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by David H. Rose, Anne Meyer, Nicole Strangman and Gabrielle Rappolt
Table of Contents
In this chapter, you will learn how familiarity with brain research can help you understand your students better.
The brain, the most powerful learning tool that a student brings to the classroom, is highly complex and something of a mystery. In recent years, scientists have made unprecedented progress toward unlocking the secrets of how our brains learn, driven in part by remarkable new technologies and techniques for imaging the brain'sactivity.
Neuroscience for Kids, a Web site for students and teachers, includes a concise and informative overview of brain imaging techniques.http://faculty.washington.edu/chudler/neurok.html
Scientists are using this new knowledge to address many questions that concern educators, students, parents, and policymakers:
Even when scientists explore learning directly, the educational significance of scientific research is rarely self-evident. In this chapter, we forge connections between neuroscience and the classroom by interpreting brain research from an educational perspective. We show how this research can illuminate and refine our understanding of differences between learners. These insights help us understand our students better and tailor learning experiences in ways that will maximize their opportunities to progress.
Most pictures of the brain show only the tissue visible on the surface—the deeply fissured and folded gray matter called the cortex. The complex organization and unique structure of this tissue gives it a central role in learning. Cortical tissue features astonishing connectivity: The approximately 1 trillion neurons in the cortex are linked by approximately 10 trillion connections, creating an incredibly dense network. Similar to a telephone or computer network, these multifaceted connections help individual parts of the brain communicate flexibly and along multiple pathways, regardless of whether they are close to each other or on opposite sides of the brain.
Within this large network, many smaller networks are specialized for performing particular kinds of processing and managing particular learning tasks. Three primary networks, structurally and functionally distinguishable but closely connected and functioning together, are equally essential to learning. We identify these networks by terms that reflect their functions: therecognition, strategic, and affective networks. The activities of these networks parallel the three prerequisites for learning described by the Russian psychologist Lev Vygotsky (1962): recognition of the information to be learned; application of strategies to process that information; and engagement with the learning task. In brief:
These three neural networks work together to coordinate even simple acts like signing a birthday card for a friend. 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.
These three networks share two common characteristics that have particular significance for learning: (1) Processing is distributed laterally across many brain regions operating in parallel (enabling, for example, simultaneous processing of color and shape); and (2) Processing is hierarchical,enabling simultaneous processing of sensory information entering low in the hierarchy(“bottom-up”) and contextual influences entering high in the hierarchy(“top-down”).
Although all brains share these general characteristics, individual brains differ substantially—a point that bears critical implications for teaching. Understanding the specialized functions of the recognition, strategic, and affective networks can help us appreciate the unique strengths and weaknesses of individual students. Let's take a closer look at each of the networks.
Located in the back of the brain, as shown in Figure 2.1, recognition networks enable us to identify and interpret patterns of sound, light, taste, smell, and touch. These networks enable us to recognize voices, faces, letters, and words, as well as more complex patterns, such as an author's style and nuance, and abstract concepts like justice.
Not available for electronic dissemination.
Adapted with permission from Principles of Neural Scienceby Eric R. Kandel et al. (Eds.). © 2000 by The McGraw-Hill Companies.
This schematic drawing of the lateral surface of the human brain shows the regions primarily responsible for recognition.
Take a look at the picture in Figure 2.2. Instantly, you probably recognize many of the objects depicted—people, furniture, doorways. If asked, you could identifyparts of these objects, such as eyes, table legs, or doorknobs. Some of these objects are partially hidden; others are at odd angles or clustered in poor light, yet your recognition networks are so powerful that you have no difficulty determining what these objects are.
Reprinted by permission of the publisher from Eye Movements and Visionby Alfred L. Yarbus. © 1967 by Plenum Publishers, Inc.
We can do more than just recognize many objects at essentially the same time; we can also recognize the same object in a number of different ways. For example, even out of context you can recognize the shape in Figure 2.3 as a chair. This is remarkable, given that this particular representation does not show the features usually associated with chairs, such as four legs and a seat. And chances are you can recognize it not only as a chair, but also as the chair from “The Unexpected Visitor” picture in Figure 2.2. Your recognition networks enable you to distinguish this specific chair from all the other chairs you have ever identified. Without articulating it, you also recognize the chair as a member of the category “furniture.”
Recognition, which seems simple, is actually an incredibly complex feat. As scientists identify the salient characteristics of recognition networks, we understand more clearly how recognition actually works.
How does the brain accomplish the complex work of recognition in just a fraction of a second? Positron Emission Topography (PET) scan images give us some important clues. In Figure 2.4,we see a PET scan of the brain in the act of recognizing one set of words under two different sensory conditions. The same words have been presented orally to the brain pictured on the left and visually to the brain pictured on the right.
Adapted with permission from Nature (see Petersen, Fox, Posner, Mintun, & Raichle, 1988).© 1988 by MacMillan Magazines, Limited.
