learning to read in the digital age

MIND, BRAIN, AND EDUCATION

Learning to Read in the DigitalAgeDavid Rose1 and Bridget Dalton2

ABSTRACT—The digital age offers transformative opportu-nities for individualization of learning. First, modern imagingtechnologies have changed our understanding of learning andthe sources and ranges of its diversity. Second, digital tech-nologies make it possible to design learning environments thatare responsive to individual differences. We draw on CAST’sresearch and development on universal design for learning tosuggest the potential of digital reading environments that aredesigned to support learning and engagement by addressingthe diversity in learners’ representation, strategic and affec-tive networks. Optimal customization depends on continuedadvances in the digital tools of the neurosciences and thedesign and enactment of digital learning environments.

Bruce Fullan, one of the most widely read educationaltheorists, recently published a slim and provocatively titledbook called Breakthrough (Fullan, Hill, & Crevola, 2006). Inthis book, he and his coauthors argue persuasively that mosteducational reform movements ultimately falter because theyhit the same glass ceiling, a ceiling imposed by the limits ofmass, standardized, educational methods. A true breakthroughin education can come only, he argues, when that ceilingis broken—when instruction is ‘‘personalized’’ rather thanstandardized.

The argument for personalization—for making more effec-tive matches between instructional methods and individuallearners—is not new or unique. The failure of our schoolsto individualize instruction stems not from resistance to theidea itself but from the inability to implement it effectively.Effective implementation would require at least two impor-tant things: (a) a clearly articulated view of learners and theirdifferences that would be relevant and significant for making

1CAST, Inc., Wakefield, MA2Vanderbilt University’s Peabody College, Nashville, TN

Address correspondence to David Rose, CAST, Inc., 40 Harvard MillsSquare, Suite 3, Wakefield, MA 01880-3233; e-mail: [email protected]

individual instructional decisions and (b) methods and mate-rials that are flexible and differentiated enough to effectivelyrespond to those individual differences in actual practice.

Neither of these are typical realities in most existingclassrooms where a single teacher faces 20–30 students ata time and where every student must use the same materialsand methods (e.g., a standardized textbook) to follow the samepath to success. Indeed, the final chapters of Fullan’s book,where ‘‘practical’’ strategies for differentiating instruction aredescribed, is the weakest part of the book. The ‘‘practical’’strategies require extraordinary amounts of extra preparationand effort by the classroom teacher, demands that seemdisappointingly impractical in reality.

In this article, we would like to revisit the breakthrough ofindividualization in the context of the digital age. We believe itis timely to do so because new tools—tools primarily based onthe modern digital technologies that are pervasive everywherebut in schools—have the potential to radically transform theecology of teaching and learning. These new technologies aretransformative in two key respects.

First, modern digital technologies have transformed ourability to articulate (and understand) learning and itsindividual differences. Modern imaging technologies arepowerful because of their ability to collect enormous amountsof data and transform that data into intelligible information.The results of those digital transformations allow us, andeven require us, to be far more articulate about learning andits diversity than we could ever have been without them.These new digital tools for imaging the brain (e.g., positronemission tomography [PET]; functional magnetic resonanceimaging [MRI]; magnetoencephalography [MEG]) have had atransformative effect on cognitive neuroscience, changing notonly our views of what learning is, but also our understandingof the sources and range of its diversity.

Second, similar modern digital technologies are transform-ing our ability to design educational environments that areflexible enough to address those individual differences prac-tically and effectively. Traditional classroom methods andmaterials have, for centuries, been based on the affordances ofprint. But print is a fixed and standardized medium that is a

74© 2009 the Authors

Journal Compilation © 2009 International Mind, Brain, and Education Society and Blackwell Publishing, Inc. Volume 3—Number 2

David Rose and Bridget Dalton

poor platform for differentiation of instruction. As the inter-active multimedia technologies of the digital world replacethe static technologies of print, a far richer palette opens upfor instructional designs, and especially for designs that areflexible enough to meet the challenge of individual differences.

