, contrasting group and dimensional approaches show differences between adequate and inadequate responders to instruction in their cognitive characteristics (Fletcher et al., 2011; Nelson et al., 2003;Vellutino et al., 2006). In addition, brain imaging studies show differences in brain activation patterns and that these patterns predict instructional response (Rezaie et al., 2011). However, the brain imaging patterns in responders resemble those of typically developing children and are largely normalizing, not compensatory. Similarly, cognitive patterns reflect a continuum of severity that parallel the levels of difficulties on reading tasks (Fletcher et al., 2011; Vellutino et al., 2006). Thus, although decisions must be made about resource allocation, instructional response seems to represent a dimension with no evidence thus far for discrete groups. From a classification perspective, these results support the validity of instructional response as an inclusionary criterion.

A HYBRID APPROACH TO LD IDENTIFICATION Given the difficulties I have identified in classifying and defining LD, what are the solutions? Psychometric approaches that take into account the measurement issues by applying confidence intervals and defining costs and benefits of decision errors would improve psychometric decision making. Although we can acknowledge the problems with dichotomizing decisions as if there were discrete groups, the allocation of resources usually requires a decision of this sort. Such decisions should not be based

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Learning about Learning Disabilities

on a single criterion and apply it as a gate keeping device for accessing services. All too often, identification is focused on finding the “right” child. The origins of special education are in entitlement programs through which protections and services are provided based on specifically identified disadvantages. However, consistent with the obesity analogy I used in the opening of this chapter, the purpose of LD identification should be both the civil rights protections accorded to people with disabilities and to identify children who would benefit from intervention. Over identifying people for services relative to some criterion indicating LD (i.e., false positive errors) may be undesirable from an accounting view, but such error may be more acceptable if the goal is to enhance academic and behavioral outcomes for children at risk for these difficulties. Focusing on instructional models, looking at relations of different indicators of LD in terms of triggers for interventions, and moving away from the preoccupation identifying the “right” child would do much to reduce the difficulties of identification. A hybrid model would combine attributes of LD that are evidencebased and measurable with exclusionary criteria traditionally used to identify other disabilities and conditions associated with LD. A consensus group convened by the US Department of Education Office of Special Education Programs proposed identification criteria based on features of a low achievement method and methods based on instructional response (Bradley et al., 2002). This group posed three primary criteria: (1) Student demonstrates low achievement (inclusionary); (2) There is insufficient response to effective research-based interventions (inclusionary); (3) Exclusion factors: intellectual disabilities, sensory deficits, serious emotional disturbance, language minority status (where lack of proficiency in English accounts for measured achievement deficits), and lack of opportunity to learn. In an evaluation, children who meet both inclusionary criteria and in whom the exclusionary criteria can be eliminated as explanations of low achievement and inadequate instructional response would be considered LD.

CONCLUSIONS If the attributes of LD represent unbroken continuums, identification may be improved by moving away from categorical decisions and considering the likelihood or probability of LD. Although conceptual models for the construct of LD show consensus for the attributes of LD, there is no consensus over which attributes are best for defining LD. As I discussed,

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currently implemented identification methods for identification of LD have issues with the implementation of thresholds that are usually not addressed. The measurements used to operationalize LD are correlated and have small amounts of unreliability, problems that make identification of LD less reliable than is desirable at the level of the individual person. When cut-off points are used, they should be set above the desired threshold to avoid missing people who fluctuate around the threshold on single assessments. Alternatively, confidence intervals could be constructed around the cut-off point. More generally, ignoring issues related to resources, false positive errors are less detrimental than false negative errors because the former can be evaluated in the context of intervention response. Presently many children do not get intervention or go into remediation with infrequent progress monitoring, languishing in an intervention that is not working. Moreover, the decision process for identifying LD may need to shift to a clearly defined multi-axial, consensus method as in the hybrid model proposed in Bradley et  al. (2002). The strongest methods for identifying LD should ultimately relate to outcomes. As such, I hope that classifications based on instructional methods continue to be refined, with unexpected underachievement viewed in part as intractability to instruction. Even in research, many studies combine children who may or may not have been recipients of adequate instruction. The increasing focus on classifications and interventions for children who respond inadequately to instruction that is generally effective would facilitate research and practice involving people at risk for and who meet criteria for LD.

ACKNOWLEDGMENT This research was supported in part by grant P50 HD052117, Texas Center for Learning Disabilities, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD or the National Institutes of Health.

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Ruscio, J., Haslam, N., & Ruscio, A. M. (2006). Introduction to the taxometric method: A practical guide. Hillsdale, NJ: Lawrence Erlbaum Associates. Rutter, M. (1978). Dyslexia. In A. L. Benton & D. Pearl (Eds.), Dyslexia: An appraisal of current knowledge. New York: Oxford. Rutter, M., & Yule, W. (1975). The concept of specific reading retardation. Journal of Child Psychology and Psychiatry, 16, 181–197. Satz, P., & Fletcher, J. M. (1980). Minimal brain dysfunctions: An appraisal of research concepts and methods. In H. Rie & E. Rie (Eds.), Handbook of minimal brain dysfunctions: A critical view (pp. 669–715). New York: Wiley–Interscience. Shalev, R. S., Auerbach, J., Manor, O., & Gross-Tsur, V. (2000). Developmental dyscalculia: prevalence and prognosis. European Child and Adolescent Psychiatry, 9, 58–64. Shaywitz, S. E., Escobar, M. D., Shaywitz, B. A., Fletcher, J. M., & Makuch, R. (1992). Evidence that dyslexia may represent the lower tail of a normal distribution of reading ability. New England Journal of Medicine, 326, 145–150. Shaywitz, S. E., Fletcher, J. M., Holahan, J. M., Shneider, A. E., Marchione, K. E., Stuebing, K. K., et al. (1999). Persistence of dyslexia: The Connecticut longitudinal study at adolescence. Pediatrics, 104, 1351–1359. Siegel, L. S. (1992). An evaluation of the discrepancy definition of dyslexia. Journal of Learning Disabilities, 25, 618–629. Silva, P. A., McGee, R., & Williams, S. (1985). Some characteristics of 9-year-old boys with general reading backwardness or specific reading retardation. Journal of Child Psychology and Psychiatry, 26, 407–421. Skinner, H. (1981). Toward the integration of classification theory and methods. Journal of Abnormal Psychology, 90, 68–87. Snowling, M. J., & Hulme, C. (2012). Annual research review: The nature and classification of reading disorders: a commentary on proposals for DSM-5. Journal of Child Psychiatry and Psychology, 53, 593–607. Stanovich, K. E. (1988). Explaining the differences between the dyslexic and the gardenvariety poor reader: The phonological–core variable difference model. Journal of Learning Disabilities, 21, 590–604. Stecker, P. M., Fuchs, L. S., & Fuchs, D. (2005). Using curriculum-based measurement to improve student achievement: Review of research. Psychology in the Schools, 42, 795–819. Still, G. F. (1902). Some abnormal psychological conditions in children. Lancet, 1, 1077–1082. Strauss, A. A., & Lehtinen, L. E. (1947). Psychopathology and education of the brain-injured child: Vol. 2. Progress in theory and clinic. New York: Grune & Stratton. Stuebing, K. K., Barth, A. E., Molfese, P. J., Weiss, B., & Fletcher, J. M. (2009). IQ is not strongly related to response to reading instruction: A meta-analytic interpretation. Exceptional Children, 76, 31–51. Stuebing, K. K., Fletcher, J. M., Branum-Martin, L., & Francis, D. J. (2012). Simulated comparisons of three methods for identifying specific learning disabilities based on cognitive discrepancies. School Psychology Review, 41, 3–22. Stuebing, K. K., Fletcher, J. M., LeDoux, J. M., Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2002). Validity of IQ-discrepancy classifications of reading disabilities: A meta-analysis. American Educational Research Journal, 39, 469–518. Tanaka, H., Black, J., Hulme, C., Leanne, S., Kesler, S., Whitfield, G., et al. (2011). The brain basis of the phonological deficit in dyslexia is independent of IQ. Psychological Science, 22, 294–302. Torgesen, J. K. (2002). Empirical and theoretical support for direct diagnosis of learning disabilities by assessment of intrinsic processing weaknesses. In R. Bradley, L. Danielson, & D. Hallahan (Eds.), Identification of learning disabilities: Research to practice (pp. 565–650). Mahwah, NJ: Erlbaum.

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U.S. Department of Education (2004). Individuals with disabilities education improvement act. Washington DC: Author. 20 U.S.C. § 1400. U.S. Office of Education (1968). First annual report of the National Advisory Committee on Handicapped Children. Washington, DC: Author. United States Office of Education (1977). Assistance to states for education for handicapped children: Procedures for evaluating specific learning disabilities. Federal Register, 42, G1082–G1085. VanDerHeyden, A., & Burns, M. (2010). Essentials of response to intervention. New York: John Wiley. Vellutino, F. R., Scanlon, D. M., & Lyon, G. R. (2000). Differentiating between difficult to remediate and readily remediated poor readers: More evidence against the IQ Achievement discrepancy definition of reading disability. Journal of Learning Disabilities, 33, 223–238. Vellutino, F. R., Scanlon, D. M., Small, S., & Fanuele, D. P. (2006). Response to intervention as a vehicle for distinguishing between children with and without reading disabilities: Evidence for the role of kindergarten and first-grade interventions. Journal of Learning Disabilities, 39, 157–169. Willcutt, E. G., Betjemann, R. S., McGrath, L. M., Chhabildas, N. A., Olson, R. K., DeFries, J. C., et  al. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46(10), 1345–1361. Willcutt, E. G., Pennington, B. F., Duncan, L., Smith, S. D., Keenan, J. M.,Wadsworth, S., et al. (2010). Understanding the complex etiologies of developmental disorders: Behavioral amd molecular genetics approaches. Journal of Behavioral and Developmental Pediatrics, 31, 533–544.

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Learning Disabilities and Memory H. Lee Swanson, and Danielle Stomel Graduate School of Education, Educational Psychology/Special Education, University of California, CA 92521, USA

Chapter Contents Introduction27 A Historical Perspective 28 Understanding Memory Differences for Students with LD 31 Parallels to Normal Memory Development 32 Mapping Memory Components that Might Be Deficient for Students with LD 33 Everyday Memory 43 Memory Intervention 44 Memory Strategies Serve Different Purposes 45 Good Memory Strategies for NLD Students are not Necessarily Good Strategies for Students with LD and Vice Versa 45 Effective Memory Strategies do not Necessarily Eliminate Processing Differences 46 The Strategies Taught are not Necessarily the Ones Used 46 Memory Strategies in Relation to a Student’s Knowledge Base and Capacity 47 Comparable Memory Strategy May not Eliminate Performance Differences 47 Memory Strategies Taught do not Necessarily become Transformed into Expert Strategies48 Strategy Instruction Must Operate on the Law of Parsimony 48 Training WM Directly 49 Summary and Conclusions 51 References52

INTRODUCTION Memory is the ability to encode, process, and retrieve information that one has been exposed to. As a skill, it is inseparable from intellectual functioning and learning. Individuals deficient in memory skills, such as children and adults with learning disabilities (LD), would be expected to have difficulty on a number of academic and cognitive tasks. Although memory is linked to performance in several academic (e.g., reading) and cognitive areas (e.g., problem solving), it is a critical area of focus in the field of LD for three reasons. First, it reflects applied cognition; that is, memory functioning reflects

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all aspects of learning. Second, several studies suggest that the memory skills used by students with LD do not appear to exhaust, or even to tap, their ability, and therefore we need to discover instructional procedures that capitalize on their potential. Finally, several intervention programs that attempt to enhance the overall cognition of children and adults with LD rely on principles derived from memory research. This chapter characterizes and selectively reviews past and current research on memory skills, describes the components of processing that influence memory performance, and discusses the implications of memory research for the instruction of children and adults with LD. A more comprehensive, historical review and analysis of contemporary memory research on LD is reported elsewhere (e.g., Swanson, 2011; Swanson, Cooney, & McNamara, 2004).

A HISTORICAL PERSPECTIVE The earliest link between LD and memory was established in the literature on reading disabilities in the works of Kussmaul. In 1877, Kussmaul called attention to a disorder he labeled “word blindness”, which was characterized as an inability to read, although vision, intellect, and speech were normal. Following Kussmaul’s contribution, several cases of reading difficulties acquired by adults due to cerebral lesions, mostly involving the angular gyri of the left hemisphere, were reported (see Hinshelwood, 1917, for a review). In one important case study published by Morgan (1896), a 14-year-old boy of normal intelligence had difficulty recalling letters of the alphabet. He also had difficulty recalling written words, which seemed to convey “no impression to this mind”. Interestingly, the child appeared to have good memory for oral information. This case study was important because word blindness did not appear to occur as a result of a cerebral lesion. After Morgan’s description of this condition, designated as a specific reading disability, research on memory was expanded to include children of normal intelligence who exhibited difficulties in reading. Hinshelwood’s (1917) classic monograph presents a number of case studies describing reading disabilities in children of normal intelligence with memory problems. On the basis of these observations, Hinshelwood inferred that reading problems of these children were related to a “pathological condition of the visual memory center” (p. 21). At the same time Hinshelwood’s monograph appeared, a little known text by Bronner (1917) reviewed case studies linking memory difficulties to children of normal intelligence. For example, consider Case 21:

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Henry J., 16 years old, was seen after he had been in court on several occasions. The mental examination showed that the boy was quite intelligent and in general capable, but had a very specialized defect. The striking feature of all the test work with this boy was the finding that he was far below his age in the matter of rote memory. When a series of numerals was presented to him auditorially, he could remember no more than four. His memory span for numerals presented visually was not much better … he succeeded here with five. Memory span for syllables was likewise poor … On the other hand when ideas were to be recalled, that is, where memory dealt with logical material, the results were good. (Bronner, 1917, p. 120)

A majority of case studies reviewed in Bronner’s text suggested that immediate (short-term) memory of children with reading disabilities was deficient and that remote (long-term) memory was intact. Bronner also noted that little about memory and its application to complex learning activities was known. For example, the author stated: Very many practically important laws of memory have not yet been determined; those most firmly established concern themselves mainly with nonsense or other type of material quite unlike the activities of everyday life. In a common sense way we are aware that both immediate and remote memory are essential, that we need to remember what we see and hear … that to remember an idea is probably more useful in general, than to have a good memory for rote material, but a defect for the latter may be of great significance in some kinds of school work. (Bronner, 1917, p. 110)

Researchers from the 1920s to the 1950s generally viewed reading difficulties as being associated with structural damage to portions of the brain that support visual memory (e.g., see Geschwind, 1962, for a review; also see Monroe, 1932). A contrasting position was provided by Orton (1925, 1937), who suggested that reading disorders were reflective of a neurological maturational lag resulting from a delayed lateral cerebral dominance for language. Orton described the phenomenon of a selective loss or diminished capacity to remember words as strephosymbolia (twisted symbols). Orton (1937) noted that: Although these children show many more errors of a wide variety of kinds it is clear that their difficulty is not in hearing and not in speech mechanism … but in recalling words previously heard again or used in speech, and that one of the outstanding obstacles to such recall is remembering (emphasis added) all of the sounds in their proper order. (Orton, 1937, p. 147)

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In cases of visual memory, Orton stated that such children with reading disabilities have major difficulties in “recalling the printed word in terms of its spatial sequence of proper order in space” (p. 148). Thus, for Orton, reading disabled children’s memory difficulties were seen as reflecting spatial sequences in visual memory or temporal sequences in auditory memory. Although the conceptual foundation of much of Orton’s research was challenged in the 1970s (see Vellutino, 1979, for a review), much of the clinical evidence for linking LD and memory processes was established from the earlier clinical studies of Morgan, Hinshelwood, and Orton. It was not until the late 1960s and early 1970s that experimental (nonclinical) studies appeared comparing children with LD and nondisabled (NLD) children’s performance on memory tasks. The majority of these studies focused on modality-specific memory processes (i.e., auditory vs. visual memory) and cross-modality (e.g., visual recognition of auditorially presented information) instructional conditions. For example, Senf and Feshbach (1970) found differences between good and poor readers’ memory on cross-modality presentation conditions. That is, students were compared on their recall of digits presented auditorially, visually, and audiovisually and retrieval responses were verbal or written. The sample with LD exhibited poor recall of stimuli organized into audiovisual pairs, which was attributed to problems of cross-modality matching. Older, normal achieving children recalled the digits in audiovisual pairs more accurately than their younger counterparts, whereas older children with LD recalled no better than younger children with LD. The sample with LD also exhibited a higher prevalence of visual memory errors. The implication of this research was that some prerequisite skills of pairing visual and auditory stimuli had not developed in the children with LD, and the possession of these skills was essential for reading. In contrast to this study, Denckla and Rudel (1974) found that poor recall of children with LD was not related to visual encoding errors, but rather to temporal sequencing. Their results suggested that children who had difficulties in temporal sequencing would have difficulty recalling information from spatial tasks or tasks that required matching of serial and spatial stimuli (as in the study of Senf & Feshbach, 1970). To summarize, studies in the late 1960s and early 1970s, although contradictory, did establish that children with LD experienced memory difficulties on laboratory tasks that required the sequencing of information presented visually and auditorially. Differences in results were most likely due to variations in how the ability groups were defined and selected. We now turn to a discussion of the conceptualizations related to memory problems of children and adults with LD from the mid 1970s to the

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mid 1980s. During this time period, memory performance of children and adults with LD was strongly influenced by the hypothesis that variations in memory performance are partly rooted in children’s acquisition of mnemonic strategies. Strategies are deliberate, consciously applied procedures that aid in the storage and subsequent retrieval of information. Studies during this time period focused on memory activities such as clustering, elaboration, and rehearsal. The emphasis in these studies was on teaching children with LD under various conditions or with different types of memory strategies how to remember presented material (e.g., see Scruggs & Mastroperi, 2000, for a review). In general, earlier studies showed that children with LD could be taught through direct instructions (e.g., Gelzheiser, 1984), modeling (e.g., Dawson, Hallahan, Reaves, & Ball, 1980), and reinforcement (e.g., Bauer & Peller-Porth, 1990) to use some simple strategies that they do not produce spontaneously (e.g., Dallego & Moely, 1980). Further, the strategy hypothesis was generalized into other areas beside memory, such as reading comprehension (e.g., Wong & Jones, 1982), writing (e.g., Graham & Harris, 2003), mathematics (e.g., Montague, 1992), and problem solving (e.g., Borkowski, Estrada, Milstead, & Hale, 1989). Since the 1990s, the majority of memory research has moved in a different direction, towards an analysis of nonstrategic processes that are not necessarily consciously applied. Many of these studies are framed within Baddeley’s (Baddeley, 1986, 2000, 2007; Baddeley & Logie, 1999) multiple component model (to be discussed; see Alloway, 2007; Alloway & Passolunghi, 2009; Berg, 2008; Gathercole, Alloway, Willis, & Adams, 2006; Swanson, Howard, & Sáez, 2006). The major motivation behind this movement has been that important aspects of memory performance are often disassociated with changes in mnemonic strategies, and that significant differences remain in performance between children with and without LD after using optimal strategies (a strategy shown advantageous in the majority of studies). Prior to reviewing this current focus of memory research, however, an understanding of the research conducted on the development of memory in children with LD during the late 1970s to the early 1990s is necessary.

UNDERSTANDING MEMORY DIFFERENCES FOR STUDENTS WITH LD When accounting for where, how and why students with LD’s memory is deficient in comparison to peers, two broad perspectives have been adopted, reflected in: (a) studies that parallel normal child development

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in memory and (b) studies that identify memory components in which children or adults with LD are deficient.

Parallels to Normal Memory Development There is some agreement among researchers that what we know about the memory of children with LD is somewhat paralleled by what we know about the differences between older and younger children’s memory (e.g., Jarrold & Bayless, 2007; Swanson, 1999a, 2003; Swanson, Jerman, & Zheng, 2008). Such parallels in performance do not mean that children with LD experience a lag in all memory processes or that faulty memory performance is primarily related to immature development. Rather, faulty memory performance reflects overt performance in some memory areas that is comparable to young children. The research on chronologically age matched children with and without LD parallels the research on younger versus younger NLD children and shows that performance differences (a) emerge on tasks that require the use of cognitive strategies (e.g., rehearsal and organization); (b) emerge on effortful memory tasks, but not for tasks requiring automatic processing; (c) are influenced by the individual’s knowledge base; and (d) are influenced by the individuals’ awareness of their own memory processes (metacognition). Perhaps one of the most significant studies in terms of bringing research in memory on students with LD into a developmental perspective was conducted by Tarver, Hallahan, Kauffman, and Ball (1976). In a first study, they compared children with LD of approximately 8 years of age to normally-achieving boys of the same age on a serial recall task of pictures that included central and incidental information. They found that the serial position curve of NLD children revealed the common primacy-recency effect (remembering the first and last presented items better than the middle items), whereas the performance of children with LD revealed a recency effect only. In a second study, they compared boys with LD who were 10 and 13 years of age on the same tasks. They found that the 10 and 13-year-old children with LD exhibited both a primacy and recency effect for nonrehearsal and rehearsal conditions. For both studies, an analysis of central recall (children attend to specific items based on experimenter instructions) in the three age groups revealed a constant age related increase in overall recall and in primacy (recalling the first few items presented) performance. The normal achievers recalled more information that was central to the task when compared to children with LD, whereas children with LD recalled more incidental information

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than normal achievers. Thus, although children with LD were deficient in selective attention, their selective attention improved with age. These results were interpreted as reflecting a developmental lag, in that students with LD were viewed as delayed in their utilization of the strategies for serial recall (verbal rehearsal) and selective attention. Earlier studies that covered some of the same developmental themes as the Tarver et al. (1976) study were Torgesen and Goldman (1977) in which they investigated the role of rehearsal on serial and free recall performance, Swanson (1977) in which he investigated the role of primacy performance on the non-verbal serial recall of visual information, Bauer (1977) in which he investigated the role of rehearsal and serial recall, and Wong (1978), in which she investigated the effect of cued recall and organization on children with LD.

Mapping Memory Components that Might Be Deficient for Students with LD Thus, earlier research established some parallels between the performance of younger children and those of older children with LD. Emerging research also showed that performance of children with LD may reflect deficits and not necessarily immature development. One popular means of explaining the cognitive performance of students with LD was by drawing upon fundamental constructs that are inherent in most models of information processing. Three constructs are fundamental: (1) a constraint or structural component, akin to the hardware of a computer, which defines the parameters within which information can be processed at a particular stage (e.g., sensory storage, STM, working memory, long-term memory); (2) a strategy component, akin to the software of a computer system, which describes the operations of the various stages; and (3) an executive component, by which learners’ activities (e.g., strategies) are overseen and monitored, particularly in association with WM. Briefly, the structural components are sensory, short-term, working, and long-term memory. Sensory memory refers to the initial representation of information that is available for processing for a maximum of 3–5 seconds; STM processes information between 3 and 7 seconds and is primarily concerned with storage, via rehearsal processes. Working memory also focuses on the storage of information as well as the active interpretation of newly presented information plus information from long-term memory, whereas long-term memory is a permanent storage with unlimited capacity. The executive component monitors and coordinates the functioning of the entire system. Some of this monitoring may

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be automatic, with little awareness on the individual’s part, whereas other types of monitoring require effortful and conscious processing. Earlier research relied on a multistore model of memory that viewed information as flowing through these component stores in a wellregulated fashion, progressing from the sensory register, to STM, and finally to long-term memory. These stores can be differentiated in children’s functioning by realizing that (a) STM has a limited capacity, and thus makes use of rehearsal and organizing mechanisms; (b) storage in long-term memory is mostly semantic; and (c) two critical determinants of forgetting in long-term memory are item displacement and interference, possibly as a result of a lack of retrieval strategy. In general, research on the sensory register for children with LD suggests it is somewhat intact (see Eden, Stein, Wood, & Wood, 1995; Santiago & Matos, 1994 for review). Some earlier reviews (e.g., see Worden, 1986) have also suggested that the long-term memory of children with LD is intact, but the strategies necessary to gain access to this information are impaired. However, a good deal of research has focused on ways in which STM, as well as WM and associated processes, might be problematic. Thus, attention in the remainder of this section focuses primarily in these two areas. Short-Term Memory (STM) One area that has received some consensus is that STM for verbal information is deficient in children and adults with LD (Swanson & Hsieh, 2009; Swanson & Jerman, 2006; Swanson, Zheng, & Jerman, 2009). The majority of this research has suggested that, for children with LD, the lack or inefficient use of a phonological code (sound representation within the child’s mind) impairs reading development (e.g., Siegel, 1993a,b). Several researchers have found that good and poor readers differ in the way they access phonological information in memory (see Siegel, 2003, for a review). An earlier seminal study by Shankweiler, Liberman, Mark, Fowler, and Fischer (1979) compared the ability of superior, marginal, and poor second grade readers to recall rhyming and nonrhyming letter strings. The superior readers were found to have more difficulty recalling the rhyming letter strings than the nonrhyming strings. Poor readers, however, appeared to perform comparably on rhyming and nonrhyming tasks. The authors suggested that the phonological confuseability created by the rhyming letters interfered with good readers’ recall because these readers relied on phonological information to a greater degree than poor readers.

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Thus, an interaction is usually found, in which poor readers perform better on “rhyming-word and similar letter-sounding tasks” because they have poor access to a phonological code (e.g., Shankweiler et al., 1979). That is, good readers recall more information for words or letters that have distinct sounds (e.g., mat vs. book, A vs. F) than words or letters that sound alike (mat vs. cat, b vs. d). In contrast, poor readers are more comparable in their recall of similar and dissimilar words or sounds than skilled readers. This finding suggests that good readers are disrupted when words or sounds are alike because they process information in terms of sound (phonological) units. In contrast, poor readers are not efficient in processing information into sound units (phonological codes) and, therefore, are not disrupted in performance if words or letters sound alike. Because STM is clearly the most widely researched area related to the cognitive processing problems of students with LD, a comprehensive meta-analysis (quantitative synthesis) was conducted comparing the performance of students with and without LD on STM tasks (O’Shaughnessy & Swanson, 1998). The analysis covered articles published in a 20-year period. To be included in the analysis, each study must have: (a) directly compared readers with LD to average readers, as identified on a standardized reading measure, on at least one short-term measure; (b) reported standardized reading scores which indicated that students with LD were at least 1 year below grade level; and (c) reported intelligence scores for students with LD which were in the average range (85 to 115). Although the search resulted in approximately 155 articles on memory and LD, only 38 studies met the criteria for inclusion (24.5%). Effect sizes (ESs) were computed for each experiment to reflect the relationship between the mean memory score of the learning disabled group as compared to the mean memory score of the NLD group. Negative values for ES represented poorer immediate memory performance in the learning disabled group (e.g., an ES of −0.50 suggested that the mean score of students with LD was ½ standard deviation below the mean score for normally-achieving students). For comparisons, an ES magnitude of 0.20, in absolute value, is considered small, 0.50 is moderate, and 0.80 is considered large (Cohen, 1988). Based on a review of the studies included in this analysis, two broad categories were developed to organize the results: studies that used: (1) verbal stimuli and/or (2) nonverbal stimuli. In addition, the following subcategories were developed to organize each of the broad categories: (a) free recall and serial recall memory tasks; (b) with and without instruction

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in mnemonic strategies; (c) auditory and visual presentation; and (d) age (7–8 years, 9–11 years, 12–13 years, 14–17 years, and 18 years and older). The majority of studies involved 4th, 5th, and 6th grade students. The important findings of the synthesis were as follows: 1. The learning disabled group performed more poorly on tasks requiring memorization of verbal information in comparison to the NLD group (an overall mean ES of −0.68). 2. Memory tasks that employed stimuli that could not easily be named, such as abstract shapes, did not produce large differences between good and poor readers (ES = −0.15). 3. Memory tasks that required readers with LD to recall exact sequences of verbal stimuli, such as words or digits, immediately after a series was presented yielded a much greater overall mean ES (ES = −0.80) than nonverbal serial recall tasks (ES  =  −0.17). Thus, compared to average readers, the relative serial recall performance of students with reading disabilities was much poorer with verbal material than it was with nonverbal stimuli. 4. The overall mean ES for studies which provided instructions in mnemonic strategies for verbal stimuli (e.g., rehearsal and sorting items into groups) was −0.54; which was lower, but not much, when compared to studies that did not provide instructions (ES = −0.71). This indicates that although the memory performance of students who are reading disabled improved with training in mnemonic strategies, their performance was still well below that of average readers. 5. Memory tasks that involved the auditory presentation of verbal stimuli resulted in an overall mean ES of −0.70, while those that involved a visual presentation of verbal stimuli resulted in an overall mean ES of −0.66. Thus, the inferior verbal memory performance of reading disabled students appears unrelated to the modality in which a stimulus is received. 6. Memory tasks that involved the visual presentation of nonverbal stimuli, such as abstract shapes, resulted in an overall mean ES of −0.15. This can be interpreted as a small difference between the average and learning disabled reading groups. In summary, this quantitative analysis of the literature indicates that children and adults with LD are inferior to their counterparts on measures of STM. Most critically, students with LD are at a distinct disadvantage compared to their normal achieving peers when they are required to memorize verbal information. Students with LD have difficulty remembering

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familiar items such as letters, words, and numbers, and unfamiliar items such as abstract shapes that can be named and stored phonetically in memory. Moreover, when a task demands that verbal information be recalled in sequential order, the memory performance of students with LD declines even farther. Because skillful reading involves processing ordered information (i.e., words are written from left to right and comprised of specific sequences of letters) it seems likely that memory deficits could play a role in reading disabilities. For example, beginning readers must obtain the sounds of words from their written representations. These print-to-sound codes must be stored in memory in order and then blended together, while simultaneously searching LTM for a word that matches the string of sounds. Because low verbal materials (e.g., Geometric Shapes) produce small differences between skilled and learning disabled readers in recall, the memory deficits of readers with LD do not appear to involve general memory ability. Working Memory Current perspectives on the study of memory in learning disabled samples focus on working memory. Working memory has been applied to poor performance in such academic areas as reading comprehension (e.g., Carretti, Borella, Cornoldi, & De Beni, 2009; De Jong, 1998; Savage, Lavers & Pilly, 2007; Swanson, 1999b), math (Berg, 2008; Geary, Hoard, Bryd-Craven, Nugent, & Numtee, 2007; Swanson & BeebeFrankenberger, 2004), and writing (Richards et al., 2009; Swanson & Berninger, 1996), as well as general educational attainment (Gathercole, Durling, Evans, Jeffcock, & Stone, 2008; Gathercole, Pickering, Knight, & Stegman, 2004). More recent work has focused on the relationship between working memory and reading disabilities in English language learners (e.g., Swanson, Orosco, Lussier, Gerber, & Guzman-Orth, 2011; Swanson, Sáez, & Gerber, 2006). The most popular framework used to describe working memory is Baddeley’s multi-component model (1986, 1996, 2000, 2007). Baddeley (1986; Baddeley & Logie, 1999) describes WM as a limited centralexecutive system that interacts with a set of two passive storage systems used for temporary storage of different classes of information: the speechbased phonological loop and the visual sketchpad. The phonological loop is responsible for the temporary storage of verbal information; items are held within a phonological store of limited duration, and the items are maintained within the store via the process of articulation. The visual

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sketchpad is responsible for the storage of visual-spatial information over brief periods and plays a key role in the generation and manipulation of mental images. Both storage systems are in direct contact with the central executive system. The central executive system is considered to be primarily responsible for coordinating activity within the cognitive system, but also devotes some of its resources to increasing the amount of information that can be held in the two subsystems (Baddeley & Logie, 1999). A recent formulation of the model (Baddeley, 2000) also includes a temporary multimodal storage component called the episodic buffer. However, the three factor structure has an excellent fit to the WM performance of children (Alloway et al., 2004; Gathercole, Pickering, Ambridge, & Wearing, 2004; Swanson, 2008). There are correlates in the neuropsychological literature that complement the tripartite structure, suggesting that some functional independence exists among the systems (e.g., Jonides, 2000; Ruchkin, Berndt, Johnson, Grafman, Rotter, & Canoune, 1999). Functional magnetic resonance imaging (fMRI) studies suggest separate neural circuitry for the storage and rehearsal components of both the phonological and the visual-spatial system, with phonological system activity mainly located in the left hemisphere and visual-spatial system activity located primarily in the right hemisphere (Smith & Jonides, 1997). Executive control processes, on the other hand, are associated primarily with the prefrontal cortex (e.g., Reichle, Carpenter, & Just, 2000; Smith & Jonides, 1999). Neuropsychological evidence also suggests that children with LD in the areas of reading (RD) and/or math (MD) experience difficulties related to these structures. Based on the type of task, of course, studies suggest that children with RD have processing difficulties related to regions of the frontal lobe (e.g., Lazar & Frank, 1998), left parietal lobe (e.g., Pugh et al., 2000; Shaywitz et al., 1998), as well as problems related to the interhemispheric transfer and coordination of information across the corpus callosum (e.g., Swanson & Mullen, 1983; Swanson & Obrzut, 1985). Likewise, a casual review of the literature shows that MD 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. Clearly, the biological correlates of the various subcomponents in WM in RD and/or MD samples are just beginning to be identified with advances in technology. How does this WM formulation help us understand LD better than the concept of STM? First, it suggests that strategies play a smaller role

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in learning and memory than previously thought. This is an important point because some studies do show that performance deficits of children with LD are not related to rehearsal, per se (e.g., Swanson, 1983a,b). Second, the idea of a WM system is useful because it is viewed as an active memory system directed by a central executive. This is important because the central executive can become a focus of instruction and influence on academic performance. Finally, and most importantly, WM processes are highly related to achievement (e.g., Daneman & Merikle, 1996), whereas with STM less so (Daneman & Carpenter, 1980). We will briefly review the psychological evidence on those components of WM that underlie LD (however also see Swanson & Siegel, 2001a,b, for an earlier review). Executive System The central executive monitors the control processes in WM. There have been a number of cognitive activities assigned to the central executive, including coordination of subsidiary memory systems, control of encoding and retrieval strategies, switching of attention in manipulation of material held related to the verbal and visual spatial systems, and the retrieval of information from LTM (e.g., Baddeley, 1996; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). Although the executive function has separable operations (e.g., inhibition, updating), these operations share some underlying commonality (e.g., see Miyake et al., 2000, for a review). Several of these activities have been reduced to three functions: (a) inhibition of irrelevant responses; (b) updating and monitoring of working memory representations; and (c) shifting between mental sets (Miyake et al., 2000). The research appears to support the notion that children with LD suffer from problems with two processes of the executive system: the suppression of irrelevant information and updating. One activity related to the central executive that has been implicated as a deficit in children with LD is their ability to suppress irrelevant information under high processing demand conditions (e.g., Chiappe, Hasher, & Siegel, 1999; De Beni, Palladino, Pazzaglia, & Cornoldi, 1998; Swanson & Cochran, 1991). These studies have investigated whether children with LD had greater trade-offs and weaker inhibition strategies than average achievers on divided attention tasks. For example, Swanson designed three experiments to reflect attentional demands on both the verbal and visual-spatial system. In one of the experiments (Swanson, 1993b, Exp. 1), a concurrent memory task, adapted from Baddeley (Baddeley, Eldridge,

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Lewis, & Thomas, 1984) was administered to LD and skilled readers. The task required subjects to remember digit strings (e.g., 9, 4, 1, 7, 5, 2) while they concurrently sorted blank cards, cards with pictures of nonverbal shapes, and cards with pictures of items that fit into semantic categories (e.g., vehicles—car, bus, truck; clothing—dress, socks, belt). Demands on the central executive capacity system were manipulated through the level of difficulty (three vs. six digit strings) and type of sorting required (e.g., nonverbal shapes, semantic categories, blank cards). The results showed that readers with LD could perform comparably to chronological age (CA)matched peers on verbal and visual-spatial sorting conditions that involved low demands (i.e., three digit strings), and that only when the coordination of tasks became more difficult (e.g., six digit strings) did ability group differences emerge. More important, the results for the high memory load condition indicated less recall for readers with LD than for CA-matched (and achievement-matched) peers during both verbal and nonverbal sorting. Because recall performance was not restricted to a particular storage system (i.e., verbal storage), one can infer that processes other than a language-specific system accounted for the results. Several studies (e.g., Swanson, 1994, 1993a,b; Swanson & Ashbaker, 2000; Swanson, Ashbaker, & Lee, 1996; Swanson & Sachse-Lee, 2001b) on executive processing have focused on updating. Updating requires monitoring and coding of information for relevance to the task at hand, and then appropriately revising items held in WM. Thus, studies have included tasks that follow the format of Daneman and Carpenter’s Sentence Span measure, a task strongly related to student achievement (see Daneman & Merikle, 1996, for a review) that requires simultaneous juggling of storage and processing requirements. For example, in the reading span task by Daneman and Carpenter (1980), participants are required to read sentences and verify their truthfulness (processing requirement) while trying to remember the last word of each sentence (storage requirement). These studies have consistently found LD readers to be more deficient than skilled readers in WM performance using this task format, which taps central executive processes related to updating (Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). In general, a number of studies show that some participants with LD matched to NLD participants on IQ are deficient on tasks that measure specific components of executive processing. For example, a cross-sectional study (Swanson, 2003) compared skilled readers and LD readers across four age groups (7, 10, 13, 20) on phonological, semantic and visual-spatial

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WM measures administered under conditions referred to in Swanson et al. (1996): initial (no probes or cues), gain (cues that bring performance to an asymptotic level), and maintenance conditions (asymptotic conditions without cues). The results clearly showed that the LD readers had less WM recall than skilled readers for all task conditions, tasks that involved the processing of phonological, visual-spatial, and semantic information. Further, the study provided no evidence that LD readers’ WM skills “catch up” with skilled readers as they age, suggesting that a deficit model rather than a developmental lag model best captures such readers’ age-related performance. Further studies (Swanson, 1992, 1993b; Swanson et al., 1996) have found evidence of domain general processing deficits in children and adults with LD, suggestive of executive system involvement. Those components of the executive system deficient in individuals with LD are related to updating (e.g., Siegel & Ryan, 1989; Swanson, Ashbaker & Lee, 1996) and the inhibition of irrelevant responses (e.g., Chiappe, Hasher, & Siegel, 2000; Carretti et al., 2009). Some alternative explanations to these findings on executive processing (Swanson, 2001a,b), for example that deficits are due to ADHD, domain specific knowledge, and/or low-order processes (such as phonological coding), have been addressed elsewhere (see Swanson, 2005, 2011; for a review of studies). Phonological Loop In Baddeley and Logie’s model (1999), the phonological loop is specialized for the retention of verbal information over short periods of time. It is composed of both a phonological store, which holds information in phonological form, and a rehearsal process, which serves to maintain representations in the phonological store (see Baddeley, Gathercole, & Papagno, 1998, for an extensive review). Thus, the ability to retain and access phonological representations has been associated with verbal STM—but more specifically the phonological loop. The phonological loop has been referred to as STM because it involves two major components discussed in the STM literature: a speech-based phonological input store and a rehearsal process (see Baddeley, 1986, for review). Thus, although phonological loop is viewed as a component of Baddeley multiple component model, its activities and functions fit our previous discussion on STM. A substantial number of studies support the notion that children with RD experience deficits in phonological processing (e.g., see Stanovich & Siegel, 1994), such as forming or accessing phonological representations

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of information. This difficulty in forming and accessing phonological representations impairs their ability to retrieve verbal information from STM. In general, several studies suggest that difficulties in forming and accessing phonological representations impair the ability to learn new words in individuals with RD. A recent quantitative synthesis (Swanson, Zheng, & Jerman, 2009) shows that deficits in STM emerge across a host of measures. Our analysis shows that these deficits are primarily related to verbal information (also see O’Shaughnessy & Swanson, 1998, for an earlier synthesis) and persist across age. Visual-Spatial Sketch Pad The visual-spatial sketch pad is specialized for the processing and storage of visual material, spatial material, or both, and for linguistic information that can be recoded into imaginable forms (see Baddeley, 1986, 2007, for a review). Measures of visual-spatial WM have primarily focused on memory for visual patterns (e.g., Logie, 1986). A major study by Gathercole and Pickering (2000a,b) found that visual-spatial WM abilities, as well as measures of central executive processing, were associated with attainment levels on a national curriculum for children aged 6 to 7 years. Children who showed marked deficits in curriculum attainment also showed marked deficits in visual-spatial WM. Thus, there is a strong relationship between visual-spatial WM and academic performance in the younger grades. However, the literature linking RD to visual-spatial memory deficits is mixed. For example, as described earlier, several studies in the STM literature suggest RD children’s visual STM is intact (see O’Shaughnessy & Swanson, 1998, for a comprehensive review). When visual-spatial WM (combined storage and processing demands) performance is considered, however, while some studies, again, find that visual-spatial WM in students with RD is intact when compared with their same age counterparts (e.g., Swanson, Ashbaker, & Lee, 1996, Exp. 1), others suggest problems in various visual-spatial tasks (Swanson et al., 1996, Exp. 2). Most studies suggest, however, that depending on the type of academic disability, greater problems in performance are more likely to occur on verbal than visual-spatial WM tasks. We found that the evidence on whether children with RD have any particular advantage on visual-spatial WM when compared to their normal achieving counterparts fluctuates with processing demands. Swanson (2000) proposed a model that may account for these mixed findings. There are two parts to this model. The first part of the model assumes that

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executive processes (domain general system) are used to maintain associations across high demand processing conditions. A child with a reading disability has difficulty efficiently maintaining these associations. The predictions of the first part of the model are consistent with current models of executive functions that are called into play only when the activities of multiple components of the cognitive architecture must be coordinated (e.g., Baddeley, 1996; Engle, Cantor, & Carullo, 1992). The second part of the model assumes that when excessive demands are not made on the executive system, performance differences between children with RD and without RD are limited to the verbal system. The second part of the model is consistent with earlier work suggesting that the visual-spatial system of RD readers is generally intact, but when excessive demands are placed on the executive system, their visual-spatial performance is depressed compared with chronological age-matched readers (Swanson et al., 1996). Taken together, there is evidence that children with RD (and to some degree MD) have problems in various components of the WM system.

EVERYDAY MEMORY Although a consistent finding in the literature is that children with LD suffer deficiencies on verbal memory tasks as well as complex tasks that exceed the processing capacity of WM, conclusions are open to question because most of the findings are related to laboratory tasks. Thus, we have little understanding of how the memory of children and adults with LD operates in everyday life. Only two studies were identified in the memory literature that linked laboratory measures of memory to everyday cognition in children with LD. Swanson, Reffel, and Trahan (1991) assessed naturalistic memory of 10-year-old children with LD in three experiments. In Experiment 1, readers with and without LD were compared on their recall of common objects and events, such as the name of their kindergarten teacher, items on a telephone and a penny, as well as information related to the 1986 space shuttle disaster. (These children had watched the Space Shuttle Disaster 2 years earlier on television in a classroom setting.) Also studied, via questionnaire, was the relationship between the children’s memory and their strategies for recalling activities of their daily life. There were three important findings when the ability groups were compared. First, recall differences on the coin task (recalling information

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on a penny) indicated that children with LD were poorer than skilled readers in their recall of common visual and verbal information. Second, children with LD were less likely to remember facts about a consequential event (e.g., date of the space shuttle disaster) or facts that include their earlier experiences in school (e.g., name of their kindergarten teacher). Finally, the results from the questionnaire suggested that LD readers were less likely to report using an external memory aid (e.g., write a note to themselves) so they would remember information than skilled readers. A more recent study by McNamara and Wong (2003) compared 11-year-old children with and without LD on their recall of complex academic information and information encountered in children’s everyday lives. As the researchers were interested in WM, children with LD were screened to include those with poor verbal WM skills. The academic recall measures included a sentence listening span test, a rhyming words WM test, and a visual matrix WM task. The everyday WM tasks included recall of an experienced event (a dance workshop), recall of an everyday procedure (checking a book out of the school library), and recall of common objects (information on the face of a coin, the components of a telephone, and the features of a McDonald’s sign). Additionally, children’s cued recall of all the tasks was measured. Compared to children without LD, those children with LD performed poorly on both the academic recall tasks as well as the everyday recall tasks. Results support the notion that some students with LD may have WM problems that affect their performance on tasks beyond reading. Further, results of the cued recall condition showed that the availability of cues decreased significantly the ability group differences on many of the academic and everyday tasks. Taken together, the results of this type of research suggest that memory deficits in children with LD are pervasive across everyday and laboratory measures.

MEMORY INTERVENTION Based on this extensive literature on memory function in children and adults with LD, some very practical concepts and principles from memory research can serve as guidelines for instruction. We can assume that effective instruction must entail information (a) about a number of strategies; (b) about how to control and implement those strategies; and (c) about the importance of effort and personal causality in producing successful performance. Furthermore, any of these components taught in isolation is likely to have diminished value in the classroom context. The following

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section describes eight major principles that must be considered if strategy instruction is to be successful (also see Montague, 1993; for further application of these principles).

Memory Strategies Serve Different Purposes One analysis of the memory strategy research suggests there is no single best strategy for children with LD. A number of studies, for example, have looked at enhancing these children’s performance by using advanced organizers, skimming, questioning, taking notes, summarizing, and so on. But apart from the fact that students with LD have been exposed to various types of strategies, the questions of which strategies are the most effective is not known. We know in some situations, such as remembering facts, the key word approach appears to be more effective than direct instruction models (Scruggs & Mastropieri, 2000), but, of course, the rank ordering of different strategies changes in reference to the different types of learning outcomes expected. For example, certain strategies are better suited to enhancing students’ understanding of what they previously read, whereas other strategies are better suited to enhancing students’ memory of words or facts. The point is that different strategies can affect different cognitive outcomes in a number of ways.

Good Memory Strategies for NLD Students are not Necessarily Good Strategies for Students with LD and Vice Versa Strategies that enhance access to knowledge for normally developing students will not be well suited for all children with LD. For example, Wong and Jones (1982) trained LD and NLD adolescents in a self-questioning strategy to monitor reading comprehension. Results indicated that although the strategy training benefited the adolescents with LD, it actually lowered the performance of NLD adolescents. To illustrate this point further, Swanson (1989) presented students with LD, intellectual disabilities, giftedness, and average development a series of tasks that involved base and elaborative sentences. Their task was to recall words embedded in a sentence. The results of the first experiment suggested that children with LD differ from the other groups in their ability to benefit from elaboration. That is, while the other groups clearly benefited from the elaborative when compared to the base sentence condition, there was no clear advantage for either type of sentence for participants with LD. In sum, these results suggest that strategies that are effective for NLD students may be less effective for students with LD.

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Effective Memory Strategies do not Necessarily Eliminate Processing Differences It appears logical that if children with LD use a strategy that allows them to process information efficiently, then improvement in performance is due to the strategies’ affecting the same processes that they do in NLD students. This assumption has emanated primarily from studies that have imposed organization on seemingly unorganized material. For example, considerable evidence indicates that readers with LD do not initially take advantage of the organizational features of material (e.g., Lee & Obrzut, 1994). However, the notion that readers with disabilities process the organizational features of information in the same manner as NLD students is questionable (Swanson, 1986). For example, Swanson and Rathgerber (1986) found in categorization tasks that readers with disabilities can retrieve information without interrelating superordinate, subordinate, and coordinate classes of information, as the NLD children do. Thus, children with LD can learn to process information in an organizational sense without knowing the meaning of the material. The point is that simply because children with LD are sensitized to internal structure of material via some strategy (e.g., by cognitive strategies that require the sorting of material) it does not mean they will make use of the material in a manner consistent with what was intended from the instructional strategy.

The Strategies Taught are not Necessarily the Ones Used The previous principle suggests that during intervention different processes may be activated that are not necessarily the intent of the instructional intervention. It is also likely that students with disabilities use different strategies on tasks in which they seem to have little difficulty and these tasks will likely be overlooked by the teacher for possible intervention. It is commonly assumed that although students with LD may have isolated memory deficits (verbal domain) and require general learning strategies to compensate for these processing deficits, their processing of information is comparable with that of their normal counterparts on tasks with which they have little trouble. Several authors suggest, however, that there are a number of alternative ways for achieving successful performance, and some indirect evidence indicates that students with LD may use qualitatively different mental operations (Shankweiler et al., 1979) and processing routes (e.g., Swanson, 1988) from their NLD counterparts.

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Memory Strategies in Relation to a Student’s Knowledge Base and Capacity One important variable that has been overlooked in the LD intervention literature is the notion of processing constraints. Memory capacity seems to increase with development, with a number of factors potentially contributing to the overall effect. STM capacity increases with age (Case, Kurland, & Goldberg, 1982). A number of component processes increase in speed with development, with faster processes generally consuming less effort than slow processes and thus the same amount of capacity can seem greater (i.e., there is a functional increase of capacity with increasing efficiency of processing). Older children are likely to have more organized prior knowledge which can reduce total number of chunks of information that are processed and decrease the amount of effort to retrieve information from LTM. These developmental relationships may play a role in strategy effectiveness. To test this possibility, Pressley, Cariglia-Bull, and Schneider (1987) studied children’s ability to execute a capacity demanding imagery representation strategy for the learning of sentences. Children in an experimental condition were presented a series of highly concrete sentences (e.g., the angry bird shouted at the white dog, the turkey pecked the coat). They were asked to imagine the meanings of these sentences. Control condition participants were given no instruction. Children benefited from imagery instruction. However, performance depended on the child’s functional STM capacity, as reflected by individual differences in performance on classic memory span task. That is, the imagery versus control difference in performance was only detected when functional STM was relatively high.

Comparable Memory Strategy May not Eliminate Performance Differences Several studies earlier have indicated that residual differences remain between ability groups even when ability groups are instructed and/or prevented from strategy use. For example, in a study by Gelzheiser et al. (1987), discussed earlier, children with and without LD were compared on their ability to use organizational strategies. After instruction in organizational strategies, the two groups were compared on their abilities to recall information on a posttest. The results indicated that children with LD were comparable in strategy use to NLD children, but were deficient in overall performance.

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Memory Strategies Taught do not Necessarily become Transformed into Expert Strategies Children who become experts at certain tasks often have learned simple strategies and, through practice, discover ways to modify them into more efficient and powerful procedures. In particular, the proficient learner uses higher-order rules to eliminate unnecessary or redundant steps to hold increasing amounts of information. A child with LD, in contrast, may learn most of the skills related to performing an academic task and perform appropriately on that task by carefully and systematically following prescribed rules or strategies. Although children with LD can be taught strategies, some studies suggest that the difference between NLD (experts in this case) and children with LD is that the former have modified such strategies to become more efficient (Swanson & Cooney, 1985). It is plausible that a child with LD remains a novice in learning new information because he or she fails to transform memory strategies into more efficient forms (see Swanson & Rhine, 1985).

Strategy Instruction Must Operate on the Law of Parsimony A number of multiple-component packages of strategy instruction have been suggested for improving LD children’s functioning. These components have usually encompassed some of the following: skimming, imagining, drawing, elaborating, paraphrasing, using mnemonics, accessing prior knowledge, reviewing, orienting to critical features, and so on. No doubt there are some positive aspects to these strategy packages in that: 1. These programs are an improvement over some of the strategies seen in LD literature to be rather simple or ‘quick-fix’ strategies (e.g., rehearsal or categorization to improve performances). 2. These programs promote a domain skill and have a certain metacognitive embellishment about them. 3. The best of these programs involve (a) teaching a few strategies well rather than superficially; (b) teaching students to monitor their performance; (c) teaching students when and where to use the strategy to enhance generalization; (d) teaching strategies as an integrated part of an existing curriculum; and (e) teaching that includes a great deal of supervised student practice and feedback. The difficulty of such strategy packages, however, at least in terms of theory, is that little is known about which components best predict student performance, nor do they readily permit one to determine why the

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strategy worked. The multiple-component approaches that are typically found in a number of strategy intervention studies must be carefully contrasted with a component analysis approach that involves the systematic combination of instructional components known to have an additive effect on performance.

Training WM Directly Before leaving research linking performance instruction to memory, there is a question as to whether WM can be trained directly. As reviewed, most memory training interventions for children focus on teaching memory strategies (e.g., keyword method, rehearsal, clustering, elaboration). However, the effects of these training studies remain task specific and usually are not transferable to a wide variety of classroom or academic tasks. A promising alternative towards providing strategy interventions is a process specific approach, such as WM training. With WM training, the goal is not to train additional processes in the sense of strategies, such as the mnemonics or rehearsal, but instead to train the WM system directly. In one of the few studies on the effects of training on the WM performance in children, Klingberg, Fernell, Olsen, Johnson, Gustafsson, Dahlstrom et al. (2005) found that when children with ADHD were exposed to a computerized WM training program that significant improvements emerged on measures of verbal and visual-spatial memory and complex reasoning (Raven Colored Progressive Matrices Test) relative to the control conditions. Improvements in WM and their links to reasoning were attributed to activities of the central executive system (e.g., response inhibition). However, the study did not address whether the treatment effects influenced academic performance. A recent study by Swanson, Kehler and Jerman (2010) addressed the question as to whether direct strategy instruction can improve WM performance between children with and without reading difficulties. All children were randomly assigned clinical trials that involved rehearsal training or nontraining. Swanson et al. (2008) found that the use of rehearsal strategy instruction positively influenced post-test span scores on a WM task (Operation Span task). Both groups in this study (children with and without reading problems) benefited from strategy instruction. However, the WM gains were no greater for children with reading difficulties than children without reading problems. These findings are comparable to Engle et al. (1992) showing that strategies do not drive the relationship between WM and reading ability.

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Testing-the-Limits Studies Another approach to directly improving WM performance is through a dynamic testing procedure referred to as testing-the-limits. This procedure has found that performance conditions that include simple feedback and memory cues contribute unique variance to reading and math beyond traditional testing conditions (e.g., Swanson, 1999a, 2003). For these studies, WM tasks were presented to the same children under three conditions: initial (no probes or cues), gain (cues that bring performance to an asymptotic level), and maintenance conditions (asymptotic conditions without cues). Previous studies (Swanson, 1992, 1999a, 2003, 2010; Swanson et al., 1996) have shown that the gain conditions improve WM performance by as much as 1 standard deviation. This occurred because the systematic cuing procedures emphasized sequential processing strategies and thereby reduced the number of competing strategies employed. The maintenance condition allowed for the examination as to whether WM difficulties in children reflected capacity constraints in accessing what had been previously stored (as well as retrieved) in the gain condition. For the maintenance condition, the same WM tasks that matched each participant’s highest WM span level (gain score) were again administered, but without cues. The general findings across several studies (Swanson, 1992, 2003; Swanson & Howard, 2005) have been that skilled readers performed better than children with reading difficulties in all processing conditions and that concurrent reading comprehension performance was best predicted by the maintenance testing than the other WM testing conditions. Further, the magnitude of the difference (effect size) between high and low readers increased on gain and maintenance testing conditions when compared with the initial conditions, suggesting that the performance gap between ability groups was increased by using testing-the-limits procedures. In general, a number of studies have shown that WM can be improved upon. In addition, simple feedback on WM performance adds significant variance with predicting academic outcomes. However, few studies have shown that WM training directly influences performance on academic measures, such as reading and math. Some studies have found some generalization to nontargeted related processes (visual WM training was related to recognizing visual spatial patterns), but WM training to date has not been shown at this point to make substantial improvement on important classroom tasks such as reading comprehension and/ or math performance.

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SUMMARY AND CONCLUSIONS In summary, we have briefly characterized research on memory and LD. Our knowledge of the memory of individuals with LD somewhat parallels our knowledge about the differences between older and younger children’s memory. The parallel relies in effortful processing, the focus on cognitive strategies, the development of a knowledge base, and the awareness of one’s own memory processes. Most memory research emanates from an information-processing framework. Earlier research tended to emphasize the integration of information across modalities (visualauditory) and perception (visual memory), whereas more recent studies have tended to focus on the representation, control, and executive process (e.g., strategies) of memory. Current research on memory is beginning to examine the interaction of structures and process on performance. Most of the current research is occurring in the area of WM. The limitations of previous models are highlighted as well as recent trends in memory research on students with LD. A number of principles related to memory strategy instruction have emerged that have direct application to the instruction of children and adults with LD. Some of these principles are related to the purposes of strategies, parsimony with regard to the number of processes, individual differences in strategy use and performance, learner constraints, and the transfer of strategies into more efficient processes. The important conclusion from this review is that children and adults with LD, who have normal intelligence but experience difficulties in specific academic areas (e.g., reading, math), suffer memory difficulties. These memory difficulties are not pervasive but are related to two components of working memory: the phonological loop and the executive system. The phonological loop specializes in the retention of speech-based information. The research also finds that situations that place high demands on processing (e.g., comprehension), which in turn exert demands on controlled attentional processing (such as monitoring limited resources, suppressing conflicting information, updating information), place children and adults with LD at a clear disadvantage when compared with their chronological aged counterparts. Children and adults with LD executive processing difficulties may include (a) maintaining task relevant information in the face of distraction or interference; and (b) suppressing and inhibiting information irrelevant to the task if necessary. We also find evidence that the WM of children and adults with LD can be improved (i.e., with dynamic testing).

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Swanson, H. L. (2000). Are working memory deficits in readers with learning disabilities hard to change? Journal of Learning Disabilities, 33, 551–566. Swanson, H. L. (2003). Age-related differences in learning disabled and skilled readers’ working memory. Journal of Experimental Child Psychology, 85, 1–31. Swanson, H. L. (2005). Working memory, intelligence and learning disabilities. In O. Wilhelm & R. W. Engle (Eds.), Handbook of understanding and measuring intelligence (pp. 409–429). NY: Sage Publications, Inc. Swanson, H. L. (2008). Working memory and intelligence in children: What develops? Journal of Educational Psychology, 100(3), 581–602. Swanson, H. L. (2010). Does the dynamic testing of working memory predict growth in nonword fluency and vocabulary in children with reading disabilities? Journal of Cognitive Education and Psychology, 9, 51–77. Swanson, H. L. (2011). Dynamic testing, working memory, and reading comprehension. Journal of Learning Disabilities, 44, 358–371. Swanson, H. L. (2011). The influence of working memory growth on reading and math performance in children with math and/or reading disabilities. In P. Barrouillet & V. Gaillard (Eds.), Cognitive mechanisms and constraints (pp. 203–230). London: Psychological Press. Swanson, H. L., & Ashbaker, M. (2000). Working memory, Short-term memory, articulation speed, word recognition, and reading comprehension in learning disabled readers: Executive and/ or articulatory system? Intelligence, 28(1), 1–30. Swanson, H. L., Ashbaker, M., & Lee, C. (1996). The effects of processing demands on the working memory of learning disabled readers. Journal of Experimental Child Psychology, 61, 242–275. Swanson, H. L., & Beebe-Frankenberger, M. (2004). The relationship between working memory and mathematical problem solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 96(3), 471–491. Swanson, L. H., & Berninger, V. W. (1996). Individual differences in children’s working memory and writing skill. Journal of Experimental Child Psychology, 63, 358–385. Swanson, H. L., & Cochran, K. (1991). Learning disabilities, distinctive encoding, and hemispheric resources. Brain and Language, 40(2), 202–230. Swanson, H. L., & Cooney, J. (1985). Strategy transformations in learning disabled children. Learning Disability Quarterly, 8, 221–231. Swanson, H. L., Cooney, J. B., & McNamara, J. K. (2004). Learning disabilities and memory. In B. Y. L. Wong (Ed.), Learning about learning disabilities (pp. 41–92). San Diego, CA: Elsevier Academic Press. Swanson, H. L., & Howard, C. B. (2005). Children with reading disabilities: Does dynamic assessment help in the classification? Learning Disability Quarterly, 28, 17–34. Swanson, H. L., Howard, C. B., & Sáez, L. (2006). Do different components of working memory underlie different subgroups of reading disabilities? Journal of Learning Disabilities, 39(3), 252–269. Swanson, H. L., & Jerman, O. (2007). The influence of working memory on reading growth in subgroups of children with reading disabilities. Journal of Experimental Child Psychology, 96(4), 249–283. Swanson, H. L., Jerman, O., & Zheng, X. (2008). Growth in working memory and mathematical problem solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 100(2), 343–379. Swanson, H. L., Jerman, O., & Zheng, X. (2009). Math disabilities and reading disabilities: Can they be separated? Journal of Psychoeducational Assessment, 27, 175–196. Swanson, H. L., & Hsieh, C.-J. (2009). Characteristics of adults with reading disabilities: A meta-analysis of the literature. Review of Educational Research, 79(4), 1–28.

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Brain and Behavioral Response to Intervention for Specific Reading, Writing, and Math Disabilities: What Works for Whom? Virginia W. Berninger1, and Michael Dunn2 1

University of Washington, Seattle, WA 98195-3600, USA Washington State University,Vancouver, WA 98686-9600, USA

2

Chapter Contents Learning about the Brain   60 Defining Brain   60 Brain Geography   60 Brain Imaging Technologies   61 Systems Approach   62 Working Memory   64 Controlled and Automatic Processing   65 Nature–Nurture Interactions   65 Comparing Reading, Writing, and Math Brains   66 Brain Differences of Individuals with and without SLDS   66 Reading  66 Writing  67 Math  67 Behavioral and Brain Response to Intervention (RTI)   68 RTI as a Nature–Nurture Perspective   68 Behavioral RTI   70 Brain Response to Intervention (RTI)   74 Individual, Developmental, Gender, Language, and Cultural Differences   76 Longitudinal Studies   76 Gender Differences   77 Language and Cultural Differences   77 Defining SLDs in Reading, Writing, and Math   77 Conclusions and Recommendations   80 References  80

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LEARNING ABOUT THE BRAIN Defining Brain The human brain is a complex electrochemical organ with texture like jello. Weighing only about three pounds, this organ supports an individual’s inner mental activity and interactions with the external environment. Brain initiates behaviors, and changes in response to environmental events; also, brain and genes in each neuron mediate response to intervention. Thus, brain is an independent variable, dependent variable, and intervening variable, respectively (Berninger & Richards, 2009). Understanding the brain requires research on its structures, physiological functions, and behaviors, all of which are interrelated but not in a simple one-to-one way (Mesulam, 1990). Only the sensory and motor systems have direct contact with the external world, but these systems create connections with the inner language and/or cognitive systems as well as with each other so that the inner systems can communicate with the external world through the sensory and motor end organs (Berninger & Richards, 2011; Berninger, Fayol, & Alamargot, 2012, Chapter 4, Table 4.4). Four separable functional language systems— language by ear (listening), language by mouth (speaking), language by eye (reading), and language by hand (writing)—are created which can function alone or in concert (Berninger & Abbott, 2010). Cognitions can be translated into language (Fayol, Alamargot, & Berninger, 2012) or nonlanguage format (Dunn, 2012; also see section on Behavioral RTI, writing in this chapter). Most human cognition exists outside conscious awareness, but with support of working memory can be brought into consciousness for temporary goal-related tasks (Berninger, Rijlaarsdam, & Fayol, 2012, Tables 3.1 through 3.5). Yet, what is probably the most remarkable about the human brain was best captured by a poet, not a neuroscientist, namely, the capacity of the brain to create an inner cognitive world to represent and conduct its own thinking as well as to receive incoming messages from the environment and behave in the environment. In the words of the poet Emily Dickenson (Poem 632 quoted by Diamond (1999, page 38)): The Brain—is wider than the sky— For—put them side by side— The one the other will contain With ease—and You—beside.

Brain Geography Over the years scientists have developed systems for locating brain regions (neuroanatomical structures) in 3-dimensional space and labeling these regions

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with names or numbers (e.g., for Brodmann’s areas). Neuropsychologists have conducted postmortem studies and more recently brain imaging of living people to identify functions associated with the various specific regions. Books written specifically for educators and psychologists to learn about regionspecific brain structures and functions include Berninger and Richards (2002), Blakemore and Frith (2005), and Posner and Rothbart (2007). Technologysupported ways to access and learn the regions and associated functions include (a) Carter et al. (2009, which includes an illustrated book and interactive CD); (b) for PC users, the Brain Atlas accessed at www.cabiatl.com/ mricro/mricro/mricro.html#Installation; and (c) for Mac Users, SPM, which requires metlab, should first be installed and then go to the Brain Atlas at http://en.wikibooks.org/wiki/SPM/Installation_on_Mac_OS_%28Intel%29. It is important to keep in mind that many illustrations in books label structures on the surface; yet these structures are 3-dimensional and extend below the surface and many other structures exist below the surface that are not as easily depicted in 2-dimensional drawings. In addition, brain regions are often reported for layers (slices) of brain images from top-to-bottom, or from right to left, or from back of the brain to the front. To identify specific brain structures or regions of brain activation, it is best to rely on reports by neuroscientists with specialized training and expertise in using a Brain Atlas.

Brain Imaging Technologies For an overview of brain imaging technologies used to study the living human brain, see the Appendix in Blackmore and Frith (2005), introductory material in Carter (2009), or Chapter 3 in Berninger and Richards (2002). In general, studies of specific learning disabilities (SLDs) use noninvasive techniques such as (a) structural (MRI), which constructs via computer programs, visualization of neuroanatomical structures (not photographs of them); (b) functional (fMRI) magnetic resonance imaging of region-specific blood oxygen-level dependent (BOLD) activation, which shows specific brains regions that are using glucose to provide energy for processing; or (c) electrophysiological recordings of event-related potentials (ERPs), which record changes in brain wave activity over time from stimulus onset to response. Recently developed new techniques assess (a) both where and when activation occurs during scanning; (b) functional connectivity for which regions activate at the same time given a specific brain region source; and (c) structural connectivity of white fiber tracts that connect pathways distributed across brain regions (diffusion tensor imaging,

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DTI). In contrast, invasive techniques use radioactively labeled dyes to trace brain activity over time (PET) or radiation (CT scans). Typically institutional review boards (IRB) do not approve use of invasive imaging techniques with developing children or youth with or without SLDs.

Systems Approach A research-supported general principle is that brain function involves both local and global activity. Jackson (1887) startled fellow neurologists by claiming that the brain has multi-level organization. Subsequent research has supported these claims. Brain mechanisms depend (a) on structures and functions in individual neurons, (b) the momentary functional connectivity between individual neurons separated by a small space (synapse), (c) the pathways consisting of many synapsed neurons distributed across brain regions, and (d) the computations of the six layers of cerebral cortex that periodically coordinate the brain activity distributed in space and sequenced over time (see Berninger & Richards, 2002, 2011). Also, primary brain regions specialize in uni-modal sensory or motor messages; secondary brain regions specialize in hetero-modal messages, which integrate across sensory input and motor output regions, or between language regions and a sensory or motor output region; and tertiary brain regions specialize in processing at an abstract level independent of sensory, motor, or sensory-motor codes, for example, in cognitive operations such as thinking. Luria (1962, 1973), the Russian neuropsychologist, further contributed to understanding of functional brain systems in the working brain with these four insights based on careful clinical observations and assessments: a. Multiple brain regions distributed throughout the brain are involved in performing a specific function. b. It follows that functional systems for performing a specific task or function have multiple structural and functional components. c. Different tasks draw on common as well as unique brain regions in the interrelated pathways. d. Thus, the same brain region may participate in more than one functional system. Minsky (1986), a leading architect of artificial intelligence, talked to neuroscientists throughout the country, built robots to test computational models, created a model that involved multiple systems or a society of mind, and consulted with a poet to find this metaphor to explain the model to the general public. In the Society of Mind Model, a typical agent in a system

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knows its job—to switch other agents or pathways “on” (excitatory) or “off ” (inhibitory), but is typically unaware of the activities of other agents, even when its activities may exert indirect influences on agents far down the communication loop. Thus, the Society of Mind conceptual framework accounts for most human cognition being outside conscious awareness. Moreover, the different distributed brain regions are on different temporal scales (momentary time). That is, time, just like Euclidean space, is multidimensional. Periodically, cerebral cortical computations synchronize the various brain activities occurring in momentary time to a common scale, based on linear time, in what is often referred to as real time. This synchronizing gives rise to brain waves. Patterns of communication across local and global societies of mind in space and time change across development and learning, but are always partial in that agencies and societies (collections of agents) do not code in the same way and only have indirect knowledge of each other through models they create for transforming codes in one domain to codes in other domains. Thus, the Societies of Mind model is consistent with cognitive-linguistic translation as a cross-domain transformation process (see Berninger, Fayol et al., 2012; Berninger & Hayes, 2012; Berninger, Rijlaarsdam, & Fayol, 2012; Fayol et al., 2012). Fuster (1997), who devoted his career to studying working memory in laboratory rats, contributed ground-breaking knowledge about the three distributed neural networks that serve as the brain basis of the working memory system, which supports goal-related activity. a. A back-to-front pathway originates in primary brain systems in the back of the brain, receives incoming sensory messages (e.g., visual, auditory, touch), and sends them forward to secondary association areas where they are integrated with each other or other systems. b. A top-down pathway originates in dorsal lateral prefrontal cortex (DLPFC) that projects to midlevel premotor and supplementary motor areas and then to lower-level primary motor areas in the frontal brain regions, and then to spinal cord, which supports the elements of movement in behavior that acts in or on the external world. c. A cortical-subcortical pathway from cerebrum to cerebellum provides temporal coordination of the sequential and parallel processes unfolding in momentary time and periodically synchronized in real time (see Minsky, 1986), and thus serves as executive functions for selfregulating attention, working memory, learning, and behavior (see Posner & Rothbart, 2007).

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Posner, Peterson, Fox, and Raichle (1988), who did the first brain imaging study of reading, introduced a metaphor for brain—the orchestration of the mind—that captures what the brain is from a complex systems perspective. Each of the brain regions, with specialized computation expertise, is analogous to the individual musicians and their various instruments in the orchestra. For the orchestra to create music, each of these musicians must not only produce the technically correct sounds, but also must coordinate them in temporal synchrony. If any of the musicians (brain structures) lacks expertise or momentarily does not play the instrument correctly or synchronously with the other instruments (brain function), the result will be noise rather than music. In an analogous fashion, if any brain structure is underdeveloped or impaired or cannot function in concert with other structures in the brain system, the brain and mind it constructs will not develop, learn, or function normally.

Working Memory The University of Washington Interdisciplinary Learning Disability Center (UWLDC), conducted genetics, brain imaging, assessment, and instructional research for students in grades 4 to 9 who had not responded adequately to reading and/or writing instruction in school and also had multi-generational history of SLDs affecting written language acquisition. The interdisciplinary research findings across a decade were captured in a systems model of working memory with (a) three word-form storage and processing units (phonological for spoken words, orthographic for written words, and morphological for word parts that signal meaning and grammar); (b) syntax storage and processing units for accumulating, serial words; (c) phonological and orthographic loops for cross-code integration; and (d) executive functions for working memory (focusing, switching, and sustaining attention, and self-monitoring over time) (e.g., Berninger, Raskind, Richards, Abbott, & Stock, 2008; Berninger & Richards, 2010). Likewise, studies across alphabetic (Paulesu et al., 2000) and non-alphabetic orthographies (Tan, Spinks, Eden, Perfetti, & Siok, 2005) found differences between participants with and without reading disability in brain regions associated with working memory. Also, brain imaging studies have documented the role of working memory in math learning (Meyer, Salimpoor, Wu, Geary, & Menon, 2010; Wu, Meyer, Maeda, Salimpoor, Tomiyama, Geary, & Menon, 2008). Behavioral studies have shown that in English-Language Learners (ELLs) working memory skill differentiates those who do and do not respond to reading and math instruction (Swanson, Jerman, & Zheng, 2008; Swanson, Sáez, & Gerber, 2006; Swanson, Sáez, Gerber, & Leafstedt, 2004).

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Controlled and Automatic Processing Schneider and Shriffin (1977) and Shriffin and Schneider (1977) conducted pioneering studies that compared controlled, strategic processing in learning new skills and automatic processing once skills are practiced and mastered. Which brain regions are engaged change when processing transitions from controlled to automatic processing (for review of examples from using writing tools, see Berninger & Richards, 2002, Chapter 7). Meta-analyses have shown that struggling writers benefit from learning explicit strategies for self-regulation of the writing process (Graham & Perrin, 2007), which can increase writing fluency, that is, the amount that is written within a given time (see Dunn 2001d later in this chapter). Yet, neither writing nor reading fluency (smooth coordination of multiple processes in time) is the same as automatic writing or reading (fast direct retrieval), even though both are assessed with timed measures. Reading speed (total time) or rate (number of seconds on average for producing a behavior) is often mistakenly equated with reading fluency (fast, smooth coordination of multiple processes) and automaticity (rapid, effortless, direct retrieval), none of which assesses the same brain process (e.g., see Berninger, Abbott, Trivedi et al., 2010).

Nature–Nurture Interactions In a ground breaking study, Hoeft et al. (2007) showed that both behavioral and brain imaging measures uniquely predicted reading achievement outcomes. Likewise, twin studies (e.g., Byrne et al., 2009; Pennington, 2008; Plomin & Bergeman, 1991) and longitudinal family studies (Lyytinen et al., 2004) support nature–nurture interactions between genes and the environment. Thus, investigations are warranted for behavioral and brain differences and behavioral RTI and brain RTI for SLDs in reading, writing, and math. Also, although a sizable body of research has shown that targeted reading interventions normalize brain function in specific brain regions, longitudinal research showed that young adults who appeared at the behavioral level to be functionally compensated were, in fact, not normalized in terms of normal functional connectivity among regions (e.g., Shaywitz et al., 2003). Functional connectivity is relevant to whether all the components of a functional brain system for reading, writing, or math are orchestrated in time and thus working in concert and supporting fluent reading, writing, or math (c.f., Posner et al., 1988). Thus, brain RTI should consider not only whether normalization occurs in specific

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brain regions but also in functional connectivity across brain systems, but at present much more brain research focuses on regions of interest than connectivity.

Comparing Reading, Writing, and Math Brains Although some may think of reading, writing, and math as separate content domains, indeed much academic learning requires integration across domains (e.g., reading-writing, Altemeier, Abbott, & Berninger, 2008) and math draws on reading (word problems), oral language (understanding teacher’s instructional talk or math-specific vocabulary), quantitative knowledge, and visual-spatial skills (e.g., Robinson, Abbott, Berninger, & Busse, 1996). Moreover, both math learning and language learning may draw on concepts in the nonverbal domain (e.g., Halberda, Mazzocco, & Feigenson, 2008); and the hand plays a role in math learning ( Jordan, Kaplan, Raminemi, & Locuniak, 2008) as well as writing acquisition. It follows that the reading, writing, and math brains draw on common as well as unique brain regions (Berninger & Richards, 2002, 2009).

BRAIN DIFFERENCES OF INDIVIDUALS WITH AND WITHOUT SLDS Reading On the one hand, which brain regions activate during brain scanning depends on the task and stimuli employed. On the other hand, results across many imaging studies using a variety of imaging tools indicate that, in general, predictable regions are activated during reading. Findings of brain imaging studies over the past four decades of living human beings reading have been reviewed periodically. For a recent overview, see McCardle, Miller, Lee, and Tzeng (2011), and the introduction to Rezaie, Simos, Fletcher, Cirino, Vaughan, and Papanicolaou (in press). Also, see Figure 1 in Perfetti (2012) for four regions that typically activate during reading: (a) association areas integrating occipital and temporal regions; (b) lower and middle temporal regions referred to as word form regions; (c) temporal-parietal regions involved in cross-code integration and word storage and processing; and (d) inferior frontal regions, which Mesulam (1990) described as the executive functions for the functional language system. Cutting-edge research with imaging that tracks over time, records

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eye movements, and employs computational modeling shows that the phonological core deficit (for storing and processing spoken words and making connections with written words) in children with reading disabilities can persist into the young adult years (Magnuson et al., 2012). Cohen, Lehe´ricy, Chhochon, Lemer, Rivaud and Dehaene (2002)’s pioneering study provided evidence for a brain region that specializes in processing the orthographic word form (often referred to as visual word form for visible language) in the lower temporal regions where visual and language inputs are integrated. Evidence has accumulated from both brain and behavioral studies that both phonological and orthographic word-form storage and processing units tend to be impaired in dyslexia and other written language disabilities (reviewed in Berninger & Richards, 2010; Richards, Berninger, & Fayol, 2012).

Writing Until recently most brain research on writing and writing disabilities focused on acquired writing disorders in individuals who previously had normal function. The UW LDC studies found brain activation differences between children with and without dyslexia, which is a word decoding and spelling disability, on spelling tasks in regions associated with mapping interrelationships among the phonological, orthographic, and morphological word-forms and their parts (Richards, Berninger, Nagy, Parsons, Field, & Richards, 2005). In UW Literacy Trek studies of children with dysgraphia (impaired handwriting and spelling without dyslexia) brain differences were found during finger sequencing (e.g., brain regions involved in cognition, working memory, and language, all of which required serial organization), handwriting (e.g., fusiform, the orthographic word form region), spelling (e.g., motor, touch, language, and cognitive), and idea generation before composing (e.g., working memory). For a review of these studies, see Berninger and Richards (2011) and Richards et al. (2012).

Math For an introduction to and overview of the accumulating research on the brain and math development and learning, see Dehaene (2009, 2011), Menon (2010), and Zamarian, Ischebeck, and Delazer (2009). As was the case with reading, timing (Menon, Mackenzie, Rivera, & Reiss, 2002) and specific brain regions activated often depend on task, for example (a) counting

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related to math fact retrieval (Cho, Ryali, Geary, & Menon, 2011); (b) calculation (Davis et al., 2009b; Rosenberg-Lee, Chang, Young, Wu, & Menon, 2011); or (c) mental arithmetic (Rivera, Reiss, Eckert, & Menon, 2005), and (d) symbolic and magnitude estimations (Rosenberg, Lee, Tsang, & Menon, 2009). Yet, it is clear across studies that the parietal lobe plays a major role in math learning (e.g., Zhang, Majid, Caspers, Eickhoff, & Menon, 2009), even though multiple parietal regions or pathways may be involved (Wu, Chang, Majid, Caspers, Eickhoff, & Menon, 2009) and other brain regions are also involved. Brain differences have been found when imaging the brain during math tasks between: (a) genders (Keller & Menon, 2009); (b) children who are and are not at-risk for math disability (Davis et al., 2009a); and (c) individuals with and without dyscalculia (Rykhlevskaia, Uddin, Kondos, & Menon, 2009).

BEHAVIORAL AND BRAIN RESPONSE TO INTERVENTION (RTI) The most important finding from the brain imaging studies of children with and without dyslexia before and after instructional intervention was that many of the brain differences, especially those related to language, could be eliminated (that is, normalized) in specific brain regions. Even though SLDs have a genetic basis in DNA, instruction may create epigenetic modifications in gene expression (Cassidy, 2009). Thus, it follows that both behavioral RTI and brain RTI are important to assess and investigate. An overview of representative studies of both behavioral RTI and brain RTI is presented next.

RTI as a Nature–Nurture Perspective RTI is a pedagogical paradigm offering ongoing assessment and strategy instruction (Gresham, 2002) because, despite the brain and genetic basis of SLDs, children who are given appropriate, specialized instruction often do response to instruction and make achievement gains. To illustrate the application of RTI for preventing SLDs or reducing its severity, we provide an overview of representative research in RTI in reading, writing, and math. RTI is typically implemented within a threetier model (Fuchs, Mock, Morgan, & Young, 2003). In Tier 1, students receive research-based general education classroom programming, which should address the needs of 80% of the children. At three points during

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the school year, students complete short assessments of core academic skills to define if instruction is meeting this 80% target and which students need more intensive programming (e.g., more instructional time and/or in a smaller group working with a teacher on skills and strategy instruction); these students who need additional support receive Tier 2 intervention. Tier 2 includes three options. First, a standard protocol approach may be employed in which struggling students are grouped based on type of need and then receive an already-created instructional program such as the writing components of the Read 180 Program (Scholastic, n.d.). Second, the teacher, in collaboration with the school multi-disciplinary team, may engage in problem solving consultation to (a) create intervention components tailored to the individual needs of that child; (b) implement them; (c) collect weekly or bi-weekly progress-monitoring data over a defined period of time (e.g., 8–12 weeks); (d) return to the school team to review the results; and (e) then decide, as a team, to either discontinue the intervention given sufficient progress or redesign the intervention and provide it again. Third, the team might devise a blend between the standard protocol approach and problem-solving model; for example, a school team could alter the components of a published intervention program and provide the intervention to a small group of children all of whom have similar instructional needs. For students who do not progress well in Tier 2, more intensive and longer-term programming can be provided: Tier 3, although there is no consensus on the nature of Tier 3 intervention. To some (e.g., Harn, Kame’enui, & Simmons, 2007), Tier 3 is a more intensive type of Tier 2 intervention in which children receive instruction in a smaller group and/ or over a longer period of time. To others, (e.g., Fuchs & Kearns, 2008), Tier 3 represents an opportunity for teachers to move beyond the academic nature of Tier 2’s components to address deficits in cognitive processing (e.g., inattention, processing speed) during intervention sessions. Support staff can help identify these processing needs. For example, the school psychologist could administer cognitive tests to identify specific areas of need in cognitive processing. The speech and language pathologist (SLP) could administer language tests to identify specific language processes that are impaired. Another Tier 3 definition is that of special education classification (Vaughn & Fuchs, 2003). Children complete diagnostic assessments such as the Woodcock-Johnson Tests of Achievement, 3rd Edition (WJ III)

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(Woodcock, McGrew, & Mather, 2001) to define their strengths and weaknesses. Then the school team uses all of the collected assessment and anecdotal information, along with parental input and consent, to determine if the student is eligible for special education services based on state and school system criteria for implementing federal special education law.

Behavioral RTI Behavioral Reading RTI A sizable body of research exists on effective reading interventions (e.g., Morris & Mather, 2008). However, although many children show behavioral RTI to early intervention, demonstrating that reading disabilities can be prevented or their severity reduced, challenges remain for using RTI to identify and classify reading disabilities. The RTI approach for this purpose has been studied in depth by the Florida Learning Disabilities Center, and their findings showed that RTI is difficult to operationalize for this purpose and also has challenges in obtaining acceptable reliability for classifying (Wagner, Waesche, Shatschneider, Maner, & Ahmed, 2012). Nevertheless, adding RTI to assessment with tests only has value for identifying who needs modified or specialized instruction (e.g., Berninger & Miller, 2011). Behavioral Writing RTI For two reasons Writing RTI is as important as Reading RTI. First, of the three core academic skills (i.e., reading, writing, and math), writing may be the most challenging because a student needs to first generate and organize ideas and then encode them into text while re-reading drafts to make edits for a final product. Second, recent national (NAEP, 2007) and state assessments (e.g., OSPI Washington State Report Card, 2010) of writing skills indicate that as many as 40% of children struggle with writing at a basic level. Despite research showing effective ways to teach handwriting, spelling, and composing early in schooling (e.g., Morris & Mather, 2008; Troia, 2009), the writing requirements of the curriculum increase in volume and complexity as children progress through the grades. Those with SLD in writing, whether due to handwriting, spelling, selective language impairment in syntax processing and production, or executive functions for self-regulation of attention and the writing process, may require modified or specialized instruction in order to meet writing standards.

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Dunn’s research on Tier 2 interventions for struggling writers addresses effective ways to help such students who continue to struggle and need more than Tier 1 intervention1. His writing intervention research is based on his experience as a special education consultant teacher for six years and as a university faculty member who now trains pre-service and in-service general education and special education teachers. In the first two projects (Dunn & Finley, 2008, 2010) children received intervention in an arts-based/integrated curriculum summer program named Thirsty Thinkers, which draws on the cognitive, language, sensory, and motor systems of brain as children’s minds engage in the writing process. In the first study in the summer of 2006, in a small-group format, children were taught a mnemonic strategy, Ask, Reflect, Text (ART) (Dunn & Finley, 2008), which drew upon and extended Graham and Harris (1989): Students ask themselves a series of questions and their answers cue them what to include in a narrative story (e.g., Who is in the story? Where does it take place?). As students reflect on their answers, they also illustrate their ideas to support translation of ideas into written language without transcription requirements (handwriting and spelling), which are often weak or impaired in struggling writers. After generating a story plan, students write the text of their story. When children arrived at the Thirsty Thinkers Program, Dunn would read a published story book to the children to exemplify what a published story entailed. He then explained the ART strategy and resources/materials available to children in the center (e.g., pencils, paper, paints, playdough, laptops with writing-assistance software, books on CD), which they could use to generate their own story. Of note, in the first project (Dunn & Finley, 2008), some children chose to 1 

To date, Dunn has completed eight intervention studies that included the ART strategy with children who struggle with writing: (a) Dunn and Finley (2008) (action research format during July 2006; N = 45, grades 2–7); (b) Dunn and Finley (2010) (action research format during July 2007; N = 43, grades 1–5); (c) Dunn, Tudor, Scattergood, and Closson (2010) (action research format during spring 2007 with a fourth-, a sixth-, and an eighth-grade student); (d) Dunn (in press) (fall 2008 project with second-grade students, N = 9, who were struggling writers but not classified with a learning disability); (e) Dunn (2011-a) (spring 2009 project with fourth-grade students, N = 3, who were struggling writers but not classified with a learning disability); (f ) Dunn (2011b) (Fall 2009 project with fourth-grade students, N = 4, who were struggling writers but not classified with a learning disability); (g) Dunn (2011c) (Spring 2010 project with fourth-grade students, N = 4, who were struggling writers and classified with a learning disability); and (h) Dunn (2011d) (Fall 2010 project with fourth-grade students, N = 12, who were struggling writers but not classified with a learning disability). In these studies Dunn defined a struggling writer as either (a) classroom teacher-referred students in the bottom 20% of the class for writing ability with minimal proficiency in writing a story; or (b) a student’s already having qualified for special education under a learning disability category and having writing goals/objectives included in their Individual Education Plan IEP).