These contrasting images illustrate the fact that visual stimuli are recognized in one part of the cortex and auditory stimuli in another (Kandel, Schwartz, & Jessell, 1991).In other words, the general task of recognition is distributed across different areas, each specialized to handle a different component of recognition. (From this point on, we will refer to these specialized areas of the brain as “modules.”) Distributed processing is not limited to differences between distinct sensory modalities, such as vision and hearing. The subprocesses within each sense modality are also distributed. For example, visual recognition is distributed across at least 30 different modules, so that elements like vertical lines, diagonal lines, color, and motion are all processed in physically discrete areas of the brain (Gazzaniga, 1995; Mountcastle, 1998; Roland & Zilles, 1998; Zeki, 1999).
An analogy may clarify how distributed modular specialization works. Think of the brain as a kitchen full of food processors. Imagine that all the processors are the same basic make and model, but each comes with a specialized attachment for blending dough, shredding cabbage, or performing another specialized task. Although each processor performs the same general function, their output is as different as piecrust is from coleslaw. By keeping a kitchen full of processors, a chef needn't switch the blade for each new task or worry about getting cabbage in the piecrust! In the brain, distributed processing provides a similar advantage. All the modules have the same basic structure, but the tissue in each region is fine-tuned to process one type of input extremely efficiently. This works more effectively than would “all-purpose” brain tissue that would have to adapt to each new task.
Recognition is quick and efficient because all the modules are working in parallel. Through parallel processing—the simultaneous performance of multiple tasks by interconnected modules—our brains process and pool information that is distributed throughout our recognition networks, all in less than half a second. The brain's modules are interconnected through multiple pathways, enabling visual, auditory, olfactory, and tactile recognition to influence one another. This accounts for the interesting observation that an auditory or tactile stimulus can bias our interpretation of a visual pattern (see Martino & Marks, 2000).
The distributed nature of recognition has profound implications for individual differences. If recognition were the product of one homogeneous area of brain tissue, recognition abilities would vary from person to person in only a limited number of ways. Differences in recognition would have global effects. For example, if an entire modality, like vision, were the product of one sub network, any difference would affect vision as a whole. But because recognition is actually a coordinated act of many different modules, each very small component of recognition has the potential to exhibit person-to-person differences. The differences between us may affect one module, and therefore one aspect of recognition, or many modules, and therefore many aspects of recognition.
We have learned that individual aspects of patterns, such as color, shape, orientation, and motion, are processed in parallel by separate pathways within the recognition networks. Each of these pathways is organized into a hierarchical continuum, containing some brain regions that are highly complex, some that are comparatively simple, and others that are somewhere in between.
Let's continue to use vision as an example. As visual sensory information we take in though our eyes departs from our retinas, it travels up through an increasingly complex hierarchical network, eventually reaching the visual cortex. This is called “bottom-up” processing, and it is part of the way we extract visual details from an image such as “The Unexpected Visitor.” This type of processing is responsible for identifications based on particular sensory features, meaning the quality of sensory input is very important. Poor lighting, low-quality photocopies, or mumbled speech can all impede bottom-up processing and make everyday recognition tasks difficult.
Just as important as the information flowing up the hierarchy of recognition structures is the information that travels down the hierarchy. To facilitate the recognition of details, our brains make use of higher-order information, such as background knowledge, context, and the overall pattern. When examining “The Unexpected Visitor,” you applied knowledge about the kind of room the scene is set in (gleaned from bottom-up recognition of the room's more visible objects) to help you identify other objects that are difficult to recognize based on visual detail alone.
Bottom-up and top-down recognition processing both play critical roles in learning. Consider learning to read: The common assumption used to be that reading was mainly a bottom-up activity, wherein letters are recognized by their features, synthesized into words and sounds, and then analyzed for meaning. But research has shown that it is easier and faster to recognize letters in the context of words than it is to recognize them in isolation. This phenomenon, termed the word superiority effect(Adams, 1994), occurs because familiarity with the larger pattern (the word) constrains the bottom-up process of individual letter recognition and leads a reader to rely more on his or her expectation of what letters will come next and less on the actual visual features of those letters. That's why proofreading is so difficult. We miss errors because our word expectations are so powerful that they influence how we see the individual letters. Use of context and meaning to predict what is coming next is another familiar example of the top-down processing used in reading.
This online activity demonstrates the “word superiority effect”—how our brains use context to help recognize visual patterns:http://coglab.wadsworth.com/experiments/WordSuperiority/index.html
Because smoothly functioning recognition networks take advantage of both top-down and bottom-up processes, teaching to both processes rather than focusing exclusively on one or the other is the wisest choice. A positive example is the recent truce in the “phonics wars.” Most programs have now adopted a form of reading instruction that incorporates both the top-down whole language method and bottom-up phonics. This balanced approach is consistent with the way the learning brain works.