In this article, we seek to explore the question at theintersection of these two applications of digital technology.Can the advances in learning technologies take advantage ofthe advances in the cognitive neurosciences, and vice versa? Weshall focus in this article only on learning to read, although thearguments would apply as well to other domains of learning.

LEARNING TO READ TEXT IN A WORLD OF PRINT

For 500 years, the preferred medium of intellectual communi-cation and literacy has been printed text. Concepts of learningand teaching have been driven by printed text, and our class-room pedagogies have been organized around the strengthsand weaknesses of print.

Both the strengths and weaknesses of printed text are tied toits essential quality—its permanence or ‘‘fixedness.’’ The revo-lution started by Gutenberg was one of capturing the ephemeraof language and thought in a more permanent form, one thatcould withstand transformation over time and distance andthe vagaries of human memory. The fixed nature of printprovided consistency—everyone saw the same words, sen-tences, images—although not necessarily the same meaning.The stability and permanence of a document was its strength.

The strength of printed text, permanence, is also itsweakness, especially as a pedagogical instrument. In a universe

of highly individual readers, ‘‘one-size-fits-all’’ printed textoffers a gateway to knowledge for some, and a hurdle oroutright barrier for others. The range and severity of barriersin printed text are often underestimated, and students whoexperience difficulty learning from text are often labeledwith aggregate terms such as dyslexia, learning disabilities,or struggling reader which mask underlying variations inetiologies and instructional needs.

Cognitive and neuroscientific research, on the other hand,reveals that a differentiated and complex set of processes areinvolved in learning to read. Let us turn to the ways in whichnew technologies allow us to ‘‘see’’ the brain at work; then wewill focus on reading itself.

RESEARCH ON LEARNING IN THE BRAIN

New digital imaging techniques allow us to view not only theanatomy of the brain, but the way it functions as it worksand learns. PET scans, for example, provide images of theworking brain by revealing what parts of the brain are mosturgently metabolizing glucose. The more active the region, themore glucose it metabolizes, creating a ‘‘hot spot’’ of energyconsumption. The ‘‘hotter’’ the spot, the more brightly coloredit looks when rendered in a PET scan image.

The very early PET scans in Figure 1 (Petersen, Fox,Posner, Mintun, & Raichle, 1988) illustrated for all to seethe underlying activity of a nervous system while subjects areengaged in four language-related tasks. The distribution of‘‘hot spots’’ showed that the brain allocates energy differentlydepending on the demands of the task—from seeing words, to

Fig. 1. PET scan images of human brains while engaging in four different language-related tasks. Adaptation reprinted with permission fromNature (see Petersen, Fox, Posner, Mintun, & Raichle, 1988) copyright 1988 by MacMilan Magazines Limited.

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Learning to Read in the Digital Age

listening to words, to speaking words, to speaking a specificclass of words (verbs).

The research of Petersen et al. suggested two generaliza-tions. First, most mental tasks involve a distinctive patternof activity in the cortex. The pattern of activity for hearingwords is quite different from the pattern for seeing words.Second, this pattern of activity is ‘‘distributed’’ around thecortex, rather than localized in one area. There is not a single‘‘language’’ center at all, but a large number of regions thatmight become active in a language-related task. These regionswork together, rather like an ad hoc committee formed to per-form some specific function. Each member of the committee(when it works well) does its component of the task, and theywork in parallel for maximum efficiency.

But Figure 1 illustrates only a static image of the workingbrain. One of the most powerful capabilities of the new digitalimaging technologies is their capacity to examine the dynamicchanges that occur over time—including the changes thatwe call learning. Figure 2 presents three images of the brainengaged in the same task at three different stages of learning(Peterson, VanMeir, Fiez & Raichle, 1998). The first imageshows a brain which is just beginning to learn the task,responding with a verb when a noun is given as the stimulus(e.g., experimenter says dog; subject replies run). The brainshows ‘‘hot spots’’ of activity in distinctive regions of bothfrontal and temporal lobes of the brain. The second imagedisplays data from subjects performing the same task, but ata later point in their learning. The brain allocates its energy

differently when the task has been ‘‘practiced,’’ allowing usto see the effect of learning itself. The third image shows theeffect of introducing new stimuli while the task remains thesame, a classic instance of the kind of learning commonlycalled generalization. The energy distribution is localized inmuch the same way as in the naive condition, but there is‘‘savings’’ from prior learning.

