Universal Access in the Information Society

, Volume 5, Issue 4, pp 381–391

Universal Design for Learning: meeting the challenge of individual learning differences through a neurocognitive perspective


    • The Center for Applied Special Technology (CAST)
  • Nicole Strangman
    • The Center for Applied Special Technology (CAST)
Long Paper

DOI: 10.1007/s10209-006-0062-8

Cite this article as:
Rose, D.H. & Strangman, N. Univ Access Inf Soc (2007) 5: 381. doi:10.1007/s10209-006-0062-8


The traditional “one-size-fits-all” approach to curriculum denies the vast individual differences in learning strengths, challenges, and interests. The focus of this article is a novel approach, called Universal Design for Learning, to addressing the challenge of individual learner differences. Cognitive science research suggests the joint action of three broad sets of neural networks in cognition and learning: one that recognizes patterns, one that plans and generates patterns, and one that determines which patterns are important. These networks, referred to in this paper as recognition, strategic, and affective networks, are subject to individual differences that impact how individual students learn. This paper describes these networks and how the Universal Design for Learning framework makes use of this networks-based perspective to structure the consideration of individual learner differences and guide the design of a flexible, technology-rich curriculum that provides rich options for meeting diverse student needs.


DisabilitiesBrainEducational technologyCognition and learningUniversal Design for Learning

1 Introduction

The diversity of students in today’s classrooms is unprecedented. Particularly notable is the increased number of students with disabilities served in the general education classroom [32, 77]. The Individuals with Disabilities Education Act Amendments of 1997 require that these students have the same opportunity of other students to access, participate, and progress in the general education curriculum. However, this opportunity has been difficult to realize in a system where curricula are designed to be “one-size-fits-all,” based on the assumption that students have generally uniform interests and abilities [64]. There remains a large gap in academic performance between students with and without disabilities [9, 26, 50].

This article argues that meeting the challenge of student diversity, and ensuring all students equal learning opportunity, require a new approach to curriculum that is founded on an understanding of individual differences in learning. For the past 20 years, The Center for Applied Special Technology (CAST)1 has sought to harness the power of technology to improve education for individuals with disabilities and their peers, both children and adults, through the research and design of new learning environments. Emerging from this research and development is an innovative approach, called Universal Design for Learning (UDL), which can be used to design technology-rich curricula that are more flexible, providing students with a range of options to meet their diverse needs. This approach is now being widely adopted in the United States and promoted by the US Department of Education [62]. The first part of this article describes the cognitive neuroscience underpinnings of UDL, understandings about learning and three learning networks in the brain. The second part of the article shows how UDL uses a networks-based framework together with technology to guide the design of more flexible and supportive curricula, drawing on examples from previous work of the authors.

2 A networks-based perspective on learning

The UDL framework is grounded in cognitive and neural perspectives on learning. Recent advances in the cognitive sciences, and especially in the cognitive neurosciences, have made one thing abundantly clear—there is no simple way to characterize, or localize, cognition. The journal Cognitive Psychology takes a typical approach to this problem: instead of directly defining cognition, it lists related processes: memory, language processing, perception, problem solving, and thinking. Unfortunately, these processes are also difficult to define or categorize. In fact, the explosion of imaging studies in cognitive neuroscience has demonstrated that none of these processes are truly coherent or localizable functions at all. For example, few neuroscientists or cognitive scientists think that memory, language, and perception are simple or unitary functions. They are all highly distributed, with many different subcomponents that are activated in different tasks and contexts, and in practice they are surprisingly difficult to separate from each other. With attention, problem solving, and thinking, the picture grows even more complicated.

Although neuroscience suggests that every act of cognition is considerably complex, psychological and neuropsychological research recognize three broad but anatomically and psychologically distinct functions that are involved in every act of cognition [17, 40]. Broadly speaking, one component recognizes patterns, a second one plans and generates patterns, and a third one determines which patterns are important. Each of these components is involved not only in the general act of cognition, but also in specific functions, including memory, language, problem solving, and possibly thinking.