Although human brains all share the same basic recognition architecture and recognize things in roughly the same way, our recognition networks come in many shapes, sizes, and patterns. In anatomy, connectivity, physiology, and chemistry, each of us has a brain that is slightly different from everyone else's.
PET scan images, such as those shown in Figure 2.4, usually represent averages across individuals. These averages highlight commonalities between individuals but obscure the fact that each individual brain actually reveals a unique pattern of activity. For example, most people, when they recognize an object visually, show increased activity in the back part of their brains; however, the exact magnitude, location, and distribution of that increased activity varies quite a bit. The active area of the cortex may be larger or smaller, more localized to the right or left hemisphere, or more widely or closely distributed. These variations undoubtedly manifest in the way people recognize things in the world—their recognition strengths, weaknesses, and preferences.
Individual differences that affect learning are apparent in brain images such as those available athttp://www.cast.org/TeachingEveryStudent/brain
The distributed nature of processing in the brain leads to myriad subtle differences in recognition between individual learners. Unlike the global notion of ability suggested, for example, in a Stanford-Binet IQ score (see Thorndike, Hagen, & Sattler, 1986),learners' abilities are multifaceted. When two students perform the same academic task, the patterns of activity in their brains are as unique as their fingerprints. The uniqueness may not be visible in the overall level of brain activity, but rather lies in the pattern of activation: how the activity is distributed across different brain regions. For this reason, no one measure of brain activity—and no one learning score or variable—differentiates or describes individual learners in any meaningful way.
Traditional views of disability, also based on an implied assumption of unitary brain functioning, suggest that a person either does or does not belong to the category “disabled.” New understanding about the distributed nature of neural processing shows that abilities in many domains fall along a very large number of continua. Further, the importance of a particular strength or weakness depends upon what is being asked of the learner. This is why, for example, a youngster with perfect pitch who has difficulty recognizing letters is seen as disabled, but a child who is tone deaf but can read words easily is not.
Specific differences in the recognition networks of individual learners range from the subtle to the profound. The recognition cortex in Albert Einstein's brain was disproportionately allocated to spatial cognition (Harvey, Kigar, & Witelson, 1999).He had difficulty recognizing the letter patterns and sound-to-symbol connections required for reading, but he was a genius at visualizing the deepest fundamentals of physics. Awareness of these differences across his recognition networks could have helped Einstein's teachers shape instruction that would both capitalize on his spatial genius and support his areas of weakness.
Although all three brain networks—recognition, strategic, and affective—are involved in learning any task, curricular teaching goals and methods tend to cluster into broad types that coincide with each network. For example, subject-verb agreement, the causes of The War of 1812, the structure of the atom, and the nuances of Dostoyevsky's style are all patterns to be recognized; these things are the “what” of learning.
Traditionally, when teachers teach these kinds of patterns, they tend to present them in one way for the entire class. But the overt and subtle differences in how students best recognize patterns suggest that more varied means of presentation can reach more students. Being aware of the distributed nature of recognition processes and the combination of top-down and bottom-up processing can help teachers understand individual learners better and shape instruction and assessment accordingly.
In the examples that follow, we illustrate classroom applications of these concepts and introduce you to some of the students and teachers we will use as examples throughout the book.
The power, flexibility, and speed of recognition networks are critical to how humans experience the world and are thus worthy of the attention of teachers and curriculum designers. As teachers, understanding the pattern of strengths and weaknesses within a learner's recognition networks can help us individualize the kind of challenge and support we provide, thus maximizing every student's opportunity to learn.
Recognition represents one way of “knowing” the world: building factual knowledge and relating new information we encounter to what we already know. Traditional curricula focus primarily on recognition, at times overlooking the other kinds of “knowing” served by strategic and affective networks. And yet these networks are no less important to effective learning.
Meet Mr. Costa . . . and Sophia
In a suburban middle school outside of Philadelphia, Mr. Costa's 7th grade English students show widely varying talents and difficulties. He is working with the technology specialist to develop flexible approaches to accommodate these learner differences. One student in particular he finds both challenging and inspiring.
Sophia, a soprano in the school's top singing group, is legally blind but has significant residual vision. She cannot sight-read music, but she has such a good ear that she is able to learn her parts quickly. The rest of the group depends on her as one of its most talented and consistent members.
Although Sophia reads Braille well, she prefers to use a desktop magnifier to read printed text. This technology worked well for her in the self-contained classrooms of elementary school, but it is proving awkward in middle school, where she must move the magnifier from class to class. Furthermore, the magnifier does not allow Sophia to skim and scan text; as a result, she is having a tough time staying on top of the increasing volume of reading assignments.
Mr. Costa understands that Sophia's visual acuity is only one small part of her recognition capacity. Equally important is her good ear for music; this ability to differentiate patterns in sound also enables her to understand and use language effectively.