Two things are notable in the images therefore far byPosner’s group and in a great many studies publishedsince (e.g., Cabeza & Nyberg, 1997; Palmer, Brown,Petersen, & Schlaggar, 2004; Sandak, Mencl, Frost, &Pugh, 2004).

First, the brain is a distributed processor. In any learningopportunity, many different regions of the brain may beinvolved, and the differences may be quite specific. Forexample, when we learn to recognize an object, visual cortex‘‘lights up’’ but not uniformly. The object’s color is processedin one part of visual cortex, its shape in another. Still a differentarea processes where the object is in space and yet anotherin learning its path of motion. All of these processors operatein parallel, each contributing only a small part to learningabout the object (e.g., Farah, 1999). Our knowledge of theobject is an amalgam of many different kinds of knowing,each specialized in a different part of the brain. Second, theexact pattern of brain activity revealed by imagery dependsnot only on the task, but on learner variables, such as skill, age,gender, motivation, genetics, etc. (Palmer et al., 2004; Posner& Raichle, 1994; Sandak et al., 2004).

Fig. 2. PET scan images of human brains engaging in the same task but at three different stages of learning. Figure 2 is from Petersen, S. E.,van Mier, H., Fiez, J. A., & Raichle, M. E. (1998). The Effects of practice on the functional anatomy of task performance. Proceedings of theNational Academy of Sciences, USA, 95(3): 853–860. Adaptation reprinted with permission from the National Academy of Sciences (see Petersen,VanMeir, Fiez & Raichle, 1998).

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LEARNING TO READ IN THE BRAIN

What parts of the brain are most active in reading? Whetherreading isolated words (Cabeza & Nyberg, 1997) or engagingin more complex tasks such as understanding the moralof a fable, reading is a highly distributed activity withmany different processors in the brain contributing to thevarious components of competent reading (Misra, Katzir,Wolf, & Poldrack, 2004; Rumelhart, 1994; Sandak et al., 2004;Wolf, 2007). The way that the reading brain distributes itsprocessing differs not just by the nature of the task, but byother factors such as text difficulty, background knowledge,motivation, and whether the individual has a specific readingdisability (e.g., Sandak et al., 2004; Shaywitz & Shaywitz,2004, etc.). For example, Shaywitz and Shaywitz (2004) showthe different distributions that characterize ‘‘typical’’ learnersand ‘‘dyslexic’’ learners when reading single words (Figure 3).

In summary, reading, like other complex tasks, requires ahighly distributed set of processors in the brain. The exactdistribution depends on many variables related to the natureof the task, text, and individual doing the reading.

PROVIDING A BRAIN-BASED FRAMEWORK FORUNDERSTANDING THE DEMANDS OF READING

While brain images suggest considerable complexity intasks like reading, it is possible to organize the processorsin the brain into three large networks, as have manyneuropsychologists before (e.g., Cytowic, 1996; Luria, 1973).Broadly speaking, one system recognizes patterns, secondgenerates patterns, and the third determines which patternsare important to us. At all stages of learning, all three systemsare crucial. Successful reading instruction requires attentionto all three of these interconnected systems.

Recognition Systems in the BrainMost of the posterior (back) half of the brain’s cortex is devotedto recognizing patterns (e.g., Farah, 1999; Mountcastle, 1998).

Fig. 3. Comparison of activation patterns during reading for dyslexicand non-dyslexic students. Overcoming Dyslexia (p. 83), by S.Shaywitz, 2003, New York: Alfred A. Knopf. Copyright 2003 bySally Shaywitz. Reprinted with permission.

