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59Learning about Learning Disabilities© 2012 Elsevier Inc.

All rights reserved.2012

Brain and Behavioral Response to Intervention for Specific Reading, Writing, and Math Disabilities: What Works for Whom?Virginia W. Berninger1, and Michael Dunn21University of Washington, Seattle, WA 98195-3600, USA 2Washington State University, Vancouver, WA 98686-9600, USA

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Chapter Contents

Learning about the Brain 60Defining Brain 60Brain Geography 60Brain Imaging Technologies 61Systems Approach 62Working Memory 64Controlled and Automatic Processing 65Nature–Nurture Interactions 65Comparing Reading, Writing, and Math Brains 66

Brain Differences of Individuals with and without SLDS 66Reading 66Writing 67Math 67

Behavioral and Brain Response to Intervention (RTI) 68RTI as a Nature–Nurture Perspective 68Behavioral RTI 70Brain Response to Intervention (RTI) 74

Individual, Developmental, Gender, Language, and Cultural Differences 76Longitudinal Studies 76Gender Differences 77Language and Cultural Differences 77Defining SLDs in Reading, Writing, and Math 77

Conclusions and Recommendations 80References 80

CHAPTER

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LEARNING ABOUT THE BRAINDefining BrainThe 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 initi-ates 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, respec-tively (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 exter-nal 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 func-tion alone or in concert (Berninger & Abbott, 2010). Cognitions can be translated into language (Fayol, Alamargot, & Berninger, 2012) or non-language 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 containWith ease—and You—beside.

Brain GeographyOver 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 region- specific brain structures and functions include Berninger and Richards (2002), Blakemore and Frith (2005), and Posner and Rothbart (2007). Technology-supported ways to access and learn the regions and associated functions include (a) Carter et al. (2009, which includes an illustrated book and inter-active 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 sur-face that are not as easily depicted in 2-dimensional drawings. In addi-tion, 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 TechnologiesFor an overview of brain imaging technologies used to study the living human brain, see the Appendix in Blackmore and Frith (2005), introduc-tory material in Carter (2009), or Chapter 3 in Berninger and Richards (2002). In general, studies of specific learning disabilities (SLDs) use non-invasive techniques such as (a) structural (MRI), which constructs via computer programs, visualization of neuroanatomical structures (not pho-tographs 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 stim-ulus onset to response. Recently developed new techniques assess (a) both where and when activation occurs during scanning; (b) functional connec-tivity for which regions activate at the same time given a specific brain region source; and (c) structural connectivity of white fiber tracts that con-nect 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 insti-tutional review boards (IRB) do not approve use of invasive imaging tech-niques with developing children or youth with or without SLDs.

Systems ApproachA 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 con-nectivity between individual neurons separated by a small space (syn-apse), (c) the pathways consisting of many synapsed neurons distributed across brain regions, and (d) the computations of the six layers of cere-bral 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 mes-sages; secondary brain regions specialize in hetero-modal messages, which integrate across sensory input and motor output regions, or between lan-guage 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 func-

tional system.Minsky (1986), a leading architect of artificial intelligence, talked to neuro-scientists throughout the country, built robots to test computational mod-els, 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 frame-work accounts for most human cognition being outside conscious aware-ness. 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 synchro-nize the various brain activities occurring in momentary time to a com-mon scale, based on linear time, in what is often referred to as real time. This synchronizing gives rise to brain waves. Patterns of communica-tion 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 transla-tion 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 move-ment 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 unfold-ing in momentary time and periodically synchronized in real time (see Minsky, 1986), and thus serves as executive functions for self-regulating 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 cap-tures 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 cor-rect 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 func-tion), 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 MemoryThe University of Washington Interdisciplinary Learning Disability Center (UWLDC), conducted genetics, brain imaging, assessment, and instruc-tional 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 gram-mar); (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 atten-tion, 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 par-ticipants 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 ProcessingSchneider and Shriffin (1977) and Shriffin and Schneider (1977) con-ducted 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 exam-ples from using writing tools, see Berninger & Richards, 2002, Chapter 7). Meta-analyses have shown that struggling writers benefit from learn-ing 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 InteractionsIn a ground breaking study, Hoeft et al. (2007) showed that both behav-ioral 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 nor-malized 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 support-ing 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 BrainsAlthough 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 vocabu-lary), 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 SLDSReadingOn 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 mak-ing 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 lan-guage disabilities (reviewed in Berninger & Richards, 2010; Richards, Berninger, & Fayol, 2012).

WritingUntil 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 morpho-logical 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 dif-ferences 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).

MathFor 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 spe-cific 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 imag-ing 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 epi-genetic modifications in gene expression (Cassidy, 2009). Thus, it follows that both behavioral RTI and brain RTI are important to assess and inves-tigate. An overview of representative studies of both behavioral RTI and brain RTI is presented next.