In the middle school environment, barriers for Sophia include
Instead of regarding these barriers as Sophia's problem, Mr. Costa and Sophia's other teachers seek new ways to present content that will make learning more accessible to her, and in the process, offer new options for others in the class. Their approach relies on making text and images available in digital form and via a network, which will allow supports such as text-to-speech translation, voice recognition, and on-screen text and image enlargement to be readily available in classrooms throughout the school.
Meet Ms. Sablan . . . and Paula
Ms. Sablan, an experienced 3rd grade teacher, has a particularly diverse class this year, increasing the challenge she feels to help all her students develop strong literacy skills before they move on to 4th grade.
Paula is a particular concern. Despite excellent single-word decoding and spelling, Paula's reading comprehension is poor. She has difficulty grasping meaning from connected text, and her limited fluency suggests that she has trouble using context to predict words and ideas as she reads. Paula also tends to miss many of the subtle cues carried by tone of voice and other vocal nuances; she interprets spoken language very literally and misses the intended humor in the things her classmates say. As a result, many of Paula's peers see her as rather odd. Fortunately, Paula is able to connect with some of her classmates through a shared love of bike riding, and she has found opportunities to join in with others by participating in the school's weekend cycling club.
Considering Paula's recognition strengths and weaknesses, we can see she is highly skilled at bottom-up processing—synthesizing parts, such as the letters and sounds that make up words. But she has trouble with top-down processing—connecting what she is learning to other knowledge and understanding content and context of both written text and spoken language.
Ms. Sablan knows that efficient recognition involves both top-down and bottom-up processing, and she wants to help Paula build her ability to use context and prior knowledge when reading. She decides to reduce Paula's focus on word decoding and instead help Paula to develop explicit strategies for understanding the content of what she reads.
It is through strategic networks that we plan, execute, and monitor our internally generated mental and motor patterns—actions and skills as diverse as sweeping the floor, deciding a chess move, or choosing a college. During some activities, such as playing sports, orchestrating an April Fool's joke, or composing an essay, we maybe conscious of applying strategy. What most of us do not realize is that conscious or not, strategy is involved in essentially everything we do.
The strategic components of everyday tasks serve to illustrate the centrality of strategy for cognition and learning. Take another look at “The Unexpected Visitor” (Figure 2.5). Try to identify the type of roomin which the scene is set.
Most likely you glanced at the image and had no trouble determining that it shows a living room or a parlor. Without being aware of it, you relied on your strategic networks to figure this out. You identified the goal of the task, came up with a plan to achieve it, executed that plan, and evaluated its outcome, all the while avoiding distractions that might carry you off track. This underlying strategy is evident in Figure 2.6,which shows eye movements of someone examining “The Unexpected Visitor.”
Notice that Figure 2.6 shows three eye movement maps. Each reflects the same individual looking at the same image, yet the patterns of movement are different. Why? The answer isdifferent goals. First, the viewer was told to look at the image, but was given no specific instructions about what to look for (map 1, at the top of the figure).Second, he was instructed to identify the ages of the people in the picture (map 2, at the lower left). Third, he was asked to determine what the people in the picture were doing before the visitor arrived so unexpectedly (map 3, at the lower right).Each instruction required a different viewing strategy, and each new strategy resulted in a different pattern of eye movement.
As this example shows, even a simple action like searching a picture involves a multistep strategic process:
The neural networks responsible for generating patterns of mental and motor action occupy their own unique territory, located primarily in the part of the brain called the frontal lobes (see Figure 2.7). Research into the effects of selective damage to the frontal lobes has revealed that like recognition, the ability to think and act strategically is distributed across specialized modules
Adapted with permission from Principles of Neural Science, by Eric R. Kandel et al. (Eds.). © 2000 by The McGraw-Hill Companies.
This schematic drawing of the lateral surface of the human brain shows the regions primarily responsible for strategy.
Neurological and brain imaging studies tell us that within the frontal lobes, the prefrontal cortex oversees complex strategic capacities and is critical for identifying goals, selecting appropriate plans, and self-monitoring. If we were to show “The Unexpected Visitor” to a person with damage to this area of the brain, a map of his eye movements might not reveal the distinctive tracing patterns shown in Figure 2.6.Instead, the map might show random movements, indicating a focus on seemingly unrelated details. Even if this viewer's eye movements did suggest some kind of plan, he would not be able to alter this plan in response to unexpected variables, such as a change of instructions. For example, a directive to figure out the visitor's age and another to speculate on the prior activities of the woman in the foreground might produce no difference in pattern.
The pattern of activity distributed across the modules of the frontal lobes shapes how we plan and execute actions. These modules function in parallel, enabling us to perform highly complex actions with ease. Consider, for example, what's involved in playing the organ. If it were not for parallel processing within strategic networks, organists would never be able to simultaneously coordinate not one but several different keyboards and numerous pedals and switches!