Pattern recognition systems make it possible to identify visual,auditory, and olfactory stimuli—to know that a particularstimulus pattern is a book, your dog’s bark, the smell of burn-ing leaves. Damage to the posterior cortex can affect the brain’srecognition capacity. Depending on the degree and kind ofdamage, an individual may lose the ability to recognize objectsby their color or shape, by the way they move, or by the way theysound. Normal variation in people’s ability to recognize pitch,timing, location, color orientation, or shape is largely due to dif-ferences in the amount of cortex, or extent of neural networks,allocated to visual, auditory, and olfactory recognition.

Obviously reading depends on the recognition systems.The ability of the brain to quickly recognize basic patternsin orthography, phonology, semantics, as well as the manyhigher-level patterns of written syntax, story grammar, style,etc., is critical to reading and understanding text (for a review,see Shaywitz & Shaywitz, 2004). But it is not enough. Moreof the brain is required.

Strategic Systems in the BrainThe anterior part of the brain (the frontal lobes) comprisesthe strategic networks responsible for knowing how todo things. Actions, skills, and plans are highly patternedactivities, requiring the strategic brain systems responsible forgenerating patterns. Strategic systems work in tandem withrecognition systems to learn to read, compute, write, solveproblems, and complete projects (see Fuster, 2002; Goldberg,2001; Jeanerrod, 1997; Stuss & Knight, 2002). Damage to dif-ferent parts of the frontal lobes can lead to difficulties withplanning and organization, weak fine or gross motor coor-dination, impulsivity, or paralysis. Physiological differencesin frontal networks account for much of the normal individ-ual variation in fine motor skill, physical coordination, andcapacities for planning, organization, and strategic thinking.

Frontal systems are also critical in learning how to read (e.g.,Sandak et al., 2004; Shaywitz & Shaywitz, 2004). Much ofreading is not just recognizing patterns in text, but knowinghow to look for patterns—knowing how to ‘‘sound out’’ anunfamiliar word, knowing how to look for an author’s point ofview, knowing how to monitor progress while reading. Theseare all critical, but they are not enough for successful reading.More of the brain is required.

Affective Systems in the BrainAt the core of the brain (the extended limbic system) liethe networks responsible for emotion and affect. Neitherrecognizing nor generating patterns per se, these networksdetermine whether the patterns we perceive matter to usand help us decide which actions and strategies to pursue(Damasio, 1994; Lane & Nadel, 2000; Ledoux, 2003; Panksepp,1998). What is important to us varies a great deal over time



and depends on our present status, history, expectations,and many features of what we call our personality. For theindividual with low blood sugar, food becomes very importantand patterns in the environment that signal food (like a bigupright white metal box) to attract our attention and effort.

The affective systems, such as strategic and recognitionsystems, are distinctive parts of a distributed system forlearning and knowing (Lane & Nadel, 2000; Ledoux, 2003).For example, amnesiacs may be unable to recognize a personwhile reacting appropriately to their affective significance(e.g., fearing someone who has hurt them in the past, evenwhen they have no recollection of ever having seen him before).Damage to the limbic system can impair the ability to establishpriorities, select what we value or want, focus attention, orprioritize actions. Physiological as well as experiential factorscontribute to individual variation in affective factors.

Affective factors are also critical in learning to read (Guthrie&Wigfield, 2000). Fink (1998) showed that highly successfulindividuals who are dyslexic persisted in learning to read due toa driving interest in a particular topic. Children’s dispositionstoward reading are also influenced by their history of successand failure with reading in school, as well as the reading valuesand practices of their family, peers, and community.

THE BRAIN’S INTEGRATED SYSTEMS AND READING

All three of the systems—recognition, strategic, andaffective—work together, in parallel. Each contributes anessential kind of knowing that is central to what we callintelligence. And each is crucial to our ability to read.