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create a strategy based on a movie’s storyline in lieu of generating a story plan, and then created a first draft and wrote subsequent drafts until they attained an acceptable story product. Although no given strategy may work for all children, the provision of a strategy can help many struggling writers manage a task effectively (Graham & Perrin, 2007). Dunn also conducted a collaborative project with local special education teachers, who agreed to employ the ART strategy with children for whom they provided writing instruction during the school day (Dunn et al., 2010). In the single case design studies (Dunn, 2011a,b,c) children demonstrated their pre-treatment writing abilities during baseline. An intervention specialist, a recent graduate and trained by Dunn, taught students the ART strategy employing the self-regulated strategy development (SRSD) model (Graham & Harris, 2005). In the remaining sessions, children employed the ART strategy. All children demonstrated that they could apply the ART strategy. Growth in story content was assessed on two criteria: (a) ratings on the 0–7 scale of response to WWW, W = 2, H = 2 cue questions (Graham & Harris, 1989) in their stories; and (b) a 0–7 rating for each story as compared to that of a proficient fourth-grade student’s story text. Participants in these studies made good improvement in story content, but not necessarily story quality. Children either made minimal improvement or no progress between baseline and intervention levels on story quality. Change in story content focused on students’ answering the WWW, W = 2, H = 2 questions which required as little as a single word (e.g., when did the story take place? Monday). Story quality involves a wide range of tasks (e.g., spelling, composing phrases, paragraphing, maintaining a progressive story line). For these reasons, story quality could be more of a challenge. Dunn (2011d) employed randomized control trial methods to compare ART with another strategy: The What I think, I can say, What I can say, I can write (or Think-Talk-Text; T3) mnemonic strategy (Katahira, 2011). Traweek (1993) found that the kindergarten children who used the T3 strategy throughout the year improved with writing and also reading (by the end of kindergarten, the children read at 90th %tile or above except one at 70th percentile) even without formal reading instruction. In the current study twelve 4th-grade children (11 White, one Hispanic, all proficient in oral English) participated during the 18-session project (45 minutes per session; four days for baseline probe assessments, four days of mnemonic-strategy instruction for ART and T3, and 4 days of post-training/intervention probe assessments). Participants, in groups

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of three, met with the intervention specialist in the media center. Dunn assessed control group participants in a location near their classroom but not near the media center. Students were given paper for planning, art media (for ART group participants to illustrate their ideas; for T3 students to illustrate their story after writing their texts), and 10 minutes to plan their text and 15 minutes to write. All probes were scored for story content using Graham and Harris’ (1989) WWW, W = 2, H = 2 questions: Who is in the story? Where does it take place? When does it take place? What happens? What happens next? How does the story end? How do the characters feel? In addition, story quality was scored with a rubric crafted by Dunn based on Harris and Graham’s (1996) rubric as well as the 6 + 1 Traits of Writing, and each participant’s story product number of words written (NWW) was calculated by WORD (2010). Dunn trained a graduate student in scoring the story probes and completed inter-rater reliability with her so as to attain 100% agreement. Dunn then analyzed participants’ repeated measures across baseline and intervention for story content, quality, and number of words written (NWW) within each group using paired-samples t-tests (Vogt, 2007). Probe categories (e.g., intervention stories’ content) were analyzed using MANOVA to assess for differences among groups within a given measure. Dunn also computed effect sizes (Cohen’s d;Vogt, 2007). Given the small sample size (N = 12) of this pilot-study, alpha level was set at .25. Significant differences were found between ART (Dunn & Finley, 2008) and T3 (Katahira, 2011) for (a) story content; (b) story length and (c) story quality. ART students did better with story content and number of words written. The T3 group did best on story quality. The WWW, W = 2, H = 2 cue questions (Graham & Harris, 1989) that focused children’s attention on specific aspects of their story that needed to be included and the nonverbal illustrations may have facilitated access to both verbal and nonverbal knowledge in long-term memory, resulting in better content and more words (fluent production) (see Dockrell, Lindsay, Connelly, & Mackie, 2007; McCutchen, 2006). However, the ART students in this study did not attain a score close to seven for the story-content variable, which probably requires more than 18 instructional sessions. T3’s significant superior story quality could stem from the think aloud strategy of verbalizing ideas before having to translate them into written language (Berninger, Richards et al., 2009; Donovan & Smolkin, 2006), thus reducing load on working memory. Writing is a challenging task which may require multiple intervention phases

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(Gresham, 2002; Haager et al., 2007; Jiménez-Glez & Rodrigo-López, 1994) for improvement and sustained improvement in story quality and content to that of typically-achieving peers. Planned studies with larger samples will evaluate whether these results replicate and if there is an advantage for combining ART and T3. Behavioral Math RTI Large scale, school-based, programmatic research being conducted by the Vanderbilt group is advancing knowledge of evidence-based math instruction for students who have specific kinds of math difficulties, including (a) counting and math facts (Fuchs et al., 2010a); (b) math facts (Fuchs et al., 2010b); (b) math facts and word problems (Fuchs et al., 2009); and (c) math problem solving (Fuchs, Fuchs, Craddock, Hollenbeck, Hamlett, & Schatschneider, 2008; Powell, & Fuchs, 2010). L. Fuchs, D. Fuchs and their associates have studied both children with math difficulties only (Fuchs et al., 2009) and math and reading difficulties (Powell, Fuchs, Fuchs, Cirino, & Fletcher, 2009). Just as behavioral RTI for reading is challenging when used for purposes of identification and classification of reading disability (Wagner et al., 2012), so is behavioral RTI for math because it is difficult to identify a single cut-off (Murphy, Mazzacco, Hannah, & Early, 2007).

Brain Response to Intervention (RTI) Brain Reading RTI Three recent studies in which children’s brains were imaged while performing reading tasks before and after reading instruction have added important new knowledge. This now sizable body of research on the brain’s RTI in children with and without reading problems shows that the brain is plastic and changes as a result of reading instruction. Myler, Keller, Cherkassky, Grabieli, and Just (2008) studied brain RTI of 5th graders whose brain was scanned while they performed a sentence reading comprehension task before receiving remedial reading instruction, after 100 hours of the reading instruction, and a year after the instruction was completed. Compared to good readers, prior to the instruction the poor readers were significantly less activated in bilateral parietal regions. Following intervention, the poor readers showed behavioral RTI in reading and brain RTI in left angular gyrus and superior parietal lobule and a year later showed increased activation and normalization in these regions, which probably support word- and sentence-level assembly. However, they did not normalize in the medial frontal cortex where they over-activated,

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probably because reading was effortful and taxed their supervisory attention for self-regulating language processing. Davis et al. (2010) studied structural connectivity of neuroanatomical pathways as a brain predictor of behavioral RTI. They acquired high angular resolution diffusion images from a group of first grade children who received a year-long reading intervention. Probabilistic tractography was used to calculate the estimated strength of connections among nine cortical regions of interest; these connections were correlated with the children’s scores on four standardized reading measures. Eight correlations between brain structural connectivity and behavioral RTI were significant. Of these, four involved the connections between insular cortex and angular gyrus. Rezaie, Simos, Fletcher, Cirino, Vaughan, and Papanicolaou (in press), in contrast, studied brain RTI for magnetoencephalography for middle school students with and without reading disabilities. A year after the intervention those who completed it were classified as adequate or inadequate responders. Before intervention the adequate responders had increased activation in left middle, superior, and ventral occipital temporal regions and right mesial temporal cortex. The brain activation at baseline prior to intervention contributed uniquely in addition to baseline behavioral measures of word reading or fluency to predicting improvement in reading efficiency. Thus, engagement of specific temporal lobe regions predicts response to instruction in adolescents who still struggle with reading. Brain Writing RTI Such studies are not available for pure writing disability but are for children who have both writing and reading disability due to dyslexia, which interferes with both word spelling and reading. For example, Richards et al. (2005) showed that children who had completed grades 4 to 6 not only improved on behavioral measures of writing (Berninger, Winn et al., 2008, Study 1) but also on spelling during brain imaging. Brain Math RTI In their ground breaking study, Rosenberg-Lee, Barth, and Menon (2011) did not deliver a math intervention but rather studied brain RTI in the regular math program from the second to third grade. Not only were there changes at the behavioral level (third graders scored higher on addition computation than second graders) but also at the brain level. Compared to second graders, third graders showed greater activation in

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(a) dorsal stream of right superior parietal lobule, intraparietal sulcus, and angular gyrus; (b) ventral stream of bilateral lingual gyrus, right lateral occipital cortex, and right parahippocampal gyrus; and (c) left dorsal lateral prefrontal cortex; the third graders also showed greater deactivation in the ventral medial prefrontal cortex. At both grade levels, activation increased in two cognitive regions (right inferior frontal sulcus and anterior insula) and two regions associated with arithmetic calculation (left intraparietal sulcus and superior parietal lobule) during complex calculations. Of most interest, functional connectivity between the left dorsal lateral prefrontal cortex and multiple posterior brain areas was greater in third graders than second graders. However, the differences in functional connectivity were larger in the dorsal stream parietal areas (superior parietal lobule and angular gyrus) compared to the ventral stream areas (lingual gyus, lateral occipital cortex, and medial prefrontal cortex). The third and second graders did not differ in functional connectivity between the ventral medial prefrontal cortex and posterior brain regions.

INDIVIDUAL, DEVELOPMENTAL, GENDER, LANGUAGE, AND CULTURAL DIFFERENCES Longitudinal Studies Longitudinal studies track individual differences over development. Even though brains may change in response to intervention, it does not follow that there are not individual differences that remain relatively stable across development or that development undergoes systematic changes, maybe not in discrete non-overlapping stages but probably in cascading, overlapping phases characterized by recursive gains, plateaus, and regressions. Both the individual differences and developmental changes need to be taken into account in identifying students with SLDs in reading, writing, and math and assessing their RTI. In this chapter we call attention to progress being made in understanding (a) longitudinal change in math skills ( Jordan, Kaplan, & Hanich, 2002; Jordan, Hanich, & Kaplan, 2003a) and in math-related skills that influence math learning, including but not restricted to number sense ( Jordan, Glutting, & Ramineni, 2010) and executive functions (Mazzocco & Kover, 2007); (b) the predictive relationship of early math skills and math outcomes later in schooling ( Jordan, Kaplan, Ramineni, & Locuniak, 2009; Locuniak, & Jordan, 2008); and (c) math development in students with math problems only or math and reading problems ( Jordan, Hanich, & Kaplan, 2003b). The implication

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is that for both biological (brain and genetic) variables and environmental variables that influence reading, writing, and math, the individual’s developmental status and trajectory and profile of relevant skills related to others (inter-individual differences) and to one’s own average skills (intra-individual differences) (see Berninger & Abbott, 2010) need to be considered in planning, implementing, and evaluating an appropriate instructional program.

Gender Differences Gender differences have been documented in writing disabilities but not reading disabilities (Berninger, Nielsen, Abbott, Wijsman, & Raskind, 2008). Even though environmental variables may influence gender differences in math achievement, so do biological variables (e.g., Halpern, Benbow, Geary, Gur, Hyde, & Gernsbacher, 2007; Keller & Menon, 2009).

Language and Cultural Differences Two kinds of language variables can influence reading, writing, and math achievement. One kind is whether the child’s first language is the language spoken at school; in the United States these students whose first language is not English are called English Language Learners (ELLs). Although these students may encounter challenges in academic learning, Swanson and colleagues’ research discussed earlier indicates that individual differences in working memory influence ELL students’ RTI over and beyond challenges of learning a second language. The second kind is whether the child has selective language impairment (SLI), which does have a genetic and brain basis that can affect learning to read and write and do math (understand the vocabulary, word problems, and the teacher’s instructional talk). Many cultural factors can also influence academic learning of students with brain-based SLDs, but scarcely any research has been done on this timely issue.

Defining SLDs in Reading, Writing, and Math Patterns in Developmental Domains, Learning Skills, and Working Memory Components Silliman and Berninger (2011) proposed an alternative that goes beyond IQ-achievement discrepancy or RTI. Because not all reading, writing, or math problems are the same, especially if the individual’s development is not within the normal range, they proposed obtaining a developmental profile of the five domains of development: cognitive and memory,

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receptive and expressive language, attention and executive functions, social and emotional, and sensory and motor. Many tests and/or rating scales are available for this purpose. The goal is to determine if the child has pervasive developmental disability (PDD) across all five domains or selective developmental disability (SDD) in one or more but not all domains. These children often have reading, writing, and math learning problems, but not the same ones as children with SLDs in reading, writing, and math and otherwise normal development, and need instruction at their developmental level. SLDs in reading, writing, and math should be diagnosed based on learning profiles of key reading, writing, and math skills and working-memory phenotype profiles based on research identifying behavioral markers of underlying genetic variations. For example, some reading disabilities have only word-level impairments and others syntaxlevel impairments in working memory, which are associated with different impairments in the learning profile—word reading and spelling or reading comprehension and writing sentences, respectively (also see Berninger & Miller, 2011). For examples of math problems associated with different neurogenetic disorders that are not dyscalculia, see Mazzocco (2009) and Dennis, Berch, and Mazzocco, (2009). Instructional Relevance of Evidence-Based Definitions of SLDs in Written Language (SLD-WL) This approach to definition links patterning of developmental, learning, and phenotypes to identifying appropriate instruction. See Berninger and O’Mally May (2011) for an example of how students began to respond after years of non-responding once the nature of their SLD was diagnosed (dysgraphia—impaired handwriting) or oral and written language learning disability (OWL LD), also referred to as selective language impairment (SLI) and an instructional program was designed for their specific LD. With the current focus on phonological decoding, children with these SLDs are often not identified or treated. Instructional Guidelines for Dysgraphia, Dyslexia, and OWL LD Because of the underlying genetic and brain-based working memory problems, our intervention programs for students with SLDs affecting written language (SLD-WL) have been based on instructional design principles that are not business as usual. First, because they have trouble sustaining language processing over time and their brains habituate more quickly than age peers to language, we keep all instructional activities

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brief and keep varying them. Also, to facilitate all the components of the working memory system working in concert to support fluent reading and writing, we teach to all levels of language close in time (first subword, then word, and then text) and engage both the phonological and orthographic loops of working memory. At the subword level we teach automatic, procedural knowledge of the alphabetic principle by looking, touching, saying, and listening to form automatic associations between graphemes (1- and 2-letters) and corresponding phonemes (in and out of word context) or in the opposite direction. We do not teach declarative knowledge (verbalized rules) that overloads their working memory. At the word level, we teach transfer of alphabetic principle and other orthographicphonological connections to words that vary in number of syllables and morphemes and are from different word origins with different soundspelling-morpheme relationships. The goal is to make the morphophonemic orthography of English explicit. At the text level, we teach transfer of word decoding to reading text for meaning or of word spelling to composing text using the T3 strategy Dunn described in the behavioral writing RT section or explicit strategy instruction for planning, translation, or reviewing/revising. For additional applications of these and other instructional design principles to teaching students with dysgraphia, dyslexia, or OWL LD, see Berninger and Wolf (2009a,b). Dyscalculia Progress is being made in understanding the multifaceted nature of dyscalculia, which is also marked by individual differences and changes in expression across math development (e.g., Geary, Hoard, Byrd-Craven, Nugent, & Numtee, 2007; Jordan & Hanich, 2003). One evidence-based diagnostic system differentiates among a procedural subtype, a semantic memory subtype, and a visuospatial subtype; see Table 12.3 in Geary (2003) for linking these with what was known at that time about the brain or genetic basis. Both cognitive and neuroscience research has identified differences between children with and without math disability in the mental representation of the number line (e.g. Geary, Hoard, Nugent, & Byrd-Craven, 2008), partwhole relationships (Mazzocco & Devlin, 2008), timed performance for arithmetic skills (Mazzocco, Devlin, & McKenney, 2008), and math estimation (Mazzocco, Feigenson, & Halberda, 2001). Fortunately, evidencebased instructional interventions for math disabilities are available (Gersten, Jordan, & Flojo, 2005). Also see, the programmatic research by Fuchs and colleagues that was showcased under Behavioral Math RTI.

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CONCLUSIONS AND RECOMMENDATIONS Above all, all consumers of brain research on reading, writing, and math in children with or without SLDs should be cautious and critical. Much brain research is currently based only on group analyses and not individual brain analyses which are needed for educational applications. Many who advocate brain-based education practices are not trained, qualified neuroscientists or experienced psychologists with the expertise to make the claims they do. The Appendix provides questions to ask about who is making claims about the brain and learning or learning disability and evaluate whether they are qualified and a credible source of information. The Appendix also has a schema for reading research articles on the brain. Brain research has increased understanding of SLDs, but much work remains until the role of brain in learning and development is fully understood. An approach that only takes brain into account should be avoided. More appropriate is an interdisciplinary systems approach that is grounded in the contributions of multiple disciplines and recognizes the multiple components of the brain and the mind it constructs, the learning environment with which it interacts (physical, social, and instructional), and the individual differences and developmental status of the learner, languages spoken at school and home, and cultural differences. The current approach of asking “What Works?” may need to be modified for students with SLDs in reading, writing, and math. That approach implies “One Size Fits All.” Future research might combine group research designs and analyses (e.g., randomized, controlled studies) with single subject designs (as recommended by the National Panel on Single Subject Designs, Kratchowill et al., 2009) to evaluate effectiveness of interventions for SLDs in writing and reading and math in general and also RTI for individuals with SLDs and modifying their interventions according to their individual needs. Thus, we might reframe the question to ask “What Works for Whom?”

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Rosenberg-Lee, M., Tsang, J., & Menon, V. (2009). Symbolic, numeric, and magnitude representations in the parietal cortex. Behavior Brain Sciences, 32, 350–351. Rykhlevskaia, E., Uddin, L. Q., Kondos, L., & Menon,V. (2009). Neuroanatomical correlates of developmental dyscalculia: combined evidence from morphometry and tractography. Frontiers Human Neuroscience, 3(510). Schneider, W., & Shiffrin, R. (1977). Controlled and automatic human information processing: Detection, search, and attention. Psychological Review, 84, 1–66. Scholastic, Inc. (n.d.). About READ 180. Retrieved September 19, 2011, from . Shaywitz, SE, Shaywitz, BA, Fulbright, RK, et al. (2003). Neural systems for compensation and persistence: Young adult outcome of childhood reading disability. Biological Psychiatry, 54, 25–33. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing II: Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. Silliman, E., & Berninger,V. (2011). Cross-disciplinary dialogue about the nature of oral and written language problems in the context of developmental, academic, and phenotypic profiles. Topics in Language Disorders, 31, 6–23. Swanson, H. L., Jerman, O., & Zheng, X. (2008). Growth in working memory and mathematic problem solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 100, 343–379. Swanson, H. L., Saez, L., & Gerber, M. (2006). Growth in literacy and cognition in bilingual children at risk for reading disabilities. Journal of Educational Psychology, 98, 247–264. Swanson, H. L., Sáez, L., Gerber, M., & Leafstedt, J. (2004). Literacy and cognitive functioning in bilingual and nonbilingual children at risk for reading disabilities. Journal of Educational Psychology, 96, 3–18. Tan, L., Spinks, J., Eden, G., Perfetti, C., & Siok, W. (2005). Reading depends on writing, in Chinese. Proceedings of the National Academy of Sciences, 102, 8781–8785. Traweek, D. (1993). Teacher and learner variables in early literacy instruction: Treatment, evaluation and ethnographic studies. Ph.D. Dissertation, University of Washington. Troia, G. (Ed.). (2009). Instruction and assessment for struggling writers. Evidence-based practices. New York: Guilford. Vaughn, S., & Fuchs, L. (2003). Redefining learning disabilities as inadequate response to instruction: The promise and potential problems. Learning Disabilities Research & Practice, 18, 137–146. Vogt, W. (2007). Quantitative research methods for professionals. Boston: Pearson. Wagner, R. K., Waesche, J., Shatschneider, C., Maner, J., & Ahmed,Y. (2012). Using response to intervention for identification and classification. In P. McCardle, B. Miller, J. Lee, & O. Tzeng (Eds.), Dyslexia across languages: Orthography and the brain–gene–behavior link (pp. 202–213). Baltimore, MD: Paul H. Brookes. Woodcock, R., McGrew, K., & Mather, N. (2001). Woodcock-Johnson III Psychoeducational Achievement Test Battery. Itasca, IL: Riverside. Wu, S., Meyer, M., Maeda, U., Salimpoor, V., Tomiyama, S., Geary, D., et al. (2008). Standardized assessment of strategy use and working memory in early mental arithmetic performance. Developmental Neuropsychol, 33(3), 365–393. Wu, S. S., Chang, T. T., Majid, A., Caspers, S., Eickhoff, S. B., & Menon,V. (2009). Functional heterogeneity of inferior parietal cortex during mathematical cognition assessed with cytoarchitectonic probability maps. Cerebral Cortex, 19, 2930–2945. Zamarian, L., Ischebeck, A., & Delazer, M. (2009). Neuroscience of learning arithmetic— Evidence from brain imaging studies. Neuroscience and Biobehavioral Reviews, 33, 909–925. Zhang, T. T., Majid, A., Caspers, S., Eickhoff, S. B., & Menon, V. (2009). Functional heterogeneity of inferior parietal cortex during mathematical cognition assessed with cytoarchitectonic probability maps. Cerebral Cortex, 19, 2930–2945.

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Appendix Questions to Ask Those Making Claims about the Brain and Education or Brain and SLDs 1. What is their training, experience, credentials for research in neuroscience, cognitive neuroscience, neuropsychology? 2. What is their training, experience, credentials for research on education, educational diagnosis, instruction? 3. Has the individual participated in an interdisciplinary training program or research team that engaged in cross-disciplinary dialogue and collaboration? Schema for Reading Brain Research as Critical Consumer As you read ask these questions: 1. What was each question the research was designed to address. Why or why not might each be an interesting or important question? 2. How was the research designed to address each research question? Who were the participants? What were their characteristics and how were these determined? What were the research design and methods for collecting and analyzing the data? Include the kind of brain imaging technology employed. If participants had to perform one or more tasks, describe them. Evaluate whether and how the design and methods addressed the research questions. 3. Explain what the results were and why they are important. Briefly list the results and then discuss their significance to a critic who wonders, So what? Why should this research and its findings be of importance? Evaluate why the findings contribute important new knowledge (basic or applied) and suggest what the next step(s) might be in this line of research.

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Social Relationships: The 4th R Judith Wiener, and Victoria Timmermanis Department of Applied Psychology and Human Development, OISE/University of Toronto, Canada

Chapter Contents Self-Perceptions 92 Self-Esteem and Domain-Specific Self-Concepts 93 Achievement Motivation 98 Knowledge of LD, Identity, and Stigma 99 Summary 101 Peer Relationships 102 Peer Status 102 Friendship 103 Bullying 104 Predictors of Peer Relationship Difficulties 105 Meaning of Peer Relations Difficulties 109 Summary 110 Parenting and Family Relationships 110 Emotional and Behavioral Difficulties 112 Interventions in the Affective and Social Domain 113 School- and Classroom-Based Intervention 114 Social Skills Training 116 Individual and Group Therapy 122 Parenting Interventions 124 Summary 125 Conclusions and Implications 126 Acknowledgment 129 References 129

The purpose of this chapter is to review the research on social, emotional, and behavioral functioning of individuals with learning disabilities (LD) and interventions that address their challenges in this domain. Although learning disabilities are currently defined in terms of academic achievement difficulties (e.g., Swanson, 2000), when the term was first used by Kirk in 1963, he described children with LD as having disorders in the development of language, speech, reading, and the associated communication skills needed for social interaction. Furthermore, Kavale and Forness’ (1996) Learning about Learning Disabilities

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meta-analysis showed that approximately 75% of individuals diagnosed with LD have social, emotional or behavioral difficulties. School curricula typically consider the 3 R’s—reading, writing, and arithmetic—to be the basic skills that students need to acquire. The 4th R—relationships— is usually only implicitly taught at school and in family and recreational contexts. Having high quality relationships, however, is foundational to social and emotional adjustment (e.g., Bukowski & Adams, 2005). Morrison and Cosden (1997) proposed that the concepts of risk and resilience become a framework for conceptualizing nonacademic outcomes in individuals with LD. These outcomes include child and adolescent social and emotional adjustment, substance use and abuse, involvement in the criminal justice system, family functioning, secondary and postsecondary school completion, and adult adaptation in terms of career and interpersonal relationships. Resilience refers to the process of, capacity for, or outcome of successful adaptation despite challenging or threatening circumstances (Masten, Best, & Garmezy, 1990). These challenging or threatening circumstances are risk factors. Protective factors are attributes of the individual or environment that promote resilience. The concepts of risk and resilience are integral to the bioecological theory proposed by Bronfenbrenner (2005) and research in developmental psychopathology. One of the principles underlying Bronfenbrenner’s theory is that the form, power, content, and direction of the proximal processes producing development vary systematically as a joint function of the characteristics of the developing person (including genetic inheritance); of the environment—both immediate and more remote—in which the processes are taking place; of the nature of the developmental outcomes under consideration; and of the continuities and changes occurring in the environment over time, through the life course, and during the historical period in which the person has lived (pp. 6–7). The implication of bio ecological theory for research on social, emotional and behavioral functioning of individuals with LD is that it is not sufficient to determine whether, on average, individuals with LD differ from individuals without LD. Research on individuals with LD must also consider developmental processes, and the interaction of their characteristics and key aspects of their environment including family, school, community, and culture. Thus, in addition to markers of the problems of individuals with LD (e.g., children with LD are more apt to be rejected by peers than children without LD), researchers should investigate developmental and environmental predictors of their difficulties including moderators, mediators, and mechanisms (Bukowski & Adams, 2005). Moderators are variables that increase

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or decrease the strength of an outcome variable (e.g., having comorbid ADHD might increase the extent to which children with LD would be rejected by peers). Mediators are variables that are correlated with predictor and outcome variables and fully or partially explain the relationship between predictor and outcomes (e.g., once aggressive and oppositional behavior are considered, the relationship between LD and bullying others is no longer significant). As the words imply, mechanisms describe a process (e.g., the process children with LD use to process social information) and meanings describe individuals’ views of phenomena (e.g., the emotional impact of being rejected by peers). Wong (2003) edited a special issue of Learning Disabilities Research and Practice in which she discussed several issues pertaining to the risk and resilience framework including interpretation of previous longitudinal studies that elucidate factors that predict both positive and negative outcomes in individuals with LD, and the need for research that develops this model. Respondents to Wong’s proposal concurred that risk and resilience theory is a useful framework to guide research in this area. Donahue and Pearl (2003) cautioned that these concepts should be clarified. Several respondents indicated directions for future research based on this model including investigating family functioning and parental support (Wiener, 2003), the role of emotions and reciprocity in relations with adults and peers (Margalit, 2003), and the role of friendship (Cosden, 2003). Bryan (2003) suggested that once factors that contribute to resilience in individuals with LD are identified, it may then be possible to teach these strategies to individuals who are at risk. Early research using a resilience model sought to identify the characteristics of adults with LD who are successful in their careers (e.g., Gerber, Ginsberg, & Reiff, 1992; Goldberg, Higgins, Raskind, & Herman, 2003). Goldberg et  al. followed up 41 adults who graduated from the Frostig School (a private school for students with LD in the United States) 20 years earlier. Although both successful and unsuccessful adults with LD were aware of the nature of their difficulties, successful adults were able to compartmentalize the disability, viewing it as only one aspect of their identities. Successful adults with LD were also described as proactive— they believed they had the power to control the direction of their lives. They were involved with their families and communities, were leaders, accepted support, but also supported others. Although they were incredibly persistent, they were also flexible, adapting their goals and strategies when they were not able to attain the original goal. Both successful and

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unsuccessful adults set goals but, in contrast to the unsuccessful adults, the goals of the successful adults were realistic, specific, flexible in terms of taking advantage of opportunity, and accompanied by viable strategies. Most of the adults with LD referred to significant others who supported or mentored them including family, friends, teachers, and co-workers in early adulthood. Once they were 30 years of age or older, the successful adults referred to these people as influential to the direction of their lives but were not dependent on them. They also discussed mentoring and supporting others. The unsuccessful adults, however, remained in a dependent role in relation to mentors and significant others. Almost all of the adults with LD described experiencing stress and exhaustion as a result of their learning difficulties that, at times, caused depressive symptoms and high levels of anxiety. By the time they were 30 years of age or older, the successful adults had acquired coping strategies such as avoiding triggers of anxiety and people who are critical, delegating tasks at work, changing activities to minimize stress, taking advantage of family and peer support, planning ahead, and seeking therapy. Although the retrospective longitudinal studies have identified the characteristics of successful adults, this research does not elucidate risk and resilience factors in children and youth. Discussing these factors is a major focus of this chapter. The social, emotional, and behavioral difficulties that are common in children and youth with LD, the factors that put them at risk for these difficulties and factors that are associated with resilience in this domain are discussed below. In the past 35 years many studies have been devoted to investigation of the self-perceptions, and peer and family relationships of children and youth with LD. In this chapter we begin with a description of this research that not only includes markers but also includes examination of risk and protective factors such as cognitive and social cognitive development of individuals with LD, attachment, teacher qualities, special education support, and the presence of co-occurring disorders such as ADHD, anxiety, and depression. We then describe evidence-based interventions in the social, emotional, and behavioral domain. The chapter concludes with a discussion of the limitations of the existing research, and implications for future research and practice.

SELF-PERCEPTIONS Due to their academic failure and the fact that academic achievement is highly valued in many cultures (Zadeh, Geva, & Rogers, 2008), the

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hypothesis that individuals with LD would differ in their self-perceptions from average and high achievers has been investigated in several studies. The areas of investigation mainly focus on academic self-concept and selfesteem. Recent studies have examined correlates and predictors of academic self-concept and self-esteem in children and youth with LD with the goal of identifying risk and protective factors. Children and youth with and without LD have also been compared in terms of their self-efficacy, whether their self-appraisals differ from parent and teacher appraisals in areas such as strategy knowledge and effort, and their attributions for success and failure. Several studies have been devoted to children’s, adolescents’ and adults’ knowledge about LD and how this knowledge is incorporated into their identity.

Self-Esteem and Domain-Specific Self-Concepts Harter (1999) defined self-esteem as a global judgment of one’s selfworth, and domain specific self-concepts as self-appraisals in specific domains such as scholastic competence (often referred to as academic self-concept), social competence, behavioral conduct, physical appearance, and athletic competence. Although in the literature the terms selfesteem and self-concept are sometimes used synonymously, in this chapter Harter’s distinction is maintained. Having low self-esteem is a risk factor for internalizing psychopathology, most notably depression (Harter, 1999). According to Harter’s model, which has extensive research support, a negative self-concept in a specific domain does not put a person at risk for low self-esteem unless the person views competence in the area assessed by the self-concept as extremely important. Differences between Individuals with and without LD Bear, Minke, and Manning (2002) conducted a meta-analysis of 61 studies comparing self-esteem and domain specific self-concepts of children and adolescents with and without LD. Most of these studies used the Harter SelfPerception Profile for Children (SPPC; Harter, 1985), the Harter Self-Perception Profile for Children with Learning Disabilities (SPPLD; Renick & Harter, 1988) or the Piers-Harris Children’s Self-Concept Scale, Second Edition (Piers, Harris, & Herzberg, 2002). Consistent with the conclusions from other recent qualitative reviews (e.g., Burden, 2008; Zeleke, 2004) and an earlier metaanalysis (Chapman, 1988), the results of Bear et  al.’s meta-analysis showed that children with LD have a more negative academic self-concept than children without LD, and that the effect size is large; i.e., d = −.69 based on

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47 comparisons (k  =  47). Children with LD also had lower self-concepts in reading, spelling, writing, and math than children without LD (d ranges from .49 to .79; k ranges from 2 to 4). Furthermore, the low academic selfconcepts of children with LD are stable over time (e.g., Vaughn, Elbaum, Schumm, & Hughes, 1998). These results indicate that the low academic self-concept of children and adolescents with LD is a robust finding. The research findings are less consistent in relation to self-esteem and social selfconcept. Bear et  al.’s (2002) meta-analysis, however, provided some clarification. Although children and youth with LD had lower self-esteem and lower social and behavioral self-concepts than children without LD, the effect sizes were small to moderate (Self-Esteem, d = −.22, k = 38; Social, d = −.22, k = 39; Behavioral Conduct, d = −.27, k = 28). The results of a meta-analysis by Nowicki (2003) that was confined to studies conducted after 1990 were similar. Predictors of Academic Self-Concept The research described above showed that having a negative academic self-concept is so common among individuals with LD that it might be construed as a marker of the disability even though it is not a defining characteristic. Therefore, several studies have been devoted to understanding the specificity, accuracy, predictors, and outcomes of a negative academic self-concept. With regard to specificity, an important question is whether students with LD are able to differentiate in their self-perceptions of academic competence between subjects where they achieve relatively well and relatively poorly. Two studies with German and American samples of children in grades 3 through 9 suggest that they do (Hanich & Jordan, 2004; Moller, Streblow, & Pohlmann, 2009). Moller et  al., for example, found positive correlations between achievement of students with LD in grades 5 through 9 in math and German and their corresponding domain specific self-concepts, but verbal and math self-concepts were not correlated. In spite of their academic self-concept being lower than that of students without LD, both elementary school and secondary school students with LD tend to overestimate when asked to predict their performance on academic tasks or in comparison to ratings by parents and teachers (Bear & Minke, 1996; Heath & Glen, 2005; Klassen, 2007, 2008; Stone, 1997; Stone & May, 2002). Why might they engage in this overestimation? Heath and Glen (2005) found that their self-ratings are more calibrated with their performance after being given positive feedback, suggesting that

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their positive illusions serve a self-protective function. Bear and Minke (1996), on the other hand, concluded on the basis of interviewing the children that their inflated self-perceptions of academic competence may be a function of positive teacher feedback on their performance designed to maintain the children’s self-esteem. Owens, Goldfine, Evangelista, Hoza, and Kaiser (2007) provided other hypotheses in relation to the positive illusory bias of children with ADHD that should also be explored with children with LD. As children with LD have challenges with executive functioning, including self-monitoring (Meltzer & Krishnan, 2007), their positive illusions may simply reflect these cognitive difficulties. There is also a developmental progression in terms of understanding of the self; children become more realistic as they get older (Harter, 1999). If children with LD are immature in their social cognitive development, they may overestimate their competence in comparison with same-age peers. Research on academic self-concept in students with LD does not explicitly identify mediators or moderators, but several studies were devoted to correlates of positive academic self-perceptions. Students with LD in grades 4 to 9 who had a more positive academic self-concept were more likely to work hard and had teachers who believed that the students were making an effort to achieve than students with a low academic selfconcept (Meltzer, Reddy, Pollica, Roditi, Sayer, & Theokas, 2004). Positive teacher feedback was associated with positive student academic selfconcept in grades 3 and 6, and both teacher feedback and comparison with peers were associated with student academic self-concept in grade 6 (Bear, Minke, Griffin, & Deemer, 1998). Providing effective instructional interventions in a specific domain was associated with increases in academic self-concept in that domain. These instructional interventions included assistive reading software for secondary school students (Chiang & Jacobs, 2009) and strategy instruction infused into the classroom curriculum in grades 4 to 9 (Meltzer, Katzir, Miller, Reddy, & Roditi, 2004). The assistive reading software instructional intervention was associated with increased reading self-concept on the SPPLD, and the classroombased strategy instruction was associated with higher perceptions of competence in reading, spelling, and writing. Protective Factors in Relation to Self-Esteem Given that self-esteem is central to healthy social and emotional development (e.g., Harter, 1999), it is important to determine how individuals with LD protect their self-esteem in spite of academic failure and a

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negative academic self-concept. Using a structured interview and a combination of quantitative and qualitative analyses, Singer (2008) examined strategies that children use to protect their self-esteem in the face of academic failure in a sample of 60 Dutch 9 to 12 year old children diagnosed with dyslexia. The children used combinations of four main strategies leading the investigators to classify them in accordance with four profiles: 1. Approximately 21% of the children engaged in strategies oriented towards improving academic performance including working hard, seeking assistance from parents, friends, and peers, and using adaptive self-talk (e.g., telling themselves they are not stupid, repeating phrases their parents have used such as I can do it, it will just take more effort and time). Children who used this combination of strategies had understanding and insight into the nature of their reading and writing difficulties. 2. About 36% of the children tried to hide the problem from peers and teachers and to regulate negative emotions (i.e., sadness, anger, or frustration) through distraction. They changed the subject when academics came up in peer conversations, and got involved in other activities. Students who used the second strategy often complained that teachers humiliated them in class and that they were afraid of being teased and bullied. About half of the children who fit this profile had parents who provided emotional support; the remainder indicated that their parents did not accept their dyslexia. Students fitting the second profile tended to use maladaptive self-talk that suggested they viewed themselves as helpless and blameless. 3. About 20% of the children fit a profile that was in between profiles 1 and 2; in the face of academic failure they worked hard, obtained adult support, but did not seek out peer support. 4. Approximately 20% of the students fit the fourth profile. They did not try to perform better in school and did not seek help from peers. They tended to rely on parent and teacher support to help them feel better when they were faced with academic failure. Their main strategy, however, was to put their academic challenges into perspective by differentiating between their performance in reading and spelling and their overall intelligence. They tried to distract themselves from thinking about their academic problems (e.g., joking around, thinking about pleasant things, doing an activity they enjoy). They compartmentalized their dyslexia—saying that it should not dominate their lives. About half of these children had parents or siblings with dyslexia and identified with them.

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The Singer (2008) study is the most comprehensive investigation of the mechanisms that children with LD use to protect self-esteem. Other studies corroborated their findings. Elementary school students with dyslexia who had high self-esteem had a more positive attitude towards their reading difficulties and more solid peer social support, and their parents had a more positive attitude toward their children’s reading difficulties than students who did not have high self-esteem (Terras, Thompson, & Minnis, 2009). For children with LD in elementary and middle school, higher self-esteem was associated with higher nonacademic (i.e., social competence, behavioral conduct, and physical appearance) self-concepts (Cosden, Elliot, Noble, & Keleman, 1999). Children in grades 3 through 6 who viewed their disability as delimited (i.e., only affecting one part of their lives) and modifiable (i.e., controllable) had higher self-esteem (Rothman & Cosden, 1995). Young adults with LD who reported having a mentor in their adolescence (e.g., a teacher or guidance counselor) had higher secondary school graduation rates and higher self-esteem (Ahrens, DuBois, Lozano, & Richardson, 2010). Role of Special Class Placement To what extent does special class placement affect self-esteem and domain specific self-concepts of children and youth with LD? Some advocates of inclusion claim that labeling and segregated special education placement damage self-esteem (e.g., Coleman, 1983). Social comparison theory (Festinger, 1954), however, predicts that children with LD in more segregated settings might have higher self-esteem and domain specific self-concepts because they compare their own competencies with other students with LD. The evidence does not strongly support either position according to the results of two meta-analyses (Bear et  al., 2002; Elbaum, 2002). For most comparisons, children’s self-esteem and domain specific self-concepts did not differ by type of placement. Both meta-analyses, however, showed that children with LD in regular classrooms who did not receive any special education support had lower academic self-concept than children receiving support. Whitley (2008), however, provided evidence that the issue is more complex. Using a longitudinal database that is a representative sample of Canadian children and youth, she investigated predictors of self-esteem in a sample of school-identified 10- to 14-year-old children with LD. The model suggested that the relationship between inclusive special education placements and self-esteem was indirect; more inclusive placements were

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associated with lower social self-concept, the perception that teachers were unfair and that parents had high expectations. These factors predicted lower self-esteem.

Achievement Motivation Due to their academic problems, students with LD must work hard to achieve (Goldberg et  al., 2003). When students, including students with LD, put more effort into achieving in school, they typically improve their performance (Meltzer et  al., 2004). Students with LD in grades 4 to 9 rate themselves as putting a lot of effort into their schoolwork and their self-reports of their effort do not differ from students without LD. Their teachers’ ratings, however, are lower than their self-reports and teachers’ ratings of the effort of students without LD (Meltzer, KatzirCohen, Miller, & Roditi, 2001). It is possible that students with LD are inflating their ratings of their own effort for social desirability reasons, or that teachers rate students’ effort on the basis of actual achievement and do not really know how much effort they put into tasks. Furthermore, teachers view students with LD as using fewer appropriate strategies than students without LD. Students with LD may therefore be working as hard as other students but because they do not use effective work strategies, it may appear as if they are not working hard. Students with LD, however, vary in the amount of effort they exert in relation to academic achievement (Lackaye & Margalit, 2006). Their effort is positively associated with their actual achievement, their ratings of their academic self-efficacy, their mood, and feelings of hope. Achievement motivation is associated with attributions for success and failure (Weiner, 2000). The meta-analysis by Kavale and Forness (1996) showed that students with LD often have different attributions for academic success and failure than students without LD. They are more likely than their counterparts without LD to attribute success to luck (d = .50, k = 14) and failure to internal causes such as low ability (d = .54, k = 22) and effort (d  =  .43, k  =  14). Núñez et  al. (2005) identified two subgroups of 9 to 14-year-old children in relation to their attributional style. Students with an Adaptive Profile (45% of the sample) typically attributed success to high ability and high levels of effort, and seldom attributed success to external causes. There was no clear pattern in relation to their attributions for failure. Students with a Helpless Profile (approximately 55% of the sample) attributed their success to external causes and their failure to inadequate ability and low levels of effort. Compared to the students

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with the Helpless Profile, students with the Adaptive Profile had higher teacher ratings of academic achievement, were more persistent following failure, had higher self-perceptions in the academic and social domains, better relations with parents and peers, and were more motivated to learn in order to achieve and receive social reinforcement. Currently, it is not reasonable to conclude that low levels of effort is a marker variable, but there is clearly data to suggest that many students with LD use ineffective strategies that might lead others to conclude that they do not put in the necessary effort to achieve. The research suggests that a significant proportion of students with LD use adaptive strategies and that these students have positive self-perceptions in terms of academic self-concept and self-efficacy, positive mood, and an attributional style that is consistent with making an effort to achieve. It is possible that these are protective factors in relation to academic achievement. The research discussed above described how children, adolescents and adults with LD differ from individuals without LD in terms of their selfperceptions and risk and protective factors in relation to academic selfconcept, self-esteem, and achievement motivation. None of these studies described what having a learning disability means to individuals who have this diagnosis. Several recent studies have explored this question.

Knowledge of LD, Identity, and Stigma Most individuals with LD have learning difficulties through the lifespan and are diagnosed with LD as children. As it is adaptive for individuals with diseases or disabilities to acquire knowledge about the symptoms, etiology, and ways of coping with their difficulties (Shany, Wiener, & Feingold, 2011), several studies have explored the understanding of people with LD about their learning disability. These studies have varied in methodology; one study analyzed children’s postings on an Internet site www.sparktop. org) for children with LD (Raskind, Margalit, & Higgins, 2006), and others used semi-structured or structured interviews (Higgins, Raskind, Goldberg, & Herman, 2002; Ingesson, 2007; McCray, Vaughn, & Neal, 2001; Shany et  al., 2011). The participants ranged in age from 9 through 40. Children tended to be more responsive if the prompts were indirect (e.g., tell me everything you know about learning disabilities) rather than direct (e.g., tell me about your learning disability) (Shany et al., 2011). Children with LD tended to describe their LD in terms of one or more academic problems. They often were quite specific such as describing reading difficulties in terms of decoding, fluency, and comprehension,

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and describing LD in terms of impact on everyday life (e.g., being able to read directions) (McCray et al., 2001; Shany et al., 2011). The children who wrote to SparkTop, however, had many questions about LD including how they would know if they might have LD, its causes, and how they might have gotten it (Raskind et al., 2006). Adults with LD tend to have a more sophisticated understanding of the cognitive processes that underpin the disability (e.g., working memory, processing speed) than children (Reiff, Gerber, & Ginsberg, 1993). Children and adults with LD vary in terms of the degree to which LD is part of their identity. Participants in most studies acknowledged that they had been diagnosed with LD (Higgins et  al., 2002; Raskind et  al., 2006; Shany et  al., 2011). Some of them accepted the diagnosis as a valid reflection of their difficulties, whereas others denied it. Higgins et  al. recounted the process of acceptance as described by adults in their late 30s who had attended the Frostig School 20 years earlier. Participants indicated that they went through several stages in terms of accepting the diagnosis and incorporating it into their identity. The first stage, which occurred prior to being diagnosed, was being aware that they were different from other children in terms of academic skills, motor skills, and social problems. They claimed that at the time they felt that society was judging them negatively due to these problems, that they experienced strong emotions including fear, sadness, confusion, frustration and anger, and that they reacted by being withdrawn or aggressive. The second stage involved the process of acquiring a diagnosis, including being tested, and sometimes being given inappropriate diagnoses. The experience of being assessed was anxiety producing, especially if their parents were anxious. The third stage involved the participants and their parents struggling with understanding what the learning disability meant in terms of the child’s abilities and skills, and determining the type of help that was needed. Many of the participants indicated that no one explained to them what LD meant in general or the nature of their learning disability. At the time they were diagnosed sources such as the Internet were not available. It is possible that many of the children writing to SparkTop were at stage 3. The fourth stage, compartmentalization, involved the individuals with LD viewing their disability as only one small part of their identity. Most of the adults who were interviewed had achieved that stage. Higgins et  al. called the fifth stage transformation. Some of the participants had concluded that there were advantages to being LD in terms of having to

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develop strategies, be tolerant of others, and keep going in spite of adversity. Raskind et al. (2006), however, reported that few of the children writing to SparkTop were able to comment about positive aspects of LD. Within society, learning disabilities and intellectual disabilities are often confused, which contributes to the stigmatization of LD (May & Stone, 2010). Children, adolescents, and adults with LD feel this stigma deeply (Higgins et al., 2002; Raskind et al., 2006). Many children indicated that they wanted to hide their LD from friends because they felt they would be rejected if their friends found out. Both children and adults expressed a willingness to relinquish accommodations and supports they received in order to prevent others from finding out. The children and adolescents who wrote to SparkTop expressed their distress, stating that they did not feel they could ask for help from teachers, family members, or friends. They described their experiences of being harassed by punitive teachers and bullied by peers including being teased, ostracized, and physically assaulted. The adults in the Higgins et al. study described these experiences vividly, suggesting that that they had been deeply affected by them.

Summary The finding that individuals with LD have a negative academic selfconcept is so robust that it might be considered a marker variable. Effective interventions that teach children and youth with LD strategies are associated with increases in academic self-concept. Findings regarding low self-esteem in individuals with LD are inconsistent. Factors that protect self-esteem in children and adults with LD include family and peer support, having a mentor, having high self-perceptions in nonacademic areas such as physical appearance, athletic competence and social competence, and viewing the learning disability as only a small part of their identity. Although it is clear that these factors are associated with higher self-esteem, it is not clear from the research whether they are mediators or moderators. Some students with LD have an adaptive profile; they attribute success to ability and effort, whereas others display helplessness. The adaptive profile is associated with higher academic selfconcept and achievement. Several studies have investigated the meaning of LD to individuals with LD, showing that they tend to define LD in terms of their academic difficulties. Many individuals with LD struggle with incorporating LD into their identity and feel profoundly stigmatized by the disorder.

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PEER RELATIONSHIPS In 1974, Tanis Bryan published a study showing that children with LD in elementary school were less accepted by their classmates than other children. This pioneering study stimulated research on the peer relations of children with LD. Researchers have investigated their peer status, friendships, and peer victimization experiences.

Peer Status Peer status is defined as the extent to which people are accepted by a social group of same-age peers. In terms of children, the research has focused on the degree to which they are accepted by classmates. Most investigations of peer status use peer sociometrics. The roster rating scale method involves asking all of the members of a class to rate each other in terms of how much they would like to play or work with each other. The nomination method typically involves giving children a class list and asking them to nominate three children they like most and three they like least. Studies using peer nominations allow researchers to calculate social preference scores (i.e., z-score of liked most nominations minus z-score of liked least nominations) and social impact scores (i.e., z-score of liked most nominations plus z-score of liked least nominations), and to classify children as popular (higher than average number of liked most and few, if any, liked least nominations), neglected (few nominations of any kind), rejected (higher than average number of liked least and few, if any, liked most nominations), and average. Consistent with the findings of the Bryan (1974) study, almost every sociometric study comparing children with and without LD in terms of peer status have found that children with LD are less accepted by classmates than children without LD. In their meta-analysis, Kavale and Forness (1996) found that children with LD were more likely to be rejected (d = .79, k = 41) and less likely to be accepted (d = .78, k = 57) by peers than children without LD. The Nowicki (2003) meta-analysis of studies conducted after 1990 found that children with LD had a lower social preference score (d  =  1.00, k  =  23) than children without LD. Children with LD tend to maintain their peer status from year to year, even when they have a different set of classmates doing the ratings (Bryan, 1976; Estell et  al., 2008). Furthermore, their peer status typically deteriorates within a school year with children who have average peer status in the fall term being rated as neglected or rejected by the end of the academic year

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(Kuhne & Wiener, 2000). Thus, the findings regarding low peer acceptance of children with LD are robust.

Friendship Friendship is defined as a mutual and reciprocal relationship (e.g., Schneider, Wiener, & Murphy, 1994). By middle childhood, having a close friend provides many benefits in terms of social development including improved self-esteem and the opportunity to acquire social interaction skills (Keefe & Berndt, 1996). With regard to individuals with LD, researchers have examined the number of friends they have, the characteristics of their friends, the stability of their friendships, and the quality of their relationships. Although children and adolescents with LD indicate that they have similar numbers of friends as typically developing individuals, when researchers ask the nominated friends themselves, parents, or teachers to corroborate the nominations, children and adolescents with LD have been found to have fewer friends than other children (Estell, Jones, Pearl, & Van Acker, 2009; Tur-Kaspa, Margalit, & Most, 1999; Wiener & Schneider, 2002). Students with LD have proportionally more friends who do not attend their schools, younger friends, and friends with learning problems than other children (Wiener & Schneider, 2002). Their friendships are also less stable (Estell et al., 2009; Wiener & Schneider, 2002). In addition, during the course of a school year, children with LD acquired fewer friends and more enemies, whereas the converse was true for children without LD (Tur-Kaspa et al., 1999). The dimensions of friendship quality in childhood typically include companionship (the amount of time spent together in mutually satisfying activities), providing help and guidance, validating each other (i.e., communicating satisfaction when the friend succeeds), trust and caring, and intimacy and disclosure. Friends also have conflicts with each other, but stable friends typically find a way of resolving the conflicts and repairing the relationship (Fonzi, Schneider, Tani, & Tomada, 1997; Parker & Asher, 1993). Children with and without LD reported similar levels of companionship, help and sharing, trust and caring, and intimacy and disclosure with their very best friends (Wiener & Schneider, 2002). The parents of children with LD, however, perceived their children’s friendships as often being superficial, claiming that their children only interacted with their friends briefly at school, seldom interacting outside school (Wiener & Sunohara, 1998). The parents’ perceptions were supported by findings that

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dyads of best friends where at least one child had LD were characterized by lower levels of companionship and intimacy compared to dyads where neither child had LD (Wiener & Schneider, 2002). Both children and parents reported higher levels of conflict within friendships and challenges with repairing the relationship among children with LD (Wiener & Schneider, 2002: Wiener & Sunohara, 1998). The quantitative data from published studies on friendships in individuals with LD are confined to elementary and middle school students. Goldberg et  al. (2003), however, reported that adults with LD claimed that their biggest struggles in terms of developing friendships were in adolescence. The greater degree of autonomy and control of their lives that they experience in adulthood allows them to develop interests and skills that help them feel successful and to meet friends who share their interests.

Bullying Bullying is defined as the chronic performance of negative actions that are intended to cause harm toward an individual who is weaker than the perpetrator (Olweus, 1995). Bullying may involve physical aggression, verbal aggression such as name calling, or relational aggression such as spreading rumors or exclusion from group activities. Children who bully others are at risk for being abusive in their relationships as adults; they are more likely to engage in violent crime, spousal and child abuse. Children who are victimized by peers often experience anxiety and depression, and during adulthood are at risk for being victimized by others in the workplace and in social relationships (Pepler, Craig, Jiang, & Connolly, 2008). Mishna (2003) reviewed the literature on bullying in relation to children and youth with LD. Only a few studies have investigated the problem with all of them showing that, compared to children without LD, children with LD were more likely to be victimized by peers but were not more likely to be perpetrators of bullying (Martlew & Hodson, 1991; Nabuzoka & Smith, 1993; Thompson, Whitney, & Smith, 1994; Whitney, Nabuzoka, & Smith, 1992). Mishna reviewed risk factors for peer victimization and social competence of children with LD, concluding that children with LD were in double jeopardy—they more often than other children had characteristics that put them at risk of being victimized. Although she has called for more research on this question, this research has not been done.

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Predictors of Peer Relationship Difficulties The research literature suggests three broad risk factors for the peer relationship difficulties of individuals with LD: (1) social skill and social cognitive deficits; (2) comorbid disorders such as ADHD; and (3) school and classroom environment. Although these broad risk factors are discussed separately below, they actually overlap in various ways. Difficulties with peer relations predict low self-esteem, loneliness, and internalizing and externalizing behavior disorders, which may exacerbate the peer relationship difficulties and relationships with teachers. Social Cognitive Abilities Children and adolescents with LD are less skilled at social interaction than typically developing peers, and their social skill deficits are associated with peer rejection. Wiener, Harris and Shirer (1990), for example, found that 9 to 13-year-old children with LD were more likely to be rejected and to be viewed by peers as disruptive and dependent (i.e., seek help when they do not need it). Furthermore, peer rated disruptive behavior and dependence was associated with peer rejection. Similarly, adolescents with LD are more likely than adolescents without LD to experience challenges with several social skills including engaging in conversation, following instructions, accepting and giving negative feedback, giving positive feedback, negotiating, resolving conflicts, and resisting peer pressure (Schumaker, Hazel, Sherman, & Sheldon, 1982). The factors underlying the social skill deficits of children and adolescents with LD, including pragmatics of language, social perception, and social information processing/social problem solving are the subject of considerable research. In several early studies with elementary school children Bryan and her colleagues investigated the pragmatic or conversational skills of children with LD. They found that, compared to children without LD, children with LD engaged in fewer cooperative and more hostile conversational interactions in the classroom (Bryan, Wheeler, Felcan, & Henek, 1976). When teaching a game to other children of different ages they were less likely to adjust their speech to the level of the listener (Bryan & Pflaum, 1978). Although they responded to both direct and indirect requests for information when doing a referential communication task, they did not ask as many clarification questions as children without disabilities and as a result made more errors (Bryan, Donahue, & Pearl, 1981). When engaged in a joint decision making task, children with LD contributed to the

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conversation as much as other children in terms of quantity, but were less likely to disagree with other children’s opinions and were less persuasive. Bryan, Donahue, Pearl, and Strum (1981) analyzed the discourse of children with and without LD while engaging in a dyadic interaction where the children played the roles of talk show host and guest. Although children with LD engaged in as many conversational turns as their partners, when they were hosts, children with LD were not as skilled at initiating and maintaining a conversation or taking on the dominant speaker role. Not only were they less likely to direct questions to their guests, the type of questions they did ask were less likely to elicit an elaborated response. Children with LD tended to ask product questions that typically elicit specific information (e.g., what is your favorite TV show), or choice (yes/no) questions (e.g., do you like videogames?) as opposed to process questions that typically elicit descriptions or explanations (e.g., tell me what you think about Harry Potter books and movies). Not surprisingly, children without LD displayed more signs of discomfort when paired with the children with LD than with children without LD. Early research showed that children with LD experience difficulties with social perception (i.e., the ability to interpret critical social cues and thus understand the complexities of social situations) and social perspective taking (taking the perspective of another person who does not have the knowledge they have; also called role taking) (e.g., Stiliadis & Wiener, 1989; Wong & Wong, 1980), and that these difficulties are associated with low peer acceptance (Meaden & Halle, 2004; Stiliadis & Wiener, 1989). Adolescents with LD were also found to have social problem solving difficulties when they were asked to play the role of an advisor in a telephone hotline conversation (Hartas & Donahue, 1997). More recent research has examined the social information processing skills of children with LD using the Crick and Dodge (1994) model. This model comprises six steps: (1) encoding—attending to social cues in the environment, processing the information, and using memory strategies to store the information; (2) developing a mental representation and interpretation of the information; (3) selecting a desired goal or outcome for the social interaction; (4) response decision—evaluating potential benefits and consequences of alternative responses keeping in mind the environmental context and the child’s own capacities; (5) response enactment; and (6) monitoring whether the response was appropriate in the context and changing the response if necessary. Tur-Kaspa (2004) found that children who were identified as at risk for LD in kindergarten had

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more difficulty on the response decision and enactment steps than typically functioning children. Children with LD in grades 4 to 6 had even more pervasive difficulties when compared with children without LD (Bauminger, Edelsztein, & Morash, 2005). At the encoding stage, they processed fewer units of information, but there were no differences in their interpretation of the scenarios. Children with LD articulated fewer goals and fewer potential solutions to the problem than comparison children and their solutions were less likely to be positive and more likely to be ineffective (e.g., aggressive, passive avoidant). Their chosen solutions were also less competent and less likely to be consistent with the social goals they articulated than the solutions adopted by comparison children. When asked to recognize emotions in pictures and stories, describe their own experiences of feeling these emotions, and define the emotions, children with LD were as competent as children without LD in recognizing and describing the simple emotions such as happiness and sadness but had more difficulty with complex emotions such as loneliness, embarrassment, pride, and guilt. One of the questions raised by the research described above is whether the differences between children with and without LD in pragmatic language and social cognitive abilities are associated mainly with nonverbal LD. With regard to making inferences from stories, children with nonverbal LD (ages 9 to 13) differed from typically achieving comparison children in making inferences about the emotions of characters in stories but not in making other types of inferences. Children with language-based LD differed from typically achieving comparison children in making general inferences but not in making inferences about emotions. The nonverbal LD and language-based LD groups, however, did not differ from each other on any of the variables (Worling, Humphries, & Tannock, 1999). Galway and Metsala (2011) examined social information processing based on the Crick and Dodge (1994) model. Their findings in relation to a small sample of 9- to 15-year-old children with nonverbal LD were similar to those of Bauminger et al. (2005). Given the small sample sizes of the studies that assessed children with nonverbal LD separately, and that the Galway and Metsala study did not have a sample of children with language-based LD, the research does not permit a conclusive answer to the question of whether the pragmatic language and social information processing difficulties of children with LD are mainly associated with participants with nonverbal LD in the samples. How might the social perspective taking and social information processing difficulties of children with LD impact them when interacting

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with peers? Bryan and her colleagues conducted several studies illustrating how pragmatic language and social cognitive abilities are implicated in the challenges adolescents with LD have in resisting peer pressure (Bryan, Werner, & Pearl, 1982; Bryan, Pearl, & Fallon, 1989; Pearl & Bryan, 1992; Pearl, Bryan, & Herzog, 1990). Adolescents with LD were more likely than typically achieving peers to be pressured into misconduct, especially if the person exerting the pressure was a friend. Youth with LD typically expected that the requests would be simple and straightforward (e.g., let’s go to the store and steal a video game) as opposed to requests that attempt to persuade them to engage in the action (e.g., we really want to play this video game so we should take it from the store … everybody does it … we won’t get in trouble). Youth with LD were also less likely to say that they would refuse to engage in the misconduct because they would get in trouble. Comorbid ADHD Approximately 30% of children and adolescents with LD meet criteria for a diagnosis of comorbid ADHD (e.g., Willcutt et  al., 2010). As children with ADHD are more often rejected by peers than typically functioning children (see Stormont, 2001 for a review of this literature), San Miguel, Forness, and Kavale (1996) argued that social skills and social relationship difficulties of children with LD might be due to the large proportion of children with comorbid ADHD in the samples. Although comorbid ADHD is an additional risk factor for peer rejection in children with LD, children with LD who do not have comorbid ADHD are also more likely to be socially rejected than children with neither disorder (Flicek, 1992; Kellner, Houghton, & Douglas, 2003; Wiener & Harris, 1993). Children with ADHD (with or without LD) are more likely to be viewed by peers and teachers as aggressive and disruptive than children with LD and typically functioning children (Wiener, Harris, & Duval, 1993). McNamara and his colleagues (McNamara, Vervaeka, & Willoughby, 2008; McNamara, Willoughby, & Chalmers, 2005) compared adolescents with LD, adolescents with LD and comorbid ADHD, and adolescents without LD on several psychosocial variables and risk taking behaviors. Both groups of students with LD reported engaging in higher levels of risk taking behaviors including smoking, marijuana use, minor delinquency, and acts of direct aggression than youth without LD but did not differ from each other on any of these variables. Engagement in extracurricular activities at school was associated with less risk taking behaviors. Although both groups of youth with LD reported being victimized

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by peers more than youth without LD, self-reported ADHD diagnosis was associated with an increased risk. Special Education Placement Several studies examined peer relations of elementary school students with LD in inclusive classrooms without comparing them to students in pullout or self-contained settings. In most of these studies, compared to students without LD, students with LD had lower peer acceptance (Sale & Carey, 1995; Vaughn, Elbaum, & Schumm, 1996). Students with LD were also more likely to report peer victimization (Luciano & Savage, 2007). Two studies compared the peer relations of children with LD in different placements. Placements where the children remain in the regular classroom all day and special education teachers work with them within the classroom appear to have a small positive impact on peer acceptance and friendship quality of students with LD (Vaughn et  al., 1998; Wiener & Tardif, 2004). Children with relatively severe LD in grades 4 and 5 placed in inclusion programs with a general education and a special education teacher were more likely to report more satisfying relationships with their best school friends, were less lonely, and had fewer self-reported and teacher-reported problem behaviors than students placed in self-contained special education programs for at least half of each school day (Wiener & Tardif, 2004).

Meaning of Peer Relations Difficulties Peer rejection, having few friends, and peer victimization have an impact on the emotional well being of children with LD. Loneliness is a negative experience that results from the perception that a person’s network is deficient in terms of number and quality of relationships (Peplau & Perlman, 1982). Margalit and her colleagues found that Israeli children and youth with LD (preschool through adolescence) are more lonely than their counterparts without LD (e.g., Margalit, 1998; Margalit & Ben Dov, 1995; Most, Al-Yagon, Tur-Kaspa, & Margalit, 2000). Children with LD who are lonely tend to have higher rates of being rejected by peers and internalizing behavior disorders (Tur-Kaspa, Weisel, & Segev, 1998), and lower quality friendships (Margalit & Efrati, 1996). Adolescents with LD who exclusively have virtual friends (i.e., have friends on the Internet and social media but have no face-to-face friends) experience higher levels of loneliness than adolescents who interact with their friends face-to-face in addition to Internet communication (Sharabi & Margalit, 2011). With the

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exception of a study conducted with a sample of children in grades 4 to 6 in Beijing, China (Yu, Zhang, & Yan, 2005), all of the published studies reporting differences between children and youth with LD in loneliness had Israeli samples. Several qualitative studies describe the suffering of children and youth with LD who experience peer rejection and bullying or who have few friends (Goldberg et al., 2003; Higgins et al., 2002; Raskind et al., 2006; Wiener & Sunohara, 1998).

Summary Being rejected or neglected by classmates is so common in children with LD that it could be viewed as a marker variable. The research on friendship and bullying is not as strong as the research on peer status because there are fewer studies. Nevertheless, there is considerable evidence that children with LD have fewer reciprocated friendships, and less stable and lower quality friendships than children without LD. A few studies have also indicated that they are at risk for being victims of bullying. The research has not explicitly examined mediators, moderators, and mechanisms, but there is an enormous body of literature describing their challenges in terms of conversational skills, social perception and social problem solving. Self-contained special education class placement for at least half the school day and comorbid ADHD are risk factors for peer relations difficulties among children and adolescents with LD. The peer relationship difficulties of children and youth with LD have a significant impact on their wellbeing.

PARENTING AND FAMILY RELATIONSHIPS Do parents of children and youth with LD differ from parents of children and youth without LD in terms of parental expectations and attributions and their interactions with their children in relation to their children’s learning and behavior? Do families with and without a child with LD differ in terms of attachment patterns, parent/child relationships, parenting stress, social support, and impact on siblings? Do these parenting and family factors impact social and emotional adjustment and academic achievement of children and youth with LD? Research in this area provides partial answers to these questions. Stone, Bradley, and Kleiner (2002) reviewed previous studies on parents’ expectations, attributions, and parent–child interactions, all of which were conducted in the United States. Parents of children with LD

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have lower expectations for their children’s performance than parents of children without LD (Bryan, Pearl, Zimmerman, & Matthews, 1982) and when they rate their children’s skills they tend to provide ratings that are lower than those of teachers or the children themselves. Similar to their children’s attributions, parents of children with LD tend to attribute their children’s success to luck and their failure to low ability. Parents use somewhat different communicative strategies when they communicate with or teach their children with LD or children with language disorders. Compared to parents of typically developing children, parents of children with LD are more directive and less contingent in their scaffolding; i.e., they are less likely to respond to their children’s errors with helpful instruction and gradually withdraw their support. Thus, the parent–child interactions of mothers and their young children with language disorders are characterized by briefer chains of responses that are calibrated to the children’s needs than interactions of mother–child dyads where the children do not have language disorders. The mothers of the children with language disorders are also more likely to take over and do the task rather than instruct their children (Wertsch & Sammarco, 1985). Parents of children with LD have also been found to communicate less clearly with their children than parents of children without LD. Differences in functioning between families of children and youth with and without LD are subtle. Several studies, all conducted with Israeli families, indicated that children with LD ages 8 to 12 were less securely attached than children without LD (e.g., Al Yagon, 2007; Al Yagon, 2009; Al Yagon & Mikulincer, 2004). Their parents tend to experience higher levels of parenting stress and anxiety than parents of children without LD (e.g., Antshel & Joseph, 2006; Dyson, 2010; Margalit & Heiman, 1986) but less parenting stress than parents of children with ADHD (Baker & McCal, 1995). The family climate among families of children with LD involves more emphasis on organization and control and less emphasis on free expression than families of children without LD (Margalit & Heiman, 1986). The research conducted in Israel suggests that among children with LD their attachment style and their parents’ emotional resources play a central role in predicting children’s social and emotional wellbeing but these are not associated with academic achievement. Al-Yagon and Mikulincer (2004) classified children with and without LD as resilient if they had low scores on a loneliness measure and high scores on a measure of sense of coherence (a person’s feeling that he or she understands the environment, is likely to have a positive outcome, and motivation and

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interest in devoting effort toward achieving a goal). Children with LD who were classified as resilient were more likely to be securely attached than children who were not classified as resilient (Al-Yagon & Mikulincer, 2004). Maternal avoidant coping (i.e., mothers’ efforts to deny or escape from a stressor) moderated the relationship between learning disabilities and children’s attachment security, loneliness and hope (Al-Yagon, 2007). Fathers’ self-confidence and positive affect predicted children’s sense of coherence, and the effort children exerted on tasks and fathers’ negative affect were associated with children’s higher levels of internalizing and externalizing problem behaviors. Mothers’ self-confidence predicted children’s sense of coherence and mothers’ negative affect was associated with children’s internalizing and externalizing behavior problems (Al-Yagon, 2009). Al-Yagon (2010) confirmed a model that shows that mothers’ avoidant attachment is directly associated with negative child sense of coherence and indirectly with children’s feelings of hope. Children’s secure attachment with their mothers was associated with higher sense of coherence and fewer problem behaviors. Maternal anxiety and negative affect predicted higher levels of child problem behaviors and maternal positive affect predicted lower levels of child problem behaviors. Attachment security and emotion regulation in children with LD also predicted their social information processing abilities (Bauminger & Kimhi-Kind, 2008). Parenting and family functioning are highly influenced by cultural differences. Parents of children with learning difficulties from different cultural groups may have different explanations for their children’s academic difficulties. Although, as stated above, American parents may attribute their children’s success to luck and failure to low levels of ability, Iranian parents who are new immigrants to Canada are more likely than Canadian parents to attribute their children’s failure to lack of effort. As they become acculturated to Canadian society, however, their attributions change; they are more likely to acknowledge that their children might have a learning disability (Zadeh, Geva, & Rogers, 2008).

EMOTIONAL AND BEHAVIORAL DIFFICULTIES Children and youth with LD are at increased risk to experience anxiety and depressive symptoms than children without LD. A recent meta-analysis showed that both elementary and secondary students with LD experience higher levels of general anxiety than children and youth without LD (Nelson & Harwood, 2011a). The effect size based on 58 studies was large

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in size (d  =  .61). As discussed by Nelson and Harwood, there are three possible explanations of this increased anxiety: the anxiety is due to an increased incidence of a comorbid anxiety disorder; high levels of anxiety lead to learning impairment; and anxiety is a secondary reaction to academic difficulties. Although secondary reaction theory is the most common explanation given, there is very little research to support any of the theories. In support of secondary reaction theory is the finding that students with LD experience heightened levels of test anxiety (Peleg, 2009; Whitaker Sena, Lowe, & Lee, 2007). Shany et  al. (2011), however, found that among students with reading disabilities in grades 5 and 6, rumination about their reading disabilities was negatively associated with reading comprehension. Rumination was predicted by high levels of trait anxiety and the children’s perception that their mothers were worried about their reading disability. Two recent meta-analyses demonstrate that children and youth with LD are at risk for experiencing depressive symptoms. The effect size based on 14 self-report studies was medium (d = .35; Maag & Reid, 2006) and the effect size based on 31 studies using parent or teacher report measures was large (d  =  .75; Nelson & Harwood, 2011b). Studies have not clarified whether children and adolescents with LD are at risk for clinical levels of depression or whether elevated, but not clinical, depressive symptoms are a secondary reaction to LD. Apostol and Wiener (2008) compared 9 to 14-year-old children with LD and comorbid ADHD, children with LD who did not have ADHD symptoms, and children with neither ADHD nor LD in terms of their self-reported depressive symptoms. Elevated depressive symptoms were evident in the children with co-occurring LD and ADHD. Although youth who are involved in the criminal justice system have a higher incidence of LD than the general population (see Grigorenko, 2006, for review), there is no clear evidence that children and youth with LD are more likely to experience conduct problems than children and youth without LD. The evidence suggests that conduct problems in children and youth with LD is associated with comorbid ADHD (Miranda, Soriano, Fernandez, & Melia, 2008; Richards, Symons, Greene, & Szuszkiewicz, 1995).

INTERVENTIONS IN THE AFFECTIVE AND SOCIAL DOMAIN Several types of interventions have been employed to ameliorate the emotional and social difficulties of children and youth with LD. Studies have

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examined the effects of classroom-based interventions, social skills training, individual and group psychotherapy, and parenting interventions. The outcomes measured in most studies are social skills, peer acceptance, academic self-concept, and self-esteem. Some interventions (e.g., social skills training) are conducted in both school and clinical settings.

School- and Classroom-Based Intervention Children and adolescents typically spend 25 to 30 hours per week in school and schools are settings where they typically interact with peers. Educators talk about the 3 R’s—Reading, Writing, and Arithmetic. Schools, however, are crucial for teaching the 4th R—Relationships. Consequently, identifying the characteristics of schools and classrooms that are effective for promoting academic self-concept, self-esteem, and positive peer interactions has been a focus of considerable research. This research has focused on the beliefs and practices of teachers, the impact of academic instruction, and the use of specific peer mediated instructional practices such as cooperative learning and peer tutoring, social skills training, and counseling interventions. As social skills training and counseling interventions are not exclusively conducted in school settings, they are discussed as separate topics below. Teacher Beliefs and Practices The literature on effective schools and classrooms has mainly been devoted to identifying crucial teacher attitudes that are associated with the use of effective instructional practices to promote positive academic outcomes in children with learning difficulties. Some of these studies, however, have shown that teachers who attribute student academic, social and behavioral difficulties to an interaction between student characteristics and classroom demands and who believe that they, as teachers, have a responsibility to accommodate and remediate these student challenges engage in practices that enhance student outcomes (e.g., Elik, Wiener, & Corkum, 2010; Poulou & Norwich, 2002; Stanovich & Jordan, 1998; Stanovich, Jordan, & Perot, 1998). These practices include pre-referral interventions, collaborating with parents, and adapting and scaffolding instruction. Positive outcomes among students with learning difficulties in classrooms with teachers who hold these positive beliefs and engage in these interventionist practices include enhanced academic self-concept and positive peer relationships in elementary school ( Jordan & Stanovich, 2001; Stanovich et  al., 1998). Furthermore, an early study indicated that when

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teachers are punitive towards students with LD in elementary school classrooms, the students with LD are more likely to be rejected by peers (Siperstein, Bopp, & Bak, 1978). Peer Mediated Instruction: Cooperative Learning and Peer Tutoring Peer mediated instructional strategies involve students working together on academic tasks in a manner that promotes responsibility for their own and their partners’ learning (Wolford, Heward, & Alber, 2001). Two commonly used types of peer-mediated instruction are cooperative learning and peer tutoring. Cooperative learning typically involves three to five students working together, whereas peer tutoring involves two students, a tutor and a tutee (roles may alternate). Cooperative learning and peer tutoring have been proposed as instructional strategies that promote inclusion and create a climate that encourages peer interaction between students with and without LD (Fuchs, Fuchs, Mathes, & Martinez, 2002; Gilles & Ashman, 2000; Taylor, Peterson, McMurray-Schwarz, & Guillou, 2002). Johnson and Johnson (1994) highlight important conditions for maximizing the effectiveness of cooperative learning, claiming that it is important to establish “positive interdependence” in which students believe that they must coordinate their efforts with group mates because they cannot succeed unless their group mates do (and vice versa). The activities should be structured to include individual accountability such that each member’s efforts are required and each has a complementary role that makes a unique contribution. Although cooperative learning groups and peer tutoring may provide students with positive social environments and opportunities to practice social skills, some have argued that merely placing students with LD into groups with their peers does not ensure they will interact in socially appropriate ways (Bryan, Burstein, & Ergul, 2004; Wolford et  al., 2001). When students with LD (and other low-achieving students) are working in cooperative learning groups, they typically retreat to passive roles or are sometimes inadvertently marginalized by peers who are more knowledgeable, verbal, and assertive (O’Connor & Jenkins, 1996). Consequently, for students who have emotional or social skill difficulties it may be necessary to supplement cooperative learning or peer tutoring with explicit instruction (including role play practice) in social skills such as compromising, negotiating, and asking for assistance from peers, and reinforce the generalization of these skills when students display them in the group setting (Bryan et al., 2004; Gilles & Ashman, 2000, Wolford et al., 2001).

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Wanzek, Vaughn, Kim, and Cavanaugh (2006) conducted a synthesis of 27 studies to examine the social outcomes of early reading interventions for children with LD that compared group interactive learning (i.e., cooperative learning and reciprocal teaching) and cross-age/peer tutoring with teacher-led group or one-on-one remedial instruction. Group interactive learning was associated with gains in aspects of peer interaction and peer acceptance for students with reading disabilities. The effects for crossage/peer tutoring were smaller. Students with LD continued to experience more social difficulties than average and high achieving students. The effects sizes for both group interactive learning and peer tutoring were commensurate with those from social skills training studies (Forness & Kavale, 1996). School-Based Interventions for Self-Perceptions Given the low academic self-concept and self-esteem experienced by many students with LD, considerable research has been conducted examining the efficacy of interventions designed to enhance self-esteem and domain specific self-concepts within school settings. Elbaum and Vaughn (2001) conducted a meta-analytic review of 82 studies evaluating schoolbased interventions for self-concept of children and adolescents with LD. Overall, school-based interventions for self-concept are most effective for middle school students (compared to elementary or high school students) and have the largest impact on academic self-concept (d = .28) followed by social self-concept (d  =  .18) and self-esteem (d  =  .15). Effect sizes, however, are small. Academic skill interventions (aimed at helping students increase their academic knowledge or develop more effective learning strategies) were more effective in enhancing the self-concept of elementary school students (d  =  .17), while counseling interventions based on therapeutic models (see below) were most effective for middle (d  =  .61) and secondary school students (d = .32). Self-concept interventions, however, are only beneficial for students with negative self-perceptions prior to the intervention (Elbaum & Vaughn, 2003).

Social Skills Training Social skills training approaches involve explicit teaching of appropriate social behaviors and social problem solving to children and youth with LD. Most social skills training programs are manualized cognitive behavioral group treatments. Social skills training programs that use literature (i.e., social stories, short stories, drama) have been developed and evaluated

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recently. Some of the social skills training interventions have been specifically directed at developing self-determination (e.g., teaching selfadvocacy and coping skills) in adolescents and adults. Cognitive-Behavioral Approaches Cognitive-behavioral social skills training approaches aim to enhance the social skills of students with LD by developing social problem solving skills and teaching appropriate social behaviors. Participants are taught strategies to assist them with navigating novel and difficult interpersonal situations assertively while simultaneously accommodating the perspectives and goals of the individuals involved. Aggressive and passive responses to interpersonal problems are discouraged. Participants are also taught appropriate social behaviors such as initiating and maintaining conversation, joining an ongoing conversation or activity, cooperating and including others, communicating needs and responding to the negative behaviors of others. The specific skills taught vary from intervention to intervention. Some approaches teach a general battery of skills designed to benefit all participants, others select problem solving scenarios and social behaviors catered to the needs of the participants based on referral concerns or assessment, and others encourage participants to discuss situations or problems they have seen or experienced in real life. Several strategies are employed in social skills training programs using a cognitive behavioral approach including problem solving exercises, coaching/modeling/providing performance feedback, role playing, repetition, and monitoring generalization to other settings. Children are encouraged to practice problem solving and decision-making in a class, small group, or one-to-one discussion using hypothetical interpersonal conflicts/situations (Conte et al., 1995) (e.g., “If you attempted to join a group of other students and one of them said ‘What do you want egg-head?’ how would you respond?”). With scaffolding and modeling if necessary, children are encouraged to generate possible solutions, evaluate the consequences of each option, select the best solution and explain or demonstrate what the response would be. Coaching, modeling and performance feedback are used to explicitly teach appropriate social behaviors. Role playing solutions to problems and acting out appropriate social behaviors help children develop and practice the skills they are learning in a safe environment. Repetition and practice are important to solidify the skills and increase the probability of generalizing them beyond the intervention setting. Encouraging children to self-monitor and evaluate their own use

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of the skills they are taught throughout the day also fosters generalization outside of the intervention. Children are encouraged to observe people using these skills in social situations, practice these skills between meetings and talk about what happened in subsequent meetings. Social skills training programs using a cognitive behavioral approach are implemented in a variety of settings including clinical settings, regular classrooms including children with and without LD, and special education classrooms including only children with LD. Social skills training groups are led by school counselors, psychologists or people who work with the children on a daily basis such as classroom teachers and educational assistants. Training school staff on the use of these intervention techniques facilitates the on-going teaching of social skills and social problem solving in the classroom. Formats such as games (e.g., Social Life—Wiener & Harris, 1997; Social Skills Game—Utay & Lampe, 1995), token systems, and cooperative goal structures (i.e., pooling of reinforcers earned by all members of the group to attain a group goal such as a class trip) are employed in some programs to engage and motivate the participants. Efforts to remediate the social skill difficulties of children and youth with LD through social skills training have met with limited success (Kavale & Mostert, 2004). Forness and Kavale (1996) conducted a metaanalysis of 53 studies that indicated that children and adolescents with LD who participated in social skills training programs demonstrated only modest gains compared to control groups, and that the effect size is small (d = .21). The effectiveness of social skills training did not differ as a function of the age of children at time of intervention or length of exposure to training. Forness and Kavale (1996) hypothesized that the poor treatment outcomes may have been due to insufficient training intensity, programs designed without a clear rationale or pilot testing, shortcomings in research design and a possible need to address academic, cognitive, and linguistic targets in addition to social skills. The studies included in this meta-analysis, however, assessed a range of outcomes including peer acceptance, cooperative behaviors, self-concept, locus of control, teacher-rated conduct problems, academic competence and hyperactivity. McIntosh, Vaughn, and Zaragoza (1991) reviewed 22 studies of social skills interventions to examine characteristics and components that were most effective; 21 of these studies were included in the Forness and Kavale (1996) meta-analysis. Selecting participants who had social skills difficulties or low peer acceptance, using cognitive-behavioral intervention procedures,

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providing individual or small group instruction and applying long-term training were associated with more successful approaches. Other investigators have identified additional components that predict intervention efficacy including targeting the skills taught to the needs of the participants, combining social problem solving and explicit teaching of social skills, and interventions done in settings where the children typically interact including classrooms (Court & Givon, 2003; Conte, Andrews, Loomer, & Hutton, 1995; Hepler, 1997; Utay & Lampe, 1995; Wiener & Harris, 1997). Children with LD who participate in interventions involving these relatively more effective strategies often maintain their social status across time, while the social status of children with LD who do not participate experience a decline (Conte et al., 1995; Hepler, 1997; Wiener & Harris, 1997). Thus, this type of intervention may prevent peer rejection, but not enhance peer acceptance. Other positive outcomes include improved social problem solving when responding to hypothetical situations (Conte et  al., 1995), teacher- and self-rated social skills (Utay & Lampe, 1995; Wiener & Harris, 1997), and prosocial behaviors in classroom and playground rated by observers (Wiener & Harris, 1997). Literature-Based Approaches Using literature to promote social skills is a strategy that has been recommended for children and adolescents with LD because when students enjoy stories, they may be motivated to learn from them (e.g., Anderson, 2000; DeGeorge, 1998; Womack, Marchant, & Borders, 2011). Stories provide examples of social behavior and interactions that may become the basis for modeling, explanation, and discussion of specific social skills. According to the advocates of the use of literature-based approaches, selected stories should be ones that allow children and youth to make connections with their own experience and to identify characters that share similarities with people they know. Some advocates of a literature-based approach suggest using short stories or dramas written by authors of children’s literature or literary works that are appreciated by adolescents to teach social problem solving. For example, adolescents often have personal experiences involving parental disapproval of boyfriends and girlfriends that might allow them to relate to Romeo and Juliet and explore themes of interpersonal conflict, taking others’ perspectives, and assessing consequences of actions (Anderson, 2000). Other advocates of literature-based approaches suggest the use of social stories intentionally written to teach a specific social skill.

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Social stories often include a conflict or goal and a character whose feelings, thoughts, and behaviors are described as they model appropriate steps to reach the goal or solve the conflict (see Womack et al., 2011 for suggestions). Social stories is a strategy that has been found to be effective with children with autism spectrum disorders (Gray & Garand, 1993). Agaliotis and Kalyva (2008) provided preliminary empirical evidence that social stories can be used to enhance the interpersonal conflict resolution skills of children with LD. In their study, children with LD read a story in which two parties involved in an interpersonal conflict were able to express what they wanted and reach a peaceful conflict resolution. While all children chose mainly hostile and avoidant strategies in response to hypothetical interpersonal conflicts prior to the intervention, after reading the story, children in the intervention group chose predominantly positive strategies (accommodation and cooperation) compared to children who received no intervention. It is not clear from this study whether the knowledge they acquired about conflict resolution generalized to actual peer interactions. Self-Determination Adults who are successful in their careers and relationships in spite of their learning disabilities (i.e., who are resilient) typically set goals, persevere, access help when they need it, use effective strategies for coping with stress, and are self-aware and creative in finding alternative strategies in the face of difficulty (e.g., Gerber et  al., 1992; Goldberg et  al., 2003). Some researchers also highlight the importance of control or taking charge of one’s life (e.g., Reiff, Gerber, & Ginsberg, 1997) and adaptive coping and perceived control in the face of the challenges that disabilities present (Firth, Frydenberg, & Greaves, 2008). The term self-determination is often used to describe the constellation of these characteristics (Field, Martin, Miller, Ward, & Wehmeyer, 1998). Self-determination interventions geared toward individuals with LD often aim to teach self-advocacy skills in adolescents and adults (Agozzine, Browder, Karvonen, Test, & Wood, 2001; Test, Fowler, Brewer, & Eddy, 2005), perceived control, and adaptive coping (Firth et al., 2008). Individuals who self-advocate effectively have a specific knowledge base and skill set. First, they need to be aware of their strengths, needs, learning style, and necessary accommodations. Second, they need to know about the rights of individuals with disabilities in their jurisdictions. Third, they must be able to communicate to relevant teachers, counselors and administrators

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about their learning style and the accommodations they need (Test, Fowler, Wood, Brewer, & Eddy, 2005). Effective interventions aim to develop many of these components of self-advocacy (Durlak, Rose, & Bursuck, 1994; Campbell-Whatley, 2008; Kotzer & Margalit, 2007; Lancaster, Schumaker, & Deshler, 2002; Phillips, 1990). With regard to self-awareness, students are provided with information about LD, characteristics of successful individuals with LD, and names and attributes of famous people who have LD. They are encouraged to develop an awareness of their own individual strengths, weaknesses, learning and testing styles including strategies that work and do not work for them by recording this information in a learning log or journal. This self-awareness provides the basis for students to identify individual goals, needs, strategies, and accommodations related to their own disability. They are asked to brainstorm solutions to difficulties associated with their disability and taught to problem solve when they face challenges. In order to help them learn about their rights, students are provided with information about relevant legislation and policies, and services and accommodations that are available in secondary and post-secondary settings. Communication skills are typically taught through strategies such as modeling, role-play, and performance feedback. Group leaders may model how to communicate in specific situations. Students are encouraged to plan what they would say when they meet with teachers, disability counselors, or administrators and to practice through role-plays. Group leaders and other students in the self-advocacy groups provide feedback. Individuals of different ages and with many types of disabilities are able to acquire self-advocacy skills (Test, Fowler, Brewer, & Wood, 2005). Students who have participated in self-advocacy interventions learn to explain their learning disability, know about available services (Philips, 1990), and experience a greater sense of competence (Kotzer & Margalit, 2007). Additionally, participation in self-advocacy interventions has been shown to increase the frequency with which students approach teachers about needed accommodations and increase students’ feelings of responsibility for their education (Phillips, 1990). Students who complete these interventions are better able to effectively share information about their strengths, weaknesses, and learning and testing preferences, to articulate their learning goals, and to contribute to the development of their own individual educational plan (Bos & Van Reusen, 1994; Lancaster et al., 2002). However, it should be noted that some participants express discomfort and embarrassment about advocating for their needs, and in some cases are unable to generalize the skills outside the training setting (Durlak et al., 1994).