School yourself in Brain Injury 101 at The Brain Injury Association of Washington's site:http://www.biawa.org
Strategic modules, although operating in parallel, are also interdependent. The connections between modules enable modules doing different things to influence one another. In fact, elements of our plans-of-action that come later in a series can influence those that come before (Fowler, 1981). This is why, for example, you will pick up a bowling ball one way if you intend to bowl the ball yourself and another way if you intend to hand it to a friend. Similarly, when we speak, separate but connected modules process syllables and words simultaneously, so that the pronunciation of any syllable or word is highly influenced by those that follow it. That's why prerecorded “voices” like the automated flight-arrival information broadcast in airports sound so odd to us: Both the words and sentences are spliced together from prerecorded individual sounds, and each sound is articulated the same way regardless of the linguistic context. Cursive writing provides yet another example: When we write, we form individual letters differently depending on which letters precede and follow them.
As teachers, being mindful of the parallel nature of strategic processing can help us better understand individual learners and design optimal supports for each. For example, school requires students to learn discrete strategic skills (such as listening, extracting relevant information, and writing down the information) and to execute these skills simultaneously (as when taking notes in class). From what we know about strategic networks, we can appreciate that these patterns of actions are not “built” by putting together a step-by-step sequence. Different layers of an action are added on at the same time and mutually influence one another. For this reason, skill instruction is often more effective when the various components of the process are learned simultaneously rather than one at a time (Gopher, 1996). Thus, a tennis instructor may model the whole serve and encourage the learner to try it out, only analyzing individual steps (ball toss, backswing, step forward, swing, and follow-through) when particular aspects must be corrected. Likewise, each subcomponent of a task like writing an essay makes the most sense to our students if it is taught in the context of the whole task.
Like recognition modules, strategic modules form part of a two-way hierarchical pathway. Neural signals travel from higher-order regions in the cortex down to the spinal cord, where the neurons that innervate muscles are found, enabling internally driven strategies(“I will pick up this pencil”) to influence how we act on—and in—the world (picking up the pencil). Modules specialized to carry out different steps within a skills sequence reside at different levels along that path.
The top of the hierarchy orders the steps, “commands” our muscles to act, and keeps track of whether or not the goal is reached, modifying the plan as needed. As actions are practiced and perfected, they require decreasing amounts of monitoring from the top. Anyone who knows how to touch-type will remember that as a beginner, you had to rely heavily on conscious monitoring capacities to check finger placement and letter sequence. With practice, though, the pattern of movements necessary to hit the right keys became automated, requiring little if any conscious monitoring.
The top-down flow of information in strategic networks makes intuitive sense. We can understand that top-down processing enables us to carry out a plan formed high up in the neural hierarchy. When we as teachers express goals clearly, give verbal instructions, or offer models for students to work from, we are supporting students' top-down processing by stressing the importance of strategic skills and encouraging students to be guided by clear goals and plans.
Within strategic networks, information travels not only down from the cortex to the muscles, but also up from the muscles to the cortex. One source of bottom-up strategic pathways is the cerebellum, the cauliflower-shaped structure located at the back and base of the brain, overlaying the brainstem (see Figure 2.8). Pathways from the cerebellum to strategic modules in the cortex serve an important role in learning skills and strategies. The cerebellum takes sensory feedback, which clues it in to how actions are being executed, and compares it to other signals that convey the actions we intended. Then, through these bottom-up projections, the cerebellum informs our strategic networks about whether our actions are “on target.” Although this is best described for motor patterns, bottom-up processing operates in a similar way to refine mental patterns. It works much like a thermostat, but regulating skills and strategies rather than temperature.
Illustration by Lydia Kibiuk. Reprinted by permission of the Society for Neuroscience.
Thus, to acquire skills, students need support for both top-down and bottom-up strategic processing. They learn best when they have not only good instruction and good models but also plenty of opportunities to practice and to receive ongoing, relevant feedback. The kinds of models and supports most suitable for individual learners depend on the students' particular strategic strengths and weaknesses.
The distributed organization of strategic networks introduces a level of complexity that would not exist in a homogeneous network in which all tissue looks and acts the same and a deficit in one part of the network has the same effect as a deficit in another. In reality, deficits and strengths can affect very specific aspects of strategic skills. For example, a student may be skilled at making a plan but have difficulty self-monitoring when executing the plan. Another student might be an expert at finding information, but have difficulty organizing and keeping track of that information. Recent brain imaging experiments provide a novel illustration of individual differences in strategy. When two people are confronted with the same problem but solve it using different cognitive strategies, the brain images reveal two very different patterns of activity (Burbaud et al., 2000).
Differences in strategic networks manifest themselves in various ways in the classroom. For example, learners differ dramatically in their abilities to acquire and automate pattern-based routines such as forming letters, typing, spelling, and multiplying. Learners also differ in their ability to enact higher-level strategies such as planning, organizing, monitoring progress, devising alternative approaches, and seeking help when they need it. For example, students with executive-function disorders (disorders affecting reasoning, logic, hypothesis-testing, and similar high-level abilities) can have difficulty at all levels of reading. When decoding words, they may make impulsive guesses rather than apply their phonics knowledge or search for context cues. When reading a paragraph, they may fail to use organizing strategies to help them focus on the key points.