The three systems we have outlined here may seem familiarto educators as well as neuroscientists. In the NRC Report(1998), ‘‘Preventing Reading Failure in Children,’’ Snowdescribes three potential stumbling blocks in learning to read:

The first obstacle, which arises at the outset of reading acquisition,is difficult in understanding and using the alphabetic principle—theidea that written spellings systematically represent spoken words.It is hard to comprehend connected text if word recognition isinaccurate or laborious. The second obstacle is a failure to transferthe comprehension skills of spoken language to read and to acquirenew strategies that may be specifically needed for reading. The thirdobstacle to reading will magnify the first two: the absence or loss of aninitial motivation to read or failure to develop a mature appreciationof the rewards of reading (National Reading Council, 1998, p.4)

The resemblance to the three ‘‘systems’’ described in thebrain is not coincidental. Both emphasize that there are not justtwo aspects of teaching reading that need our attention; butthere are three. Students may fail because we fail in teachingthem three important things: to recognize the patterns ofthe written code, to apply successful strategies for obtaining

meaning, and to see the purpose for reading in their ownlives. Failure to address any of them results in a program ofreading that lacks balance and is destined to fail for manystudents.

READING FAILURE: DISTRIBUTED SOURCES

The images of modern neuroscience are not the only indicationsof the distributed processing that is involved in reading.The natural experiments of individuals with brain-baseddisabilities are a second source of information, especially sincemany of these individuals exhibit reading difficulties. First,there are individuals who have sensory/perceptual disabilitieswho fall into the ‘‘recognition’’ category discussed earlier. Itis no surprise that people who are blind or partially blindexhibit difficulties in learning to read. But the magnitude ofthe problem, even for Braille readers, is often underestimated(Dunlea, 1989). Other sensory disabilities reveal less obviousbarriers. Deaf adults, for example, read on average at a fourthgrade reading level (Stern, 2001). Though one might expectthat lack of phonemic awareness would be the major stumblingblock, the best predictor of good reading among deaf adultsis the amount of expressive language in the home—evenif that language is without phonemes (i.e., sign language).Similarly, many individuals with higher order language–basedimpairments also have reading-related learning disabilities(Simmons & Kameenui, 1998).

Second, there are individuals with various motor andexecutive function disabilities (the strategic systems). It isoften a surprise to clinicians to find that the incidence ofreading disabilities is elevated in students with cerebral palsyor related ‘‘motor’’ disabilities who have difficulty managingthe technology of print—e.g., turning the pages or tracking thetext visually. It is less of a surprise to find that students withexecutive function disabilities face reading barriers (Adams &Snowling, 2001; Graham & Harris, 1996). Because of their weakstrategic abilities, these individuals have difficulty soundingout unfamiliar words and struggle to apply and monitorcomprehension strategies. Students with attention deficithyperactivity disorder (ADHD) have strengths in lower levelreading skills, but often lack the attentional skills necessaryfor deep understanding of text (Adams & Snowling, 2001).

Third, there are individuals with affective or emotionaldisorders. While the reading problems of these individuals areinfrequently studied, it is apparent that making meaning fromtext is more difficult for individuals who are distracted ordisengaged by emotional intrusions or instabilities (Coleman,1996; Wehby, Falk, Barton-Arwood, Lane, & Cooley, 2003).At the extremes, individuals with schizophrenia or autismhave notable difficulty engaging with text (Maugham, 1995;Frith, 1991). Children with Asperger’s syndrome are oftenprecocious decoders, but they are poor overall readers because



the ‘‘semantic’’ aspects of reading pose barriers. They struggleto construct meaning from what they can easily decode(Seymour & Evans, 1992).

These few examples of diverse reading difficulties illustratethe numerous challenges that reading poses and the barriersthat different students find when learning to read. Both neuro-imaging research and observation of students with disabilitiesconfirm the complex and multifaceted challenge of learning toread and reading to learn.

READING TEXT IN A DIGITAL WORLD

While readers are highly differentiated, printed books are‘‘one-size-fits-all.’’ Printed books thus place a heavy burdenon teachers to customize methods and materials ‘‘post hoc.’’Some do it very well, but often at great cost in terms of timeand effort. In this context, text’s uniformity and consistencyis not a virtue.