The overall faculty of cognition and each of the above-mentioned specific functions are of obvious and unquestionable importance to learning. However, what may be less obvious is a deeper connection between learning and the three neural components recognized to cut across acts of cognition. These three neural components neatly parallel what Vygotsky [79] identified as three essential learning constituents: recognition of the information to be learned, application of strategies to process that information, and engagement with the learning task. In previous work by the authors of this paper, these three components have been referred to as recognition, strategy, and affect, and their respective neural substrates as recognition networks, strategic networks, and affective networks [62]. The following sections detail the role of each of these broad networks in learning and the potential impact of individual differences each one.

2.1 Recognition networks

Recognition is a key part of understanding and relating to the world. We are constantly bombarded with stimuli: visual, auditory, tactile, olfactory, and gustatory. As these stimuli impinge on us they are received by various sensory organs and converted into signals that are ultimately interpreted by broad networks of neurons in the back half of the brain’s cortex: recognition networks.

Recognition networks are specialized for recognizing patterns [25, 48]. These networks enable people to identify objects based on how they look, sound, smell, taste, and feel and to differentiate complex combinations of features. They enable, for example, to identify a familiar face, a musical instrument, a perfume, a favourite food, and a light switch in a dark room.

Recognition networks are essential to many higher cognitive tasks, and pattern recognition pervades all academic content areas. In reading, these networks are relied upon for letter recognition, decoding, and comprehension, among other things. In mathematics, they are critical for recognition of numbers, shapes, and algebraic expressions. In social studies, they underlie the ability to identify historical figures and themes.

Individual differences in the structure and function of recognition networks manifest as differences in how various recognition tasks are managed. Damage or dysfunction in the recognition networks can impact recognition capacity in any variety of ways, both general and specific. Neurological studies have characterized a multitude of disorders associated with deficits in the recognition networks, including disorders specifically affecting the ability to recognize color, movement, faces, music, spoken words, and written words. Dyslexia, for example, is accompanied by structural change and reduced activation in parts of the recognition networks [69, 70].

Differences in recognition networks can also emerge as a product of practice and training. For example, it has been shown that the recognition networks of individuals with different levels of musical training show different patterns of activation during the same music task [67]. Thus, recognition difficulties are not exclusively neurological in origin. The term recognition—re-cognition—emphasizes the experiential role in knowing. What we perceive, what we remember, what we are able to imagine in order to solve problems, how we can understand spoken or written language, all depend on the ability to re-cognize, to remember and reconstruct the patterns that we have previously experienced, the patterns that we have learned. Recognition is a key component of any cognition, and any learning, but it is by no means the only component.

2.2 Strategic networks

In addition to receiving stimuli and constructing meaning from the world around us, it is critical that we interact with what we perceive. We need to not only recognize patterns but also generate patterns of our own. In the front of the cortex (the frontal lobes) are a set of networks, the strategic networks, that manage the complex act of responding to the world, both physically and cognitively. The strategic networks are specialized to plan, coordinate, self-monitor, and execute physical movement and cognitive action. Strategic networks manage the so-called executive functions, such as selective attention, goal-setting, planning, organization, coordination, and self-monitoring. Strategic networks guide our behaviors by enabling us to set goals, identify strategies, focus, monitor progress, and correct our course of action.

Both simple and complex acts fall under the influence of strategic networks. How we look at a picture—where we direct our eyes and when—is a function of our goals, attention, and coordination, all subserved by strategic networks [62]. Luria [40] showed that individuals with damage to frontal cortex, much like inexperienced children, do not examine pictures skillfully or strategically; these individuals do not know how to look at, or learn from, the content of an image. Decision-making under more complex circumstances, such as gambling, is also influenced by strategic networks [4].