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Individual and Group Therapy Cognitive-Behavioral Therapy Cognitive-behavioral therapy (CBT) is a psychotherapeutic approach in which the goal is to change distorted, maladaptive or dysfunctional beliefs, attitudes, and behaviors using a variety of therapeutic techniques. Individuals are encouraged to examine the interconnection between thoughts, emotions, and behaviors, and to analyze and reality-test existing patterns of thinking, emotional reactions, and behaviors (Sheldon, 2011). For example, an individual might be encouraged to identify how negative self-talk impacts their feelings and behaviors. A student might notice that when she can’t answer a homework question in math, she thinks, I’m horrible with math and school and will never pass the course (thought). This results in feelings of nervousness, increased heart rate and feeling sick to her stomach (emotion). Ultimately she gets frustrated and puts her books away (behavior). A CBT approach might use techniques such as challenging thoughts (e.g., Does having difficulty with a single question mean you will fail the course or is that jumping to the worst case scenario), increasing positive self-talk (e.g., If I was able to solve the last three correctly, I can figure out this one too), problem-solving and coping skills (What strategy worked the last time I had trouble like this? Who can I ask for help?), and relaxation techniques. CBT is a documented evidenced-based treatment for many problems experienced by children and adolescents with LD (Shechtman & Pastor, 2005). CBT has been shown to be highly effective in the treatment of childhood depressive and anxiety disorder, as well as a wide range of disorders in adulthood including depression, generalized anxiety disorder, panic disorder, social phobia, and post-traumatic stress disorder (Butler, Chapman, Forman, & Beck, 2006). Research with high school and college age students with LD has found that CBT techniques such as progressive muscle relaxation, guided imagery, and self-instruction training, combined with academic skill instruction reduced test anxiety and increased academic self-concept (Wachelka & Katz, 1999). In addition to treating their anxiety and depressive symptoms, cognitive-behavioral techniques have been used with individuals with LD to enhance social skills (as discussed above) and proactive coping. Recent programs have been developed to enhance the use of proactive coping strategies in response to difficulty among individuals with LD (Shectman & Pastor, 2005; Firth et  al., 2008). Firth and colleagues used a “coping program” to increase levels of proactive coping strategies and positive thinking in adolescents with LD (Firth, 2006; Firth et  al., 2008). In this

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program, adolescents are taught to work toward their goals and problem solve difficult situations using productive coping strategies (e.g., working hard, accessing help, working on the problem, physical exercise) as opposed to strategies that may be nonproductive (e.g., ignoring the problem, worrying, keeping to oneself). The program also focused on positive thinking strategies such as challenging negative self-talk (self-blame) and replacing it with more realistic and empowering self-talk. Lastly, adolescents were taught to be assertive when trying to communicate effectively, responding to criticism, and making requests. Adolescents who participated in the program demonstrated more internal locus of control and used more active coping responses such as working hard and persevering with solving a problem. However, the intervention was not associated with changes in nonproductive coping strategies. Counseling to Enhance Self-Concept A meta-analysis that addressed the impact of school-based programs designed to enhance self-concept of students with LD found that counseling (as opposed to academic) interventions are effective for students in middle school and secondary school (Elbaum & Vaughn, 2001). These interventions include reality therapy and rational-emotive therapy. Many of the specific counseling strategies employed are similar to those used in CBT including training in relaxation, behavioral monitoring, visualization, coping skills, communication skills, elimination of self-defeating behaviors, and bibliotherapy (reading and discussion of stories in which the characters confront challenges similar to those of students in the intervention). Some also include a social component designed to foster supportive interactions among group participants. According to Mishna and Muskat (2004) it is therapeutic for adolescents to have mutual support from peers and to feel that others are “in the same boat”. Mindfulness Meditation Mindfulness is a particular way of paying attention, described by KabatZinn (2003, p. 145) as the awareness that emerges through paying attention on purpose, in the present moment, and nonjudgmentally to the unfolding of experience. Many mindfulness-based approaches use mindfulness meditation, a sitting-based meditation technique aimed to increase an individual’s awareness of present, moment-to-moment, experience (Burke, 2010). Through practice, people learn to bring their attention back to the present, using the breath or another object as an anchor, whenever

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they notice their attention has been directed to streams of thoughts, worries or general lack of awareness (Segal, Williams, & Teasdale, 2002; Beauchemin, Hutchins, & Patterson, 2008). Mindfulness meditation has been successfully used in adult populations to enhance physical and psychological functioning including treatment of chronic pain, binge-eating, anxiety, and depression and to enhance social interaction and sustained attention (Baer, 2003; Chambers, Lo, & Allen, 2008; Ornter, Kilner, & Zelazo, 2007). Although research on mindfulness interventions has largely been undertaken with adult participants, an increase in applications to children and adolescents has produced a small body of research literature providing support for the feasibility of mindfulness-based interventions for children and adolescents (Burke, 2010). Burke’s (2010) review of the childhood literature indicates that mindfulness training may hold promise for enhancing executive functioning, attention, and social skills, and reducing anxiety, depression, and externalizing behaviors. However, this early stage of the research is limited by methodological problems and insufficient empirical evidence of the efficacy of intervention in these younger populations (Burke, 2010). As individuals with LD experience many of the issues targeted by mindfulness meditation (e.g., executive functioning and social difficulties, anxiety and depression), this intervention technique may be beneficial for this population. Indeed, preliminary evidence indicates that mindfulness meditation is associated with decreases in self-rated anxiety, and enhanced self- and teacher-rated social skills, and academic performance in adolescents with LD (Beauchemin et  al., 2008; Haydicky, Wiener, Badali, Milligan, & Ducharme, in press).

Parenting Interventions Parents of children and adolescents with LD experience difficulties in their parenting role including parenting stress and anxiety. Some of these challenges are similar to those experienced by parents of children with other learning and behavioral problems (e.g., ADHD) (Johnston & Mash, 2001). As such, some of the intervention work for parents of children with LD has been conducted in mixed samples with parents of children with other disabilities or learning difficulties (e.g., Margalit & Raskind, 2009; Shechtman & Gilat, 2005). Group counseling and online communities are two strategies that have been used to enhance social support and parenting strategies for parents of children with learning difficulties. Both enhance social support by providing parents with the opportunity to share and discuss difficulties related to their children, explore thoughts,

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feelings, and behaviors in difficult situations, and receive guidance by discussing effective and ineffective solutions to the problems they encounter. Additionally, such programs typically provide psychoeducational information to increase parents’ understanding of LD and enhance parent– child interaction. This information usually includes symptoms, diagnosis, etiology, and management of learning disorders, and teaching of effective communication and problem solving skills (Uslu, Erden, & Kapci, 2006). Gains associated with participation in counseling and psychoeducational groups include improved parent–child interaction (i.e., decrease in criticism and increase in warmth and positive remarks) and parent–child relationship, lower levels of parenting stress, and increased parenting selfefficacy and control (Shechtman & Gilat, 2005; Uslu et al., 2006). Online communities also provide parents with valid and reliable information and strategies for parenting and educating their children, as well as emotional support. Parents who participated in an online community valued receiving information, empathy, companionship, and emotional support from others in similar circumstances (Margalit & Raskind, 2009). Additionally, the confidential nature of online communities provides an opportunity for parents to disclose emotions in a setting perceived as private and safe. Innovative methods are being developed to enhance parents’ knowledge and awareness of learning disabilities. Virtual reality has been effectively used to simulate the reading errors, cognitive perceptions and personal experiences such as helplessness and frustration associated with dyslexia. Parents who participated in a virtual reality simulation increased their knowledge of reading disabilities and reported a greater understanding of the way their children experienced reading (Passig, Eden, & Rosenbaum, 2008).

Summary The social, self-perception, and emotional difficulties of individuals with LD are heterogeneous and idiosyncratic; they can vary significantly from one individual to the next. Similarly, the interventions that have been studied to remediate these problems come in many different forms and draw on a range of techniques. Given the heterogeneity of social and emotional difficulties experienced by individuals with LD, as well as the range of interventions available, a comprehensive assessment of an individual’s needs is essential for effective treatment planning. Teachers and other professionals working with individuals with LD should ensure that interventions are selected to target the specific needs of the child. From

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a research perspective, while some methods such as social skills training have an established history of research, other emerging methods such as mindfulness meditation and parent support programs would benefit from further evaluation to determine their efficacy.

CONCLUSIONS AND IMPLICATIONS The research on social, emotional, and behavioral functioning of individuals with LD described above clearly shows that children and adolescents with LD are at risk for having a low academic self-concept and low peer acceptance. There is also some indication that they have fewer reciprocal friendships, lower quality friendships, less stable friendships, are victimized by peers, and are more likely to succumb to peer pressure. Their social cognitive patterns have been described in several studies. They tend to be awkward when carrying on a conversation; in dyads they are reluctant to take the floor, and when they do are less likely than other children to ask open-ended questions that sustain conversation. They also have difficulty with interpreting social cues, social perspective taking, and social problem solving. With regard to family relationships, they are at risk for insecure attachment, and their families tend to be less cohesive. Furthermore, their mothers tend to be more directive than mothers of children without LD when teaching them tasks. They tend to be more lonely and anxious, and to have higher levels of depressive symptoms than children without LD. These differences between individuals with and without LD are only markers of the disorder in the sense that children, adolescents, and adults with LD are more at risk for these challenges; they do not define the disability in that not every individual with LD has difficulties in these areas. Recent research has, as a result, examined risk and protective factors among individuals with LD for these difficulties. At the outset of this chapter we suggested that Bronfenbrenner’s bioecological theory might be a basis for conceptualizing the social, emotional, and behavioral challenges of individuals with LD. LD, by definition, is a disorder that is presumed to have a biological basis. Although studies have provided information about the neurological and genetic correlates of dyslexia and nonverbal LD, this is not the case for the specific social, emotional, and behavioral difficulties described above. Nevertheless, research has consistently indicated that children and youth with LD have social cognitive deficits suggesting that their social, emotional, and behavioral difficulties are a product of their biology and environmental factors.

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These social cognitive difficulties have been weakly linked with their peer relationship challenges. The research has suggested that comorbidity with other disorders, mainly ADHD, may be a factor contributing to some of the behavioral difficulties of individuals with LD, especially with regard to externalizing behaviors. Although individuals with LD have higher levels of anxiety and depressive symptoms than typically developing individuals, it is not clear whether these symptoms are typically clinical or subclinical. Higher levels of anxious and depressive symptoms may logically flow from persistent academic and social failures accompanied by low levels of social support. The research has examined classroom and family environments as potential risk factors for social, emotional, and behavioral difficulties of children with LD. Some studies suggest that more segregated special education placement (i.e., for at least half of the school day) is a risk factor for peer neglect, lower self-perceptions of behavioral conduct, and lower quality friendships. Special education placement does not per se seem to be associated with academic self-concept or self-esteem. In inclusive classrooms, however, teacher attitudes and teaching processes appear to be important. Students with LD and other mild disabilities are more socially accepted and have higher academic self-concepts when they have teachers who have empathy for their students, respect them, believe that they must and can accommodate their diverse needs, and seek resources to do so. Children with LD who have mothers who are highly anxious or have fewer emotional resources tend to be less securely attached, have lower sense of coherence, and lower levels of social adjustment. For the most part, studies exploring risk and protective factors in relation to social, behavioral, and emotional functioning of children and youth with LD were set up to explore risk factors as opposed to protective factors. Protective factors are biological or environmental characteristics that differentially impact individuals at risk and as such are not exactly the opposite of risk factors. Attachment, for example, predicts psychological adjustment in children in general—not specifically children who are at risk such as children with LD. Consequently, insecure attachment among children with LD increases their risk of having social and emotional difficulties, but secure attachment is not a protective factor. Given the importance of self-esteem, the strong correlation between academic self-concept and self-esteem, and the finding that most students with LD have a low academic self-concept, the fact that differences between children and adolescents with and without LD in self-esteem are small is astonishing.

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This might be due to children with LD having a positive illusory bias (Heath & Glen, 2005), as described above. Singer’s (2008) qualitative study, however, suggested that some children with LD use adaptive strategies such as high levels of effort, asking for help from parents and peers, positive self-talk, and compartmentalizing their disability. Whether these strategies are protective factors would need to be determined through appropriately designed quantitative research. Intervention studies in relation to social, behavioral, and emotional functioning of children and youth with LD typically report small effects of interventions. There are several reasons for this. First, individuals with LD are heterogeneous in terms of the nature of their difficulties and most studies do not select participants who have the specific problem the intervention is targeting. Furthermore, some interventions may have different effects on different participants depending on their needs. For example: in an evaluation of a mindfulness cognitive-behavioral therapy program (Integra-Mindfulness Martial Arts), findings showed that children who were hyperactive and impulsive benefitted in terms of reduction of externalizing behaviors and social problems whereas children with high levels of anxiety reported less anxiety (Haydicky, Wiener, Badali, Milligan, & Ducharme, in press). Consequently, evaluation research on interventions should either target individuals with LD who have the specific problem the program is designed for, or break down the sample into smaller subgroups. What are the major limitations of current research and the concomitant implications for future research? Although there are many limitations, we are restricting our discussion to four major ones. First, studies describing social cognitive deficits are typically designed to determine whether individuals with and without LD differ on specific tasks such as social information processing. Although this is an important first step, it is important to establish whether these social cognitive deficits are associated with markers of social adjustment such as peer acceptance, neglect and rejection, friendship, and peer victimization. If so, social skills training interventions that ameliorate functioning in these social cognitive skills may impact these major markers of adjustment. Second, most research is conducted with children with LD, with fewer studies investigating social, emotional, and behavioral functioning in adolescents and adults. Furthermore, few studies are developmental. Thus, the research does not allow for any conclusions about the various developmental trajectories of individuals with LD.

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Third, approximately 30% of individuals with LD have co-occurring ADHD, and many have higher rates of anxiety and depressive symptoms than the general population. Furthermore, early research studies might have included individuals with Asperger Disorder or High Functioning Autism in the samples because these disorders were less often diagnosed prior to the 1990s. Careful sample selection and description may help tease out whether the social, emotional, and behavioral difficulties of individuals with LD are associated with LD or these other disorders. Finally, most studies of individuals with LD in the social, behavioral and affective domain have been conducted in the United States, Israel, Canada, Australia, and New Zealand. The dominant culture in these countries is individualistic; pursuit of autonomy is of great importance. In these countries, emotional expression and assertiveness are also valued (e.g., Schneider, 1993). Schneider documents how peer relations are affected by whether the culture tends to be individualistic versus collectivistic (where group goals are paramount), and the degree to which diversity and ambiguity are accepted. In addition, cultural attitudes in relation to the importance of academic achievement may affect how families, teachers, and peers treat individuals who are not achieving at the expected level. Research on basic processes contributing to reading disabilities has been conducted in many countries in order to establish whether specific language and orthographic factors contribute to reading difficulties. Similarly, research on the social, behavioral, and emotional functioning of individuals with LD would benefit from research conducted in many different societies and from cross-cultural research.

ACKNOWLEDGMENT The authors are grateful to Susan Carlson-Lishman for her valuable critique of the manuscript.

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Self-Regulation among Students with LD and ADHD Robert R. Reid1, Karen R. Harris2, Steve Graham2, and Marcia Rock3 1

University of Nebraska-Lincoln, NE 68583-0732, USA Arizona State University, Tempe, AZ 85069-3151, USA 3 University of North Carolina-Greensboro, NC 27402-6170, USA 2

Chapter Contents Defining and Understanding Self-Regulation 142 Self-Regulation and Self-Regulated Learning 143 Metacognition 144 Executive Functioning 144 Theoretical Bases for Research on Self-Regulation 145 Operant Theory 145 Information Processing Theory 146 Social Constructivist and Social Cognitive Theories 146 Common Self-Regulation Processes: Application with Students with LD and ADHD 147 Self-Monitoring 148 Self-Evaluation 148 Self-Instruction 148 Goal-Setting 149 Self-Reinforcement 150 Final Thoughts 150 Self-Regulation and Major Dependent Variables 151 On-Task Behavior 151 Academic Preparedness 153 Academic Productivity 154 Academic Accuracy 156 Disruptive Behavior 157 Self-Regulation in the Social Context 158 Factors Influencing and Influenced by Self-Regulation 159 Self-Efficacy 159 Self-Regulation of Strategic Performance 161 SRSD 161 Outcomes of SRSD 162 Findings Regarding the Self-Regulation Components of SRSD 164 Conclusion 166 References 166

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DEFINING AND UNDERSTANDING SELF-REGULATION Throughout history, the ability to control and regulate one’s behavior has been considered desirable. Aristotle, for example, praised the virtues of self-awareness, and Benjamin Franklin was a staunch proponent of selfregulation. Franklin described a number of self-regulation procedures he used in his quest for self-improvement (Zimmerman & Schunk, 1989). He defined 13 virtues, such as temperance and order, which he wished to develop. He kept a record in which he established goals to increase each virtue, monitored his successes and failures, and established new goals. The Scottish poet Robert Burns considered prudent, cautious self-control to be the root of wisdom, and William Penn did not consider a person fit for commanding others if they could not “command themselves” (cf. Harris, Friedlander, Saddler, Frizelle, & Graham, 2005; Harris & Graham, 1992). Today, the area of self-regulation has become a major focus of research in many areas of education and educational psychology, and self-regulation per se an important construct in research and intervention in the areas of learning disability (LD) and attention a construct in research and intervention in the areas of learning disability (LD) and attention-deficit hyperactivity disorder (ADHD; Graham, Harris, & Reid, 1992; Schunk & Zimmerman, 2003). While more basic research is still needed, researchers have provided evidence that students with LD have difficulties with or deficiencies in self-regulation processes (cf. Barkley, 2006; Harris, 1986a). Both academic and social difficulties encountered by students with LD may arise, in part, from problems in self-regulation of organized, strategic behaviors (Graham et al., 1992). Further, current conceptualizations of ADHD suggest that this disorder is the result of deficient self-regulation (Barkley, 2006; Cutting & Denckla, 2003). Researchers in ADHD have conceptualized self-control as having subdomains, including cognitive control and social-emotional control, and have focused on deficits in executive functions. While students with LD and those with ADHD are a heterogeneous group, difficulties in self-regulation appear common among students with either disorder. Further, underachievement is common among students with ADHD, with up to 80% of these students exhibiting academic achievement problems and as many as 20–40% of students with ADHD are also diagnosed as having LD (Barkley, 2006; Cutting & Denckla, 2003). Students challenged by either or both LD and ADHD commonly experience difficulties with inhibition of behavior, delay of gratification, persistence while engaged in activities requiring self-regulation, producing

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the amount and quality of work they are capable of, maintaining on task behaviors, following through when given instructions, and planning and directing goal-directed, future-oriented actions. Given the importance of self-regulation in learning and development, and the recognition that students with LD and/or ADHD commonly have deficits in self-regulation, it is not surprising that a great deal of intervention research has been conducted with these students. In fact, intervention research in self-regulation has become evident not only in the areas of LD and ADHD, but across many specialties in education and psychology. In this chapter, we provide an overview of the theoretical bases for research in selfregulation; common self-regulation strategies or processes; the effects of selfregulation interventions on major dependent variables or outcomes; factors that can influence the use and effectiveness of self-regulation among learners, with an emphasis on self-efficacy; and the effects of combining explicit development of self-regulation abilities with strategies instruction. First, however, we turn to definitions of common terms in this area, including selfregulation, self-regulated learning, metacognition, and executive function.

Self-Regulation and Self-Regulated Learning The relatively large number of researchers working in self-regulation across a number of domains has resulted in a plethora of definitions for self-regulation and self-regulated learning; a number of definitions can similarly be found for metacognition and executive function. As research has progressed in these areas, definitions have evolved and been refined, and we expect that this will continue to be the case. Further, there is some ambiguity and overlap among these constructs and their definitions (Boekaerts & Corno, 2005), which will also continue to be addressed as research and practice evolve. Here, we present definitions currently offered by leading researchers in these areas. Self-regulation can be defined as the “process whereby students activate and sustain cognitions, behaviors, and affects, which are systematically oriented toward attainment of their goals” (Schunk & Zimmerman, 1994, p. 309), and can be seen as referring to the “degree that individuals are metacognitively, motivationally, and behaviorally active participants in their own learning process” (Schunk & Zimmerman, 1994, p. 3). Self-regulated learning, therefore, encompasses thoughts, feelings, and actions generated by the student and then monitored and adapted by the student over time in order to attain learning goals. Zimmerman (2000) argued that students

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can self-regulate aspects of their own learning behaviors, environmental conditions, and their cognitive and affective states, and has offered a model of the cyclical phases of self-regulation that includes forethought, performance, and self-reflection. He also has identified different subprocesses of self-regulation, such as task analysis, self-motivational beliefs, and selfcontrol processes, which occur during these phases. The construct, and thus the definition of, self-regulation is obviously complex. Boekaerts, Pintrich, and Zeidner (2000) stated that self-regulation involves a number of “integrated microprocesses, including goal-setting, strategic planning, use of effective strategies to organize, code, and store information, monitoring and metacognition, action and volitional control, managing time effectively, self-motivational beliefs (self-efficacy, outcome expectations, intrinsic interest, and goal orientation, etc.), evaluation and selfreflection, experiencing pride and satisfaction with one’s efforts, and establishing a congenial environment” (p. 753). We will return to these behavioral, affective, and cognitive components and processes of self-regulation as we continue to discuss research in this area with students with LD and ADHD.

Metacognition Boekaerts et al. (2000) noted that the distinction between self-regulation and metacognition is sometimes unclear in the literature and that there is little consensus on the nature of the relationship between these two terms. Metacognition is commonly agreed to encompass students’ awareness of the skills, strategies, and resources needed to perform a task effectively, as well as their knowledge of how to regulate their behavior in order to successfully complete the task (cf. Boekaerts et al., 2000). Today, many researchers see self-regulation as the broader term, and define it as encompassing metacognitive knowledge and skills (Boekaerts & Corno, 2005). Self-regulation is seen as going beyond metacognition because it incorporates affective/emotional, motivational, and behavioral monitoring and self-control processes.

Executive Functioning The last term to be defined here, executive functioning, has also been seen as overlapping with the terms metacognition and self-regulation, making precise understanding and use of all these terms somewhat challenging. Cutting and Denckla (2003) argued that the term executive functioning should not be elevated to the position of a synonym for metacognition. The term executive function is somewhat more commonly used

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by cognitive neuropsychologists and cognitive psychologists, whereas the term self-regulation is more commonly used by educational and educational psychology researchers. Barkley (2006) defined executive function as serving inhibitory functions. Executive functions are seen as self-directed mental activities that occur during the delay in responding, and serve to modify the eventual response to an event. This in turn serves to improve the long-term future consequences related to that event. He further noted that the term executive function incorporates self-directed actions; organization of behavior across time; the use of self-directed speech, rules, or plans; deferred gratification; and goal-directed, future-oriented, purposive, effortful, or intentional actions. In sum, executive functions are those types of actions performed to direct ourselves so as to accomplish self-control, goaldirected behavior, and the maximization of future outcomes. The nuances in the definitions of the terms self-regulation, metacognition, and executive function have much to do with the fields of study from which they have arisen and the times at which these definitions were developed. While the overlap among these terms and the lack of clear distinctions in these constructs may be confusing at first, each view has informed research on self-regulation or self-control. These terms are also related to the different theoretical perspectives of self-regulation, which we address below.

THEORETICAL BASES FOR RESEARCH ON SELF-REGULATION In this section, we briefly describe four of the most relevant theories from which research on self-regulation among students with LD or ADHD has evolved: operant theory, information processing theory, social constructivist theory, and social cognitive theory (Schunk & Zimmerman, 2003). More detailed discussion of these theories and their contributions to selfregulation research, as well as contributions from other perspectives, can be found in Schunk and Zimmerman (1994, 1998, 2003) and in Boekaerts et al. (2000). Note that these theoretical perspectives have areas of overlap, and each of these theories continues to evolve and develop. Thus, our portrayal of these theories can be seen only as a general overview.

Operant Theory Operant, or behavioral, theorists have traditionally explained human behavior through environmental antecedents and consequences, with research

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focusing on observable and measurable overt behaviors. Some behaviorists have seen cognitions as having no place in the science of behavior, while more moderate behaviorists do not deny the existence of the mind, yet see cognitive components of behavior as either irrelevant or trivial (Harris, 1982). As early as the 1970s, however, some proponents of behavioral theory began expanding it to include a greater role for cognition. Kanfer and Karoly (1972) wrote an early and extremely influential article regarding self-regulation from a behavioral perspective, entitled, interestingly, “Selfcontrol: A behavioristic excursion into the lion’s den.” Just two years later, Mahoney and Thoresen (1974) published an influential book that reviewed behavioral and social learning perspectives on self-regulation, entitled, “Self-control: Power to the Person.” These early and important works are a must read for those who want to understand the progression of research in self-regulation. From an operant theory perspective, a student decides what behaviors to regulate, establishes discriminative stimuli for their occurrence, evaluates performance according to whether or not it meets standards, and administers reinforcement (Schunk & Zimmerman, 2003). Key selfregulation processes studied by behavioral researchers include goal-setting, self-instructions, self-monitoring (including both self-assessment and selfrecording), and self-reinforcement. These same processes, however, have also been studied by researchers of other theoretical persuasions.

Information Processing Theory Schunk and Zimmerman (2003) noted that there are several models of information processing but, in general, this theory emphasizes that students need to compare present activities and abilities against standards and then take steps to resolve discrepancies. Metacognition, or awareness about task demands, personal capabilities, and strategies for the task, is seen as necessary for self-regulated learning. Learning is further seen as the encoding of information into long-term memory; new knowledge is related to existing information in working memory. Self-regulation or self-control processes are used in creating new learning and in moving information from working memory to long-term memory.

Social Constructivist and Social Cognitive Theories Schunk and Zimmerman (2003) described the social constructivist theory of self-regulation as grounded in theories of cognitive development that postulate that human beings are intrinsically motivated, active learners. Mental representations and refinements in understandings develop over

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time, with reflection, experience, social guidance, and acquisition of new information. Self-regulation, then, is seen by social constructivists as the process of students “acquiring beliefs and theories about their abilities and competencies, the structure and difficulty of learning tasks, and the way to regulate effort and strategy use to accomplish goals” (Schunk & Zimmerman, 2003, p. 66). Students’ beliefs and theories are related to their level of development and change due to ongoing development and experiences. The works of Vygotsky, Luria, Flavell, and others are frequently seen as fundamental to this theory, and are also seen as informing social cognitive theory (Harris, 1982, 1990). Social cognitive theory is grounded in Bandura’s (1986) emphasis on the reciprocal nature of interactions between behaviors, environmental factors, and cognition and affect. Self-regulation is seen by social-cognitive theorists as situation specific, and as strongly influenced by students’ selfefficacy beliefs (Schunk & Zimmerman, 1994, 1998, 2003). Zimmerman’s three-phase model of the cyclical processes of self-regulation (forethought, performance, and self-reflection) mentioned earlier evolved from a social cognitive theoretical base. Various self-regulatory processes are seen as coming into play across the three phases and the interaction of personal, behavioral, and environmental factors. We turn next to components of self-regulation commonly studied across these differing theoretical views of self-regulation: self-monitoring, self-instruction, goal-setting, selfevaluation, and self-reinforcement.

COMMON SELF-REGULATION PROCESSES: APPLICATION WITH STUDENTS WITH LD AND ADHD There are a number of self-regulation strategies that can be effectively taught to students with deficiencies or difficulties in self-regulation to aid in their development of these capabilities. These include self-monitoring (also called self-assessment or self-recording), self-evaluation, selfinstruction, goal setting, and self-reinforcement. All of these aspects of selfregulation have been thoroughly researched and classroom tested, and have demonstrated efficacy for students with LD and ADHD (Mace, Belfiore, & Hutchinson, 2001; Reid, 1999). Though we discuss each separately, we stress that these self-regulation procedures are commonly and effectively combined in practice. Once we have explained each of these selfregulation procedures, we turn to the major target behaviors, or dependent variables, that have been studied using self-regulation interventions.

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Self-Monitoring Self-monitoring is one of the most thoroughly researched self-regulation techniques and has been called one of the most important subprocesses of self-regulated learning (Reid, 1996; Shapiro, Duman, Post, & Levinson, 2002). It was originally developed as an assessment procedure designed to allow psychologists to gather information from patients regarding behaviors, feelings, or cognitions in order to evaluate effectiveness of interventions (Kanfer, 1977). However, it was found that merely being aware of and recording behaviors caused changes in the frequency of their occurrence (Nelson & Hayes, 1981). This behavior change, termed “reactivity,” led to the use of self-monitoring as an intervention in and of itself. Selfmonitoring is defined as occurring when an individual first self-assesses whether or not a target behavior has occurred, and then self-records the occurrence, frequency, duration, or so on of the target behavior (Nelson & Hayes, 1981). Typically, self-monitoring does not involve the use of external reinforcers; however, in some cases, notably involving children with ADHD, self-monitoring is combined with external reinforcement (Barkley, Copeland, & Sivage, 1980). Teaching a student to use selfmonitoring is both quick and straightforward, and procedures are well established (see Graham et al., 1992; Reid, 1993).

Self-Evaluation Self-evaluation is closely related to self-monitoring. Self-evaluation differs from self-monitoring in the use of external comparisons and reinforcers. It has been used frequently and very effectively with children with ADHD (e.g., Shapiro, DuPaul, & Bradley-Klug, 1998). Self-evaluation requires students to rate a behavior at set intervals (Shapiro & Cole, 1994). For example, students might rate their behavior on a scale of 1 (did not follow directions or finish work) to 5 (followed all directions and finished all work). Students’ ratings are then compared to the evaluation of an external observer (e.g., teacher, paraprofessional), and students receive points or tokens based on how closely they match the external rating. After students have attained consistently accurate ratings, the external matching is faded and children self-award points based on their self-evaluation.

Self-Instruction Self-instruction techniques involve the use of self-statements to direct or self-regulate behavior (Graham et al., 1992). Put simply, children

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quite literally learn to “talk themselves through” a task or activity. Selfinstruction techniques grew from Vygotsky’s (1934/1962) observation that children used overt verbalizations to help regulate behavior. Self-talk (often termed “private speech”) is used by children to self-regulate and guide behavior and is a part of the normal developmental process (Harris, 1990). Self-instruction techniques mimic the manner in which language is normally used to self-regulate behavior. Graham et al. (1992) identified six basic forms of self-instruction: 1. Problem definition—defining the nature and demands of a task. 2. Focusing attention/planning—attending to task and generating plans. 3. Strategy related—engaging and using a strategy. 4. Self-evaluation—error detection and correction. 5. Coping—dealing with difficulties/failures. 6. Self-reinforcement—rewarding oneself. Self-instruction techniques have a well-demonstrated record of effectiveness for children with LD (Swanson, Hoskyn, & Lee, 1999). They are also commonly used as a component in strategy instruction interventions (e.g., Graham & Harris, 1996).

Goal-Setting Effective learners are goal-oriented (Winne, 1997), and goal-setting is viewed as an important aspect of self-regulation (Bandura, 1986). Goals serve important functions for learners. Goals structure effort, provide information on progress, and serve to motivate performance (Schunk, 1990). Goals may be either absolute (i.e., with a fixed standard such as completing 20 math problems correctly in 6 minutes) or normative (i.e., doing as well as another student on the math problems). There is some evidence that the most appropriate goals for children with LD might be normative as these types of goals may enhance self-efficacy and motivation (Schunk, 1987). Students who see satisfactory progress toward a goal are more likely to sustain effort (Bandura, 1986). In some instances, goals can be maladaptive. Students with ADHD sometimes exhibit performance avoidance goals, namely to avoid appearing incompetent (Barron, Evans, Baranik, Serpell, & Buvinger, 2006). There are three salient features of effective goals: specificity, proximity, and difficulty (Bandura, 1988). Specificity refers to how well a goal is defined. Goals that are vague (e.g., do your best on the test) are not as effective as those that are well specified (e.g., achieve at least 80% correct on the test). Proximity refers to temporal aspects of goals. Proximal goals can be completed in the near term (e.g., copy my spelling words

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three times by the end of class), and are generally more effective than distal goals, which can only be completed in the far future (e.g., learn 100 new spelling words by the end of the year). Note, however, that it is possible to use a series of proximal goals to accomplish a distal goal. Difficulty refers to how much challenge a goal poses an individual. Goals that are easily attained do not serve to enhance or maintain effort ( Johnson & Graham, 1990). The most effective goals are those that are moderately challenging. That is, those which are neither too easy nor too difficult. Goal-setting often involves a self-judgment process that entails comparing current performance with a goal (Schunk, 2001). For goal-setting to affect behavior, goals must be valued. If a goal has little or no importance to the student, then it is unlikely to improve performance or maintain motivation or effort. Additionally, attributions (the perceived cause of an outcome) must be considered (Schunk, 2001). Individuals must perceive progress toward a goal as being the primary result of their own efforts rather than simply luck or outside help (e.g., the teacher helped me).

Self-Reinforcement Self-reinforcement occurs when a student selects a reinforcer and selfawards it when a predetermined criterion is reached or exceeded (e.g., when I write three pages, I get a break) (Graham et al., 1992). This process is analogous to the natural developmental process where a child learns that meeting expectations often results in positive reinforcement while the opposite typically results in no response or a negative response (Zimmerman & Schunk, 1989). As a result, children learn to self-reinforce (or self-punish) their own behavior. Implementing self-reinforcement involves: (1) determining standards for rewards; (2) selecting a reinforcer; (3) evaluation of performance; and (4) self-awarding reinforcement when criterion is reached. Self-reinforcement is often combined with other self-regulation techniques; it is frequently the final step in a sequence of self-regulation processes and can set the stage for further self-regulation. The notion that individuals can actually engage in self-reinforcement may be seen by some as counter to a strict operant perspective of self-regulation (see Mace et al., 2001, for a detailed critique); regardless, the technique itself is quite effective.

Final Thoughts We have briefly defined and discussed major self-regulation techniques that research indicates are effective for children with LD and ADHD. There are other promising self-regulation techniques such as visualization

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(Zimmerman, 1998), correspondence training (Paniagua & Black, 1990) and strategic planning and self-consequences (Zimmerman, 1998) that may also be useful for children with self-regulation difficulties. In closing, we stress that self-regulation does not take place in a vacuum. The environment is a significant factor in self-regulation from both the socialcognitive and operant perspectives (Mace et al., 2001; Schunk, 2001). At the most basic level, environmental manipulations can enhance or enable self-regulation (e.g., taking a limited amount of cash prevents overspending; Mace et al., 2001). Students also may self-regulate their environment to enhance performance (e.g., creating a study space that is quiet and free of distractions to enhance studying or improve homework completion). Providing children with a structured environment with predictable stable routines is a necessary prerequisite for self-regulation and can greatly increase the likelihood of effective self-regulation. Children with LD or ADHD will likely have some degree of problem with self-regulation even in the best possible environment. In a disordered, chaotic environment, successful self-regulation is doubtful, at best. There are numerous environmental changes that can enhance self-regulation, such as providing students with folders to serve as organizers for assignments, taping prompts to lockers (Did you remember to bring...), or using Job Cards (which list the steps for a task and serve to cue performance; Pfiffner & Barkley, 1998; Reid, 1999).

SELF-REGULATION AND MAJOR DEPENDENT VARIABLES We turn now to the effects of self-regulation interventions on major dependent variables. These include on-task behavior, academic preparedness, academic productivity, and academic accuracy. Next, we discuss effects on disruptive behaviors. Finally, we discuss the use of self-regulation techniques in the social context.

On-Task Behavior On-task behavior is the most studied outcome in self-regulation interventions by a wide margin (Briesch & Chafouleas, 2009; McDougall, Skounge, Farrell, & Hoff, 2006; Reid, 1996). This is understandable because increasing on-task behavior is a natural focus for self-regulation interventions among students with LD and ADHD. Though not sufficient in isolation, attending to a task and maintaining effort are important prerequisites to academic success. Moreover, increasing on-task behavior can

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have positive effects on classroom climate and the teacher–child relationship (Hallahan & Lloyd, 1987). Self-monitoring has a long-standing record of effectiveness for increasing on-task behavior for children with LD (Briesch & Chafouleas, 2009; McDougall et al., 2006; Reid, 1996). More than 30 studies involving children with LD have reported increased on-task behaviors. The effects of self-monitoring have been demonstrated across age levels and educational settings. The majority of studies focused on participants in the 9 to 11 age group; however, self-monitoring has been effective for children as young as 7 (e.g., Hallahan, Lloyd, Kneedler, & Marshall, 1982; Hallahan, Lloyd, Kosiewicz, Kauffman, & Graves, 1979) and as old as 18 (e.g., Blick & Test, 1987; Prater, Joy, Chilman, Temple, & Miller, 1991). Self-monitoring is effective across individual, small group, and large group settings (e.g., Hallahan et al., 1979; Hallahan, Marshall, & Lloyd, 1981; Prater, Hogan, & Miller, 1992). An important factor in self-monitoring intervention is the durable nature of the effects. Several researchers have demonstrated that benefits of selfmonitoring have been maintained over several months of classroom use (e.g., Harris, 1986b; Harris, Graham, Reid, McElroy, & Hamby, 1994; Lloyd, Bateman, Landrum & Hallahan, 1989; Rock & Thead, 2007). Self-monitoring interventions have also been successful in increasing on-task behavior for children with ADHD (DuPaul & Stoner, 2002). In a meta-analysis, Reid, Trout, and Schartz (2005) reported a mean effect size of 1.94 for self-monitoring, indicating strong practical impact. Mathes and Bender (1997) used self-monitoring of attention successfully with three elementary-age, resource students. Harris, Friedlander, Saddler, and Frizelle (2005) reported meaningful increases in both on-task behavior and spelling study performance using self-monitoring procedures with students with ADHD. Shimabukuro also reported increased on-task behavior for three 12- to 13-year-old students with LD and ADHD (Shimabukuro, Prater, Jenkins, & Edelen-Smith, 1999). Similar results were reported by DeHaas-Warner (1990) for a preschool student during readiness tasks and by Christie, Hiss, and Lozanoff (1984) for three elementary-school students in the general education classroom. It is noteworthy that self-monitoring resulted in increases in on-task behavior over and above psychostimulant medication in two studies (DeHaas-Warner, 1990; Mathes & Bender, 1997). Self-monitoring plus reinforcement (SM + R) interventions have also had a positive effect on on-task behavior for students with ADHD (Reid et al., 2005). Combining self-monitoring with a signaling system, contingent teacher praise, and planned ignoring, Stahr, Cushing, Lane, and Fox

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(2006), improved time on-task for a 9-year old boy with ADHD in a self-contained school. In a residential setting, staff provided small rewards for accurate self-monitoring to improve adolescents’ on-task homework behavior (Axelrod, Zhe, Haugen, & Klein, 2009). Using self-monitoring with and without external reinforcement, Graham-Day, Gardner, and Hsin (2010) reported increased on-task behaviors for three tenth-grade students with ADHD during a study hall period. Interestingly, only one participant needed reinforcement to achieve benefit. Other researchers have combined self-monitoring and self-reinforcement to increase on-task behavior for children with ADHD (e.g., Barkley et al., 1980; Edwards, Salant, Howard, Brougher, & McLaughlin, 1995; Rock, 2005; Rock & Thead, 2007). Although self-monitoring alone can be effective, adding reinforcement may be necessary for some students with ADHD. Reid et al. (2005) reported a large mean effect size of 2.53 for multi-component interventions, such as these. Self-evaluation techniques have also been used successfully to improve on-task behavior for children and youth with ADHD. For example, Ervin, DuPaul, Kern, and Friman (1998) reported improvements for a 14-year-old student in a residential placement; similar results were found by Shapiro et al. (1998) for two 12-year-old children, one in general education and one in a self-contained setting. More recently, Barry and Messer (2003) reported increased on-task behavior in five sixth-grade students with ADHD. Terenzi, Ervin, and Hoff (2010), who used self-monitoring with whole class and teacher ratings to increase on-task behavior in students with LD and ADHD, and also reported favorable findings. Selfreinforcement has also demonstrated effectiveness for children with ADHD (Ajibola & Clement, 1995; Bowers, Clement, Fantuzzo, & Sorensen, 1985). However, effects were small compared to self-monitoring and selfevaluation (Reid et al., 2005).

Academic Preparedness Researchers have successfully used self-regulation procedures (i.e., selfmonitoring with goal setting and self-evaluation) to improve the classroom preparedness skills (e.g., coming to class on time, bringing needed materials) of students with LD (Snyder & Bambara, 1997). Gureasko-Moore, DuPaul, and White (2006) replicated Snyder and Bambara’s study, and reported improved classroom preparedness skills for three seventh grade students with ADHD. In a subsequent study, Gureasko-Moore, DuPaul, and White (2007) extended these findings, and increased both classroom

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preparation skills and homework completion in six middle school students with ADHD. Merriman and Codding (2008) used self-monitoring with goal setting and systematic fading procedures, and improved math homework completion for three high school students with ADHD. Effects were maintained for two students during fading and post-treatment. Self-monitoring (with and without reinforcement) has also improved homework completion with students with ADHD. Axelrod et al. (2009) reinforced students for accurate self-monitoring and increased the number of homework assignments adolescents completed on time. In a larger scale study, involving 42 sixth, seventh, and eighth graders, Meyer and Kelley (2007) found that self-monitoring without reinforcement resulted in increased homework completion.

Academic Productivity The effects of self-monitoring on academic productivity—the amount or rate of academic responding—for children with LD have been documented in a number of studies (Reid, 1996). However, effects are less clear than for on-task behavior. Some early studies found clear effects (e.g., Roberts and Nelson, 1981), while others reported equivocal effects (e.g., Hallahan et al., 1979, 1982) or no effects (Lloyd et al., 1982). Methodological problems and issues in the design of the self-monitoring interventions may have contributed to the lack of effects in these studies. In some studies, new material was introduced without regard to mastery; this is a potential problem because self-monitoring will not affect skills not already in a child’s repertoire. Simply put, self-monitoring something a student does not know how to do will not help the student do it better. In other studies, students were required to perform previously mastered tasks for prolonged periods, which may have resulted in resistance or boredom. Later studies reported that selfmonitoring can meaningfully improve academic productivity (e.g., DiGangi, Maag, Rutherford, 1991; Harris, 1986b; Harris, Friedlander, Saddler, Frizelle, & Graham, 2005; Harris et al., 1994; Lloyd et al., 1989; Maag, Reid, & DiGangi, 1993; Reid & Harris 1993). In a review of the effects of selfmonitoring on reading performance of students with learning disabilities, Joseph and Eveleigh (2011) reported a mean effect size of 1.74, indicating strong practical impact. However, the effects on students’ achievement (i.e., new learning) remain largely unstudied. Effects of self-regulation interventions on academic productivity with students with ADHD have not been well studied. One study reported that self-monitoring increased academic productivity for three 12- to

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13-year-old students with ADHD for reading, math, and written expression tasks (Shimabukuro et al., 1999). However, McDougall and Brady (1998) did not find that self-monitoring improved productivity for a fourth-grade student on a math task. Ajibola and Clement (1995) used self-reinforcement to increase the academic productivity for six children with ADHD on a reading comprehension task. We were able to locate only study using self-evaluation with children with ADHD that targeted students’ academic productivity. Barry and Messer (2003) improved five sixth-grade students’ percentage of completed assignments. Because of the well-documented difficulties of children with ADHD in academic productivity and assignment completion (e.g., DuPaul & Stoner, 2003), this is an area that should continue to receive increased research attention. Self-Monitoring of Attention AND Self-Monitoring of Performance for on-Task and Academic Performance Although self-monitoring of attention (SMA) and self-monitoring of performance (SMP) have both been used effectively, until recently there were no studies in which participants used a combined approach (i.e., SMA + SMP). In two inclusive elementary classrooms, involving nine students with and without disabilities—three of whom were diagnosed with LD and one with ADHD, Rock (2005) demonstrated efficacy of a concurrent SMA and SMP intervention. Not only was the combined procedure effective in increasing students’ on-task behavior during math independent seatwork, but also most students increased productivity, which was defined as the number of math problems completed. In 2007, Rock and Thead extended this work by investigating the effects of using and fading SMA + SMP with three students with LD and ADHD. Consistent with previous findings, students’ on-task behavior and productivity increased when SMA and SMP procedures were used concurrently; however, results were mixed during fading. In both studies, the students’ mastery levels and the complexity of the math independent seatwork assignments were not well controlled. Future researchers need to address these limitations. Self-Monitoring of Attention VERSUS Self-Monitoring of Performance for on-Task and Academic Performance Harris et al. (2005) reported the first study on the relative effects of both self-monitoring of performance and self-monitoring of attention among students with ADHD on on-task behavior and spelling study performance. Both self-monitoring of attention and self-monitoring of performance

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had positive effects on students’ on-task and spelling study behaviors. While improvements in on-task behavior were comparable across the two self-monitoring interventions, self-monitoring of attention produced substantially higher gains in spelling study behavior among four of the six elementary students in their study. While this is the first study to investigate differential effects of these two self-monitoring interventions among students with ADHD, previous studies of differential effects among students with LD found the opposite result—self-monitoring of performance tended to result in higher rates of spelling study than did self-monitoring of attention (Harris, 1986b; Harris et al., 1994; Reid & Harris, 1993). Theory suggests that SMA might be more effective for students with ADHD because of the more frequent self-recording provides more feedback on behavior (Barkley, 2006). Clearly, further research on the comparative effects of these two self-monitoring procedures among students with LD and ADHD is needed.

Academic Accuracy The effects of self-monitoring on academic accuracy are by no means clearcut. For children with LD, only three studies have included reports of data on accuracy (Crabtree, Alber-Morgan, & Konrad, 2010; Dunlap & Dunlap, 1989; Maag et al., 1993). Two studies (i.e., Dunlap & Dunlap and Maag et al.) involved math computation tasks and both reported clear effects on accuracy. In reading, Crabtree, et al. (2010) investigated high school seniors’ use of structured self-monitoring, which included recording written responses of prompted comprehension questions. They reported favorable results for all three students not only on immediate recall accuracy, but also on comprehension quiz accuracy. Similarly, researchers have reported positive effects on accuracy for children with ADHD (Edwards et al., 1995; Shimabukuro et al., 1999;Varni & Henker, 1979). Most recently, Farrell and McDougall (2008) found that self-monitoring of accuracy combined with self-graphing and goal setting improved the number and rate of correct responses for six ninth-grade students with LD and ADHD during 3-minute drills of simple addition and subtraction problems. Questions remain whether self-monitoring alone should result in increased accuracy (Reid, 1996). As previously noted, self-monitoring does not create new behaviors; it only affects behaviors already in a child’s repertoire. While self-monitoring may increase awareness of accuracy, theoretically it does not provide a means of improving accuracy in and of itself. Self-monitoring may, however, result in increased practices, which could

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improve accuracy. Self-monitoring may also be combined with a strategy, as was the case in the Dunlap and Dunlap (1989) study. Here, students self-monitored the use of correct procedures in solving math problems (e.g., start in ones column, remember to carry). Clearly, the usefulness of self-regulation techniques for situations that involve new learning continues to be an under researched area.

Disruptive Behavior Children with LD and ADHD often exhibit problem behavior in the classroom (Lauth, Heubeck, & Mackowiak, 2006; Smith-Bonahue, Larmore, Harman, & Castillo, 2009). This may take the form of inappropriate verbalizations, impulsive or inappropriate behaviors, or excessive motor activity. These types of behaviors have a deleterious effect on the classroom learning environment—there is less time spent in instruction and learning activities—and may also have a detrimental influence on teacher–student relationships. Several researchers have demonstrated that self-regulation approaches may be useful for disruptive behaviors of children with ADHD. Three studies conducted in hospital and research settings (Barkley et al., 1980; Horn, Chatoor, & Connors, 1983; Kern, Ringdahl, Hilt, & Sterling-Turner, 2001) included a combination of selfmonitoring and external reinforcement procedures to reduce disruptive behaviors. Researchers have also shown that self-monitoring can reduce students’ disruptive behavior in the classroom. Coogan, Kehle, Bray, and Chafouleas (2007) used a multicomponent intervention that included selfmonitoring and reinforcement and decreased disruptive behaviors in five 12-year-olds. Christie et al. (1984) successfully used self-monitoring in a general education classroom. Similarly, Stewart and McLaughlin (1992) reported reduced off-task behaviors in a self-contained special education setting. In contrast to the studies carried out in hospital or residential settings, they did not use external reinforcers. Self-evaluation has also been effective at reducing problem behaviors of students with ADHD in the general education classroom and selfcontained settings (Davies & Witte, 2000; Hoff & DuPaul, 1998; Shapiro et al., 1998). The effectiveness of self-regulation techniques in the general education setting is particularly salient. Most children with LD and/or ADHD will spend the majority of their school day in the general education classroom (Schnoes, Reid, Wagner, & Marder, 2006). High rates of disruptive behaviors are a barrier to effective inclusion. Methods aimed

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at reducing these behaviors can improve the chances that these children will be effectively included in the general education setting, and thus are particularly important. Self-regulation interventions are extremely promising because typically they are acceptable to classroom teachers and require little if any additional time from teachers after implementation. However, more research is needed to determine how self-regulation can best be used to integrate children with LD and ADHD into the general education setting ( Jull, 2009; McDougall et al., 2006; Reid, 1996). Whether there are differences in the magnitude of effects across different interventions (e.g., self-monitoring or self-evaluation) and what behaviors are best targeted (i.e., should interventions focus on reducing inappropriate behavior or on increasing desired behavior?) are important questions for future researchers.

Self-Regulation in the Social Context The difficulties of children with LD and ADHD are not limited to academics. The social milieu also poses problems for many of these children. Bryan (1997) estimated that over a third of children with LD also have impaired social skills. In fact, some have proposed that social deficits be included in the definition of LD (Lerner & Johns, 2009). The problem is even more serious with children with ADHD as many of the symptomatic behaviors reflect difficulty in social functioning (Miller, Miller, Bloom, Hynd, & Craggs, 2006). From 40 to 60% of children with ADHD will also develop severe problems that affect social relations, such as Oppositional Defiant Disorder (Barkley, 2006). These socially based problems are not trivial. Deficits in social functioning may be even more disabling than academic difficulties because they are more pervasive (Mikami, Jack, & Lerner, 2010). There is evidence to suggest that, for many children with LD and ADHD, the cause of the social skill problem is not due to a lack of social skills, but rather to an inability to activate skills already in the repertoire or to difficulty overcoming impulsive (but inappropriate) behaviors (Barkley, 2004; Gumpel, 2007; Nijmeijer et al., 2008). This distinction is important because self-regulation techniques require prerequisite skills to be present if they are to be employed effectively. Unfortunately, the research base on self-regulation for children with LD and ADHD in the social context remains sparse (Shapiro et al., 2002). As a result, it is not yet possible to assess how effective self-regulation approaches may be in this area. There have, however, been some successful applications with children

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with ADHD. In an experimental program, Hinshaw, Henker, and Whalen (1984) used a combination of self-evaluation and self-evaluation plus medication in a playground setting and found that both were effective for reducing negative social behaviors. In another interesting study, Gumpel and David (2000) taught a 10-year-old to self-monitor his playground behavior. The child was taught to set an inexpensive kitchen timer and to use a small notebook to self-record whether he was engaged in appropriate behaviors (e.g., I succeeded in playing without hitting) at 4-minute intervals. Self-monitoring effectively decreased the rate of aggressive playground behavior and increased the rate of positive social interactions for an elementary school child. Gains were maintained 6 weeks following cessation of treatment.

FACTORS INFLUENCING AND INFLUENCED BY SELF-REGULATION Researchers from multiple theoretical perspectives (Boekaerts et al., 2000) and domains (Boekarts & Corno, 2005) have proposed a host of factors that can influence the use and effectiveness of self-regulation among learners and that can, in turn, be influenced by the process of selfregulation. As Zeidner, Boekaerts, and Pintrich (2000) noted, there is as of yet little agreement regarding the “phases or facets in the structure and morphology of self-regulation” (p. 753). Key factors could include environmental determinants and influences (family, social, religious, etc.), affective and motivational factors (self-efficacy, attributions, goal orientation, ability beliefs, achievement values, mood, etc.), and individual differences (gender, age, cognitive ability, personality, etc.). Multiple models of the temporal placement of these factors and their reciprocal relationships and interactions with each other and selfregulation processes have been suggested, with little research yet available to fully explain the role of any given factor or to support any particular model (Boekaerts et al., 2000; Schunk & Zimmerman, 1994, 1998). In this chapter, we further address one of the constructs researchers have found evidence for in relationship to self-regulation and that has been investigated by researchers in the areas of LD or ADHD: self-efficacy.

Self-Efficacy The term “self-efficacy” refers to students’ pre-task judgments—their expectations or beliefs regarding whether or not they can perform a

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given task or activity. While the role of self-efficacy in self-regulation and the effects of self-regulation on self-efficacy are not yet clearly established, some researchers view self-efficacy as an integral part of the self-regulation process (Zeidner et al., 2000). Many researchers believe that change in self-efficacy is a critical factor in changes in behavior (cf. Zimmerman, 2000). Self-efficacy is believed to have a strong influence on performance as it affects choice of activities, the amount of effort expended, and persistence in the face of difficulty. In other words, individuals who believe they are capable of successful performance are likely to choose challenging activities, work hard, and persist when difficulties are encountered (Harris & Graham, 1996). Thus, strong self-efficacy may lead to greater and more effective selfregulation, while successful self-regulation and completion of a task may, in turn, strengthen self-efficacy. Interestingly, students with LD may not only experience low selfefficacy regarding some tasks that are within their capabilities; some research indicates that very young children and students with LD frequently exhibit unrealistically high pre-task expectancies. They expect to be able to successfully complete activities they are not yet capable of (Graham & Harris, 1989; Sawyer, Graham, & Harris, 1992). Unrealistically high expectations among students with LD may be due to misperceptions of task demands or difficulties in comprehending the task, inaccurate selfknowledge, selective attention to what has been mastered as opposed to what has not, inability to match demands to ability level, or employing a self-protective coping strategy (Sawyer et al., 1992). Unrealistically high expectations are also common among students with ADHD both for academics and social behavior (Evangelista, Owens, Golden, & Pelham, 2008). When unrealistically high pre-task expectancies are followed by failure or extreme difficulties, negative, maladaptive attitudes and beliefs, including lowered self-efficacy, may be the result. In terms of self-regulation processes, not only must students believe they can perform an appropriate task, but in order to have positive pretask expectancies for their ability to self-regulate the task, they must also believe that they are using self-regulation processes and skills to assist in reaching their goals (Zeidner et al., 2000). Pintrich (2000) emphasized that not only can learners regulate their cognition and behavior, they can also regulate their motivation and affect. Self-regulation of motivational beliefs can include regulation of goal-orientation; beliefs about the importance, utility, and relevance of the task; personal interest in the task; and selfefficacy (Boekaerts et al., 2000; Pintrich, 2000). Here again, the complex

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relationships among self-regulation and factors such as self-efficacy are evident, and remain critical targets for future research. While little research regarding self-efficacy and self-regulation has been done among students with ADHD, some research involving the combination of strategies instruction and development of self-regulation of strategic performance and the effects of such intervention on performance and self-efficacy among students with LD has been conducted. We turn next to consideration of the integration of strategies instruction with self-regulation development and the resulting effects among students with LD and ADHD.

SELF-REGULATION OF STRATEGIC PERFORMANCE The target behaviors for self-regulation discussed so far have largely been relatively discrete behaviors such as academic productivity or accuracy, and on-task behavior. A number of researchers have noted that one or more self-regulation procedures could also be critical in successful use of more complex learning strategies (cf. Boekaerts et al., 2000; Harris & Graham, 1996; Schunk & Zimmerman, 1994, 1998). In fact, approaches to strategies instruction across a number of fields have been strongly influenced by the seminal work of both Donald Meichenbaum and Ann Brown and her colleagues (Harris, 1982; Harris & Graham, 1992, 1999; Wong, et al., 2003). Meichenbaum (1977) noted that a number of self-regulation procedures could be critical in strategy training, and developed guidelines for selfinstructional training. Brown and her colleagues (cf. Brown, Campione, & Day, 1981) emphasized the importance of self-control components in strategy learning, which they described as planning and executing the strategy, monitoring strategy use, and evaluating strategy effectiveness and outcomes. A substantial body of research exists among students with LD, and recently, researchers have begun to study the effects of integrating academic and self-regulation strategies in instruction for students with ADHD.

SRSD Space precludes a broad discussion of the integration of self-regulation with strategies instruction (see Wong et al., 2003, for a review). Rather, one model, Self-Regulated Strategy Development (SRSD) is discussed. SRSD has been in development by Harris, Graham, and their colleagues since the early 1980s (Harris, 1982; Harris & Graham, 1992, 1999). It is perhaps the most thoroughly researched strategy instruction model and its effectiveness is well documented for some academic areas,

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particularly writing (for reviews see Graham & Harris, 2003; Graham & Perin, 2007; Rogers & Graham, 2008). SRSD was designed to address the self-regulation difficulties of students with learning difficulties through incorporation of self-regulation strategies. It also addresses factors such as self-doubts, learned helplessness, low self-efficacy, maladaptive attributions, and unrealistic pre-task expectancies. Low motivation and engagement in academic tasks are also addressed by helping students develop a more positive attributional style (Harris, 1982; Harris & Graham, 1992). Thus, SRSD is particularly appropriate for students with LD or ADHD who may exhibit difficulties in all these areas. Because SRSD instruction has been described in some detail elsewhere (e.g., Harris, Graham, & Mason, 2003) we do not offer a description of it here. Rather, we will briefly summarize what research on SRSD has informed us about the effectiveness of this approach for students with LD, and particularly on what we have learned about the role of selfregulation development within this model. Greater detail on SRSD in the classroom can be found and is available; for example detailed lesson plans and support materials for instruction are provided in Harris, Graham, Mason, and Friedlander (2008), and a detailed discussion of classroom implementation is provided by Harris, Graham, and Mason (2003). Online interactive tutorials are available at http://iris.peabody.vanderbilt.edu/ pow/chalcycle.htm. Finally, an example of the complete stages of instruction in an inclusive fourth grade classroom can be seen in the video, “Using Learning Strategies,” produced by the Association for Supervision and Curriculum Development (2002).

Outcomes of SRSD SRSD research has focused primarily on writing. More than 30 studies using the SRSD model of instruction in the area of writing involving LD and normally achieving students in the elementary through secondary grades have been reported since 1985, with several additional studies reported in reading and math (Graham & Harris, 2003). Writing strategies across a number of genres have been developed and researched, typically with the assistance of teachers and their students, including personal narratives, story writing, persuasive essays, report writing, expository essays, and state writing tests (Graham & Harris, 2003; Harris & Graham, 1996). SRSD has resulted in improvements in four main aspects of students’ performance: quality of writing, knowledge of writing, approach to writing, and self-efficacy regarding writing (Graham & Harris, 2003; Harris &

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Graham, 1999). The quality, length, and structure of students’ compositions have improved across a variety of strategies and genres. In some studies, students with LD have improved to where their performance is similar to that of their normally achieving peers in the same classrooms. Maintenance of these improvements has been found for the majority of students with LD, with some students needing booster sessions for longterm maintenance. Generalization, while not as robust as maintenance, has been found across settings, persons, and writing media. Meaningful improvements have been found for normally achieving students as well as students with LD, making this approach a powerful fit for inclusive classrooms. It is important to note, in addition, that although normally achieving students typically do not need as extensive or scaffolded instruction, research indicates that students with LD do not show meaningful gains unless the complete, scaffolded, and collaborative SRSD instructional stages are implemented (cf. Danoff, Harris, & Graham, 1993; Harris & Graham, 1999). The effectiveness of SRSD for students with ADHD has not been as thoroughly researched. However, researchers are now beginning to investigate the use of SRSD with students with ADHD and there is a small but growing body of research. Two studies with three elementary-school students with ADHD (Lienemann & Reid, 2008; Reid & Lienemann, 2006) found that SRSD strategy instruction in story writing and persuasive essay writing resulted in compositions that, compared to baseline, were longer, included more genre elements, and were qualitatively superior. In fact, the quality of the compositions was equivalent to normally achieving peers. Effects were maintained over a two-week maintenance check. Mason, Kubina, and Taft (2011) taught Quick Writes (10-minute writing responses) for persuasive essays to three seventh-grade students with ADHD. All students improved the number of essay elements and the quality of essays also improved markedly. Gains were maintained at two-week maintenance checks. Jacobson and Reid (2010) taught a persuasive writing strategy to three high-school students with ADHD. All students improved the length, number of essay parts, and overall quality of their essays. Additionally, students planning time increased markedly and all students independently used the planning strategy to help organize their essays. This is significant because students with ADHD often fail to maintain strategy use after only a short time (Kofman, Larson, & Mostofsky, 2008). Two studies have examined effects of SRSD on reading comprehension. Rogevich and Perin (2008)

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taught 31 high-school students with ADHD and comorbid behavior disorders a summarization strategy. They found that student recall of details and main ideas improved significantly. Johnson, Reid, and Mason (2011) used a paraphrasing strategy with three high-school age students with ADHD and reported similar increases. We would characterize the results of SRSD with students with ADHD to date as “promising.” This is not surprising because there are many aspects of SRSD that directly address learning problems experienced by students with ADHD as noted in current conceptualizations of ADHD (e.g., Johnson & Reid, 2011; Martinussen & Major, 2011): 1. Students are explicitly taught effective strategies. Many students with ADHD lack strategies or are unable to create effective strategies. 2. Students are provided with metacognitive information on the value of strategies. Many students with ADHD use ineffective strategies simply because they require less effort; they do not realize that a more effortful strategy may also be more effective. 3. Performance goals are explicitly set and made salient through ongoing feedback on progress. Students with ADHD often do not realize the goal of an academic task (e.g., the purpose or reading is to gain information) or fail to maintain the goal in working memory. 4. Planful and organized approaches to academic tasks are inculcated. Students with ADHD rarely plan or organize their efforts and as a result often fail to maximize their potential. 5. Self-regulation strategies infused in SRSD can help students to better maintain effort. Students with ADHD commonly experience difficulties persisting at tasks that require effortful processing of information. 6. Working memory difficulties are ameliorated through systematic scaffolding and teaching strategy use to a high degree of mastery. We would caution, however, that much more research is required. Most notably there is a need to investigate long-term maintenance of strategy use. Generalization, or transfer, to other settings and tasks also requires further research.

Findings Regarding the Self-Regulation Components of SRSD A basic premise underlying the SRSD model is that inclusion and explicit development of self-regulation contributes to students’ mastery of the strategies they are learning and to the maintenance and generalization of these strategies. One important research objective, then, is to determine the relative contribution of the self-regulation components of SRSD

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(Harris & Graham, 1999). Two studies have examined the contribution of self-regulation development to the acquisition, maintenance, and generalization of writing strategies. In the first study, Graham and Harris (1989) provided fifth and sixth graders with LD strategies instruction in planning and writing stories using either the full SRSD instructional approach (including self-regulation) or SRSD minus explicit goal setting and self-assessment, including graphing of progress. No differences were found between the two groups in terms of strategy use, writing performance, or self-efficacy. Thus, in this study, the inclusion of explicit instruction in goal-setting and self-assessment did not enhance the strategic behavior, beliefs, or writing performance of these students with LD. However, this first study (Graham & Harris, 1989) did not compare the presence of explicit self-regulation instruction to the absence of such instruction. Not only were self-instructions explicitly developed in both conditions, it is likely that many of the remaining instructional components in SRSD (such as cognitive and collaborative modeling involving the use of self-regulation) generated and induced selfregulatory behavior (Harris & Graham, 1999; Sawyer et al., 1992). Thus, in a second study, Sawyer et al. (1992) investigated the effects of three versions of SRSD on planning and story-writing among fifth and sixth grade students. The first version was the complete SRSD model; the second version removed explicit instruction in goal-setting and selfassessment (including graphing of progress); the third version further removed additional explicit and implicit components that promote selfregulation, including self-instructions and cognitive and collaborative modeling of strategy use involving self-regulation. The students with LD made meaningful gains in story-writing in all three conditions (Sawyer et al., 1992). However, the contribution of self-regulation components was evident in this study at two points. First, students who received the first and second versions, which included self-instructions and modeling, had significantly higher schematic structure scores than did either students in the third version (where further self-regulation components were removed) or students in a writing practice control condition. Further, students in the first, or full SRSD version, performed significantly better than students in all other conditions on the generalization probe administered in a new setting by the regular classroom teacher. Thus, findings from this study support the use of the full SRSD model with explicit development of self-regulation (Harris & Graham, 1999; Sawyer et al., 1992). Further research and replication of findings are clearly needed.

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CONCLUSION After 30 years of research on self-regulation and self-regulated learning among students with LD, ADHD, and their normally achieving peers it seems safe to say that self-regulation interventions have been established as effective and are useful across a wide range of school settings and behaviors. However, many questions still remain unanswered in this intriguing area. We have noted directions for further research throughout this chapter. Detailed discussions of the work that has been done and directions and needs in future research have been established in seminal works by Boekaerts et al. (2000); Schunk and Zimmerman (1994; 1998); and Zimmerman and Schunk (1989). We close by noting, although more research is needed, we know enough about the importance of and how to assist in the development of self-regulation among students with LD and ADHD to use this knowledge base in making a difference in the lives of these students.

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Reading Interventions for Students in Early Primary Grades Sylvia Linan-Thompson, and Jeremy Miciak The University of Texas at Austin, TX 78712, USA

Chapter Contents Introduction 175 Overview of Literacy Development 177 Effective Instruction for Students with Reading Difficulties 180 Assessment 183 Initial Screening 183 Progress Monitoring 186 Decision-Making 187 Summary 188 References 188

INTRODUCTION If we were to stop and think about all the processes required to learn to read, we would not be surprised by the time and effort needed to become a proficient reader or wonder why some children have difficulty learning to read. Beginning readers must recognize that oral language can be written down and read back. Additionally, they have to learn to decipher the code that rules the language and to interact with the text to create meaning. For many of us, this was a gradual process that was supported by literate persons in our lives and by our environment. Consequently, we do not remember the myriad of skills and knowledge that we acquired during that period unless we had difficulty in learning to read or were learning in a second language. Additionally, when proficient readers encounter new words or fail to understand something, they have an automatic process for addressing those difficulties. Our understanding of how children learn to read has grown exponentially in the last decade. Across languages there are universal skills and knowledge that all beginning readers need in order to develop into

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competent readers. Furthermore, it is clear that many reading difficulties are preventable, particularly those due to lack of English proficiency or educational opportunity. To prevent reading difficulties, classroom teachers, reading intervention specialists, and administrators must work together to ensure that all students, particularly those at-risk for reading difficulties and those with learning disabilities, receive effective reading instruction based on scientific research. Coordinating the delivery of effective reading instruction to meet the needs of all students can be logistically challenging. Debates about the structure of reading interventions, that is grouping and time requirements, as well as considerations of educator responsibilities have occasionally overshadowed what we believe is the most important consideration in designing and delivering effective reading interventions to early elementary students—the nature and characteristics of instruction. It is our contention that instruction for students at-risk for reading difficulties cannot be piecemeal, wherein a supplemental reading program is implemented without consideration of the entirety of reading instruction. Instead, successful intervention for students at-risk for reading difficulty begins with effective core reading instruction, characterized by data-driven teaching, strategic interventions, and variations in group size and instructional intensity. Because students who experience difficulty learning to read have difficulty in one or more of the basic components of reading: phonemic awareness, decoding, vocabulary, fluency, or comprehension, all reading instruction provided to students should include, at a minimum, the core components of reading and systematic and explicit reading instruction. Additionally, measures of literacy skills are necessary to identify students who may experience difficulty learning to read. The implementation of instruction in tiers or levels is a characteristic of Response to Intervention (RtI) models. RtI, when properly implemented, represents a framework for delivering effective, data-driven teaching that can prevent reading difficulty and serves as a means for identifying children with learning disabilities. Finally, RtI, with its attention to the differing educational needs of students at each of the tiers of intervention helps focus our attention on instruction and the crucial determination of each individual student’s response to that instruction. Tiered models of intervention typically contain three or four tiers of instruction. Each tier is differentiated by instructional intensity. Within this structure, Tier I instruction is core reading instruction, delivered to all students by their general education teacher. Tier II instruction is for

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students that either do not meet benchmarks on universal screenings or who fail to make adequate progress in Tier I. Tier II instruction is supplemental instruction delivered in small groups to enhance Tier I instruction. Within Tier II, student progress is continually monitored and students that do not make adequate progress are moved to Tier III, which consists of supplemental instruction of even greater intensity (Fletcher & Vaughn, 2009; Fuchs, Compton, Fuchs, Bryant, & Davis, 2008; Gersten et al., 2008). Tier II and Tier III instruction are designed to supplement, not supplant, core reading instruction. Identifying a student as at-risk for reading difficulty and providing Tier II instruction does not diminish the importance of systematic, targeted instruction in Tier I. Through this framework, the appropriate level of instruction can be provided to each student when needed, minimizing risk and maximizing opportunities to learn. This chapter provides an overview of literacy development, with special attention to universal features across languages. It then discusses the characteristics of effective reading instruction at differing tiers of intensity. A discussion of assessment, for both screening and progress monitoring purposes, follows, concluding with important considerations in the decision-making process.

OVERVIEW OF LITERACY DEVELOPMENT Literacy development is similar across languages. All children progress through three general phases: (1) pre-literate; (2) becoming literate; and (3) literate. The pre-literacy phase begins at birth and continues until children begin to develop an understanding of the conventional uses of print. Most children enter school with some knowledge of reading and writing (Graves, 1983). Among the skills and knowledge that children develop before schooling are a well-developed capacity to hear phonological distinctions, phonological awareness, knowledge of letter shapes and names; experience “reading” environmental print; the command of several thousand vocabulary words; and an understanding of the grammar and discourse rules of the language/s they speak (Snow, Griffin, & Burns, 2005). These skills are invaluable resources as students begin formal instruction that includes how letters represent sounds, to expect meaning from texts, to use context to support the decoding of irregular or complex words, and how to bring syntactic and pragmatic linguistic understanding to their reading (Snow et al., 2005). However, children who have had very limited or no exposure to print begin school less prepared to benefit from

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formal instruction. Though all will have developed phonological awareness, vocabulary, and knowledge of the grammar and discourse rules of their home language, the introduction to letter shapes, names, and sounds, for most, will begin in school. It is for this reason that children need explicit instruction if they are to catch up with their peers. It is widely accepted that to develop literacy, children need instruction in the five components of reading: (1) phonemic awareness (PA); (2) alphabetics; (3) fluency; (4) vocabulary; and (5) comprehension (National Reading Panel, National Institute of Child Health and Human Development [NICHD], 2000). More specifically, students should understand that print corresponds to speech; and that words are composed of phonemes. Students with these skills are more likely to gain the understanding that words are composed of individual letters and that these letters correspond to sounds. Phonological awareness, the ability to hear and manipulate sounds, appears to follow a “language universal” sequence (Goswami, 2006, p. 463). Across alphabetic languages, an awareness of larger units of speech such as syllables and onset/rime precedes the development of phonemic awareness, the ability to identify and manipulate individual sounds. The ability to manipulate phonemes in a word usually requires formal reading instruction (Adams, 1990; Goikoetxea, 2005) and is contingent on a teaching method that emphasizes symbol-sound learning and decoding. Thus, children learning to read benefit from explicit instruction of lettersound relationships. In addition to phonological awareness, students need to learn both the names and sounds of letters in the first year of schooling. Letter names are important because “a beginning reader who does not know the letters of the alphabet cannot learn to which sounds those letters relate” (Whitehurst & Lonigan, 1998, p. 851). Learning the sounds of letters is important because mapping sounds to print is a foundational skill in decoding (Ehri, 1991; Juel, 1991; Liberman & Liberman, 1990; Stanovich, 1986). This knowledge helps children build an alphabetic schema which they will use as a resource to read words. As children add letter/sound correspondences to their alphabetic schemata, the number of words they will be able to read will increase. This process is facilitated when children are introduced to letters and their sounds in a systematic manner. Opportunities to practice newly acquired word reading skills in a variety of contexts will move children from phonological recoding to automatic word reading. When children no longer have to sound out a word each time they see it, they have achieved automaticity. When words are known

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well, both the pronunciation and meaning are accessed automatically (LaBerge & Samuels, 1974). Eventually, chunking larger units is a more efficient way of reading new words. As children become familiar with letter patterns that recur in different words, the grapheme/phoneme connections in the words become consolidated into larger units. This skill is valuable in reading multisyllabic words. All children should ultimately learn to read accurately and without effort. Fluent reading—the ability to read with speed, accuracy, and expression—is essential to reading comprehension because these skills are indictors that the reader is processing text efficiently. To develop fluency, students need opportunities to read daily, both orally and silently. Oral reading provides students with the opportunities to receive feedback while reading new text or reading text at their instructional level. This helps them build accuracy and speed. Although, fluency is necessary for comprehension it is not sufficient. Students also need to develop comprehension strategies. Thus, instruction in both code- and meaning-focused reading skills is necessary. Each of these components, individually and when acquired and integrated, allow children to convert sounds to recognizable words, to read fluently, and comprehend what they read. Therefore, they must have the opportunity to learn and practice their emerging skills in isolation and then in context. It is during this phase that reading difficulties often become apparent. Students’ with reading difficulties and learning disabilities begin to lag behind their peers and often need additional instruction in their areas of difficulty. Instruction, then should specifically address those areas. Characteristics of students with reading difficulties: Inability to hear small differences between sounds, not attributable to a hearing loss, particularly vowel sounds. Poor decoding skills. Poor reading fluency. Lack of self-monitoring reading skills. Poor comprehension and/or retention. Difficulty identifying important ideas in context. Extreme difficulty building ideas and images. Difficulty integrating new ideas to existing knowledge. Weak vocabulary skills. Extreme difficulty understanding words or grammar. Difficulty recognizing high frequency words. Oral comprehension is noticeably stronger than reading comprehension. Extreme difficulty focusing attention on the printed marks. Difficulty controlling eye movements across the page. l

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Understanding how children learn to read and causes of difficulty is essential in planning and implementing successful reading interventions.

EFFECTIVE INSTRUCTION FOR STUDENTS WITH READING DIFFICULTIES Research indicates that effective reading instruction is balanced, systematic, and explicit. Balanced instruction addresses the five core components of reading: phonemic awareness, alphabetic principle, fluency, vocabulary, and comprehension (National Reading Panel, NIHCD, 2000). Systematic instruction rejects ad hoc, “as needed” instruction. Instruction is carefully backward mapped to reflect the sum of skills and knowledge that students need to acquire to meet district and state standards for the year. In addition to planning and delivering balanced, systematic, and explicit instruction during Tier I reading instruction, teachers are responsible for differentiating instruction to meet the needs of all students, especially those at-risk for reading difficulty. To successfully differentiate instruction to meet student needs, teachers must first analyze formal and informal assessment results to group students and determine what skills should be taught and what level of materials should be utilized. Instruction can be differentiated by pacing, content, the level of materials, teacher presentation style, and the method of participation by the student. Each of these methods plays a part in successful differentiation of instruction and should be utilized by the general education teacher. Skilled general education teachers utilize multiple grouping formats each day. The utilization of whole group, small group, and individual instruction can provide opportunities to teach target skills to specific students. Based on formal and informal assessment data, teachers identify groups and/or individual students that may need targeted instruction in specific skills and directly teach the target skill(s). Unfortunately, providing targeted small group or individual instruction also requires that the teacher create and maintain appropriate classroom management systems and design a series of self-directed lessons that individual or small groups of students can complete while the teacher works with his target group. Orchestrating this level of organization is taxing for even the most experienced and skilled teachers, and novice teachers should not be shy in seeking help from more experienced colleagues and administrators. Beyond providing targeted instruction to meet individual students’ needs, general education teachers should be careful to ensure that Tier I

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reading instruction is delivered in the most accessible manner possible. As teachers, we often enjoy explaining the nuances and reasons behind reading concepts. Unfortunately, such explanations are often incomprehensible to early elementary students, particularly those students that are learning in their second language. Further, extraneous teacher talk can often distract students from the important reading concept we are trying to teach. For this reason, it is important that we limit teacher talk in early reading instruction. One way to facilitate this reduction in teacher talk is to utilize consistent teaching routines across reading content. These routines help us focus on the specific skill we would like students to demonstrate and provide multiple practice opportunities. A typical teaching routine would consist of four steps and minimal teacher talk: (1) Set the focus (Teacher: Today we will practice reading words that begin with the letter b); (2) Model the skill (Teacher: My turn. I read the sounds of the word, then read the word: /b/ /a/ /t/, bat); (3) Group practice (Teacher: Let’s practice together. Teacher and students: /b/ /a/ /t/, bat. Repeat with other words); and (4) Independent practice (Teacher: Now we will practice as individuals, Maria? Maria: /b/ /a/ /t/, bat. Teacher continues with other students and other words). When implementing this routine, the teacher should correct mistakes in a timely fashion, with direct, clear language. To facilitate this process, teachers can utilize a similar routine: (1) Teacher stops task when a mistake is made; (2) Teacher points out error; (3) Teacher explains why it is wrong; (4) Teacher describes/models the correct way to complete the task; (5) Teacher asks student to complete another example. These routines, when used consistently, can be helpful in reducing teacher talk and providing multiple opportunities for group and individual practice. Some students will struggle to learn to read even with access to effective core reading instruction because they: need additional instruction or practice; failed to develop phonemic awareness; failed to develop the alphabetic principle; failed to master basic decoding skills; were accurate but not automatic; or learn very slowly. For these students, more intensive instruction may be necessary to prevent the development of reading difficulties. Tier II instruction is designed to provide this preventive instruction. Tier II instruction typically differs from Tier I in several ways: (a) target population (all students vs. students that do not meet benchmark on screening measure or make adequate l

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progress in Tier I); (b) group size (whole class vs. homogeneous small group); (c) group characteristics (heterogeneous grouping vs. homogenous grouping); (d) time (90 minutes vs. 25–30 minutes); and (e) assessment period (benchmark assessments vs. weekly progress monitoring). Tier II reading interventions, particularly at the early elementary level, should focus on instruction in all five critical components of reading: phonemic awareness, alphabetic principle, fluency, vocabulary, and comprehension (Vaughn, Cirino et al., 2006; Vaughn, Linan-Thompson et al., 2006). The amount of time spent on each of these skills will vary by grade. For example, in kindergarten it is essential that students master phonemic awareness skills (Torgesen, Morgan, & Davis, 1992). Tier II interventions in kindergarten, then, should dedicate more time to the development of these skills than a Tier II intervention in second grade. Similarly, in second grade it is important that students have opportunities to read with appropriate fluency (Chard,Vaughn, & Tyler, 2002). Students typically participate in Tier II reading interventions for 25–30 minutes every day, although in some circumstances 3 days a week may be sufficient. The smaller group size allows reading intervention specialists ample opportunity to interact with students. Such interaction allows for an increased rate of learning opportunities than what is provided in Tier I. Intervention specialists, like general education teachers, should consistently model target skills, provide multiple opportunities for group and individual practice, and provide direct corrective feedback in a timely manner. The smaller group size also allows the intervention specialist to more closely monitor a student’s progress in reading. Students participating in Tier II reading interventions should be assessed each week, using an appropriate progress-monitoring tool. Curriculum-based measures (CBM), like those described below, have proven effective for monitoring student progress in reading. To monitor student progress, a measure must: (a) include multiple, equivalent probes; (b) be sensitive to small changes in student performance; and (c) measure an important, foundational skill that indicates a change in overall reading or pre-reading skill level. If, after a year of being provided supplemental instruction, students are still struggling, more intensive instruction is necessary. At this point students may either continue to receive supplemental instruction in the form of Tier III instruction or they are referred for special education services. If Tier III is conceptualized as more intensive instruction, then criteria can be set by the school, district, or state. However, if Tier 3 is conceptualized

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as special education, the existence of a disability must be documented and all of the factors in the exclusionary clause addressed. Tier III, when provided as supplemental instruction, is characterized by increased intensity and explicitness. Intensity is increased by reducing group size and increasing instructional time. Providing multiple and extended instructional sessions daily is an effective way to increase student reading outcomes. Explicitness is achieved by focusing on fewer high priority reading skills during lessons and integrating high levels of individualized feedback. Also important is teaching to mastery to ensure retention of skills. This instruction is implemented by a specialist using materials that are selected to meet students’ individual needs. Finally, progress needs to be monitored regularly to determine whether adjustments to instruction should be made. Tier III services may be provided year-long or until the learning deficit is no longer apparent.

ASSESSMENT To ensure that students are receiving the instruction they need, assessment data are needed.

Initial Screening In medicine, screening to identify risk or disease is a well-established practice. Indeed, the moment we arrive in the examination room, we begin to be poked and prodded. Our temperature is taken. We are weighed. The physician listens to our heart and lungs. She may order a blood test to check our cholesterol or blood glucose levels. And, although few of us likely look forward to these medical rituals, almost all acquiesce, because we recognize the inherent value of such tests. The sorts of screening tests described above are quick, inexpensive, and able to detect risk or disorder early, before the problem is so acute as to be untreatable. Universal screening in reading shares the same goal and, ideally, is just as quick, inexpensive, and as accurate as medical screeners. Before discussing specific screening measures used with students in kindergarten through second grade, it is important to understand more specifically the characteristics of an acceptable screening measure in reading. First, any screening measure must accurately identify students that will have reading difficulty absent effective intervention. This is the sensitivity of the test and refers to the number of true positives a test identifies. An astute reader will likely recognize that this is only half the equation, as any test

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could be calibrated to achieve 100% sensitivity if the benchmark is set high enough that no student meets it. In a world of unlimited resources, this might be a desirable course of action. Unfortunately, reading instruction occurs in a world of limited time and resources, and we must be thoughtful about how we allocate them. It is important, then, that a screening measure also correctly identify those students that will not have subsequent difficulty in reading. This is the specificity of the test and refers to the number of true negatives a test identifies. An ideal screening measure has high sensitivity and specificity, and therefore misclassifies very few students—it has high classification accuracy ( Jenkins, Hudson, & Johnson, 2007). In this way, we are assured that very few students that are truly at-risk do not receive the reading intervention they need, while few students that are not at-risk are identified as needing labor- and time-intensive interventions that will prove unnecessary. In addition to accuracy, a screening measure should be brief and easy to administer and interpret ( Jenkins & Johnson, 2011). The identification of a measure or set of measures with each of these characteristics is not without complications. First, screening in kindergarten through second grade is complicated by the rapid development of basic reading processes (Snow, Burns, & Griffin, 1998). Many students enter kindergarten unable to read single words and leave second grade reading over 100 words a minute. Identifying the skill or set of skills that each student should have mastered at each time point is a difficult task. Second, a screening measure is ipso facto a predictive measure, and as such, is subject to the vagaries of time. Third, a screening measure is designed to be brief. While this is certainly one of its attractive qualities, it must also be understood as a limitation. Despite these complications, recent research has identified a number of promising measures and practices that can be utilized for identifying reading risk among students in kindergarten through third grade. These measures are often forms of curriculum-based measures (CBM; Deno, 1985; Fuchs 1986; Shinn, 1989). Potential Screening Measures in Grades K-2 Letter naming fluency (LNF). LNF is a fluency-based measure that assesses the student’s ability to identify letter names aloud. It is typically a oneminute assessment, and the student score is reported as the number of letters correctly named in one-minute. LNF is most appropriate in kindergarten and the beginning of first grade (Speece, Mills, Ritchey, & Hillman, 2003). Phoneme segmentation fluency (PSF). PSF is a fluency-based measure that assesses the student’s ability to segment whole words into

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constituent phonemes. The examiner says a word aloud, and the student tells all the sounds in the word (e.g. T: cat, A: /k/ /a/ /t/). The student score is reported as the number of phonemes correctly provided (for the example above, the student would receive three points, for correctly identifying all the phonemes in the word cat) in one minute. PSF is most predictive in kindergarten and the beginning of first grade. By the middle of first grade, students have acquired enough basic word reading skills to assess that skill more directly. Nonsense word fluency (NWF). NWF is a fluency-based measure that assesses the student’s ability to read phonetically regular nonsense words. In English, the words are typically monosyllabic three and four letter words. The students has one minute to read as many of the words or sounds as possible. A student may receive credit for reading the whole word, or for correctly reading sounds in the word. NWF is most appropriate in kindergarten and first grade (Good & Kaminski, 2002). By second grade, students should be reading connected text, which allows us to directly assess that skill. Word identification fluency (WIF). WIF is a fluency-based measure that assesses the student’s ability to read whole words, presented in a list. The student has one minute to read as many words as possible, and the student score is reported as the correct number of words read in one minute. In contrast to NWF, which presents only single syllable nonsense words, WIF allows us to assess the student’s ability to read two and three syllable words. WIF is most appropriate in first and second grades (Fuchs, Fuchs, & Compton, 2004). Similar to NWF, the importance of WIF declines as students develop proficiency with connected text. Oral reading fluency (ORF). ORF is a fluency-based measure that assesses the student’s ability to read connected text aloud. The student reads for one minute, while the examiner marks mistakes and skipped words. Mistakes are subtracted from the total number of words read, to yield correct words per minute (WPM), the scoring metric. ORF can be assessed beginning in first grade, although significant floor effects may be present in early first grade. ORF is an important assessment for screening and monitoring progress throughout elementary school (Fuchs, Fuchs, Hosp, & Jenkins, 2001). Improving Screening Processes There are a number of steps that can be taken to improve the classification accuracy of screening measures. The utilization of multiple screening

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measures can significantly improve the classification accuracy of screening measures (Foorman et al., 1998; O’Connor & Jenkins, 1999). By carefully selecting a battery of measures that assess multiple skills (e.g. phonological awareness, word reading, and oral reading), we are able to more confidently identify those students at-risk or not. Another way to include multiple screening measures is to create a multiple gating procedure, wherein students that fail to meet universal benchmarks are referred for a more intensive assessment battery (Foorman et al., 1998). This is not dissimilar to what frequently happens in medicine, wherein the detection of an abnormality is cause for further testing to determine the nature and magnitude of the problem. Finally, the classification accuracy of screening measures may be improved by extending the screening process to include some progress monitoring (Compton, Fuchs, Fuchs, & Bryant 2006). Within this model, students who do not meet initial benchmarks are referred for a pre-determined period of progress monitoring after which a determination of risk is made. Students that failed to reach the benchmark and meet growth targets are then referred for more intensive interventions.

Progress Monitoring Following initial screening, students that fail to meet established benchmarks can be referred for more intensive interventions, when appropriate. An important feature of Tier II and Tier III interventions is more frequent assessment, often called progress monitoring. Typically, progress monitoring occurs at regular intervals, ranging from once a week to once every month ( Jenkins, Graff, & Miglioretti, 2009). Similar to the screening process, progress monitoring is most frequently done with CBM, which are characterized by (a) standard tasks; (b) standardized administration and scoring; and (c) multiple equivalent forms (Deno, 2003; Shinn, 1989). This attention to standardization in task, form, and scoring allows teachers to attribute changes in student performance to changes in underlying reading proficiency. Many of the same CBM used for screening can also be employed for progress monitoring, including: phoneme segmentation fluency nonsense word fluency word identification fluency oral reading fluency. Progress monitoring data can be used for multiple decisions with an RtI framework. First, progress monitoring data on individual students in Tier II and Tier III interventions can be used to determine the adequacy l

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of each student’s response to instruction. If, for example, one student in a Tier II intervention group is making less progress than other students in the group, it may be appropriate to provide her additional instruction, or move her to a Tier III intervention. Progress monitoring data plays a critical role in those decisions. Further, progress monitoring data can assist in maintaining homogeneity in intervention groups. By periodically evaluating progress monitoring data of students within groups, teachers, interventionists, and administrators can make more informed decisions about the most appropriate grouping arrangements. Finally, by evaluating group progress, teachers are able to determine the effectiveness of the reading interventions themselves. A lack of aggregate progress would indicate that the present reading intervention program, as delivered, may not be optimal. Such data provides more timely feedback on program effectiveness.