Reading in a digital medium can be very different fromreading in a print medium. Unlike printed text, digital text ishighly flexible. Because of word processors, most people areaware of the rudiments of this flexibility. Once a documentis ‘‘opened,’’ the user can change from one font, size, orcolor to another with ease. These display alterations are notparticularly interesting to most readers. For individuals withvisual disabilities, however, they are critical.

This flexibility of visual representation on the screen isonly the tip of the iceberg. The real power and flexibility ofdigital content stems from another key aspect of digital text:the separation of content and its ‘‘display.’’ Unlike printedtext, where content and display are fused, digital content canbe displayed in countless ways, even in multiple modalities.For example, digital text can automatically be ‘‘displayed’’ asspoken words (through modern text-to-speech technologies)and the words can be highlighted as they are spoken, makingthe connection between written and spoken forms moreevident. The same digital words can also be displayed as tactilewords through a refreshable Braille device. For students whoface barriers in the language itself, a click on a word can bringup a contextually appropriate definition with graphics and inmultiple languages. In these and many other ways, digital textcan reduce barriers for students with disabilities, strugglingreaders, English language learners, and so forth.

Another benefit of digital content’s flexibility is its ability tobe variably and reversibly ‘‘marked.’’ With hypertext markuplanguage (HTML) the same content can be displayed on dif-ferent computers and devices, in unique ways for differentusers, without losing the integrity of the original content. Forexample, a single web page can be displayed on a large desktop,a small laptop, and a handheld device—all because the webpage is marked up in HTML. HTML works by tagging differentpieces of content. Structural tags, for example, can indicate that

one line of text is a header and another piece of text is a ‘‘side-bar.’’ Once tagged, the different components can be assigneddifferent display characteristics, chosen by the user. Headerscan be large or small, purple or blue; content can be displayedor hidden, depending on the user’s needs and preferences.

With the newer markup language of extensible markuplanguage (XML), the tags are not only structural, but alsosemantic, enabling elements to be identified based on theirmeaning and not just their structure or syntax. For example,a body of text can be labeled as a summary or a question andthen at a later time be selectively displayed for one studentor a group of students, while hidden for others for whom itwould be unnecessary or distracting.

With semantic tagging, it is possible to begin to createdigital texts that are strongly pedagogical rather than simplyeditorial. The Universal Design for Learning (UDL) Groupat CAST (http://www.cast.org) is beginning to get researchresults from this pedagogical tagging, embedding scaffolds forlearning directly into text documents (see Figure 4).

The key thing about these digital editions is that the sup-ports for learning are embedded universally, but displayedindividually. Every student reads the same content—e.g.,the folktale Snake and Eagle—but supports for learning areselected and displayed individually for each student. Usingthis approach to differentiation, ‘‘smart’’ learning materialscan support students in their ‘‘zone of proximal development’’(Vygotsky, 1978), much as research has shown that a skilledhuman tutor does (Wood, Bruner, & Ross, 1976). What kindsof support can be provided? What kinds of support should beprovided?

AN INSTRUCTIONAL DESIGN FRAMEWORK FROMTHE DIGITAL NEUROSCIENCES

The potential in the flexibility of digital learning environ-ments can only be realized to the extent that it allows better‘‘matches’’ between individual learners and their pedagogy—tothe extent that more individuals are in their ‘‘zone of prox-imal development.’’ Better matches depend on two kinds ofcapabilities: better articulation (and understanding) of indi-vidual differences in learning and better articulation (andunderstanding) of the ways in which curricula can addressthose differences.