Strategic networks play as fundamental a role in the classroom as do recognition networks [27, 30, 34, 75]. For example, in the mathematics classroom, strategic networks are needed to identify the goal for a particular word problem, for ignoring irrelevant stimuli in the problem text, for selecting a problem-solving strategy, and for manipulating pencil and paper to derive the solution. Similarly, writing prose is a highly strategic task, with frontal networks playing an essential part throughout the planning, drafting, and revising stages. Although not always recognized as such, reading is no less strategic a process, and the frontal cortex is active during skilled reading [66, 68]. Skilled readers call upon the strategic networks as they identify the reading purpose, select among various reading strategies, monitor progress, and revise as needed their course through the text [31, 59]. Reading competency also requires knowing how to look for patterns in the text: for example, how to examine the critical features of the letters, how to decode an unknown word, how to use the headings to leverage understanding, and how to investigate stylistic devices.

Structural or functional aberrations in the strategic networks are associated with movement disorders and executive function disorders. For example, when performing executive control tasks such as searching a visual stimulus, children with attention deficit/hyperactivity disorder show a different pattern of frontal brain activation than do children without the disorder [35]. Differences in the ability to negotiate academic tasks, such as problem solving, reading and writing, are associated with differences in strategic networks. For example, studies have shown that readers with dyslexia show greater activation of some frontal regions during a reading task than do average readers [69]. This likely reflects the fact that reading is less automatic for these individuals and requires greater executive control. Differences in strategic networks can also be associated with unusual talents. For example, a recent brain imaging study demonstrated that when mathematically gifted adolescents mentally rotate images, a task that comes into play during mathematical reasoning, their strategic networks show a different overall pattern of activation relative to their peers with average mathematical ability [51].

2.3 Affective networks

Recognition and strategic networks oversee how we recognize patterns and plan and generate patterns. There is also an affective component to how we process and manipulate patterns. Each individual continually evaluates and prioritizes patterns in terms of their appeal and significance. Indeed, a pattern’s affective significance holds unique meaning. Clinically, it has been shown that an individual who cannot recognize a person or object in any conventional respect can still respond in an emotionally appropriate way [37, 38]. The task of interpreting the world in terms of its emotional import and significance belongs to a set of networks localized to the core of the brain (known as the limbic system): affective networks. These networks orchestrate emotions and regulate hormones that affect biological drives such as thirst and hunger. They determine our baseline emotional state and shape our emotional responses to the world. They help to respond appropriately to divergent stimuli, such as a crying friend, a humorous cartoon, and a dangerous animal. Affective networks essentially act as an emotional filter, influencing our everyday actions and decision-making based on emotion, motivation, and biological drives [22, 37, 38, 52, 56]. They enable us to prioritize and persist in spite of difficulty, but they can also influence us to desist and shut down when the challenge is too great.

Affective networks contribute in a unique way to learning and knowing. Whether a student is successful depends to a large extent on whether he or she is motivated and engaged [55]. Vygotsky [79] recognized the fundamental role of affect in learning; his concept of the zone of proximal development embraces the notion that optimal learning requires careful consideration of what is motivating to a child: the task must offer challenge without discouraging effort. Generally, however, affect has received less attention from educational research and practice. This is changing, and the influences of affect, engagement, and motivation on learning are increasingly receiving the attention they deserve [3, 8, 24, 33]. Indeed, recent work shows that instructional approaches focused on developing positive emotion and motivation can improve academic performance [28, 29]. For example, Garcia and De Caso [28] demonstrated a significant positive impact on middle school students of a writing program that incorporated motivational training. Students participating in the program made significantly greater gains in writing quality and attitude toward writing than did students participating in traditional instruction. The impact of affect extends across content areas; for example, research has shown that students’ mathematical and reading performance is related to their self-concept in the respective content areas [15, 24].