Decision-Making Although the assessment process seems to be straight forward, there are additional factors to consider particularly as they relate to children from disadvantaged backgrounds. In selecting measures to make decisions about which students will benefit from supplemental interventions, Tier II or Tier III, also consider the following: Were measures of both code-focused and meaning-focused reading skills used? Do they give adequate information about students’ level and rate of growth? Do they provide enough information about student needs in other areas that may be affecting progress? When assessing students’ response to instruction multiple data can help make the most appropriate determination. Of primary importance, of course, is the student’s current level and rate of response. Only students that are below level and responding at a lower rate should be considered for more intensive interventions. Before that decision is made, however, it is also necessary to gather specific information about the student and the instruction received. If the student is an English language learner, for example, it is important to consider her current English language proficiency, to determine if the results of assessment more accurately reflect limited English proficiency. Problems with vision and hearing should be ruled out. Further, it is important to gather information about the duration, frequency, and nature of the interventions that have been tried. How long has he been receiving supplemental instruction? How often does he l

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receive supplemental instruction? Have alternative interventions or combinations of interventions been tried? By evaluating student level and progress, student characteristics that may impact instructional response, and the intervention itself, we are allowed more confidence in the appropriateness of our decisions.

SUMMARY Learning to read is a specific phase in reading development that all children must pass through. Research has provided a wealth of data to inform practice. Attention to both the content of instruction and the delivery of instruction is necessary as is the use of assessment to guide instruction. In planning instruction that will meet students’ needs in a cohesive and systematic manner, ensure that there are common goals across the curriculum and contexts. Response to instruction provides a means for providing such instruction. When implemented with fidelity it includes: (1) high quality, research-based instruction; (2) data-based decision-making; (3) clearly articulated procedures; and (4) clearly articulated criteria for movement between more intensive tiers of instruction.

REFERENCES Adams, M. J. (1990). Beginning to read: Thinking and learning about print. Cambridge, MA: The MIT Press. Chard, D. J., Vaughn, S. R., & Tyler, B. J. (2002). A synthesis of research on fluency effective interventions for building fluency with elementary students with learning disabilities. Journal of Learning Disabilities, 35, 386–406. Compton, D. L., Fuchs, D., Fuchs, L. S., & Bryant, J. D. (2006). Selecting at-risk readers in first grade for early intervention: A two-year longitudinal study of decision rules and procedures. Journal of Educational Psychology, 98, 394–409. Deno, S. L. (1985). Curriculum-based measurement: The emerging alternative. Exceptional Children, 52, 219–232. Deno, S. L. (2003). Developments in curriculum-based measurement. The Journal of Special Education, 37(3), 184–192. Ehri, L. C. (1991). Development of the ability to read words. In R. Barr, M. L. Kamil, P. Mosenthal, & P. D. Pearson (Eds.), Handbook of reading research (pp. 383–417). New York: Longman. Fletcher, J. M., & Vaughn, S. R. (2009). Response to intervention: Preventing and remediating academic difficulties. Child Development Perspectives, 3(1), 30–37. Foorman, B. R., Fletcher, J. M., Frances, D. J., Carlson, C. D., Chen, D., Mouzaki, A., et al. (1998). Technical report: Texas primary reading inventory (1998 edition). Houston: Center for Academic and Reading Skill and University of Houston. Fuchs, D., Compton, D. L., Fuchs, L. S., Bryant, J., & Davis, G. N. (2008). Making “secondary intervention” work in a three tier responsiveness-to-intervention model: Findings from the first grade longitudinal reading study of the National Research Center on Learning Disabilities. Reading and Writing, 21, 431–436.

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Fuchs, L. S. (1986). Monitoring progress of mildly handicapped pupils: Review of current practice and research. Remedial and Special Education, 7(5), 5–12. Fuchs, L. S., Fuchs, D. J., Hosp, M. K., & Jenkins, J. R. (2001). Oral reading fluency as an indicator of reading competence: A theoretical, empirical, and historical analysis. Scientific Studies of Reading, 5(3), 239–256. Fuchs, L. S., Fuchs, D., & Compton, D. L. (2004). Monitoring early reading development in first grade: Word identification fluency versus nonsense word fluency. Exceptional Children, 71(1), 7–21. Gersten, R., Compton, D., Connor, C. M., Dimino, J., Santoro, L., Linan-Thompson, S., et al. (2008). Assisting students struggling with reading: Response to Intervention and multi-tier intervention for reading in the primary grades. A practice guide. (NCEE 2009-4045). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from . Goikoetxea, E. (2005). Levels of phonological awareness in preliterate and literate Spanishspeaking children. Reading and Writing, 18, 51–79. Good, R. H., & Kaminski, R. (2002). Nonsense word fluency: Dynamic indicators of basic early literacy skills (6th ed.). Eugene, OR: Institute for Development of Educational Achievement. Goswami, U. (2006). Orthography, phonology, and reading development: A cross-linguistic perspective. In R. M. Joshi & P. G. Aaron (Eds.), Handbook of orthography and literacy (pp. 463–480). Mahwah, New Jersey: Lawrence Erlbaum Associates. Graves, D. H. (1983). Writing: Teachers and children at work. Exeter, NH: Heinemann Educational Books. Jenkins, J. R., & Johnson, E. S. (2011). Universal screening for reading problems: When and how should we do this. Retrieved from RTI Network August 11, 2011 from: . Jenkins, J. R., Hudson, R. F., & Johnson, E. S. (2007). Screening for at-risk readers in a response to intervention framework. School Psychology Review, 36, 582–600. Jenkins, J. R., Graff, J. J., & Miglioretti, D. L. (2009). Estimating reading growth using intermittent CBM progress monitoring. Exceptional Children, 75(2), 151–163. Juel, C. (1991). Beginning Reading. In R. Barr, M. L. Kamil, P. Mosenthal, & P. D. Pearson (Eds.), Handbook of reading research (pp. 759–788). New York: Longman. LaBerge, D., & Samuels, J. (1974). Toward a theory of automatic information processing in reading. Cognitive Psychology, 6, 292–323. Liberman, I. Y., & Liberman, A. M. (1990). Whole language v. code emphasis: Underlying assumptions and their implications for reading instruction. Annals of Dyslexia, 40(1), 51–76. National Institute of Child Health and Human Development (2000), Report of the national reading panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. Washington, DC: U.S. Government Printing Office. (NIH Publication No. 00-4769) O’Connor, R. E., & Jenkins, J. R. (1999). The prediction of reading disabilities in kindergarten and first grade. Scientific Studies of Reading, 3, 159–197. Shinn, M. R. (1989). Curriculum-based measurement: Assessing special children. New York: Guilford. Snow, C. E., Burns, M. S., & Griffin, P. (1998). Preventing reading difficulties in young children. Washington, DC: National Academies Press. Snow, C. E., Griffin, P., & Burns, M. S. (2005). Knowledge to support the teaching of reading: Preparing teachers for a changing world. San Francisco, CA: Jossey-Bass Education. Speece, D. L., Mills, C., Ritchey, K. D., & Hillman, E. (2003). Initial evidence that letter fluency tasks are valid indicators of early reading skill. Special Education, 36, 223–233.

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Stanovich, K. E. (1986). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21(4), 360–407. Torgesen, J. K., Morgan, S. T., & Davis, C. (1992). Effects of a two types of phonological awareness training on word learning in kindergarten children. Journal of Educational Psychology, 84(3), 364–370. Vaughn, S., Cirino, P.T., Linan-Thompson, S., Mathes, P. G., Carlson, C. D., Cardenas-Hagan, E., et al. (2006). Effectiveness of a Spanish Intervention and an English Intervention for English Language Learners at Risk for Reading Problems. American Educational Research Journal, 43(3), 449–488. Vaughn, S., Linan-Thompson, S., Mathes, P. G., Cirino, P.T., Carlson, C. D., Pollard-Durodola, S. D., et al. (2006). Effectiveness of Spanish intervention for first-grade English language learners at risk for reading difficulties. Journal of Learning Disabilities, 39(1), 56–74. Whitehurst, G. J., & Lonigan, C. J. (1998). Child development and emergent literacy. Child Development, 68, 848–872.

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Highlights in Reading Comprehension Intervention Research for Students with Learning Disabilities Linda H. Mason1, and Jessica L. Hagaman2 1

The Pennsylvania State University, University Park, PA 16802, USA University of Wisconsin-Whitewater, WI 53190, USA

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Chapter Contents Introduction191 Reading Comprehension for Students with Learning Disabilities 192 Impact of Decoding and Fluency on Comprehension 193 Narrative Text 194 Narrative Text Interventions 195 Expository Text 199 Text Enhancements 200 Cognitive Strategy Instruction 203 Multicomponent Interventions 206 Peer-Assisted Intervention in the Inclusive Classroom 207 CSR208 PALS208 Discussion209 References210

INTRODUCTION In 1993, Durkin referred to reading comprehension as the “essence of reading.” Given that the underlying purpose of reading is to construct meaning from text, Durkin’s statement is even more applicable today; skills for constructing meaning from text are vital to success in the 21st century. As students progress through school, reading for comprehension becomes important not just in language arts classes, but also in content area classes such as science and social studies where information derived from text becomes a primary source of knowledge (Smagorinsky, 2001). Moreover, assessment data suggests that ability to read predicts academic Learning about Learning Disabilities

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achievement in core areas such as mathematics (e.g., The American College Testing Program, Inc.: ACT, 2006). However, the importance of reading for understanding is not limited to school. Proficient reading comprehension skills are becoming increasingly important beyond school and are required for success in society. Today’s youth, for example, are required to have more advanced literacy skills (i.e., advanced comprehension of text) than any other generation to keep up with the increasing demands of the workplace (Biancarosa & Snow, 2006). It has been predicted that almost 90% of high-wage jobs will require at least some postsecondary education (U.S. Department of Labor, 2006). Another estimate suggests that the 25 fastest growing professions, such as professional, business, education, and health services, have far-greater-than-average literacy demands, with roughly 50% of all job growth between 2004 and 2014 requiring high-level literacy skills (Barton, 2000). These predictions suggest that without proficient literacy skills, employment is limited. In addition to the increase in literacy demands in the workplace, students must acquire strong literacy skills to sort through the flood of information, such as web-based resources, that confronts them daily. This requires students to gather, comprehend, evaluate, and synthesize information from a wide range of narrative and expository media forms (e.g., print and nonprint; Council of Chief State School Officers [CCSSO, 2010]). For students to successfully navigate information, they must have the skills to differentiate between the various narrative and expository text types and be able to understand the different purposes for reading.

READING COMPREHENSION FOR STUDENTS WITH LEARNING DISABILITIES Reading comprehension is often a challenge for many students especially those with learning disabilities (LD) who may still be gaining reading skills such as decoding and fluency. In addition, for some students with LD, the struggle with reading comprehension continues, even with improved decoding and fluency skills. Generally speaking, students with LD may have difficulty comprehending what they read for several reasons. For example, students with LD often have difficulty attending to the meaning of text, relating what is being read to prior knowledge, making inferences, and identifying main ideas (Taylor, Alber, & Walker, 2002). Many students with LD lack the metacognitive skills required for comprehension and as a result fail to engage in strategic reading behaviors such as monitoring understanding as

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they read, and using strategies when comprehension breaks down (Torgesen, 1977). Moreover, students with LD frequently confuse different text structures (i.e., narrative, expository), which interferes with reading strategy selection and subsequent comprehension of material (Klingner, Vaughn, & Boardman, 2007). It is also important to note that the difficulties experienced by students with LD are not independent of one other. That is, there appears to be a multifaceted, reciprocal relationship between the underlying processes of comprehension that often result in complex reading problems (Graham & Bellert, 2004). To understanding these complex difficulties, it is important to consider the prerequisite reading skills of decoding and fluency.

IMPACT OF DECODING AND FLUENCY ON COMPREHENSION Students with LD are likely to experience difficulties with important foundational reading skills such as decoding and fluency. Difficulties with these skills interfere with students’ ability to effectively comprehend what they read. To successfully comprehend text, readers must identify letters and words; must process the meaning of those words; and integrate the meaning of individual words with the overall meaning of the sentence, paragraph, and text (Best, Floyd, & McNamara, 2008). Hence, reading requires the coordination of many processes such as word or text decoding, fluency, and vocabulary knowledge. Readers who cannot identify and decode words cannot focus the necessary attention on comprehension. This is because their cognitive resources are consumed by laborious efforts at decoding, leaving little for text comprehension. Conversely, readers’ automatic or effortless decoding of words frees up their cognitive resources to focus on constructing meaning of the text (Kuhn, 2005). It follows for the same reason that rate, speed, and accuracy in readers’ text reading also influence how much is understood (Chard,Vaughn, & Tyler, 2005). Students with LD often struggle to read fluently (Therrien, Wickstrom, & Jones, 2006). These difficulties may stem from problems in reading sight words or decoding words, highlighting the complex relationship among the composite skills that make up reading comprehension. Whatever the cause(s) of decoding problems may be for individual students with LD, their effortful decoding can negatively affect their motivation to read because so much time and effort is spent on decoding text that they do not see reading as an enjoyable task. Thus for students with LD, the negative impact of effortful decoding goes beyond reading comprehension.

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Understanding the possible underlying deficits that influence comprehension is important. Specifically, if students with LD lack a sufficient foundation in decoding and fluency, intervention planning should include skill building, as both decoding and fluency have an important role in students’ ability to comprehend text. In fact, studies have found that interventions targeted at improving a foundational reading skill and reading comprehension can have positive effects on both skills. For example, in a repeated reading for fluency experimental study (i.e., randomized control), 60 students in grades four through eight were taught how to generate questions while reading narrative text (Therrien et al., 2006). The researchers paired repeated reading with question generation in an eight-step intervention called “Reread-Adapt and Answer-Comprehend” (RAAC). Students were taught to answer five questions while reading: (1) Who is the main character? (2) Where and when did the story take place? (3) What did the main character do? (4) How did the story end? and (5) How did the main character feel? Following a four-month period of instruction, students performed significantly better than control on the DIBELS oral reading fluency test (Effect Size: ES = .89)1 and the WJ-III Broad Reading Scale (ES = .69). Another common reason that students with LD may struggle with reading comprehension, as noted previously, is because they fail to differentiate between different types of text (Englert & Thomas, 1987). As a result, they may not understand that texts may be read for different purposes, have different structures, and therefore require different strategies. Two of the most common types of text that students will encounter in schools are narrative and expository. This review of intervention research is framed within these two broad textual contexts.

NARRATIVE TEXT From an early age, students are exposed to narratives in various forms. Narratives, for example, can be presented aurally, through pictures, on television, or from text (Kremer, Lynch, van de Broek et al., 2008). The structure of narrative texts is often referred to as a “story grammar”. Story grammars contain similar elements and structure such as characters; setting (when and where); action or problem to be solved; a conclusion or resolution; and emotion (Short & Ryan, 1984). Understanding the structure of 1 

Effect sizes for group studies are considered to be small (.20), medium (.50), or large (.80) as suggested by Cohen (Huck, 2000).

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narrative texts helps the reader to make predictions, to organize information, to generate inferences (Trabasso & Wiley, 2005), and to construct a mental representation of the text (van den Broek et al., 2005), thus facilitating comprehension. Researchers suggest that students with LD may struggle with multiple aspects of narrative text. Specifically, students with LD may struggle to identify relevant information and theme in stories, and fail to identify important story components or structures such as setting and plot (Williams, 2005). Griffith and colleagues found that students with LD did not have a strong sense of story grammar when compared to their nondisabled peers (Griffith, Dastoli, Ripich, & Nwakanma, 1985; Griffith, Ripich, & Dastoli, 1986). In the two studies, students with LD recalled fewer details from simple stories than their nondisabled peers. In addition, when stories became more complex and included more detail, the recall of students with LD was much lower than their nondisabled peers, suggesting that students with LD struggle to attend to important information in narrative text.

Narrative Text Interventions Studies have shown that explicit instruction in underlying narrative text structure, or story grammars, can help students with LD improve comprehension (Gersten, Fuchs, Williams, & Baker, 2001). Explicit instruction is critical as it provides students a framework for organizing thinking and for generalizing understanding to new situations. The following sections describe research that has examined improving students’ narrative comprehension by applying story grammar to text that is read aloud to students, and by teaching story mapping and self-questioning strategies. Interventions that combine approaches for reading comprehension are also noted. Story Grammar for a Read Aloud Researchers have suggested that students may benefit from instruction in story grammar components, even before students are able to read a story on their own. Garner and Bochna (2004), for example, taught first graders to use a story structure strategy while listening to stories. Following instruction, students who were taught story grammar were able to recall more information from the story than students who were not taught story structure. In a quasi-experimental study for young struggling learners with and without disabilities, Stevens, Van Meter, and Warcholak (2010) trained

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four kindergarten (K) teachers three first grade teachers, two primary (K—second grade) self-contained special education class teachers, to teach story structure elements. Teachers read stories aloud while explicitly teaching elements of story structure (e.g., main character, setting). Results indicated that the intervention resulted in significant increases in comprehension with small effects, ES = .23 and .39 for free and prompted recall respectively. There were no significant differences for students of low and high ability or special education status. These findings suggest that inserting instruction in story grammar elements into common reading routines (e.g., reading a story aloud) can have positive effects on students’ ability to comprehend narrative text and should be considered for further evaluation for young students with LD who may be nonreaders. Story Mapping Story mapping, a well-studied intervention for students with LD, is a visual representation or graphic organizer, of the structure of narrative text. Generally, when teaching story mapping, students are asked to create an outline or detailed picture that includes main elements of a narrative text, creating a schema between what the reader knows and the text read. In two multiple-baseline studies, Idol (1987) and Idol and Croll (1987) investigated the effects of story mapping with five third and fourth grade, and five second through fifth grade students with LD. After using a model-lead-test approach with question prompts for teaching students to complete a story map (setting—characters, time, place; the problem; the goal; action; and the outcome) while reading, students significantly improved performance in answering comprehension questions. In both studies student performance on comprehension questions maintained after instruction and generalized to listening comprehension and standardized reading comprehension tests. Curriculum-based measures were also improved in Idol (1987); however, mixed with the two reading programs tested in Idol and Croll (1987). Spontaneous retelling also yielded mixed results (Idol & Croll, 1987); researchers noted that explicit instruction in story retelling and for specific reading programs, for some students with LD, is needed. Vallecorsa and DeBettencourt (1997) examined the effects of story mapping on reading comprehension performance with three middle school students with LD. Story mapping instruction included elements (characters; time; place/locale; the problem; the goal—starter event, actions/episodes, reaction; and outcome/ending) developed by Graham

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and MacArthur (1987) and question prompts developed by Idol (1987) and Idol and Croll (1987). Researchers in this study used oral and written retelling as the outcome measures. Significant gains in level and trend data for oral retelling were noted following story mapping instruction; however, the results for the written retell were mixed and only improved after explicit story writing instruction was provided. The importance of explicit instruction for each literacy area, reading and writing, was noted. Gardill and Jitendra (1999) used explicit instruction in advanced story map note sheets to help six middle school students identify information that was explicitly stated in passages, and to identify information inferred or unstated. Students addressed story components developed by Carnine, Silbert, and Kame’enui (1997) while reading: (1) Name the problem or conflict; (2) Identify the main characters and describe them; (3) Where did the story take place? (4) Tell how the characters try to solve the problem; (5) Is there an added twist or complication in the story? (6) Tell how the problem is or is not solved; and (7) What is the theme of the story? What is the author trying to say? Students were taught the strategy in pairs over a 14–20 week period. Following instruction, students were able to recall more story elements than were initially recalled in baseline, with a mean percent increase of 26%. In addition, increased generalization (as measured with a novel passage and oral story retells) was noted across all six participants. The authors, however, noted that identifying story themes was difficult and required additional instruction. In a later descriptive ABC (baseline, intervention, maintenance) study, six 3rd and 4th grade students with LD were taught to create story maps after reading narrative text (Boulineau, Fore, Hagan-Burke, & Burke, 2004). After five days of instruction in story mapping (setting/time; main character; episode(s)—problem, solution, outcome; reaction; theme) the authors reported that five of the six participants showed mean increases, from what was noted in baseline, in recall of character, setting, and time. As in Gardill and Jitendra (1999), narrative elements such as theme, outcome, and reaction/emotions were difficult for the students, and required additional instruction. Self-Questioning Interventions Self-questioning is the act of asking questions before, during, and after reading a text to facilitate understanding (Wong & Jones, 1982). Good readers do many of these things naturally when reading narrative text. For example, a good reader might read the title of a passage and make a

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prediction about what they will be reading. Struggling readers often approach reading passively. By explicitly teaching struggling readers how and when to self-question, reading comprehension can be enhanced. Taylor and colleagues (2002) compared story mapping and self-questioning with five students with LD in third through fifth grade. The authors reported that both story mapping and self-questioning resulted in higher levels of comprehension as measured by quizzes. They noted that, while not statistically significant, it appeared that students taught self-questioning performed higher than those taught the story mapping strategy. In an extensive program of research that spans narrative and expository text comprehension, Williams and colleagues (see Williams, 2005 for an overview of the program) developed a self-questioning intervention variation, Themes Instruction Program, targeting the complex task of theme. The approach, for narrative text, was tested in four randomized control studies: two studies with seventh and eighth grade students with LD (Williams, Brown, Silverstein, & DeCani, 1994), one study with middle students with LD (Wilder & Williams, 2001), and one study with elementary students (Williams, Lauer, Hall, Lord, Gugga, Bak et al., 2002). The intervention, which was scaffolded for student age and ability level, included: (1) discussion before reading; (2) story reading (reading aloud for younger students); (3) self-questions for discussing key elements; (4) story theme identification and generalization to a variety of situations; and (5) theme knowledge and story structure transfer to other story examples and/or real-life experience. Results were impressive with significant effects noted after instruction across grade levels and settings. Elementary students, for example, were able to successfully identify themes in stories they had not been exposed to during instruction with strong post instruction results, ES  =  .68 to 2.71. Although the authors note the challenges in teaching theme to students with LD (e.g., “both teachers and students felt that the program was excessively slow and redundant;” Williams et al., 1994, p. 219), the results of this line of inquiry indicate promise for this difficult area of reading comprehension. Combining Comprehension Interventions Several researchers have investigated the effects of combined interventions on student comprehension of narrative text. For example, in an experimental components analysis study with 47 fourth through sixth grade students with LD, Johnson, Graham, and Harris (1997) implemented a story grammar strategy intervention (Who, When, Where, What did the

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character(s) do? What happened next? How did the character(s) feel? How did the story end?). Strategy instruction alone was compared with strategy instruction combined with explicit instruction for student selfregulation for goal setting, self-instruction, or goal setting and selfinstruction. Results of a story retelling assessment indicated significant results pre- to post-test and pre- to maintenance-test for all groups; however, no additive effect for self-regulation procedures was noted for the students in this study. Researchers noted that explicit instruction for the story grammar strategy, in all conditions, was intense and extensively supported and scaffolded; therefore many components of self-regulation may have been implicitly imbedded. Faggella-Luby, Denton, and Deshler (2007) investigated the effects of teaching Embedded Story Structure (ESS) to a heterogeneous group of ninth grade students, 14 identified with LD. ESS instruction included instruction for three reading strategies: student self-questioning, storystructure analysis, and summarizing. Students randomly assigned to ESS instruction outperformed students assigned to comprehension skill instruction (CSI) on measures of strategy use and story structure knowledge. On a measure of unit reading comprehension after instruction, ESS students scored significantly higher (ES = .78) than students in CSI. Moreover, results indicated equivalent gains for ESS students regardless of disability versus nondisability category. In summary, the highlighted research for narrative text reading comprehension clearly indicates the benefits of explicit instruction for story grammar strategies across multiple measures of reading comprehension. Researchers noted for more complex tasks such as story retelling, theme development, and writing, additional and intense instruction is often required for students with LD.

EXPOSITORY TEXT Expository text differs substantially from narrative as it is written, often in an inconsistent style, to communicate truth or empirical facts, theories, and dates (Bakken & Whedon, 2002; Graesser, Leon, & Otero, 2002). Four characteristics—unfamiliar text structures, conceptual density, vocabulary complexity, and expectation of prior knowledge—make expository text challenging for students with LD (Saenz & Fuchs, 2002). Expository texts are written using unfamiliar structures such as lists, compare-contrast, time-sequence, procedural, problem-solution, classification, concept, and

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cause-effect or are written in a hybrid, mixed-text style (Mason & Hedin, 2012). Expository text is often conceptually dense with abstract logicalcausal relationships (Gertsen et al., 2001) and contains vocabulary that is more difficult than narrative vocabulary. In addition, expository text provides little opportunity for vocabulary development placing students with LD at a great disadvantage (Roberts, Torgesen, Boardman, & Scammacca, 2008). As noted previously for story grammar, exposure to expository material should begin early, for both emerging and struggling readers. Teachers should provide time for comprehension acquisition during a read-aloud and support students’ awareness of text features (Smolkin & Donovan, 2003). Fortunately for more proficient readers, researchers have documented the positive effects of expository reading comprehension interventions that address comprehension difficulties by enhancing text and by providing cognitive strategy instruction. Several thorough meta-analyses of expository text interventions have been published (Berkley, Scruggs, & Mastropieri, 2010; Dexter & Hughes, 2011; Gajria, Jitendra, Sood, & Sacks, 2007) and have been utilized in this review, when appropriate, for reporting effect sizes.

Text Enhancements Text enhancements include routines and practices that assist students in identifying, organizing, comprehending, and recalling key information. Mastropieri and Scruggs (1994) noted large comprehension effects (ES = .92) when text enhancement instruction was used for students with LD; however, caution that although text enhancements support students’ learning, students with LD need systematic instruction in how to use these supports. Research-based enhancement interventions for students with LD can be categorized as graphic organizers, mnemonics, and computer assisted instruction. Graphic Organizers Graphic organizers promote students learning by making learning and abstract concepts more concrete. In a recent meta-analysis of 55 group experimental studies, Dexter and Hughes (2011) noted five categories of graphic organizers: cognitive mapping, semantic mapping (SM), semantic features analysis (SFA), syntactic/semantic features analysis (SSFA), and visual display. Results of the meta-analysis yielded a large post-test effect for students with LD in factual comprehension, vocabulary, and written

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recall following graphic organizer instruction, ES  =  .91. Effects generally maintained over time, ES  =  .56, with the drop explained due to interventions of short range. Findings also indicated that students generalized instruction to near and far transfer tasks. Graphic organizers, as reviewed by Dexter and Hughes, were noted to have the strongest effects for science learning. Several key studies, reviewed next, illustrate effective graphic organizer instruction for students with LD. Cognitive Mapping A cognitive map assists students by linking ideas and relationships with cues such as lines, arrows, and spacial arrangements. Two experimental studies (Boyle, 1996, 2000) tested cognitive mapping (linking main ideas and details in study one with middle school students; a Venn diagram in study two with high school students) for improving students’ literal and inferential reading comprehension. Students receiving the cognitive mapping intervention outperformed control on literal comprehension in both studies with strong effects for literal comprehension and inferential comprehension. SM, SFA, and SFFA In seven experimental studies, Bos, Anders, and colleagues investigated the effects of SM, SFA, and SFFA instruction for middle and high school students with LD. SM includes arrangements of concepts that demonstrate the relationship of a superordinate concept with coordinate concepts, examples, functions, and/or characteristics and are generally created collaboratively by the teacher and the students (Bos & Anders, 1990, 1992). SFA is similar to SM with key information included in a matrix format with vocabulary for superordinate concepts listed at the top and coordinate concepts, examples, functions, and/or characteristics along the side of the chart (Bos, Anders, Filip, & Jaffee, 1989). SSFA extends SFA by replacing key vocabulary with cloze sentences in place of vocabulary (Bos & Anders 1992; Study 1 and 2). In 1990, Bos and Anders compared the effects of SM, SFA, SSFA, and definition instruction on vocabulary learning, reading comprehension, and written retells with 61 middle school students with LD. Results indicated that students in SM, SFA, and SSFA outperformed students in definition instruction at post-test, SSFA outperformed at maintenance on reading comprehension measures. Results for the vocabulary post-test indicated that SM and SFA were more effective than definition instruction; however, in maintenance testing the SM, SFA, and SSFA conditions outperformed the definition comparison. There were

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no significant differences for SM, SFA, or SSFA on any written retell measure; however, SFA and SSFA did have significant scores at maintenance when compared to definition instruction. There were no differences in SM, SFA, and SSFA during post-test or maintenance for reading comprehension, vocabulary, or written retell. Visual Display The use of visual display for graphically organizing expository text information has been tested in six experimental studies with students with LD in grades four through high school (Darch & Carnine, 1986; Darch, Carnine, & Kame’enui, 1986; Darch & Eaves, 1986; Datch & Gersten,1986; Griffin, Simmons, & Kame’enui, 1991; DiCecco & Gleason, 2002). In visual displays, concepts are clearly presented temporally as in timeline or life cycle, spatially as in a decision tree, sequentially, hierachally, or comparatively (Hughes, Maccini, & Gagnon, 2003). In the first four studies conducted by Darch and colleagues, students were provided a visual display organizer prior to reading and then completed the organizer by filling in information (e.g., writing main ideas) during reading and learning activities. In these studies, students demonstrated significant improvement, compared to the comparison treatment (basal reading approach, formal teacher presentation/teacher-led discussion, and text/teacher-led discussion), in comprehension as measured by free recall and/or cued comprehension tests. Interestingly, in the next study (Griffin et al., 1991), students receiving the visual display treatment performed comparably to students receiving the comparison treatment, “list of facts”. Griffin and colleagues noted that the geometric shapes and successive presentation in the graphic organizer may not have adequately communicated idea relationships or may have been visually distracting or confusing to students with LD, and caution that great care should be given when creating graphic organizers. Dicecco & Gleason (2002) used graphic organizers as an after reading activity; results of students’ written knowledge essays indicated significant differences favoring intervention students over a “no graphic organizer” control for relational knowledge statements. No differences were found for factual content knowledge. Mnemonics By enhancing student learning through mnemonic instruction, students with LD learn to improve retention of essential information in expository text. In four randomized control experiments, Mastropieri, Scruggs, and colleagues (Brigham, Scruggs, & Mastropieri, 1995; Mastropieri, Scruggs, &

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Levin, 1987; Scruggs, Mastropieri, McLoone, Levin, & Morrison, 1987), for example, found that mnemonic illustrations inserted into text, compared to inserted descriptive illustrations, significantly improved students’ text recall. Use of a keyword mnemonic method (i.e., using a familiar/keyword such as conquer to learn an unknown word such as conquistador) has also been noted to improve comprehension (Mastropieri, Scruggs, & Fulk, 1990). Results of meta-analysis indicate that mnemonic interventions have yielded a large overall effect (ES  =  1.62) for elementary and secondary students with LD, indicating the benefits of this intervention for students with LD (Scruggs & Mastropieri, 2000). Computer Assisted Instruction Multi-media technology and computers have been effectively used to enhance text in two studies. In an experimental study with 21 middle school social studies students, Okolo and Ferretti (1996) investigated the effects of integrating multi-media technology with project-based learning; a small effect (ES = .21) was obtained on a knowledge test, favoring students in the enhanced treatment condition. McArthur and Haynes (1995) evaluated the effects of a hypermedia format with embedded enhancements on science text comprehension of ten students with LD in grades nine and ten. Enhanced text versions included highlighted main ideas, visual questions related to various text sections, teacher comments and notes, and a glossary. All but one student demonstrated improved comprehension with medium effects (ES = .50).

Cognitive Strategy Instruction The most consistent positive gains in expository reading comprehension for students with LD follow explicit cognitive strategy instruction (CSI). For example, Gajria et al. (2007) found, in their meta-analysis of 15 cognitive strategy instruction studies, an aggregate effect size of 1.83. As will be described, CSI intervention may include a combination of strategies and procedures to teaching and supporting self-regulated learning. Main Idea Identification and Summarization Students with LD often miss the main ideas and important details when reading expository text (Wong, 1980) and do not consistently attempt to repair misunderstandings while reading (Carlisle, 1999). Reviewed research-based interventions for supporting remediation of these deficits include self-questioning, paraphrasing, summarization, and questionanswer relationships (QAR).

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Self-Questioning Wong and Jones’ (1982) experimental study and Wong, Wong, Perry, and Sawtsky’s (1986) single subject design study evaluated the effects of selfquestioning training for concept text reading with eighth and ninth grade students. Training included five steps: (a) Ask, “What are you studying this passage for?” (b) Find and underline the main idea; (c) Think of a question about the main idea; (d) Learn the answer to the question; and (e) Look back at questions and answers. Although small effects (ES  =  .49; Gajria et al., 2007), for the experimental study, were obtained on measures of reading comprehension, these early studies laid the groundwork for the use of selfquestioning in cognitive strategy development for students with LD. Paraphrasing Instruction for a main idea strategy (i.e., RAP: Read a paragraph, Ask yourself what the paragraph was about, Put the main idea and two details in your own words) was tested in three experimental studies with students with LD. The interventions included principles of direct instruction (e.g., teacher-led modeling, guided practice, and feedback). In each study, main idea strategy instruction was enhanced by including self-monitoring training (ES = 4.59 in Graves, 1986; ES = 2.55 in Graves & Levin, 1989), or repeated reading (ES  =  4.17 in Ellis & Graves 1990) resulting in strong gains (Gajria et al., 2007). In 2000, Jitendra, Hoppes, and Xin taught 33 middle school students (29 with LD) specific steps for identifying main ideas through selfquestioning. Lesson passages included a different text structure (e.g., single subject and action, cause and effect, time sequence, description or procedure). Students received scaffolded support for main idea strategy implementation and for self-monitoring with a prompt card (Does the paragraph tell: What or who the subject is? What action? Why something happened?). Significant gains with large effect sizes of 2.13 and 1.41 on multiple-choice and production tasks, respectively, were noted when student performance was compared to a traditional reading control condition. Summarization Summarization strategies are based on Brown and Day’s (1983) five-step approach: 1. Delete trivial information. 2. Delete redundant information. 3. Substitute super ordinate terms for a list of terms.

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4. Select a topic sentence. 5. Invent a topic sentence. In a study with sixth through ninth grade students with LD, Gajria and Salvia (1992) tested the effects of explicit instruction for summarization. After the intervention, students performed better on condensation and factual questions when compared to students in a no-treatment control group (ES = 4.45, Gajria et al., 2007). Performance for students with LD was comparable to average readers for factual questioning, but exceeded average readers for condensation questioning. In a study that compared summarization to summarization with selfmonitoring, Malone and Mastropieri (1992) found that both instructional approaches were equally effective in improving social studies text comprehension as measured by free-recall when compared to traditional instruction (ES  =  2.95; Gajria et al., 2007) however, only summarization with self-monitoring resulted in differential effects on the far transfer measure. Researchers noted that the complexity of the far transfer measure was better supported by the addition of self-monitoring. QAR The effects of question-answer-relationships (QAR) have been tested in one experimental study with students with LD in grades one to nine (Simmonds, 1992). Students were taught to answer three question types in social studies content: right there or literal; think and search or text implicit; and on my own or script implicit. When compared to students receiving traditional instruction, QAR students had stronger comprehension test results (ES = 1.57; Gajria et al., 2007). Text Structure Instruction Several researchers have explored the effects of teaching students to identify and use text structure in supporting expository text understanding. In an early study, Wong and Wilson (1984) demonstrated that when fifth- and sixth-grade students with LD were provided a strategy for reorganizing expository text, retelling was improved. Bakken, Mastropieri, and Scruggs (1997) found that text structure instruction improved comprehension of eighth-grade students with LD for three passage types: (a) main idea; (b) list; and (c) sequence of events. The intervention included text-structurebased booklets highlighting keywords and strategy clues in text. Results indicated large effect differences (ES = 2.27 to 3.29; Gajria et al., 2007), favoring the text structure intervention when compared to students in

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traditional instruction or paragraph restatement, in immediate, delayed, and transferred recall testing. In two studies, the effects of compare-contrast text structure, situated within the context of a theme scheme program (Williams, Hall, Lauer, Stafford, Desisto, & deCani, 2005) and embedded in a second grade science program (Williams et al., 2009), on comprehension has been evaluated. In both studies, students were explicitly taught to use: clue words, a graphic organizer, and a series of questions to cue to important information. Students’ post-instruction performance was compared to students’ performance in content instruction classrooms receiving no text structure instruction. Large effects (ES = 1.36 to 7.56) were obtained, for students with and without disabilities, across strategy, structure, and content measures. Williams et al. (2009) noted that findings support the use of explicit comprehension instruction for primary students and provide evidence that text structure training can lead to transfer, and can be accomplished without compromising content delivery.

Multicomponent Interventions Multicomponent reading comprehension training systems combine several evidence-based practices into one intervention package. These approaches present strategies for prior knowledge acquisition, comprehension monitoring, and summarization of information throughout reading (Baker, Gertsen, & Scanlon, 2002). Reciprocal Teaching procedures such as predict, question, clarify, and summarize (Palincsar & Brown, 1984), are generally implemented. Interventions evaluated with students with LD include Multipass, POSSE (Predict, Organize, Search, Summarize, Evaluate), and TWA (Think before reading, While reading, and After reading). Multipass During Multipass instruction students are taught to review (e.g., “survey, size up, and sort out”) chapter material (Schumaker, Deshler, Denton, Alley, Clark, & Warner, 1984). Each component includes multiple strategy procedures. During “survey,” for example, students are taught to attend to the organization of the chapter and main ideas, and then complete summaries. In “size-up,” chapter questions are answered through paraphrases. Finally, to “sort out” students self-test their understanding. Ten steps are included for teaching Multipass strategies: (a) pre-assessment; (b) strategy description; (c) teacher-led modeling; (d) strategy memorization; (e) guided practice with controlled materials; (f ) feedback; (g) progress assessment; (h) guided practice with grade-level materials; (i) feedback; ( j) progress assessment. Multipass

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was proven effective for improving content test scores in a single subject design study with eight high school students with LD. POSSE Five steps are included in POSSE instruction: 1. Predict, students are cued to use text signals (e.g., titles, headings, pictures, keywords) and brainstorm predictions. 2. Organize their ideas. 3. Search for the text structure. 4. Summarize the main ideas. 5. Evaluate understanding (Englert & Mariage, 1991). Graphic organizers to support main idea identification and summarization, with sentence stems (e.g., I predict that…) are included to promote strategy use (Mariage, 1995). Experimental research for POSSE small group instruction demonstrated significant positive effects on free recall (ES = 1.90; Gajria et al., 2007) for students with LD in fourth, fifth, and sixth grades. Lederer (2000) replicated these positive findings, measured by summary composition, with fourth and fifth grade students with LD (ES = .82; Gajria et al., 2007). TWA TWA, taught within the Self-Regulated Strategy Development (SRSD) model, has nine strategies in three phases of reading—before reading, while reading, and after reading (Mason, 2004). In the first phase, “Think before reading,” students identify text structure and develop statements and questions for what they know and want to learn. Steps in “While reading”— monitor speed, monitor understanding, and make connections - are taught next. The “After reading” phase includes main ideas and summarizing strategies. Students then orally retell what has been read and learned in the passage. TWA has been evaluated in eight studies for struggling students with and without LD; small to large effects have been found (ES  =  .46 to .94; Mason, in press). In two single case studies with fourth-grade and fifth-grade students with LD, similar positive effects were noted (Hedin, Mason, & Gaffney, 2011; Mason, Hickey Snyder, Sukhram, & Kedem, 2006).

PEER-ASSISTED INTERVENTION IN THE INCLUSIVE CLASSROOM Researchers have also investigated the effect of peer-assisted instruction to supplement reading comprehension in the inclusive classroom. Two

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established lines of research, Collaborative Strategic Reading (CSR) and Peer-assisted Learning Strategies (PALS) are described.

CSR Klingner and Vaughn (1996) developed CSR to improve informative comprehension skills of students with linguistically diverse learning needs. The intervention includes a mix of whole class instruction, followed by small group peer-assisted learning. CSR assists students in self-monitoring comprehension while implementing three strategy steps: “Click and Clunk, Preview, and Wrap up.” CSR was developed and has been evaluated in extensive case study research (Klingner & Vaughn, 1996, 1998, 1999, 2000). In two quasi-experimental studies (Klingner, Vaughn, Arguelles, Hughes, & Leftwich, 2004; Klingner, Vaughn, & Schumm, 1998), the effects of CSR were evaluated for social studies comprehension in 4th grade classrooms. Significant effects favoring CSR compared to the control classrooms were noted on reading comprehension measures (ES = .51 and .43 respectively). Recently, CSR was evaluated in a large experimental study (Hitchcock, Dimino, Kurki, Wilkins, & Gersten, 2010) without significant findings. Researchers suggested findings were limited due the use of convenience sampling, the lack of specificity of English Language Learner (ELL) status, and weak fidelity of implementation documentation. For CSR to be effective, it is critical that intensive training and coaching be provided to teachers.

PALS The PALS instructional approach, developed by Doug and Lynn Fuchs, supports reading fluency and comprehension by providing students with multiple opportunities for reading in the general education classroom. There are three versions of PALS: K-PALS, PALS for elementary students, and PALS for high school students (McMaster, Fuchs, & Fuchs, 2006a). In all PALS instruction high-performing students are paired with lowperforming students. Instruction includes: (1) partner reading with 2 min retell; (2) paragraph shrinking; and (3) prediction relay. In K-PALS instruction for kindergarten and first grade students, more teacher guidance through extended coaching, and activities for phonological awareness and for reading words and sentences are included. For high school students, PALS is extended by changing partners more frequently than is done with younger students, by adding a system for managing and spending PALS dollars for points earned, and by focusing on reading with expository text. PALS has been evaluated in over 15 years of pilot study, component

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analyzes, and experimental studies; and has been approved for inclusion in the National Diffusion Network on effective practices (McMaster, Fuchs, & Fuchs, 2006b). Researchers caution that although PALS is effective for a majority of students, approximately 20% of low-achievers and more than 50% of students with disabilities appear to be nonresponsive to the intervention (Al Otaiba & Fuchs, 2006; McMaster, Fuchs, Fuchs, & Compton, 2005). Researchers note supporting intervention fidelity through teacher training and support; consistent/frequent intervention scheduling; and progress monitoring (McMaster et al., 2006a; b).

DISCUSSION Despite increasing literacy demands in schools, recent reports from the National Center for Education Statistics (2009) suggest that reading comprehension remains a concern for our nation’s youths. For example, only 33% fourth, 34% eighth, and 38% twelfth grade students perform above proficiency in reading (National Center for Education Statistics, 2009). Reported results on reading achievement across grade levels have remained relatively stable, with no statistically significant growth over latest testing periods for students with disabilities. In other words, gaps in reading achievement for many students, including students with LD, persist. It is evident, from this current review of literature, that there are many available research-based strategies that can remediate reading comprehension deficits. However, barriers remain in getting research-based interventions into effective practice. Instruction, for example, should begin early, and not be withheld until students can read fluently. For those students who cannot decode fluently, basic reading skill instruction needs to be included in a language arts program, however, not at the expense of comprehension instruction. As noted previously, text enhancements and reading comprehension strategies must be explicitly taught and supported over a sufficient time period. It is also critical that teachers received adequate training and coaching to insure that interventions are implemented with a high degree of fidelity. In conclusion, reading comprehension intervention research remains to be completed. In a review of intervention research quality, Jitendra, Burgess, and Gajria (2011) found that group designed research generally met evidence standards; however, many group design studies conducted prior to 1997 and single subject studies did not meet evidence standards. Although reading comprehension interventions have been established as

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effective (Berkley et al., 2010; Dexter & Hughes, 2011; Gajria et al., 2007; Gersten et al., 2001), many studies have either demonstrated small effects or noted nonresponse. Future research should attend to quality indicators established in recent years ( Jitendra et al., 2011) and to establishing the effectiveness of interventions for all students with LD.

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Instructional Interventions for Students with Mathematics Learning Disabilities Margo A. Mastropieri1, Thomas E. Scruggs1, Clara Hauth1, and Dannette Allen-Bronaugh2 1

George Mason University, Fairfax,VA 22030-4444, USA James Madison University, Harrisonburg,VA 22807, USA

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Chapter Contents Introduction 217 Learning Disabilities and Mathematics Achievement 221 Effective Math Interventions 222 Behavioral Interventions on Computation 223 Early Behavioral Interventions: Improving Computation and Basic Skills 223 Metacognitive Instruction in Math 224 Metacognitive Instruction to Improve Computation 224 Use of Manipulatives to Enhance Concreteness and Metacognitive Training 225 Metacognitive Problem Solving with Visual Diagrams and Schemas 227 Algebra and Metacognition 229 Response to Intervention Research in Math 230 Tier 2 RtI Math Studies 231 What Are Evidence-Based Math Practices? 233 Discussion and Future Directions 235 References 236

INTRODUCTION Mathematics education is in the forefront of our national discussion on education reform. Recent laws mandate education accountability measures for all students in reading and mathematics (NCLB, 2001). The focus of both the No Child Left Behind Act of 2001 and the recent Elementary and Secondary Education Act: Blueprint for Reform (USDOE, 2010) incorporate similar requirements of strong academic performance by all students and accountability measures that reflect that performance (NCLB, 2001). The ESEA blueprint has an added emphasis on preparation for college and career training (USDOE, 2010). In addition, NCLB (2010) Learning about Learning Disabilities

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emphasizes the use of evidence-based practices and expresses the goal for K-12 teachers to use scientifically based research to help direct and guide instructional practices and interventions in the classroom. Another critical legislative piece is the Individuals with Disabilities Education Act of 2004, (IDEA, 2004), which mandates that students with disabilities have access to the same curriculum and are taught using best practices and high standards of learning. The National Council of Teachers of Mathematics (NCTM) also incorporates these same tenets of equal access for all students in their principles and standards in mathematics education. Mathematics instruction in the United States is guided by the National Council of Teachers of Mathematics (NCTM) which is a national organization for mathematics educators whose stated focus is “to ensure equitable mathematics learning of the highest quality for all students” (http://www.nctm.org). NCTM has identified six principles and ten standards for students in kindergarten through 12th grade to succeed in mathematics education (NCTM, 2000). The principles for school mathematics include equity, curriculum, teaching, learning, assessment and technology. These principles help to provide teachers, schools, and district and state administrators with the tools needed to guide educational decisions in mathematics for all students. Equity Principle: Excellence in mathematics education requires equity, high expectations and strong support for all students. Curriculum Principle: A curriculum is more important than a collection of activities: it must be coherent, focused on important mathematics, and well articulated across grades. Teaching Principle: Effective mathematics teaching requires understanding what students know and need to learn and then challenging and supporting them to learn well. Learning Principle: Students must learn mathematics with understanding, actively building new knowledge from experience and prior knowledge. Assessment Principle: Assessment should support the learning of important mathematics and furnish useful information to both teachers and students. Technology Principle: Technology is essential in teaching and learning mathematics; it influences the mathematics that is taught and enhances student learning (www.nctm.org/standards/content; NCTM, 2000). These principles engage educators in the overarching discussion regarding the design of curriculum for all students in K-12 mathematics education. l

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In addition, NCTM emphasizes the incorporation of content and process standards to address the actual concepts and instruction indicative of an excellent mathematics education. These standards are used throughout the United States as state and local school leaders plan and create programs of study for mathematics. The mathematics standards are delineated by content and process areas incorporating the skills, knowledge and understanding that students in grades preK-12 should attain in the educational system. The five content area standards include numbers and operations, algebra, geometry, measurement, and data analysis and probability. The five process standards include problem solving, reasoning and proof, communication, connection, and representation (NCTM, 2000). The content standards incorporate guidelines for curricula which are expanded upon throughout the four grade bands of prekindergartengrade 2, grades 3–5, grades 6–8 and grades 9–12. As an example for the number and operations standard, all students should have an understanding of number sense which may begin in preK programs as simple counting and ordering of natural numbers {1,2,3…} and which continues to develop across the grade bands to understanding the relationships between number systems {irrational, rational, integers…}. The content area standards are embedded in each level of mathematics instruction, scaffolding to more complex problem sets. Process area standards were developed to guide educators as they teach the content area in the classroom. The process area standards integrate critical thinking methodology by teaching math content via the processes of problem solving, reasoning and proof, communication, connections to the real world, and the use of alternate representational models. These areas are fostered by teachers as they introduce, teach and assess learners in the four grade bands. For example, with the problem solving standard, teachers are encouraged to teach with various problem solving techniques and to encourage a range of problem solving skills which may include diagrams, patterns, and manipulatives to discover content area concepts (NCTM, 2000). Educators must be cognizant of both content and process standards to fully develop curricula which will meet the needs of all learners. The NCTM provides a focus and direction for mathematics education, which is also validated by the recent National Math Advisory Panel (NMAP, 2008) government report (USDOE, 2008). The National Math Advisory Panel report emphasizes the need for consistent high standards in math education with an emphasis on equity for all students in

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mathematics. The report was commissioned by the U.S. government to address the growing concerns regarding our nation’s falling standings in mathematics achievement. Mathematics is a critical skill area for students as they graduate and enter into an increasingly global economy. They placed an increased emphasis on preparation for and instruction of algebra as a critical area for increasing American student achievement in mathematics for higher education and career readiness. Much like the NCTM standards, the NMAP findings place a strong emphasis on curriculum standards and learning processes for all students. The report also included recommendations, or areas of focus, for the future of mathematics education in the United States. The main findings and recommendations include an increased focus on (a) curricular content; (b) learning processes; (c) teachers and teacher education; (d) instructional practices; (e) instructional materials; (f) assessment; and (g) research policies and mechanisms to report findings (USDOE, 2008). Students with disabilities often have difficulties meeting the academic benchmarks outlined by the NMAP report and the NCTM content and process standards. These students also have difficulty passing high stakes standardized tests (Thurlow, Altman, Cormier, & Moen, 2008). To meet the needs and differentiated learning styles of students with disabilities, evidence-based practices (EBP) and intervention are the focus for special educators in the mathematics classroom (Gersten, Chard, Jayanthi, Baker, Morphy, & Flojo, 2008). The criteria for effective math instruction are, however, another area of noted importance as educators search for and implement strategies for the students they teach. The No Child Left Behind Act (NCLB, 2001) recommends that schools use evidence-based practices when educating all students. Section 6516 of public law 110 encourages schools to: …implement a comprehensive reform program that has been found through scientifically based research to scientifically improve the academic achievement of students participating in such program … or a program has been found to have strong evidence that such program will scientifically improve the academic achievement of students. (Public law 107–110)

Evidence-based practices and scientifically-based research are important to help direct and guide instructional practices as well as interventions in the classroom. Although the NCTM standards are presently exerting substantial influence on mathematics education reform in the United States, many

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special education professionals have expressed concern about the NCTM standards as applied to students with disabilities (e.g., Montague, 1996c; Rivera, 1997). These concerns are essentially based on fears that methods (e.g., “discovery,” “inquiry”) that require independent insight on the part of the learner will not be effective for students with disabilities, for whom insight or deductive inference can be relative weaknesses. However, historically special education professionals have perhaps overemphasized rote learning of facts and procedures, to the extent that students have had little opportunity to experience and practice mathematical reasoning (e.g., Cawley, Miller, & School, 1987).

LEARNING DISABILITIES AND MATHEMATICS ACHIEVEMENT Research has documented that students with learning disabilities can lag far behind other students in the area of mathematics. Previous and current research has consistently identified that students with learning disabilities score well below their typical peers on standardized math tests (e.g., Fuchs & Fuchs, 2003; Scruggs & Mastropieri, 1986; Parmar, Cawley, & Frazita, 1996). For example, McLeskey and Waldron (1990) reported that 64% of 906 students with learning disabilities in the state of Indiana from ages 5–19 were achieving below grade level in mathematics. Recent research refers to students with math learning difficulties (MLD) as those individuals who perform lower in math who may or may not have been identified as having learning disabilities ( Jordan, Hanich, & Kaplan, 2003). Geary (2004) estimated that between 5 and 8% of school children may have MLD. Findings across math studies conclude that students with MLD and LD frequently struggle learning math, including basic skills and more conceptually based problem solving (e.g., Fuchs & Fuchs, 2002; Geary, 1993; 2003; Swanson & Beebe-Frankenberger, 2004). Evidence also indicates that when children experience early difficulties in math, challenges may persist well into upper elementary grades (e.g., Jordan, Kaplan, Ramineni, & Locuniak, 2009). Researchers have identified potential difficulties many students with learning disabilities with math disabilities may exhibit (Montague, 2007; 1996a, b; see also Swanson & Jerman, 2006). Students with LD and MLD have difficulties with memory, working memory, memory for numbers, and number sense (e.g., Swanson & Sachse-Lee, 2001, Fuchs et al., 2005). Such challenges may differentially affect mathematics performance. Language

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and communication difficulties may also impact math performance when students are required to read and discuss math problems (e.g., Montague, 1996). Insufficient metacognitive strategies in solving math problems have also been related to students’ weaker math performance (e.g., Woodward & Montague, 2002). Finally, low motivation can impact individuals’ affect and attitude toward the content area of math (Montague, 1996). Given the potential learning challenges, what math intervention strategies have been shown to be effective with students with learning disabilities?

Effective Math Interventions Although math intervention research with students with learning disabilities has been conducted, there is considerably less research than in other areas such as literacy. For example, Mastropieri et al. (2009) reported that of all the research published in special education major journals over a 19-year period only 15.9% represented intervention research. Of that percentage 49% represented literacy research while only 15% were math intervention studies. Fortunately over the years reviews of research have synthesized much of that research. Earlier reviews (e.g., Jitendra & Xin, 1997; Mastropieri, Scruggs, & Shiah, 1991) and more recent reviews (e.g., Gersten et al., 2008; Kroesbergen, & Van Luit, 2003; Swanson & Jerman, 2006) have described effective research practices and discussed the importance of strong research designs for the provision of evidencebased math practices. The purpose of this chapter is to update previous reviews and research on mathematics performance of students with learning disabilities, in grades K-12 that have occurred since earlier research reviews. This review provides information on mathematics interventions for students with LD. The studies considered in this review fell broadly into behavioral interventions in computation and meta cognitive strategies for problem solving. Strategies for problem-solving research has focused on meta cognitive strategies instruction, self-regulation, schema-based strategies, and embedded number combination practice within tutoring for problem solving. The most efficacious studies appeared to include: explicit instruction, self-regulation including verbalization, visualization of strategies, concrete manipulatives, and multiple examples, including real world applications. Finally, future issues including evidence-based practices and use of Response to Intervention (RTI) to prevent long-term math difficulties are presented.

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BEHAVIORAL INTERVENTIONS ON COMPUTATION The vast majority of early research on mathematics with students with learning disabilities (LD) focused on methods for improving computation skills. The studies included methods for developing proficiency in basic numeration concepts, facts, and operations (addition, subtraction, multiplication, and division) with whole numbers, fractions, or decimals. A small selected sample of these is now described.

Early Behavioral Interventions: Improving Computation and Basic Skills Early research employed behavioral approaches, including reinforcement and direct instruction to improve computational skills. For example, Pavchinski, Evans, and Bostow (1989) described an intervention with a 12-year-old student with learning disabilities to increase basic reading and math skills. Using a changing criterion design and a token reinforcement system, the student was presented with Dolch sight words, and a second set of measures consisting of a list of 220 simple arithmetic problems. Tokens earned for meeting the target criterion could be exchanged for privileges. Similarly, Hastings, Raymond, and McLaughlin (1989) used task-analysis and direct instruction procedures and successfully trained seven students, including two with learning disabilities, to count money rapidly. Other researchers varied presentation formats for students with learning disabilities (e.g., Albers & Greer, 1991). For example, Cooke, Guzaukas, Pressley, and Kerr (1993) investigated the effects of interspersed drill and practice and found that more drill and practice facilitated recall. Koscinski and Gast (1993b) used a 4-second constant time delay procedure (correct response was provided four seconds after the stimulus presentation, see Koscinski & Gast, 1993a; Koscinski & Hoy, 1993) to teach multiplication facts. Houten (1993) compared rote drill with a rule learning procedure in learning subtraction. Several early studies examined the effects of reinforcement for improving math homework completion (e.g., O’Melia & Rosenberg, 1994; Patzelt, 1991). Studies typically concluded that behaviorally oriented intervention improved basic computation skills, although most were single subject designs conducted over relatively short intervals involving very few participants. Peer mediation during instruction was examined to determine whether the computational skills of students with LD would be improved.

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For example, Beirne-Smith (1991) investigated the effects of peer tutoring on acquisition of addition facts for elementary age students with LD. Hawkins et al. (1994) investigated the use of peer guided practice or independent practice during instructional pauses from lecture in arithmetic computation. Fuchs et al. (1996) investigated the quality and effectiveness of students’ (grades 2–4) mathematical explanations to 20 tutees with learning disabilities as a function of the ability of the tutor in mathematics. Their results indicated that high-achieving tutors were rated higher on conceptual and procedural characteristics as well as on overall quality, as compared with tutors of average ability. This study provided foundational guidance for later peer mediation research in math. Summary Early behavioral research provided information on the positive effects of a variety of behavioral techniques on the math computation performance of students with LD. These techniques have included presentation rate, drill and practice, direct instruction, cumulative review, reinforcement and behavioral contracting, commonly reported to be generally effective teaching strategies (e.g., Mastropieri & Scruggs, 2010). In addition, further information was provided on rule learning and peer mediation, including tutoring and cooperative homework teams. However, the early studies were typically short in duration, examined a single task (e.g., math facts), and failed to examine higher level math problem solving.

Metacognitive Instruction in Math Metacognitive interventions teach individuals the processes involved in learning by using verbal self-instructions, self-monitoring, self-evaluation and self-regulation as instructional components integrated within instruction (e.g., Bandura, Gusec, & Menlove, 1966; Meichenbaum, 1977). Early studies examined basic computation and time on task, but later studies were extended to include manipulatives, problem solving strategies, and higher level math using more complex instructional packages

Metacognitive Instruction to Improve Computation Earliest math research in LD examined self-instructions to improve time on task (e.g., Prater, Hogan, & Miller, 1992), which required students to check off their on-task or off-task behavior on hearing an audible tone. Other studies employed variations of metacognitive training to enhance computation skills. For example, Dunlap and Dunlap (1989) investigated the effects of error

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self-monitoring to improve subtraction with regrouping. Laird and Winton (1993) compared the effectiveness of three self-instructional checking procedures on math performance. Wood, Rosenberg, and Carran (1993) studied the effects of tape-recorded self-instruction cues to solve addition and subtraction problems and Shiah et al. reported success with embedded self-instruction into computer-assisted problem solving. One notable study, for example, by Kamann and Wong (1993) investigated the effectiveness of self-statement instruction to reduce math anxiety in students with LD when solving problems involving fractions. The trainer demonstrated a self-talk procedure, which consisted of positive, neutral, and negative statements. Oral and visual representations of poor self-talk, which affect performance, were presented to evoke awareness of students’ maladaptive styles of thinking. Next, coping strategies were introduced. The trainer modeled the strategies as outlined on two cue cards which described the steps in the coping process and the coping self-statements: situation assessment, identifying and controlling negative thoughts, coping thought, and reinforcement. The confronting/coping/controlling self-statements included the following: Don’t worry. Remember to use your plan. Take it step by step—look at one question at a time. Don’t let your eyes wander to other questions. Don’t think about what others are doing. Take it one step at a time. When you feel your fears coming on...take a deep breath, think, “I am doing just fine. Things are going well” (Kamann & Wong, 1993, p. 632).

The earlier interventions investigating metacognitive strategies to improve math computation skills of students with LD were largely effective, providing further evidence for successful training to improve performance in students with LD, but were relatively short in duration and taught only isolated skills. In the research presently reviewed, effective metacognitive training was implemented in the areas of self-monitoring of on-task behavior, error self-monitoring, making positive affective selfstatements, self-instructional checking procedures, self-instruction of calculation procedures, and math anxiety.

Use of Manipulatives to Enhance Concreteness and Metacognitive Training Use of manipulatives can aid conceptual understanding during math instruction (NCTM, 2000) and studies have documented that the effective use of manipulatives has enhanced the performance of students with LD (e.g., Marsh & Cooke, 1996). Miller and Mercer (1993a) examined

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an instructional sequence from concrete, using manipulatives, to abstract to determine the effectiveness of a sequence of concrete–semiconcreteabstract (CSA) instruction of math facts (see also Mercer & Miller, 1992). The teacher provided a verbal organizer, demonstrated the skill by thinkaloud and modeling, provided guided practice with prompts, cues, and feedback, and provided independent practice. The instruction sequences progressed from concrete, semiconcrete, to abstract, on each instructional level. The concrete level instruction employed manipulatives; illustrations were provided on the semiconcrete level; and on the abstract level, drawings were provided only when students could not recall the facts. The authors concluded that CSA was an effective means for teaching math computation. Miller and Mercer (1993b) replicated the effectiveness of a graduated word problem sequence strategy for teaching math problem solving. Each instructional level (concrete, semiconcrete, and abstract) contained four steps: (a) providing an advance organizer; (b) demonstrating and having students model skills; and (c) guided; and (d) independent practice with feedback. The language used in the word problems matched the manipulative objects in the concrete and semiconcrete levels. For example, if students were learning to subtract using cubes, the word “cubes” was used in the problem: 4 cubes −2 cubes ? cubes During the abstract level, the difficulty of word problems increased gradually from simple words, phases, and sentences, such as: 8 pieces of candy −8 pieces of candy sold ? are left (p. 172) to more elaborate sentences: Jennie had 4 pens She lost 2 of them She has _____ pens left (p. 172) to having the student created his/her own word problems. Post-test scores suggested that the intervention had been effective, although students predictably scored lowest in creation of their own problems.

In a follow-up study Harris, Miller, and Mercer (1995) investigated the effect of using a concrete-representational-abstract teaching sequence to improve multiplication skills in mainstream classrooms. Understanding of multiplication concepts was taught (lesson 1–3) to students by using

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concrete manipulatives with language parallel to that used in the problems. On the representational level (lesson 4–6), students were taught to use pictures of objects and tallies to solve problems. The mnemonic acronym DRAW was taught in the seventh lesson. Students were encouraged to use the DRAW (Discover the sign, Read the problem, Answer (or draw), check, and Write the answer) mnemonic to solve problems without using manipulatives, pictures, or tallies for the following three lessons. Word problems and increasing computation rate began at lesson 11. In this lesson, students were taught a mnemonic device, FAST DRAW, which combined the FAST (Find what you’re solving for, Ask yourself “What are the parts of the problem?”, Set up the numbers, and Tie down the sign) device and previous learned DRAW device, to set up and solve word problems. During lesson 12 to 21, students independently practiced word problems with or without extraneous information and filled in blank spaces to create their own word problems. Results indicated that students with disabilities substantially increased their math performance, although not to the level of the normally achieving students. In these studies instruction-using manipulatives was very explicit, systematic and included teacher instruction, guided practice and independent practice.

Metacognitive Problem Solving with Visual Diagrams and Schemas Problem solving has been the focus of many studies with students with LD that have included metacognitive training with self instruction, selfregulation, and the use of visual diagrams (e.g., Walker & Poteet, 1989– 1990). Jitendra and Hoff (1996) evaluated the effects of schema-based instruction for one-step addition and subtraction word problem solving. One third- and two fourth-grade students with learning disabilities whose difficulty involved using incorrect equations to solve word problems participated in this study. During the problem schemata phase, students learned how to recognize relations in the problem and to distinguish three types of problems (change, group, and compare). Students mapped features of situations onto the appropriate schemata diagrams after reading story situations. For example, in a change problem, the procedure involved reading the change word (e.g., verb) to determine whether an increase or decrease had occurred to the beginning amount. When the ending amount was determined to be more than the beginning amount, the word “total” was written under the ending amount on the schema diagram; otherwise the word “total” was written under the beginning

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amount. When the total and one of the other numbers was known, using subtraction was appropriate; when the total was unknown but the other two numbers were known, using addition was appropriate. Students successfully applied this strategy to solve addition and subtraction word problems. The results indicated that not only the overall percentage of correct word problems increased substantially over baseline, but also that at least some generalization was evident for all students. Students circled the keyword consistently, but were less likely to draw pictures. Students reported that they found the instruction beneficial. Jitendra et al. (1998) and Xin, Jitendra, and Deatline-Buchman (2005) followed up on this study with variations of problems and schematic diagrams and in a series of single subject designs (e.g., Jitendra et al., 1998; Jitendra et al., 2002). All findings replicated earlier results with one- and two-step word problems involving schematic instruction. In the Xin et al. study students were taught multiple schemata diagrams to solve multiple problem types. Then they were taught to read problems, identify which schema diagram and math procedure was required to solve the problem, write out the diagram, and solve the problem. The visual diagram and the specific explicit systematic instruction combined with self-monitoring checklists appeared to benefit students with LD. Montague and colleagues have also examined problem-solving strategies using metacognitive strategies and Montague’s Solve It materials. Montague (1992) assessed the effects of cognitive and metacognitive strategy instruction (CMSI) on mathematical problem solving. Strategy training consisted of demonstration, guided practice, and testing sessions. Students learned seven cognitive processes (read, paraphrase, visualize, hypothesize, estimate, compute, and check) and the initial letters (RPVHECC) by memory without being taught how to apply those processes. Metacognitive strategy training included self-instruction, self-questioning, and self-monitoring to monitor and control strategy usage. Students in the CMSI group learned to apply metacognitive activities to each cognitive process by using say, ask, and check activities without memorizing. Results indicated that the combination of cognitive and metacognitive strategies may be more effective than either cognitive or metacognitive strategies alone. Students improved their performance on mathematical problem solving. In a follow-up study Montague, Applegate, and Marquard (1993) evaluated the effectiveness of CMSI on 2- to 4-step math problem solving.

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One group received training on cognitive strategies for problem solving; a second group received metacognitive training; while a third group received a combination of cognitive and metacognitive strategy training. After seven days of training, the first two experimental groups received an additional five days of training in the complementary component of the instructional program, while the third group received an additional five days of cognitive and metacognitive instruction. Although there were no differences on immediate tests, delayed tests indicated a slight advantage for students who received the combination cognitive and metacognitive strategy training. Findings across Montague’s studies demonstrated that when students were taught how to proceed, to ask themselves questions while solving the problems, and to monitor by checking responses throughout the process they were more successful.

Algebra and Metacognition Algebra has also gained some research attention (e.g., Lang, Mastropieri, Scruggs, & Porter, 2004; Witzel, 2005; Witzel, Mercer, & Miller, 2003). In one of the first studies, Hutchinson (1993) investigated the effects of cognitive strategy instruction on algebra problem solving. Twenty adolescent students with learning disabilities were randomly assigned to either the instructional or comparison condition. Instructional condition students were provided with cognitive strategy training for representing and solving algebra word problems. Three types of word problems (relational, proportional, and two-variable) with each surface structure or story line (work, age, distance, money, and number) were used through the study. Each student in the instructional condition met with the instructor individually on alternate days for a period of four months. Students in the comparison condition received an equivalent amount of instruction in the resource room. Students in the instructional condition were taught to apply the following self-questions for representing word problems: 1. Have I read and understood each sentence: Are there any words whose meaning I have to ask? 2. Have I got the whole picture, a representation, for this problem? 3. Have I written down my representation on the worksheet? (goal; unknown(s); known(s); type of problem; equation). 4. What should I look for in a new problem to see if it is the same kind of problem? (Hutchinson, 1993, p. 39).

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For solving algebra word problems, students were taught to ask themselves the following: 1. Have I written an equation? 2. Have I expanded the terms? 3. Have I written out the steps of my solution on the worksheet? (collected like terms; isolated unknown(s); solved for unknown(s); checked my answer with the goal; highlighted my answer). 4. What should I look for in a new problem to see if it is the same kind of problem? (Hutchinson, 1993, p. 39). Students were also provided with a structured worksheet to assist with organizing, representing and solving problems. Although individual students varied on the amount of material mastered, post-tests, transfer, and maintenance tests all demonstrated very substantial gains relative to those of students in the comparison condition. Summary Successful findings to date have added to the accumulating problem solving research using meta cognitive strategies and real world problems (e.g., Bottge, Rueda, LaRoque, Serlin, & Kwon, 2007; Bottge, Rueda, Grant, Stevens & Laroque, 2010). Teaching fact families, and providing schema, diagrams, visualizations, and training in multiple-step cognitive strategies proved to be very effective in increasing the problem-solving skills of students with learning disabilities in areas of arithmetic and algebra. Effective research shares common instructional features including: explicit instruction, teaching systematically, embedding metacognitive instructions such as self-regulation, self-instructions or self-monitoring strategies, providing sufficient opportunities for guided and independent practice, and a wide range of examples to help encourage generalized learning of the concepts and principles. One area of recent investigation in math with MLD students has been in the Response to Intervention (RtI) arena.

RESPONSE TO INTERVENTION RESEARCH IN MATH Response to Intervention (RtI) is a multi-tiered prevention of failure delivery system designed including general screening for all and increasing intensity tiers of instruction that begin in the general education inclusive classroom with Tier 1. All students are screened in the general class while the first instructional tier is intended to provide evidence-based math

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(or other content area) instruction to all students. Students who fail to make adequate progress in Tier 1 are moved into Tier 2 instruction, which is frequently smaller group intensified instruction. If students fail to make adequate progress in Tier 2, they are moved to Tier 3, which may be special education in some models (e.g., Fuchs, Fuchs, & Compton, 2012). Since students are assessed periodically to determine movement within the tier system, the model is referred to as RtI. Recent research in math using RtI has been conducted and is described below, because although samples are not directly identified as LD, students are MLD and or at risk for MLD and LD in math. Note throughout that the “packages” of intervention in this research appear to contain explicit instruction, use of manipulatives or sequencing of instruction from concrete, semiconcrete to abstract or visuals or diagrams, multiple opportunities for practice, and embedded meta cognitive strategies, all of which appear to be highly related to efficacious practices.

Tier 2 RtI Math Studies Fuchs and colleagues (Fuchs et al., 2005) have investigated Tier 2 math instruction designed to improve learning and understanding of word problem solving and computation. Sample interventions are multifaceted, include explicit instruction, with modeling, guided and independent practice, on schema-based problem solving involving concrete to semi concrete to abstract representations of content. Students are taught to set up and solve various word problems, and to check and label responses. Counting strategies using fingers to assist in computation are also taught. Peer tutoring is employed during the process as a component of the math lesson and reinforcement with students earning tokens is a component. In a representative study, students in first grade were provided regular math instruction plus 40-minute interventions consisting of 30 minutes of intensive small group instruction using a concrete representation to abstract instructional sequence with peer tutoring and 10 minutes working individually with computers on a math facts program. The tutoring component often emphasized number sense and operation activities. In a third grade study students were taught RUN to solve word problems where R = read math problems, U = underline the question, and N = name the problem type. Findings have been generally positive for students in experimental conditions across first and third graders (see Fuchs et al., 2002; Fuchs et al., 2004; Fuchs et al., 2010a, b), with less

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consistent findings on fluency measures at the first grade and on standardized math measures at the third grade. Similarly, Bryant, Bryant and colleagues have investigated the effects of Tier 2 early numeracy training programs for first graders with math difficulties (Bryant et al., 2008a, b; 2011). The first study examined the effects of early numeracy intervention covering number concepts, and operations, basic facts, and place value four days a week in 15-minute sessions over 18 weeks. However, no significant differences were obtained which the authors speculated may be due to an insufficient duration and intensity of the intervention (Bryant et al., 2008a). In a follow-up study the intervention was increased in intensity and duration and significant differences were obtained for the Tier 2 instruction (Bryant et al., 2008b). In a third investigation, additional details were provided on the interventions, which were split between two 10-minute sessions. Lessons included warm-up fluency activities and activities designed to teach conceptual knowledge using concrete manipulatives and visual representations and to learning meta cognitive strategies such as count on, and fact families to improve efficiency in solving problems more efficiently. All lessons were taught using explicit instruction using modeling, guided practice, and independent practice, progress monitoring and multiple opportunities for practice. These findings were mixed with positive effects on progress monitoring measures but not on problem-solving measures. Summary Research in RtI math has promise and is beginning to demonstrate some effects for providing more intensive early preventative math instruction to at risk learners. The studies reviewed here all employ rigorous research designs and implement what most have described as components of efficacious practice for teaching math to students with LD, including explicit instruction, use of concrete manipulatives and a teaching sequence beginning with concrete and moving towards abstract as students gain understanding, multiple practice opportunities, including a range of examples to build generalization, and metacognitive components including selfinstruction, self-regulation, self-monitoring. These preliminary findings are based on research conducted with first and third graders and the field waits for replications and for extensions to the upper grade levels. Moreover, more Tier 1 and Tier 3 research is needed to provide additional guidance to the field and high quality research is needed that meets evidenced-based standards.

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WHAT ARE EVIDENCE-BASED MATH PRACTICES? NCLB (2001) emphasized the importance of teachers using evidence-based practices to guide instructional practice and interventions within the K-12 setting. In addition there is an increased emphasis on teacher accountability for the progress of the students within their classrooms. New programs and curricula inundate teachers; however the programs are not always evidence based. An unanswered question is where do teachers go to find evidence-based practices to use within their classroom? Most teachers are familiar with What Works Clearing House (http://ies.ed.gov/ncee/wwc/), and may utilize this resource to locate evidence-based practices to use in their classroom. In order to facilitate the investigation of evidence-based practices in mathematics, the Institute of Education Sciences (IES), Council for Exceptional Children (CEC) and National Council for Mathematics (NCTM) websites were searched to identify each organization’s guidelines for evidence-based practices. After exploring these organizational websites, the criteria for evidence-based practices outlined by IES were utilized. The following four factors: randomized controlled trial or quasi-experimental design, statistical significance, a significant effect size to show that the strategy worked, and consistency in findings across studies, are required by IES in order for a study to be awarded a positive rating of effectiveness. The What Works Clearing House was utilized to see if any math curriculum received a positive ranking based on the IES factors. In addition to the curriculum reviews, articles listed on the What Works Clearing House website that investigated math were examined to determine whether any of them included students with disabilities. It was found that IES reviewed 361 studies, and from these, only four met IES standards for evidence-based practices in middle school math for core comprehensive math curricula (http://ies. ed.gov/ncee/wwc/reports/middle_math/topic/, 2007). Of noted importance, of these four studies, none of them involved students with disabilities. Since the studies listed on the IES website did not demonstrate adequate resources for special educators, further investigation into CEC’s website was warranted. Like IES, CEC recognizes experimental and quasi-experimental studies as the gold standard, however they include criteria for single subject research to their category of research-based practice. In order to obtain a ranking of an evidence-based practice, CEC’s criteria include the following: a. experimental and quasi-experimental research must have a minimum of four acceptable quality studies or two high quality studies (Gersten et al., 2005, p. 152) with

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b. a significance level of .05. In order for a single subject research study to receive a research-based practice rating by CEC there must be: a. a minimum of five studies that meet quality indicators and document experimental control (Horner et al., 2005, p 14) b. by a minimum of three different researchers across a minimum of c. three different locations, with at least d. twenty total participants between these studies. A report by NCTM (2000) calls for enhancing the rigorous math standards for all students. NCTM states that all rigorous mathematics curricula should include the following five content areas: numbers and operations, algebra, geometry, measurement, and data analysis and probability. In addition, NCTM also recommends that the following five process standards: problem solving, reasoning and proof, communication, connections, and representations (Miller & Hudson, 2007) should be integrated within each of the content areas. Moreover, a literature search using ERIC and PsycInfo was conducted using the keywords: evidence-based practices, school mathematics, and special education to locate articles written about evidence-based practices and middle school mathematics (Allen-Bronaugh & Hauth, 2010). This search revealed limited curricular materials that have been researched and found to be evidence based for middle school mathematics. This is significant, because many times teachers go to professional development or trainings that focus on specific curriculum to use in order to teach mathematics to their students; however most of these materials lack research to determine if they are evidence based. More readily available are instructional practices teachers can incorporate into their teaching that have been studied and found to be evidence based. These practices include using visual and graphic depiction (Artus & Dyrek, 1989), systematic and explicit instruction (Xin, Jitendra, & Deatline-Buchman, 2005), student think-alouds (Shunk & Cox, 1986), structured peer assisted learning (Bahr & Rieth, 1991), and range and sequence instruction (Witzel, Mercer, & Miller, 2003). It is evident from this search that the need persists for continued research and dissemination of evidence-based practices and remains a critical area in special education. IES, CEC and NCTM are organizations that are available for teachers to utilize in order to stay up to date on evidencebased practices. These websites are continually changing in the digital era and educators need to frequently check these websites as well as professional journals to access new information.

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DISCUSSION AND FUTURE DIRECTIONS A review of research on mathematics instruction involving students with learning disabilities revealed a variety of behavioral, cognitive, and metacognitive approaches, which have been found to be effective in improving both mathematical computation and mathematical problem solving in students with LD. Most metacognitive instruction has been combined with very explicit instructional strategies during which students are taught, and then provided ample opportunities of practice. Research in metacognitive strategy training has revealed the effectiveness of such training across an expanding area of tasks, as well as types of training. Of particular interest in recent years is the research on explicit problem solving, including the expansion to higher-level math including algebra and research on affectively oriented self-instruction. This research has extended our knowledge of the potential breadth of metacognitive training, with respect to types of intervention as well as content area. Similar to earlier reviews (Mastropieri, Scruggs, & Shiah, 1991), a number of investigations in the present review were concerned with calculation performance. More recently instructional packages have included practice on number combinations within instructional packages that also contained problem solving (see RtI section). This finding appears to contrast strongly with the expressed views of the National Research Council (1989) and the National Council of Teachers of Mathematics (1989), who have repeatedly argued against the emphasis on computation over conceptual development. However, it has been clearly demonstrated that students with LD and MLD frequently exhibit persistent difficulties mastering basic number facts and computational skills (Geary, 2004), as well as in simple verbal problem solving (Lerner & Johns, 2012). Early word problem-solving interventions involved relatively simple and straightforward problems of the sort typically found in math workbooks (for an exception see Hutchinson, 1993). Such problems do not generally correspond to the NCTM (1989) emphasis on “word problems of varying structures” (p. 20), such as problems that require analysis of the unknown, problems that provide insufficient or incorrect data, problems that can be solved in more than one way, or that have more than one correct answer (see Baroody, 1987; Parmar & Cawley, 1996). However, more recent research with students with LD and MLD and in the areas of RtI appears to meet more recent NCTM (2000) standards. These research packages are multifaceted and integrate metacognitive problem-solving

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strategies, including computational practice and strategies, concrete to abstract teaching sequences using manipulatives, explicit instruction and multiple practice opportunities that use a range of exemplars to facilitate generalized learning (e.g., Bryant et al., 2011). Nevertheless, finding evidence-based practices in math remains challenging for teachers. Currently few studies meet established criteria by the What Works Clearing House which places teachers in the awkward position of searching for math programs. This is especially true for teachers of students with LD and MLD. Future research efforts would do well to conduct rigorous studies which could yield more available evidence-based practices and programs for students with LD and MLD. Overall, it can be stated that research in mathematics education for students with LD and MLD is progressing steadily. Future researchers and practitioners will be able to benefit greatly from the insights gained from the present research and look forward to more research in the future.

REFERENCES Albers, A. E., & Greer, R. D. (1991). Is the three-term contingency trial a predictor of effective instruction? Journal of Behavioral Education, 1, 337–354. doi:10.1007/BF00947188. Allen-Bronaugh, D., & Hauth, C. (2010, April). Putting your finger on it: Finding relevant evidence-based practices in middle school mathematics. Paper presented at the annual meeting of the Council for Exceptional Children Conference, Nashville. Artus, L. M., & Dyrek, M. (1989). The effects of multiple strategy intervention on achievement in mathematics. Chicago: Saint Xavier College. Unpublished master’s thesis. Bahr, C. M., & Rieth, H. J. (1991). Effects of cooperative, competitive, and individualistic goals on student achievement using computer-based drill-and practice. Journal of Special Education Technology, 11, 33–48. Baroody, A. J. (1987). Children’s mathematical thinking. New York: Teachers College Press. Beirne-Smith, (1991). Peer tutoring in arithmetic for children with learning disabilities. Exceptional Children, 57, 330–337. Bottge, B. A., Rueda, E., LaRoque, P.T., Serlin, R. C., & Kwon, J. (2007). Integrating reformoriented math instruction in special education settings. Learning Disabilities Research & Practice, 22, 96–109. Bottge, B. A., Rueda, E., Grant, T. S., Stephens, A. C., & LaRoque, P. T. (2010). Anchoring problem-solving and computation instructions in context-rich instruction in contextrich learning environments. Exceptional Children, 76, 417–437. Bryant, D. P., Bryant, B. R., Gersten, R., Scammacca, N., & Chavez, M. (2008). Mathematics intervention for first- and second-grade students with mathematics difficulties: The effects of Tier 2 intervention delivered as booster lessons. Remedial and Special Education, 29, 20–32. doi:10.1177/0741932507309712. Bryant, D. P., Bryant, B. R., Gersten, R., Scammacca, N., Funk, C., Winter, A., et al. (2008). The effects of Tier 2 intervention on first-grade mathematics performance of first-grade students who are at risk for mathematics difficulties. Learning Disability Quarterly, 31, 47–63. doi:10.2307/20528817.

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Bryant, D. P., Bryant, B. R., Roberts, G., Vaughn, S., Pfannenstiel, K. H., Porterfield, J., et al. (2011). Early numeracy intervention for first grade students with math difficulties. Exceptional Children, 78, 7–23. Cawley, J. F., Miller, J., & School, B. A. (1987). A brief inquiry of arithmetic word-problemsolving among learning disabled secondary students. Learning Disabilities Focus, 2, 87–93. Cooke, N. L., Guzaukas, R., Pressley, J. S., & Kerr, K (1993). Effects of using a ratio of new items to review items during drill and practice: Three experiments. Education and Treatment of Children, 16, 213–234. Dunlap, L. K., & Dunlap, G. (1989). A self-monitoring package for teaching subtraction with regrouping to students with learning disabilities. Journal of Applied Behavior Analysis, 22, 309–314. doi:10.1177/0145445508317132. Fuchs, D., Fuchs, L. S., & Compton, D. L. (2012). Smart RtI: A next-generation approach to multi-level prevention Exceptional Children, 78, 263–279. Fuchs, L. S., Compton, D. L., Fuchs, D., Paulsen, K., Bryant, J. D., & Hamlett, C. L. (2005). The prevention, identification, and cognitive determinants of math difficulty. Journal of Educational Psychology, 97, 493–513. Fuchs, L. S., & Fuchs, D. (2003). Enhancing the mathematical problem solving of students with mathematics disabilities. In H. L. Swanson, K. R. Harris, & S. E. Graham (Eds.), Handbook on learning disabilities (pp. 306–322). New York: Guilford. Fuchs, L. S., Fuchs, D., Hamlett, C. L., & Appleton, A. C. (2002). Explicitly teaching for transfer: Effects on the mathematical problem-solving performance of students with mathematics disabilities. Learning Disabilities Research and Practice, 17, 90–106. Fuchs, L. S., Fuchs, D., & Prentice, K. (2004). Responsiveness to mathematical problemsolving instruction: Comparing students at risk of mathematics disability with and without risk of reading disability. Journal of Learning Disabilities, 37, 293–306. doi:10.1002/9780470373699.speced1091. Fuchs, L. S, Powell, S. R., Seethaler, P. M., Cirino, P. T., Flewtcher, J. M., Fuchs, D., et al. (2010). Remediating number combination and word problems deficits among students with math difficulties: A randomized Control trial. Journal of Educational Psychology, 101, 561–576. Fuchs, L. S., Fuchs, D., Powell, S. R., Seethaler, P. M., Cirino, P. T., & Fletcher, J. M. (2008). Intensive intervention for students with mathematics disabilities: Seven principles of effective practice. Learning Disability Quarterly, 31, 854–856. Fuchs, L. S, Powell, S. R., Seethaler, P. M., Cirino, P. T., Flewtcher, J. M., Fuchs, D., et al. (2010). The effects of strategic counting instruction, with and without deliberate practice, on number combination skill among students with math difficulties. Learning and Individual Differences, 20, 89–100. Fuchs, L. S., Fuchs, D., Karns, K., Hamlett, C. L., Dutka, S., & Katzaroff, M. (1996). The relation between student ability and the quality and effectiveness of explanations. American Educational Research Journal, 33, 631–664. Geary, D. C. (1993). Mathematical disabilities: Cognitive, neuropsychological, and genetic components. Psychological Bulletin, 114, 345–362. Geary, D. C. (2003). Learning disabilities in arithmetic: Problem solving differences and cognitive deficits. In K. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 199–212). New York: Guilford. Geary, D. C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37, 4–15. Gersten, R., Chard, D., Jayanthi, M., Baker, S., Morphy, P., & Flojo, J. (2008). Mathematics instruction for students with learning disabilities or difficulty learning mathematics: A synthesis of the intervention research. Portsmouth, NH: Research Corporation, Center on Instruction. Harris, C. A., Miller, S. P., & Mercer, C. D. (1995). Teaching initial multiplication skills to students with disabilities in general education classrooms. Learning Disabilities Research & Practice, 10, 180–195.

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Hastings, F. L., Raymond, G., & McLaughlin, T. F. (1989). Speed counting money: The use of direct instruction to train learning disabled and mentally retarded adolescents to count money efficiently. Journal of Special Education, 13, 137–146. doi:10.1111/j.1467-8578.1996.tb0096x. Hawkins, J., Brady, M. P., Hamilton, R., Williams, R. E., & Taylor, R. D. (1994). The effects of independent and peer guided practice during instructional pauses on the academic performance of students with mild handicaps. Education and Treatment of Children, 17, 1–28. Houten, R. V. (1993). Rote vs. rules: A comparison of two teaching and correction strategies for teaching basic subtraction facts. Education and Treatment of Children, 16, 147–159. Hutchinson, N. L. (1993). Effects of cognitive strategy instruction on algebra problem solving of adolescents with learning disabilities. Learning Disability Quarterly, 16, 34–63. Individuals with Disabilities Education Act of 2004. Pub. L. No. 108-446. Jitendra, A. K., & Hoff, K. (1996). The effects of schema-based instruction on the mathematical word-problem-solving performance of students with learning disabilities. Journal of Learning Disabilities, 29, 422–431. Jitendra, A. K., & Xin, Y. P. (1997). Mathematical word-problem-solving instruction for students with mild disabilities and students at risk for math failure: A research synthesis. Journal of Special Education, 30, 412–438. doi:10.1177/105345129403000205. Jitendra, A. K., Griffin, C. C., McGoey, K., Gardill, M. G., Bhat, P., & Riley, T. (1998). Effects of mathematical word problem solving by students at risk or with mild disabilities. The Journal of Educational Research, 91, 345–355. doi: 10.111/j.1540-5826.2005.00135.x Jordan, N. C., Hanich, L. B., & Kaplan, D. (2003). A longitudinal study of mathematical competencies in children with specific mathematics difficulties versus children with comorbid mathematics and reading difficulties. Child Development, 74, 834–850. Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (2009). Early math matters: Kindergarten number competence and later mathematics outcomes. Developmental Psychology, 45(3), 850–867. Kamann, M. P., & Wong, B.Y. L. (1993). Inducing adaptive coping self-statements in children with learning disabilities through self-instruction training. Journal of Learning Disabilities, 26, 630–638. doi:10.1177/002221949602900410. Koscinski, S.T., & Gast, D. L. (1993). Computer-assisted instruction with constant time delay to teach multiplication facts to students with learning disabilities. Learning Disabilities Research & Practice, 8, 157–168. Koscinski, S. T., & Gast, D. L. (1993). Use of constant time delay in teaching multiplication facts to students with learning disabilities. Journal of Learning Disabilities, 26(567), 533–544. doi:10.1177/002221949302600807. Koscinski, S. T., & Hoy, C. (1993). Teaching multiplication facts to students with learning disabilities: The promise of constant time delay procedures. Learning Disabilities Research & Practice, 8, 260–263. Kroesbergen, E. H., & Van Luit, J. E. H. (2003). Mathematics interventions for children with special educational needs: A meta-analysis. Remedial and Special Education, 24, 97–114. Laird, J. L., & Winton, A. S. W. (1993). A comparison of self-instructional checking procedures for remediating mathematical deficits. Journal of Behavioral Education, 3, 143–164. Lang, C, Mastropieri, M. A., Scruggs, T. E., & Porter, M. (2004). The effects of self-instructional strategies on problem solving in algebra for students with special needs. In T. E. Scruggs & M. A. Mastropieri (Eds.), Research in secondary schools: Advances in learning and behavioral disabilities (Vol. 17, pp. 29–54). Oxford, UK: Elsevier Science/JAI Press. Lerner, J. W., & Johns, B. (2012). Learning disabilities and related mild disabilities: Teaching strategies and new directions (12th ed.). Belmont, CA: Wadsworth Cenage Learning.