The pedagogy of decades ago lacked both. The articulationof learning was too limited, too aggregated to representthe distributed intelligence that cognitive neuroscience hasrevealed. Also, the articulation of instructional options was toolimited. Today, with multimedia learning technologies that arefar more flexible than any before, we can revisit the challengeof individualizing instruction. To do so, we draw heavily ona framework drawn from digital cognitive neuroscience toarticulate the individual differences and suggest a framework



Fig. 4. Screen shot of CAST’s Thinking Reader, a scaffolded reading environment (Dalton et al., 2001). Reprinted with permission by CAST[Computer software]. Wakefield, MA: CAST.

for the design of digital text and environments that isresponsive to individual differences, UDL (Rose & Meyer,2002). For the purposes of this article, we focus on developingstudents’ reading strategies, a well-validated approach toimproving comprehension (National Institute of Child Healthand Human Development, 2000; Block & Paris, 2008; for amore in-depth consideration of UDL reading environments, seeDalton & Proctor, 2007; Dalton & Strangman, 2006; Meyer &Rose, 1998; Dalton & Rose, 2008). Interactive examples of UDLreading editions are available at http://udleditions.cast.org/.

RECOGNITION SYSTEMS: PROVIDE MULTIPLE MEANSOF REPRESENTATION

One way in which readers clearly differ is in the easewith which they recognize the patterns in text—processesidentified with posterior cortical networks. Students withdyslexia have difficulty recognizing the sound–symbolpatterns that underlie decoding; hyperlexics have difficultyrecognizing the larger semantic and relational patternsthat underlie comprehension, and students with cognitivedisabilities often lack the background knowledge required forinferencing (e.g., Shaywitz, 2003; Wolf, 2007).

Unlike a fixed print environment, digital text can be dis-played in ways that support student differences. There are twoadvantages in having multiple representations. First, we canensure that all children have sensory and perceptual access tothe text (e.g., students who are blind, deaf, dyslexic, etc, willbe able to perceive the words because they are represented inalternative sizes, modalities, etc.). Second, once customizedaccess to the text is provided through multiple representations,cognitive capacity is freed up so that we can focus students’

learning strategies on our goal, reading comprehension. That is,we can present text in individualized ways to reduce the barri-ers that might interfere with learning to comprehend: text that‘‘talks’’ itself aloud for the blind or dysfluent reader, has hyper-links to vocabulary definitions or second language translationsfor the English as a second language (ESL) reader and hasbackground information links for the student with cognitivedisabilities. Such scaffolding, common in apprenticeships ofevery kind, does not actually teach the strategies themselves,however. To develop reading comprehension strategies, wewill need to return to the framework.

STRATEGIC SYSTEMS: PROVIDE FLEXIBLE MEANSOF ACTION AND EXPRESSION

The second part of the framework, identified with networksin frontal cortex, is strategic systems. Students differ fromone another in the ease with which they can learn toact skillfully and strategically in any domain, includingtext comprehension. Students with physical disabilities havedifficulty mastering the strategic patterns of eye movementsthat underlie effective comprehension, students with ADHDhave difficulty mastering the sustained self-monitoring skillsrequired for comprehension, and students with executivefunction disorders lack the ‘‘meta’’ skills that allow them tochoose strategies that are effective in a particular context (fora comprehensive review of executive function in educationand reading, see Meltzer, 2007).

In a print environment, students are presented with thesame text; differentiation of strategic support must comeexternally from a teacher or peer. In a digital environment,it is possible to embed supports within the text to help



the apprentice reader build strategic skills. This makes thelearning activities accessible to a broader range of studentsand provides differential supports for learning.

In our work on strategic digital reading with the UDLGroup at CAST, we are guided by the literature oncognitive apprenticeship (Collins, Brown, & Newman, 1989)and cognitive neuroscience of motor action (Fuster, 2002;Goldberg, 2001; Jeanerrod, 1997). Both of these literatureshighlight scaffolds characteristic of apprenticeship: (a) modelsof the skill or strategy; (b) guided practice with a gradualrelease of support; and (c) relevant and timely feedback. Whenreading a UDL text, students find each of these supports. Forexample, when learning the summarization reading strategy,coach avatars model the strategy in practice. The coach,unlike a real teacher, is available on demand. As they practicesummarizing in context, the text transforms to highlight keyinformation or to present key points (along with distractors) tohelp build a summary. Feedback is provided that is timely andrelevant. These scaffolds can be gradually withdrawn as theygain independence. The key is that they are individualized andthus more likely to support learning within students’ zones ofproximal development.