Damage to the affective networks has been shown to affect social judgement [2], emotion perception [1, 11], and the ability to exhibit conditioned biological responses to emotionally salient stimuli [5]. For example, individuals with damage to one part of the affective networks, the amygdala, have difficulty recognizing fearful or sad facial expressions [11]. Children with Asperger’s syndrome also have difficulty interpreting emotional facial expressions, as well as emotion in speech sounds [39]. Recent findings also suggest that children who are shy may process emotion differently than those who are not shy [76].

It is clear that emotion and emotional regulation are subject to both biological and environmental influences [80], and individual differences are readily apparent to the observer. Students with many kinds of cognitive disabilities are vulnerable affectively – either as a primary or secondary aspect of their disability. These differences may present as interindividual variation in mood, biological drives, or what is interesting and motivating. These in turn lead to differences in how individuals filter the world, which can influence relative performance in the classroom. Students who have learned to expect failure, students who are anxious in a competitive learning environment, and students who have weak attentional controls do not enter the learning environment with the same opportunities to learn as do other students.

Recognition, strategic, and affective networks work in concert, each contributing an essential kind of knowing that is central to cognition. Take, for example, the simple act of signing a birthday card:

“Through recognition networks, we understand the concept of a birthday and identify the card, the pen, our hands as we write, and our signature. Through strategic networks, we set our goal of signing the card, form a plan for picking up the pen and moving it to produce our signature, monitor our progress, and make small course corrections, such as reducing the size of the letters if we begin to run out of space. Affective networks connect us to our feelings for our friend, motivate us to sign the card, and keep us on task (p. 12, [62]).”

Successful learning requires recognizing patterns in the environment, identifying and implementing successful strategies to act on those patterns, and evaluating patterns and strategies based on their relative importance. Thus, learning impairments may result from weakness in any one of the three broad learning networks. As a simple confirmation, consider the 1998 report of the National Research Council [71] on preventing reading difficulties in young children, which identified three primary obstacles to learning to read:

“The first obstacle, which arises at the outset of reading acquisition, is difficulty understanding and using the alphabetic principle—the idea that written spellings systematically represent spoken words. It is hard to comprehend connected text if word recognition is inaccurate of laborious. The second obstacle is a failure to transfer the comprehension skills of spoken language to reading and to acquire new strategies that may be specifically needed for reading. The third obstacle to reading will magnify the first two: the absence or loss of an initial motivation to read or failure to develop a mature appreciation of the rewards of reading” (pp. 4, 5).

Given the clear role of recognition, strategy, and affect throughout the curriculum, the approach proposed in this paper generalizes beyond this example and suggests that students with deficits in any one of the three related cognitive networks may struggle with learning. Thus, addressing obstacles to student learning requires focused attention to all three networks and related aspects of the curriculum. This belief forms the foundation for UDL, a framework for considering and anticipating individual differences in learning and providing a more flexible and supportive curriculum.

3 Using Universal Design for Learning to address differences in the three learning networks

How can modern information technology support individuals who have difficulties in any of the cognitive networks described in the previous section? CAST believes that the answer is a re-envisioning of curriculum design using the principles of UDL, which are structured according to the three-network framework detailed above. Universal Design for Learning was inspired by the concept of universal design in architecture, where structures are designed to accommodate the widest spectrum of users, and broad accessibility is built in, eliminating the need for costly and inelegant retrofits [16]. The UDL approach is to design curricula that will succeed with the widest possible range of learners, with explicit consideration of students with disabilities.