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Marsh, L. G., & Cooke, N. L. (1996). The effects of using manipulatives in teaching math problem solving to students with learning disabilities. Learning Disabilities Research & Practice, 11, 58–65. Mastropieri, M. A., & Scruggs, T. E. (2010). The inclusive classroom: Strategies for effective instruction (4th ed.). Upper Saddle River, NJ: Prentice Hall. Mastropieri, M. A., Berkeley, S., McDuffie, K., Graff, H., Marshak, L., Conners, N., et al. (2009). What is published in the field of special education? An analysis of 11 prominent journals. Exceptional Children, 76, 95–109. Mastropieri, M. A., Scruggs, T. E., & Shiah, S. (1991). Mathematics instruction with learning disabled students: A review of research. Learning Disabilities Research & Practice, 6, 89–98. McLeskey, J., & Waldron, N. L. (1990). The identification and characteristics of students with learning disabilities in Indiana. Learning Disabilities Research, 5, 72–78. Meichenbaum, D. (1977). Cognitive-behavior modification: An integrative approach. New York: Plenum. Mercer, C. D., Jordan, L., & Miller, S. P. (1996). Constructivistic math instruction for diverse learners. Learning Disabilities Research & Practice, 11, 147–156. Miller, S. P., & Mercer, C. D. (1997). Educational aspects of mathematics disabilities. Journal of Learning Disabilities, 30, 47–56. doi:10.1177/002221949703000104. Miller, S. P., & Mercer, C. D. (1993). Using data to learn about concrete-semiconcreteabstract instruction for students with math disabilities. Learning Disabilities Research & Practice, 8, 89–96. Miller, S. P., & Mercer, C. D. (1993). Using a graduated word problem sequence to promote problem-solving skills. Learning Disabilities Research & Practice, 8, 169–174. Montague, M. (1992). The effects of cognitive and metacognitive strategy instruction on the mathematical problem solving of middle school students with learning disabilities. Journal of Learning Disabilities, 25, 230–248. doi:10.1177/002221949202500404. Montague, M. (1996a). Assessing mathematical problem solving. Learning Disabilities Research & Practice, 11, 238–248. Montague, M. (1996b). Student perception, mathematical problem solving, and learning disabilities. Remedial and Special Education, 18, 46–53. doi:10.1177/074193259701800108. Montague, M. (1996c). What does the “New View” of school mathematics mean for students with mild disabilities? In M. C. Pugach & C. L. Warger (Eds.), Curriculum trends, special education, and reform: Refocusing the conversation (pp. 84–93). New York: Teachers College Press. Montague, M. (2007). Self-regulation and mathematics instruction. Learning Disabilities Research & Practice, 22, 75–83. doi:10.1111/j.1540-5826.2007.00232.x. Montague, M., Applegate, B., & Marquard, K. (1993). Cognitive strategy instruction and mathematical problem-solving performance of students with learning disabilities. Learning Disabilities Research & Practice, 8, 223–232. National Council of Teachers of Mathematics, (1989). Curriculum and evaluation standards for school mathematics. Reston, VA: Author. (ERIC Document Reproduction Service No. ED 304 336) National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author. National Mathematics Advisory Panel, (2008). Foundations for success: The final report of the national mathematics advisory panel. Washington, DC: U.S. Department of Education. No Child Left Behind Act. Reauthorization of the Elementary and Secondary Education Act. Pub. L. 107-110, § 2102(4) (2001). National Research Council, (1989). Everybody counts: A report to the nation on the future of mathematics education. Washington, DC: National Academy Press.

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O’Melia, M. C., & Rosenberg, M. S. (1994). Effects of cooperative homework teams on the acquisition of mathematics skills by secondary students with mild disabilities. Exceptional Children, 60, 538–548. Parmar, R. S., Cawley, J. F., & Frazita, R. R. (1996). Word problem-solving by students with and without mild disabilities. Exceptional Children, 62, 415–429. Patzelt, K. E. (1991). Increasing homework completion through positive reinforcement. East Lansing, MI: National Center for Research on Teacher Training. (ERIC Document Reproduction Service No. ED 343 306) Pavchinski, P., Evans, J. H., & Bostow, D. E. (1989). Increasing word recognition and math ability in a severely learning-disabled student with token reinforcers. Psychology in the Schools, 26, 397–411. doi:10.1002/1520-6807(198910)26:4 3.0.CO;2-8. Prater, M. A., Hogan, S., & Miller, S. R. (1992). Using self-monitoring to improve on-task behavior and academic skills of an adolescent with mild handicaps across special and regular education settings. Education and Treatment of Children, 15, 43–55. Rivera, D. (1997). Mathematics education and students with learning disabilities. Journal of Learning Disabilities, 30, 2–19. doi:10.1177/002221949703000101. Schema-based instruction on word problem solving performance of students with learning disabilities. East Lansing, MI: National Center for Research on Teacher Training. (ERIC Document Reproduction Service No. ED 381 990) Scruggs, T. E., & Mastropieri, M. A. (1986). Academic characteristics of behaviorally disordered and learning disabled children. Behavioral Disorders, 11, 184–190. Shiah, R. L., Mastropieri, M. A., Scruggs, T. E., & Fulk, B. J. M. (1994–1995). The effects of computer assisted instruction on the mathematical problem solving of students with learning disabilities. Exceptionality, 5, 131–161. doi:10.1207/s15327035ex0503_2. Swanson, H. L., & Beebe-Frankenberger, M. (2004). The relationship between working memory and mathematical problem solving in children at risk and not at risk for serious math difficulty. Journal of Educational Psychology, 96, 471–491. doi:10.1037/0022-0663.96.3.471. Swanson, H. L., & Jerman, O. (2006). Math disabilities: A selective meta-analysis of the literature. Review of Educational Research, 76, 249–274. doi:10.3102/00346543076002249. Swanson, H. L., & Sachse-Lee, (2001). Mathematical problem solving and working memory in children with learning disabilities: Both executive and phonological processes are important. Journal of Experimental Psychology, 79(3), 294–322. doi:10.1006/ jecp.2000.2587. Thurlow, M. L., Altman, J. R., Cormier, D., & Moen, R. (2008). Annual performance reports: 2005-2006 state assessment data. Minneapolis, MN: University of Minnesota, National Center on Educational Outcomes. Retrieved from U.S. Department of Education, Office of Planning, Evaluation and Policy Development. (2010). ESEA blueprint for reform. Washington, DC. Retrieved from . Witzel, B. S. (2005). Using CRA to teach algebra to students with math difficulties in inclusive settings. Learning Disabilities: A Contemporary Journal, 3(2), 49–60. doi:10.1177/1053451208314734. Witzel, B. S., Mercer, C. D., & Miller, M. D. (2003). Teaching algebra to students with learning difficulties: An investigation of an explicit instruction model. Learning Disabilities Research & Practice, 18, 121–131. doi:10.1111/1540-5826.00068. Wood, D. A., Rosenberg, M. S., & Carran, D. T. (1993). The effects of tape-recorded selfinstruction cues on the mathematics performance of students with learning disabilities. Journal of Learning Disabilities, 26(269), 250–258.

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CHAPTER

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Writing Instruction Charles A. MacArthur1, Zoi Philippakos1, Steve Graham2, and Karen Harris2 1

University of Delaware, Newark, DE 19716, USA Arizona State University, Tempe, AZ 85069-3151, USA

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Chapter Contents Introduction  243 Goals of Writing Instruction   244 Writing Tasks   244 Writing Processes   245 Writing to Learn   246 Basic Skills and Conventions   247 General Recommendations for Writing Instruction   248 Provide Time for Writing   248 Create a Supportive Social Environment   249 Integrate Writing and Reading   249 Writing Instruction for Students with LD and other Struggling Writers   250 Explicit, Systematic Instruction in Self-Regulated Strategies   251 Explicit, Systematic Instruction in Basic Skills   257 Application of Technology in Writing Instruction   260 Computer Support for Planning Processes   265 Concluding Comments   266 References  267

INTRODUCTION Writing is a complex cognitive and social process. Proficient writing requires awareness of the audience and purpose for particular tasks, knowledge of content, effective strategies for planning and revising, critical reading, language ability, motivation, and self-regulation, as well as fluent text production. Even for skilled adults, writing is challenging, and most students find writing difficult. The most recent National Assessment of Educational Progress (Salahu-Din, Persky, & Miller, 2008) reported that only 33% of eighth-grade, and 24% of twelfth-grade students were proficient. Among special education students at eighth grade, only 6% scored proficient,

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and 46% were below basic. Students with learning disabilities (LD) perform more poorly than their normally achieving peers on all aspects of writing. They have less knowledge about writing, fewer skills with language, greater difficulties with spelling and handwriting, and less effective strategies for writing (Troia, 2006). The purpose of this chapter is to review research relevant to the writing needs of students with LD and struggling writers and to provide specific instructional recommendations to educators. We begin with a discussion of the goals of writing instruction for all students and classrooms. Next, we provide general recommendations for a sound program of writing instruction for all elementary and secondary students. The largest part of the chapter focuses on instructional approaches that have been studied intensively with students with LD and other struggling writers. We focus on three instructional approaches: instruction in self-regulated strategies for planning and revising; instruction in the basic skills of handwriting, spelling, and sentence writing; and assistive technology for writing. Throughout the chapter, whenever possible, recommendations are based on empirically validated procedures. However, we have not limited our discussion just to evidence-based practices. Promising practices are emphasized as well.

GOALS OF WRITING INSTRUCTION A reasonable place to begin a discussion of writing instruction is with the desired outcomes. What do students need to know and be able to do in order to be successful in later education and in the workplace? Desired outcomes include knowledge about writing tasks, proficiency with cognitive strategies and processes, ability to use writing to support learning, and skills with writing conventions and fluent text production.

Writing Tasks Students need to learn to write for a wide range of audiences and purposes in a variety of forms. The Common Core State Standards (2010) organize writing outcomes according to three tasks with associated communicative purposes that are common to writing in school and the workplace: arguments to persuade; informative/explanatory texts to convey information clearly; and narratives to convey experience, real or imagined. The Writing Framework for the National Assessment of Educational Progress (National Assessment Governing Board, 2007) specifies the same three writing purposes: to persuade, to

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explain, and to convey experience. Within these broad purposes, there are many specific purposes and forms of writing that differ in text structure, content, language, tone, style, and other characteristics. For example, personal narratives, fables, adventure stories, and mysteries are all narratives with characters and problems, but they differ in content, structure, language, and style. For older students, science reports, literary analyses, and interpretations of historical events all seek to explain, but have dramatically different conventions for content, organization, and style. If the writing curriculum in a school focuses on a relatively narrow range of writing tasks, such as the five-paragraph essay, then students may not be prepared for the wide variety of writing tasks they will encounter in later schooling and employment. One way to broaden the range of writing tasks is to teach writing in all content-area classes, so that students learn some of the purposes and conventions of writing in the disciplines (Shanahan & Shanahan, 2008). Knowledge of genres develops gradually over the school years (Donovan & Smolkin, 2006). Cognitive studies (Hayes & Flower, 1980; Torrance & Galbraith, 2006) have found that expert writers have considerable knowledge of common text structures and genres, which they use in generating and organizing content. In contrast, struggling writers have limited knowledge of and sensitivity to such text structures (Englert & Thomas, 1987).

Writing Processes In addition to knowledge about types of writing, students need to acquire strategies for planning and revising. Proficient writers use a range of strategies to manage the complex processes of planning and revising. In general, younger and less skilled writers engage in far less planning than older and more proficient writers. One key difference between proficient and novice writers is that proficient writers engage in task analysis and goal setting during planning (Bereiter & Scardamalia, 1987; Hayes & Flower, 1980). Writers begin with general tasks but need to set sub-goals to direct their planning. In the process, they analyze the task, using knowledge about audience, purpose, and content. Experimental studies of goal setting have shown that directing students to set goals for planning an argument (Ferretti, MacArthur, & Dowdy, 2000) or for revising an argument to consider audience (Midgette, Haria, & MacArthur, 2008) can improve the quality of their writing. In addition to goal setting, proficient writers engage in a variety of planning strategies to generate and organize content, including brainstorming, outlining, mapping, free-writing, self-questioning, and using

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text structure. Recent reviews of research on cognitive strategy instruction show that teaching planning strategies, especially strategies based on text structure with mnemonics or graphic organizers as scaffolds, can have substantial effects on the organization and overall quality of written products (Graham, 2006; Graham & Perin, 2007). Proficient writers also differ from less skilled writers in revising processes. Expert writers evaluate their texts throughout the writing process and revise to improve meaning, organization, language, and conventions, whereas, novice writers typically revise primarily for mechanical problems (Fitzgerald, 1987). Revision is dependent on critical reading and self-evaluation processes. Prompting writers to ask evaluative questions, directly teaching evaluation criteria, and instruction in peer review all have been shown to support increased revision and improved writing quality (MacArthur, 2012). Students also need to learn self-regulation strategies. Self-regulation is important in all areas of academic performance, but it is especially so in writing because of its complexity and challenge. Use of strategies clearly helps writers to self-regulate the writing process, and self-regulation has been recognized as a critical aspect of strategy development since the 1980s (Brown & Palincsar, 1982). In addition to writing strategies, specific self-regulation processes play an important role in proficient writing (Graham & Harris, 2005; Schunk & Zimmerman, 2007). Several types of self-regulation strategies have been studied with regard to writing, including self-monitoring, self-instructions, goal setting, self-reinforcement, and management of time and environment. Further discussion of strategies and self-regulation is included in a later section on strategy instruction.

Writing to Learn One of the main purposes of writing in school settings is to support learning. Teachers ask students to summarize reading assignments, analyze texts or learning activities, take essay exams, and write research papers, all with the purpose of enhancing content learning. Research reviews show that writing activities do enhance content learning (Bangert-Drowns, Hurley, & Wilkinson, 2004). Furthermore, writing about texts that have been read supports enhanced reading comprehension (Graham & Hebert, 2010). Graham and Hebert found moderate to strong effects on reading comprehension from responding to a text in writing (e.g., personal reactions, analysis), summarizing texts, writing notes, and answering questions about a text in writing. Writing about texts one has read involves

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substantial skills that go beyond writing activities based on personal experience or existing knowledge. For example, summarizing requires comprehension of the main ideas in a text and skill in paraphrasing those ideas in one’s own words, and responding to a text requires comprehension plus some critical analysis. This reprocessing of information enhances understanding of the content of the reading and increases future ability to read that type of text. Learning to write a research paper requires many additional skills, such as searching for information, selecting and evaluating the information, taking notes, integrating information from multiple sources, and developing a main idea or thesis that integrates the information effectively. A solid writing instruction program should provide ample opportunities for students to write about what they read in the service of learning.

Basic Skills and Conventions Issues of grammar and writing conventions are critical to any discussion of struggling writers. Berninger and Swanson (1994) modified Hayes and Flower’s (1980) model of writing, elaborating the empty “translation” component to include sentence production and transcription components. Transcription factors, including handwriting, spelling, typing, and punctuation, have a significant impact on writing quality for young writers (Graham, Berninger, Abbott, Abbott, & Whitaker, 1997) and older struggling writers. Graham et al. (1997) found that handwriting fluency and spelling accounted for 25 to 42% of variance in writing quality for elementary school children, and there is evidence that typing fluency similarly affects quality for secondary students (Christensen, 2004) and primary students (Connelly, Gee, & Walsh, 2007). Many struggling writers have difficulties with handwriting that interfere with fluent writing and affect motivation and writing quality. In addition, ability to write correct Standard English is important in its own right because errors can interfere with understanding and can affect the judgments of others about one’s ability. Students with LD and other struggling writers, of course, make far more errors than proficient writers. Unfortunately, problems with grammar can be persistent and difficult to remediate. Some difficulties stem from the fact that written text is more complex than oral text, so students need to learn more complex sentence structures for writing than they use in speaking. In addition, students who speak nonstandard English and non-native speakers have additional sources of difficulty with the complexities of English syntax. Despite the difficulties, the ability to write using correct and varied sentences and the ability

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to produce text with reasonable fluency via handwriting and typing are important outcomes of writing instruction.

GENERAL RECOMMENDATIONS FOR WRITING INSTRUCTION Before considering the special needs of students with LD and other struggling writers, we would like to present briefly some recommendations about writing instruction for all students. The first step in preventing writing problems is to provide a solid general writing curriculum based on research, where available, and recommendations of expert practitioners. The elements of effective writing instruction will vary by grade, but some key principles remain the same. From the goals discussed above, it follows more or less directly that a sound writing curriculum should include: Instruction in writing for a wide range of audiences and purposes in a variety of forms. Instruction in the writing processes involved in planning, drafting, and revising as well as strategies for self-regulation of the overall process. Instruction in writing that is integrated with learning in content areas. Instruction in the basic skills required for fluent text production and use of standard English. In addition, we would like to make three other general recommendations for a sound writing curriculum: Provide ample time for writing. Create a supportive social environment. Integrate writing and reading. l

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Provide Time for Writing One essential element of an effective writing instruction program is adequate time for students to write. They need time to learn and practice the basic skills required. They need time to learn strategies for planning, drafting, and revising. They need time to learn about the various purposes that writing can serve. They need time to learn to evaluate their writing. And they need time to develop motivation and confidence in their ability. Teachers should establish regular routines for writing instruction and practice that include daily opportunities for students to practice writing. In elementary school classes, teachers should set aside time devoted specifically to writing plus arrange opportunities to write across the school day as part of instruction in reading and the content areas. Students should

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spend time during the school day writing in class as well as writing for homework. In-class writing is important because it provides opportunities for teachers to observe and evaluate students’ writing processes and difficulties as well as time to assist individual students. At the secondary level, writing assignments may be divided across many content area classes, which requires coordination by grade-level teams of teachers to ensure that students are receiving adequate time and instruction.

Create a Supportive Social Environment Teachers should establish a supportive environment for writing in which students form a community of writers. Writing is a social process as well as a cognitive process. To learn writing as a meaningful process of communication with others, student writers need response from readers. In a supportive environment, students feel connected to each other and the teacher, and they feel safe to share their ideas and writing. They can then learn to revise and improve their writing by giving and receiving feedback and revising their writing. Publication of their writing for peers enhances motivation and emphasizes the purpose of writing. The community should not be limited to peers in the classroom. Teachers should provide opportunities to write for a variety of audiences beyond the classroom, which can expand students’ sense of audience. Opportunities to share writing with other classes or external audiences have always been available and have increased with new forms of communication via the Internet (MacArthur & KarchmerKlein, 2010). All of these social aspects of writing are emphasized in process approaches to writing, which have research support (Graham & Perin, 2007).

Integrate Writing and Reading Reading and writing are often taught as separate subjects at all levels from first grade through college, but it makes more sense to integrate them. Reading and writing are related from multiple perspectives (Tierney & Shanahan, 1996). From a social perspective, reading and writing are reciprocal processes. Writers need responses from readers, and readers need to learn to think about the authors of the books they read. From a cognitive perspective, reading and writing draw on much of the same knowledge about print and text structure. From a pragmatic perspective, in most real world and academic settings, except for writing class itself, people rarely write without reading. One way to integrate reading and writing is to give students opportunities to read many examples of a type of text before asking them to write

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that type. When elementary school children are asked to write fictional stories, or subgenres of story like fables, they have usually had considerable experience reading such stories. However, students of all ages have had less experience reading the types of informative and persuasive text they are asked to write. For example, high school students often have difficulty writing persuasive essays, as shown by performance on the NAEP (Saludin et al., 2008). Perhaps, one explanation is that they are seldom asked to read persuasive essays in their classes. With proper experience, even young children can learn to write persuasively. For example, to introduce persuasive writing to second-grade students, McCraw (2011) had children read numerous book reviews for the authentic purpose of choosing books for read-aloud lessons. Then children were guided to analyze the claims and evidence in several reviews before they finally wrote their own book reviews. We think it is good practice to give students at all ages the opportunity to read and analyze model papers before attempting to write their own. Reading and writing are also integrated when students write about the texts they have read. Students write summaries of reading passages, analyze the readings, take essay exams on what they learned from reading, and read sources for research papers. All of these types of integrated reading and writing instruction require careful instruction and practice. Content area classes provide many opportunities to teach these skills. Students generally see writing to learn as a meaningful purpose for writing. As noted earlier, research indicates that writing about reading enhances reading comprehension (Graham & Hebert, 2010), writing skill, and content learning (Bangert-Drowns, Hurley, & Wilkinson, 2004).

WRITING INSTRUCTION FOR STUDENTS WITH LD AND OTHER STRUGGLING WRITERS In the remainder of this chapter, we focus on aspects of instruction that have been studied with students with LD in attempts to find instructional methods that meet the special needs of these struggling writers. We focus on three topics: instruction in self-regulated strategies for planning and revising; instruction in the basic skills of handwriting, spelling, and sentence writing; and assistive technology for writing. All students need instruction in strategies for planning and revising, but students with LD can benefit from more explicit systematic instruction. All students need to master basic skills, but students with LD often find these skills particularly difficult and need specialized instruction. Finally, all students need to learn

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to use technology for writing, but special technological tools can be of particular benefit to struggling writers.

Explicit, Systematic Instruction in Self-Regulated Strategies As noted earlier, all students need to develop strategies for planning, drafting, and revising. Younger and less skilled writers engage in far less planning and revising than older and more proficient writers. Process approaches teach writing as a process that involves planning, drafting, and revising and aim to help students learn to engage in those processes. However, students with LD may need quite explicit and systematic instruction to master strategies well enough to use them independently. Most of the research on strategies instruction in writing has been done by researchers interested in students with LD or other struggling writers (e.g., Deshler & Schumaker, 1986; Englert, Raphael, Anderson, Anthony, & Stevens, 1991; Graham & Harris, 1993; Wong, Hoskins, Jai, Ellis, & Watson, 2008) though the research has demonstrated positive effects for students of all ability levels (Graham, 2006). A recent meta-analysis of strategy instruction in writing (Graham, 2006) found large and consistent effects on both the organization of written texts and their overall quality. Effects were found for narrative, persuasive, and comparison writing; for planning and revising; and for elementary and secondary students. In addition, effects were found for students with LD, low achieving students, and typically achieving students, though considerably more research focused on students with LD. A review of all experimental research on writing instruction for students in grades 4 to 12 (Graham & Perin, 2007) also reported large effects for strategy instruction in writing. Both reviews also found that strategy instruction was more effective when it included self-regulation strategies. One well known program of strategy instruction is the Cognitive Strategies Instruction in Writing (CSIW) program developed by Englert and her colleagues (Englert et al., 1991). In CSIW, the writing process is divided into strategies for planning, organizing, writing, editing, and revising (with the mnemonic POWER). The strategies are supported by “think sheets” that guide students through the process. For example, the planning think sheet asks students to consider audience and purpose, to brainstorm content, and to begin to group ideas into categories. The organize think sheets are graphic organizers that are specific to text structures. For example, the procedural graphic organizer prompts students to consider where the activity will take place, what materials are needed, and the sequence

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of steps involved. The edit and revise think sheets include questions specific to the text structure as well as general evaluation criteria. To help students internalize the strategies incorporated in the think sheets, teachers explained and modeled the strategies, guided student practice until coaching was no longer needed, led class discussions on the strategies, and helped students to understand when and where the strategies could be used. In a study in which fourth and fifth grade students with LD and low and high achieving students were given CWIS instruction, the students wrote papers of higher quality. Their papers contained more ideas, and were better organized than the papers written by students in the control classes that followed a general writing process approach. In addition, students of all ability levels made gains in metacognitive knowledge. In subsequent writing, Englert and her colleagues (2006) have emphasized the critical role of interactive dialogue, peer support, and meaningful writing tasks in the development of students’ strategies and writing achievement. Self-Regulated Strategy Development The most extensive program of research on writing strategies has focused on the Self-Regulated Strategy Development (SRSD) model of Graham and Harris (Graham & Harris, 2005; Harris, Santangelo, & Graham, 2008). Like other approaches to teaching writing strategies, the SRSD model includes instruction in the characteristics of good writing, explicit explanation and think-aloud modeling of specific writing strategies, discussion of when and where to use strategies, and extensive guided practice. Instructional support is gradually withdrawn as students master the strategies. In addition, SRSD includes instruction in metacognitive, self-regulation strategies. Students learn to set goals for performance to motivate their efforts. They learn to selfmonitor their use of strategies and self-evaluate their writing performance. An important aspect of self-regulation in the SRSD model is the use of self-statements during the writing process to direct their efforts and manage motivation. Self-statements help students to replace prior negative thoughts with more constructive ones. Six instructional stages provide the framework for SRSD (Harris & Graham, 2005). These stages provide a “metascript”, or general instructional guidelines that can be modified, combined, or reordered to meet teacher and student needs. The stages are recursive; if a specific concept is not mastered when introduced at a specific stage, it can be revisited as instruction proceeds to another stage. Depending on students’ understanding, a particular stage may not be needed at all. For example, if students

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already have a clear understanding of the purposes of a particular genre, and demonstrate understanding of criteria of good writing, the first stage (Develop Background knowledge) may be omitted or require less time for development. The stages also provide for flexible adaptation to the needs of individual students; for example, the types of self-regulatory procedures (e.g., self-instructions, goal setting, self-monitoring) taught to students are determined on an individual basis. Two examples of strategies studied using the SRSD model are included in Boxes 9.1 and 9.2. Box 9.1 shows a strategy focused on planning a persuasive essay with rebuttals that might be appropriate for high school students. Box 9.2 displays a peer-revision strategy focused on personal narratives that might work well in elementary schools. Stage 1: Develop background knowledge. In the first stage, students learn the background knowledge needed to apply the strategies, usually knowledge about types of writing and the characteristics of good writing. For example, before learning a strategy for persuasive writing (see Box 9.1), students need to understand the common elements of persuasive writing, such as position, reasons, evidence, responses to opposing positions, and conclusion. Teachers might spend time discussing situations in which students have used persuasion or reading persuasive texts to make sure that students understand the elements and the language used

Box 9.1  Strategy for Writing a Persuasive Essay THINK Who will read this? Why am I writing this? STOP Suspend judgment (generate ideas for each side of the issue) Take a side (decide your position) Organize ideas (select ideas to include and order them for writing) Plan more as you write (continue planning while writing) DARE: REMEMBER Develop your topic sentence Add supporting ideas Reject arguments for the other side End with a conclusion l

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Source: Harris, Graham, Mason, & Friedlander, 2008.

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Box 9.2  Peer Revising Strategy LISTEN and READ along as the author reads the story. TELL what it is about and what you liked best. READ it to yourself and make NOTES about: CLARITY? Is there anything you don’t understand? DETAILS? What information/details could be added? DISCUSS your suggestions with the author. Author: Make changes on the computer. (Note that evaluation questions in step 3 should be tailored to the ability of the students, the genre, and the focus of instruction.) Source: MacArthur, Schwartz, & Graham, 1991.

to discuss them. Often instruction begins with an examination of model pieces of writing to analyze what makes them effective. Stage 2: Discuss it. The second stage includes an examination of students’ current strategy use and writing performance and discussion of the strategy to be learned. Students examine their current writing performance and discuss the strategies they use to accomplish specific writing tasks. The examination of current performance should be positive in anticipation of the gains expected from learning and applying the strategy. This initial guided evaluation of students’ writing provides a baseline against which to measure improvement. Students may graph their current performance and identify goals for learning. For example, they might graph the number of story elements included in their story. The target writing strategies are then introduced, and their purpose and benefits are discussed with the students. At this point, students are asked to make a commitment to learn the strategies and be active collaborators in the learning process. The teacher may also decide to explore with students any negative or ineffective self-statements or beliefs that currently affect their writing. Stage 3: Model it. In this stage the teacher models the strategy, thinking aloud while planning, drafting, and revising a composition. Think-aloud modeling makes the cognitive processes visible to the students and is essential to strategy instruction. The modeling should be done at the level of the teacher’s goal for the students and should include some of the common problems that students might experience. The modeling should also include appropriate self-instructions, including problem

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definition, planning, strategy use, self-evaluation, coping and error correction, and self-reinforcement statements. After the writing strategy has been modeled, the students and teacher discuss the strategy and the selfstatements used by the teacher. The students may then develop and record their own self-statements. Stage 4: Memorize it. At this stage the steps of the writing strategies, any mnemonics or graphic organizers, and self-statements developed at the previous stage are memorized. The students may paraphrase the steps of the strategies; however, it is important to maintain the meaning of each step. The memorize stage is especially needed by students who may face memory difficulties. Although memorization is a low-level process, it is important because students will not be able to use a strategy independently if they do not remember the steps. Stage 5: Support it. In this stage teachers and students collaboratively apply the strategies and self-statements to complete writing assignments. Teachers gradually release responsibility for management of the strategies to students. Self-regulation procedures, including goal setting and self-assessment, may be introduced at this time. Students set goals to improve specific aspects of their writing and use the strategies and selfinstructional procedures to mediate their performance. The guided support stage is the longest in time and is challenging for teachers. Teachers need to evaluate the progress of the students to mastery of the strategies and provide just the right amount of support for individual students. It is important for teachers to give feedback to students both on their mastery of the strategy and on their progress in writing. Students also need to develop their self-evaluation and goal-setting abilities to progress toward independent self-regulation. Stage 6: Independent performance. Independent performance is less a separate stage than the natural endpoint of guided practice. SRSD is a mastery model, so students should continue to receive guided practice until they demonstrate that they can apply the strategy independently and produce improved writing. Stopping short of this goal has negative effects on student motivation as they conclude that strategy instruction is one more approach that will not work for them. From a motivational perspective, one goal of SRSD is to convince students that they can be successful if they use the right strategy. Procedures for promoting generalization and maintenance, including the use of self-reflection are integrated throughout all stages of SRSD. These include class discussions of opportunities to use the strategies in

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other settings, analyses of how the strategies might need to be modified to work for different assignments, and evaluation of the success of attempts at generalization. Further resources on SRSD. SRSD has met criteria for evidencebased practice as established by panels of independent researchers (see Baker, Chard, Ketterlin-Geller, Apichatabutra, & Doabler, 2009; National Center for Response to Intervention, www.rti4success.org/instructionTools/). Resources to support implementation of SRSD are available. Descriptions and further discussion can be found in Graham and Harris (2005); Harris, Graham, and Mason (2003); and Sandmel et al. (2009). Lesson plans and support materials are provided in Harris, Graham, Mason, and Friedlander (2008). All of the stages of SRSD instruction can be seen in both elementary and middle school classrooms in the video, “Teaching students with learning disabilities: Using learning strategies” (ASCD, 2002). Online interactive tutorials on SRSD are available at: http://iris.peabody.vanderbilt.edu/resources.html. Finally, a website devoted to strategies instruction can be found at www.unl.edu/csi/. Final Comments on Strategy Instruction Strategy instruction should not be viewed as an isolated part of writing instruction or as a complete writing program. Rather, strategy instruction is most effective when it is integrated in an overall writing program that has all the characteristics of good writing instruction discussed above (Graham & Harris, 2005; Englert, 2006). Strategies are compatible with the emphasis on the writing process, teacher conferencing, and peer collaboration found in classrooms following an overall process approach (MacArthur, Schwartz, Graham, Molloy, & Harris, 1996). Within a process approach to writing instruction, strategies instruction provides the explicit instruction and persistent guided practice needed by many students with LD. Maintenance and generalization of strategies is one of the most difficult challenges for instruction. Although students may possess the knowhow, they may not possess the will to use the strategies or to use them with sufficient care. Consequently, teachers should consider students’ goal orientations and attitudes when providing strategy instruction, and employ procedures that facilitate mindful use of strategies. It is important to monitor whether students continue to use the strategies over time and whether they adapt them to new settings and tasks. Continuing discussions with students about application of strategies in other settings should continue throughout the school year. Working together with other teachers at

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the same grade level or across grades to emphasize common strategies is another way to promote generalized use. Learning to use strategies independently is a long-term process that requires concerted effort over time.

Explicit, Systematic Instruction in Basic Skills There is a strong relation between the fluency and quality of children’s writing and their proficiency with text production skills such as handwriting and spelling (Graham et al., 1997). Moreover, efforts to improve such skills can also result in corresponding improvements in writing performance (Berninger et al., 2005, 2008; Graham, Harris, & Chorzempa, 2002; Graham, Harris, & Fink, 2000; Christensen, 2005). Consequently, we recommend that teachers devote instructional time to teaching text production skills, especially handwriting and spelling, to students with LD. Such instruction should not dominate the writing program, however, and should focus on those skills that are most likely to make a difference. Handwriting The basic goals of handwriting instruction are to help students develop writing that is legible and can be produced quickly with little conscious attention. Legibility and fluency can support students’ written production and communication. However, if text production is slow or tedious, the writer is more likely to resent writing or to produce papers limited in content and poor in ideas. Therefore, explicit instruction in handwriting is critical. This involves teaching students an efficient pattern for forming individual letters as well as how to hold their pen or pencil and position the paper they are writing on (Graham, 1999). Also, posture while writing is important. Handwriting instruction does not and should not require hours of time practicing individual letters. Instead, once a letter is introduced, students should spend a short time carefully practicing the letter, receive help as needed (including subsequent review), and evaluate their own efforts. Fluency in handwriting is best promoted through frequent writing, and develops gradually over time (Graham & Weintraub, 1996). We would also like to point out that providing extra handwriting instruction to young children experiencing difficulty with this skill may help to prevent later writing problems. For instance, Graham et al. (2000) provided first grade children (who had slow handwriting and generally poor writing skills) with approximately 7 hours of additional handwriting instruction. Three times a week, each child met with a tutor for 15 minutes of instruction. Each 15-minute lesson involved four activities. For

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the first activity, Alphabet Warm-up, students learned to name and identify the letters of the alphabet. With the second activity, Alphabet Practice, three lower case letters, sharing common formational characteristics (e.g., l, i, and t) were introduced and practiced. The tutor modeled how to form the letters, followed by the student practicing each letter by tracing it three times, writing it three times inside an outline of the letter, copying it three times, and circling the best formed letter. Three lessons were devoted to mastering each letter set, with the second and third sessions primarily involving letter practice in the context of single words (e.g., lit) or hinky-pinks (rhyming word such as itty-bitty). The third activity, Alphabet Rockets, involved asking the child to copy a short sentence quickly and accurately for a period of three minutes. The sentence contained multiple instances of the letters that were emphasized in Alphabet Practice during that lesson (e.g., Little kids like to get letters.). The number of letters written was recorded on a chart and during the next two lessons, students tried to beat their previous score by writing at least three more letters during the specified time period. With the fourth activity, Alphabet Fun, the student was taught how to write one of the letters from Alphabet Practice in an unusual way (e.g., as long and tall or short and fat) or use it as part of a picture (e.g., turning an i into a butterfly or an s into a snake). Students who received this extra instruction became quicker and better handwriters than peers assigned to a contact control group receiving instruction in phonological awareness. They also evidenced greater gains in their ability to craft sentences and generate text when writing a story. Spelling The basic goal of spelling instruction is to help students become proficient and fluent in spelling words they are likely to use in their writing. This involves learning the common regularities and patterns underlying English orthography; the correct spelling of frequently used words; as well as strategies for studying new words, applying knowledge of spelling (e.g., spelling by analogy), and proofreading (Graham, 1999). Frequent reading and writing contribute to spelling development, as they serve as a source for additional learning, a context for practicing newly learned skills, and a reminder on the importance of correct spelling in practical and social situations. Children also need to become familiar with external aids to spelling such as the dictionary, thesaurus, spell checkers, or asking another person for help. In addition, spelling instruction and practice play a role in the development of reading skills. In early literacy, invented spelling, that is,

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encouraging children to say the sounds of words to spell unknown words, helps to develop phonemic awareness and letter-sound associations. As students learn more complex orthographic and morphological patterns, spelling and reading instruction are mutually reinforcing. A survey conducted by Graham and colleagues (2008) with 168 primary teachers suggested that teachers did employ a number of strategies for spelling instruction with or without peer interaction and the majority of them reported teaching spelling 90 minutes a week. We recommend devoting 60–75 minutes a week to spelling instruction in the elementary grades. As with handwriting, providing extra spelling instruction to young children experiencing difficulty with this skill may help to prevent later writing problems. For example, Graham et al. (2002) provided second grade children who were poor spellers and writers with approximately 12 hours of additional spelling instruction. Pairs of students met with a tutor three times a week for 20 minutes each lesson. Instruction involved six units with six lessons each, and each unit contained five instructional activities. During the first lesson of each unit, children completed a word sorting activity (activity one) that focused on the spelling patterns taught in that unit (these primarily centered on long or short vowel patterns). With the tutor’s help, students first sorted word cards into two or three spelling pattern categories. Each category was represented by a master word (e.g., the words “made,” “maid,” and may were the master words for the three patterns representing the long /a/ sound), and children placed each word card in the appropriate category. If the children placed a word in the wrong category, the tutor corrected the mistake and modeled out loud how to decide where the word should be placed. Once all words were placed, the tutor helped students state rules for the patterns emphasized in that word sort (e.g., When you hear a long /a/ in a small word, the “a” is often followed by a consonant and silent “e.”). Students then generated words of their own that matched the patterns. Next, the word cards were reshuffled and students completed the word sort again, getting help and feedback as needed. At the end of each lesson, students were encouraged to “hunt” for words that fit the target patterns (activity two). During the second lesson and continuing through lesson five, students studied eight new spelling words (activity three). These were words that students had misspelled previously, and each word matched one of the spelling patterns emphasized in that unit. Students used two

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basic procedures to study these words. One procedure, “Graph Busters,” involved students recording the number of times they correctly practiced the words during a lesson using a traditional study strategy. The second procedure involved studying words while playing a game with a peer partner. Spelling Road Race was one of the games. It consisted of a laminated board with a racing track divided into 30 segments. When children correctly spelled one of the spelling words, they moved a place for each letter or word. Also starting in the second lesson and continuing through lesson five, students practiced sound-letter associations for consonants, blends, diagraphs, and short vowels (activity four). Using flash cards with a picture on one side (e.g., a “cat”) and the corresponding letter on the other side (“c”), students practiced 9 to 16 associations during each lesson. Students completed a word building activity (activity five) during lessons two through five. This involved building words with rimes that fit the target spelling patterns. They were asked to create as many real words as they could from a rime (e.g., “ay”) and 18 different consonants, blends, and digraphs. In the final lesson of each unit, students completed three tests. One test was on the eight words they studied, a second was on words that were studied in the previous two units, and a third was on words that matched the rimes used during the word building activity. Students not only learned and maintained almost all of the words taught, but their performance on two standardized tests of spelling improved dramatically as well. Even more importantly, there was a corresponding improvement in their writing and reading skills. Spelling instruction is an important component of writing development. Frequent reading and writing activities can support students in better learning and applying conventional spelling. Activities that allow students to compare words to learn patterns and derive rules are more effective than teaching rules. Also, the application of research based practices for spelling is more likely to support students’ needs.

Application of Technology in Writing Instruction The new Common Core State Standards (2010) include facility in writing with computers as a core part of writing proficiency, based on the argument that the use of computers to write has become nearly universal in academic and workplace writing, as well as in everyday life. The next round of writing assessments in the National Assessment of Educational

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Progress will require students to write using word processors (National Assessment Governing Board, 2007). Fortunately, research indicates that technology, if used properly, can enhance the writing of students with LD and other struggling writers. Word processors with spell checkers can make it easier for struggling writers to produce and revise written work. Related applications like word prediction, speech synthesis, and speech recognition offer additional support for transcription. Outlining programs and concept mapping software can help with planning. The Internet offers new opportunities for students to learn by sharing their writing with wider audiences and by integrating multiple media. Before discussing specific technological tools, we discuss two general issues that teachers need to consider in planning instruction (MacArthur, 2009). First, technological tools can support the writing process and help students to write more fluently and correctly. However, tools also impose new burdens and challenges; they may require training or make writing more complex in some ways. For example, word processing compensates for problems with handwriting, but it requires learning to type. Dictating using speech recognition removes most concerns with the mechanics of writing, but it requires clear enunciation and introduces new editing issues. Whether a particular technological tool will increase or decrease the overall challenge of writing depends on the skills and motivation of individual students and the quality of training. Second, the effects of writing tools for struggling writers depend very much on how the tools are integrated with instruction. For example, word processors make revising much easier, and skilled writers may revise more frequently and effectively when they write on a computer. However, for students with limited knowledge of how to evaluate their own writing, using a word processor is unlikely to improve their revising or writing. Similarly, search tools on the Internet greatly expand the amount of information available to use in writing, but most students, especially those with limited reading and writing skills, will need careful instruction to take advantage of this opportunity. Computers are powerful tools for writing but their effects depend on instruction that takes advantage of that power. Word Processing Word processors are flexible writing tools that can support the physical, cognitive, and social processes involved in writing. Most obviously, they ease the physical processes involved in revising, which can support instruction in writing as a process involving cycles of planning, drafting, and

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revising. For struggling writers, the editing capabilities make it possible to produce texts that are free of errors and easy to read. Typing is a major help for students with handwriting problems though it is a skill itself that must be learned. Writing on a computer can also support the social processes involved in writing by increasing opportunities for publication and collaborative work. It is far easier to work collaboratively at a computer screen than via handwriting on paper. Completed work can be published in many forms, both print and electronic. A moderate amount of research has investigated the effects of word processing in combination with instruction. A recent review of 19 studies with students in grades 4 to 9 (Graham & Perin, 2007) found a moderate effect on quality of writing (ES = 0.51) for writers in general but a larger effect (ES = 0.70) for low achieving writers. Research on the effects of word processing on revising suggests that typically achieving students may make more revisions with a word processor (Goldberg, Russell, & Cook, 2003). However, MacArthur and Graham (1987) found no differences in the amount or quality of revisions made with paper and pencil or word processing by students with LD. To take advantage of the editing capabilities of word processing, students with LD need instruction and support in learning to revise for meaning as well as errors. The revising skills of students with LD can be improved by instruction in strategies for evaluating and revising their writing. Several studies have combined word processing and strategy instruction in teaching revisions, resulting in increases in the number of substantive and mechanical revisions made by students with LD as well as improvement in the quality of their texts (Graham & MacArthur, 1988; MacArthur, Schwartz, & Graham, 1991; Stoddard & MacArthur, 1993). In the peer revision strategy developed by MacArthur et al. (1991), students worked in pairs applying specific evaluation criteria (see Box 9.2). In addition to supporting revision, word processors enhance publishing by making it possible to produce neat, printed work in a wide variety of professional-looking formats, including newsletters, illustrated books, business letters, and signs and posters. The motivation provided by printed publications may be especially important for students with LD who often struggle with handwriting and mechanics. For classrooms that have just one or two computers, the best use of the equipment may be for publishing projects. Students can work together on a class newsletter or other project, or they can select their best writing for inclusion in a literary magazine. In recent years, the Internet has provided even more

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opportunities for publication of work, and publication that goes to much wider audiences than school and family. An important practical consideration in using word processing is that students must develop some proficiency in typing. Otherwise, the attention required by typing and the slower rate of production may negatively affect the length and quality of writing. Although there is not much research on the effects of typing proficiency, one study (Russell, 1999) did find that the effect of word processing depended on typing skill; it had a positive effect on essay quality for high school students with above average typing speed (20+ wpm) but a negative effect for students with below average typing. Handwriting fluency predicts writing quality (Graham et al., 1997), so it makes sense that typing fluency would have a similar effect. Although typing can be easier than handwriting for many students with LD, students will need instruction and practice to develop adequate skill. A variety of software programs are available to provide the needed practice. Spell checkers are very helpful for students with LD and other writers who struggle with spelling. However, they will not automatically correct all errors. In one study (MacArthur, Graham, Haynes, & De La Paz, 1996), middle school students with LD corrected 37% of their spelling errors with a spell checker compared to 9% unaided. College students with LD (McNaughton, Hughes, & Clark, 1997) fixed 60% of their errors using a spelling checker compared to 11% with handwriting. The most significant limitation of spell checkers is that they fail to identify about one-third of spelling errors because the errors are other real words, including homonyms and other close approximations (e.g., “whet” for “went”). Furthermore, spell checkers do not always suggest the correct spelling, especially when words are badly misspelled. Finally, some students fail to pick out the correct suggestion from the list, especially if it is not the first suggestion. Students need instruction in strategies for using spell checkers successfully. Students can be taught to try alternate, perhaps phonetic, spellings when the desired word does not appear. They also need to learn to proofread for the errors not flagged by the checker. One important practical problem is access to computers. To gain the full benefit from word processing, students need to complete the entire composing process from first draft to publication on the word processor. Drafting with pencil and paper and then typing on the word processor is difficult for students without touch-typing skills. It is slow and they often introduce new errors. Potential solutions include computer labs devoted to writing or sets of inexpensive laptops designed just for word processing.

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Assistive Technology to Support Transcription Word processors with spelling checkers can offer significant help to struggling writers in translating their words into text fluently and accurately. In this section, we consider three tools that go beyond word processing to offer further support in transcription—speech synthesis, word prediction, and speech recognition. Speech synthesis. Speech synthesis, or text-to-speech, software converts text into speech. Many standard word processors have some speech capabilities. Reading support software designed for individuals with reading disabilities generally includes speech along with word-by-word or sentence highlighting on the screen, so that it is possible to follow along with the reading. Similar capabilities are included in some word processing programs designed for young children or students with literacy problems. Speech synthesis may help students monitor the adequacy of their writing, including spelling and grammar. Little research has investigated the effects on writing. In a study of college students with LD (Raskind & Higgins, 1995), students detected more errors with speech synthesis than without, although the difference was not large. One promising idea is to use speech synthesis after a spell checker to find the errors missed. Word prediction. Word prediction was originally developed for individuals with physical disabilities to reduce the number of keystrokes required to write, but it is also helpful for students with severe spelling problems. As the user begins to type a word, the software predicts the intended word and presents a list of words from which to choose. Depending on the sophistication of the software, predictions are based on spelling, word frequency, individual patterns of use, and syntax. Generally, speech synthesis is available to read the choices. MacArthur (1998, 1999) found that word prediction resulted in substantial improvements in the readability and spelling accuracy of writing by students with LD with severe spelling problems. As with any tool, there are new burdens as well. Students need to attend to the list of suggestions and make choices, which can slow text production. For students who make relatively few errors, it is probably better to follow the common recommendation to ignore spelling during drafting and use a spelling checker later. Speech recognition. Dictation is one clear way to avoid difficulties with transcription, and students with LD generally can produce higher quality text through dictation than via handwriting (Reece & Cummings, 1996). However, dictation to a person is not practical in most settings, and it compromises the writer’s independence. Speech recognition software

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makes dictation possible without the support of another person. It also makes it possible for the writer to see the developing text, which is important in ongoing planning and sentence generation. However, currently available speech recognition still has some limitations. Accuracy of recognition is not perfect. Furthermore, users must articulate clearly, dictate punctuation and formatting, and monitor the accuracy of the text. In addition, users must learn new editing procedures; instead of misspelled words, they need to learn to look for incorrect words. Several studies have found that speech recognition can provide significant benefits to students with LD who need the support. Higgins and Raskind (1995) found positive effects on the quality of writing for college students with LD. Quinlan (2004) found positive effects for middle school students with problems in writing fluency. MacArthur and Cavalier (2004) studied speech recognition as a test accommodation for high school students with LD, comparing it to handwriting and dictation to a person. Students with LD wrote essays of higher quality with speech recognition than handwriting, and even better essays when dictating to a person. No differences in quality were found for students without LD. In addition, the rate of errors was lower with speech recognition than handwriting. Effective training in the use of speech recognition involves training the system to recognize students’ speech and training students to speak clearly and monitor and correct errors. In addition, planning strategies become more important because a good plan facilitates smooth dictation. Using speech recognition also presents some difficult practical issues. It is difficult to use in a school setting because it requires a relatively quiet environment, and it is probably socially unacceptable to dictate in public. Thus, schools may need to provide separate facilities or encourage use at home.

Computer Support for Planning Processes Students with LD often have difficulty with planning processes such as setting goals, generating content, and organizing their ideas. Computer applications that support outlining and concept mapping or that prompt students with questions to help with idea generation and organization have potential to enhance planning processes. Outlining and concept mapping are common practices for organizing ideas prior to writing, both in schools and among experienced writers. As discussed earlier in this chapter, planning strategies based on text structure and mapping have been shown to be effective ways to improve the writing of students with LD. Many word processors include

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outlining programs and software for concept mapping is commonly available. Electronic outlining and mapping are more flexible than paper and pencil versions. Ideas can be inserted and rearranged easily. In addition, ideas on concept maps can be automatically converted to outlines to provide a linear organization for writing. A few studies have explored the use of concept-mapping software. Anderson-Inman and her colleagues (e.g., Anderson-Inman & Horney, 1998) have conducted a number of descriptive and qualitative studies of concept mapping as a tool to support reading and studying, though not specifically for writing. Sturm and Rankin-Erickson (2002) compared planning with concept mapping software, hand-drawn concept maps, and no maps with adolescents with learning disabilities. Essays were longer and higher in quality in both concept-map conditions than in the no-map condition. Our colleagues (Klein, MacArthur, & Najera, 2008) studied concept mapping with normally achieving fifth-grade students and found a positive impact on organization but not overall quality. The interactive capabilities of computers can be used to develop programs that prompt writers to engage in planning processes. Englert and her colleagues (Englert, Zhao, Dunsmore, Collings, & Wolbers, 2007) developed an online writing support program that included prompts for idea generation, organization, and revision and that included graphic organizers. They reported increased length and quality of writing for students with LD. Overall, the effects of concept mapping software and prompting programs, like other applications, depends on the quality of the instruction in using them for planning.

CONCLUDING COMMENTS The success of schools teaching writing should be judged not only in terms of how well students develop the skills necessary for meeting academic and occupational demands, but also on whether students are motivated to write and understand how to use writing for social communication and recreation. In our efforts to improve the writing of students with LD, we do not want to lose sight of the critical goals of helping students learn to appreciate writing and to enjoy doing it. In this chapter we have presented a variety of procedures that, when applied in concert, should help students realize all of these goals. Finally, if we are to improve in any meaningful way how and what students with LD write, we must be dedicated to the importance of writing.

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Too often special education teachers have made writing instruction the stepchild to reading or math. We have found through our own experiences in working with schools and teachers that they are often hesitant and sometimes resistant to allocating sufficient time for writing instruction; they often fear that making such a commitment will have negative consequence because students will get less of something really important like reading. We would argue that writing and reading are mutually reinforcing. Teaching writing has powerful effects on students’ reading achievement. Therefore, we would like to encourage teachers to provide daily writing instruction, integrate reading and writing across the curriculum, and attempt to engage students in meaningful and purposive writing activities.

REFERENCES Anderson-Inman, L., & Horney, M. A. (1998). Transforming text for at-risk readers. In D. Reinking, M. C. McKenna, L. D. Labbo, & R. D. Kieffer (Eds.), Handbook of literacy and technology (pp. 15–44). Mahwah, NJ: Erlbaum. Association for Supervision and Curriculum Development. (2002). Teaching students with learning disabilities in the regular classroom: Using learning strategies [videotape 2]. Retrieved 11/15/11 from . Baker, S. K., Chard, D. J., Ketterlin-Geller, L. R., Apichatabutra, C., & Doabler, C. (2009). Teaching writing to at-risk students: The quality of evidence for Self-Regulated Strategy Development. Exceptional Children, 75, 303–318. Bangert-Drowns, R. L., Hurley, M. M., & Wilkinson, B. (2004). The effects of school-based writing-to-learn interventions on academic achievement: A meta-analysis. Review of Educational Research, 74(1), 29–58. Bereiter, C., & Scardamalia, M. (1987). The psychology of written composition. Hillsdale, NJ: Lawrence Erlbaum. Berninger, V. W., & Swanson, H. L. (1994). Modifying Hayes and Flower’s model of skilled writing to explain beginning and developing writing. In E. C. (1994). Butterfield (Ed.), Children’s writing: Toward a process theory of the development of skilled writing (Vol. 2, pp. 57–82). Greenwich, CN: JAI Press. Berninger, V. W., Rutberg, J. E., Abbott, R. D., Garcia, N., Anderson-Youngstrom, M., Brooks, A., et al. (2005). Tier 1 and Tier 2 early intervention for handwriting and composing. Journal of School Psychology, 44, 3–30. Berninger, V. W., Winn, W. D., Stock, P., Abbott, R. D., Eschen, K., Lin, S. J., et al. (2008). Tier 3 specialized writing instruction for students with dyslexia. Reading and Writing, 21, 95–129. Brown, A. L., & Palincsar, A. S. (1982). Inducing strategic learning from texts by means of informed, self-control training. Topics in Learning & Learning Disabilities, 2(1), 1–17. Christensen, C. A. (2005). The role of orthographic-motor integration in the production of creative and well-structured written text for students in secondary school. Educational Psychology, 25, 441–453. Common Core State Standards Initiative. (2010). Common Core State Standards for English Language arts & literacy in history/social studies, science, and technical subjects. Connelly, V., Gee, D., & Walsh, E. (2007). A comparison of keyboarded and handwritten compositions and the relationship with transcription speed. British Journal of Educational Psychology, 77, 479–492.

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Deshler, D. D., & Schumaker, J. B. (1986). Learning strategies: An instructional alternative for low-achieving adolescents. Exceptional Children, 52(6), 583–590. Donovan, C. A., & Smolkin, L. B. (2006). Children’s understanding of genre and writing development. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research (pp. 131–143). New York: Guilford. Englert, C. S., & Thomas, C. C. (1987). Sensitivity to text structure in reading and writing: A comparison between learning disabled and non-learning disabled students. Learning Disability Quarterly, 10(2), 93–105. Englert, C. S., Mariage, T.V., & Dunsmore, K. (2006). Tenets of sociocultural theory in writing instruction research. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research (pp. 208–221). New York: Guilford. Englert, C. S., Raphael, T. E., Anderson, L. M., Anthony, H. M., & Stevens, D. D. (1991). Making writing strategies and self-talk visible: Cognitive strategy instruction in writing in regular and special education classrooms. American Educational Research Journal, 28, 337–372. Englert, C. S., Zhao,Y., Dunsmore, K., Collings, N.Y., & Wolbers, K. (2007). Scaffolding the writing of students with disabilities through procedural facilitation: Using an internetbased technology to improve performance. Learning Disability Quarterly, 30(1), 9–29. Ferretti, R. P., & MacArthur, C. A. (2000). The effects of elaborated goals on the argumentative writing of students with learning disabilities and their normally achieving peers. Journal of Educational Psychology, 92, 694–702. Fitzgerald, J. (1987). Research on revision in writing. Review of Educational Research, 57, 481–506. Goldberg, A., Russell, M., & Cook, A. (2003). The effect of computers on student writing: A metaanalysis of studies from 1992 to 2002. Journal of Technology, Learning, and Assessment, 2(1), 1–51. Graham, S. (1999). Handwriting and spelling instruction for students with learning disabilities: A review. Learning Disability Quarterly, 22, 78–98. Graham, S. (2006). Strategy instruction and the teaching of writing: A meta-analysis. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research (pp. 187–207). New York: Guilford. Graham, S., & MacArthur, C. (1988). Improving learning disabled students’ skills at revising essays produced on a word processor: Self-instructional strategy training. Journal of Special Education, 22, 133–152. Graham, S., & Harris, K. J. (2005). Writing better: Effective strategies for teaching students with learning difficulties. New York: Brooks. Graham, S., & Hebert, M. (2010). Writing to read: Evidence for how writing can improve reading. New York: Carnegie Corporation. Graham, S., & Perin, D. (2007). A meta-analysis of writing instruction for adolescent students. Journal of Educational Psychology, 99(3), 445–476. Graham, S., & Weintraub, N. (1996). A review of handwriting research: Progress and prospects from 1980 to 1994. Educational Psychology Review, 8(1), 7–87. Graham, S., Berninger, V. W., Abbott, R. D., Abbott, S. P., & Whitaker, D. (1997). Role of mechanics in composing of elementary school students: A new methodological approach. Journal of Educational Psychology, 89(1), 170–182. Graham, S., Harris, K. J., & Chorzempa, B. F. (2002). Contribution of spelling instruction to the spelling, writing, and reading of poor spellers. Journal of Educational Psychology, 94, 669–686. Graham, S., Harris, K. J., & Fink, B. F. (2000). Is handwriting causally related to learning to write? Treatment of handwriting problems in beginning writers. Journal of Educational Psychology, 92, 620–633.

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Graham, S., Morphy, P., Harris, K. R., Fink-Chorzempa, B., Saddler, B., Moran, S., et al. (2008). Teaching spelling in the primary grades: A national survey of instructional practices and adaptations. American Educational Research Journal, 45(3), 796. Harris, K. R., Graham, S., & Mason, L. (2003). Self-regulated strategy development in the classroom: Part of a balanced approach to writing instruction for students with disabilities. Focus on Exceptional Children, 35, 1–16. Harris, K. R., Graham, S., Mason, L., & Friedlander, B. (2008). Powerful writing strategies for all students. Baltimore, MD: Brookes Publishing. Harris, K. R., Santangelo, T., & Graham, S. (2008). Self-regulated strategy development in writing: Going beyond NLEs to a more balanced approach. Instructional Science: An International Journal of the Learning Sciences, 36, 395–408. Hayes, J., & Flower, L. (1980). Identifying the organization of writing processes. In L. W. Gregg, & E. R. Steinberg (Eds.), Cognitive processes in writing (pp. 3–30). Hillsdale, NJ: Erlbaum. Higgins, E. L., & Raskind, M. H. (1995). Compensatory effectiveness of speech recognition on the written composition performance of postsecondary students with learning disabilities. Learning Disability Quarterly, 18, 159–174. Karchmer-Klein, R., MacArthur, C. A., & Najera, K. (2008, Dec.). The effects of concept mapping software on fifth grade students’ writing. Paper presented at the annual meeting of the National Reading Conference, Orlando, FL. MacArthur, C. A. (1998).Word processing with speech synthesis and word prediction: Effects on the dialogue journal writing of students with learning disabilities. Learning Disability Quarterly, 21, 1–16. MacArthur, C. A. (1999). Word prediction for students with severe spelling problems. Learning Disability Quarterly, 22, 158–172. MacArthur, C. A. (2009). Technology and struggling writers: A review of research. Teaching and Learning Writing: Psychological Aspects of Education—Current Trends: British Journal of Educational Psychology Monograph Series II, 6, 159–174. MacArthur, C. A. (2012). Evaluation and revision processes in writing. In V. W. Berninger (Ed.), Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology (pp. 461–483). London: Psychology Press. MacArthur, C. A., & Cavalier, A. (2004). Dictation and speech recognition technology as accommodations in large-scale assessments for students with learning disabilities. Exceptional Children, 71, 43–58. MacArthur, C. A., Graham, S., Haynes, J. B., & DeLaPaz, S. (1996). Spelling checkers and students with learning disabilities: Performance comparisons and impact on spelling. Journal of Special Education, 30, 35–57. MacArthur, C. A., Schwartz, S. S., & Graham, S. (1991). Effects of a reciprocal peer revision strategy in special education classrooms. Learning Disabilities Research and Practice, 6, 201–210. MacArthur, C. A., Schwartz, S. S., Graham, S., Molloy, D., & Harris, K. R. (1996). Integration of strategy instruction into a whole language classroom: A case study. Learning Disabilities Research and Practice, 11(3), 168–176. McCraw, S. B. (2011). Little writers—Big opinions:The impact of exposing first graders to persuasive writing. Unpublished Dissertation, University of Delaware, Newark. DE. McNaughton, D., Hughes, C., & Clark, K. (1997). The effect of five proofreading conditions on the spelling performance of college students with learning disabilities. Journal of Learning Disabilities, 30, 643–651. Midgette, E., Haria, P., & MacArthur, C. A. (2008). The effects of content and audience awareness goals for revision on the persuasive essays of fifth- and eighth-grade students. Reading and Writing, 21, 131–151.

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National Assessment Governing Board, (2007). Writing framework for the 2011 National Assessment of Educational Progress. Iowa City, IA: ACT, Inc. Quinlan, T. (2004). Speech recognition technology and students with writing difficulties: improving fluency. Journal of Educational Psychology, 96, 337–346. Raskind, M. H., & Higgins, E. (1995). Effects of speech synthesis on the proofreading efficiency of postsecondary students with learning disabilities. Learning Disability Quarterly, 18, 141–158. Reece, J. E., & Cummings, G. (1996). Evaluating speech-based composition methods: Planning, dictation, and the listening word processor. In C. M. Levy & S. Ransdell (Eds.), The Science of Writing (pp. 361–380). Mahwah, NJ: Erlbaum. Russell, M. (1999). Testing writing on computers: A follow-up study comparing performance on computer and on paper. Educational Policy Analysis Archives, 7, 20. Salahu-Din, D., Persky, H., & Miller, J. (2008). The Nation’s Report Card: Writing 2007 (No. (NCES 2008–468). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Sandmel, K. N., Brindle, M., Harris, K. R., Lane, K. L., Graham, S., Nackel, J., et al. (2009). Making it work: Differentiating tier two Self-Regulated Strategies Development in writing in tandem with school-wide positive behavioral support. Teaching Exceptional Children, 42(2), 22–35. Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children’s self-efficacy and self-regulation of reading and writing through modeling. Reading & Writing Quarterly, 23, 7–25. Shanahan,T., & Shanahan, C. (2008).Teaching disciplinary literacy to adolescents: rethinking content-area literacy. Harvard Educational Review, 78, 40–59. Stoddard, B., & MacArthur, C. A. (1993). A peer editor strategy: Guiding learning disabled students in response and revision. Research in the Teaching of English, 27, 76–103. Tierney, R. J., & Shanahan, T. (1996). Research on the reading-writing relationship: Interactions, transactions, and outcomes Handbook of Reading Research, vol. 2 (Vol. 2). Torrance, M., & Galbraith, D. (2006). The processing demands of writing. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research (pp. 67–81). New York: Guilford. Troia, G. A. (2006). Writing instruction for students with learning disabilities. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research (pp. 324–336). New York: Guilford. Wong, B. Y. L., Hoskyn, M., Jai, D., Ellis, P., & Watson, K. (2008). The comparative efficacy of two approaches to teaching sixth graders opinion essay writing. Contemporary Educational Psychology, 33, 757–784.

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Assessment and Interventions for English Language Learners with Learning Disabilities Esther Geva1, and Katherine Herbert2 1

Ontario Institute for Studies in Education of the University of Toronto, Toronto, Ontario, Canada, M5S 1V6 2 Ministry of Children and Family Development of British Columbia, Victoria Child and Youth Mental Health, Victoria, BC, Canada, V8W 9S3

Chapter Contents Introduction271 Historical Perspective 273 Contextual Considerations 274 Language Development 275 Cross-Language Transfer 275 “BICS” vs. “CALP” 277 Development of English Language Proficiency in ELLs 278 The Simple View of Reading 279 ELLs’ Literacy Development in English 279 Word-Level Skills: Decoding and Spelling 280 Text-Level Skills: Reading Comprehension and Writing 280 Typically Developing ELLs 282 LD Subtypes in ELLs 282 Poor Decoders 283 Poor Comprehenders 284 Assessment285 The Assessment of Intelligence—Implications for L2/LD 285 Assess in the L1 and/or in English? 287 Assessing Oral Proficiency 289 Do L1-Based Interventions Work for ELLs? 289 Conclusion291 References292

INTRODUCTION With increasing cultural and linguistic diversity in global communities, research that may influence educational policy regarding multilingual Learning about Learning Disabilities

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youth is extremely pressing. In English speaking countries that absorb immigrants there is steady growth in the number of school children from an immigrant background whose first language is not the societal language. This trend is most noticeable, though not limited to, large metropolitan areas where newcomers tend to settle. English Language Learners (ELLs) may be the children of immigrant parents (second generation), they may have immigrated with their families when they were infants, or they may have arrived after having attended school in another country for a number of years. Recent estimates of school aged children speaking a home language other than English in the United States vary by region from 12 to 34% (Federal Interagency Forum on Child and Family Statistics, 2009). In Canada, 22% of the population reported speaking a mother tongue other than one of the country’s two official languages (English and French), with approximately 55% of school aged children in the large “gateway” cities of Vancouver and Toronto speaking English as a second language (Statistics Canada, 2006). In the United Kingdom, approximately 12 to 16% of students countrywide speak a home language other than English, with some urban schools in London recording figures as high as 75% (UK Department for Education, 2011). Australian statistical reports suggest that approximately 1 in 5 of the total population speak English as a second language (Australian Bureau of Statistics, 2006). These consistently high figures can leave little doubt that issues related to the education of ELLs are both timely and salient, and will become even more critical as continued immigration contributes to the increasing diversification of English speaking countries. This chapter focuses on children and youth whose challenges navigating English school systems in a second language (L2) are compounded by having learning disabilities (LD). English Language Learners (ELLs) with LD represent a vulnerable group of students, whose difficulties are frequently misunderstood, misattributed, or simply missed. This chapter introduces theoretical perspectives on L2 development, touching on topics such as cross-language transfer, and the development of basic vs. academic language proficiency. Research concerning LD subtypes in ELLs (i.e. “poor decoders” and “poor comprehenders”) is also discussed. Additionally, issues related to assessment are analyzed, focusing on how to tease apart expected L2 development from an ELL-LD profile. Lastly, clinical applications related to educational interventions for ELLs with LD are presented, and suggestions are offered for much needed future research. We argue that in spite of the challenges, ELLs who may have an LD should

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be entitled to research based, well informed, culturally sensitive assessment that can lead to appropriate educational intervention.

HISTORICAL PERSPECTIVE In the school system, how to identify and provide essential supports to ELLs who have LD has been controversial due to problems related to both over-identification (Cummins, 1991) and under-identification (Limbos & Geva, 2001). Historically, students from ethnic, cultural and linguistic minorities were over-represented in special education classes. In an effort to counteract this prejudice in the school system, classification criteria require ruling out social factors (e.g., lack of exposure to reading materials due to low socioeconomic status) in the identification of an LD (Learning Disabilities Association of Canada, 2002). In response to research, and in order to allow students time to acculturate and become proficient in English, many school districts introduced policies advocating delaying psychoeducational assessments of ELLs for 5 to 7 years (Cummins,1984,1991). While this policy was well-intentioned, an unintended consequence was that ELLs with LD often waited an extremely long time to receive necessary assessment and special education services. One may imagine that after 5 to 7 years of struggling to read and write, ELLs who may actually have a LD may be so far behind their peers that it may be difficult for them to catch up. This may partially contribute to very high school dropout rates among ELLs (e.g., Brown, 2006; Ferguson, Tilleczek, Boydell, Rummens, & Roth-Edney, 2005; Watt & Roessingh, 2001). Further, access to educational intervention services within schools is often reliant on referrals from classroom teachers. However, subjective teacher judgment may have limited effectiveness at differentiating typical development of skills in ELLs from red-flags for reading disabilities among ELLs (Limbos & Geva, 2001). As many as 9 out of 10 ELLs with objectively measured indicators of reading problems may “fall through the cracks” if the access to service relies on teacher referrals alone, because classroom teachers tend to misattribute ELLs’ reading and writing difficulties to the fact that their oral language skills are not sufficient to enable them to perform at grade level (Limbos & Geva, 2001). Understanding the language, cultural, and literacy development of L2 learners is crucial to enhance sensitive, bias-free, and accurate assessment, and to provide appropriate instruction and program adaptations when the need arises (Gottardo, Collins, Baciu, & Gebotys, 2008; Hamayan & Damico, 1990).

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CONTEXTUAL CONSIDERATIONS While this chapter emphasizes individual differences among students, it is important to acknowledge broad systemic factors that exert a significant influence on the developmental course of language and literacy skills in ELLs, but are outside the scope of the current discussion. For instance, contextual and community variables can exert an influence on academic achievement over and above individual- and family-level socio-economic indices (Gould, Lavy, & Paserman, 2003; Hart & Risley, 1999; Rumberger & Willms, 1992; Willms, 1999). Factors such as the extent to which ELLs live in homogenous communities where the first or home language (L1) is widely used, and there is less exposure to English, are related to rate of language attainment. Age of arrival in the host country is typically inversely related to children’s acquisition of English, while parental education is positively related (Garnett, 2010). Another important consideration is whether learning of the L1 or L2 was disrupted, as is often the case for children who are refugees, move schools frequently, or alternate between living in the country of origin and the host country. The educational challenges faced by some immigrant children and youth may be exacerbated by poverty, social-emotional problems that might be related to acculturation, parents who may be stressed or have minimal education, cultural differences in the attributions that community members may have about the possible source of learning difficulties experienced by their children, and limited access to services. Specifically, newcomers’ ability to access services may be affected by the city or area in which they settle. There are large discrepancies in the support available for ELLs in various jurisdictions (e.g., Provinces, States), due to differences in policies and professional training relating to the provision of appropriate educational accommodations and modifications for ELLs. A recent policy paper (Geva, Gottardo, Farnia, & Byrd Clark, 2009) revealed differences among Canadian provinces in the amount of funding available to support ELLs. This in turn can affect the duration and quality of educational support. The policy paper also highlighted that most jurisdictions do not have clear policies about the education of ELLs who also have a LD, a problem that is compounded by the fact that traditional, standardized assessment methods may not be valid for many of these children. Similar concerns have been voiced in other recent reviews (e.g., Durgunog˘ lu, 2002; Klingner, Artiles, & Barletta, 2006; Lipka, Siegel, & Vukovic, 2005; McCardle, Mele-McCarthy, & Leos, 2005). It is important

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to underscore that although the contextual factors mentioned here do not cause LD, they may exacerbate the struggle with LD.

LANGUAGE DEVELOPMENT Cross-Language Transfer Frequently, in the context of considering the potential relationships and influences between the L1 and L2, the concept of cross-language transfer emerges. Theoretical and applied questions often asked by researchers and educators concern the extent to which the learner’s home language may enhance the process of learning to comprehend, communicate, read, and write in the L2 (i.e., “positive transfer”), or whether features of the target language or the L1 hamper the acquisition of the L2 (i.e., “negative transfer”). Cross-language transfer has been considered from four theoretical orientations: L1-L2 interdependence (Cummins, 1984; Geva & Ryan, 1993), L1-L2 contrastive perspective (Lado, 1964), target-language influences (Dulay & Burt, 1974), and interlanguage (Selinker, 1972). For the purposes of this discussion, we will focus on the interdependence hypothesis and the contrastive perspective due to their prominence and relevance to a discussion of ELL-LD. The interdependence hypothesis, formulated by Cummins (1981, 2000) postulates that the acquisition of L1 and L2 skills is interdependent, and that development of the L1 can influence and facilitate development of the L2. This framework has been highly influential in the literature on L2 literacy development (Genesee & Geva, 2006). According to this framework, not all aspects of L1 development are expected to be equally facilitative of L2 development. Instead, two dimensions—the extent of contextual support and the cognitive demands of communication—create situations that vary in terms of how facilitative existing L1 skills may be for L2 learning. Cummins (2000, p. 173) hypothesizes that “academic proficiency transfers across languages such that students who have developed literacy in their first language will tend to make stronger progress in acquiring literacy in their second language” because the academic language skills are developmentally linked to common underlying proficiencies across the languages. For example, L2 learners can use knowledge or learning strategies acquired in one language when they read in another language (Cummins, in press; Royer & Carlo, 1991). It follows that clinicians may be able to gain important insights into what ELLs can do by exploring relevant or parallel conceptual knowledge in the L1, or by allowing the learner

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to respond in the L1. In line with this theory, clinicians should gather detailed information about the learner’s academic performance in L1 in the home country, because pre-existing academic difficulties may suggest an ELL-LD profile. In contrast, lack of exposure to formal schooling (e.g., refugees who did not have the opportunity to attend school consistently and are not literate in the L1), may display academic troubles stemming from a lack of opportunity to learn rather than an LD. Theoretically linked to the interdependence hypothesis is the central processing framework. While the interdependence hypothesis framework tends to focus on higher order academic skills that may be related crosslinguistically, the central processing framework highlights common underlying cognitive processing across languages that can help to understand the relationships between L1 and L2 reading components (Geva & Ryan, 1993). These underlying abilities are thought to account for individual differences in the rate and success of language and literacy skills development whether in the L1 or L2. Processes such as working memory, phonological shortterm memory, phonological awareness, and rapid automatized naming are thought to be part of one’s general innate cognitive endowment. As discussed in more detail below, the research literature suggests that, unlike the higher level cognitive skills and strategies that Cummins has articulated as being transferrable (provided that the learners have attained a sufficient level of proficiency in the L2), the performance of ELLs on underlying cognitive processing skills is “hard wired” and therefore is less dependent on having developed high levels of proficiency in the L2. Deficits in these cognitive processes therefore tend to be reliable indicators of LD. The contrastive framework (Lado, 1964) focuses on structural similarities and differences between the L1 and L2 that may enhance or impede the acquisition of specific language or reading elements in the L2. Languages that are typologically similar (e.g., English and French) share more structural features than languages that are typologically distant (e.g., English vs. Arabic or Japanese). Employing a contrastive stance adds nuance to understanding how ELLs from different L1 backgrounds develop their language and literacy skills in English. For example, specific errors in the phonological awareness or decoding skills of ELLs may reflect negative transfer from the home language when the distance between the two languages is larger (e.g., Bialystok, Majumder, & Martin, 2003; Wang & Geva, 2003). Typological effects may delay the development of accurate and fluent decoding skills in English due to the “deep” or less “transparent” nature of the rules that govern phoneme to grapheme correspondences, as opposed

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to “shallow” or “regular” orthographies such as Spanish, Dutch, or voweled Hebrew (Share, 2008). Evidence of typologically-related negative transfer in the emergence of spelling skills in the L2 comes from studies that applied error-analysis to the spelling of novel phonemes in the L2 (e.g., Fashola, Drum, Mayer, & Kang, 1996; Mumtaz & Humphreys, 2002; Wade-Woolley & Geva, 2000; Wang & Geva, 2003b). For example, Wang and Geva (2003) tracked the spelling development of ELLs whose first language was Cantonese and of monolingual English-speaking controls over two years. A fine-grained error-analysis revealed that at the beginning of Grade 1, the ELLs experienced difficulties in spelling specific phonemes that do not exist in Cantonese (e.g., /th/ in the word “teeth”). However, as their language proficiency and exposure to literacy skills increased these difficulties disappeared, and by the end of Grade 2 these Cantonese-as-L1 learners had acquired the /th/ phoneme and were able to spell it correctly in various words. The implication for assessment of reading disability in ELLs may be that on the surface, students may not have a known history of decoding or spelling problems in the language (L1 or L2) that is associated with a more shallow orthography such as Spanish, but some students might show difficulties in developing decoding or spelling skills in English because it has a “deep” orthography (see Frost, 1994; Katz & Frost, 1992). However, even when ELLs with LD are able to benefit from the regularity of the orthography and decode with accuracy, they typically are less able to read with fluency. Clinically, it is very useful to examine and analyze error patterns. However, the interpretation and conclusions based on this analysis needs to be based on a consideration of developmental and typological factors.

“BICS” vs. “CALP” In considering the course of language development, the distinction between Basic Interpersonal Communicative Skills (BICS) and Cognitive Academic Language Proficiency (CALP) offered by Cummins (1984) is important to consider, particularly with regard to the general issue of ELL/LD. Cummins’ framework highlights the importance of distinguishing between everyday or social knowledge and academic or conceptual knowledge, and the conditions under which the learner can communicate this knowledge. BICS involves context-embedded, daily communication such as the language shared by family members at the dinner table. CALP refers to the language one needs for academic and higher-order cognitive purposes. CALP is the kind of language one may use in discussing

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topics where high-level, subject-specific vocabulary is required, such as a discussion about the theory of evolution. It is useful to think of BICS and CALP as relative points along a continuum. The distinction between BICS and CALP is important because even though ELLs may understand conversational English well, expressing themselves with fluency (and perhaps even with no “foreign” accent) when communicating about everyday topics (i.e., BICS), ELLs are likely to struggle with the language of academic text and higher level, schoollike language (i.e., CALP). Therefore, ELLs may appear fluent, but in fact, their command of the L2 may not enable them to fully understand and participate when language demands are higher. The challenge when working with ELLs is to be able to distinguish between these different levels of proficiency and consider the extent to which ELLs’ language skills reflect lack of opportunities to develop CALP or (perhaps in addition) an underlying and persistent difficulty in developing language skills. This discussion underscores the caution needed to avoid erroneously assuming that ELLstatus is not an issue and that test norms developed on the basis of monolingual samples can be used to interpret performance when in fact the learner’s language skills are still developing.

Development of English Language Proficiency in ELLs Despite rapid development in many areas, typically developing ELLs take longer to achieve native-like English language proficiency than do monolingual peers. ELLs tend to lag behind their monolingual peers on various aspects of English oral language proficiency, including vocabulary breadth (August, Carlo, Dressler, & Snow, 2005; Bialystok & Feng, 2009; Farnia & Geva, 2011; Goldenberg & Coleman, 2010; Roessingh & Elgie, 2009), academic vocabulary (Jean & Geva, 2009), morphological skills (e.g., Pasquarella, Chen, Lam, Yang, & Ramirez, 2011; Ramirez, Chen, Geva, & Kiefer, 2010), and listening comprehension (e.g., Geva & Farnia, 2011; Lesaux, Rupp, & Siegel, 2007; Proctor, Carlo, August, & Snow, 2005). Such language skills are related to each other and are all essential for coping with the school curricula. For example, a longitudinal study in which the development of vocabulary in ELLs and monolinguals was tracked has shown that the gap in vocabulary depth and breadth does not disappear even after six years of attending school in English (Farnia & Geva, 2011; Jean & Geva, 2009). Consequently, when assessing ELLs, one might expect to see somewhat lower scores on aspects of English language proficiency. However, this lower performance does not necessarily reflect a disability,

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and should be mild only. More significant delays and lack of improvement may be due to an underlying disorder and should warrant further investigation.

THE SIMPLE VIEW OF READING Before we turn to describing the research on ELLs with LD it is important to briefly mention a prominent theory that has received much attention in the reading research literature—the Simple View of Reading (SVR), which can help in conceptualizing subtypes of reading disabilities. According to the SVR (Gough & Tunmer, 1986) reading comprehension is made up of two interacting sets of skills: decoding (word reading) and language comprehension. Decoding involves the visual and visual-phonological mapping skills needed to derive word meanings from print with both accuracy and fluency (Wolf & Katzir-Cohen, 2001). Language comprehension involves a variety of language skills including vocabulary, morphology, syntax, semantics, and pragmatics. The SVR provides a general framework for explaining how different types of reading difficulties (i.e., poor decoding vs. poor comprehending) can occur as an outcome of a breakdown in the ability to decode, the ability to comprehend language, or both. A number of recent studies suggest that the SVR framework is adequate for understanding the factors that contribute to the reading comprehension of ELLs as well (e.g., Gottardo & Mueller, 2009; Manis, Lindsey, & Bailey, 2004; Proctor, Carlo, August, & Snow, 2005; Yaghoub Zadeh, Farnia, & Geva, 2010). Some researchers have critiqued the SVR and offered ways in which it can be improved, however a discussion of these aspects is beyond the scope of this chapter (for a critique and suggestions for expansion to the SVR see Cain, Oakhill & Bryant, 2004; Cutting & Scarborough, 2006; Kirby & Savage, 2008). On the whole, a rather consistent finding is that word reading skills and language skills in English predict reading comprehension in ELLs.

ELLs’ LITERACY DEVELOPMENT IN ENGLISH The question of the relationship between language proficiency and literacy is crucial to understanding expected developmental trajectories for ELLs, thereby shedding light on whether reading/writing difficulties can be attributed to insufficient command of the language, or whether they may reflect a LD.

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Word-Level Skills: Decoding and Spelling Isolated word reading is modular and relatively self-contained (Perfetti, 1999; Share & Leikin, 2004). Children with “dyslexia” have difficulties with word reading and spelling skills, including the ability to recognize words and decode/spell new words. One of the key underlying characteristics of monolingual children and ELLs with “dyslexia” is their difficulty with phonological awareness (e.g., Durgunog˘ lu, Nagy, & Hancin-Bhatt, 1993; Ehri, 1998; Elbro, 1996; Geva et al., 2000; Scarborough, 1990; Shankweiler, 1999; Torgesen, 1999). Phonological awareness refers to children’s understanding of the relevant phonemic units in words, and involves the ability to break words into units such as syllables, onsets-rimes, and phonemes (Durgunog˘ lu et al., 1993). Phonological awareness measured prior to or at the onset of formal schooling and reading instruction predicts later word recognition and spelling, and subsequent reading and writing development. Research has shown that performance on phonological awareness tasks predicts word reading and spelling in L1 and L2 for bilingual children (e.g., Comeau, Cormier, Grandmaison, & Lacroix, 1999; Guron & Lundberg, 2003; Jared, Cormier, & Wade-Woolley, 2011; MacCoubrey, Wade-Woolley, Klinger, & Kirby, 2004; Saiegh-Haddad & Geva, 2007). Of high relevance to the topic of ELL-LD is the finding that ELL status does not undermine the ability of ELLs to perform with accuracy on cognitive processing tasks such as phonological awareness, naming speed, and verbal working memory, which are causally related to word level reading and spelling skills (for a review see Geva, 2006; see also Lesaux & Siegel, 2003). These “hard-wired” processing skills may be less dependent on language proficiency, meaning that ELLs should be able to score in the normal range on measures of these skills very early on (Genesse & Geva, 2006).

Text-Level Skills: Reading Comprehension and Writing Unlike word-level skills, text-level skills such as reading comprehension and writing are complex and multifaceted constructs that require multiple levels of knowledge, ranging from basic linguistic knowledge to higherlevel cognitive and language processes and relevant background knowledge. ELLs, by definition, have less well-developed oral language skills than their monolingual counterparts. They also tend to perform more poorly than their monolingual peers on reading comprehension tasks

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(e.g., Aarts & Verhoeven, 1999; Carlisle, Beeman, Davis, & Spharim, 1999; Geva & Farnia, 2011; Hutchinson, Whiteley, Smith, & Connors, 2003; Nakamoto, Lindsey, & Manis, 2008). Various oral language skills, including vocabulary knowledge, listening comprehension, and syntactic and morphological skills have been shown to be strongly related to reading comprehension and reading fluency of ELLs (e.g., Carlisle et al., 1999; Droop & Verhoeven, 2003; Geva & Farnia, 2011; Lesaux et al., 2007; Miller, Heilmann, Nockerts, Iglesias, Fabiano, & Francis, 2006; Nakamoto et al., 2008; Proctor et al., 2005; Yaghoub Zadeh et al., 2010). It is interesting to point out that language comprehension plays a more substantial role in reading comprehension in higher grades than in the lower grades. The reason for this differentiation is that much of the emphasis in the lower grades is on developing accurate and fluent decoding skills but that attention shifts to comprehension later on (e.g., Catts, Adlof, & Ellis-Weismer, 2006). There is growing evidence that reading fluency, the ability to recognize printed words with accuracy and automaticity, contributes to reading comprehension in both monolingual students and ELLs. The ability of ELLs to read isolated words with fluency, that is the ability to read words with accuracy and speed, may be less dependent on having well developed English oral proficiency (Geva & Yaghoub Zadeh, 2006; Lesaux & Siegel, 2003). Yet, ELLs’ fluent reading of texts is more closely linked to their English oral proficiency (e.g., Al Otaiba, Petscher, Pappamihiel, Williams, Drylund, & Connor, 2009; Crosson & Lesaux, 2010; Geva & Farnia, 2011; Nakamoto et al., 2008). In other words, typically developing ELLs who have better developed English language skills can read texts with more fluency than their peers whose language skills are developing at a slower pace (Geva & Yaghoub Zadeh, 2006). This means that when the reading of ELLs is consistently dysfluent it is important to find out whether the source of the difficulty resides in having decoding difficulties, lack of language skills, or both. As is the case for reading comprehension, in addition to having well developed aspects of language proficiency, to become good writers ELLs need to be able to draw on other knowledge and skills, such as spelling, familiarity with cohesive devices, audience awareness, familiarity with writing various text genres, having relevant background knowledge, and writing skills in the L1 (Cummins, in press). There is a dearth of research on the writing development of ELLs (Geva & Genesse, 2006). One available study suggests that ELLs who begin school at a young age in Grade 1

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are able to achieve story-writing skills within age and grade expectations by Grades 4 to 6 (Ndlovu, 2010). Results indicated that verbal short-term and working memory, word-level literacy skills (i.e., reading and spelling), and reading comprehension were the strongest predictors for ELL’s storywriting development over time, supporting theories which suggest that writing requires the successful coordination of multiple systems and skills (e.g., Berninger, 2009).

Typically Developing ELLs It is important to remember that unlike monolingual children, ELLs need to learn spoken and written language simultaneously. Canadian research suggests that typically developing ELLs who begin school in English at a young age should be doing well academically by the time they are in Grades 3 or 4 (Geva, Yaghoub Zadeh, & Schuster, 2000; Lesaux & Siegel, 2003; Ndlovu, 2010). This body of research has demonstrated that these young ELLs matched their EL1 peers in multiple intellectual (e.g., nonverbal intelligence), cognitive-linguistic (e.g., phonological processing, verbal memory, rapid automatized naming), and literacy skills (e.g., realand nonsense-word reading, reading fluency, spelling, written syntax, and story-writing). This means that staff conducting classroom and/or psychoeducational assessments of ELLs can expect students to perform at a similar level to their first language peers in these areas after a few years of school in English, particularly if they began language instruction at a young age and benefitted from consistent instruction. On the whole, much less is known about the development of ELLs who begin school in English as adolescents, but they tend to have lower language and literacy skills than their monolingual counterparts (Kieffer & Lesaux, 2011 ).