Variation within students' strategic networks also influences their abilities to use different kinds of learning tools. Students with motor difficulties may be marginally able or unable to use a keyboard or a mouse, to scan a line of text, or to turn the pages of a book. Speech difficulties may impede oral presentations, and students with language and learning difficulties may find that they expend so much energy attending to the mechanics of producing written text that they have difficulty communicating effectively in that medium. These are just a few of the obvious examples; often the strengths and weaknesses in strategic networks are more subtle.
Variations in the degree of bottom-up and top-down processing influence how students acquire skills. We have all seen students with the uncanny ability to watch someone else do something and then do it almost perfectly the first time; this is an indication of strong top-down strategic processing. On the other hand, we also know students who seem to learn best by doing; these are the students who achieve expertise only after lots of practice and feedback—an indication of strong bottom-up strategic processing. Awareness of these subtle differences can help teachers design optimal strategic teaching for different kinds of learners.
Understanding the way strategic networks function and the differences in students' strategic networks is a useful guide when teaching skills and strategies such as predicting, summarizing, and determining the steps needed to solve a problem or write an essay. Differences in strategic skills manifest as preferences, proclivities, or significant strengths and weaknesses. The following set of classroom examples illustrates some of these differences.
These classroom examples illustrate the multiple and varied influences of strategic networks on students' performance. Understanding the many facets of strategic learning, the approaches most suited to supporting strategic networks, and the patterns of strength and weakness in different students can help shape instruction to support every learner's unique needs.
Meet Mr. Mitchell . . . and Jamal
Mr. Mitchell teaches 5th grade in an urban Chicago school. This year's class is the largest he's ever taught, and it includes several students with disabilities. One of his students is Jamal, a young man with cerebral palsy. Jamal is an enthusiastic student and well on his way to becoming an expert on military tanks and submarines. From his home computer, he has found and collected hundreds of photos, stories, and Web sites devoted to this weaponry. Jamal speaks slowly but intelligibly. He uses a wheelchair for mobility and a variety of assistive technologies to help him operate his computer. Although Jamal cannot write or draw with pen and paper, he has learned to do these things with the support of a computer equipped with an expanded keyboard and a voice recognition system.
Jamal is integrated into Mr. Mitchell's classroom for all academic subjects. Science and social studies particularly engage him, and he uses his strong strategic skills (such as his ability to seek, locate, and save information) to good effect in these classes. But Jamal's motor difficulties affect the pace of his work. He is keeping up, but barely—challenged by the large amounts of required reading and writing. Despite his interest and abilities, Jamal must invest tremendous effort to avoid falling behind, and at times, he becomes discouraged.
Jamal's story to date is one of strategic success, but he works slowly and the academic demands he faces will only increase as he progresses from grade-to-grade. Realizing that Jamal's motor difficulties are a potential threat to success, Mr. Mitchell plans to scaffold Jamal's areas of difficulty and draw upon his particular strategic strengths. Among other things, Mr. Mitchell will make sure assigned text is available in digital form so that Jamal can navigate it on the computer with keyboard or voice-activated commands. He will also make sure Jamal has access to computer-based drawing and composition tools rather than just pencil and paper. Mr. Mitchell hopes these methods will help Jamal stay engaged and enable him to develop new ways to manage his increasing workload.
Meet Ms. Chen . . . and Charlie
Ms. Chen has taught 6th grade in rural Iowa for three years. One of her major goals for this year is to find a way to adapt her techniques and materials so that she can reach all students in the class, who range from “highly focused” to “highly distractible.”
Ms. Chen describes Charlie as a constant source of classroom energy. He dives headlong into activities, jumps out of his seat to answer questions, and constantly seeks new things to do and join. He finishes few of the things he starts. When boredom descends or something new comes up, Charlie quickly abandons his task, regardless of whether it is complete. This is true not only for extended projects, such as a science fair experiment or a book report, but also for short-term tasks like looking up a word in the dictionary. Unless Ms. Chen or one of Charlie's parents structures him closely, he rarely completes his schoolwork. He also forgets his homework and textbook nearly every day, and despite his enthusiasm, he is rarely ready to begin an activity with the rest of the class.
At first glance, it seems that Charlie's problem is distractibility. However, Ms. Chen has found that if she minimizes external distractions, Charlie will create his own. Further, when engaged in an activity that interests him, like a Nintendo game or a certain school project, he can focus for long periods of time. Ms. Chen realizes that she needs to help Charlie develop strategic skills, particularly the ability to plan, self-monitor, and complete tasks.