In order to guide the consideration of learner differences, the UDL framework is structured according to three principles, each one addressing potential individual differences in one of the three learner networks, and prescribing a specific type of curriculum flexibility (Table 1) [46,62]. Thus, UDL curricula anticipate differences in recognition, strategy, and affect, and incorporate alternatives in instructional materials, methods, and assessment, intended to minimize barriers to student learning.
Table 1

Three core principles of Universal Design for Learning

To support recognition learning, provide multiple, flexible methods of presentation

To support strategic learning, provide multiple, flexible methods of expression and apprenticeship

To support affective learning, provide multiple, flexible options for engagement

Key to UDL is leveraging the power of new technologies. In traditional curricula text is the dominant instructional medium. While effective for some students, text is a barrier to access and understanding for many other students, including those with visual deficits, learning disabilities, and certain physical disabilities. Either these students must adapt to the print-driven curriculum or their teachers must try to adapt instructional materials and methods to meet their individual needs, a process that is costly and time consuming. In contrast, digital technologies are innately flexible and amenable to changes in format, medium, and content that can provide greater and more efficient individualization of the curriculum [62].

While digital media do not by definition provide extensively greater learner support, digital materials designed to support UDL offer considerably greater potential to meet the needs of individuals with a wide range of cognitive abilities [57]. The three UDL principles provide a framework for using technology to enrich the curriculum and support student needs by flexibly supporting recognition, strategy, and affect. The following sections describe how this can be accomplished, using examples from CAST’s universally designed digital learning environments. A more detailed description of these environments and the research around them is reported in Rose and Meyer [62] and Rose and Dalton [61].

3.1 Providing flexible means of representation

With printed books, there are major limitations to meeting the needs of students with different recognition capabilities. For one, there are limited modalities for information display—primarily text and images. In addition, content and display are preset and fixed. Every student must work with the same content, format, sequence, and display. This is problematic given that these characteristics are typically selected on the basis of the “average” student [64].

Digital media offer a number of advantages for flexibly supporting students’ recognition capabilities. First, content can be separated from display. This means that the content can be generated once and displayed or transformed in a variety of ways, based on the needs of the particular student. Information in text, for example, can be presented as speech, with or without text highlighting for the student with learning disabilities, and font size can be adjusted for the student with visual acuity limitations. The sequence, layout, and subset of elements can also be customized to the student. Second, there is a wider palette for representation: not only text but also images, animation, video, sound, networks, even virtual reality. This is important given that scholars have argued that media should be selected with consideration of the task and learner [36, 65]. Third, flexible, on-demand supports can be embedded within the text, such as hyperlinks to multimedia vocabulary definitions, a graphic organizer depicting the relationships among the content, or background information [41, 58].

All of these simple alternatives remove what may be barriers to recognition for some students. In addition, they open new pathways for constructing meaning to all students. Research shows that non traditional media, such as computer simulations and graphic organizers, can be highly effective for learners [73, 74], and multimedia materials provide students with a valuable opportunity to make connections across symbol systems [53]. There are two primary advantages to having multiple representations. The obvious one is that it improves access to information. In addition, however, it can serve a remedial role: with flexible ways of presenting information, the learning environment can be tailored to the student in such a way as to allow him or her to focus practice and effort on specific target skills and strategies.

CAST has developed a series of prototype digital reading environments that integrate reading supports into novels and other student reading materials. These reading environments provide multiple representations in the form of text-to-speech, hyperlinked multimedia vocabulary definitions, American Sign Language (ASL) translations in video format (Figure 1), and Spanish translations, thus providing customized recognition support for a range of learners, including students with learning disabilities, students who are deaf or hard of hearing, and English Language Learners [19, 20, 60]. In one study, students who used a universally designed digital supported reading environment offering reciprocal teaching instruction made significantly greater pre to post gains on a standardized test of reading achievement than did their peers participating in reciprocal teaching instruction with only print materials [20].
Fig. 1

Screenshot from a prototype universally designed digital reading environment showing ASL video, an alternative means of content representation (Gall)