AFFECTIVE SYSTEMS: PROVIDE FLEXIBLE MEANSOF ENGAGEMENT

A third part of the framework, identified with the extendedlimbic system and cingulate cortex, is affective systems. Itis no surprise that students display considerable intra- andinterindividual variation in the ways in which they can bemotivated and engaged in learning. Students with disabilitiesare vulnerable affectively—either as a primary aspect oftheir disability, or as a secondary effect. Students who aredepressed, schizophrenic, anxious, etc., obviously do not enterthe learning environment with the same opportunities to learnas other students. Whether a student is successful in learningcomprehension strategies depends to a large extent on whetherthey are motivated to do so, whether they are engaged by thegoals, by the lesson, by the teacher (Koskinen, Palmer, Martin,Codling, & Gambrel, 1994).

Printed texts offer few options for meeting the challengeof individual differences in engagement. In our work ondigital universal learning editions, we focus on threeimportant scaffolds for engagement: choice, media, andinteraction. Students may choose appearance, level and typeof support, method of response, content, etc. Multimediaoptions for representation and expression are possible, as areopportunities to act on your environment, obtain feedback, andinteract with peers and coach avatars. This type of customizedlearning that also develops competence can contribute tostudents’ overall reading engagement. Most importantly, asWolf and Barzillai (2009) have recently pointed out, these

digital learning environments have the potential to engagelearners of all kinds in the truly ‘‘deep reading’’ that modernliteracy demands.

RESEARCH ON EFFECTIVENESS

Does digital text designed for individual differences inrecognition, strategic, and affective systems improve students’reading achievement? A growing body of research on hypertextand intelligent tutoring systems shows the value of access andlearning supports for reading (for a review, see Dalton &Strangman, 2007; Strangman & Dalton, 2005). Our researchapplying UDL principles for the development of digital readingenvironments has also produced promising results. A studyof struggling adolescent readers found that students readingdigital texts with embedded reciprocal teaching strategies(Palincsar & Brown, 1984) significantly outperformed peersapplying reciprocal teaching strategies with printed texts ona standardized reading achievement test, after controllingfor initial reading achievement and gender (Dalton, Pisha,Eagleton, Coyne, & Deysher, 2002). They also demonstratedsignificantly higher on-task behavior. Similar positive resultswere obtained with sixth-grade students who are deaf orhard of hearing using American sign language (ASL) videoand signing avatars (Dalton, Schleper, Kennedy, Lutz &Strangman, 2005).

While there is much yet to be learned, publishers haverecognized the potential of these new kinds of supportedreading environments. Tom Snyder Productions of Scholastichas released nine award-winning middle school trade books(e.g., The Giver; Bud, Not Buddy) in this format, calling themThinking Readers. Other publishers are preparing to distributecore digital textbooks with flexible supports like those inrecent research. The US Department of Education recentlyendorsed a National Instructional Materials AccessibilityStandard for the publication of textbooks in digital formats sothat they are more accessible to students of all kinds (Stahl,2004; see NIMAS website: http://nimas.cast.org/).

SUMMARY

Learning to read in a digital world offers new opportunitiesnot available in an instructional world defined by printedtext. More individualizable than a printed book, digital text’sflexibility is key to its accessibility and effectiveness as ascaffolded learning tool for diverse learners. The same flexiblepower is also available for other digital media such as video,audio, and even virtual reality.

This flexibility of digital text can only be taken advantageof, however, when we understand the learning differencesamong our students and the interactions between those



individual differences and learning supports. Digital toolsin the neurosciences are one important asset in understandingthose differences. Education has much to learn from thecontinued interaction between these two digital advances inlearning.

REFERENCES

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