Learning requires interaction with the external world-with varied materials, tools, people, and contexts. But different students experience the same situations in very different ways. A well-known poem by e. e. cummings illustrates this idea:
maggie and milly and molly and maywent down to the beach (to play one day)and maggie discovered a shell that sangso sweetly she couldn't remember her troubles, andmilly befriended a stranded starwhose rays five languid fingers were;and molly was chased by a horrible thingwhich raced sideways while blowing bubbles: andmay came home with a smooth round stoneas small as a world and as large as alone.For whatever we lose (like a you or a me)it's always ourselves we find in the sea.*
“maggie and molly and milly and may.” Copyright 1956, 1984, 1991 by the Trustees for the E.E. Cummings Trust. From SELECTED POEMS by E. E. Cummings, Introduction & Commentary Richard S. Kennedy. Used by permission of Liveright Publishing Corporation.
With the closing line “it's always ourselves we find in the sea, ”cummings summarizes a principle long known to poets and now confirmed by neuroscientists: What individuals “see” is determined partly by their own internal state—a melting pot of emotions, needs, and memories. Each girl in the poem experiences the beach in her own distinctive manner.
To illustrate this further, let's return to “The Unexpected Visitor”(see Figure 2.9). Look at the picture again and note what grabs your attention. A variety of factors determines what attracts your eye and how long you inspect the image. There's your emotional state, your familiarity with the picture, your interest (or lack of interest)in the content or form, and your state of energy or fatigue, to name just a few. More generally, we can say that your memory, personality, motivation, mood, interest, and biological state all influence how you interact with the picture.
Of course, these kinds of characteristics and states vary tremendously across viewers. A psychologist might attend to the expressions on the people's faces, while an interior designer might take note of the room's décor. The mother of a toddler might be drawn to the child seated at the table, whereas a musician might first notice the piano. If asked to comment on the state of mind of those in the picture, each viewer would offer a unique perspective.
The power of affective variables to influence what we see and how we interpret the environment is exploited in the Rorschach, a projective test used by psychologists. The inkblot in Figure 2.10 is not from the Rorschach itself, but it presents a similarly ambiguous image.
Image courtesy of Helge Malmgrem, Department of Philosophy, Göteborg University, Sweden. Reprinted with permission from the HTML version of Moving Toward the Other,a poster presented at the “Tucson III” conference, April 27–May 3, 1998.
Clearly, inkblots are not representational images. Still, most people, when asked to describe what they see in an inkblot, find figures, animals, or objects. By collecting many responses, researchers develop norms based on what people without emotional disturbances are likely to find in these patterns. (Because the strong emotions experienced by people with affective disorders tend to significantly influence what they see in the outside world, they tend to register unusual responses on tests like the Rorschach.) By examining a patient's responses across many different stimuli and looking for commonalities and patterns, psychologists can deduce a patient's fears, preoccupations, and desires.
To some extent, the entire world is a Rorschach. At any instant, multiple facets of the environment compete for our attention. These demands require not only that we recognize objects and formulate strategies, but also that we evaluate their significance and importance to ourselves. How we do so largely reflects our own emotions and motivations.
Emotion, like recognition and strategy, belongs to circumscribed networks within the brain. Recognition networks help us to identify objects, such as coffeepots and cars, and strategic networks enable us to act on these objects—reaching to pour, turning to steer. Affective networks attach emotional significance to these objects and actions, influencing in a third way what we see and do.
Like recognition and strategic networks, affective networks are extremely efficient. Murphy and Zajonc (1993) have demonstrated that our brains can evaluate the importance of an object within just a quarter of a second! In spite of their complexity, affective evaluations are performed rapidly and effectively. How is this so?
The connection between thinking and feeling is the subject of this text interview with neuroscientist Joseph Ledoux. Online athttp://www.edge.org/3rd_culture/ledoux/ledoux_p1.html.
Intuitively, emotion seems to be a more complex and elusive phenomenon than recognition or strategy. However, the neural processing of emotion occurs in a very similar way. Affective networks are made up of many specialized modules, located predominantly at the core of the brain and associated with the limbic system (Damasio, 1994). Some of these modules are visible in Figure 2.11. Because affective networks are distributed across many modules, learners exhibit many differences along many continua that influence their motivation to learn and their subsequent and ongoing engagement with learning tasks.
Reprinted with permission from Principles of Neural Science,by Eric R. Kandel et al. (Eds). © 2000 by The McGraw-Hill Company.
This medial view of the brain shows the limbic lobe, site of the affective networks. The limbic lobe includes primitive cortical tissue (stippled area), the frontal lobes, and underlying cortical structures (hippocampus and dentate gyrus, not shown).