Flexible means of representation are also important to assessment. Current assessments offer one representation to every student, thereby often confounding the target skills and knowledge with the ability to decode the assessment medium. For example, mathematics assessments, by virtue of their singular format, confound mathematical ability with the ability to read text. Computer-based assessment, while posing challenges [6], presents the opportunity to design tests more considerate of students’ diverse recognition capabilities and more targeted to the skills and knowledge of interest. Not only may such tests reduce barriers to recognition, they may also make assessment more relevant and insightful [7]. To take a very simple example, a computer-based mathematics test offering customizable font size and text-to-speech could greatly improve test accessibility and accuracy [23, 45]. In a recent study, two equivalent forms of a National Assessment of Educational Progress United States history and civics test were delivered to ten high school students with learning disabilities. Students completed one form administered via traditional paper-and-pencil and another via a computer-based system with optional text-to-speech. Scores for questions with reading passages greater than 100 words in length were significantly higher with the computer-based versus paper-and-pencil version, suggesting that students were better able to demonstrate their knowledge with the more flexible, bimodal test. Multimedia test content and computerized adaptive assessment offer even more promising possibilities [7, 13, 43].

3.2 Providing flexible strategic support

Traditional printed textbooks and materials, and pencil-and-paper tasks, require all students to respond in the same way, when in fact students’ strategic networks are highly variable. Meeting the challenge of students’ extensive strategic differences demands flexible materials that can support students as they build and implement strategic skills.

Here, too, digital materials provide unprecedented opportunity. There are a variety of assistive technologies available to help students with physical disabilities, enabling them to respond using an adaptive switch, for example, rather than paper and pencil. Digital technology offers the possibility for responding in many different media: constructing a diagram, producing a photographic collage or video, or audio recording. The use of diverse media for responding supports intermediality, learning to construct meaning and communicate across symbol systems [53].

Digital technology also provides opportunity to support students in using cognitive strategies as they work. Development of computer-based training in cognitive strategies is an exciting new area of research [42, 44]. For example, McNamara et al. [44] have developed a Web-based program that provides reading strategy training to college students. Previous work by the authors of this paper has focused on embedding support for cognitive strategies into learning materials and has been influenced by research into cognitive apprenticeships [14], the cognitive neuroscience of motor function [27, 30, 34], and reading comprehension instruction. Reciprocal teaching is a highly validated approach to reading comprehension instruction that is based on an apprenticeship model [54, 63]. Students learn to construct meaning from texts through a process of teacher modeling, teacher-student and student-student dialogue, and teacher scaffolding that decreases over time. CAST’s prototype digital reading environments, in addition to providing recognition support, integrate reciprocal teaching instruction and flexible strategic support. As students read through the text, they periodically encounter prompts that ask them to “stop and think” and apply a reading comprehension strategy such as question, predict, summarize, visualize, or clarify (Fig. 2). Students can consult a help page for information about these strategies at any time. In addition, they can access the on-demand support of a virtual tutor, an animated agent that will provide a hint, model, or think aloud. Students may choose from multiple response options such as typing, audio recording, or signing responses using a Web cam [18, 19]. Once students submit a response, they receive immediate feedback from the program. Also, students can choose from multiple levels of challenge and support, differing with respect to the answer format and the availability of hints, models, and thinkalouds [20]. The key is that these supports are individualized, offering targeted support based on the student’s needs. Thus, students can develop expertise in a supported environment, with a means to gradually reduce the level of support as they develop skills. CAST has adapted this basic paradigm to offer customized support to students with different language backgrounds and cognitive abilities, including students who are deaf or hard of hearing, English Language Learners, struggling readers, and students with cognitive disabilities [1820, 60]. Studies have shown a significant improvement in reading achievement for students using one of these prototype digital reading environments over an extended intervention (4.5 weeks–6 months) [19, 20].
Fig. 2

Screenshot from a prototype universally designed reading environment showing the variety of strategic and recognition supports

More recently, CAST has developed a universally designed digital environment for writing science reports, which incorporates strategic supports such as research-validated writing strategies, models, and scaffolded prompts [49]. Pilot data suggest a very positive impact on students’ writing quality.