LD SUBTYPES IN ELLs Thanks to research advances it is now possible to begin to discuss subtypes of LD in ELLs, thereby allowing for more refined theoretical frameworks, and for more sensitive and informed assessment and intervention for ELLs with LD. In general, the research conducted to date suggests that the subtype classification for reading disorders into “poor decoders” and “poor comprehenders” (or “unexpected poor comprehenders”) is applicable to ELLs as well. In what follows we briefly describe these groups in relation to recent research findings.

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Poor Decoders As we have discussed earlier, longitudinal and cross-sectional research has shown that young, typically developing ELLs make rapid progress in terms of their English word-level decoding and spelling skills in the early grades, in spite of being behind in their English oral language skills. Children who have poor decoding skills constitute a group of students that has more traditionally been thought of as having reading disorders, or “dyslexia”. As discussed above, the best indicators of underlying word-based learning disabilities in ELLs are persistent weaknesses in decoding and phonological skills. In general, as is the case with monolinguals, phonological awareness, rapid automatized naming (RAN), and working memory have been shown to be sources of individual differences associated with word level reading and spelling skills in ELLs (e.g., Arab-Moghaddam & Sénéchal, 2001; Da Fontoura & Siegel, 1995; Durgunog˘ lu et al., 1993; Everatt, Smythe, Adams, & Ocampo, 2000; Geva et al., 2000; Gottardo, Chiappe, Yan, Siegel, & Gu, 2006). ELL and EL1 at-risk readers with low phonological processing skills have very similar learning profiles on many cognitive and linguistic measures (Geva et al., 2000; Ndlovu, 2010; Lesaux & Siegel, 2003). Recent research from a Canadian longitudinal study tracking EL1 and ELLs from Grade 1 to 6, demonstrated that poor decoders can be reliably and fairly identified regardless of home language (Ndlovu, 2010). These researchers used a cut-off point of the 30th percentile on a standardized test of word reading and demonstrated that all language groups were proportionally represented, suggesting that ELLs were neither over- nor under-identified as being poor decoders. The profiles of ELL and EL1 participants who were designated as poor decoders were characterized by phonologically-based difficulties, weaknesses in short term and working memory, more limited vocabulary and syntactic skills, as well as some difficulties in listening comprehension. ELL and EL1 poor decoders had significant delays in literacy development, showing marked difficulties in both word- and text-level reading and writing. The conclusion from the available body of research is that it is possible to reliably assess phonological processing skills in young ELLs, and that poor performance on tasks assessing phonemic awareness, rapid automatized naming, and verbal memory predict word-level reading and spelling difficulties in ELLs as well.

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Poor Comprehenders Poor comprehenders who are fluent decoders are attracting an increasing amount of attention in recent research with monolingual EL1 populations. Poor comprehenders represent a vulnerable group because their difficulties tend to be overlooked by teachers in the classroom due to their fluent oral reading (Leach, Rescorla, & Scarborough, 2003; Nation, Snowling, & Clark, 2007). The fact that this group of learners tends to have below average oral language proficiency has been observed with various aspects of oral language, including vocabulary, listening comprehension, semantics, morphosyntax, and oral story-telling (Cain, 2003; Catts et al., 2006; Leach, Scarborough, & Rescorla, 2003; Tong, Deacon, Kirby, Cain, & Parilla, 2011). Poor comprehenders have problems with higher level and metacognitive skills such as inference making and comprehension monitoring (Cain, Oakhill & Bryant, 2004). They also tend to have below average cognitive abilities, and deficits in short-term verbal memory (Catts et al., 2006; Nation, Clarke, Marshall, & Durand, 2004; Swanson, Saez, & Gerber, 2006). Given their weaknesses in oral language, there is some debate as to whether poor comprehenders have “Specific Language Impairments” (SLI). There is some overlap between poor comprehenders’ skill deficits and those of SLI (Nation et al., 2004), but researchers point out that poor comprehenders typically have intact phonological awareness, whereas students with SLI do not, and that this distinction might affect the focus of intervention (Bishop & Snowling, 2004). It may be rather complex to identify ELLs who are poor comprehenders, or what Kirby et al (2011) refer to as “unexpected poor comprehenders”, for a number of reasons. First, like their EL1 counterparts, the difficulties that ELLs who are poor comprehenders may have are less likely to be recognized, especially in the lower grades, because they are often able to read with relative fluency and have good decoding skills. In addition, as discussed above, ELLs with LD often have their difficulties misattributed to their second language status (Limbos & Geva, 2001). Furthermore, typically developing ELLs can be expected to have less well developed oral language skills than their monolingual peers. This means that teasing apart poor language and reading comprehension skills that are related to normal L2 developmental trajectories from language and reading comprehension difficulties that reflect a “true” LD and perhaps a language impairment is challenging. Sensitive assessment is needed to determine whether the reading comprehension difficulties of ELLs are in excess of what would be expected from normally developing ELLs.

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The task is also complicated by the fact that to date there are no available yardsticks for determining what ‘normal’ reading comprehension is for ELLs coming from different learning and demographic contexts. One way to overcome this challenge is to define ELLs who appear to have persistent difficulties in reading comprehension relative to their own language group. This approach was used in two recent studies, based on the same longitudinal research project (Ndlovu, 2010) in which the performance of ELLs and EL1 students in Grades 4 to 6 was compared on various language and reading measures. These studies highlighted the distinct profiles of three reading groups: poor decoders, poor comprehenders who had average decoding skills, and typically developing students who had average word reading and reading comprehension skills. Importantly, in order to disentangle ELL status from LD, the designation of poor comprehenders was done relative to the ELL/EL1 language groups. Due to the fact that ELLs had lower reading comprehension scores as a whole group, the relative cut-off point of the 30th percentile on a test of reading comprehension represented a lower raw score than that used for EL1s. This procedure minimized over-identification of ELLs as poor comprehenders. The EL1 students and ELLs who were poor comprehenders exhibited similar and pervasive difficulties in comprehending and producing oral language. They were especially challenged when the tasks demanded higher-level semantic and syntactic processing, the integration of factual information, or the ability to draw inferences. In terms of their literacy development, while poor comprehenders had intact word-level skills, their text-level skills (i.e., reading comprehension and story writing) were severely compromised (Ndlovu, 2010). Of particular concern is the fact while some EL1 poor comprehenders who were identified in Grade 4 had improved their reading comprehension by Grade 6, the majority of the ELL poor comprehenders showed little, if any, improvement. On the whole, these studies suggested that poor comprehenders experienced persistent difficulties with various aspects of oral language proficiency and literacy skills, regardless of L2 status.

ASSESSMENT The Assessment of Intelligence—Implications for L2/LD Much has been written about the (in)appropriateness of assessing intelligence in minority children. The difficulties in the use of standardized

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intelligence measures with linguistically diverse populations has usually been presented with regard to serious questions about the validity of obtained scores. A discussion of these issues is beyond the scope of this chapter. For an excellent overview of the theoretical perspectives readers are referred to: Hamayan and Damico, (1991); Sattler (2008); Scientific American, (1998). We do wish to address, however, one specific aspect of intelligence testing that pertains to the diagnosis of LD in ELLs. It has to do with the practice of establishing a significant discrepancy between assessed intellectual functioning and academic achievement in order to qualify for a diagnosis of a learning disability. Among monolinguals, children who experience reading difficulties that are commensurate with their intelligence are often referred to as “garden-variety” poor readers (Stanovich, 1988). They may therefore not be eligible for special education services that are offered to children who meet the discrepancy criteria and are therefore deemed to be LD. The discrepancy requirement has been under attack by researchers. In particular it has been shown that the symptoms associated with a reading disability such as dyslexia in young children are identical for both discrepant and nondiscrepant poor decoders, and it appears that the same root causes (i.e., impaired phonological processing) underlie that difficulty (Stanovich & Siegel, 1994). Phonological processing skills show a very strong, causal relationship with decoding skills, while intelligence is only weakly associated with the acquisition of basic decoding skills in EL1s (Stanovich & Siegel, 1994) and in ELLs (e.g., Geva et al., 2000). Moreover, for ELLs there is another strong argument for avoiding the use of the discrepancy criterion to establish a reading disability. In addition to concerns about the validity of the obtained intelligence scores it is important to consider the fact that for ELLs who are in the process of developing proficiency in English, it may be difficult to obtain a verbal intelligence score that is high enough to establish a discrepancy. Some might argue that to avoid this vicious circle it maybe preferable to rely on nonverbal intelligence to establish the discrepancy. However, concerns about the application of L1 norms to an L2 population remain. Furthermore, nonverbal intelligence tests do not have predictive validity for decoding skills in L2 learners (Geva & Siegel, 2000; Geva et al., 2000). It is also important to remember that global factors such as oral language proficiency and general intelligence may be more subject to the influences of cultural knowledge, socioeconomic factors, and the social context of language acquisition. Instead of relying on

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the possibly erroneous measurement of such global factors, it may be prudent to target the underlying cognitive processes such as working memory, phonological short-term memory, phonological awareness, and rapid automatized naming, which, as discussed, have been shown to be important sources of individual differences in ELLs’ reading acquisition for early identification and intervention.

Assess in the L1 and/or in English? There are ongoing debates about the extent to which it is useful to examine both L1 and L2 reading and language skills. This clinical question is motivated by theoretical questions about the extent to which it is possible to predict performance on literacy measures in L2 by considering performance in the L1 and the L2. From a theoretical perspective the underlying argument here is whether linguistic processes are universal or languagespecific (Geva & Siegel, 2000). These discussions are related to the interdependence and typology frameworks discussed above. It is important to remember, however, that these two perspectives are not mutually exclusive and their realization may depend on the specific language or reading features that are considered. For example, phonological awareness is a metalinguistic skill that is acquired in the process of developing language and literacy skills. It has been argued that once acquired it does not have to be learned again in the context of another language. This means that phonological awareness in one language is likely to correlate positively and significantly with phonological awareness in another language (e.g., English, the L2). Indeed, there is ample research to support this observation with typically developing children (Comeau et al., 1999; Jared et al., 2011; MacCoubrey et al., 2004; Saiegh-Haddad & Geva, 2007) as well as in studies of children with learning difficulties (Durgunog˘ lu, 2002; Guron & Lundberg, 2003; Marinova-Todd & Zhao, & Bernhardt, 2010; Paradis, Genesee, & Crago, 2010). Other aspects of language such as morphological or syntactic skills may be somewhat more closely aligned with L2 language proficiency. For example, Saigh-Haddad and Geva (2008) have shown in a study of primary school children that the correlation between phonological awareness in English and Arabic was positive and significant, while the correlation between morphemic awareness in English and Arabic was not. SaighHadded and Geva explain that these children were able to carry out the phonological awareness tasks in both languages because language proficiency in either language did not exert a toll. Yet, having well developed

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morphological skills in English could not be used to perform the task in Arabic. Typological differences between different languages exert an influence when it comes to morphological skills, as does language proficiency. Morphological awareness requires linguistic knowledge and children whose L2 proficiency is minimal may not be able to “transfer” their morphological knowledge from one language to the other if the two languages are very different from each other, as is the case in the EnglishArabic dyad for example. This typological effect has also been illustrated in other studies that compared morphological skills across languages with different structures (e.g., Pasquarella et al., 2011). The implications for assessment are clear; one should be cautious about attributing difficulties to a learning disability when the difficulty is contained to the L2, and is not noted when other linguistic elements that are common to both the L1 and L2 are involved. In contrast, one may conclude not only that the learner is thwarted by negative transfer from the L1, but that the learner’s performance is considerably delayed if their performance is low relative to other students with similar home language and instructional experiences. Some have argued that children’s performance should be assessed in both the L1 and L2 because limiting assessment to the L2 only (as is often the case when working with ELLs) may underestimate the learners’ linguistic ability (e.g., Oller & Pearson, 2002) and other sources of knowledge (Cummins, 1984, in press; Sparks, Patton, Ganschow, & Humbach, 2009). For example, learners may be familiar with the meaning of words in their L1 but not in the L2. They may also be able to use higher-level reading and writing strategies in their “better” language (Cummins, in press). Even in the domain of phonological awareness research has shown that although phonological processes assessed in the L1 and L2 correlate positively with each other (i.e., there is a lot of overlap), L1 and L2 phonological processes may load on different factors (e.g., Durgunog˘ lu et al., 1993; Gottardo & Mueller, 2009; Manis et al., 2004; Swanson, Saez & Gerber, 2006). Assessing only in the L2 may be related to bias in assessment that may reflect injudicious reliance on test norms developed on the basis of monolingual speakers, unfamiliarity with testing procedures, and so on. Therefore, from an equity perspective it is useful to gather information on parallel skills in the L1 and L2 where this is feasible, or at least gather information about past performance in the L1. At the same time, it is important to acknowledge a myriad of challenges to the assessment

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and interpretation of tests administered in the L1 as well. These challenges include factors such as: lack of appropriate tests in children’s L1, lack of qualified personnel who can administer tasks in the L1, and the inappropriateness of L1 norms for ELLs who have begun to lose their L1 due to the fact that their schooling may be occurring entirely, or primarily, in the societal language (Geva & Wade Woolley, 2004). On the whole, we suggest that where possible assessment should be carried out in both the L1 and L2, and gathering information about performance in the L1 and L2 is useful. In principle, one would be looking for consistent information in the L1 and L2. However, the interpretation of performance should be carried out with caution, and with full awareness of possible mitigating factors. In this respect it is important to remember that comparison of performance with that of siblings or with other learners with a similar background is highly informative.

Assessing Oral Proficiency Assessing the oral language skills of ELLs is a complex task that requires a good understanding of language development in various domains including phonology, semantics, morphosyntax, discourse comprehension, and pragmatics. It is also important to be cognizant of within-child, typological, demographic and contextual factors that influence L2 language and literacy development. These factors may impact decision making with regard to possible language impairment or learning disability. Within-child factors include the previously discussed underlying, “hard-wired” skills, such as rapid automatized naming or phonological short-term memory. Individual differences in such skills are related to language and literacy development in L1 and L2. The challenge is to figure out whether poor performance reflects lack of opportunities to learn and dearth of appropriate educational exposure, or whether it reflects an underlying disability. The approach that we take to untangling this challenge is two-pronged. It involves adopting a developmental perspective that considers children’s response to instruction and opportunities to learn, and comparing struggling learners to peers with similar linguistic and demographic background.

DO L1-BASED INTERVENTIONS WORK FOR ELLs? Only a handful of studies have systematically examined the impact of research-based interventions on ELLs at-risk for having a learning

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disability and the majority of those have focused in on enhancing phonological awareness. An important and well designed series of studies has been conducted in Texas. For example, Cirino et al. (2009) report on the outcomes one year post-intervention of ELLs at-risk for reading problems. Reading performance was evaluated at the end of Grade 2 for ELLs from a Latino/a background, who were at-risk at the beginning of Grade 1, and were assigned to an intervention or a comparison group either in English or Spanish, depending on the language in which they were receiving reading instruction. The intervention focused on explicit phonemic awareness, phonics instruction, application of this knowledge to reading at the word and text levels, and meaning making strategies. The intervention was further enhanced by including an oral and vocabulary component, and by emphasizing language support activities. Students who received the intervention in Grade 1 in English showed improvement in English oral language, decoding, comprehension, reading fluency, and spelling, with modest effect sizes. This was generally also true for the students who received the parallel intervention in Spanish (i.e., the home language). Participants made and sustained gains regardless of whether the intervention was delivered in Spanish or English. However, there was very little evidence of transfer between English and Spanish. One wonders whether evidence of transfer would have emerged by lengthening the treatment period or with explicit teaching to transfer. In a Canadian study, Lovett et al. (2008) examined the extent to which struggling readers from different ELL backgrounds could benefit from a phonologically based intervention. Reading and reading related outcomes of 166 participants were assessed before, during, and following an intervention of 105 hours. Participants were randomly assigned to one of three interventions, or to a special education reading control program. The research-based interventions were more effective than the special education reading control program on reading outcomes, and rate of growth. Of high relevance in the present context was the finding that there were no differences between ELLs and EL1s in terms of responsiveness to the treatments. In another study, Wise and Chen (2010) identified French Immersion children in Grade 1 who were at-risk for reading disability. Those at-risk students received a systematic intervention designed to improve phonological awareness skills. The intervention program was first delivered in English (children’s L1) but then there was a shift to French, the language of instruction at school. This intervention study also yielded significant improvement in reading.

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These studies vary in the methodology used and the procedures used to identify learning disabilities. Nevertheless they point to the merit of providing L2 learners who are at-risk for a reading disability or who were identified as LD with rigorous and timely interventions that are researchbased. The available research suggests that L2 learners, like their monolingual peers, can benefit from such interventions, particularly when common underlying processes such as phonological awareness are targeted. However, various questions still remain when one considers the impact of interventions applied to ELLs who have LD. Additional research is needed that explores the conditions that might enable L1-L2 transfer of training and intervention in subgroups of ELLs with LD. More is known about what may be helpful for poor decoders, while very little research is available on how to intervene with ELLs who are poor comprehenders. The effects of assessment and intervention with ELLs who enter school in English in adolescence and may have an LD is another “unchartered territory”, as is research on the efficacy of training clinicians and educators to identify and tailor interventions that are sensitive to the needs of ELLs with different profiles.

CONCLUSION This chapter has provided an overview of recent research on language and literacy development in typically developing ELLs and those who might have an LD. Due to the implications for assessment, special attention was given to a discussion of reading domains that are more closely related to L2 language proficiency, and those hard-wired, modularized aspects of word reading that are less closely aligned with language proficiency. Additional sections provided a review of other complementary factors that pertain to the development of language and literacy skills in typically developing ELLs and those with LD. These include the contribution of contextual and family factors, cross-language transfer and its relevance to L2 learning, the contribution of typological linguistic factors, and withinchild, processing factors. We have shown that the cognitive and linguistic profiles of monolingual and ELL poor decoders (dyslexics) are rather similar to each other, as are the profiles of monolingual and ELL students who are “unexpected” poor comprehenders. Recommendations for assessment and for students at-risk for decoding and/or reading comprehension problems include: taking a developmental approach that considers response to instruction, fine-grained

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error-analysis, the importance of assessing in both the L1 and L2 where possible, and of gathering detailed information about past academic performance in the L1. Other recommendations involve the need for caution in using standardized norms and intelligence testing, and the value of comparing students’ progress to that of students with similar backgrounds such as siblings. The available research on educational interventions for ELLs with LD suggests that research-based instructional programs work equally well for monolinguals and ELL. Understanding the course of language and literacy development of ELLs in elementary and secondary school is important because recent research indicates that too many of the ELLs who have an LD are not identified and are therefore denied proper program adaptation and intervention. School staff who are hesitant to refer ELLs are likely trying to be sensitive to their students’ need for time to learn English and acculturate. However, as this chapter suggests, tools are available for sensitively assessing ELLs and ensuring they receive appropriate intervention if necessary. It appears that systematic, research-based teaching methods that work when applied to monolingual learners should also work when applied to ELLs. To minimize the drop-out rate of ELLs with LD it is vital that schools support students with LD, regardless of whether they speak English as a first or second language.

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Improving Outcomes for Adolescents with Learning Disabilities Patricia Sampson Graner, and Donald D. Deshler University of Kansas, Center for Research on Learning, Lawrence, KS 66045, USA

Chapter Contents Introduction  299 Summary of Research Findings on Adolescents with LD   300 Reading  300 Writing  301 Mathematics  302 An Integrated Intervention Framework for Struggling Adolescent Learners   303 Instructional Supports   305 Professional Learning Supports   311 System Supports   314 Summary  319 Future Directions   319 References  320

INTRODUCTION During the past decade, a growing amount of attention has been given to adolescent learners who struggle academically. This is evidenced by the large number of special reports issued specifically addressing the unique challenges facing these students as they move into secondary schools as well as the various interventions that enable them to successfully respond to rigorous curriculum demands (e.g., Biancarosa & Snow, 2006; Carnegie Council on Adolescent Literacy, 2010; Faggella-Luby, Ware, & Capozzoli, 2009; Graham & Perin, 2007a, b; Heller & Greenleaf, 2007; McPeak & Trygg, 2007; Short & Fitzsimmons, 2007; SREB, 2009). While these reports do not specifically address adolescents who have been formally identified as having a learning disability (LD), their findings and recommendations are in alignment with what much of the research done on adolescents with LD has found. Thus, frequent reference will be made to Learning about Learning Disabilities

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important findings from these reports that are relevant to designing effective solutions to the vexing challenges adolescents with LD face. The purpose of this chapter is three-fold: (1) to summarize some key findings that have emerged from the empirical literature during the past decade on adolescents with LD; (2) to describe an intervention framework that has emerged from the research and development (R & D) work of the University of Kansas Center for Research on Learning (KUCRL) since 1978 in its attempt to design and validate instructional interventions and infrastructure supports that enable adolescents with LD to dramatically improve their academic achievement, to thrive in their content classes, and to become career and/or college ready; and (3) to specify issues that should be addressed by educational leaders, practitioners, and researchers to enhance the chances of adolescents with LD being able to find success in schooling, work, and their personal lives.

SUMMARY OF RESEARCH FINDINGS ON ADOLESCENTS WITH LD A growing number of studies have appeared in the professional literature during the past decade describing the characteristics of struggling adolescent learners and the types of interventions that result in the most favorable outcomes. This is in marked contrast to the relative paucity of studies on adolescent learners overall. A summary of research findings in the areas of reading, writing, and mathematics follows.

Reading Four major research syntheses and meta-analyses have been recently completed describing the types of instructional interventions and instructional procedures that have been found to have the greatest effects on enhancing outcomes for students who struggle (Edmonds, Vaughn, Wexler, Reutebuch, Cable, Klingler-Tackett, et al., 2009; Faggella-Luby & Deshler, 2008; Scruggs, Mastropieri, Berkley, & Graetz, 2010; Wanzek, Wexler, Vaughn, & Ciullo, 2010). These reviews all point to a converging evidence base that has emerged regarding adolescent reading comprehension instruction. Specifically, these reviews indicate that reading comprehension for both students with LD and those at-risk for failure was improved when instruction mirrored what good readers do. That is, comprehension improved when readers learned to identify narrative and expository text structures, discover word meaning, tap prior knowledge, and use cognitive

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strategies. Reading comprehension instruction embodied a host of approaches including: teaching how to recognize and navigate various text structures (e.g., narrative, expository), teaching cognitive strategies (e.g., self-questioning, visual imagery), and teaching students how to participate in cooperative learning to increase their task engagement. The specific cognitive strategies that were found to be remembered best and used most frequently in post-intervention included self-monitoring, summarizing, and story grammar self-questioning. Of particular note was the repeated finding that strategy instruction that was overt and explicit resulted in the greatest effect size. Additionally, in a large descriptive study, Hock, Brasseur, Deshler, Catts, Marquis, Mark, and Stribling (2009) found that in all component areas of reading (i.e., alphabetics, fluency, vocabulary, and comprehension), struggling adolescent readers were found to perform statistically lower than their proficient reader counterparts. By and large, the struggling readers scored approximately one standard deviation below the mean in each reading area and 20 to 25 or more standard score points lower than the proficient reader group. While the areas of greatest deficit were in fluency and comprehension, many poor readers showed significant deficits at the word level as well (word attack, decoding, word recognition, and rate). Of particular note is the finding that the profiles of students with disabilities were similar across component areas; however, the size of the deficit was greater in Alphabetics and Fluency (10.56 points and 6.65 points lower respectively).

Writing The National Assessment of Educational Progress (NAEP) 2007 writing exam measured the writing skills of 8th and 12th graders and translated their scores into three levels of proficiency: Basic, Proficient, or Advanced (Salah-Din, Persky, & Miller, 2008). One sobering finding was that only 33% of eighth graders and 24% of twelfth graders performed at or above the Proficient level across the grades, and very few were found to write at the Advanced level (Salah-Din et al., 2008). Very troubling was the fact that high proportions of students, two thirds of eighth graders and threequarters of twelfth graders, were found to be at or below the Basic level. Unfortunately, while scores for adolescents with disabilities were somewhat better in 2007 than 2002 or 1998, the greatest number scored below Basic in both the 8th and 12th grade samples. Over the past three administrations, the percentages of eighth grade students identified with disabilities scoring below Basic were 45, 53, and 57% respectively (NAEP

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Writing Data, 2007). For the same three years, percentages of students with disabilities in twelfth grade scoring below Basic were 56, 70 and 67% respectively. These results clearly underscore the importance of large numbers of adolescents receiving interventions to help them become better writers, and employing interventions that will help teachers improve the writing skills of students with disabilities is imperative. Graham and his colleagues have recently completed several research syntheses and meta-analyses on writing instruction for adolescents (Graham, 2008; Graham & Hebert, 2010; Graham & Perin, 2007a, b; Rogers & Graham, 2008). The following encapsulate the findings with the largest effects that emerged from these reviews: teach students how to plan (draft) both narrative and expository text; directly teach grammar skills and usage (i.e., capitalization, subject/verb agreements, incomplete sentences, etc.); teach students how to set clear and specific goals for increasing their writing productivity and quality; teach students specific strategies for editing their written products for meaning, capitalization, punctuation, spelling, and overall appearance; facilitate access to and usage of word processing software; reinforce students for their writing quality and productivity (i.e., number of words, variety of words, new words, variety of sentence structures, etc.); engage students in pre-writing activities for gathering and organizing ideas in advance of writing; instruct students how to construct complex sentences (i.e., compound, complex, compound-complex sentences); and teach students strategies for constructing different types of paragraphs (i.e., descriptive, expository). l

l

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Mathematics Some research has suggested that as many as 5–8% of school-aged students experience some sort of mathematics LD (Geary, 2004). Students with LD tend to commit procedural errors, have difficulty organizing information, and evidence working and long-term memory deficits when performing mathematical tasks. Additionally, they frequently have difficulty with basic computation and problem-solving curricular demands (Geary, 2004; Miller & Mercer, 1997). A study by Montague and Applegate (2000) found that students with LD perceived math problems to be more difficult. They also found that these students required more time to complete problems and evidenced fewer strategies than their peers without disabilities.

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Maccini and her colleagues (Maccini, Mulcahy, & Wilson, 2007; Maccini, Strickland, Gagnon, & Malmirgren, 2008) have conducted literature reviews to determine the nature and focus of math interventions that are effective for assisting adolescents with LD. Their reviews of the empirical literature found that the practices resulting in the largest effect sizes included: (1) mnemonic strategy instruction (i.e., use of mnemonics to help students remember each step in a problem-solving strategy); (2) graduated instructional approach (i.e., employing a three-phase instructional process involving concrete instruction to introduce students to concepts via manipulatives, semi-concrete or representational instruction using pictures to represent objects, and abstract instruction using numbers and symbols); (3) cognitive strategy instruction involving planning (i.e., using self-monitoring while solving the math problem, focusing while solving the problem, addressing and using various data to solve problems, and solving the math problem in a specific order); and (4) schema-based instruction (i.e., explicit instruction that focuses on helping learners understand the structure of math word problems such as proportion or comparison). Across these various approaches they found a common thread of effective instruction (Rosenshine & Stevens, 1986) including components of direct and explicit instruction such as: modeling, guided practice, independent practice, monitoring student performance, and corrective feedback. In sum, adolescents with LD face substantial academic challenges that can prevent them from being successful in being college or career ready.

AN INTEGRATED INTERVENTION FRAMEWORK FOR STRUGGLING ADOLESCENT LEARNERS The problems that adolescents with LD face when trying to succeed within the rigorous general education curriculum are significant. Unless students have the necessary skills and strategies to respond to the heavy curriculum demands, they will encounter failure and frustration. Figure 11.1 illustrates the dilemma faced by teachers and students with LD in secondary schools. The straight solid line represents both the path of normal acquisition of skills, strategies, or knowledge by students performing on grade-level and the demands of the curriculum. That is, these demands increase with each succeeding year that students are in school. Hence, at the end of one year, students should have gained a year’s worth of knowledge, as signified by the distance between point A and point B. To the degree that students acquire the skills, strategies, and knowledge that are expected of students

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The Performance Gap

Skills/achievement

12

6

B A

1 1 A1

B1

6 Grade in School

12

Growth of: Average achieving students At-risk students Students with disabilites

Figure 11.1  The performance gap.

at their grade level, they will be in a position to successfully respond to the demands of the curriculum. For students with disabilities or who fail to acquire skills, strategies and knowledge at the typical rate of achievement, the performance trajectory from one year to the next would not follow the same path but instead, on average, be similar to the curved, dotted line with a year’s worth of progress indicated by the distance between point A1 and point B1 (Warner, Schumaker, Alley, & Deshler, 1980). As these students continue to struggle in school, the gap between their academic proficiency and the demands placed on them by the curriculum continues to grow. To complicate this situation, some research has shown that the acquisition of skills and strategies for adolescents with LD tends to plateau at about the 5th or 6th grade level (Warner et al., 1980). The National Center for Learning Disabilities reported that on average, 23% of students with LD lag by one grade level, almost half (45% in reading and 44% in math) test more than three grade levels behind, and at least one fifth are five or more grade levels behind the grade in which they are enrolled (Cortiella, 2011). Inasmuch as the demands continue to increase, the size of the gap gets larger with each succeeding year that students are in school and may contribute to the high dropout rate among students with LD.

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Given this dynamic, it is easy to understand why adolescents who struggle in learning may get discouraged, may begin to act out, or may give up trying altogether because of the size of the gap between where they are and where they are expected to be. Researchers and developers at the KUCRL and their partners in hundreds of schools have been designing and testing a broad array of interventions to find ones that will have an impact on narrowing the size of this gap and put adolescents with LD in a position to be successful in school. Our research has shown that no one intervention or element is sufficiently powerful to close the gap. Rather, significant progress occurs when multiple components are implemented with fidelity, in a coordinated fashion, and over a sustained period of time. The addition of three categories of supports to the current supports has been found to be critical in helping to close the achievement gap: (1) Instructional supports; (2) Professional learning supports; and (3) System supports. Each category consists of several components given that current supports are not sufficiently powerful to change the trajectory of student achievement. In the following paragraphs, each category and its components will be described. There are three overriding goals of this instructional model: (1) dramatically improve academic achievement; (2) thrive in content classes; and (3) become college and/or career ready.

Instructional Supports Instructional supports are those actions, procedures, structures, or strategies that are used to improve the academic achievement of struggling adolescent learners. High quality instruction is foundational to bringing about any changes in the performance of students. Too frequently, emphasis is placed on factors other than instruction. Elmore (2004) has argued that most efforts to improve a school’s focus are on aspects that are not central to the instructional process or what he refers to as the “core of educational practice.” This core he defined as the standard solutions of “… how teachers understand the nature of knowledge and the students’ role in learning, and how ideas about knowledge and learning are manifested in teaching and classwork” (p. 8). Included as well is the grouping of students for instruction, the time allocated to content, and the assessment of student work, which in Elmore’s perspective have changed little and change only fractionally. Interventions or innovations that require substantive change in the core educational practices do not typically breach the practice barrier of most schools. Instructional supports embody components that relate to what should be taught, how instruction should be provided, and what should be done to engage and motivate students (see Figure 11.2).

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Skills & Demands

INSTRUCTIONAL SUPPORTS • Literacy-based instruction (SIM)

Instructional Supports

• Standards-informed curriculum planning • Motivation strategies • Engaging instructional materials & activities

Current Supports

Years in School

Figure 11.2  Instructional supports.

Literacy-Based Instruction Most adolescents with LD struggle with language-based learning problems. Because of this, they have difficulty responding to and navigating rigorous curriculum demands that they encounter in middle and high school settings. In order to be good problem solvers and to be able to engage in higher order thinking, students must have a solid foundation of language and skills. Figure 11.3 depicts the relationship between higher order thinking and the acquisition of critical subject matter, strategies, skills, and language. We refer to these elements as the “building blocks of content literacy”. That is, underlying the ability to be a good problem solver and higher order thinker in various content disciplines (science, mathematics, history, literature, etc.) are four building blocks: 1. Language: foundational to any academic success is strong language skill. When students fail to acquire a strong language base in their pre-K years that includes facility with language structures as well as a rich vocabulary, future learning is markedly impaired (Risley & Hart, 1995). Hence, language is the foundational block in this structure. Language is shown at the base of each of the additional building blocks because it plays a central role in each and students must learn how to use and navigate the unique and increased language structures at each level. 2. Skills: these are automatic procedures that do not require thought, interpretations or choice. Skills are observable behaviors and are acquired through repeated practice until they become habitual responses to particular tasks. In the case of reading, one example would involve the fluent, automatic application of phonemic skills in decoding words.

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HIGHER ORDER THINKING Language

SUBJECT MATTER Language

STRATEGIES Language

SKILLS Language

LANGUAGE

Figure 11.3  Building blocks of content literacy.

3. Strategies: these are conscious plans and actions under the control of the learner that are used before, during, and after the performance of a task. Strategies are process-oriented, cognitive operations used by students to purposefully navigate curricular demands. 4. Subject matter: each curriculum content area (e.g., history, biology, algebra, literature) has a body of information consisting of core concepts and underlying details that are structured in unique ways (e.g., hierarchies, taxonomies). The acquisition of critical subject matter content enables learners to problem solve and engage in higher order thinking. Understanding the building blocks of content literacy helps to determine which particular building block or blocks require attention to properly equip struggling learners to be successful in school. Regardless of the focus of what is taught during the instructional process, how that instruction is offered is very important. Among the defining features of effective instruction that have emerged from our research (Bulgren, Deshler, & Lenz, 2007; Deshler & Lenz, 1989; Ellis, Deshler, Lenz, Schumaker, & Clark, 1991; Schumaker & Deshler, 2006) are the following: 1. The purpose and benefits of learning a strategy should be explained to students. 2. The physical and mental actions embedded within a strategy should be fully described to students. 3. Students should participate in goal setting to promote and guide learning.

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4. Multiple models of the strategy should be provided by the teacher. 5. The key steps of the strategy should be memorized so it can be used fluently in multiple settings and situations. 6. Practice should begin with controlled and guided practice and ultimately conclude with advanced independent practice. 7. A measurement system should provide ongoing information that will demonstrate to the student and teacher that the strategy is being learned and used. 8. While generalization should be prompted throughout the strategy acquisition process, specific efforts to promote generalization should follow strategy acquisition. Standards Informed Curriculum Planning Increasingly, schools are attempting to orient their instructional planning and delivery to a set of standards defined by an external educational entity, such as a state department of education. Since 2009, the vast majority of states have adopted the Common Core State Standards (CCSS) (www.corestandards.org/). The CCSS is a state-led effort coordinated by the National Governors Association Center for Best Practices (NGA Center) and the Council of Chief State School Officers (CCSSO). The standards were developed in collaboration with teachers, school administrators, and experts to provide a clear and consistent framework to prepare children for college and the workforce. The instruction provided to adolescents with LD needs to be informed by and in alignment with these kinds of standards if students are to acquire the kinds of skills, dispositions, and knowledge that will equip them to successfully compete in postschool environments. An important step in successfully aligning instruction with external standards is carefully assessing the specific demands of the curriculum to understand the expectations that students must meet from a literacy perspective. As students move into the secondary grades, literacy demands change markedly in the following areas: texts become longer (i.e., more sophisticated learning strategies are required to complete assignments), word complexity increases (i.e., the density of technical and subject-matter specific vocabulary increases thus requiring instruction in segmenting and pronunciation), sentence complexity increases (i.e., longer sentences must be parsed automatically for fluency and cohesive devices and connective words must be effectively used to understand relationships), structural complexity increases (i.e., interrelationships among and across text sections is not always

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apparent), increased importance of graphics to comprehend content (i.e., graphics are critical to understand ideas and to synthesize information across sections), conceptual challenges increase (i.e., relationships across a conceptual domains must be built since the acquisition of new knowledge is dependent on sophisticated knowledge of previously learned concepts), and texts vary widely across subject-matter area (i.e., each content area demands a different approach to reading, thinking, and writing) (Carnegie, 2010). When literacy demands are understood and when the subject matter and associated skills to successfully access that content is the basis of the instructional planning process, teaching becomes focused and goal directed. Motivation Strategies By the time adolescents with learning disabilities reach the secondary grades, they probably will have encountered a great deal of academic failure. Their willingness to continue to enthusiastically engage in the schooling process is greatly diminished. In some instances, their hopes and dreams for the future have been shattered and the future they face is filled with much uncertainty. Their emotional reaction may be one of fear or anxiety as a result of the gap that they sense existing between how they perform as learners and what they see their peers doing. On the other hand, some have given up altogether or are very reluctant to try yet one other thing or one more time. While learning skills and strategies are critical to academic success, if students lack the motivation to engage in learning, growth will be limited (Guthrie & Wigfield, 1999). Guthrie and Wigfield have defined reading motivation as an “individual’s personal goals, values, and beliefs with regard to the topics, processes, and outcomes of reading” (p. 405). This construct of motivation assumes that reading motivation is likely to differ from individual to individual. Additionally, they contend that motivation to learn can be affected by the context within which learning occurs (e.g., which class setting), the type of text (e.g., science, history), or who is serving as the student’s teacher or tutor. In short, motivation to learn is a multifaceted and very complex construct. Considering the level of student motivation is important when planning an instructional program. Extrinsically motivated students act for reasons outside of themselves; that is, to gain a reward or to avoid a punishment. Multiple studies have shown a positive relationship between the amount of reading completed and extrinsic motivation (e.g., Guthrie, Wigfield, Metsala, & Cox, 1999). However, they have found that both extrinsic and intrinsic motivational

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strategies explain increases in the amount of learning more than each construct alone. In spite of that finding, research has shown that middle and high school teachers use practices that emphasize extrinsic motivation more than intrinsic motivation. Because we are interested in students’ long-term success and their ability to learn and perform independently, our work has emphasized intrinsic motivational strategies. One of the approaches that the KUCRL has taken to enhancing the academic motivation and commitment of adolescents with LD is to use the construct of “possible selves”. Possible selves are ideas about what one might become in the future (Markus & Nurius, 1986). Markus and her colleagues reported that ideas about oneself in the future can be very motivating. That is, individuals with clear ideas and goals about what they want to do, be and be like seem more willing to put forth the effort needed to attain these hoped-for ideals. Similarly, Markus has reported that some individuals will work just as hard to avoid the possible selves that they fear. Based on this foundational research, Hock, Deshler, and Schumaker (2006) developed the Possible Selves Program. This program is designed to be used with students to increase their motivation by having students examine their future and think about goals that are important to them. Specifically, students participating in the program with their teacher think about and describe their hoped-for possible selves (selves they would like very much to create, a wish or a dream), expected possible selves (selves they are fairly sure they can create), and feared possible selves (selves they wish to avoid). Once students describe their possible selves, they create a Possible Selves Tree (Borkowski, Day, Saenz, Dietmeyer, Estrada, & Groteluschen, 1992; Day, Borkowski, Dietmeyer, Howsepian, & Saenz, 1994), a drawn picture of a tree that has branches and other elements representing their possible selves. The tree is used as a metaphor to help students examine the key roles they will assume in life, their hopes, expectations, and fears for the future, and the overall condition of their “tree”. In effect, students examine their personal tree and are challenged to evaluate and take action to nurture their tree so it can become a strong, well-balanced, beautiful tree. Finally, they set goals related to the actions they need to take to nurture their trees, make plans for reaching the goals, and then work toward those goals. Engaging Instructional Materials and Activities As situations of failure continue to mount, struggling learners often acquire an aversion to most kinds of texts. One of the top instructional priorities is to re-engage students in the learning process. One of the

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best ways to do this is to make highly engaging reading materials available to them. Too frequently students become very frustrated when they are expected to read books that are too difficult for them to decode and understand. Students need access to reading materials that are below their frustration level; however, they need to be of high interest to the students. Some authors (e.g., Biancarosa & Snow, 2006) have used the term “diverse texts” to mean that the materials should represent both a wide range of difficulty levels as well as a wide range of topics. Topical diversity provides students more choices for self-selecting their learning. Biancarosa and Snow believe that “… students should be able to find representatives of themselves as well as others about whom they wish to learn or even become.” School librarians can be an excellent resource for assisting teachers and students in identifying books and other reading materials to get and keep students engaged in reading in such a way that the volume of what they read increases as well as the vocabulary, background knowledge, and critical concepts that they are requiring (Neuman, 2002).

Professional Learning Supports Professional learning supports (see Figure 11.4) are those mechanisms and practices that enable teachers and school leaders, on an ongoing basis, to improve and refine their craft so they are better able to understand and meet the learning needs of all students including those with disabilities. Providing meaningful, high quality, effective instruction for all adolescents in academically diverse classes is extraordinarily challenging. Professionals

Skills & Demands

PROFESSIONAL LEARNING SUPPORTS

Professional Learning Supports Instructional Supports

Current Supports

Years in School

Figure 11.4  Professional learning.

• Protocols for observing, describing, analyzing practice • Team planning for cohesion • Instructional coaching • Student driven professional development

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require multiple, job-embedded learning opportunities to critically analyze, discuss, and refine their instructional practices. Many studies have underscored the power of teacher effects on student achievement in reading and mathematics (e.g., Rowan, Correnti, & Miller, 2002). In light of these findings, it is important that attention be given to supporting the professional learning and growth of all teachers and school leaders. Professional learning supports embody components that relate to protocols for studying and talking about instruction, planning to ensure coordination across the curriculum, instructional coaching, and strategies for designing professional development in light of student needs. Protocols for Observing, Describing, and Analyzing Practice Teaching and leading in schools is often a very solitary, isolated experience. Educators seldom have the opportunity to work together, to observe each other practice their craft and to dialogue with each other for the purpose of improving and refining their practice. However, instructional improvement requires continuous learning (Elmore, 2004). Fullan (2008) has said that three elements must be present in schools to improve the quality of instruction: (a) transparency of instruction (i.e., all teachers must be open to having their instruction observed because it will serve as the basis of dialogue among staff in their collective attempt to improve their craft of teaching); (b) nonjudgmentalism (i.e., to have open, candid analysis and dialoguing about instructional practices, professionals must work to create an environment of psychological safety and support in which professional practice is the focus of interactions and not individuals); and (c) instructional coaching (i.e., foundational to improving any complex behavior is high quality coaching that provides the necessary support, problem solving, and feedback—instructional coaching is described in greater detail below). Team Planning for Cohesion Students are more likely to learn and remember critical content and skills/ strategies if they are taught in a systematic, scaffolded, and related fashion in which the various elements of content and skills/strategies are deliberately linked together so new knowledge can be built upon and reinforce that which was previously learned. All students learn best when their instruction is characterized accordingly; however, for adolescents with LD, this type of instruction is imperative. When instruction of critical content and skills/strategies is sporadic, fragmented, and not carefully linked, little learning occurs. Inherent to the structure of secondary schools in

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which students have as many as six or seven teachers across the course of a school day and from one semester (or year) to the next is fragmentation. That is, unless teachers deliberately plan and work toward a cohesive instructional plan, what transpires in one class will seldom carry over or be reinforced in other classes. From a content knowledge standpoint, this is critical because what is taught in a lower level class should provide students with the necessary background knowledge, vocabulary, and habits of mind (Costa & Kallick, 2008) required to succeed in higher-level courses. Similarly, in order to build into students the necessary skills and learning strategies to successfully access and navigate the curriculum, teachers must deliberately plan for ways to build and reinforce one skill/strategy on top of previously learned ones as well as to systematically program into instruction across classes and teachers ways for students to use and generalize their application of various skills and strategies. This only occurs when efforts are made at team planning to ensure cohesive instruction. Instructional Coaching Instructional coaching is a process in which professional development supports are provided to educators on how to use proven teaching methods (Knight, 2008). Instructional coaches employ a variety of professional development procedures to foster widespread, high-quality implementation of interventions, providing “on-the-job learning”. An emerging approach to the instructional coaching practice that has effectively engaged teachers to employ evidence-based practices in their instruction of students with LD is an approach developed at the KUCRL based on a partnership approach in which instructional coaches respect teachers’ professionalism and focus their efforts on conversations that lead to creative, practical application of research-based practices. Instructional coaches see themselves as equal partners with teachers in the complex and richly rewarding work of teaching students. More than anything else, instructional coaches work in partnerships to accelerate teachers’ professional learning through mutually enriching, healthy relationships. Instructional coaches are colleagues, friends, and confidantes who listen with care and share valuable information with teachers at the time when teachers most need it. Student-Driven Professional Development Professional development generally refers to ongoing learning opportunities available to teachers and other education personnel through their

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schools and districts. Effective professional development is often seen as vital to school success and teacher satisfaction, but it has also been criticized for its cost, often vaguely determined goals, and for the lack of data on resulting teacher and school improvement that characterizes many efforts. However, with schools today facing an array of complex challenges—from working with an increasingly diverse population of students, to integrating new technology in the classroom, to meeting rigorous academic standards and goals—observers continue to stress the need for teachers to be able to enhance and build on their instructional knowledge (Gallimore, Ermeling, & Goldenberg, 2009). Given the cost and vital role that professional development can potentially play in improving the quality of schooling, it is imperative that the vast majority of any professional development experiences be directly tied to the needs of students. Too frequently, as school districts, or even schools, make professional development decisions and plans, they do not take into account the breadth of academic diversity in their districts, schools, and classrooms. By not doing so, there is a high likelihood that the needs of students who are in minority (e.g., students with disabilities, English language learners, etc.) will not be sufficiently factored into overall plans for professional development and the subsequent follow-up instructional coaching supports to ensure improved student outcomes. In short, professional development plans should begin with a very thorough and careful assessment of student needs and staff competencies in relation to those needs.

System Supports System supports (see Figure 11.5) are those mechanisms and practices that create and maintain the kind of environment in schools that enables teachers to provide effective instruction and for students to learn in safe and civil settings where optimal growth and learning can occur. As a result, the most important work that influences student achievement is directly tied to the quality of instruction that takes place within individual classrooms. However, unless a broad array of system supports are designed and thoughtfully implemented, the overall quality of instruction provided within these classrooms will be compromised and/or diminished. Thus, how teachers come together to work with one another to solve problems around issues of curriculum, assessment of student learning, and instruction are of paramount importance. System supports embody components that relate to how instruction should be planned and implemented in a coordinated manner, how accountability mechanisms should

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SYSTEM SUPPORTS (CLC) • Literacy leadership teams

Skills & Demands

System Supports Professional Learning Supports Instructional Supports

• Tiered instructional supports-CLC • Data guidance tools • Behavioral supports • Responsive scheduling • Strong building & district leadership • Internal accountability mechanisms

Current Supports

• Parental engagement

Years in School

Figure 11.5  System supports.

be conceptualized and data gathered to monitor student growth, and how school schedules should be designed to provide teachers with optimal flexibility to organize instruction to meet student needs. Literacy Leadership Teams Every secondary school needs a mechanism that enables teachers and administrators to come together on a regular basis (at least monthly) for the purpose of explicitly examining student data relative to literacy outcomes and assessing the degree to which student needs are being met under current instructional offerings and practices. One such mechanism is a literacy leadership team (LLT). A LLT is composed of teachers from various academic departments (not necessarily the department leader), administrators, and support personnel (e.g., counselors, special educators). The LLT team is responsible for driving literacy improvement initiatives within a school—not only from an advocacy and leadership standpoint, but also to ensure that instruction is organized and delivered in an effective manner and that all members of a secondary school faculty assume ownership for and play a role in improving literacy outcomes. Tiered Instructional Supports The needs of struggling adolescent learners (including those with LD) vary considerably. Some of these students’ needs can be met within the general education setting; whereas, the needs of other students can only

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be met if marked changes are made in the nature and intensity of instruction that is provided. Responsiveness to intervention (RTI) is a multi-level prevention system that includes several levels of intensity of instruction. The primary prevention level includes high quality core instruction. The next level includes evidence-based intervention(s) of moderate intensity. The final levels include individualized intervention(s) of increased intensity for students who show minimal response to previous levels of instructional intensity. At all levels, attention should be given to the fidelity of implementation. A model that has been specifically designed for application in secondary schools is called the Content Literacy Continuum (CLC) (Ehren, Deshler, & Graner, 2010; Lenz, Ehren, & Deshler, 2005). CLC is a whole school Tiered model of supports in which literacy instruction is woven across the curriculum into all subject-matter areas, giving students the tools necessary to learn and master course content while providing a relevant context for developing and applying their literacy skills. BenHanania Lenz and Bulgren (in press) conceptualize CLC in which Tier 1 involves teacher-directed content learning interventions, Tier 2 involves classroom-assisted content learning interventions, and Tier 3 involves support class content learning interventions. Tier 1 instructional approaches could involve: (a) the use of content enhancement routines (e.g., Bulgren, Deshler, & Lenz, 2007) such as a routine for learning critical concepts (Bulgren, Schumaker, & Deshler, 1988) or one for exploring guiding questions in a unit of instruction (Bulgren, Marquis, Lenz, Deshler, & Schumaker, 2011); (b) the use of individual accommodations as embodied in universal design for learning (UDL) principles (Rose & Meyer, 2002); or (c) direct learning strategy instruction as a part of content-area instruction in which the content teacher provides multiple models of discipline specific learning strategies that are unique to a specific content area (Shanahan & Shanahan, 2008). Ben-Hanania Lenz and Bulgren (in press) conceptualize Tier 2 interventions (classroom assisted content learning interventions) as including (a) peer-assisted learning structures (e.g., peer tutoring, student learning teams, and cooperative learning) in which students are expected to help each other learn content or complete tasks with the teacher planning for and monitoring these activities; or (b) adult-assisted learning structures in which adults provide intensive support to enable content learning to occur. One model with some empirical support is Strategic Tutoring (Hock, Pulver, Deshler, & Schumaker, 2001) in which the tutor teaches students

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strategies in how to learn the content as the tutor helps the student master the content that must be learned for a specific content-area class. Finally, the greatest level of intervention support and intensity is offered in Tier 3. In these situations, students may be provided intensive interventions on targeted skill and/or strategy deficits. Because the size of the gap is so large, the amount of time afforded in a supplemental class in which intensive skill/strategy instruction can be offered on a daily basis can be advantageous (e.g., Ehren et al., 2010). Some of the defining features of supplemental classes are: the establishment of classroom management procedures to ensure the optimal use of class time, the use of goal setting procedures by students so their engagement and learning of literacy competencies can be directly tied to life goals they wish to achieve, the use of highly engaging reading and instructional materials/assignments, the teaching of high-leverage learning strategies taught in an integrated fashion, and explicit generalization of newly learned strategies to their subject matter classes. Data Guidance Tools It is imperative to carefully monitor the effectiveness of instructional decisions and subsequent practices on student growth relative to critical learning targets. One of the most powerful learning procedures is databased decision making. Using data to make decisions about policies, programs, and individual students is a hallmark of schools that want to stay on the path of continuous improvement. These schools have incorporated thoughtful data collection, analysis, and use into their improvement plans. Teachers and administrators in these schools know how to use the feedback provided to pinpoint areas in need of improvement, get to the root cause of problems, guide resource allocation, and communicate with stakeholders as needed. Although the exact nature of data collection, analysis, and use may vary from school to school, the key elements of an effective data program include (1) purposeful data collection and analysis; (2) designated resources and other supports, such as time and an appropriate data management system; and (3) strategies for communicating about the process of data collection and use as well as the findings. Behavioral Supports In order for learning to occur, students must feel safe and secure. Additionally, an orderly, productive environment must exist in each classroom. Regardless of how powerful or effective the academic strategies are that are being used, their impact on students’ achievement will be

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thwarted in the absence of a learning environment that is under control. During the past three decades, an impressive database has emerged on a system known as positive behavioral interventions and supports (PBIS) that demonstrates the impact that a system of behavioral supports can have on student achievement (pbis.org). PBIS is an empirically validated, function-based approach to eliminate challenging behaviors and replace them with prosocial skills. Use of PBIS decreases the need for more intrusive or aversive interventions (i.e., punishment or suspension) and can lead to both systemic as well as individualized change. PBIS targets an individual student or an entire school, as it does not focus exclusively on the student, but also includes changing environmental variables such as the physical setting, task demands, curriculum, instructional pace and individualized reinforcement. Thus it is successful with a wide range of students, in a wide range of contexts, with a wide range of behaviors. Blending behavioral science, empirically validated procedures, durable systems change and an emphasis on socially important outcomes, PBIS always involves data-based decision making using functional behavioral assessment and ongoing monitoring of intervention impact. Responsive Scheduling One of the greatest barriers to implementing instructional interventions and systems in secondary schools is the rigid structure presented by class periods and overall school schedule. This problem is compounded further by the requirement to fulfill certain credit hour requirements (e.g., Carnegie Units). In order to fully implement a Tiered model of academic supports, it is imperative that school leaders critically examine their school schedule and determine ways to creatively increase flexibility for student placement and movement across classes and teachers. Scheduling includes ensuring that students with disabilities have sufficient time to learn (dosage) specific strategies/ skills essential to success in their school careers, and teachers have sufficient time to instruct students in research and evidence-based tools with fidelity. Strong Building and District Leadership The important role that the school principal plays in instructional leadership has been broadly discussed and empirically demonstrated. For example, in describing nearly two decades of data analysis from school reform in the Chicago Public Schools, Bryk, Sebring, Allensworth, Luppescu, and Easton (2010) describe a theory of action in which the school principle is the “driver for change”. In the absence of a school leadership team that

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possesses the vision and the proper set of routines for implementing that vision (e.g., use of data-based decision making, internal accountability systems, etc.) school wide achievement gains are unlikely. However, Marzano and Waters (2009) in a meta-analysis of research on the effects of district leadership on student achievement concluded effective district-level leadership can impact student achievement by as much as 9.5 percentile points. The critical leverage points of change that can be addressed from a district level are the following: (a) collaborative goal setting; (b) non-negotiable goals for achievement and instruction; (c) board alignment with and support of district goals; (d) monitoring achievement and instruction goals; and (e) tying resources to achievement and instruction. Internal Accountability Mechanisms In an era of school improvement, much of the accountability for that improvement is “external” in nature—that is, it comes from agencies outside of individual schools (i.e., district office or state department of education). While there is a role for this kind of accountability, when taken to the extreme or used as a sole strategy, it may convey a lack of trust to those being held accountable. “Internal” accountability, on the other hand, is a system of self-imposed standards by teachers and administrators that are designed to improve outcomes through individual goal setting, progress monitoring, to accomplish socially significant outcomes. In short, ultimate responsibility for change resides with the individual professional who is a member of a team pursuing shared goals of improved student outcomes.

Summary The successful implementation of the integrated intervention framework described in the previous section requires significant, focused efforts over sustained periods of time. The prevailing assumption underlying this model is the following: the only way the gap closes in literacy outcomes for struggling adolescent readers is through an integrated, school-wide approach in which everyone owns part of the problem and believes substantial changes in achievement can happen.

FUTURE DIRECTIONS According to the U.S. Department of Education (2010), 79% of students with disabilities are enrolled in general education classes for at least 40% of their day. Given the diversity of the population entering the school, this

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information should be of critical concern to not only special educators but also to content teachers who find themselves accountable for the outcomes of these students via federal legislation such as NCLB (2001) and IDEA (2004). Certainly, whether a district subscribes to Responsiveness to Intervention (RTI) or Multi-Tiered Systems of Support (MTSS), points of service delivery for all students begins in the general education classroom. The most recent initiative, Common Core State Standards (CCSS) (2010) presented as a route for providing clear, consistent understanding of what students in American schools are expected to learn while building a foundation for success in college and for career readiness is inclusive of all students. A variety of factors have contributed to the achievement/performance gap discussed in this chapter. Hence a variety of efforts are necessary to change the current standard. While this chapter first suggests three categories of supports that will be challenging for any school to implement, the authors would also suggest issues encompassed in this work to be tackled in the future. For instance, where do, or do the “new” literacies such as disciplinary literacy (Shanahan & Shanahan, 2008), socially just/social justice pedagogy (Moje, 2007) or out of school literacy spaces (Moje, 2004) fit into the schema presented here? Another issue to tackle is the application of technology supports for teachers via the latest hardware, apps and software available for use in the classroom. And, of course, each support suggested should be examined from a Universal Design for Learning (UDL) perspective to ensure that all individuals have equal opportunities to learn. Finally, if using data to make decisions about what and how to teach enhances outcomes for students, a wide-open area for examination, especially at the high school level, is new ways to conceptualize and measure fidelity of implementation and determine appropriate dosage. Development of such tools that are valid, reliable, and easy to use and interpret would make the job of data-based decision making more palatable. Until these issues are tackled, however, continuing to draw upon the empirical base and teachers and systems willing to implement instructional practices well can position students and teachers to be uniquely successful academically. Concerted effort to employ an integrated intervention framework can place a successful future within reach for adolescents who struggle.

REFERENCES Ben-Hanania Lenz, B. K., & Bulgren, J. (in press). Improving academic outcomes in the content areas. In D. J. Chard, B. Cook, & M. Tankersley (Eds.), Best practices for improving academic outcomes. Boston: Pearson.

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Effective Instruction for History and the Social Studies: What Works for Students with Learning Disabilities Susan De La Paz University of Maryland, Department of Special Education, College Park, MD 20742, USA

Chapter Contents What are the Social Studies?   325 Traditional Approaches for Teaching History and Social Studies   327 Instructional Approaches for Learning from Textbooks   328 Vocabulary  329 Reading Comprehension Strategies   330 Incorporating Text Structure Features in Strategy Instruction   331 Content Enhancement Routines   332 Alternative Textbooks and Computerized Study Guides   333 Summary  334 Reform Approaches to History Instruction   335 Reframing Goals for Instruction   335 Historical Reading and Writing in the Discipline of History   337 Historical Discussions and Debates   339 Inquiry Instruction   342 Virtual History Museum (VHM)   346 Discussion and Recommendations   347 References  348

WHAT ARE THE SOCIAL STUDIES? The National Council for the Social Studies (NCSS) in the United States (1992, 2011) defines social studies as both multidisciplinary and as central for the education of youth: [The social studies include an] integrated study of the social sciences and humanities to promote civic competence. Within the school program, social studies provides coordinated, systematic study drawing upon such disciplines as anthropology, archaeology, economics, geography, history, law, philosophy, political

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science, psychology, religion, and sociology, as well as appropriate content from the humanities, mathematics, and natural sciences. The primary purpose of social studies is to help young people make informed and reasoned decisions for the public good as citizens of a culturally diverse, democratic society in an interdependent world.

It is through studying these topics that students gain a sense of our collective past—by learning about people, places, problems, solutions, and the outcomes of those solutions students may “discover how their personal identity is shaped by their culture, by groups, and by institutional influences such as schools, churches, families, and government systems” (McCoy, 2005, p. 2). Although reforms in social studies have been complicated by disputes over the most important goals of instruction (Stanley, 1985), it is generally agreed that the subject of history holds special prominence in the social studies curriculum, in part because knowledge of our political processes, institutions (including the founding documents of a nation), people and events from the past allows us an understanding of the traditions and acts that have shaped a country, which in turn is requisite for functioning as effective citizens (c.f., 2011 NCSS position statement on citizen education). Thus, in the elementary and middle grades, history, geography, civics, and economics are often interwoven at each grade level with history receiving more attention than other domains (Scruggs, Mastropieri, & Okolo, 2008). For example, in the United States, the curriculum focus of at least one grade (typically the fourth) is devoted to information about state history, often including stories about indigenous peoples, exploration and statehood, and its role in the nation. Moreover, United States history is taught chronologically across multiple grades, on the early development of America, the growth and conflict in the nation, and turning points in the twentieth and twenty-first centuries (e.g., fifth, eighth, and eleventh; California Department of Education, 2011). Finally, in high school, students study Government, European history, economics, and increasingly, psychology, anthropology and sociology. In sum, effective K-12 social studies programs require multiple disciplines to enable students to learn to think critically about complex human dilemmas and make informed personal decisions within a democratic society. The purpose of this chapter is to provide teachers who work with students with learning disabilities (LD) with information on the teaching of history and the social studies in general education classrooms. I begin by considering traditional approaches for teaching and learning history and the social studies, which can contribute to the academic challenges faced

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by students with LD. Effective interventions that promote specific skills such as learning vocabulary and content learning from textbooks are summarized. The next part of the chapter considers alternative approaches to instruction, with an emphasis on interventions that focus on teaching and learning of history. This section explains the relevance of historical thinking goals, such as learning to think like a historian, to develop contextualized understandings, and to apply domain-specific approaches when reading and writing with primary source documents. Promising approaches that highlight historical discussion and interventions that allow students to engage in inquiry are then shared as examples of instructional models that facilitate students’ development of deep understandings within the discipline of history. Finally, I end this chapter with implications for research and practice in history and the social studies.

TRADITIONAL APPROACHES FOR TEACHING HISTORY AND SOCIAL STUDIES Most secondary students with LD receive social studies instruction in the general education classroom; unfortunately, studies indicate that students with LD often receive little instruction beyond basic literacy in history and social studies classrooms (Gersten, Baker, Smith-Johnson, Dimino, & Peterson, 2006; O’Brien, 2000). A second fundamental problem with traditional forms of instruction is that textbooks drive socials studies instruction more than any other school subject (Bean, Zigmond, & Hartman, 1994; Twyman & Tindal, 2006) and when in use, they are presented as authoritative sources of information (Bain, 2006; Paxton, 1999) rather than nuanced interpretations of past events. Social studies textbooks also cover too much content, lack coherence, focus on examples rather than concepts, and are just plain boring (Beck, McKeown, & Gromoll, 1989; Carnine, Miller, Bean, & Zigmond, 1994; Jitendra, Nolet, Gomez, & Xin, 1999; Kinder & Bursuck, 1992; O’Brien, 2000). In addition to these criticisms, textbooks present special difficulties for students with LD in reading. First, there is a well-established literature that shows how students with LD are frequently referred for special education services because of reading problems (Fletcher et al., 1994; Kavale & Forness, 2000) making it likely that they will have difficulty reading grade level textbooks. Expository text, which is common in social studies texts, is critical to content-area learning (Williams, Stafford, Laver, Hall, & Pollini, 2009) and formidable for students with disabilities (Gersten et al., 2006).

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Moreover, content area reading comprehension involves different subskills across types of texts and content (Duke, 2005). Texts have different purposes and represent different genres, and within genres, there is tremendous variability with respect to the type of texts they subsume. For example, expository text as a genre subsumes many different text subtypes, including description, sequence, compare-contrast, causation, and problem-solution (Meyer & Freedle, 1984; Meyer et al., 2002). Students must gain experience with various genres, and the emergence of genre knowledge involves developing understandings of the linguistic features of different types (Donovan & Smolkin, 2006). However comprehension difficulties that are common among students with LD may result from inadequate background knowledge, impoverished vocabularies, poor awareness of text structures employed in different genres, and limits in the self-regulatory skills needed to monitor their comprehension (Faggella-Luby & Deschler, 2008; Gersten, Fuchs, Williams, & Baker, 2001; Mastropieri, Scruggs, & Graetz, 2003; Vaughn et al., 2000; Ward-Lonergan, Liles, & Anderson, 1998). Finally, social studies instruction can be problematic for students with LD because of other deficits in verbal learning and memory (c.f., Mastropieri & Scruggs, 2010). Students with LD have been reported to show limitations in their ability to consider multiple perspectives (Bouck et al., 2008) and, along with other young learners, to evidence delays in cognitive and socio-emotional development that constrain their ability to differentiate time and to develop historical empathy (Okolo & Ferretti, 1997). In addition, students with LD may present difficulties in actively transforming information, and in differentiating relevant from irrelevant details (Bulgren, Deshler, & Lenz, 2007). Perhaps as a result, students with LD reportedly learn less content in comparison to students without LD after engaging in the same instruction (Ferretti, MacArthur, & Okolo, 2001). These problems are then compounded in middle and high school, where content area learning begins to incorporate complex disciplinary standards (Shanahan & Shanahan, 2008).

INSTRUCTIONAL APPROACHES FOR LEARNING FROM TEXTBOOKS The work of many special education researchers has resulted in several approaches to instruction that support students with LD as they acquire new vocabulary, and attempt to comprehend text and glean important

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factual information in history and social studies classrooms where learning from textbooks is the dominant mode of instruction. Promising approaches include mnemonic instruction for learning vocabulary as well as strategies for helping students improve their reading comprehension. Pedagogical tools, such as the use of content enhancement routines, providing alternative types of textbooks, and computerized study guides are also beneficial.

Vocabulary Mastropieri and Scruggs and their colleagues provide seminal work in facilitating the performance of students with and without LD in learning content area vocabulary through the use of mnemonics; in particular, they established the utility of the keyword approach to learning (Mastropieri, Sweda, & Scruggs, 2000; Scruggs, Mastropieri, Berkeley, & Marshak, 2010). Their work initially focused on teaching students with and without LD factual information, such as recalling states and capitals (Mastropieri, Scruggs, Bakken, & Brigham, 1992), locations and dates of Revolutionary war battles (Brigham, Scruggs, & Mastropieri, 1995) and learning the names and chronological order of U.S. presidents (Mastropieri, Scruggs, & Whedon, 1997). Although the focus of their early work was on helping students learn specific vocabulary terms and simple facts, their more recent mnemonic instruction is focused on helping students learn more complicated concepts, such as relationships between terms and associated facts and ideas. It is important to note that throughout this body of research, effect sizes, which gauge the impact of learning gains among students who have access to the target intervention, are strong in comparison to students who attempted to learn the same content without pictorial representations, thus establishing the benefits in using a mnemonic association for student learning. In addition, recent work by these researchers and their colleagues have established the utility of embedding mnemonic strategies within a classwide peer tutoring program for middle school students with and without LD, which extends the applicability of this approach. Marshak, Mastropieri, and Scruggs (2011) taught students to work with partners using “fact sheets” to study factual information for upcoming unit tests on industrialization, the progressive movement, and imperialism. Students used mnemonic elaborations when they were unable to recall information. They worked together four times per week during each unit. Learning outcomes were based on 20 item multiple-choice post-tests. Gain scores

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based on class means favored students in the experimental condition for items that were practiced during tutoring sessions (ES  =  1.23) and for items that were related to items that students practiced (ES  =  .49). Mastropieri and Scruggs’ program of research has established the utility of mnemonic strategies as an evidence-based approach to helping students learn content vocabulary.

Reading Comprehension Strategies Early work by Kinder and Bursuck (1993) and Harmon, Katims, and Whittington (1999) illustrates that reading and note-taking strategies that focus students’ attention on key historical concepts and information results in better understanding of social studies texts. In Kinder and Bursuck’s (1993) study, students learned to analyze a series of textbook selections for “problem–solution–effect” relationships by answering four questions: What was the problem? Why was it a problem? What was the solution? What was the effect of the solution? Students learned to determine the meaning of vocabulary words from context, write definitions for these words, and develop timelines. In Harmon et al.’s (1999) investigation, students with and without disabilities used predictions about social studies section headings (whether they were about a person, event, or place) to facilitate their ability to take notes, clarify important words, and answer questions in the passage specific to each category. The instruction prompted students to make important connections about social studies concepts, such as the fact that what is perceived as a solution to one historical problem may have effects that become the source of new problems. Students also learned how information about persons, events, and places contributes to one’s overall understanding of an issue. A recent study by Berkeley, Marshak, Mastropieri, and Scruggs (2011) also establishes the utility in prompting students to attend closely to information in textbook sections. In this study, students learned to reframe information from textbook headings into questions and to self-regulate whether they could answer each question. To illustrate, students learned to change the heading, “The election of 1976” into “What was important about the election of 1976?” or, “Who won the election of 1976?” Students self-regulated their learning via attempting to (a) re-read sections; (b) check their understanding of vocabulary; (c) look at text features such as maps, graphs, and pictures, and, when these attempts were unsuccessful; (d) ask their teacher. In this study, students who learned the self-questioning

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strategy outperformed those who did not learn to focus on textbook headings or to self-regulate their learning on content learning tests involving multiple choice (ES = 0.92) and open-ended items (ES = 1.61).

Incorporating Text Structure Features in Strategy Instruction Carol Sue Englert, Cindy Okolo, and Troy Mariage (2009) have developed and validated ACCelerating Expository Literacy (ACCEL), an instructional program designed to help students apply reading and writing strategies as they read common expository text structures (cause/effect, problem/solution, compare/contrast, sequential order, classification, and explanation). In this instructional model, students learn to use “artifacts and tools” (notes, diagrams, and concept maps) as meditational devices to coordinate use of individual strategies within an inquiry process. The inquiry process begins as teachers help students to plan to learn, for example, by considering the purpose for reading (P: Why am I reading this?) and overview of the topics (L: List Topics—What is it about?), activating prior knowledge (A: Activate prior knowledge and connect to self, text, world—What do I know?), asking questions (N: Note your questions—What do I want to know?) and surveying the structure of the passage (S: Structure and organization—How is it organized?). Learning continues with students’ execution of additional reading and writing strategies that integrate planning (Plans-It), reading (Highlight-It, Mark-It), organizing (Note-It, Map-It), and writing (Respond-to-It, Report-It); moreover, these strategies are situated within a learning-to-learn inquiry process, in which students apply their knowledge about text comprehension and composition to support the development of disciplinary knowledge in social studies (Englert et al., 2009). The ACCEL program has been evaluated by comparing learning outcomes for students with LD to learning outcomes for students without learning problems. In their social studies program evaluation, students were asked to select and use main ideas and facts from material they read to write an informational news article. This activity required students to construct, represent, and establish informational relationships among main ideas and details (Englert et al., 2009). The results from this evaluation indicated that students without disabilities outperformed students with LD on measures related to note taking, highlighting, comprehension, and writing. Moreover, organizing information was difficult for all students, and reliable gains in overall writing ability were not evident for students in either group. However, students with special educational needs made larger gains than students in general

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education in their ability to identify main ideas and details in printed texts, take well-organized notes, and generate written retellings that contained related details and ideas. Overall, the ACCEL approach appears to offer a promising form of instruction, especially given the fact that learning expectations are for students to apply text structure information while reading and to demonstrate content knowledge when responding to demanding writing tasks.

Content Enhancement Routines Janis Bulgren, Don Deshler, Keith Lenz, and Jean Schumaker, researchers at the University of Kansas’ Center for Research on Learning, have validated alternative approaches for teachers to extend students’ content area learning from text (c.f., Bulgren, 2006). Their instructional framework situates learning from textbooks within a broad context, by considering how to prompt effective planning decisions, as teachers attempt to facilitate students’ learning of factual information and higher order concepts. The resulting framework employs content enhancement principles for teachers to build students’ knowledge and to facilitate their attempts to select and organize content information. The overarching goal is that teachers employ content enhancement routines (CERs) to help students transform and generalize interrelated ideas. Bulgren et al. (2007) describe the potential for use of integrated CER sets to help students with LD master foundational information (e.g., vocabulary, concepts, propositions, principles and procedures); facilitate how to manipulate or use information (comparing and contrasting, developing analogies, categorizing, analyzing, explaining causes and effects, or weighing options); and extend student reasoning by prompting their application and generalization of knowledge in activities that require inference, prediction, inquiry into meaningful questions, evaluation of claims, or problem-solving. While the strongest evidence supporting the benefits of CERs has been in English and Science classrooms (Bulgren, Deshler, Schumaker, & Lenz, 2000; Bulgren, Lenz, Schumaker, Deshler, & Marquis, 2002), at least one study has involved students with LD in social studies classrooms (Bulgren, Deshler, & Schumaker, 1998). So, what do CERs look like? They are content organizers, concept diagrams, anchoring tables, and other graphical representations that teachers use to help students construct relationships between ideas that fall within broad social studies units (such as comparisons of concepts or exploration of causes and effects among events). However, while CERs rely on

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graphical representations of content, they are unlike traditional graphic organizers in at least two critical ways: (a) they are designed to both provide students with an overview of content on several topics (e.g., from several textbook chapters), that all fall within a broad unit of exploration; and (b) teachers and students complete CERs collaboratively, throughout content area instruction. Importantly, teachers use these tools to prompt students to integrate ideas from more than one completed content organizer. Thus, they are used flexibly and in combination, as students learn about interrelated concepts.

Alternative Textbooks and Computerized Study Guides Because learning with traditional textbooks is fraught with so many problems, educators and researchers have begun to generate new ways to frame this type of learning experience for students. Reform textbooks are often developed in collaboration with historians (e.g., American Odyssey: The 20th Century and Beyond, Nash, 2003) or teachers (e.g., Bring History Alive; TCI Bower, Lobdell, & Owens 2004). The latter is an example of a reform-based middle school program from California publishers Teachers’ Curriculum Institute (TCI); moreover, programs for other grade levels can be found at www.teachtci.com. Another empirical approach to circumventing traditional textbooks is to rewrite textbooks with a visible author (Paxton, 2002), providing students with a narrative voice in first person, that reveals the author’s beliefs, perspectives, and information about how historical facts and evidence have been interpreted. Special education researchers have also evaluated the benefits in using alternative textbooks for students with LD. Harniss, Caros, and Gersten (2007) conducted a study that examined the effects of an experimental social studies text on the learning of students with disabilities. The authors’ goals were to provide “big ideas” such as stages of cooperation and factors of group success, or frameworks for understanding how groups work together, and to provide a coherent approach for presenting information, such as by using a cause-effect text structure, to help students understand problemsolution-effect relationships. The text also discussed cultural and economic rationales for decisions that governments or individuals made. Despite certain limitations involved in the design of the study (students in a comparison group were identified as emotionally disturbed versus students in the experimental group who had LD, lack of pretest outcomes, and assignment of students to condition by classroom), the results indicate that students who participated in instruction involving the experimental text

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learned more content and demonstrated adequate progress in vocabulary learning and oral reading fluency. Richard Boon and his colleagues (Boon, Burke, Fore, & HaganBurke, 2006; Boon, Burke, Fore & Spencer, 2006; Higgins & Boon, 1992; Higgins, Boon, & Lovitt, 1996) have developed and evaluated the utility of providing a variety of computer-assisted approaches in an effort to supplant (or supplement) the use of traditional social studies textbooks. Examples of these efforts include computerized study guides, computerized map tutorials, and the use of cognitive organizers (e.g., using teacher created graphic organizers with Inspiration software; www.inspiration.com) to frame how students note information from texts. The computerized study guides appeared to enable students to interact more independently with textbook content. To illustrate, the use of hypertext features allowed students to access additional content, see clarifying information through the use of pop-up windows, and answer end of chapter multiple choice questions as they read, rather than afterwards. Learning outcomes for retaining content information in these studies were positive for students with LD in comparison to a traditional lecture format for learning content. Moreover, use of embedded graphic organizers facilitated students’ ability to learn factual information from their textbooks. The graphic organizers were framed to provide students with a conceptual structure for the information they were to learn, such as ideologies, foreign policy outcomes, conflict and alliances, and factors leading up to (and after) the Cold War. The results indicated that the students with LD who used the graphic organizers learned more content in comparison to students who learned the same content via teacher lecture and independent textbook reading, demonstrating the utility of guiding students’ note taking while reading.

Summary Special education researchers have traditionally focused on instruction in basic skills such as learning vocabulary as well as on teaching cognitive strategies for reading and writing from content area textbooks. An examination of the studies reviewed in this chapter reveals that special education researchers have validated a number of approaches to instruction that involve the application of general learning and literacy strategies to learning in the social studies. This focus on skills and strategies has been valuable for students with LD (c.f., Swanson & Hoskyn, 1998), and has contributed to enhanced learning outcomes. In fact, a strong argument can be made

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that instructional approaches studied by special education researchers can improve the learning of all children in diverse classrooms (Vaughn, Gersten, & Chard, 2000). Therefore, continued work on the application of general learning strategies in the social studies is certainly justified. However, special education researchers must do more than provide general reading and vocabulary strategies for social studies learning if students with LD are to have full access to academic expectations that are found in today’s general education curriculum in secondary settings. This is especially important as historical thinking goals have been emphasized in general education classrooms for some time (e.g., Bain, 2005; Lee & Dickinson, 1984; Seixas, 2006; Wineburg, 1991). In addition, an emphasis on disciplinary thinking has become embedded within efforts to develop students’ reading and writing skills as a key pathway towards developing adolescent literacy (c.f., Moje, 2008). Disciplinary interventions are also relevant for meeting students’ future needs, because it seems likely that as students learn to reason about people in the past, they will develop more sophisticated ways of reasoning about the myriad issues and viewpoints that require critical thinking today (Shanahan, 2008). Thus, the remainder of this chapter highlights model approaches to history instruction for students with LD. These may serve as examples of promising forms of teaching that promote students with and without LD to develop domain-specific ways of knowing, reasoning, and problem-solving that are particular to the discipline of history.

REFORM APPROACHES TO HISTORY INSTRUCTION The works of special education researchers who have developed innovative forms of history instruction for students with LD are now reviewed, after first establishing the relevance of historical thinking goals, such as learning to think like a historian, develop contextualized understandings, and apply domain-specific approaches when reading and writing with primary source documents. I then review the benefits of interventions that focus on (a) domain-specific reading and writing interventions; (b) historical discussion and debate; and (c) interventions such as multimedia projects, and web-based virtual learning environments, that allow students opportunities to engage in historical inquiry.

Reframing Goals for Instruction National organizations in both the United States and Canada (National Center for the Study of History in the Schools, 1996; Seixas, 2009) have

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Table 12.1  Peter Seixas’ (2009) Framework for Curricular Goals in Historical Thinking Main Focus Explanation

1.

Establish historical significance

2.

Use primary source evidence

3.

Identify continuity and change

4.

Analyze cause and consequence

5.

Take historical perspectives

6.

Understand ethical dimensions of history

People and events are significant when they impact large numbers of people over time; this evaluation requires placing the topic into a broad context or narrative. Traces left behind from the past are examined, even interrogated, so we may construct an account or argument about what it was like back then, what happened, and why. One must look beyond individual events, to ask whether and for whom change results in progress, and to consider both change and continuity across different historical periods. Ideas and decisions of individuals and groups may have unforeseen or unintended consequences; while based on precipitating actions and events, the result may be changes in long term economic, social, and political conditions. People in the past experienced their world in entirely different ways than we do now; their whole way of thinking and perhaps feeling remain a challenge for us to imagine. Although concepts like racism, sexism, and homophobia are products of very recent times, we expect to learn something from the past that helps us in facing the ethical issues of today— therefore, we must learn to make ethical judgments.

called for increased attention to viewing history as a discipline with standards related to the development of historical reasoning and from multiple perspectives. In this call, a central tenet is to evaluate historical evidence and to understand historical accounts as interpretations influenced by the purposes of historians (Lee & Ashby, 2000). Peter Seixas (2009) developed Canadian benchmarks of historical thinking that provide a framework for educational reform. This framework (see Table 12.1 for broader explanation) centers on six concepts, or “ideas that provide an understanding of

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history as a discipline…and shape the way we go about doing history” (2009, p. 28). In fact, for the past 20 years, social studies educators in North America and Europe have focused on helping students think critically in ways that approximate the thinking of professional historians1, who continue to construct new ideas about the past and have helped us come to more nuanced understandings about prior events. One of the most influential history educators in the United States is Sam Wineburg, who has observed both historians and novices (including high school students and adults who are preparing to become teachers) and made powerful inferences about cognitive processes underlying epistemological thinking in this domain. Wineburg (1991, 2000) describes how historians attempt to decipher a “subtext, a text of hidden and latent meanings” where excerpts from historical documents are viewed in different ways, considering “the text as a rhetorical artifact and the text as a human artifact” (p. 65). Wineburg’s seminal study (1991) identified three heuristics that historians use when approaching texts that have subsequently been affirmed by others (e.g., Bain, 2005; Monte-Sano, 2010). He found that historians interrogated historical documents by looking at authors and their biases (“sourcing”), situating documents in the time and place of their creation (“contextualization”), and comparing documents (“corroboration”) to find points of agreement and contradiction. In defining expert approaches to historical texts, Wineburg identified discipline-specific ways of reading and thinking. For these historians, primary documents were regarded as excerpts of social interactions that had to be reconstructed in order to render the documents comprehensible. In order to derive meaning from a text, the climate of opinion, language use, social mores, and events of the time had to be considered. Such contextualization is crucial to analyzing the documentary record and constructing an interpretation of past events.

Historical Reading and Writing in the Discipline of History Research on the kinds of reading and writing activities that foster historical thinking in students indicates that the kinds of texts with which students work can influence their reasoning processes (Paxton, 2002; Stahl, Hynd, Britton, & Bosquet, 1996). Students are more likely to think 1 

Consider Gary B. Nash, author of “The unknown American revolution:The unruly birth of democracy and struggle to create America,” (2005) who crafted a storied account of the contributions of African Americans, Native Americans, women, and both rich and poor white men to the birth of the United States.

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analytically and interact with texts if they read primary documents (Rouet, Britt, Mason, & Perfetti, 1996). Further, writing argumentative essays from multiple historical texts has been shown to help students progress from listing information to synthesizing texts into an argument (Young & Leinhardt, 1999) and develop deep understanding of content (Wiley & Voss, 1998). Finally, instruction that emphasizes historical thinking and argument can help students’ writing improve (Monte-Sano, 2008, 2011). Students with LD and other students who are struggling academically can also benefit from disciplinary approaches to reading and writing. I applied Wineburg’s (1991, 2000) work in my development of a historical reading and writing intervention with students with and without LD (De La Paz, 2005). In this study, the self-regulated strategy development (SRSD) model of instruction was applied to both a historical reasoning (reading) and writing strategy in an integrated social studies and language arts unit. Although description of the SRSD approach is beyond the scope of this chapter (see Chapter 9 in this text, by MacArthur, Philippakos, Graham, & Harris), this model incorporates a process by which students gradually take ownership of learning by (a) moving from explicit teacher modeling to collaborative (group) practice, to independent execution of specific academic and self-regulation strategies; and (b) fading procedural scaffolds such as the use of graphics or other prompts that contain strategy steps as students gain mastery. In my (2005) study, eighth-grade students with and without LD applied a historical reasoning strategy as they read primary source documents, and then applied a planning strategy to compose argumentative essays. The results indicated that in comparison to students without disabilities in a control group who did not receive either form of instruction, students who demonstrated mastery of the target strategies during instruction wrote historically more accurate and more persuasive essays regardless of their initial learning profile. Students without LD demonstrated more gains than those with disabilities; however, the students in need of instruction most improved to the same level that their most advanced peers held before instruction. My colleague Mark Felton and I replicated the utility of the historical reasoning and argumentative writing strategies in a subsequent study with eleventh-grade students who were poor or average writers (De La Paz & Felton, 2010). Teachers modeled use of the historical reading strategy by demonstrating how to annotate the sources and making the sourcing, corroboration, and contextualization strategies explicit.

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Teachers then modeled the planning strategy by transforming content that students discussed during a class debate into a plan that emphasized disciplinary thinking, and by requiring a careful examination of evidence from primary sources that could support a response to a given historical controversy. Outcomes in our (2010) study demonstrated that students who received the experimental form of instruction learned how to consider several aspects of the sources they were given to read and to corroborate and contextualize aspects across sources with events of the time period in which they were situated. They also learned how to use evidence as a means for substantiating their claims in their written arguments (83% of students in the intervention group used quotes or documents to support their claims or used quotes to further an argument as compared to just over 50% of students in the comparison group). Finally, students who received instruction wrote essays with more advanced development of claims and rebuttals after instruction, after controlling for the length of their essays.

Historical Discussions and Debates As reform efforts in teaching history shift away from a dependence on textbooks and teacher lecture to more epistemic ways of constructing meaning from primary and secondary source documents, researchers have begun to explore the benefits of oral discussion as a means for activating student interest and promoting disciplinary thinking (Nussbaum & Edwards, 2011; Reisman, 2011). Studies conducted by special education researchers provide support for use of historical discussions with students with LD, in instruction that incorporates features of anchored discussions and historical debates. Anchored Discussions Glaser, Rieth, Colburn, and Peter (1999) explored the impact of anchored instruction on student-teacher discussions in an eighth-grade social studies classroom. They use the term anchored instruction to describe an approach made popular by the Cognition and Technology Group at Vanderbilt (1990), which provides an elaborated problem context for student learning so that students understand the conditions underlying the use of knowledge (Love, 2004). It is within the context of having a shared experience that researchers facilitate students to consider multiple perspectives on historical topics, and the interventions provide information on how students’ thinking is shaped by classroom discussions.

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Students with and without LD engaged in instruction over two semesters, with curricular content related to post World War I and World War II. Teachers used commercial videos to create multimedia “anchors,” which were used to establish shared background knowledge. Goals of instruction were to use video clips to identify problems in the real world and to use information from the videos to support argumentative discussions. These discussions reportedly went beyond on themes depicted in the anchor; as an example, students began to question the role women played in society in the 1930s. Students argued from different perspectives as they evaluated the historical and geographical accuracy of information contained in the anchor. During the last phase of the study, students developed their own research questions around issues arising from small group discussions. Results from this study revealed an increase in the number of questions that students generated and in the quality of comments by classroom teachers during large-group discussions. While the authors did not report on the quality of students’ thinking during the discussions, it was apparent that as students became more knowledgeable about each anchor, they were able to look beyond past events and examine issues affecting contemporary society, such as that of street gangs. Second, students came to believe that all individuals in society need to be heard, thus demonstrating their understanding of important democratic principles. Russell Gersten and his colleagues (2006) conducted a more recent study on the benefits of anchored instruction with middle school students with and without LD. This study, which focused on the Civil Rights Movement from 1954 to 1965, used carefully selected excerpts from the documentary Eyes on the Prize, by DeVinney (1991), to ground students’ understanding of the historical era. Students read a variety of sources over the five weeks of instruction, including excerpts from their textbook and secondary sources from the time (e.g., Time and Life magazine articles on Rosa Parks, the integration of Central High School, and voter immigration training). One of the defining instructional features in this study involved the ways the teacher helped students understand content during daily discussions of each video segment. The teacher strategically inserted clarifying statements and questions that had been planned prior to the start of the instructional unit as students watched the video. These prompts allowed the teacher to explain and elaborate on the material from the video, which was initially foreign to the teenagers in the study. The questions also afforded students an opportunity to react to the content and begin to make inferences about

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events such as the trial of Emmett Till, the Voting Rights Act of 1965, and lunch counter sit-ins in the 1960s. Students engaged in “think–pair– share” discussions to compare how they would feel if they were in the situation depicted in the video or text and wrote responses on their own. Additional supports included use of compare-contrast activities with text and oral reading within student dyads to facilitate decoding and text comprehension. The effectiveness of this approach was demonstrated on a variety of outcomes in comparison to a comparable group of students who had previously been matched and randomly assigned to receive the same curriculum that differed only in terms of the interactive approach. After instruction ended, students’ comprehension and knowledge of content were determined using written exams and structured verbal interviews, which allowed students an opportunity to elaborate on central aspects of the time period. Findings from this study were strong—students with and without LD who experienced this approach demonstrated advanced performance on all measures except a vocabulary-matching task that included definitions of terms and important figures. Thus, the combination of teacher-facilitated and peer-peer discussion made a real difference in students’ learning of the historical content. Debates MacArthur, Ferretti, and Okolo (2002) employed another form of classroom discourse, debates on controversial issues, to facilitate students’ historical thinking and content area learning outcomes. The study included one sixth-grade inclusive classroom participating in an eight-week unit on immigration around 1900. As a culminating activity for the unit, students participated in debates on whether immigration should have been permitted during the period studied. Historical understanding was assessed quantitatively with a multiple-choice test and an individual interview. Substantial improvements were found for all students on a content test, an interview involving historical thinking measures, and a self-efficacy measure. On the post-test interview, students with disabilities even performed at a level equal to their peers without disabilities. The researchers hoped the debates would afford an opportunity for students to demonstrate historical understanding of the period, in particular the multiple perspectives of immigrants and people opposed to immigration. In fact, students demonstrated historical understanding of multiple perspectives. Unfortunately, their debates also revealed how they often

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failed to use relevant knowledge that they demonstrated in interviews. A second limitation was that students’ arguments were more characteristic of everyday discussions than historical reasoning, as students seldom used specific evidence from the unit to support their claims. Conversely, findings from the study also indicated that the debates supported high levels of student discussion without teacher participation. The debates did not restrict opportunities for participation by students with disabilities, indicating, in all, that they are viable ways to support students’ learning about multiple perspectives on controversial issues, but also that students may need more explicit instruction on historical reasoning to promote evidentiary arguments.