RTI as a Nature–Nurture PerspectiveRTI is a pedagogical paradigm offering ongoing assessment and strat-egy instruction (Gresham, 2002) because, despite the brain and genetic basis of SLDs, children who are given appropriate, specialized instruc-tion 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 three-tier 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 stu-dents 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 compo-nents 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 inter-vention given sufficient progress or redesign the intervention and provide it again. Third, the team might devise a blend between the standard pro-tocol approach and problem-solving model; for example, a school team could alter the components of a published intervention program and pro-vide the intervention to a small group of children all of whom have simi-lar 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 psy-chologist 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 classifica-tion (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 weak-nesses. Then the school team uses all of the collected assessment and anec-dotal 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 RTIBehavioral Reading RTIA sizable body of research exists on effective reading interventions (e.g., Morris & Mather, 2008). However, although many children show behav-ioral 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 iden-tifying who needs modified or specialized instruction (e.g., Berninger & Miller, 2011).

Behavioral Writing RTIFor 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 curricu-lum increase in volume and complexity as children progress through the grades. Those with SLD in writing, whether due to handwriting, spell-ing, 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 pro-cess. 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 chil-dren 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 soft-ware, 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 demon-strated their pre-treatment writing abilities during baseline. An interven-tion 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 cri-teria: (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, main-taining 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 read-ing (by the end of kindergarten, the children read at 90th %tile or above except one at 70th percentile) even without formal reading instruc-tion. 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 stu-dents 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., interven-tion stories’ content) were analyzed using MANOVA to assess for differ-ences 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, result-ing 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 instruc-tional 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 RTILarge 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) count-ing 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 RTIThree recent studies in which children’s brains were imaged while per-forming 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 read-ing 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 atten-tion for self-regulating language processing.

Davis et al. (2010) studied structural connectivity of neuroanatomi-cal 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 angu-lar gyrus.

Rezaie, Simos, Fletcher, Cirino, Vaughan, and Papanicolaou (in press), in contrast, studied brain RTI for magnetoencephalography for mid-dle 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 behav-ioral measures of word reading or fluency to predicting improvement in reading efficiency. Thus, engagement of specific temporal lobe regions pre-dicts response to instruction in adolescents who still struggle with reading.

Brain Writing RTISuch studies are not available for pure writing disability but are for chil-dren 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 RTIIn 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 con-nectivity 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 ven-tral medial prefrontal cortex and posterior brain regions.

INDIVIDUAL, DEVELOPMENTAL, GENDER, LANGUAGE, AND CULTURAL DIFFERENCESLongitudinal StudiesLongitudinal studies track individual differences over development. Even though brains may change in response to intervention, it does not fol-low 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 regres-sions. 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 atten-tion 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, includ-ing but not restricted to number sense ( Jordan, Glutting, & Ramineni, 2010) and executive functions (Mazzocco & Kover, 2007); (b) the predic-tive 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 environmen-tal 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 DifferencesGender differences have been documented in writing disabilities but not reading disabilities (Berninger, Nielsen, Abbott, Wijsman, & Raskind, 2008). Even though environmental variables may influence gender dif-ferences in math achievement, so do biological variables (e.g., Halpern, Benbow, Geary, Gur, Hyde, & Gernsbacher, 2007; Keller & Menon, 2009).

Language and Cultural DifferencesTwo 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 differ-ences 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 stu-dents with brain-based SLDs, but scarcely any research has been done on this timely issue.

Defining SLDs in Reading, Writing, and MathPatterns in Developmental Domains, Learning Skills, and Working Memory ComponentsSilliman 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 developmen-tal 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 per-vasive developmental disability (PDD) across all five domains or selec-tive 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 diag-nosed 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 syntax-level impairments in working memory, which are associated with different impairments in the learning profile—word reading and spelling or read-ing 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 learn-ing 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 LDBecause 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 ortho-graphic 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 con-text) or in the opposite direction. We do not teach declarative knowl-edge (verbalized rules) that overloads their working memory. At the word level, we teach transfer of alphabetic principle and other orthographic-phonological connections to words that vary in number of syllables and morphemes and are from different word origins with different sound-spelling-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 review-ing/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).

DyscalculiaProgress is being made in understanding the multifaceted nature of dyscal-culia, which is also marked by individual differences and changes in expres-sion 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 sub-type, 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), part-whole relationships (Mazzocco & Devlin, 2008), timed performance for arithmetic skills (Mazzocco, Devlin, & McKenney, 2008), and math esti-mation (Mazzocco, Feigenson, & Halberda, 2001). Fortunately, evidence-based 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 individ-ual brain analyses which are needed for educational applications. Many who advocate brain-based education practices are not trained, quali-fied 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 infor-mation. 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 compo-nents of the brain and the mind it constructs, the learning environment with which it interacts (physical, social, and instructional), and the individ-ual 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 read-ing, writing, and math. That approach implies “One Size Fits All.” Future research might combine group research designs and analyses (e.g., ran-domized, 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 modify-ing their interventions according to their individual needs. Thus, we might reframe the question to ask “What Works for Whom?”

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Appendix

Questions to Ask Those Making Claims about the Brain and Education or Brain and SLDs1. What is their training, experience, credentials for research in neurosci-

ence, 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 collabo-ration?

Schema for Reading Brain Research as Critical ConsumerAs 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 impor-tance? 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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