Equally important are studies investigating means to incorporate alternative response modes and strategic support in assessment. It is important to ensure that each student has not only appropriate opportunity to practice skills, but at the point of assessment has adequate opportunity to demonstrate skills, without being unfairly penalized for other weaknesses.

3.3 Providing flexible means of engagement

Customized support is also essential to engaging students. In typical educational settings, where materials and methods are one-size-fits-all, many students end up in one of two groups: those disengaged because the work is too easy, or those disengaged because the work is too hard. While students display considerable variation (both intra- and interindividual) in the ways in which they can be motivated and engaged as learners, printed materials and pencil-and-paper response modes offer limited opportunity to meet their needs. Although it is possible to offer students a choice of texts or content, this can be costly and impractical.

In contrast, digital learning environments can be more easily and effectively differentiated and individualized in how they meet the varying needs of students. Digital learning environments offer choice: choice in appearance, level of support, type of support, method of response, content, speed, and distractors. For example, CAST’s digital reading environments offer a choice of text views, a choice of animated agents, and the option of accessing the agents or not [1821]. They also incorporate multiple response options such as writing, audio recording, or signing responses using a Web cam. Moreover, the leveling of the program helps to ensure that each student is challenged at a level that they will find personally motivating.

Such choices, especially when they are in the hands of the individual, are key to whether students feel that the environment “is right” for them. Similarly, in the realm of assessment, computer-based testing environments may provide options and supports that can help students to overcome test anxiety and lead to more accurate test results. Future technologies may take this customization one step further by providing computer-based materials that adjust to naturally occurring changes in student motivation over time [72].

4 The promise of Universal Design for Learning

Decades after computers found their way into schools, their much anticipated role in expanding learning opportunities for all students, including those with disabilities, remains elusive. The reason is simple: they continue to be used to support old, one-size-fits-all methods of instruction and assessment that do not support what we know about learning, namely that it is as individual as DNA or fingerprints [47].

The desire to meet the unique needs of students relegated to “the margins” of everyday classrooms—those for whom traditional settings and curricula are inadequate, such as students with disabilities and/or exceptional talents—has driven our investigations into new learning environments [47]. The concept of designing a curriculum to support every student’s needs rather than the “average” student’s needs is a new one. However, it is believed that attending to individual student needs is fundamental to improving the effectiveness of curricula (a perspective similar to the one Gregor and Dickinson, also in this special issue, takes with respect to the design of information systems). Research in Universal Design for Learning suggests that every learner can benefit from the greatest range of flexible approaches that a curriculum can provide. Indeed, at a time of increasing emphasis on the need for all students, regardless of ability, to meet high academic standards, universally designed learning goals, methods, materials, and assessments are essential to helping diverse learners reach a common destination [62].

Using the three broad learning networks as a framework and attending to three simple things—the alternative ways in which information can be presented, the alternative ways in which expression can be taught and scaffolded, and the alternative ways in which students can be engaged in learning—it is possible to design learning environments that are pedagogically effective for both regular and special education students, commercially successful, and innovative enough that they are being widely copied.

Research and policy changes support a move toward more flexible, customizable curricula. The recent writing of a National Instructional Materials Accessibility Standard (NIMAS) [12], which has been endorsed by the US Department of Education, as well as in federal special education law, represents a huge step forward in the routine production of accessible digital versions of curriculum materials. Under NIMAS, publishers will routinely produce a regular printed textbook and an XML-based digital source file. It will then be possible for conversion houses, states, districts, and others to turn these digital versions into appropriate formats for students with disabilities and distribute them in a timely way at the critical point of instruction—in the classroom. In the research realm, innovative means to customize curriculum are being developed and evaluated. For example, the use of automated, adaptive systems to customize content to the individual learner is being investigated [10, 78]. Thus, it possible to envision a not-so-distant day when all students have the opportunity to learn in environments that are responsive to their unique needs, preferences, and styles.




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