Inquiry Instruction This final section provides summaries on the benefits of instruction that provides students with opportunities to create multimedia projects, and on the advantages in using a web-based virtual learning environment that provides primary and secondary source collections for students to engage in meaningful historical inquiry. Multimedia Design Projects In project-based inquiry, students work collaboratively to investigate authentic, interesting problems and then share and discuss their work with their peers. A consistent focus for research in this area has been the examination of teaching practices that promote student reasoning with historical projects that aim to increase both their factual knowledge and historical thinking skills, rather than limit their explorations to the type of thinking and information that is found in textbooks (e.g., Friedman & Heafner, 2007; Lehrer, Erickson, & Connel, 1994; Saye & Brush, 2002; Tally & Goldenberg, 2005). Participation in these projects also gives students an opportunity to develop the abilities needed to participate in reasoned discussion of civic issues, a core purpose of the social studies. Ferretti, Okolo, and MacArthur (Ferretti et al., 2001; Ferretti & Okolo, 1996; Okolo & Ferretti, 1997) explored project-based inquiry in a series of studies that allowed students with LD a chance to create multimedia presentations and share the results of their inquiries with others. In each case, the researchers selected controversial topics to foster students’ understanding of multiple perspectives. Students conducted research on their topics in small groups and engaged in collaborative discussions that focused on using evidence to support their position, then used various

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software programs to create group projects and present reports to their peers. The multimedia presentations allowed students to present information in a variety of ways and circumvent some difficulties with text. The focus of these early studies (Ferretti & Okolo, 1996; Okolo & Ferretti, 1997) was to provide students with LD opportunities to investigate historical topics and to determine the extent that significant learning occurred in terms of students’ knowledge of historical events and associated causes and consequences. Ferretti et al. (2001) subsequently evaluated the effects of a multimedia project designed to help students with and without LD to learn historical content and develop more sophisticated historical thinking. Two instructional units were developed on the migration of peoples, one on westward expansion from 1840–1860 and the migration of farmers, miners, and Mormons to California, Oregon, and Utah, and the other on immigration in the late nineteenth and early twentieth centuries. Design elements involved the following. First the instructional units were organized around a conceptual framework that explained migration in terms of conflicts between migrants and prior residents and long-term outcomes. This framework was used as a strategy to help students understand the groups studied and to generalize to contemporary examples of migration, such as immigration from Central America. Second, after a period of general learning of background information, students worked in groups to investigate one migrant group and then presented their findings to the whole class. Third, students were provided with a collection of primary and secondary sources, which served to present background information about the historical period and as information sources for group projects. Fourth, students used a compare/contrast strategy as a vehicle to compare the ways of life of peoples who came into conflict. Finally, collaborative discourse with peers and the teacher was seen as critical to learning, and teachers worked with groups and the class as a whole to guide their discourse in constructive ways. Results from Ferretti et al.’s (2001) study permitted in-depth evaluation of student understanding using measures of historical knowledge, historical reasoning, and attitudes towards social studies learning. The historical reasoning measure was an interview including questions on evidence, bias, and the reasons for differing historical accounts. Students with and without disabilities made significant gains in historical knowledge and historical reasoning, although students without disabilities made larger gains. Both groups of students made gains in self-efficacy for learning social studies.

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My colleague Pedro Hernández-Ramos and I recently completed a multimedia study involving eighth-grade students with and without LD (Hernández-Ramos & De La Paz 2010). This study explored relative benefits for students who completed a technology-assisted project-based learning experience; moreover, the study design contrasted their learning outcomes with those of students who did not participate in technology-enhanced social studies instruction. Students in both conditions learned about westward expansion in a unit on “the divergent paths of the American people from 1800 to the mid-1800s and the challenges they faced,” in the Northeast, the South, and the West (California State Board of Education, 1998). Students who created multimedia projects engaged in inquiry that centered on one geographic region, which allowed for determining the extent to which they learned, not only about their assigned region, but also the two other regions. The researchers developed a digital set of primary and secondary sources that supported the state content standards and selected mPower software (Multimedia Design Corporation, 2005) for students to use during the project because it appeared ideal for the creation of multimedia presentations, and allowed individuals who viewed the projects to determine how they wanted to access content (i.e., in nonlinear ways). Instruction included lessons on reading and interpreting primary and secondary sources as well as how to use the software for their projects. At the end of the unit, each student presented his or her contributions from the group project to the class (Figure 12.1 shows three screenshots of work completed by students with LD). Several dependent measures were used to evaluate the benefits of the technology-enhanced project-based learning. Two are emphasized here to give an overview of the outcomes; moreover, a second report is in review that compares the historical thinking and work completed by students with and without LD (De La Paz & Hernández-Ramos, 2011 in review). The current summary provides a report on students’ content learning with a (a) researcher-developed measure (a 50 item, multiple-choice test before and after the multimedia unit); (b) a state-administered social studies test that was administered 2 months after instruction ended; and (c) a description of the students’ multimedia projects to determine the degree to which students’ work showed evidence of historical thinking. The results of statistical analyses revealed that whereas before instruction, students in the intervention and comparison conditions did not differ with respect to their initial levels of content knowledge, after instruction

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Figure 12.1  Work samples (screenshots of project scenes) created by students with LD showing content related to Westward Expansion in U.S. in the early to mid 1800s.

students who completed multimedia projects, and who learned about content from each other, learned more than students in a comparison group who received instruction on the same standards using instruction that centered on whole class activities. The results from additional statistical analyses also revealed significant benefits for learning content on the state-administered test for students at the intervention condition. Finally, evidence was explored on the extent to which students who participated in the multimedia unit demonstrated historical thinking. Findings were that student teams were willing to go beyond the textbook in creating their projects. Analyses revealed that 40% of their content came from textbooks, 40% came from primary sources, and 20% came from secondary sources. Analyses of each scene in the multimedia projects were then completed to determine the degree to which students engaged in sourcing, contextualization, and interpretation of primary and secondary sources. The results indicated that 29% of students with and without LD used a quote to support a claim, that 7% of the students with LD and 14% of the students without LD also provided a citation for a claim, and that

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14 and 21% of the students with and without LD provided evidence that they understood the author’s perspective, indicating important elements of historical thinking (Lévesque, 2009; Monte-Sano, 2008).

Virtual History Museum (VHM) Michigan State University researchers (notably Cindy Okolo and Carol Sue Englert) developed and validated the use of a web-based learning environment called the VHM, which was designed for teachers to create virtual museums for students to learn about history and how to think like historians (Bouck et al., 2008; Okolo, Englert, Bouck, & Heutsche, 2007). The relevance of this type of learning may seem more obvious when one considers that historical institutions such as the Smithsonian’s National Museum of American History allow students to engage in virtual artifact explorations (e.g., using the Greensboro Woolworth lunch counter that was used to spark a series of Civil Rights sit-ins during the 1960’s; www.objectofhistory.org), and that museums like the Phillips Collection provide websites for exploration of primary sources such as Jacob Lawrence’s Migration Series, a multipanel illustration of the mass exodus of African Americans from the south to the northeast and west between 1910 and 1930 (www.phillipscollection. org/migration_series/for_educators/tips_tools/index.cfm). Furthermore, Sam Wineburg and other history educators have developed websites such as Historical Thinking Matters, in collaboration with George Mason and Stanford universities (http://historicalthinkingmatters.org), that are designed to guide teachers and students’ exploration of controversial events, as well as to create instructional frameworks for teaching students contextualized approaches for historical problems (see Reisman & Wineburg, 2008 for a discussion of underlying pedagogical concepts). However, the VHM is the first project (register at http://vhm.msu.edu) to be specifically designed to meet the learning needs of students with LD. The site allows teachers to structure how students investigate nuanced historical questions, such as whether John Brown was a hero or villain, through exhibits or organized sets of primary and secondary source artifacts and a variety of learning activities. In this virtual environment, a museum metaphor is used as curators (teachers) create artifact collections (with photos, other images, music, maps, and excerpts from speeches, letters, and the like) and choose learning activities that enable members (students) to investigate and understand the exhibit. Teachers and students have choices in designing and using this tool in their exploration of historical topics. For example, teachers are able to provide different degrees of scaffolding for

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the writing process (by providing sentence starters in an outline format or by omitting this support), and students can choose to take notes and use a text-to-speech function to have documents read to them. Bouck, Okolo, Englert, and Heutsche, (2008) provide a qualitative evaluation of the VHM. In this study, learning outcomes of students with LD and other high incidence disabilities were examined after receiving instruction in co-taught eighth-grade American history classes. Students explored two VHM units that met district content standards. Classroom observations and student interviews helped the researchers understand how students used the VHM and its overarching purposes; moreover, students’ written responses to a compare-contrast activity, written predictions, and position essays provided evidence that students were able to use historically accurate information and that they developed more nuanced understandings of multiple perspectives after they engaged in corroboration. The success of the VHM demonstrates the utility of virtual explorations as another viable means for students to participate in substantive historical inquiry.

DISCUSSION AND RECOMMENDATIONS There is a widely recognized need to identify teaching strategies in history and social studies that will engage students with and without LD in their learning and equip them with an understanding of how professionals in the discipline work to help them develop the knowledge and skills necessary to think about the past imaginatively and with integrity (National Research Council, 2005), and with an appreciation of the perspectives of traditionally underrepresented or marginalized persons and groups in society. Moreover, practitioners in K-12 settings now have a considerable array of instructional approaches that help students consider problems of historical interpretation, develop analytical tools, critique sources, and learn to construct historical interpretations. In general, the rationale for reforming forms of instruction is to increase student achievement and enhance the experience of learning history by promoting deeper understanding and engagement in historical thinking (Caron, 2005; Ferster, Hammond, & Bull, 2006; Stearns, Seixas, & Wineburg, 2000). Researchers in special education should be encouraged to continue their efforts to develop instruction that enables students with LD to meet the same academic challenges as students without LD in contemporary history and social studies classrooms; as of yet, this goal has been difficult

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to realize (Buckley, 2005; van Hover & Yeager, 2003). This chapter suggests that special education teachers and researchers should consider historical thinking goals as they develop novel approaches to instruction. The chapter outlines promising practices as examples—including approaches that enable students with LD to participate in authentic historical discussion, and develop more sophisticated understandings about relationships between people, events, and issues that took place in the past. Interventions aimed at helping students read and write within the discipline of history, and those that use inquiry approaches to learning are other viable ways to provide students with LD with authentic tasks and the opportunity to practice advanced historical reasoning skills. The provision of disciplinary approaches to instruction similar to those suggested appear to be compelling ways for students with LD to have access to meaningful learning opportunities in history and social studies classrooms.

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Bulgren, J., Deshler, D. D., & Lenz, B. K. (2007). Engaging adolescents with LD in higher order thinking about history concepts using integrated content enhancement routines. Journal of Learning Disabilities, 40(2), 121–133. Bulgren, J. A., Deshler, D. D., & Schumaker, J. B. (1998). Reasoning strategies and teaching routines for use in mainstream content classrooms. Final research report submitted to the U.S. Department of Education, Special Education Services. Bulgren, J. A., Deshler, D. D., Schumaker, J. B., & Lenz, B. K. (2000). The use and effectiveness of analogical instruction in diverse secondary content classrooms. Journal of Educational Psychology, 92(3), 426–441. Bulgren, J. A., Lenz, B. K., Schumaker, J. B., Deshler, D. D., & Marquis, J. (2002). The use and effectiveness of a comparison routine in diverse secondary content classrooms. Journal of Educational Psychology., 94(2), 356–371. California Department of Education. (2011). Curriculum & Instruction Content Standards, History-Social Sciences, Adopted October, 1998. Retrieved from . Carnine, D., Miller, S., Bean, R., & Zigmond, N. (1994). Social studies: Educational tools for diverse learners. School Psychology Review, 23, 428–441. Caron, E. (2005). What leads to the fall of a great empire? Using central questions to design issues-based history units. Social Studies, Mar/Apr 2005, Vol. 96 Issue 2, p51–60. Cognition and Technology Group at Vanderbilt, (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 19(6), 2–10. De La Paz, S. (2005). Effects of historical reasoning instruction and writing strategy mastery in culturally and academically diverse middle school classrooms. Journal of Educational Psychology, 97(2), 137–156. De La Paz, S., & Felton, M. K. (2010). Reading and writing from multiple source documents in history: Effects of strategy instruction with low to average high school writers. Contemporary Educational Psychology, 35, 174–192. De La Paz, S., & Hernández-Ramos, P. (2011, December). Project-Based Learning in History: Effects on Historical Empathy for Students with and without Learning Disabilities. Paper presented at the annual meeting of the College and University Faculty Assembly of the NCSS. Washington, D.C. DeVinney, J. A., (Writer/Producer). (1991). Eyes on the prize-America's civil rights years, 1954–1965 [Documentary]. United States: Blackside Inc. Donovan, C. A., & Smolkin, L. S. (2006). Children’s understanding of genre and writing development. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 131–143). New York: The Guilford Press. Duke, N. K. (2005). Comprehension of What for What: Comprehension as a nonlinear construct. In S. G. Paris & S. A. Stahl (Eds.), Children’s reading comprehension and assessment (pp. 93–104). Mahwah NJ: Lawrence Erlbaum Associates. Englert, C. S., Okolo, C. M., & Mariage, T. V. (2009). Informational writing across the curriculum. In G. Troia (Ed.), Instruction and Assessment for struggling writers: Evidenced-based practices (pp. 132–161). New York: Guilford Press. Faggella-Luby, M. N., & Deschler, D. D. (2008). Reading comprehension in adolescents with LD: What we know; What we need to learn. Learning Disabilities Research and Practice, 23, 70–78. Ferretti, R. P., MacArthur, C. A., & Okolo, C. M. (2001). Teaching for historical understanding in inclusive classrooms. Learning Disability Quarterly, 24, 59–71. Ferretti, R. P., & Okolo, C. M. (1996). Authenticity in learning: Multimedia design projects in the social studies for students with disabilities. Journal of Learning Disabilities, 29, 450–460. Ferster, B., Hammond, T., & Bull, G. (2006). Primary access: Creating digital documentaries in the social studies classroom. Social Education, 70(5), 147–150.

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Fletcher, J. M., Shaywitz, S. E., Shankweiler, D. P., Katz, L., Liberman, I. Y., Fowler, A., et al. (1994). Cognitive profiles of reading disabilities: Comparisons of discrepancy and low achieving definitions. Journal of Educational Psychology, 86, 1–18. Friedman, A. M., & Heafner, T. L. (2007). “You think for me, so I don’t have to.” The effect of a technology-enhanced, inquiry learning environment on student learning in 11th grade United States History. Contemporary Issues in Technology and Teacher Education, 7, 199–216. Gersten, R., Baker, S. K., Smith-Johnson, J., Dimino, J., & Peterson, A. (2006). Eyes on the prize: Teaching complex historical content to middle school students with learning disabilities. Exceptional Children, 72(3), 264–280. Gersten, R., Fuchs, L., Williams, J., & Baker, S. (2001). Teaching reading comprehension strategies to students with learning disabilities: A review of research. Review of Educational Research, 71, 279–320. Glaser, C. W., Rieth, H. J., Colburn, L. K., & Peter, J. (1999). A description of the impact of multimedia anchored instruction on classroom interactions. Journal of Special Education Technology, 14, 27–43. Harmon, J. M., Katims, D. S., & Whittington, D. (1999). Helping middle school students learn with social studies texts. Teaching Exceptional Children, 32, 70–75. Harniss, M. K., Caros, J., & Gersten, R. (2007). Impact of the design of U.S. history textbooks on content acquisition and academic engagement of special education students: An experimental investigation. Journal of Learning Disabilities, 40(2), 100–110. Hernández-Ramos, P., & De La Paz, S. (2010). Learning history in middle school by designing multimedia in a project-based learning experience. Journal of Research on Technology in Education, 42(2), 151–173. Higgins, K., & Boone, R. (1992). Hypermedia computer study guides for social studies: Adapting a Canadian history text. Social Education, 56, 154–159. Higgins, K., Boone, R., & Lovitt, T. C. (1996). Hypertext support for remedial students and students with learning disabilities. Journal of Learning Disabilities, 29, 402–412. Jitendra, A., Nolet, V., Gomez, O., & Xin, Y.P. (1999, April). An analysis of four middle school geometry textbooks: Meeting the needs of students with learning problems. Paper presented at the Annual Meeting of the American Educational Research Association, Montreal, Canada. Kavale, K. A., & Forness, S. R. (2000). History, rhetoric, and reality: Analysis of the inclusion debate. Remedial and Special Education, 21, 279–296. Kinder, D., & Bursuck, B. (1992). An evaluation of history textbooks. Journal of Special Education, 25, 472–491. Kinder, D., & Bursuck, B. (1993). History strategy instruction: problem-solution-effect analysis, timeline, and vocabulary instruction. Exceptional Children, 59, 324–335. Lee, P., & Ashby, R. (2000). Progression in historical understanding among students ages 7–14. In P. N. Stearns, P. Seixas, & S. Wineburg (Eds.), Knowing, teaching, and learning history (pp. 199–222). New York: New York University Press. Lehrer, R., Erickson, J., & Connell,T. (1994). Learning by designing hypermedia documents. Computers in the Schools, 10, 227–254. Lévesque, S. (2009, April). Why can’t you just tell us? The impact of digital technology and the need for technological pedagogical content knowledge—Lessons from the virtual historian with Canadian students. Paper presented at the Annual Meeting of the American Educational Research Association, San Diego, CA. Love, M. S. (2004). Multimodality of learning through anchored instruction. Journal of Adolescent & Adult Literacy, 48(4), 300–310. MacArthur, C. A., Ferretti, R. P., & Okolo, C. M. (2002). On defending controversial viewpoints: Debates of sixth-graders about the desirability of early 20th century American immigration. Learning Disabilities Research and Practice, 17, 160–172.

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Marshak, L., Mastropieri, M. A., & Scruggs, T. E. (2011). Curriculum enhancements for inclusive secondary social studies classes. Exceptionality, 19, 61–74. Mastropieri, M. A., & Scruggs, T. E. (2010). The inclusive classroom: Strategies for effective instruction (4th ed.). Upper Saddle River, NJ: Prentice Hall. Mastropieri, M. A., Scruggs, T. E., Bakken, J. P., & Brigham, F. J. (1992). A complex mnemonic strategy for teaching states and capitals: Comparing forward and backward associations. Learning Disabilities Research & Practice, 7, 96–103. Mastropieri, M., Scruggs, T., & Graetz, J. (2003). Reading comprehension instruction for secondary students: Challenges for struggling students and teachers. Learning Disability Quarterly, 26, 103–116. Mastropieri, M. A., Scruggs,T. E., & Whedon, C. (1997). Using mnemonic strategies to teach information about U.S. presidents: A classroom-based investigation. Learning Disability Quarterly, 20, 13–21. Mastropieri, M. A., Sweda, J., & Scruggs, T. E. (2000). Putting mnemonics to work in an inclusion classroom. Learning Disabilities Research & Practice, 15, 34–43. McCoy, K. (2005). Strategies for teaching social studies. Focus on Exceptional Children, 38(3), 1–16. Meyer, B. J. F., & Freedle, R. O. (1984). Effects of discourse type on recall. American Educational Research Journal, 21, 121–143. Meyer, B. J. F., Theodorou, E., Brezinski, K. L., Middlemiss, W., McDougall, J., & Barlett, B. J. (2002). Effects of structure strategy instruction delivered to fifth-grade children using the Internet with and without the aid of older adult tutors. Journal of Educational Psychology, 94, 486–519. Monte-Sano, C. (2008). Qualities of historical writing instruction: A comparative case study of two teachers’ practices. American Educational Research Journal, 45, 1045–1079. Monte-Sano, C. (2010). Disciplinary literacy in history: An exploration of the historical nature of adolescents’ writing. Journal of the Learning Sciences, 19(4), 539–568. Multimedia Design Corporation, (2005). mPower (software). Charlotte, NC: Author. Nash, G. B. (2003). American Odyssey: The 20th century and beyond (2nd ed.). Glencoe/ McGraw-Hill. Nash, G. B. (2005). The unknown American revolution: The unruly birth of democracy and the struggle to create America. New York, NY: Viking. National Research Council. (2005). How Students Learn: History, Mathematics, and Science in the Classroom. Committee on How People Learn, A Targeted Report for Teachers, M. S. Donovan & J. D. Bransford (Eds.), Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. Nussbaum, E. M., & Edwards, O. V. (2011). Critical questions and argument stratagems: A framework for enhancing and analyzing students’ reasoning practices. The Journal of the Learning Sciences, 20, 443–488. O’Brien, J. (2000). Enabling all students to learn in the laboratory of democracy. Intervention in School and Clinic, 35(4), 195–205. Okolo, C. M., Englert, C. S., Bouck, E. C., & Heutsche, A. M. (2007). Web-based history learning environments: Helping all students learn and like history. Intervention in School and Clinic, 43(1), 3–11. Okolo, C. M., & Ferretti, R. P. (1997). Knowledge acquisition and technology-supported projects in the social studies for students with learning disabilities. Journal of Special Education Technology, 13, 91–103. Paxton, R. J. (1999). A deafening silence: History textbooks and the students who read them. Review of Educational Research, 69, 315–339. Paxton, R. J. (2002). The influence of author visibility on high school students solving a historical problem. Cognition and Instruction, 20(2), 197–248.

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Navigating the Postsecondary Landscape John G. Freeman1, Allyson Harrison2, and Ena Holtermann1 with Ingrid Jackson3, and Todd Cunningham4 1

Queen’s University, Faculty of Education, Kingston, Ontario, Canada K7M 5R7 Regional Assessment and Resource Centre, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Queens University, Nepean, Ottawa, Ontario, Canada K2G 5Z3 4 Department of Psychology, The Hospital for Sick Children, Toronto, Ontario, Canada M5G 1×8 2 3

Chapter Contents Navigating the Postsecondary Landscape   355 Academic Issues   357 Challenges  357 Promising Directions   359 Social-Emotional Issues   361 Challenges  361 Promising Directions   363 Motivational Issues   364 Challenges  365 Promising Directions   366 Promising Initiatives for Postsecondary Students with Learning Disabilities   367 Initiative in Canada: Learning Opportunities Task Force (LOTF)   368 Initiative in the United Kingdom: The BRAIN.HE Project   369 Future Directions   370 References  371

NAVIGATING THE POSTSECONDARY LANDSCAPE As an individual with a learning disability in the area of reading, writing, and auditory memory, I had to develop strategies to be able to be a successful student. However, the strategies used in high school were not adequate for me to be successful with university. The transition between high school and university was the process of abandoning the strategies that allowed me to get to university, and adopt new strategies that allowed me to be successful in completing university. (Todd Cunningham)

The number of students with learning disabilities (LD) accessing postsecondary education has increased steadily in the past two decades. In the United States, “an estimated 11% of undergraduates—more than two Learning about Learning Disabilities

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million—report having some type of disability” (Schelly, Davies, & Spooner, 2011, p. 17), an increase from 3% in 1978 and 9% in 1998. The largest growth has arisen through larger attendance rates of students with nonapparent disabilities, such as learning disabilities (Schelly et al., 2011). Similar numbers are found in Canada. Statistics from the Ministry of Training, Colleges, and Universities (MTCU) in Ontario for the 2008–2009 academic year, for example, paint a similar picture in that 36,277 students registered for disability services in the province’s colleges and universities (an increase to about 4% of the student population from 1.5% 15 years previously). Of those, 13,627 or 37.5% were identified as having LD as their primary disability (Holmes, Silvestri, & Harrison, 2011). Students with disabilities are legally entitled to provision of appropriate accommodations, services, and supports in their postsecondary programs to ensure that their impairments do not become an impediment to their college or university success (Katsiyannis, Zhang, Landmark, & Reber, 2009). As a result, these students must be provided with services such as learning strategy tutoring, coaching in the use of assistive devices, and accommodations in compiling class notes and taking examinations (Lee & Templeton, 2008). Not only do these accommodations allow students with disabilities to participate equally in their courses (Nichols, Harrison, McCloskey, & Weintraub, 2002), but, along with other transition services, they encourage students to make the transition to the postsecondary environment in the first place ( Janiga & Costenbader, 2002). But enacting policies and legislating programs is not enough to ensure academic success of postsecondary students with learning disabilities. Their graduation rates tend to trail those of their peers without disabilities (DaDeppo, 2009). The quality of the support they receive varies from institution to institution (Troiano, Liefeld, & Trachtenberg, 2010). Postsecondary students with LD still tend to believe that others hold a negative stereotype of their intelligence (May & Stone, 2010). Inclusionary practices in the K-12 system may have brought these students to the postsecondary landscape, but these practices have not shifted their perceptions. Adding to these difficulties, attendance at postsecondary institutions puts stress on undergraduate students academically, socially, and motivationally. The academic demands of the postsecondary environment generally exceed those of secondary school, demands that often outweigh the self-regulatory skills they have developed. Socially, students move from a social sphere they have learned to navigate to a new confusing sphere, peopled by strangers, with social rules not closely monitored by parents

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and teachers. Further complicating matters, motivation at the postsecondary level must largely come from within, as ongoing parental and teacher supervision is largely absent. For many students, including a large number of students with LD, these academic, social, and motivational challenges must therefore be addressed.

ACADEMIC ISSUES Text-to-speech software allowed me to scan my textbooks onto the computer and then have the textbooks read aloud. Instead of taking six hours to read a section, with the use of the text-to-speech software, I could boot to the same amount of material in half the time. This assistive technology became an essential tool to allow me to have access to the same materials that others had. It also allowed me to have some free time for the first time in my life. I could go out and have fun. (Todd Cunningham)

Challenges The transition to postsecondary education is difficult for many students with learning disabilities, and such students may encounter difficulties meeting the academic demands of the postsecondary environment (Gregg, 2007). Indeed, the majority of students with LD fail to complete the education necessary for transition to postsecondary education in the first place, as those with LD drop out of high school at a rate two-to-three times higher than that of their non-disabled peers (Young & Browning, 2005). Further, they enroll in college programs at one-tenth the rate of the general population (Wagner et al., 2005; Young & Browning, 2005). Even when accepted into college or university, such students continue to face a number of academic challenges, with differences in academic functioning varying in scope depending on the domain assessed and the assessment instrument used (Sparks & Lovett, 2009a). Barriers to postsecondary success for those with LD arise at three distinct levels: the individual, the classroom, and the institution. While we know much about the academic difficulties of children with LD, far less is known about the ongoing individual problems they experience in meeting the academic expectations at the postsecondary level with most attention being focused on postsecondary students with dyslexia (one form of LD). As a result of their underlying impairments in phonological processing, these students continue to experience difficulty with speed of reading, reading comprehension, spelling, notetaking, essay writing, and synthesizing what they have learned (Mortimore & Crozier,

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2006; Stampoltzis & Polychronopoulou, 2009). Over and above the effects of their processing impairments, postsecondary students with dyslexia have been found to be deficient in the ability to select main ideas when reading, and lack efficient and effective test taking strategies (Kirby, Silvestri, Allingham, Parrila, & La Fave, 2008). Moving beyond the specific subgroup with dyslexia, students with LD tend to not understand the exact nature of their disability, to lack the skills to be self-regulated learners, and to not have had learning strategies instruction to allow skill generalization across contexts (Kovach & Wilgosh, 1999). They have persistent difficulties in academic skills that were not mastered during the school-age years (Gerber, 1998) and can become disorganized and frustrated in learning situations (Vaidya, 1999). Students with LD may also arrive at college and university without having been exposed to, or properly trained in, the use of assistive technologies, having received minimal support regarding the most appropriate types of technology for their specific impairments (Nichols et al., 2002). These students are often unaware of the types of hardware and software available to support them, with many students for whom such technology was purchased not using these aids consistently (Abreu-Ellis & Ellis, 2006). Even for those students who purchase and use recommended assistive technology, the process of determining appropriate technology is often inappropriate or inefficient and subsequently abandoned (e.g., Holmes & Silvestri, 2009; see Simoncelli & Hinson, 2008, for a discussion of online learning). College students recognize these inadequacies themselves, realizing that the academic demands of writing increase from the secondary level to the postsecondary level without commensurate increases in amount of classroom support provided (Hadley, 2007). The differences between secondary and postsecondary classrooms exacerbate these problems for students with LD. They are underprepared by the K-12 school system to meet the academic demands of postsecondary learning, perhaps partially because the system does not provide these students a challenging enough high school curriculum, setting low academic expectations incommensurate with the students’ abilities (Gregg, 2007). A final challenge facing students with LD relates to institutional standards and policies regarding who is eligible for accommodations and supports. At the elementary and secondary school level, specific education legislation (e.g., Individuals with Disabilities Education Act of 2004 [IDEA, 2004] in the United States and specific provincial education legislation in Canada) allows schools to identify and provide academic supports

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to a wide range of learners to enhance academic success, without necessarily mandating that specific diagnostic criteria be met. At the postsecondary level, diagnosis may be more challenging (Sparks & Lovett, 2009b), as many students with LD arrive at their postsecondary institutions with documentation that is inadequate to support their need for accommodations ( Joyce & Rosen, 2006; Madaus & Shaw, 2006). Data indicate a systemic difficulty accessing comprehensive, appropriate diagnostic assessments prior to entering the postsecondary system for students with learning disabilities. Harrison, Nichols, and Larochette (2008), for instance, evaluated the disability documentation provided by 247 students seeking to register with Disability Services Offices at three Ontario postsecondary institutions (two universities and one college). Just over one-half of these students provided some type of psychological report from a professional as documentation of their disability, even though all had been accommodated during high school. More often than not, students simply provided an IEP, Student Support Plan or “identification” decisions as proof of their need for accommodations and services. These findings are similar to those reported by Reed, Lewis, and LundLucas (2006), who found half of the students with LD who were surveyed arrived at their respective Canadian postsecondary institutions without an up-to-date assessment.

Promising Directions Just as there are three types of academic challenges for postsecondary students with learning disabilities, there are three promising directions for overcoming these challenges: individual intervention or support, changes to the classroom/evaluation methods, and adjustments to institutional policies. At the individual level, support for students can come through academic support programs and assistive technology. Troiano, Liefeld, and Trachtenberg (2010) found that college students with learning disabilities who more consistently accessed support services were more likely to do well academically and to graduate from their academic programs. University preparatory courses may also provide similar support and offer an opportunity to include students with and without learning disabilities in one instructional context, given the overlapping nature of many of their challenges (Reed, Kennett, Lewis, & Lund-Lucas, 2011). These courses can help students understand the expectations of universities and use their abilities to meet these expectations in an individually tailored fashion (see Reed et al., 2009, who provide an example of the components of such a course).

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Advances in assistive technology hold the promise of serving as an equalizing agent in promoting fuller access to information and learning for students with learning disabilities (Michaels, 2000). Such technology offers the dual possibilities of circumventing academic deficits through alternative delivery means (“compensatory adaptations”) and of supporting weaker skills through complementary means (“remedial adaptations”; MartinezMarrero & Estrada-Hernández, 2009). At a postsecondary level, although both students with and without learning disabilities access assistive technology to enhance their approach to their academic work, students with LD tend to do so more frequently (Heiman & Olenik-Shemesh, in press). For students in both groups, those who reported higher levels of assistive technology use also reported higher levels of hope. Assistive technology may in fact represent a “critical need” for students with learning disabilities at the postsecondary level (Webb, Patterson, Syverud, & SeabrooksBlackmore, 2008). In the classroom, effective instruction is critical to the success of postsecondary students with learning disabilities (Scott, McGuire, & Shaw, 2003). Embracing the practice of Universal Instructional Design (UID; also known as Universal Design for Instruction [UDI]) is an effective means of providing appropriate education to all students, not just those with LD (Higby, 2009; McGuire, Scott, & Shaw, 2006). As Higby (2009) states, “implementation of UID can enhance classroom climate and ensure that students with diverse ways of knowing have an equal opportunity to learn and to demonstrate their knowledge” (p. 72). Not surprisingly, experts in the field of postsecondary LD accommodation (e.g., Gregg, 2007) have championed the adoption of UID principles in the teaching and assessment practices of postsecondary classrooms, describing in detail the benefits that accrue for all learners when such methods are adopted (see Orr & Hammig, 2009, for a review). However, many instructors express mixed willingness to provide more than minor accommodations in the university classroom (Murray, Wren, & Keys, 2008; Skinner, 2007) and might prove even more reluctant to embrace UID. Institutionally, there is a need to adopt consistent documentation criteria that apply to students with LD as they attempt to register for support services at any postsecondary institution across the country. Madaus, Banerjee and Hamblet (2010) found that only two-thirds of postsecondary institutions in the USA require that some formal testing measures to diagnose LD be provided as documentation, but there was limited consistency with respect to the criteria required for making this diagnosis. Furthermore, half

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of the institutions require demonstration of an IQ-achievement discrepancy (even though this method of diagnosis has been discredited as a reliable means of identifying LD, see Ysseldyke, 2005), with size of discrepancy also variable. While institutions have a right to determine documentation requirements, there is an expectation that these requirements are based on existing legislation for accommodating those with disabilities, so students do not encounter discrimination based on differing interpretations. Given that there is currently no consistent definition of LD employed across North America (Harrison & Holmes, 2012), it would also be important for clinicians and educational institutions to agree on an operational definition of LD. This consensus, in turn, would ensure that any student who is diagnosed with LD has met rigorous, empirically agreed upon criteria for documentation of this disability, thus allowing for better transference of documentation across school levels (National Joint Committee on Learning Disabilities [NJCLD], 2007; Harrison & Holmes, 2012). Such documentation and postsecondary institutions’ use of these standards, however, does not in any respect alleviate the responsibility for colleges and universities to provide appropriate instruction at the classroom level to accommodate the individual needs of students with learning disabilities and for students themselves to understand the nature of their learning disabilities.

SOCIAL-EMOTIONAL ISSUES I can honestly say that, being of a sensitive nature, I am convinced that because of my disability my other senses are heightened. I’m easily able to read others’ body language and to pick up on the energy, good or bad, they exude. This can be both a hindrance and a gift, depending on how one looks at it. The sole reason I agreed to share my story was to help others understand the turmoil that a learning disability can bring; from lack of confidence, to lack of trust in others, to a feeling of constantly being judged, and a feeling of not belonging. (Ingrid Jackson)

Challenges The social-emotional challenges students with learning disabilities face can include concerns with self-concept, affective disorders, and social cognition and competence (Bryan, Burstein, & Ergul, 2004; Elksnin & Elksnin, 2004; Graham-Smith & Lafayetta, 2004; Gerber, Reiff, & Ginsberg, 1996). These challenges, like learning disabilities, can be difficult to recognize and may go unnoticed as they are often perceived as

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personality traits rather than as social and/or emotional deficits that can be addressed or improved. In higher education, these misunderstandings can create unintentional barriers to learning, misconceptions of learner intellect, lack of intervention, and academic challenges (Denhart, 2008; Graham-Smith & Lafayetta, 2004; Orr & Hammig, 2009; Vogel, Fresko, & Wertheim, 2007; Zhang et al., 2010). How students with learning disabilities cope with the social-emotional demands of higher education is directly related to their self-concept, which emerges through their self-perceptions and the perceptions of others (Denhart, 2008; Graham-Smith & Lafayetta, 2004; Sideridis, 2007). For students with LD, much of their learning biography has been shaped by negative perceptions of their learning differences communicated early in life by the institutionalized social system of schooling (Osterman, 2000). These early experiences can have a profound effect on how individuals perceive themselves, how they interpret the perceptions others have of them in relation to their learning disability, and their sense of belonging in the learning community. Griffin and Pollack (2009) cite two views that individuals hold about their learning disability: a difference view, focusing on their learning disability as a “difference” encompassing both strengths and weaknesses with elements of hope and optimism, and a “medical/deficit” view where individuals hold a perception that their LD is a disadvantage. This subtle difference in LD perceptions has profound implications for students with LD in higher education as perceptions of self are often core predictors of academic, social, and emotional resilience (Denhart, 2008; Graham-Smith & Lafayetta, 2004; Griffin & Pollack, 2009; Sideridis, 2007; Zhang et al., 2010). Up to 50% of adults with learning disabilities struggle with a number of psychological concerns such as stress, anxiety, and poor mental health (Shessel & Reiff, 1999). In their work with Canadian students with selfreported learning disabilities, Wilson, Armstrong, Furrie, and Walcot (2009) found that individuals with LD aged 15–44 years were more than twice as likely to report high levels of distress, anxiety, and suicidal ideation than their nonlearning disabled peers. This higher prevalence may be in part due to cognitive challenges (information processing and memory), which in turn may contribute to negative affect in the form of stress and anxiety as individuals with LD struggle to cope with situations from multiple cognitive and affective perspectives (Bryan et al., 2004). Social cognition and competence, the ability to recognize and read social cues, make sense of nonverbal communications, and respond to

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change are all qualities students pursuing higher education must possess to be successful. These skills are generally learned in early childhood through social interactions in the school and home setting. However, for many children with LD, these skills must be explicitly taught as their learning disability not only impairs their cognitive functions but their social functioning as well. This finding underscores the need for early diagnosis and intervention as many of the skills needed to function socially (information processing skills, decision making, and communicative competence) are intertwined with learning difficulties (Bryan et al., 2004; Elksnin & Elksnin, 2004).

Promising Directions For students with learning disabilities in higher education the academic challenges they face can be exacerbated by varied and sometimes complex social, emotional, and psychosocial concerns. Because academic challenges and social-emotional concerns are of circular cause and consequence, it is difficult to elucidate on which approaches are most effective; however, students with LD in higher education identify three key social-emotional circumstances that can either help or hinder their learning: self-advocacy, faculty perceptions, and peer attitudes (Denhart, 2008; Graham-Smith & Lafayetta, 2004; Vogel et al., 2007). To what degree these circumstances are addressed impacts how well these students will cope with the interrelated academic and social-emotional challenges of higher education. From an individual learner perspective, self-advocacy is of primary importance. To be effective self-advocates, students with learning disabilities need to recognize both their strengths and their weaknesses. Because these students tend to focus on their disability as made salient by their identification, rather than their ability, there is a need to help students with learning disabilities recognize and use their personal social strengths (Farmer, 2011). Farmer’s (2011) Personal Strengths Intervention (PSI) may hold some potential in helping these students reframe their thinking. On a classroom level, instructor behavior contributes to the academic success and emotional well-being of postsecondary students with learning disabilities (Orr & Hammig, 2009; Ryan, 2007; Zhang et al., 2010). The perceptions faculty have of students with LD can create learning opportunities or barriers. How well instructors understand the interrelated learning and social-emotional challenges of students with LD determines to what degree students with LD experience success in higher education. By offering accommodations and varied instructional approaches instructors can ensure students with LD benefit from a flexible, accessible, and inclusive

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learning environment and, in doing so, improve learning for all students (Scott, McGuire, & Shaw, 2001). Beyond classroom pedagogy, faculty’s ability to understand their role in the accommodation process, how they view students with learning disabilities (students with learning differences rather than deficits), and how they communicate these understandings to all learners can have significant impact on engagement, peer acceptance, and classroom community (Denhart, 2008; Griffin & Pollak, 2009). Finally, instructor approachability and availability outside of the classroom, and how care and concern are conveyed can shift the paradigm for students with LD from one of challenge and alienation to one of possibility and belonging (Graham-Smith & Lafayetta, 2004; Osterman, 2000; Ryan, 2007). One agreed-upon approach institutions of higher learning employ to meet the needs of students with LD is to develop supportive relationships with disability support personnel, counsellors, and peer mentors, thereby addressing self-advocacy, faculty perceptions, and peer acceptance simultaneously. This intervention is intended to ensure students with LD are active participants in their support plan and active members of the learning community (Denhart 2008; Gerber, Reiff, & Ginsberg, 1996). Students with learning disabilities identify a caring and supportive peer mentor, faculty member, or disability support worker as the singular most important variable in their success or failure in higher education (Denhart, 2008; Graham-Smith & Lafayetta, 2004; Vogel et al., 2007; Zhang et al., 2010). Understanding the impact of the social-emotional challenges students with learning disabilities face is critical to developing interventions and supports in higher education. Not only must supports be available, but the perceived stigma associated with requesting and utilizing such supports must be addressed to ensure students are successful (Denhart, 2008). “When care is accepted as a central belief of a school community, structures that facilitate care are more likely to exist, and students are more likely to perceive that they are cared for, which positively affects their ability to achieve academically” (Schussler & Collins, 2006, p. 1461).

MOTIVATIONAL ISSUES As I became comfortable with my new strategies, anxiety and stress that were associated with the transition process diminished. I began to feel competent in my new way of learning. I have not stopped refining my learning strategies or the technologies I use. I strive to be as independent as my knowledge and tools allow me to be. (Todd Cunningham)

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Challenges As young people make the transition from the relatively structured nature of the secondary school environment to the more unstructured postsecondary context, a range of motivational issues can come into play, with these issues potentially having a greater influence on those students with learning disabilities than those students without. For example, in a Canadian study, academic procrastination was higher for the undergraduate students with LD in comparison to their peers, a problematic result in that higher procrastination was related to lower metacognitive self-regulation and lower self-efficacy for self-regulation within this group (Klassen, Krawchuk, Lynch, & Rajani, 2008). A follow-up interview study with 12 undergraduates with LD examined the correlates and antecedents of procrastination. The students felt that their learning disabilities influenced their levels of procrastination in that their procrastination increased when their cognitive and, to a certain extent, metacognitive skills decreased. These skill-based procrastination antecedents were intricately linked with students’ views of self. As one student stated, “I’m perfectly fine with myself and who I am, but my writing skills are low, and I know it, and my confidence is low in that area, so that affects the level of my procrastination for sure” (Klassen et al., p. 142). Other researchers have examined motivational constructs such as anxiety and self-efficacy. Greater anxiety may contribute to poorer academic performance for college students with learning disabilities (Prevatt, Welles, Li, & Proctor, 2010). The effects of short-term and long-term stress contribute to increases in anxiety that over time may lead to more profound psychological concerns and poorer overall mental health, thereby presenting additional challenges for educators when developing programs and interventions to support students with LD in higher education (Wilson, Armstrong, Furrie, & Walcot, 2009). Test anxiety may be a particularly salient and debilitating form of anxiety for these students (LaFrance Holzer, Madaus, Bray, & Kehle, 2009). With respect to self-efficacy, in one study, American college students with LD had lower levels of career self-efficacy than other students despite having engaged in more career development activities during high school. While they tended not to see their learning disabilities as a barrier to their academic success, they were also limited in their understanding of what their learning disabilities entailed (Hitchings et al., 2010). Self-determination, a critical topic with respect to the postsecondary success of students with learning disabilities, has important links to

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motivation. Self-determination can be seen with respect to developing greater autonomy in enhancing one’s competence within a network of relationships (see Self-Determination Theory [SDT]; Deci & Ryan, 2008). Self-determined college and university students with LD develop the persistence to work through their academic obstacles while building upon their areas of competence (Anctil, Ishikawa, & Tao Scott, 2008). They use their self-determination skills to seek on-campus services, to build stronger relationships with faculty members, and to increase their self-awareness, all of which they view as critical to their success (Evans Getzel & Thoma, 2008). Although her sample was limited (48 completed surveys, four interviews), Russo Jameson (2007) found that self-determined individuals with disabilities (most of which were learning disabilities) had greater retention in their program and higher GPAs than those students with low levels of self-determination. Clearly, self-determination is critical for postsecondary success for students with LD.

Promising Directions Work in addressing these motivational challenges at the postsecondary level has tended to work within a larger framework targeting motivation in the context of specific academic concerns. For example, Butler (1998), in a series of studies, demonstrated the extent to which a specific strategic approach, Strategic Content Learning (SCL), enhanced the ability of postsecondary students with learning disabilities to self-regulate, with gains in self-efficacy consistently associated with students’ self-monitoring of improved academic success. LaFrance Holzer and colleagues (2009) similarly used individualized strategy instruction to reduce test anxiety among their college students with LD, while cognitive-behavior therapy has also demonstrated benefits in this regard (Wachelka & Katz, 1999). Given its clear link to motivation for postsecondary students with learning disabilities, it is perhaps not surprising that self-determination has received attention in intervention research at this level. An eight-module intervention by Finn, Evans Getzel, and McManus (2008) targeted topics such as effective organization and time management, self-perceptions and self-management, and technology use for university students with various disabilities. The participants in the last, feedback session reported the benefits they had received from the program. Similarly, the seven students interviewed by Parker and Boutelle (2009), all of whom had learning disabilities and/or ADHD, described the largely positive outcomes they had received through executive coaching.

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While these interventions seem to be generally effective, in locating the interventions outside of the classroom context, the onus for improving motivation is placed squarely on the shoulders of the learners. Such an approach, although almost certainly justified in the current postsecondary climate where faculty members do not feel compelled to match their teaching to their students’ learning, needs to become more congruent with the inclusionary practices in the K-12 setting where the environment is seen as a focal point of change in addition to the student, thereby recognizing that certain contexts help create disabilities from impairments. Indeed, it could be argued that the K-12 system places too much emphasis on the environment without providing the individual the skills for successful management of a nonschool environment, while postsecondary institutions move the focus from environment to individual. Neither extreme approach is tenable. Therefore, for motivational interventions to be fully successful for students with learning disabilities at the postsecondary level, a paradigm shift is needed whereby the relational nature of motivational issues is more completely recognized.

PROMISING INITIATIVES FOR POSTSECONDARY STUDENTS WITH LEARNING DISABILITIES All the feelings of isolation from high school, the insecurities, came flooding back. I felt out of my depth. The physical responses were hyperventilating, chest pains, and my palms sweating, and I began to realize that I was unable to control my emotions as they were so powerful. I felt embarrassed that I was unable to control myself in this setting. I’d allowed myself to show my weakness and the pain of my struggle of some 20 years of education in front of 22 strangers. Were it not for the support of my children, my partner, Queen’s University and my lecturers, I do not know whether I could have completed the course. (Ingrid Jackson)

Ingrid is not alone in her feelings of being overwhelmed by the challenges presented by the postsecondary landscape. Fortunately for her and others who have learning disabilities, initiatives are being put in place in many different countries to help increase the resilience of students with learning disabilities, initiatives that use promising directions to meet academic, social, and motivational challenges. Herein are highlighted two such initiatives, one from Canada and one from the United Kingdom. These initiatives were selected because they simultaneously targeted academic, social-emotional, and motivational issues.

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Initiative in Canada: Learning Opportunities Task Force (LOTF) In 1997, the Province of Ontario undertook what was then a fairly radical decision: to establish a Learning Opportunities Task Force (LOTF) to develop and administer a series of pilot projects to enhance the transition of students with specific learning disabilities (SLD) into postsecondary education, and to enhance the services and supports that students with SLD received within the postsecondary educational sector such that they could complete their education successfully. Ten postsecondary institutions (four universities and six colleges) were chosen to develop and implement pilot projects, which ran from 1998–2002. Over a four-year period, 1242 students who met rigorous diagnostic validation criteria benefited from the work of LOTF by participating in the programs offered. Students with LD were as able to succeed in the postsecondary environment as were their non-disabled peers provided that the following conditions were met: (i) their transition to postsecondary education was appropriately facilitated, including access to an updated and comprehensive psycho-educational assessment; (ii) they received the necessary individualized supports, services, programs, and/or accommodations that minimized the impact of their processing impairments; and (iii) they used the individualized supports and services made available to them within the Disability Services Offices at their chosen postsecondary institution (Nichols et al., 2002). Access to a trained Learning Strategist was one of the single most helpful contributors to ultimate academic success at the postsecondary level. Training in and support for using assistive technology were additional components of postsecondary academic success. However, a substantial percentage of students came to the college or university with either a complete lack of or inadequate documentation of their learning disabilities (Harrison, Larochette, & Nichols, 2007), even though more than half of the students enrolled in this project reported that they had been identified as LD since elementary school. The task force identified that systemic difficulties related to proper diagnosis and identification at the elementary and secondary level denied students the opportunity to accurately understand or receive specific treatment for the real cause of their academic struggles, whether LD related or not, and failed to help these students learn adequate self-advocacy or coping skills necessary for postsecondary success. However, since the task force’s findings, monies have been set aside to help students with limited means underwrite the cost of an updated psycho-educational assessment or

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purchase assistive technology (https://osap.gov.on.ca/OSAPPortal/en/ A-ZListofAid/UCONT004257.html). Furthermore, subsequent to the release of the LOTF Task Force Report, all students with LD in Ontario have been able to access specialized services and supports at their postsecondary institutions at no charge. For those who do not have assistive technology of their own, every college and university in the province has a specialized computer lab where students with disabilities may use such technology. Each institution was also provided by the province with funding to hire a Learning Strategist and an Assistive Technologies Specialist to assist students with LD.

Initiative in the United Kingdom: The BRAIN.HE Project In recent years, the concept of neurodiversity, originally proposed as reflective of Autism Spectrum Disorder (Singer, 1999), has entered discussions about learning disabilities as learning differences (Griffin & Pollak, 2009; Ortega, 2009; Silverman, 2008). Proponents of neurodiversity posit that learning disabilities should be viewed as “an alternate form of cognitive functioning”; this view encompasses not only the difficulties individuals with LD may face, but the positive dimensions associated with their identification as well (Gerber, Reiff, & Ginsberg, 1996; Griffin & Pollak, 2009; Ortega, 2009). BRAIN.HE (Best Resources for Attainment and Intervention re Neurodiversity in Higher Education) is a project in the United Kingdom that aims to improve higher education’s response to neurodiversities (BRAIN.HE, 2006). The BRAIN.HE project was conceived to support institutions of higher learning in meeting the needs of increasing numbers of learning-diverse students beyond the scope of providing accommodations to students with LD to more globally inclusive practices (BRAIN. HE, 2006; Griffin & Pollak, 2009) through providing higher education institutions, university educators, and support personnel with resources, qualitative research, and analyses of learning disabilities from the perspective of students. As researchers with the BRAIN.HE Project, and in an effort to give voice to students with neurodiversities in the UK, Griffin and Pollak (2009) examined the experiences of 27 higher education students with a range of learning differences to detail their life experiences and to build a theory about the construct of neurodiversity. When faculty showed awareness and understanding of their neurodiversity through inclusive

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and innovative teaching practices, students felt strongly encouraged and inspired. These teaching practices included: multiple methods of delivery (lecture, web-based, one-on-one meetings); preferred learning styles and strategies approaches (assistive technology; visual thinking, and learning techniques); making reading materials accessible (font variety, colored overlays); and regular breaks for consolidation and organization of learning and review of key concepts. This study and the work of the BRAIN.HE project suggest that one’s sense of personal neurodiversity or “neurological self-awareness” (Singer, 1999) can be a strong predictor of academic success. The project highlights the need for intervention, resources, and support practices to be in place to meet the needs of a growing neurologically diverse student population. Higher education must be responsive to such diversities within the theoretical underpinnings of course design, delivery, and assessment. Faculty awareness of neurodiversities is needed to support the increasing variety of the student body as a whole in higher education. Rather than focusing solely on those identified as having learning disabilities, provisions must be made to ensure the pedagogies employed in higher education are accessible to all learners through multiple methods of content delivery, knowledge dissemination, and demonstration.

Future Directions The career path I had chosen in the past very much addressed my learning disability. I was not required to read or write, or compose letters. I was able to flit or jump from one thing to another, constantly moving. I thrived in this environment. I’d always dreamed of teaching, but didn’t know how to get there. Now, I’m very close to achieving that goal. It is my hope that in a few days I will finally be certified. But the uncertainty again creeps in. The inner voice, insecurity, lack of confidence that says, “Can I really do this?” I truly believe that I‘ve reached the point of my life where I’m now equipped to share 30 years of my career with others. My experience can only help me understand others and perhaps empathise with some of their hardships as they struggle to learn. My only hope is that I do my education justice and those around me that I have chosen for my support system will not feel their time was wasted. (Ingrid Jackson)

Academic, social, and motivational factors do not exist in isolation for postsecondary students with learning disabilities. In fact, Mamiseishvili and Koch (2011) linked both academic and social integration to the persistence rates of postsecondary students with disabilities from first to second year (although both factors dropped out in the logistic regression using a

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range of demographic and achievement variables, thereby indicating the complexity of the relationships). This interrelationship and the strengths of the initiatives we have described leads to the first of the four future directions. Postsecondary institutions need to place more emphasis on holistic approaches that target a multiplicity of issues simultaneously. A second future direction represents the expansion of research beyond North America to examine the challenges and opportunities for students with learning disabilities in that we found relatively few studies not conducted in either Canada or the United States (e.g., Griffin & Pollak, 2009; Stampoltzis & Polychronopoulou, 2009). Increasing the diversity of the population may well increase our understanding of best possible directions to pursue. Third, the onus in many of the studies still seems to be on the students as individuals with learning disabilities. We surmise that this focus has arisen partially because of the identification requirements for accessing services at the postsecondary level. While such stringency may be needed to avoid overtaxing support systems, it may prove a constraint to reconceptualizing the nature of learning disabilities for postsecondary students. Although students with learning disabilities need to be provided with skills to succeed in postsecondary education, learning disabilities are not a deficit but rather an impairment that results from an individualenvironmental mismatch. Equal attention must be given to the postsecondary environment. Finally, it does not seem that the K-12 inclusion movement has had much impact on the postsecondary setting. While students would benefit from such practices as Universal Instructional Design, it is difficult to imagine faculty members adopting these approaches in a wide-spread manner. Until there is a seismic shift in how postsecondary institutions work with student with learning disabilities, they will remain, in Gregg’s (2007) words, “underserved and unprepared.”

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Special Education Curriculum in an Era of High Standards John Woodward School of Education, University of Puget Sound, Tacoma, WA 98416, USA

Chapter Contents Introduction  377 The Direct Instruction Framework   381 Limitations and Lingering Questions   384 The Strategy Instruction Framework   386 Limitations and Lingering Questions   389 Constructivist Oriented Curriculum Framework   390 Limitations and Lingering Questions   393 Special Education Curriculum and the Era of High Standards   394 Conclusion: Fostering Adaptive Expertise   398 References  400

INTRODUCTION The idea of curriculum, particularly in the way it is used in day-to-day teaching, is seemingly pedestrian. Textbooks, innumerable teacher-made or commercial supplements, and computer-based resources are often what first come to mind when one thinks about curriculum. Yet the idea expands considerably when one realizes that a student’s school experience is more than what is printed on paper or the screen. Curriculum also includes pedagogy as well as assessment practices, which are logical complements to a multitude of factors that shape student learning. But even these images of curriculum only touch the surface of what can be an amorphous if not contradictory subject. The Handbook of Research on Curriculum ( Jackson, 1992) reminds us that curriculum has wide historical meaning, one that includes a course of study occurring outside as well as inside of school. From this perspective, curriculum merges with the broader concepts and purposes of schooling, whether they might be intellectual development for its own sake, citizenship, vocationalism, or school as an institution that reinforces class distinctions based Learning about Learning Disabilities

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on ethnicity, income, and/or ability. At this point, any discussion of curriculum is a long way from textbooks, supplemental materials, and computer assisted learning. One way to narrow our thinking about curriculum for students with learning disabilities is to focus on the last 40 years of federal policy and funding in special education. As special education laws were enacted and then reauthorized in the form of the Individuals with Disabilities Act (IDEA), students with disabilities were linked more and more closely to their general education peers. IDEA made access to the general education curriculum by students with learning disabilities a high priority and mandated that these students participate in statewide testing. The reauthorization of the Elementary and Secondary Education in 2001 yielded the No Child Left Behind (NCLB) agenda, which called for “scientifically-based curriculum” to be an essential part of the educational experience for all students. When IDEA was reauthorized again in 2004, it not only reinforced central tenets of NCLB such as the meaningful access by students with learning disabilities to the general education curriculum, but it ushered in the concept of Response to Treatment Intervention (RtI). RtI frames instruction around three tiers, from the regular classroom (Tier I) to “Tier II” support where students may receive additional support without the need for IEPs to Tier III special education placements if academic performance in the two prior tiers is unsuccessful. A central feature of RtI is the importance of scientifically-based curriculum and assessment procedures in the identification and remediation of learning problems, particularly in the area of beginning reading. Current conceptions of RtI now generally extend well beyond beginning reading to a host of elementary and secondary subjects. Curriculum for students with learning disabilities can now be seen on a continuum, where what happens in any one of the tiers of RtI is ultimately tied to standards, assessments, and instruction for general education students in Tier 1 or “core classrooms.” Federal funding, particularly through the US Department of Education, Office of Special Education Programs (OSEP), was also noteworthy over the last 40 years as a way of sponsoring curriculum development for students with learning disabilities. Schiller, Malouf, and Danielson (1995) note that federal legislation authorizing services for students with disabilities in the late 1970s quickly led to a search for effective instructional interventions (or curriculum). Today, one finds an impressive amount of curriculum development and related research for students with learning disabilities. In fact, the move to encourage a research foundation for

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OSEP-sponsored curriculum could be considered prescient in light of the eventual No Child Left Behind call for “scientifically-based curriculum.” Increasingly, the presence or absence of supporting empirical research has come to be a decisive factor for many teachers and school administrators when determining what an effective intervention is for students with learning disabilities. It should be noted from the onset that a distinct feature of a vast majority of special education curricula since the late 1970s is that they share common assumptions about how students with learning disabilities should be taught. Kavale (2007) notes somewhat ironically that the typical frameworks used for curriculum development and research today lie outside of special education. Until roughly 40 years ago, special education was relatively unique because its intervention methods were rooted in the processing dimensions of learning disabilities. Psycho-motor, psycholinguistic, and sensory-integration techniques distinguished curriculum for students with learning disabilities from instructional methods found in general education. It was, in his words, SPECIAL education. More recent frameworks for curriculum development and research have their origins in a larger literature, one found in any number of educational psychology textbooks intended for helping teachers understand the principles of learning and instruction for all students. Behavioral, cognitive, and constructivist theories of learning and instruction appear throughout the curriculum literature for learning disabilities. For Kavale (2007), the preponderance of research indicates that these interventions—or what will be called curriculum in this chapter—exemplify a special EDUCATION that has produced much greater educational benefits for students with learning disabilities than the more process-oriented interventions from an earlier period. The direct link to frameworks outside of special education helps explain an observation commonly made about contemporary curriculum efforts for students with learning disabilities—“well, that’s just good teaching.” This observation is also consonant with RtI and the notion that as students move from Tier I to Tier III, the issue is often one of greater intensity and individualization of instruction than it is an appeal to underlying process mechanisms such as a sensory motor integration deficit. This chapter will begin by reviewing three curriculum frameworks that appear frequently in the professional literature for students with learning disabilities. The first two frameworks—direct instruction and strategy instruction—have appeared in any number of literature reviews, and they are at the center of Swanson, Hoskyn, and Lee’s (1999) widely cited

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meta-analysis of special education interventions. Constructivism, the third framework reviewed, has emerged more recently, and it embodies different assumptions on the student’s academic needs as well as pedagogical methods. The purpose of this review is to articulate core themes, limitations, and lingering questions for each framework as well as set the stage for the last section of this chapter. The final portion of the chapter will consider these frameworks in light of our high standards educational environment. National standards such as the Common Core State Standards Initiative (2010) have direct implications for students with learning disabilities because of RtI and the longstanding fact that most of these students spend a considerable amount of time in the regular classroom. But there are reasons other than RtI for thinking about the high standards movement in this country. Standards delineate a hierarchical sequence of content, often across the K-12 spectrum of schooling. This sequence forces special educators to think not only about how much of a K-12 continuum of knowledge in a discipline is feasible for students with learning disabilities to obtain, but what kind of cognitive processes need to be developed over time. For example, the Common Core has a sustained focus on the development of analysis and interpretation skills (i.e., Craft and Structure) in the English Language Arts Standards as well as problem solving, reasoning, and argumentation (i.e., Mathematical Practice) in Mathematics Standards. Similar concerns for competence in problem solving and communication can be found in mathematics (National Council of Teachers of Mathematics, 2000), science (National Research Council, 1998) and social studies (National Council for the Social Studies, 1994). Beyond the boundaries of standards for a specific discipline is a broader, but equally salient question: What kind of intellectual dispositions and abilities do we want students with learning disabilities to achieve by the end of secondary school? How are these dispositions tied to a world of technological change that we live in today which will, in all likelihood, continue to accelerate in the future? To what extent can a broader agenda be organized around intellectual dispositions related to standards like those found in the Common Core? These questions will be addressed through the lens of a novel framework for thinking about how students learn to become more competent problem solvers and better able to transfer their learning to novel contexts. The final portion of this chapter will draw on the concept of adaptive expertise. The relevance of this concept to today’s technology laden world

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is inescapable. Adaptive expertise takes us beyond the common assumption that the acquisition of a discrete body of knowledge or specific cognitive strategies are sufficient learning outcomes for a complex world where problem solving rather than mere direct recall of information is the norm. Adaptive expertise requires that students learn to solve complex problems that may have multiple solutions. Moreover, learning is iterative. That is, students may present initial solutions to a problem only to refine them after discussion, feedback, or an examination of additional information. This kind of learning better simulates the kind of valued thinking in the world today, a world where technology enables us ready access to bodies of information once memorized as part of a traditional course of instruction. Because adaptive expertise will be an important interpretive framework for the review of curriculum for students with learning disabilities in this chapter, a disproportionate emphasis will be placed on intermediate grade and secondary curricula in content areas. The space limitations of a chapter like this do not allow an exhaustive review of published literature on all areas of curriculum development and research over the last 40 years. Furthermore there are many other excellent accounts of the research by subject area found in this volume and other special education handbooks of research (e.g., Florian, 2007; Swanson, Harris, & Graham, 2003).

THE DIRECT INSTRUCTION FRAMEWORK Gersten, Baker, Pugach, Scanlon and Chard (2001) refer to the legacy of direct instruction as one of the fundamental curricular movements in learning disabilities over the last four decades. Much of the direct instruction used in special education today can be traced to behavioral analysis in the 1970s and its potential application as a framework for curriculum design and classroom management. Behavioral psychologists and curriculum developers conceptualized how controlled presentations of content (stimuli) could be followed closely by simple reinforcement or punishment (e.g., “correct” / “incorrect”) and where possible, as informational feedback (e.g., “yes, the sound is /oˉ/ because the word ends in an e.”) Curriculum developers posited that a careful control of examples or stimuli would facilitate student learning by rendering an unequivocal presentation of a fact or concept and thus, facilitate stimulus generalization (Becker, Engelmann, & Thomas, 1975). This focus on well-sequenced examples was more fully articulated in later writing on the design of curricular material for all students (Adams

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& Carnine, 2003; Engelmann & Carnine, 1982), as well as concerns for how instruction could be delivered clearly and consistently through tightly scripted instruction. Detailed examples of direct instruction methods showing how to sequence examples and script instruction can be found in reading (Carnine, Silbert, Kameenui, & Tarver, 2004) and math (Stein, Kinder, Silbert, & Carnine, 2005). Curriculum developers continue to maintain that these principles are central to instruction for a range of students with disabilities, even those with severe cognitive deficits. Coyne, Carnine, and Kameenui (2010) updated this view of direct instruction by identifying six key principles: big ideas, conspicuous strategies, mediated scaffolding, primed background knowledge, strategic integration, and judicious review. The authors argue that these principles apply across grade levels and subject matter, and they are likely to benefit a much wider audience of students than those with special education needs. The six principles are seen as essential for all students who struggle in school, including students whose are characterized as at-risk, poor, or diverse, as well as those with learning disabilities. It should be noted that some of these principles, particularly scaffolding, have been assigned a different meaning in this context than they do in the wider educational literature (see Stone, 1993). According to Carnine and his colleagues (Carnine, 1997; Grossen, 2004), a successful application of these principles yields a curriculum that can engineer success. Teaching becomes more efficient because learners can acquire a greater amount of information in less time. Furthermore, an important but often unstated assertion of direct instruction curriculum development is that teachers do not have the time to create high quality instructional materials. Instead, teachers are better served if curriculum is developed, piloted, and refined by experts, and then broadly disseminated in a high quality form to the profession. Developers also feel that well crafted materials, particularly when detailed scripts accompany them, have a greater potential to be implemented with fidelity. This point is extremely important for secondary special education teachers who may lack the content knowledge to teach subjects like science, math, and social studies. Adams and Carnine (2003) claim that the print and digital-based materials that follow the principles described above are conceptually and empirically superior to the kind of direct instruction which focuses simply on the kinds of effective teaching methods to be discussed shortly. Carnine’s Understanding US History (Carnine, Crawford, Harniss, & Hollenbeck, 1996) exemplifies the application of the six direct instruction

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principles for designing print materials. This approach to US history is written in a way that makes big ideas much more explicit than what would typically be found in a secondary commercial textbook. Understanding US History contains embedded concept maps which stress a problem-solution-effect text structure. These maps facilitate brief student writing, and they are a central mechanism for making big ideas explicit. Furthermore, text passages contain interspersed questions that assist with comprehension and allow for teacher feedback. Finally, vocabulary is presented systematically throughout each chapter. It is defined in the context of a passage, reviewed in the interspersed questions, and covered again at the end of each section and chapter. Another view of direct instruction emerged from the 1970s, one closely linked to the process-product research described by Rosenshine (1983), Brophy and Good (1986), and others. Effective teaching principles, thought to be beneficial to all students, highlight the role of teacher modeling, guided practice, carefully monitored independent practice, teacher questioning with an emphasis on low-order questions, immediate feedback, and frequent assessment. If anything, these instructional methods fortify the connection between curriculum for general education students and those with learning disabilities, all of which remains highly pertinent in today’s discussions of RtI. Gersten and his colleagues (Gersten et al., 2001; Gersten & Santoro 2007) augment this vision of direct instruction by underscoring the role that explicitness plays in day-to-day teaching. Facts and concepts should be presented in a step-by-step manner to insure high levels of success. Teacher-to-student communication should be unambiguous in nature, though it is not clear that this necessarily means scripted instruction. Advance organizers and guiding questions should also be used to focus a student’s attention as well as prompt thinking. Denude of inductive activities or experiential learning, direct instruction fosters high levels of success because the discrete nature of tasks enables immediate feedback and mastery at each step before moving on to the next one. Dimino, Gersten, Carnine, and Blake’s (1990) implementation of story grammar accurately portrays this more pedagogically-oriented view of direct instruction. Researchers taught high school literature through a highly structured format where students learned to identify key components of fiction: character clues, setting, how characters react to events, problems that characters face and how they are resolved, as well as themes. Teachers modeled how to answer these questions as they taught short

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stories using an overhead containing the prompts just listed (e.g., “How does the main character react or feel about important events in the story?”). Students filled in their own note sheets containing the same story grammar prompts information. Gradually, the prompts were removed as the teacher focused on the more difficult components of comprehension (e.g., character reactions, themes). Once again, teacher models using highly consistent language were central to this intervention.

Limitations and Lingering Questions The two visions of direct instruction described above share many common features, but they also lead to different questions about their use in everyday practice. The first view of direct instruction merges highly scripted teacher presentations—usually in some kind of printed text or technology program—with pedagogical strategies rooted in the effective teaching or process product literature. The most dramatic attempt at this kind of direct instruction can be found in efforts in the late 1980s to embed direct instruction principles in a technology-based approach for whole class instruction. Videodiscs, the precursors to today’s CD-ROMs and DVDs, were responsible for teacher presentations involving animated examples, voice-over questions, guided practice problems, and regular quizzes and tests. Teachers assumed a very different role in instruction than a typical classroom. Their role was to monitor classroom behavior, interact with students on an individual basis, and determine if ongoing performance merited review or practice embedded in the technology program. For many, it is likely this radical shift in roles conflicted with deep-seated visions of practice. Woodward (1993) found that teachers who participated in a long-term use of this kind of technology initially had highly positive views, but when given the opportunity to use it on their own a year later, they declined to use the technology. A similar sentiment extends to the material in printed format, and it may explain Adams and Carnine’s (2003) acknowledgment that teachers “hate to follow scripts,” thus explaining one reason for the limited use of these kinds of materials. Another, related concern with highly scripted instruction has to do with the ability to format student responses. Direct instruction is grounded in the practice of frequent questions, the vast majority of which are lower order in nature (e.g., “What is this sound?”; “What’s the rule for adding /ing/ to the end of this word?”). While this format may yield predictable responses to highly convergent questions such as these, it is not clear how this kind of scripting can be employed for

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complex material (e.g., interpretations of texts, inferences about data in a science experiment). For example, Darch, Carnine, and Gersten’s (1984) study of multiplication and division word problems has been widely cited in recent mathematics literature for at-risk and special education students (see Gersten et al., 2009a). The strategies and a scripted format employed in the research were built around the identification of keywords such as each and every as well as whether or not the problem contained “the big number” (i.e., the largest number in a multiplication/division fact family). This kind of approach most certainly yields convergent answers, but it is completely at odds with current research, which discourages teaching word problems through keywords. Contemporary researchers also argue that problem solving should involve the use of multiple strategies and opportunities for classroom discussions (see Mayer & Hegarty, 1996; Woodward et al., 2012). The second vision of direct instruction, the one that encourages many of the effective teaching strategies documented in the early 1980s, seems well-suited to basic skills instruction. Gersten and Santoro (2007) argue in their review of special education curriculum that this is the key contribution of direct instruction, particularly the way explicitness fosters greater student understanding. While unambiguous communication, distributed practice, frequent questions with teacher feedback, and even choral responding might be imaginable in areas like beginning phonics instruction, it is less clear how these practices work in complex subjects with older students. Two questions seem appropriate to this view of direct instruction. Contemporary views of teaching tend to integrate the teaching of skills with other kinds of instruction. Linking basic skills instruction to the use of cognitive and meta-cognitive strategies will be a theme in the discussion of strategy instruction to follow. In this regard, extended skills practice where most, if not all of the traditional elements of direct instruction are present—particularly choral responses—seems out of place. On the other hand, if only one or two of the traditional elements of direct instruction are embedded in teaching (e.g., explicitness, guided practice), then one wonders if this is, in fact, a distinct contribution to curricular practice. After all, even constructivists acknowledge that there are times for explicitness. This observation is implicit in a recent metaanalysis of mathematics instruction for students with learning disabilities. Gersten et al. (2009b) could not find consistent uses of the term explicitness in their review. In some cases, the practice was akin to the concept

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of teacher modeling, which is a common pedagogical practice. Given the varied uses of the term “direct instruction” in research literature, one wonders how many elements of this model are necessary for instruction to be distinctively “direct.” A second question arises from an important, but little discussed principle associated with the task analytic methods used to break instruction to small steps. The desire to help students achieve high levels of success is laudable, but it is also likely that too much of this kind of structure results in a false sense of self-efficacy. Again, the Darch et al. (1984) study is instructive. Their results indicated superior performance for the direct instruction group on traditional word problems containing words like “each” and “every.” This strategy is likely to be ineffective with multi-step word problems, problems with a significant amount of irrelevant information, or ones where these keywords do not appear. These questions entail a broader consideration of strategies as part of a curricular program.

THE STRATEGY INSTRUCTION FRAMEWORK Strategy instruction attends to the role of different types of memory (e.g., long-term, short-term, working memory), knowledge (e.g., declarative, procedural, conditional) in learning as well as the mechanisms that monitor learning. Hence, cognitive as well as metacognitive strategies play essential roles in helping students reach their learning goals. Cognitive research also highlights the visual-spatial as well as textual dimensions of learning, thus placing greater emphasis on the coherent organization of information so that students can form a well-organized schema about a topic, concept, or process. Nonetheless, Swanson et al. (1999) caution that strategy instruction in practice can often be highly similar to direct instruction methods, with common features such as modeling, feedback, and step-by-step guidance in the way students are taught skills. Reading and writing are natural arenas for a strategy curriculum framework given the interplay between the different types of knowledge as well as the planning, monitoring, and evaluative dimensions of metacognition. In reading, Brown and her colleagues (Brown & Smiley, 1978; Palinscar & Brown, 1989) were instrumental in articulating productive ways to teach low achieving students how to study and enhance their ability to comprehend text by using the strategies of summarizing, questioning, clarifying, and predicting. Pressley et al.’s (1992) work in transactional strategies—identifying reading goals, monitoring progress, and evaluating

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what has been read—further solidified the importance of cognitive and metacognitive strategies for helping a range of students become independent readers. The National Reading Panel report (National Institute of Child Health and Human Development, 2000) also gave wide endorsement to strategy-based instruction including question generation, graphic and semantic organizers, and summarization. Williams (2003) encourages practitioners to use explicit and direct instruction in teaching reading strategies, a recommendation that underscores Swanson et al.’s (1999) observation about the common features of strategy and direct instruction. Writing instruction has been an equally fertile arena for the blend of strategy and direct approaches. Graham, Harris, and their colleagues (Harris, Graham, Brindle, & Sandmel, 2008; Harris, Graham, Mason, & Friedlander, 2008) created the Self-Regulated Strategy Development model based on the use of explicit instruction to develop self-regulation around goal-setting, self-assessment, self-instruction, imagery, and other strategies. As students learn to become writers, they are supported not only in their knowledge of different genres, but in self-efficacy as well. Skills instruction is essential to the model, but not in the form of isolated or decontextualized drills. Englert’s Cognitive Strategy Instruction in Writing (Englert et al., 1991; Englert et al., 1992) offers another approach for students with learning disabilities. Her model relies on graphic organizers or more specifically, “think sheets” that facilitate the planning, organizing, writing, editing, and revision process. Rather than have students simply fill out graphic organizers, they were taught as strategies for organizing and understanding information. Teachers model each step in the process, and acronyms are used as a way to help students keep track of the various steps in the writing process. The strategy curriculum framework has also been apparent in secondary content areas. The work of Bulgren, Deshler, and their colleagues (Bulgren, 2006; Bulgren, Deshler, & Lenz, 2007; Bulgren, Lenz, Marquis, Deshler, Schumaker, Davis, & Grossen, 2006) best exemplifies how learning strategies can be applied to varied subjects taught in high school. Their work is particularly important given the demands placed on secondary special educators who need to mediate their instruction for a wide range of students. Furthermore, NCLB legislation calls for highly qualified teachers—typically, general education teachers—to be primarily responsible for teaching students with learning disabilities. This means that

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curriculum that might have been used in special education settings (e.g., direct instruction print curriculum) may be perceived as inadequate for all students in a general education classroom. As a consequence, general education teachers need to find other ways to adapt their materials for a diverse audience of students. The Content Enhancement Routines model (Bulgren et al., 2007) is an ambitious effort designed to support general education teachers in their planning and instruction. Teachers are expected to create instruction around the big ideas in a unit as well as craft ways for connecting new material to previous learning. Graphic organizers or content maps for the unit are essential, and they present big ideas and supporting concepts to students as they work through the unit. This structure not only helps teachers focus their planning on big ideas, but it is intended to clarify student outcomes, usually in the form of critical questions. These maps, along with guiding questions, explicit directions for note taking, and teacher– student interactions, are a central element in daily instruction. Students are also taught specific cognitive strategies so that they can be more successful learners. Paraphrasing, mnemonics, acronyms for how to identify key information and relate it to other important ideas are all part of Bulgren and Deshler’s approach to secondary curriculum. Mnemonics and direct instruction have also been used successfully by other cognitively oriented research to teach students with learning disabilities secondary social studies (see Fontana, Scruggs, & Mastropieri, 2007). Mathematics has been another important area for cognitively-based instruction at the secondary level. Jitendra and her colleagues ( Jitendra, DiPipi, & Perron-Jones, 2002; Jitendra et al., 2009) have successfully applied graphic or schematic organizers along with cognitive and metacognitive routines to teach word problem solving. Their work is reminiscent of cognitively guided instruction (Carpenter, Fennema, Franke, & Levi, 1999) with its emphasis on the underlying structures or schemata of math problems (e.g., rate problems, compare problems). But Jitendra and her colleagues add an increased level of explicitness to the problemsolving process by focusing student attention on a variety of cognitive strategies used to solve different types of problems. Students also actively compare and contrast different solution strategies to problems. Finally, students are taught self-monitoring strategies for different problem types, initially with teachers modeling each strategy for the student then prompting them to use the strategy in the context of actual problems. It should be noted that the instructional process is highly scripted—a feature carried

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over from the direct instruction tradition—though teachers are encouraged to adapt and even abandon these scripts once they become more comfortable with the instructional routines. Finally, Montague (1992, 2003) uses a similar mix of direct instruction methods along with cognitive and metacognitive strategies in her work in mathematics. The strategic dimensions of her work in problem solving include reading and then paraphrasing the problem, creating an internal visual representation of the problem, thinking about solutions to the problem, estimating the answer as well as evaluating the solution. Metacognitive strategies also include self-instruction, self-questioning, and self-monitoring.

Limitations and Lingering Questions One of the questions evident in strategy instruction efforts has to do with an evident increase in a teacher’s pedagogical content knowledge. This is evident in writing instruction, where teachers have to know how to teach different genres of writing as well as the attendant strategies and mechanical skills. Bulgren et al.’s (2007) Content Enhancement Routines are another area where teachers need pedagogical content knowledge as well as ability to use principles like Understanding by Design (Wiggins & McTighe, 2005) to extract big ideas from curriculum materials and map them onto graphic organizers and other enhancement techniques. These demands distinguish some aspects of strategy instruction from the assumptions behind direct instruction reviewed earlier. They are also consistent with NCLB’s call for highly qualified teachers to do content instruction at the secondary level. At the same time, Lenz et al.’s (2007) research on strategy instruction in secondary classrooms is telling because it documents cases where the extensive amount of planning required to meet the needs of a diverse group of students can simply ask too much of teachers. As mentioned earlier, it was this kind of concern (as well as the hope for higher levels of fidelity of implementation) that led some in the direct instruction community to create highly scripted, print and technology-based materials for teachers. A second question is one that Swanson et al. (1999) noted in their meta-analysis of special education interventions, that is, the frequent lack of substantive differences between direct instruction and strategy instruction, at least in practice. Some researchers and curriculum developers associated with strategy instruction blur the boundaries between different frameworks even further. Harris et al. (2008) argue that multiple theories

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of learning and instruction can enhance practice, leading to more powerful interventions. They argue that good teachers use an eclectic pedagogy and thus call for theoretical pragmatism when it comes to actual interventions. Gersten and Baker (1998) have made similar arguments in efforts to merge direct instruction with constructivism. Kennedy’s (2006) detailed examination of the curricular practices of intermediate grade teachers adds further substance to this perspective. She suggests that classrooms are beset with too many interruptions and potentially unmanageable situations to be a place for just one kind of curriculum theory to dominate day-to-day instruction. Yet those researchers and curriculum developers reviewed in our next section—ones who moved from a strategy orientation to constructivism— may likely disagree with an eclectic orientation to instruction. Palincsar, Englert, Bottge, and others most certainly afford opportunities for skills to be taught directly. But many of these researchers migrated from strategy instruction to constructivism over time for a reason. Their efforts clearly indicate that substantial portions of instructional time need to be allotted for students to engage in authentic tasks and classroom discussions. Their tilt toward a student-centered, constructivist practices will be apparent in what follows.

CONSTRUCTIVIST ORIENTED CURRICULUM FRAMEWORK The constructivist perspective diverges significantly from the previous two perspectives if for no other reason than it defies a single, coherent point of view (O’Connor, 1996). For example, one strand of constructivism focuses on individual development, one that has its roots in Piaget and the centrality of guided discovery, cognitive conflict as a vehicle for intellectual growth, and fixed stages of development. Vygotsky and others tend to be an anchor point for the more socially oriented strand of constructivism, where philosophers, anthropologists, and sociologists have all helped articulate this vision of learning. The social constructivist perspective has had an increasing but nonetheless modest influence over special education in the last two decades, with its emphasis on substantive interactions between teachers and their students as a community of learners. Learning is often guided through teacher scaffolding as well as structured, peer-to-peer interactions. The latter practice is of particular theoretical importance because a struggling student is afforded the opportunity to interact with one or more other

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students who are more sophisticated in their thinking. More advanced peers as well as teachers are instructional mentors. Social constructivism also highlights authentic tools and tasks as ways to engage students and foster transfer of learning. Finally, language is a central feature of a social constructivist environment. Scaffolding and carefully orchestrated interactions allow learners to internalize the ways of thinking and become increasingly self-regulated. The importance of language is also reflected in classroom cultures that encourage discussion, debate, and as a consequence, a richer understanding of the discourse or “ways of thinking” in a discipline. Another distinguishing feature of social constructivism is the way it addresses the all too common characterization of students with learning disabilities as deficient cognitively and socially. Trent, Artiles, and Englert (1996) review the historical foundation for deficit thinking about students with disabilities, a history with a theoretical stance that deficits lie within the individual and that services should be provided in segregated environments. They subsequently argue that social constructivism creates the opportunity for students with learning disabilities to collaborate in regular classrooms with their peers through careful teacher scaffolding and classroom organizational practices. This enables these students to participate more completely in the discourse of a discipline. The theme of authentic social and intellectual participation can be seen in the early work of Palincsar (Palincsar & Brown, 1989) and later, in the shift by Englert (Englert, 2009; Englert & Mariage, 2003) from a cognitive to social constructivist framework. Palincsar’s work in reciprocal teaching (Palincsar & Brown, 1989) presents a classic example of what Lave and Wenger (1991) call legitimate peripheral participation. An expert (the teacher) initiates novices (students) into a form of discourse and practice by first modeling skills and tasks. Gradually, novices take on more active roles, not only interacting with the expert but also with fellow novices. In reciprocal teaching, metacognitive strategies take on a central role in how students learn to monitor their understanding of text and talk about it in literature conversations. The expert gradually removes him or herself to the periphery of the conversation, only providing input and commentary when necessary. Palincsar’s subsequent work in science instruction reflects many of the practices found in reciprocal teaching (Palincsar, Magnusson, Collins, & Cutter, 2001). While students engaged in communities of practice where they discussed their thinking and worked in collaborative small groups, the nature of the discourse centered on the scientific investigation of light and

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its interaction with matter. The essence of this investigation can be found in a tool fundamental to the practice of science: the notebook. Students used this medium to capture thoughts, make diagrams, and record data. It also served as a talking point for small group presentations. This approach stands in sharp contrast to science instruction in secondary education where teaching focuses on the acquisition of declarative and procedural knowledge and where the textbook provides an authoritative foundation for knowledge. Palincsar et al.’s (2001) methods are more consistent with an inquiry orientation to the discipline. Okolo and her colleagues (MacArthur, Ferretti, & Okolo, 2002; Okolo, Ferretti, & MacArthur, 2007) draw on cognitive and socio-cultural perspectives in their work with intermediate and middle school students by emphasizing authentic tasks and classroom discourse in the area of social studies. Their work underscores a number of cognitive and social constructivist principles: social mediation, authentic tasks, constructive conversations, and cognitive strategies. For example, students with learning disabilities were taught with their general education peers in cooperative, heterogeneous groups, where they conducted investigations into the lives of individuals migrating west in the US during the 19th century and the impact of this migration on the indigenous population. Rather than relying on conventional textbooks, students examined primary and secondary sources of information as a means of understanding westward expansion. Teacher-led discussions were designed to help students further interpret the complex reasons behind the westward movement as well as to afford teachers the opportunity to probe for the depth of student understanding as well as lingering misconceptions. Many of the instructional tasks were intended to help students appreciate how historians write about a period of time as well as evaluate historical interpretations for bias. Curricular materials were also organized around big ideas, including a “ways of life” schema (i.e., how political, economic, and religious belief systems of different people were affected by westward expansion) and a migration and conflict schema (i.e., causes and consequences of westward migration). A comparison and contrast strategy was used to evaluate the similarities and differences in the ways of life of the different groups the students studied. The instructional materials, which were developed by the researchers, took into consideration possible reading difficulties that students with learning disabilities might have. Furthermore, strong and struggling readers were paired in order to help with the challenge associated with their having to navigate a diverse set of documents and reading materials.

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Finally, Bottge’s work in anchored instruction (Bottge, Heinrichs, Mehta, & Hung, 2002; Bottge, Rueda, Grant, Stephens, & Laroque, 2010; Bottge, Rueda, Serlin, Hung, & Kwon, 2007) pays special attention to the role authentic transfer tasks play in helping students see connections between the formal knowledge of mathematics as it is taught in schools and how it is used in the world. Students work closely in mixed ability groups on problems requiring considerable persistence to complete. Teachers mediate learning by scaffolding student understanding where necessary, often probing for understanding, problem-solving strategies, and misconceptions. Teachers conduct brief practice sessions to solidify mathematical knowledge needed for the problem solving. Video vignettes from computer-based programs are used to present the context for the problem solving. They also allow students to learn about math concepts and even conduct measurements. Finally, students construct apparatus needed to conduct experiments or create working models of devices (e.g., hovercraft, skateboard ramps).

Limitations and Lingering Questions Few would question the complexity of constructivist-oriented curriculum. The anchored instruction work summarized above requires more than the content pedagogical knowledge discussed with strategy instruction. Bottge et al. (2007) are quick to acknowledge that the teacher’s job in orchestrating various activities, from the use of multimedia tools to the construction of actual models such as skateboard ramps, is considerable. Whether it is a science investigation or the study of US history, teachers need to be skilled in classroom discussions and in facilitating small group or paired activities. Throughout all of this, they need to be skilled in navigating students through ill-defined tasks and what sometimes can be wide ranging discussions. A second question is the inevitable concern for skill development. Englert and Mariage (2003) acknowledge that basic skills instruction is important, but that this kind of instruction should be done in context. Yet it is difficult to glean from constructivist accounts exactly what this looks like. This may be because accounts of constructivist curriculum for students with learning disabilities, whether they appear in journal articles or book chapters, tend to highlight the distinct elements of the framework. One mostly reads about the varied materials and multi-media tools or samples from transcripts of classroom discussions. Nonetheless, skills development is important.

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Ferretti, MacArthur, and Okolo (2007) acknowledge that while all students in their study learned more about US history, a number of instructional challenges were also evident. One of the key points of interest—having students use the narrative framework to understand the historical period—was only marginally successful for students with learning disabilities. These students began instruction with less background knowledge about this period of history than their peers and this remained the case throughout the unit. A number of students with and without disabilities continued to harbor significant misconceptions about the way historians evaluate information. Finally, many students failed to use the appropriate evidence in classroom discussions and debates. All of this speaks to the challenge of initiating young students into a complex discipline like history and more specifically, making sure that there is some ongoing and systematic opportunity for basic facts and concepts to be learned. Complicating this issue further, at least at the secondary level, are observations about focused skill developments from Bottge, Rueda, and Skivington’s (2006) curricular research. They found that secondary special education students “rebelled” when direct instruction techniques were employed as an ongoing part of classroom instruction. Tempering appropriate but intensive skill development for students with learning disabilities in contexts where they are learning challenging content has yet to be fully articulated by constructivist-oriented special educators.

SPECIAL EDUCATION CURRICULUM AND THE ERA OF HIGH STANDARDS One of the signature features of the No Child Left Behind legislation was the call for a scientific basis to the curricula used in our schools. When NCLB was signed, policy makers readily pointed to beginning reading research as a model for instructional materials, pedagogy, and assessment practices. NCLB, however, was intended for a much wider scope of instruction, perhaps through high school. Curricular research reviewed in this chapter varies in the extent to which a particular framework or body of work within a framework might be considered “scientifically based.” There are some in the field who argue that the only strong evidence for curriculum, at least when considered in the context of RtI decision-making, remains in the area of beginning reading (Fuchs, 2009). The debate over what constitutes science in a practical field of inquiry such as education is considerable and beyond the scope of this chapter.

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Yet the idea that only what counts is “high quality research,” the kind that turns the definition of science into methodology (e.g., experimental, quasi-experimental, regression discontinuity designs) would not only undercut much of what was summarized above, but it would endorse a naïve understanding of science. These views also tend to ignore the role clinical judgment plays in evidenced-based practices. Deciding on a curriculum is more than the amassing and weighing of empirical research studies. It involves attention to traditional external validity concerns as well as the contexts where research-based practices are actually being adopted (Darling-Hammond & Bransford, 2005). Putting these issues aside, it is important to address a fundamental observation about curricular research that is rarely entertained in the discussion of scientifically-based curriculum—the relationship between research and standards. The beginning point for this discussion is the manifest observation that research, if it is to be relevant, is time bound. During the 40 years of special education curriculum reviewed in this chapter, we have seen a number of high profile movements, standards documents, and widely-cited publications that lay claim to what students need to know and do. There has been a marked shift from an emphasis on basic skills to more complex outcomes now articulated through national policy documents like the Common Core (Common Core State Standards Initiative, 2010). In the intervening time, major disciplinary standards in mathematics (National Council for Teachers of Mathematics, 2000), social studies (National Council for Social Studies, 1994), and science (National Research Council, 1998) have appeared and spawned considerable debate, with only a portion of the debate devoted to the research support for specific standards. Hiebert (1999) articulated over a decade ago the uneven relationship between research and standards. Most certainly, there are cases where research overwhelms one aspect of a subject or discipline, providing clear guidance for curriculum development (e.g., the beginning reading research). Research on vocabulary development and comprehension instruction has produced less certainty, particularly as reading spills into disciplines like science or history. Thus, finding comprehensive research support for all of the standards contained in a document like the Common Core (Common Core State Standards Initiative, 2010) would be overwhelming, even more so if one were to consider how this research would be tailored for students with learning disabilities. Furthermore, the issue of an adequate research base extends beyond the content dimensions of these standards to the process dimensions as well.

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For Hiebert (1999), a central way to think about research is by deciding first what is valued, and then determining what research is relevant to those values. For example, if we value a student’s ability to collect and interpret data from an experiment and write a causal explanation based on the data, then studies that show significant effects favoring Method A over Method B for the simple retention of basic facts and concepts will be of marginal relevance. This may be one reason why curriculum-based measurement in beginning reading is relatively successful, while similar efforts for elementary mathematics instruction are largely irrelevant to what is deemed important to today’s math standards for the primary and intermediate grades. Hiebert’s (1999) observation also helps us understand why many syntheses of research and meta-analyses help us less than we would like in determining which curriculum or curricular framework (e.g., direct instruction, strategy instruction, constructivism) is likely to be most effective for students with learning disabilities. The typical concern in these compilations of research is with the effect sizes for a given set of studies and less with the nature of the dependent measures. Yet the dependent measures are often one of our best insights into what we value (e.g., retention of facts, problem solving, transfer). The degree of student success on specific kinds of student outcomes will enable us to determine the relevance of the research to our values or, more broadly, the standards we deem to be important. Applying Hiebert’s (1999) thinking to today’s world is complicated by the fact that there are so many standards and that within those standards, there is often a mix of content and process dimensions. Thus, those concerned with students with learning disabilities need to decide which standards—and how much of those standards—are applicable as well as what curriculum research in special education is relevant. The recently released Common Core State Standards Initiative (2010) is instructive when thinking about these issues. While the Mathematics as well as English and Language Arts Standards have received some of the closest scrutiny from educators, there are also History/Social Studies as well as Science Standards as part of the Common Core. Any perusal of the content portion of any of the high school standards would suggest that the goals are likely to be well beyond the performance levels of most students currently in schools today, not just students with learning disabilities. There is no obvious way to determine from a distance how many or what percent of the standards are

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appropriate for students with learning disabilities.Yet it might be tempting to look for the most efficient strategies for teaching as many facts, concepts, and procedures as possible to these students. There are two significant drawbacks to this approach. First, the longterm effects of research-based methods at the secondary level may simply be overstated. Most curriculum research studies tend to measure the success of their methods from pre-test to post-test with little attention to long-term retention beyond four to six weeks, if maintenance measures are administered at all. Woodward, Baxter, and Robinson (1999) documented the problem of retention in a study of decimal instruction, one based on the reputed superior effects of specific curriculum design methods for learning multi-step procedures like operations on decimals. Students in the Woodward et al. (1999) study learned about the conversion of fractions to decimals as well as operations on decimals over a four-week period of instruction. A direct instruction, technology-based program was used for the teaching, thus insuring a high fidelity of implementation. Students were held to a high criterion of daily performance (i.e., approximately 80%). Yet when assessed only ten days following the intervention, average student performance on skills directly taught over the four weeks dropped to 34% correct. In addition to struggling with computations, many students reverted to common misconceptions for converting fractions to decimals such as multiplying the numerator and denominator or dividing the numerator into the denominator. This research is far from definitive, but like classic accounts of instruction that emphasizes procedures and fact retention (see Gardner, 2011), it suggests that long-term retention through direct instruction methods alone can be problematic and that claims of students with learning disabilities knowing “more” than college students or high school honors students as the result of a pre- to post-test studies (see Grossen, 2004) are simply too good to be true. Robert Sternberg (2009) captures the problem of teaching efficiently to the content side of standards alone in his insightful, if not whimsical introduction to a recent handbook on metacognition. In his case, he critiques the typical criterion outcome for this kind of teaching, namely standardized tests: The reason the target [of standardized testing] is wrong is that, in the long run, much of the knowledge we acquire in school that is so important in tests will be forgotten anyway. I once knew how to compute a cosecant. Today, I don’t remember even what it is. I once knew what a halogen is. Those days are too long past. In my

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own field of psychology, I got a terrible start with a C in introductory psychology. When I sat down to write my own introductory psychology textbook, published 27 years after my ignominious grade, I discovered that most of the material covered by textbooks in 1968 was no longer even being taught in 1995. The knowledge had become largely irrelevant. The important things to acquire from the courses were not the textbook factoids, but rather, the learning to learn skills and the skills in accessing a knowledge base that form the heart of metacognition (p. iix).

Sternberg is not suggesting that all of the facts, basic concepts, and procedures found in standards be discarded. There is a place for this kind of learning, and a command of facts, concepts, and procedures is essential to good problem solving. For Sternberg, it is teaching students to be aware of what they know and don’t know as well as how to acquire and understand new information that is critical in education today. A second and equally significant drawback in the quest for efficiency is the importance of process dimensions to disciplinary and national standards. Many process dimensions of the Common Core are consistent across grade levels. Students are expected to interpret, analyze, reason, discuss, and argue about text and other materials. Far less special education curricular research is available to guide educators in translating these expectations to instruction for students with learning disabilities. Yet as much as these standards are tied to content, they present the same problem as mentioned earlier: What portion of the process standards are feasible for students with learning disabilities?

CONCLUSION: FOSTERING ADAPTIVE EXPERTISE A novel answer to this question resides in the concept of adaptive expertise. This concept can be found in one of the most widely cited attempts to overlay contemporary learning theory on the nature of schooling—the How People Learn (HPL) framework (Darling-Hammond & Bransford, 2005; National Research Council, 2000, 2005). This synthesis of recommendations is grounded in studies of competent performance, research into the intellectual development of young children, and what constitutes problem solving and the meaningful transfer of knowledge into varied, naturalistic contexts. What is potentially important about adaptive expertise is that it transcends the details of grade level by grade level standards and returns us to more fundamental ideas of curriculum—ones tied to the broader purposes of schooling. Adaptive expertise merges schooling for intellectual

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development for its own sake with the more contemporary focus on vocationalism. Put simply, American education has a long historical interest in developing higher order thinking, although much of it is framed around classical texts and the college preparatory courses found in many of today’s high schools. Adaptive expertise, with its emphasis on problem solving and transfer, shifts this traditional interest and frames the issue in the challenges faced everyday in a world surfeit with information. Students are not only deluged with information because of our high standards era, but it is readily available, if not confronted, through almost any interaction with technology. Similar issues face an increasing portion of today’s workers, which makes adaptive expertise part of the vocationalist purpose of schooling. Fostering development of adaptive expertise within a discipline calls for a considerable investment in well-structured learning environments. Facts and skills learned in context, rather than isolation, are critical. A flexible understanding and use of concept is also essential. Students should also be taught cognitive as well as metacognitive strategies. They should be given multiple opportunities to probe, discuss, and clarify ideas they are learning. Moreover, classroom environments need to be knowledge-centered insofar as they assume that students come to new learning with prior knowledge and (mis)conceptions of a subject matter. Schwartz, Bransford, and Sears (2005) alert us to the importance of what students “transfer in” to a problem-solving situation, because even if prior instruction hasn’t been well structured or “schematic,” then what is transferred in is likely to impede how a problem is interpreted and solved. The development of adaptive expertise calls for a community-centered classroom where answers to many questions are more than “right or wrong.” Instead, students grapple with challenging content that often calls for transfer to the outside world. In contrast to the pursuit of efficiency, what Schwartz et al. (2005) call the development of routine expertise, students are pushed to transfer their understanding of what is learned to problems that are solved in an iterative fashion. This aspect of instruction adds a new dimension to the learning environment, and it is under described or simply missing from the constructivist accounts summarized earlier in this chapter. Adaptive expertise draws attention to the first phase of problem solving where students spend time interpreting the problem. Rather than rush to execute a strategy or well-established routine method, time is devoted to understanding the problem and the extent to which knowledge used to make sense of it is relevant or even tends to constrain methods for solving

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it. Once students devise a solution to a problem, it is the subject of subsequent discussion and classroom debate. The strengths and limitations of the solution are considered, and opportunity is afforded for a revised solution. Once revised, it is subjected again to discussion and critique. While this process could continue indefinitely, the practical limits of time and the need to study other curricular topics require students to bring closure to the investigation. However, in the process students learn invaluable lessons in how to work with complex information and devise flexible solutions that are likely to take much longer than efficient routines to construct. Critics of adaptive expertise may consider it another lofty goal in the same vein as many of the high standards that now permeate American education. Yet what makes the concept compelling is the assumption that teaching the expansive content found in standards documents like the Common Core to mastery is infeasible for many students in our schools, particularly those with learning disabilities. Carl Bereiter, who ironically began his career developing direct instruction techniques for educationally disadvantaged students, captured this sentiment recently in his book on cognition, technology, and education. Bereiter (2002) laments the longstanding obsession in education with “filling the container.” The capacity to teach more in shorter periods of time—and to retain even a significant portion of this information—has passed. He, like those in the HLP framework (see Darling-Hammond & Bransford, 2005), argues for learning environments that are distributed and problem-oriented. Rather than focusing on short-term goals or ones aligned with a specific standard(s), Bereiter and others remind us that the cumulative, long-term goals of education merit equal attention. A curriculum for students with learning disabilities that develops habits of mind well-suited to today’s world—one that is technologically rich and surfeit with information—may be the best argument for innovative concepts like adaptive expertise.

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Stone, C. A. (1993). What is the metaphor of scaffolding? In E. Forman, N. Minick, & C. A. Stone (Eds.), Contexts for learning: Sociocultural dynamics in children’s development (pp. 169–183). New York: Oxford University Press. Swanson, H., Harris, K., & Graham, S. (2003). Handbook of learning disabilities. New York: Guilford Press. Swanson, H. L., Hoskyn, M., & Lee, C. (1999). Interventions for students with learning disabilities: A meta-analysis of treatment outcomes. New York: Guilford Press. Trent, S., Artiles, A., & Englert, C. (1996). From deficit thinking to social constructivism: A review of theory, research, and practice in special education. In P. Pearson & A IranNejad (Eds.), Review of Education Research, 23, 277–307. U.S. Department of Education, (2008). Foundations for success: The final report of the national mathematics advisory panel. Washington, DC: US Department of Education. Williams, J. (2003). Teaching text structure to improve reading comprehension. In L. Swanson, K. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 293–305). New York: Guilford. Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). Alexandria,VA: ASCD. Woodward, J. (1993). The technology of technology-based instruction: Comments on the RDD perspective of educational innovation. Education & Treatment of Children, 16(4), 345–360. Woodward, J., Beckmann, S., Driscoll, M., Franke, M., Herzig, P., Jitendra, A., Koedinger, K. R., & Ogbuehi, P. (2012). Improving mathematical problem solving in grades 4 through 8: A practice guide (NCEE 2012–4055). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from. http://ies.ed.gov/ncee/wwc/publications_reviews. aspx#pubsearch/. Woodward, J., Baxter, J., & Robinson, R. (1999). Rules and reasons: Decimal instruction for academically low achieving students. Learning Disabilities Research and Practice, 14(1), 15–24.

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Included, but Underserved: Rediscovering Special Education for Students with Learning Disabilities Jean B. Crockett, Elizabeth A. Filippi, and Cheryl L. Morgan School of Special Education, School Psychology and Early Childhood Studies, University of Florida, Gainesville, FL 32611, USA

Chapter Contents Introduction  405 Educating Students with LD in the Context of General Education Policy   408 Current Realities of Service Delivery   409 Policies that Govern Instruction for Students with LD   412 Research that Guides Instruction for Students with LD   415 Research on Reading Interventions   415 Research on Social Studies and Science Instruction   416 Research on Mathematics Interventions   417 Research on Social Interventions   418 The Persistent Gap Between Research and Practice   419 What Classrooms are Like for Students with LD   419 Providing Instruction for Elementary Students with LD   420 Educating Secondary Students with LD   424 Ensuring Equal Access to High Quality Education   427 Recognizing and Responding to Students’ Academic and Social Challenges   428 Maintaining the Integrity of Teachers’ Roles   429 Using Interventions Faithfully and Intensively   430 Conclusion  431 References  433

INTRODUCTION The disgrace is not that general education teachers are not adequately prepared to deliver a special education to the students with disabilities in their large and diverse classrooms. The disgrace is that we have come to believe that special education is so not-special that it can be delivered by a generalist, busy teaching 25 other students a curriculum that was generated at the school board, or state, or Learning about Learning Disabilities

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federal level. The disgrace is that we have forgotten that special education is supposed to be special and that wherever it is delivered, it is supposed to be different. (Zigmond & Kloo, 2011, p. 170)

General and special education are and should be different, argue Zigmond and Kloo (2011), who note that both branches of the educational system have distinguished histories that until recently ran parallel in the pursuit of equity for all learners. The relationship between the two branches, however, has been controversial since the inception of universal schooling. The designations of general education, regular education, and mainstream education are synonymous in referring to the basic public education available to “everyone and anyone” (p. 160). In contrast, special education is offered to individual children who have “physical, cognitive, language, learning, sensory and/or emotional abilities/disabilities that deviate from those of the general population and whose abilities/disabilities require special educational services” (p. 160). Special education provides an appropriate education “to students who otherwise would have limited access to instruction” (p. 160), and it does so across a continuum of alternative educational placements that range from general classrooms to highly specialized settings. Currently the lines are blurring between general education and special education especially in schools where substantial numbers of low achieving students could benefit from additional support in learning challenging curriculum with high expectations for success. In schools, blurring means including more special education students in general classes, and assigning special educators to consult and co-teach with general educators, or to work in tutorials with small groups of struggling learners across several tiers of support (see Fuchs, Fuchs, & Stecker, 2010). In the present context of accountability, educational equity for both special and general education students is “measured by progress toward valued and meaningful standards” (McLaughlin, Smith, & Wilkinson, 2012, p. 374). However, to measure up, some students, including those with learning disabilities (LD), require the intensive and specially designed instruction that has historically distinguished special from general education. In other words, students with LD are likely to need special education because “interventions that might be effective for this group of students require a considerable investment of time and effort, as well as extensive support” (Zigmond, 2003, p. 197). Without the support of carefully designed, special education instruction, these students might be included in general education classrooms, but essentially underserved in neighborhood schools.

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From an inclusive perspective the goal of special education is “to minimize the impact of disability and maximize the opportunities for children with disabilities to participate in general education in their natural community” (Hehir, 2005, p. 49). General education classrooms, however, can be busy and crowded places for students with disabilities, where instruction is frequently delivered to the group and driven by the clock. They can also be public places where struggling students fear the spotlight might shine on their shortcomings, and where embarrassment and frustration lead to unacceptable behaviors. In short, the characteristics of typical classrooms are often at odds with the kinds of activities, interchanges, and consistency required by special education students (Garnett, 2010). Students with LD who have neurological disorders, not tied to their overall cognitive ability, that impair their performance in reading, writing, spelling, or mathematics, cannot profit from general education without teachers’ acknowledging their special learning and behavior needs. In practice special education differs from general education not so much in kind as in degree across a number of dimensions including pacing or rate, intensity, relentlessness, structure, reinforcement, pupil–teacher ratio, curriculum in some cases, and monitoring and assessment (Kauffman & Hallahan, 2005). Special education is intended to provide instruction that is individually planned, specialized, intensive, goal-directed, research-based, and guided by student performance (Brownell, Smith, Crockett, & Griffin, 2012). How schools provide this carefully planned and delivered instruction is the focus of this discussion, and a topic that “continues to raise exciting possibilities as well as practical challenges for both general and special educators” (Eisenman & Ferretti, 2010, p. 262). In this chapter we examine how special educational services for students with LD are currently carried out in American public schools. Our guiding premise is that school-based service delivery is shaped by two often countervailing forces: the governance of educational policies, and the guidance of educational research (Bateman, 2007). How these forces interact in practice is examined through illustrations drawn from recent studies of special education service delivery for students with LD in elementary and secondary schools. In concluding our discussion we emphasize the importance of meeting the unique educational needs of students with LD and teaching them effectively in what for each is the least restrictive educational environment, using the most promising and proven practices. To do so, we argue, means drawing on the respective expertise of general and special education teachers by clarifying, not blurring, their traditional distinctions.

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EDUCATING STUDENTS WITH LD IN THE CONTEXT OF GENERAL EDUCATION POLICY In the context of public education, politics and policy are intertwined in struggles over scarce resources and who should benefit from them. Educational politics wrestles with difficult questions of “who gets what, when, and how” (Gallagher, 2006, p. 5). Educational policies in turn “serve as tools for resolving disputes, and in the case of special education, resolving them in the best interest of a student with exceptional learning needs” (Crockett, 2011, p. 349). Policies that govern education address questions of (a) who receives resources; (b) who delivers the resources; (c) what resources are delivered; and (d) under what conditions the resources are delivered to meet the intent of the policy (Gallagher, 2006). In the case of special education, the first question addresses the unique challenges of students with disabilities eligible to receive specialized services. The second question addresses the professional readiness of educators to provide specially designed instruction. The third question addresses the substance of what is provided and how schools are organized to provide students with an appropriate education. The fourth question addresses learning environments in which students with disabilities are expected to learn challenging content and teachers are held accountable for helping them be successful (Crockett, 2011). Internationally politics and policy influence how scarce resources are allocated in any country. In this discussion, we focus on describing how this influence plays out in the context of American public education. Current policies in the United States mandate that general and special education be coordinated as part of a coherent system accountable for the educational outcomes of all students. The responsibility for providing high quality basic instruction, research-based interventions, and prompt identification of students at risk for LD now belongs to general educators in collaboration with special educators and related services personnel. Interventions are to be scientifically based, and the prelude to any intervention is expected to be high quality instruction provided in general education classrooms where most students begin their schooling (U.S. Department of Education, 2011). General education policy has had important implications for special education practice over the past decade. The Elementary and Secondary Education Act, reauthorized as The No Child Left Behind Act in 2001 (NCLB), required all students including students with disabilities to learn the general curriculum, to participate in state and local assessments, and

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to make gains in achievement toward the goal of making adequate yearly academic progress. When groups of students with disabilities failed to make adequate yearly progress toward achieving 100% proficiency in reading and mathematics, their schools faced punitive accountability measures. As a result many school districts turned toward inclusion in general education classes as a means to provide special education students with greater access to the standards-based content of the general curriculum. The evidence suggests, however, that inclusive service delivery without effective, intensive, and specific instruction and social support fails to help many special education students access learning or achieve appropriate outcomes (Brownell et al., 2012).

Current Realities of Service Delivery Special education enrollments have risen with the increased demands of standards-based reforms, so that nearly 7 million children with disabilities, ages 0–21, now receive special education in the United States. Ninetysix percent of the 6.1 million special education students in kindergarten through twelfth grade are included for all or for part of their school day in general education classrooms where they are expected to participate in social activities and to learn challenging academic content (U.S. Department of Education, 2010). The most recent biennial report issued by the National Center for Learning Disabilities (Cortiella, 2011) indicates the number of students eligible for special education increased by 2% from 2000 to 2009, but the number of students with LD declined by 14% across this time span. As a result, the category of “specific learning disabilities,” that accounted for more than half of all special education students in the United States for decades, now accounts for just 42%. This downward trend reflects a steady decline in the number of students being identified as LD each year. In 2009, 2.5 million students, approximately 5% of all students in American public schools, were identified as having LD and were eligible to receive special education services under the Individuals with Disabilities Education Act (IDEA). Students with LD in the United States continue to make up the majority of the school-age population receiving special education, and, in 2008–2009, 62% of these students across age and grade levels were taught in general education classes for at least 80% of their school day. Only 28% were taught in pull-out or resource room settings and, between 1990–1991 and 2008–2009, 45 states reduced the percentage of students

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with LD taught in separate classes or separate schools (McLeskey, Landers, Hoppey, &Williamson, 2011). Data suggest that the percentage of students with LD varies by age. Students with LD comprise approximately 34% of special education students in elementary schools and 58% of special education students at the secondary level (U.S. Department of Education, 2010). Secondary schools have experienced the largest growth in the number of students with LD, and the good news is that outcomes for these students have improved in recent years. In 2008, high school dropout rates declined to 22% from 40% in 1999, and 64% of students with LD graduated with a regular high school diploma, an increase from 52% a decade earlier (Cortiella, 2011). The more sobering news is that students with LD transition to postsecondary education at a much lower rate than their nondisabled peers, and few seek specialized supports in college or complete undergraduate or advanced degrees. In 2005, only 55% of adults with LD (ages 18–64) were employed compared to 76% of adults without LD (Cortiella, 2011). These data suggest the need for paying closer attention to the pedagogical and contextual aspects of their education. Despite positive trends in the inclusion of diverse learners, the appropriate education of students with LD is still threatened by social stigma and misconceptions among educators and the general public, many of whom use the term learning disabilities to refer to a wide range of physical and intellectual challenges. Misconceptions about LD Recent studies commissioned in 2010 by the Emily Hall Tremaine Foundation, surveying the attitudes and understandings about LD of a representative sample of American parents, teachers, and administrators, indicated that (a) 70% of respondents incorrectly linked LD with mental retardation and autism; (b) close to 40% linked LD with sensory impairments such as blindness and deafness; (c) 55% of parents and the general public, 43% of teachers, and 31% of school administrators mistakenly blamed the child’s home environment as the cause of LD; and (d) 51% of respondents thought LD was the result of laziness. In addition, many parents reported that they ignored warning signs in young children ages 5–8 years, such as difficulties using a pen or pencil, matching letters and sounds, and making friends, believing their children would outgrow them (Cortiella, 2011). These misconceptions make early identification of LD and subsequent intervention more difficult.

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Providing Early Intervening Services Early intervention in general education classrooms is a high priority for students from diverse backgrounds that have not been identified as needing special education, but who need additional support to learn the core curriculum. Changes in the most recent re-authorization of the IDEA in 2004 permit the provision of funds to support early intervening services, including coaching and training for classroom teachers that might reduce the need for struggling students to receive special education. Responsiveness to intervention (RTI) is the term used for the schoolwide model of delivering interventions across multiple tiers of support to struggling students through the systematic coordination of services across general and special education (Martinez, Nellis, & Prendergast, 2006). Measuring a student’s response to intervention, meaning the change in behavior or performance that results from instruction, is not a new practice, but the RTI process derived from it is changing how schools do business in meeting the needs of struggling learners. Identifying Students with LD Preventing the over-identification of disabilities and inappropriate referrals to special education especially among students from cultural and ethnic minority groups is an objective of educational policy. As a result, the IDEA now permits teachers to consider a student’s response to scientifically-based interventions in the process of distinguishing students with suspected learning disabilities who need special education, from students with learning problems who progress when they receive more effective basic instruction (Bateman & Linden, 2006). The use of RTI to identify students with LD is highly controversial, and researchers have yet to offer reliable evidence regarding the nature and measurement of responsiveness. O’Connor and Sanchez (2011) note “it is possible that future research will define a convergent model that has sufficient reliability for identifying students with LD, but that has not happened yet” (p. 129). The RTI process is hailed by some as an alternative to the intelligenceachievement discrepancy model traditionally used to identify students as being eligible for special education under the category of LD. RTI is also seen as an antidote to the problem of over-identifying students for special education because of its emphasis on valid assessment and powerful teaching. Some say the high cost of special education is actually prompting school districts to pursue RTI in the hope that strengthening general education will reduce the number of students receiving special services, or prevent

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services from being provided later at a higher cost per student (Martinez et al., 2006). Although most discussions of RTI address special education, the activities involved in preventing learning problems—universal screening, effective and differentiated instruction, and progress monitoring—are considered to be the responsibility of classroom teachers. Tiered levels of intervention hold promise for minimizing students’ disabilities while maximizing their opportunities to learn in the general classroom, but issues critical to implementation, teacher support, and student achievement remain unresolved. Much more needs to be learned about using these approaches as components of inclusive school reform (Fuchs & Deshler, 2007; O’Connor & Sanchez, 2011) Facing the realities and dilemmas of service delivery for students with LD requires moving special education practice closer to the policies that govern it, and the educational research that guides it (Bateman, 2007). These governing policies and guiding practices are examined in the sections that follow.

POLICIES THAT GOVERN INSTRUCTION FOR STUDENTS WITH LD General and special educators in the U.S. can help students minimize the impact of their disability by attending to the key tenets of the IDEA, the federal statute that governs special education, protects the rights of students with disabilities, and funds intensive services associated with their education. The purpose of the IDEA is to ensure that “children with disabilities have available to them a free appropriate public education that emphasizes [emphasis added] special education and related services designed to meet their unique needs, and prepare them for further education, employment and independent living” (20 U.S.C. § 1400(d)(1)(A)). Special education is defined in the IDEA as specially designed instruction, which means that teachers have the responsibility to adapt the content, methodology, or delivery of instruction to meet a student’s disabilityrelated needs, and to ensure his or her access to the general curriculum and school activities. Students with LD are ensured key educational rights through the IDEA: (a) the right to receive a free appropriate public education emphasizing special education and related services; (b) the right to be taught appropriately in the most integrated and least restrictive social environment; and (c) the right to be taught by qualified teachers with expertise in state of the art instructional approaches (Crockett & Kauffman, 1999).

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Ensuring a Free Appropriate Public Education The IDEA requires schools to provide a free appropriate public education (FAPE) to students with disabilities and to develop written documentation in the form of an Individualized Education Program (IEP) that stipulates the specially designed support of special education. The IEP is the centerpiece of the IDEA, and this document is intended to outline a plan that addresses the student’s individual, disability-related needs so that he or she can access the general education curriculum. The primary responsibility of special and general educators in the IEP process is to engage with parents in planning a program of specially designed instruction, including accommodations and modifications to be used in the general classroom, so the student can benefit from his or her education (see Bateman & Linden, 2006, for an extended discussion). Individualized goal setting is currently challenged by the standards movement, which is also challenging the ways that special educators traditionally think about IEPs (McLaughlin et al., 2012). A standards-driven IEP links individually designed services and supports with grade level content standards and assessments “focused on moving the student toward attaining statedetermined content and achievement standards” (p. 372). Standards-driven IEPs seem to run counter to the individualized precept of a free appropriate education, but from an equity perspective, they “can ensure access to the same curricula and opportunity to learn the content that has been established as necessary for all other students” (p. 373). To ensure the appropriateness of the student’s program, McLaughlin and her colleagues point out the essential responsibility of focusing the IEP on the student’s educational benefit, monitoring progress, and adjusting specially designed instruction in response to changes in the student’s performance. They note “this responsibility is quite tricky, because determining what a child needs to learn is bounded by the prevailing social, economic, and political contexts that have defined various general education reforms over the years (Ladd, 2008) as well as movements in special education” (p. 373), including the inclusive schools movement. Providing Instruction in the Least Restrictive Environment The IDEA does not guarantee inclusion, but uses the legal principle of the least restrictive environment (LRE) to guide placement decisions so that students are placed in schools or classrooms based upon their individual learning needs, and not on their disability classification. The LRE principle requires that special education students, to the maximum extent appropriate to their needs, be taught in general education classes. Special and general education

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teachers have the responsibility to first restructure the general education environment to serve a student with disabilities and only consider other settings when the IEP team determines that satisfactory progress cannot be made inclusively even with the support of specialized aids and services. Policies that govern special education placement decisions require school personnel to follow a proper sequence: (a) first finding a student eligible to receive special education; (b) developing the student’s IEP; and (c) only then determining the instructional placement that constitutes the LRE for this student. The law presumes that the least restrictive appropriate placement for any student is the general education class. This presumption can be rebutted, however, if decision-making teams find a mismatched relationship between a student’s learning needs and the ecological elements affecting his or her appropriate instruction in the general education setting. For this reason, school systems are legally required to make a full continuum of alternative learning environments available that range from general classes, special classes, separate schools, residential facilities, hospitals, and home settings. With regard to teaching students with LD, it is important to note that participation in the general education curriculum does not mean the same thing as inclusion in the general education classroom. “Inclusion in a regular classroom concerns the setting where a student with a disability is educated… Participation in the general curriculum concerns what a student learns” (Sharp & Patasky, 2002, p. 3). The law expects that students will be provided with access to the general curriculum wherever they receive instruction. Using Effective Instructional Practices Teachers are responsible for providing students with LD an appropriate education in the LRE, and for using effective instructional practices in the process. The right to be taught effectively shifts the focus from where students learn, to how their instruction is actually provided. The IDEA now requires that professional development be made available to teachers and administrators so they might use practices proven to be effective with a variety of special needs learners. In responding to students’ learning needs, teachers have a responsibility to understand and properly implement educational practices based on the latest research. Special education is intended to deliver meaningful benefit to individual students, and it is unlikely that this level of benefit will be realized if ineffective, unproven strategies are used (Yell & Crockett, 2011). In responding to the needs of students with LD, teachers need to be aware of the general curriculum, evidence-based

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practices for teaching curricular content, techniques for making challenging content more accessible, and ways to facilitate positive interactions with students and colleagues (Brownell et al., 2012). The instructional provisions that govern special education are not new, but they have taken on greater significance in recent years with related policies of accountability and school reform. As pressures have risen to provide positive results for students with disabilities and other struggling learners, so have opportunities for teachers to turn to practices with a record of success, particularly in the area of reading. As a result, a growing body of knowledge based on well-conducted studies supports the use of instructional practices, such as small, interactive groups, directed questioning, and carefully controlled task difficulty that are predictive of positive outcomes for students across academic content areas (Swanson, 2000; Vaughn, Gersten, & Chard, 2000). Teachers are now in a better position to combine these evidence-based practices with their own practical knowledge to make effective instructional decisions (see Cook, Tankersley, & Landrum, 2009).

RESEARCH THAT GUIDES INSTRUCTION FOR STUDENTS WITH LD Practitioners, researchers, and public decision-makers form a triangle of intersecting interests regarding special education that converge on the need “to find out more about the nature of a disability, its biological causes or social etiology, and what to do about it from a socio-educational standpoint” (Gallagher, 2006, p. 104). Rigorous and systematic intervention research has been the main focus in special education for decades, and federal legislation now requires the use of scientifically-based educational methods in the education of all learners (IDEA, 2004; NCLB, 2001). Since the 1980s, sufficient research has been published to conduct numerous meta-analyses and research syntheses of instructional practices for students with disabilities. In the past decade additional analyses have determined interventions with positive effects within specific academic domains such as reading, social studies, science, and, mathematics, as well as across social and emotional domains such as anxiety and social competence.

Research on Reading Interventions The development of effective reading skills is a serious problem not only for most students with disabilities, but also for nearly 40% of the general

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school population, and as many as 80% of students with LD (Pullen & Cash, 2011). Reading disabilities pose problems with spelling, decoding, and word recognition skills, for which interventions in phonemic awareness, phonics, reading fluency, vocabulary instruction, and comprehension are proven practices. In the area of reading comprehension, students with LD do not read strategically or spontaneously monitor their understanding. Frequently, these students require instruction in strategies used by efficient readers, including instruction in when and how to use these strategies. In a meta-analysis of 40 studies, reported between the years 1995–2006, Berkeley, Scruggs, and Mastropieri (2010) synthesized the effects of interventions to strengthen reading comprehension used with nearly 2000 students with LD in grades K-12. Interventions were categorized as questioning/strategy instruction if the purpose was to teach students cognitive strategies or involved them in direct questioning, or in becoming independent at self-questioning, while reading. Interventions were categorized as text enhancements if their purpose was to supplement the text to increase comprehension (e.g., providing graphic organizers, or using technology for textual presentation and illustration). Interventions were categorized as fundamental reading skills if the instructional focus was on phonemic awareness and phonics, and if the study assessed the impact of the intervention on constructing meaning. The results of this analysis provided evidence of effectiveness in strengthening reading comprehension for each category of intervention. These findings reinforce the results of previous analyses (i.e., see Swanson, 2008; Vaughn et al., 2000) indicating effective interventions that improve academic outcomes for students with LD include systematic skill building, as well as the development of strategies that address skills and knowledge more broadly. Strengthening reading comprehension is essential for students with LD who typically experience difficulties with the language and literacy demands of content domain instruction in social studies, and especially in science. Reading and learning from science textbooks is considered the most academically challenging task for students with LD (Mason & Hedin, 2011).

Research on Social Studies and Science Instruction In elementary and secondary schools, instruction in social studies and science most frequently relies on textbook learning, although research indicates the value of using hands-on-approaches and supplemental activities to deepen students’ scientific thinking and conceptual understanding of social science (Brigham, Scruggs, & Mastropieri, 2011; Scruggs,

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Mastropieri, & Okolo, 2008). Mnemonics research has consistently demonstrated positive outcomes for learning vocabulary and factual content, and graphic organizers and other comprehension strategies show promising results; yet, effective practices extend beyond instruction in basic facts and concepts. Over the past decade researchers have paid more attention to the importance of domain-specific knowledge, epistemologies, and investigative procedures pertinent to the science and social studies disciplines (see MacArthur, Ferretti, & Okolo, 2002; Therrien, Hughes, & Hand, 2011). Students with LD can successfully engage in inquiry-based learning in both subjects, although with more direct support from teachers, and with more activation of prior knowledge, than might be needed by their nondisabled classmates. Purposeful dialogue and discussion with teachers and peers can also help advance students’ understanding of sophisticated ideas and complex content (Scruggs et al., 2008), although such peer mediated instruction has not been found as valuable for students with LD in mathematics instruction.

Research on Mathematics Interventions Performing calculations and solving computational problems are difficult for some students and may be considered a “signature deficit of students with mathematics disability” (Fuchs et al., 2009, p. 562). Developing fluency with number combinations and simple arithmetic problems is a necessary skill for more complex mathematical computations, yet students with LD who struggle in mathematics are characteristically “poor problem solvers” (Montague, Enders, & Dietz, 2011, p. 262). Drawing on evidence from scientific research, Fuchs and his colleagues identified seven principles of effective interventions for students with mathematics disabilities including (a) instructional explicitness; (b) instructional design to minimize the learning challenge; (c) strong conceptual basis; (d) drill and practice; (e) cumulative review; (f) motivators to help students; and (g) ongoing progress monitoring to help both students and teachers gauge improvement (Fuchs, Fuchs, Powell, Seethaler, Cirino & Fletcher, 2008, p. 85). These principles aligned with the findings from a synthesis of 42 studies evaluating the effectiveness of well-defined methods for improving mathematics proficiency that emphasized the importance of explicit instruction, visual representations, the sequence and/or range of examples, student verbalizations, and ongoing feedback (Gersten, Chard, Jayanthis, Baker, Morphy, & Flojo, 2009). Gersten et al. noted the use of

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heuristics and explicit instruction provided both practically and statistically important effects; however, peer-assisted mathematics instruction and student feedback failed to result in significant mean effects. In mathematics instruction (unlike in other academic content domains) peer-assisted learning has not been as successful for students with LD as it has been for some other students. Gersten and his colleagues suggested that the opportunity to participate in mathematical discourse may be beneficial for students with LD, but the feedback from peers may not be explicit enough to make an impact on learning. “How, when, and by whom” (Montague, 2008, p. 43) explicit and strategic interventions should be provided to students with LD remains a critical question to address in academic content areas, as well as in social domains.

Research on Social Interventions Students with LD frequently struggle socially as well as academically. A body of research, largely influenced by the scholarship of Tanis Bryan (see Wong & Donahue, 2002), has focused on the need for interventions that extend beyond literacy or numeracy to explain social developmental outcomes and to strengthen “the coping resources and sense of personal control considered to be crucial to achieving school and life success for those who have learning disabilities” (Firth, Frydenberg, & Greaves, 2008, p. 152). In a meta-analysis of 32 studies reported from 1990–2000 that assessed the social competence of students enrolled in inclusive classrooms, Nowicki (2003) concluded that the 1659 participating students with LD were at greater risk for social problems than their higher achieving peers and more likely to have inaccurate perceptions of their social acceptance. A recent meta-analysis of 58 studies addressing anxiety symptoms among school-aged students with LD confirmed the assumption that these students also experience greater anxiety than classmates without LD (Nelson & Harwood, 2011). The practical implication of these results is that classroom teachers may not be adequately prepared to address the social-emotional issues of students with LD, and that all school professionals should be aware that a student’s unique learning disability may extend beyond academic difficulties. More than 200 social skills interventions have been developed over the past four decades, but when evaluated overall, these interventions have been found to have limited effectiveness in altering social status and peer acceptance. Bryan (2005) attributes this to methodological limitations, and encourages teachers to use interventions that focus on enhancing students’

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affect and self perceptions, which have been shown to have a positive effect on student achievement. Such advice too often goes unheeded, and in spite of the emphasis in policy on the importance of using evidencebased educational methods, “the gap between research and practice persists in both general and special education” (Cook, Tankersley, Cook, & Landrum, 2008, p. 70).

The Persistent Gap Between Research and Practice Close to a half century of intervention research in the field of special education has contributed to the movement to identify and implement evidence-based practices proven to be successful in teaching students with LD (Cook & Garnett, 2012). Ensuring equitable learning opportunities for students with LD depends on recognizing and responding to unique learning needs, and providing “access to expert instruction, appropriate related services, and a quality education” (NJCLD, 2011, p. 239). Improving their educational outcomes requires teachers to identify gaps in academic learning and social growth, provide explicit and intensive intervention, and monitor progress relative to these gaps. Given the strength of accumulated evidence, one would expect to see special and general educators using effective approaches in teaching students with LD, such as controlling task difficulty; teaching in small interactive groups; modeling, and teaching questioning and think aloud strategies; using direct and explicit instruction; engaging higher order processing skills and problem solving approaches for complex learning; teaching students when, where and how to apply strategies; and closely monitoring their progress in developing specific academic and social skills (Vaughn & LinanThompson, 2003). Unfortunately very little use of evidence-based instruction is presently taking place, “particularly in general education settings, even when special education teachers are present” (Berkeley et al., 2010, pp. 433–434).

WHAT CLASSROOMS ARE LIKE FOR STUDENTS WITH LD Recent studies of elementary and secondary schools provide descriptions of what special education is like for students with LD and their teachers in the context of heightened accountability and inclusive school reforms. These studies describe classroom ecologies, constituted by interactions among students and teachers, the organization of curriculum and instruction, and the management of the learning environment (Keogh & Speece, 1996), with

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potential to influence the delivery of effective instruction. In this discussion, consideration is given first to how teachers are providing specific, intensive, and research-based instruction in the context of elementary schools. This is followed by a parallel description focused at the secondary level.

Providing Instruction for Elementary Students with LD Service delivery for students with LD has been studied most frequently in elementary schools and much of what has been written over time suggests that the instructional components used by teachers and the place where instruction is provided are both relevant to student success (Baker & Zigmond, 1995; McLeskey & Waldron, 2011). In what were among the first descriptive case studies of full inclusion for students with LD, Baker and Zigmond (1995) examined whether restructured elementary schools provided opportunities for students with LD to learn and for their teachers to provide them with intensive special education in general education classes. These studies have been described in detail elsewhere (see Crockett & Kauffman, 1998) and are mentioned because of their importance as landmark studies in illustrating how students with LD might be provided with a good general education, but miss out on receiving the special education they need to make progress toward common standards and meaningful learning goals. Although Baker’s and Zigmond’s observations pre-dated educational policies that focus on results, recent studies provide little compelling evidence of intensive and evidence-based instruction in elementary classrooms that targets the academic learning and social growth of students with LD. Classroom Interactions and Social Inclusion Given that so many students with LD are taught in general education classrooms, how they interact with their classroom teachers and peers may be critical to their success. Classroom observations have yielded data about the type and quality of interactions between students and teachers, and students and their classmates. Interactions with teachers. There appear to be risk and protective factors associated with social interactions among students and their teachers: “Some teachers mitigate and buffer children’s problems, whereas others enhance and exacerbate the risks in schooling” (Keogh & Speece, 1996, p. 9). For example, in a small observational study of 14 elementary and middle school teachers and 26 special education students included in their classes, Cooke and Cameron (2010) reported that concern and

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rejection ratings toward students with disabilities correlated with teacher– student interactions. Students for whom teachers reported concern were provided more instructional–academic interactions, such as questions or statements related to content. Students for whom teachers reported higher rejection ratings were more likely to experience non-instructional, behavioral interactions. These interactions could be positive or negative, but observers noted most interactions were negative in response to students’ “undesirable behavior” (p. 71). As a group, the included students across all categories of disability received significantly higher concern ratings than did their non-disabled classmates. This finding might be viewed positively because teacher concern could lead to positive academic interactions and outcomes. The disaggregated data, however, paint a bleaker picture. The subset of included students with LD and behavior disorders received significantly higher rejection ratings from their teachers than did their non-disabled classmates and this is cause for concern. It is unlikely that these rejected students would have their individual needs met, and rejection by teachers could likely lead to negative outcomes that exacerbate students’ risk of school failure. Teachers’ negative perceptions are also likely to be communicated to other students and influence the social acceptance of students with LD (Vaughn & Schumm, 1996). Interactions with classmates. A prime reason for placing students with disabilities in more inclusive settings has been to reduce social difficulties and to promote social acceptance; however, peer interaction studies conducted in inclusive settings suggest varied outcomes. In some studies, temporary social improvements were observed, but longer lasting social benefits were elusive because classmates persisted in holding negative social perceptions of students with disabilities over time (Salend & Duhaney, 1999). In another case, ratings of social preference for students with LD decreased over one school year, as ratings of being “least liked” by their peers increased (Kuhne & Wiener, 2000). Estell, Jones, Pearl, Van Acker, Farmer, and Rodkin (2008) studied social acceptance over three years by examining a sample of 1361 students, 55 of whom were students with LD. Using multiple measures of peer social functioning, they assessed students each semester from spring of third grade through fall of sixth grade, producing similar results to previous studies. Although students with LD were considered to be similar to their peers without disabilities, they were viewed as lower in social standing among their classmates. “These effects were maintained over

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time, indicating that long-term inclusion may not substantially affect peer social functioning among students with LD” (Estell et al., 2008, p. 5). In other words, students might be socially included, but not socially accepted, which can limit their access to student life and expert instruction. Insufficient Access to Expert Elementary Instruction With regard to academic learning, students with LD need intensive, explicit instruction to achieve academic success, yet general education teachers report not having the time or skills to deliver this kind of instruction (Berkeley et al., 2010; McLeskey & Waldron, 2011; Swanson, 2008; Vaughn & Linan-Thompson, 2003; Zigmond, Kloo & Volonino, 2009). Classroom teachers using differentiated instruction continue to make routine adaptations that are relatively easy to provide such as reduced workload, altered assignments, adjusting homework requirements, and test accommodations. These adaptations are often used with the whole class and are rarely individualized. More specialized approaches, such as modifying planned instruction, curriculum, and pacing with respect to students’ particular difficulties, is less frequently observed. In the context of educational programs for elementary students with LD, McLeskey and Waldron (2011) examined the evidence from multiple research reviews conducted over the past decade to determine whether high quality instruction can be delivered effectively in general education classrooms. They defined high quality instruction as “instruction that has strong research support for significantly improving academic outcomes for students with LD” (p. 49). Noting the disappointment of many, including themselves, who were optimistic about the prospects of inclusive instruction, they concluded “well-designed inclusive classrooms provide a very good general education and meet many of the needs of students with LD, but have not proven sufficiently malleable to offer the high-quality intensive instruction needed by most elementary students with LD” (p. 55). McLeskey and Waldron noted that resource classes, as frequently designed, have also been “ineffective in facilitating the delivery of high-quality, intensive instruction” (p. 55). Whole group instruction remains the most often observed grouping structure in both general education and special education settings, despite evidence that small group size is an effective instructional practice for students with LD. Studies included in this analysis reported little differentiation of instruction and no more time spent in resource classes on reading instruction than in general education classes, despite the lack of success in reading that prompts the need for specialized support.

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Challenging Learning Environments Barriers to providing a quality education in resource classes can be daunting, including large caseloads of students with heterogeneous disabilities that span multiple grade levels, high levels of paperwork, and a need to collaborate with many general education teachers with whom special educators shared students (McLeskey & Waldron, 2011). However, wellmanaged learning can occur in any setting, and Seo, Brownell, Bishop, and Dingle (2008) provided vivid examples of the ways in which 14 special education teachers, most of whom provided resource support, used reading practices to engage elementary students with LD. These teachers were observed over a six-month period in a variety of contexts with class sizes that ranged from 6 to 28 students, and averaged 13 students. The practices of teachers most skilled in engaging students with LD reflected four themes: (a) high quality instruction; (b) responsiveness to students’ needs; (c) a positive socio-emotional classroom climate; and (d) promotion of student autonomy through choice-making and encouragement of selfregulated learning. It should be noted that only four teachers demonstrated these practices with relative consistency, with one considered most engaging, and three considered highly engaging. Most other teachers were less consistent with six demonstrating moderately engaging practices, and four demonstrating what were considered to be low engaging practices. Those special educators who were most engaging and highly engaging were similar in many ways to highly effective general education literacy teachers, but they “provided more deliberate skill instruction, allowed less student managed instruction, engaged in more intensive teacher-led instruction, monitored student learning consistently, employed classroom management approaches that were more overt and explicit, and provided extensive feedback on student responses” (Seo et al., 2008, p. 118). As managers of instruction, the four teachers considered most and highly engaging made efficient use of instructional time, and engaged students extensively in academic rather than nonacademic activities. When students were highly engaged, teachers were observed providing “intensive teacherled instruction that involved a lot of student questioning and included small group and large group instruction” (p. 118). The special education teachers in this study used a variety of curricular materials, and had varying caseloads and levels of instructional and administrative support. Without drawing conclusions about the role that context might play in the implementation of engaging students with LD, the authors noted the two lowest engaging teachers provided instruction

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to the largest number of students. Additionally, they noted that teachers considered most engaging, highly engaging, and moderately engaging in teaching students with LD were more likely to be using structured interventions in addition to the core reading curriculum. Reflecting on the Quality of Elementary Education for Students with LD Evidence accumulated over the past two decades suggests that many students with LD experience social and academic difficulties in both general and resource classrooms intended to support their success. There is an emerging knowledge base regarding intensive early reading interventions within a school-wide RTI framework (see Katz, Stone, Carlisle, Corey, & Zeng, 2008; Vaughn, Denton, & Fletcher, 2010), but more information is needed about how school-wide models address additional curricular content as children get older. As O’Connor and Sanchez (2011) note, “even if we can inoculate young children against failure in phonemic awareness, it will prove more difficult to inoculate them against failures in reading comprehension” (p. 131). Before optimism can take root, more research is needed about school-wide models and the ecological components that effectively support the delivery of specially designed instruction. For these and other reasons, inclusive and effective instruction for elementary students with LD remains elusive.

Educating Secondary Students with LD The ecologies of secondary classrooms pose special considerations for teachers of students with LD “including an emphasis on higher level content knowledge, independent study skills, and the overall pace of general education classroom instruction” (Mastropieri & Scruggs, 2001, p. 266). In addition, the ramifications of high stakes testing often determine whether a student ultimately receives a high school diploma, increasing the pressure to succeed on both students with LD and on those who teach them. At the secondary level, the goal for using multiple tiers of instructional support is to strengthen student literacy skills so students can succeed in content classes, graduate, and be college or career ready. In secondary schools only 30% of all adolescents are proficient readers, and almost 90% of minority adolescents read below grade level (Aud et al., 2011; Daggett & Hasselbring, 2007). Curriculum demands have escalated, but data from classroom studies suggest that instruction in middle schools and high schools is fragmented, with only limited ownership of these learning problems among professionals.

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The prevailing hope is that older students will have benefited from interventions that took place in elementary grades, and that manifestations of LD would be minimized or reduced by the time they reach adolescence (Deshler, 2005). The current emphasis in policy on early intervening services underscores this hope, but research evidence indicates that “adolescents with LD have enduring and unique characteristics that are manifested in differing ways as development and setting demands change” (p. 122). As Deshler cautions, “there are reasons not to put all of our field’s eggs in the early identification and intervention basket” (p. 122). The need for effective intervention strategies for older students is equally if not more important because of the emotional overlays that typically emerge as adolescents mature and continue to experience significant failure. Unique Challenges of Adolescents with LD There is a scientific justification for addressing the social status and interactions of students with LD in middle and high school classes. Over 40 years of research demonstrates that many students with LD are depressed and lonely, with estimates consistently indicating that between 38–75% of these students have social problems (Baum, Duffelmeyer, & Greenlan, 2001; Bryan, 2005). Despite the known link between academic success and social efficacy for adolescents with LD, the fidelity with which such programs are offered suffers in schools (see Firth et al., 2008). In studying the implementation of an adaptive coping intervention with 98 adolescents in four secondary schools, Firth and colleagues referred to common problems in noting that outcomes might have been stronger if teachers had had more time to implement the lessons, and if participating secondary schools had made the acquisition of social skills a higher priority in supporting educational achievement. Insufficient Access to Expert Secondary Instruction Academic expectations prompted by the need for increasingly literate citizens, and demands for meeting yearly progress goals, require the enhancement of literacy instruction for secondary students. Significant federal investments have been used to study the reading characteristics of younger students and the instructional practices that produce positive outcomes; however, research with adolescents in secondary settings has not been well funded. Progress monitoring and the use of a student’s response to intervention to make decisions about instructional intensity and differentiation are well developed practices for elementary students, but less

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well developed at the secondary level. Consequently, there is insufficient information to define what is needed to achieve the gains that will enable struggling adolescents to compete in current economic and academic realities (Vaughn et al., 2008). Reliance on Co-Teaching Schools have responded to the diversity of students’ needs by relying on co-teaching as the most frequently used model of service delivery for students with LD (Zigmond et al., 2009). The appeal of co-teaching appears to be its efficiency; however, the practice of pairing general and special educators as co-teachers “can, depending on how it is implemented, facilitate or impede effective special education” (Cook, McDuffie-Landrum, Oshita, & Cook, 2011, p. 157). Cook and his colleagues conducted an extensive examination of the extant research base related to the impact of co-teaching on student outcomes and teachers’ practices to determine empirical support for the model. In their analysis, co-teaching remains a popular, but unproven organizational approach to resource management. Although teachers perceive co-teaching to be beneficial, as a service delivery model for students with LD it frequently falls short of its aim to blend the content expertise of general educators with the pedagogical prowess of special educators. Drawing on the extensive instructional research of Mastropieri and Scruggs, Brigham et al. (2011) refer to preliminary data suggesting positive results may be achieved when good coteaching practice is paired with effective practices for actively engaging students with LD in science instruction. More research is needed before conclusive statements can be made about co-taught science classes, and Brigham and his colleagues point to the need for educators to ensure good relationships between collaborators and adequate time for planning their instruction. It might also be added that unless the resources invested in the special education co-teacher are used specifically to enhance the learning needs of special education students, then co-teaching represents a wasted resource. Students with LD reading below grade level must obtain an advanced set of literacy skills, and to accomplish this, they require different instructional emphases and pedagogies to improve reading comprehension. How to meet this challenge when adolescents with LD are primarily served through co-teaching is a special concern, given impressive gaps in their grade level achievement (Faggella-Luby & Deshler 2008). In studies that blend IEP policy and instructional practice, data suggest that insufficient

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attention is being paid in most middle and high schools to IEP goals that address the need for intensive and extensive amounts of specialized instruction in reading and mathematics (Catone, Brady, & Brady, 2005; King-Sears & Bowman-Kruhm, 2011; Pearl & Miller, 2007). In discussing the results of an exploratory study of special education co-teachers’ perceptions of IEP goals for adolescents with LD, KingSears and Bowman-Kruhm (2011) expressed concern about teachers’ frequent use of modifications and accommodations, which could circumvent students’ need to learn to read, rather than IEP goals that stipulate the delivery of more specialized reading instruction. “If special education co-teachers consider modifications and accommodations as synonymous with specialized reading instruction to adolescents with LD, then there are misunderstandings about the intent and interpretation of these terms” (p. 182). King-Sears and Bowman-Kruhm noted that adolescents with LD make decisions in the middle grades to stay in school or drop-out, and their decisions typically hinge on their success in two subjects: English and mathematics. Viewed from a policy perspective, adolescents with LD who have specialized instruction stipulated on their IEPs, most likely in reading and/or mathematics, should definitely receive it because the stakes are very high for those who do not. Reflecting on the Quality of Secondary Education for Students with LD Many students with LD have social and academic difficulties because of their own weak skills and the expertise and expectations of their teachers. Secondary special educators rarely have expertise that extends across the content areas, and secondary general educators usually have high expectations for students to possess proficient study skills and prerequisite content knowledge (Mastropieri & Scruggs, 2001). Secondary teachers also face demands to cover the curriculum and deliver instruction at a rapid pace, and adolescents with disabilities can easily become casualties of instructional neglect. For these and other reasons, inclusive and effective instruction for secondary students with LD remains elusive, and specially designed instruction is often overlooked.

ENSURING EQUAL ACCESS TO HIGH QUALITY EDUCATION Evidence from empirical studies and research reviews suggests that students with LD are frequently included for instruction, but too often underserved in elementary and secondary classrooms when their learning

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needs are not addressed. To ensure equal access to a high quality education for students with LD, Montague (2008) calls for instruction that is: provided by expert remedial teachers who understand the characteristics of students with LD. Instruction should be provided to small groups of students (e.g., 8–10 students), who have been assessed to determine if they will benefit from instruction. Instruction should be intense and time-limited, so teachers may wish to remove students from the general education classroom for the duration of strategy instruction and include procedures to ensure that students will generalize strategy use after returning to the class. This requires collaboration between general and special education teachers. (Montague, 2008, p. 44)

This kind of collaboration is unlikely to occur, however, because of serious obstacles to implementing evidence-based practices in typical classroom settings. Students with LD are frequently placed in academic classes with teachers who have little or no background in teaching them, and limited expertise in using learning strategies and other evidencebased practices. As a result, elementary and secondary classroom teachers may need professional development and on-going support from a specialist to implement research-supported interventions with fidelity. Classroom teachers, pressured by pacing-guides and state assessments, may also feel they do not have time to implement academic or social interventions if they perceive them to be supplementary rather than complementary to the academic curriculum (Montague, 2008; Montague et al., 2011). Although research provides some reason to be hopeful about gains that might be achieved by students with LD, sustainable gains across social domains and core curriculum classes that lead to standard high school diplomas require significant changes in practice. Refocused energy must be placed on recognizing and responding to students’ needs, maintaining the integrity of general and special educators’ roles, and using researchsupported interventions with fidelity and intensity (Deshler, 2005).

Recognizing and Responding to Students’ Academic and Social Challenges Research and reasoned principles should inform policies concerning LD, but for effective policies to be established, decision makers “must understand the nature of LD and the needs of individuals with LD” (NJCLD, 2011, p. 239). There is general consensus within the scientific community that LD is a “manifestation of atypical cognitive and linguistic processes that interfere with learning” (p. 237), but there are unresolved issues in

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research and practice regarding approaches to assessment and identification. The perennial political debate over who gets what, when, and how in the distribution of educational resources is fueled by policies that push the question of whether students with LD should be identified using RTI, or “the elaborate and costly assessment gate-keeping process that determines whether a student has a particular disability and is or is not eligible for special education services” (Weintraub, 2005, p. 98). Who should receive special education remains a question to pursue as school personnel increasingly implement academic and behavioral interventions across multiple tiers of supplementary support. To date, few empirical studies have provided data to support advocacy for policies reforming the traditional structure of schooling through the implementation of RTI (O’Connor & Sanchez, 2011). As Faggella-Luby and Deshler (2008) point out, “considerable depth of understanding about the role of instructional dosage and how a continuum of service delivery can be operationalized is necessary to make student-specific instructional decisions” (p. 77), the kind of decisions historically made by special educators.

Maintaining the Integrity of Teachers’ Roles Special educators in general education classrooms often fail to maintain a student specific focus suggesting that there are sizable cultural forces pulling on classroom participants (Garnett, 2010). Garnett refers to a prevailing belief that treating students differently is somehow considered detrimental for the individual or the group, a belief that can influence special educators, working in general classes, to adapt to the prevailing focus on activity, activity flow, and the group’s overall engagement and responsiveness. When this occurs, special educators become supportive general classroom teachers, generalizing their “special” advice in stereotypical, rather than student-specific, terms. Cook et al. (2011) urge special educators “not to abandon the core principles of special education (e.g., flexibility, intensive, individualized instruction in small groups, progress monitoring and assessment; use of research-based instructional practices) while coteaching” (p. 157). The roles of educational generalists and specialists are intended to be complementary, and when teachers’ role integrity is breached, positive student outcomes are jeopardized. Students with LD typically struggle with the demands of the general curriculum because they lack the skills and strategies to process information effectively. As a result, the performance gap between students with LD and their classmates is large, and

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how special educators respond to these students greatly affects the outcomes they achieve. From Deshler’s (2005) perspective, the role of the special education teacher “should be to teach specific skills and strategies to enhance students’ effectiveness as learners in their core curriculum classes” (p. 123). This type of focused instruction for students with LD fosters independence and optimizes their chances of having genuine access to the general curriculum. Without it, they might be passed along from grade to grade, but “they will leave the educational system grossly underprepared to face the harsh realities of the post-secondary world” (p. 123), as the data suggest is currently occurring (Cortiella, 2011). For Deshler, the role of content teachers is to use their subject matter expertise to select and to organize essential information so that a wide range of students will find it easier to learn, and in the process to prompt students with LD to use the strategies special educators have taught them.

Using Interventions Faithfully and Intensively Simonsen et al. (2010) blur the lines between general and special education in advocating for a schoolwide model of service delivery that leverages academic and behavioral interventions (a) to promote the learning of all students; (b) reduce the need for special education; and (c) “effectively address the intent to provide a free appropriate public education in the least restrictive environment” (p. 22) for those students eligible to receive special education. Simonsen and her colleagues call for a redefined role for special educators as interventionists who collect and interpret data; deliver instruction; collaborate effectively with classroom teachers, support personnel, and administrators; and ensure that evidencebased instructional practices are used with fidelity. In their view, special educators will need to be prepared as “trainers, consultants, coaches, collaborators, and implementers of specially designed instruction and supports… with a flexible and comprehensive skill set to work across many settings” (p. 21). The prospects for improving outcomes for all learners with a schoolwide model of instructional and behavioral support, and its attendant role for interventionists, hinge on the adoption of elements that to this point have proven to be elusive in public schools: “careful implementation of evidence-based instruction and curriculum, administrative support, redistribution of resources, professional development, a commitment to databased decision making, and a symbiotic relationship between general and special education” (Simonsen et al., 2010, p. 22). The optimism of those

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who believe all students and teachers will function well within a multitiered system of support is reminiscent of those who once believed the implementation of full inclusion would successfully address the needs of all students with LD. Although school-wide models of support target the school-improvement priorities of general education policy under the ESEA, their utility in meeting the individually focused imperatives of special education policy under the IDEA is less clear. Fuchs et al. (2010) challenge policy makers, school administrators, researchers and advocates to confront the reality that both general and special education have failed millions of students with severe learning problems. Fuchs and his colleagues call for a fundamental change in contemporary practice that brings the blurring roles of general and special educators into sharper focus, and re-establishes the role of special education teachers as expert instructors. Fuchs et al. anchor special education’s identity in its historical mission of using instructional methods of clinical teaching “to work with the most difficult to teach children and youth” (p. 318). In calling for special education to face the future with a rediscovered appreciation for its past, Fuchs et al. ask, “Are special educators and their organizations ready to grasp an opportunity to redefine special education in historic terms, to become capable of providing most intensive instruction and to prove their value in RTI frameworks?” (p. 319).

CONCLUSION Social needs, values, and political concerns drive educational policy, but educational research often “defies stakeholders’ needs for simple conclusions to big problems” (Gersten, Schiller, & Vaughn, 2000, p. xi). Educating students with LD is a complex undertaking and research from multiple studies indicates their effective instruction requires general and special education teachers to be (a) accountable for their progress; (b) responsive to academic and social needs; (c) diligent in fulfilling their collaborative responsibilities to students with LD; (d) skillful in basing instruction on evidence and collaborative inquiry; (e) committed to acquiring the complementary knowledge, skills, and strategies needed to teach students with LD; (d) engaged in ongoing professional development; and (e) supported with adequate resources to work together and develop professionally (Brownell et al., 2012). These components form the foundation for achieving more accomplished teaching and more accomplished learning

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in classrooms where too frequently students with LD are included, but underserved when their instructional needs are not addressed. Policy decisions “cast long shadows of influence” (Gallagher, 2006, p. 1) over practices that guide the education of students with LD, and legislators have the capacity to authorize the extent to which research is funded and considered an important component in guiding their education. Ferretti and Eisenman (2010) noted both the spirit and the letter of the IDEA foster the use of collective resources in promoting human development and progress toward valued social goals: At its core, IDEA is about delivering individualized, effective instruction for every child with a disability who needs it and providing reasonable people at the local level with the tools, decision-making authority, and procedural safeguards needed to assure equitable treatment. (Ferretti and Eisenman, 2010, p. 382)

Special education pioneer and legal scholar Barbara Bateman (2005), however, predicted an unclear future for the role of law and policy in the education of students with LD, referring to “the reluctance of hearing officers, judges, and the public schools themselves to honor research establishing the effectiveness of certain techniques, strategies, and methodologies and materials” (p. 94). Policy is a blunt instrument that governs the distribution of resources, and in the final analysis, the future of special education rests on the guidance of research and its faithful implementation in schools and classrooms. Research provides teachers with clear guidance for teaching students with LD, and for eminent scholar Tanis Bryan, hope remains the most significant variable of scientific inquiry: Research lets us hope for a better understanding of disabilities. Research lets us hope that we will identify effective interventions. Research lets us track our hope that public policy will have a positive impact upon our constituents. (Bryan as cited in Donahue & Wong, 2002, p. 8).

Hope in the future provides the impetus for adopting policies and implementing research-based practices that foster success in school, at work, and in the community for those with LD, and the reason for doing so is compelling: “Individuals with LD contribute in positive and meaningful ways to the social and economic good” (NJCLD, 2011, p. 239). As we move toward the future in educating children and youth with LD, “we can all hope that special education practice moves ever closer to the legal precepts that govern it and that it follows ever more closely the research that guides it” (Bateman, 2007, p. 112).

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INDEX Note: Page numbers followed by “f ” and “t” refers to figures and tables respectively.

A ACCEL, see Accelerating Expository Literacy Accelerating Expository Literacy (ACCEL) 331, 331 Achievement, motivation 98–99 Adaptive Profile, achievement motivation 98–99 ADHD, see Attention deficit-hyperactivity disorder Adolescents integrated framework for struggling learners instructional supports engaging instructional materials and strategies 310–311 literacy-based instruction 306–308, 307f motivation strategies 309–310 overview 305–311, 306f standards informed curriculum planning 308–309 overview 303–319, 304f professional learning supports instructional coaching 313 overview 311–314, 311f protocols for observing, describing, and analyzing practice 312 student-driven professional development 313–314 team planning for cohesion 312–313 system supports behavioral supports 317–318 data guidance tools 317 internal accountability mechanisms 319 literacy leadership team 315 overview 314–319, 315f responsive scheduling 318 strong leadership 318–319

tiered instructional supports 315–317 learning disability research findings mathematics 302–303 reading 300–301 writing 301–302 prospects for learning disability studies 319–320 special education classroom experience of learning disability students access to expert instruction 425–426 co-teaching reliance 426–427 overview 424–427 quality of education 427 unique challenges 425 Algebra, see Mathematics Analysis of variance (ANOVA), learning disability classification 6–7 Anchored instruction, history class 339 ANOVA, see Analysis of variance Anxiety, learning disability association 112–113 ART, see Ask, Reflect, Text Ask, Reflect, Text (ART), writing behavioral response to intervention 71–74 Attention deficit-hyperactivity disorder (ADHD) college students 366 depression 113 learning disability classification 7–8 neurological models 8–9 parenting interventions 124–125 peer relationships 108–109 self-regulation intervention impact academic accuracy 156–157 academic preparedness 153–154 academic productivity 154–156 disruptive behavior 157–158 on-task behavior 151–153 self-monitoring of attention 155, 155–156

437

438

Index

Attention deficit-hyperactivity disorder (ADHD) (Continued) self-monitoring of performance 155–156 social skills 158–159 overview 142–145 processes goal-setting 149–150 self-evaluation 148 self-instruction 148–149 self-monitoring 148 self-reinforcement 150 self-efficacy modulation 159–161 Self-Regulated Strategy Development findings 164–165 outcomes 162–164 overview 161–162 working memory training 36

B Basic Interpersonal Communicative Skills (BICS), language development 277–278 Behavioral response to intervention, see Response to intervention BICS, see Basic Interpersonal Communicative Skills Bioecological theory 126–127 Blood oxygen level-dependent activation, see Functional magnetic resonance imaging Brain automatic processing 65 comparing reading, writing, and math brains mathematics 67–68 overview 66 reading 66–67 writing 67 defining 60 geography 60–61 imaging 61–62 longitudinal studies of developmental differences 76–77 nature–nurture interactions 65–66 systems approach to function studies 62–64

BRAIN.HE project, United Kingdom 369–370 Bullying, see Peer relationships, learning disability effects

C CALP, see Cognitive Academic Language Proficiency CBT, see Cognitive-behavioral therapy CEC, see Council for Exceptional Children CERs, see Content Enhancement Routines Classification, learning disabilities categorical versus dimensional classification 5–8 models 4–8 CLC, see Content Literacy Continuum Cognitive Academic Language Proficiency (CALP), language development 277–278 Cognitive mapping, reading intervention 201–202 Cognitive models, learning disabilities historical perspective 10–11 intelligence quotient–achievement discrepancy United States public policy 12 validity 12–13 patterns of strength and weaknesses 13–14 psychometric issues in cognitive discrepancy approaches 14–15 United States public policy 11–12 Cognitive Strategies Instruction in Writing (CSIW) 251–252, 387 Cognitive-behavioral therapy (CBT), social relationship intervention 122–123 College academic demands 355–357 academic issues with learning disability challenges 357–359 promising directions 359–361 motivational issues with learning disability challenges 365–366 promising directions 366–367 social-emotional issues with learning disability challenges 361–363 promising directions 363–364

Index

Common Core State Standards Initiative 380 Concrete–semiconcrete abstract (CSA), mathematics instruction 225–226 Content Enhancement Routines (CERs) model 388 social studies 332–333 Content Literacy Continuum (CLC) 316 Council for Exceptional Children (CEC) 233–234 Criminal activity, learning disability association 113 Cross-language transfer, see English language learners CSA, see Concrete–semiconcrete abstract CSIW, see Cognitive Strategies Instruction in Writing CSR, reading intervention 208 Cultural differences, learning disabilities 77–80 Curriculum, see Special education

D Depression, learning disability association 112–113 DLFPC, see Dorsolateral prefrontal cortex Dorsolateral prefrontal cortex (DLFPC), working memory 63 DRAW, mathematics instruction 226–227 Dyscalculia, instructional guidelines 79 Dysgraphia, instructional guidelines 78–79 Dyslexia instructional guidelines 78–79 neurological models 9–10

E Effect size (ES), short-term memory studies in learning disability 35–36 Elementary and Secondary Education Act: Blueprint for Reform 217 ELLs, see English language learners English language learners (ELLs) assessment of learning disability first language versus English assessments 287–289 intelligence 285–287 oral proficiency 289 contextual considerations 274–275

439

first language intervention efficacy 289–291 history of learning disability studies 273 language development Basic Interpersonal Communicative Skills 277–278 characteristics in second language learners 278–279 Cognitive Academic Language Proficiency 277–278 cross-language transfer central processing framework 276 contrastive framework 276–277 interdependence hypothesis 275–276 positive versus negative transfer 275 learning disability subtypes comprehenders 284–285 decoders 283 literacy development text-level skills 280–282 typical development 282 word-level skills 280 overview 271–273 prospects for study 291–292 Simple View of Reading 279 Episodic buffer 37–38 ES, see Effect size Exclusionary criteria, learning disability 2–3 Executive function, overview 144–145 Executive system, deficiencies in learning disability 39–41

F Family, see Social relationships FAPE, see Free appropriate public education FAST DRAW, mathematics instruction 226–227 fMRI, see Functional magnetic resonance imaging Free appropriate public education (FAPE) 413 Friendship, see Peer relationships, learning disability effects Functional magnetic resonance imaging (fMRI), principles 61–62

440

Index

G Gender differences, learning disabilities 77 Graphic organizers, reading intervention 200–201

H Handbook of Research on Curriculum 377–378 Helpless Profile, achievement motivation 98–99 History, see also Social studies instructional approaches for learning from textbooks alternative textbooks 333–334 computerized study guides 333–334 content enhancement routines 332–333 reading comprehension 330–331 text structure incorporation in strategy instruction 331–332 vocabulary 329–330 reform approaches to instruction anchored discussions 339–341 debates 341–342 framework 335–337, 336t historical reading and writing 337–339 multimedia design projects 342–346, 345f recommendations 347–348 Virtual History Museum 346–347 traditional teaching approaches 327–328 How People Learn (HPL) 398 HPL, see How People Learn

I IDEA, see Individuals with Disabilities Education Act IES, see Institute of Education Sciences IEP, see Individualized Education Program Inclusionary criteria, learning disability 3 Individualized Education Program (IEP) 186–187 Individuals with Disabilities Education Act (IDEA) 217–218, 378, 409, 412–415, 432 Information processing theory, selfregulation 146

Inspiration software 334 Institute of Education Sciences (IES) 233 Instructional models, learning disabilities low achievement methods 15–17 response to intervention, see Response to intervention Intelligence quotient (IQ) achievement discrepancy United States public policy 12 validity 12–13 learning disability measurement comparison 4 intervention impact IQ, see Intelligence quotient

L Language, see English language learners Language differences, learning disabilities 77 LD, see Learning disability LD. A consensus group, hybrid model for learning disability identification 20 Learning disability (LD) classification, see Classification, learning disabilities definition 1–2 exclusionary criteria 2–3 identification, see Cognitive models, learning disabilities; Instructional models, learning disabilities inclusionary criteria 3 unobservable construct features 3–4 Learning Opportunities Task Force (LOTF), Canada 368–369 Least restrictive environment (LRE) 413–414 Letter naming fluency (LNF), reading assessment 184–185 Literacy leadership team (LLT), adolescent intervention 315 Literacy, see Reading LLT, see Literacy leadership team LNF, see Letter naming fluency LOTF, see Learning Opportunities Task Force LRE, see Least restrictive environment

Index

M Magnetic resonance imaging (MRI), principles 61–62 Mathematics, see also specific learning disabilities adolescent learning disability research findings 302–303 brain function studies 67–68 interventions behavioral interventions on computation 223–224 evidence-based practices 233–234 metacognitive instruction algebra 229–230 computation improvement 227–229 manipulatives for enhancement 225–227 prospects for study 230 visual diagrams and schema 227–229 overview 222 prospects 235–236 response to intervention behavioral response to intervention 74 brain response to intervention 75–76 overview 230–233 Tier 2 studies 231–233 learning difficulty and learning disability 221–223 principles for instruction 218–219 special education research for learning disability 417–418 MBD, see Minimal brain dysfunction Memory deficiencies in learning disability everyday memory 43–44 executive system 39–41 overview 33–43 phonological loop 41–42 sensory register 34 short-term memory 34–37 visual-spatial sketch pad 42–43 working memory 37–39 development in learning disability 32–33

441

history of learning disability association studies 28–31 interventions expert strategy transformation 48 law of parsimony and instruction 48–49 learning disability versus non-learning disability students 45 performance difference impact 47 processing difference impact 46 strategies and purposes 45 student knowledge base and capacity relationship 47 taught versus used strategies 46 working memory training overview 49–51 test-in-the-limits studies 50 Metacognition, self-regulation comparison 144 Mindfulness meditation, social relationship intervention 123–124 Minimal brain dysfunction (MBD) demise of concept 10 neurological model of learning disabilities 9–10 United States public policy 11–12 Mnemonics, reading intervention 202–203 MRI, see Magnetic resonance imaging Multimedia design projects, history instruction 342–346, 345f

N NAEP, see National Assessment of Educational Progress Narrative text interventions, see Reading National Assessment of Educational Progress (NAEP) 301–302 National Council of Teachers of Mathematics (NCTM), standards 217–221 NCLB, see No Child Left Behind NCTM, see National Council of Teachers of Mathematics No Child Left Behind (NCLB) 217–218, 220, 378, 387–389, 394, 408–409 Nonsense word fluency (NWF), reading assessment 185 NWF, see Nonsense word fluency

442

Index

O Office of Special Education Programs (OSEP) 378–379 On-task behavior, self-regulation intervention impact 151–153 Operant theory, self-regulation 145–146 Oral and written language learning disability (OWL LD), instructional guidelines 78–79 Oral reading fluency (ORF), reading assessment 185 ORF, see Oral reading fluency OSEP, see Office of Special Education Programs OWL LD, see Oral and written language learning disability

P PALS, reading intervention 208–209 Patterns of strength and weaknesses (PSW), learning disability identification 13–14 PDD, see Persuasive developmental disability Peer relationships, learning disability effects, see also Social relationships bullying 104 difficulty prediction comorbid attention deficithyperactivity disorder 108–109 social cognitive ability 105–108 special education placement 109 friendship 103–104 impact of difficulties 109–110 peer status 102–103 peer-assisted reading interventions in classroom CSR 208 PALS 208–209 Persuasive developmental disability (PDD), specific learning disability defining 77–78 Phoneme segmentation fluency (PSF), reading assessment 185 Phonological loop, deficiencies in learning disability 41–42

Piers-Harris Childrens Self-Concept Scale 93–94 Positive behavioral interventions and supports (PBIS) 317–318 POSSE instruction, reading intervention 207 Postsecondary education, see College PSF, see Phoneme segmentation fluency PSW, see Patterns of strength and weaknesses

Q QAR, see Question–answer relationships Question–answer relationships (QAR), reading intervention 205

R RAAC, see Reread-Adapt and Answer Comprehend RAN, see Rapid automized naming Rapid automized naming (RAN) 283 Reading, see also specific learning disabilities adolescent learning disability research findings 300–301 assessment decision-making for intervention 187–188 grades K-2 letter naming fluency 184–185 nonsense word fluency 185 oral reading fluency 185 phoneme segmentation fluency 185 word identification fluency 185 initial screening 183–186 progress monitoring 186–187 screening improvement 186 brain function studies 66–67 cognitive strategy instruction main idea identification and summation 203 paraphrasing 204 question–answer relationships 205 self-questioning 204 summarization strategies 204–205 text structure instruction 205–206

Index

decoding and fluency impact on comprehension 193–194 development of literacy 177–179 difficulty characteristics in students 179– 180, 192–193 English language learner literacy development text-level skills 280–282 typical development 282 word-level skills 280 expository text cognitive mapping 201–202 computer-assisted instruction 203 mnemonics 202–203 overview 199–207 text enhancements and graphic organizers 200–203 visual display 202 multicomponent interventions Multipass 206–207 POSSE instruction 207 TWA 207 narrative text interventions combining comprehensive interventions 198–199 overview 194–199 self-questioning interventions 197–198 story grammar for a read aloud 195–196 story mapping 196–197 peer-assisted intervention in classroom CSR 208 PALS 208–209 response to intervention behavioral response to intervention 70 brain response to intervention 74–75 decision-making for intervention 187–188 effective response to intervention 180–183 social studies textbook comprehension 330–331 special education research for learning disability 415–416 Relationships, see Social relationships Reread-Adapt and Answer Comprehend (RAAC) 194

443

Resilience, concept in learning disability 90–92 Response to intervention (RTI) early services 411 mathematics behavioral response to intervention 74 brain response to intervention 75–76 overview 230–233 Tier 2 studies 231–233 nature–nurture interactions 65–66, 68–70 overview 17–19 reading behavioral response to intervention 70 brain response to intervention 74–75 decision-making for intervention 187–188 effective instruction 180–183 tiers of intervention 68–70, 176–177, 378 writing behavioral response to intervention 70–74 brain response to intervention 75 Risk, concept in learning disability 90, 90–92 RTI, see Response to intervention

S Science, special education research for learning disability 416–417 SCL, see Strategic Content Learning SDD, see Selective developmental disability Selective developmental disability (SDD), specific learning disability defining 77–78 Self-esteem, see Self-perception Self-monitoring of attention (SMA), selfregulation intervention impact 155–156 Self-monitoring of performance (SMP), self-regulation intervention impact 155–156

444

Index

Self-perception academic self-concept predictors 94–95 achievement motivation 98–99 counseling to enhance self-concept 123 learning disability knowledge, identity and stigma 99–101 overview and learning disability impact 92–102 school and classroom interventions 116 self-esteem definition 93–98 differences between individuals with and without learning disability 93–94 protective factors 95–97 special class placement effects 97–98 self-regulation, self-efficacy modulation 159–161 Self-Perception Profile for Children (SPPC) 93–94 Self-Perception Profile for Children with Learning Disabilities (SPPLD) 93–94 Self-Regulated Strategy Development (SRSD) overview 161–162 outcomes 162–164 findings regarding self-regulation components 164–165 writing overview 252, 253b, 254b resources 256 stages 253–255 Self-regulation college student motivational issues with learning disability challenges 365–366 promising directions 366–367 definition 143–144 enhancement 150–151 executive function overview 144–145 information processing theory 146 initiatives BRAIN.HE project 369–370 Learning Opportunities Task Force 368–369

prospects 370–371 intervention impact academic accuracy 156–157 academic preparedness 153–154 academic productivity 154–156 disruptive behavior 157–158 on-task behavior 151–153 self-monitoring of attention 155–156 self-monitoring of performance 155–156 social skills 158–159 learning 143–144 metacognition comparison 144 operant theory 145–146 overview 142–145 processes in learning disability and attention deficit-hyperactivity disorder goal-setting 149–150 self-evaluation 148 self-instruction 148–149 self-monitoring 148 self-reinforcement 150 self-efficacy modulation 159–161 Self-Regulated Strategy Development, see Self-Regulated Strategy Development social cognitive theory 147 social constructivist theory 146–147 Semantic/syntactic feature analysis (SSFA), reading intervention 201–202 Sensitivity, tests 183–184 Sensory register, deficiencies in learning disability 34 Sex differences, see Gender differences SFA, see Strategy, semantic features analysis Short-term memory (STM), see also Memory deficiencies in learning disability 34–37 overview 33–43 Simple View of Reading (SVR), English language learners 279 SM, reading intervention 201–202 SMA, see Self-monitoring of attention SMP, see Self-monitoring of performance Social cognitive theory, self-regulation 147

Index

Social constructivist theory, self-regulation 146–147 Social Life, social skills training 118 Social relationships college students with learning disability challenges 361–363 promising directions 363–364 difficulty in learning disability 89–90, 92 emotional and behavior difficulties 112–113 interventions overview 113–126 parenting interventions 124–125 psychotherapy cognitive-behavioral therapy 122–123 counseling to enhance selfconcept 123 mindfulness meditation 123–124 school and classroom peer-mediated instruction 115–116 self-perception improvement 116 teacher beliefs and practices 114–115 social skills training cognitive-behavioral approaches 117–119 literature-based approaches 119–120 self-determination 120–121 parenting and family relationships 110–112 peer relationships, see Peer relationships, learning disability effects prospects for study 126–129 self-regulation intervention impact on social skills 158–159 special education research for learning disability social interventions 418–419 Social Skills Games, social skills training 118 Social studies, see also History instructional approaches for learning from textbooks

445

alternative textbooks 333–334 computerized study guides 333–334 content enhancement routines 332–333 reading comprehension 330–331 text structure incorporation in strategy instruction 331–332 vocabulary 329–330 overview 325–327 special education research for learning disability 416–417 traditional teaching approaches 327–328 Society of Mind Model 62–63 Solve It, mathematics instruction 228 Special education classroom experience of learning disability students elementary students access to expert instruction 422 challenging learning environments 423–424 classmate interactions 421–422, 429 overview 420–424 quality of education 424 teacher interactions 420–421 secondary students access to expert instruction 425–426 co-teaching reliance 426–427 overview 424–427 quality of education 427 unique challenges 425 curriculum constructivist oriented framework 390–394 direct instruction framework 381–386 fostering adaptive expertise 398–400 overview of frameworks 377–381 standards 394–398 strategy instruction framework 386–390 equal access ensuring challenge recognition and response 428–429 intervention implementation 430–431

446

Index

Special education (Continued) overview 427–431 teacher role integrity maintenance 429–430 general education policy context 408–412 overview 377–381 peer relationship impact 109 policy effective instructional practice 414–415 free appropriate public education 413 Individuals with Disabilities Education Act overview 412–415 least restrictive environment 413–414 prospects for study 431–432 realities of service delivery early services 411 identification of students with learning disabilities 411–412 misconceptions about learning disabilities 410 overview 409–412 research guiding instruction mathematics 417–418 practice gap 419 reading 415–416 science 416–417 social interventions 418–419 social studies 416–417 self-esteem effects 97–98 Specificity, tests 183–184 SPPC 93–94 SPPLD 93–94 SRSD, see Self-Regulated Strategy Development SSFA, see Semantic/syntactic feature analysis STM, see Short-term memory Story mapping, reading intervention 196–197 Strategic Content Learning (SCL) 366 Strategic tutoring model 316–317 Strategy, semantic features analysis (SFA), reading intervention 201–202 SVR, see Simple View of Reading

T T3, see Think-Talk-Text Think-Talk-Text (T3), writing behavioral response to intervention 72–73 Thirsty Thinkers program 71–72 TWA, reading intervention 207

U UDL, see Universal Design for Learning UID, see Universal Instructional Design Understanding US History 382–383 Universal Design for Learning (UDL) 320 Universal Instructional Design (UID) 360, 371 University, see College

V VHM, see Virtual History Museum Virtual History Museum (VHM) 346–347 Visual-spatial sketch pad, deficiencies in learning disability 42–43

W WIF, see Word identification fluency WM, see Working memory Word identification fluency (WIF), reading assessment 185 Working memory (WM), see also Memory deficiencies in learning disability 37–39 neural circuitry 63–64 overview 33–43 systems model 64 training overview 49–51 test-in-the-limits studies 50 Writing, see also specific learning disabilities adolescent learning disability research findings 301–302 brain function studies 67 intervention computer-aided instruction overview 260–265 planning processes 265–266 speech recognition 264–265 speech synthesis 264 word prediction 264 word processing 261–263

Index

explicit, systematic instruction in basic skills handwriting 257–258 spelling 258–260 goals basic skills and conventions 247–248 learning 246–247 processes 245–246 tasks 244–245 general recommendations reading integration 249–250

447

social environment support 249 time adequacy 248–249 prospects for study 266–267 response to intervention behavioral response to intervention 70–74 brain response to intervention 75 Self-Regulated Strategy Development overview 252, 253b, 254b resources 256 stages 253–255 specific learning disability defining 78