Evidence of the distributed nature of affective networks comes from the selective deficits that result from disease or damage in specific locations within the brain. Some patients can express emotion but cannot recognize emotion in other people's faces or voices. Others show the reverse deficit: They can read emotional responses of others but they cannot express emotion themselves. These two types of deficits are linked to brain damage in different, specific locations (DeKosky, Heilman, Bowers, & Valenstein, 1980;Heilman, Scholes, & Watson, 1975). What this research tells us is that we use different parts of our affective networks to recognize emotion and to express emotion. Further, the subprocesses involved in recognizing emotion, like being able to interpret facial expressions and speech for emotional content, are each handled by different areas.
It should come as no surprise that affective networks operate in parallel. They process different kinds of emotional information simultaneously and communicate closely though myriad interconnections to create a whole affective impression. When students watch a teacher during a lecture, they process the expression on her face and the emotion in her voice at the same time. Further, because the brain modules are interconnected, the teacher's facial expressions influence how the students interpret her voice(de Gelder, Bocker, Tuomainen, Hensen, & Vroomen,1999).
As with the other two networks, the modules that make up affective networks are hierarchically organized, and the information travels in both bottom-up and top-down directions. Bottom-up connections in affective networks ensure that we are emotionally responsive to the outside world. Information travels from the sensory organs (such as the eyes and ears) up the hierarchical continuum. When it reaches the apex of the pathway—the limbic cortex—we “feel” emotional reactions.
We respond to emotionally reactive stimuli, such as scary faces, even when we are not consciously aware of them (Murphy & Zajonc, 1993). Before we even identify a sound or shape, our nervous system may initiate physiological responses such as an adrenaline rush, muscular contractions, and increased blood pressure—our bodies' physical manifestations of fear. This purely unconscious emotion reflects a second type of bottom-up processing, in which information travels only partway up the hierarchy, stopping short of the cortex (LeDoux, 1998). This type of processing gives rise to a cruder emotional response, one that may or may not be appropriate to the given situation.
Here's an everyday example that illustrates bottom-up affective processing. Suppose you are walking in unfamiliar woods and detect a sudden movement in your peripheral vision. Almost before you become aware of the disturbance, your eyes leap to focus on the source, and your body prepares for fight or flight. Seconds later, you identify the source of the sound: a harmless robin. Your initial responses to the noise—the physiological changes, the protective hunching of your body, and covering your head with your arms—are part of a crude defensive instinct mediated by rapid bottom-up processing. A full second may pass before your conscious awareness of fear coincides with a more analytical look at the source of the noise, so that you can decide what to do. In this case, you probably chuckle over your reaction and walk on.
Of course, instinctual emotional responses can be counterproductive. Extreme nervousness before a presentation, a recital, or an athletic competition can overwhelm and distract us, diminishing the effectiveness of our performance. Affective top-down processing helps us consciously calm ourselves through a variety of techniques such as breathing, refocusing attention, and visualizing success. Without top-down emotional processing, we would be vulnerable to intense, overblown emotions of all kinds and unable to practice the self-restraint needed to keep ourselves on task. Teachers can take advantage of top-down processing to help alleviate the negative emotion students may have learned to associate with schoolwork.
Because affective networks work in roughly similar ways across many individuals, we can make some fairly solid generalizations about how people respond to particular situations. Upon the death of a loved one we become sad; startled by a sudden loud noise or dangerous animal we become surprised and scared. However, human beings are not emotional clones. When confronted with the same life event, different people exhibit different kinds and intensities of emotion. In study after study, scientists have shown that people can be sorted into “high” and “low” emotional responders based on their patterns of self-reported emotion, changes in facial expression, or autonomic reflexes(Asendorpf, 1987; Carels et al., 1999; Cole, 1996; Dimberg, 1990; Larsen, 1987). Even animals display this variability (Adamec, 1991; Kalin, 1999; Kalin, Shelton, & Davidson, 2000).
It is easy to spot the outcomes of affective variability. The next time you find yourself in a doctor's office waiting room, take note of the other patients. Some will start to show signs of agitation the minute their appointment time has come and gone. Others will simply sit back and sigh; they might become upset only if the wait continues for an extended period. Still others will appear to simply accept the situation; they will remain relaxed and calm no matter how long the delay persists.
We might like to think that our emotional tendencies are acquired traits and therefore entirely controllable. However, brain research has revealed that some affective characteristics are strongly associated with measurable neurological differences. For example, functional brain imaging techniques have revealed that people suffering from clinical depression exhibit a characteristic asymmetry in the brain (Bruder et al., 1997; Tomarken, Davidson, Wheeler,& Doss, 1992). Depressives tend to have abnormally high resting brain activity in right-hemisphere affective networks. People suffering from panic disorder also show increased resting activity on the right side of the brain, but in different affective modules from those who are depressed (Reiman, et al., 1984).
Affective differences exert powerful influences on learners' ability to engage with learning and to progress. In studies of highly successful adult dyslexics, Rosalie Fink(1995, 1998) conclusively demonstrated the very significant positive impact affect can have on learning. The individuals in her study overcame severe deficits in recognition and strategic skills by virtue of their deep engagement with and interest in particular subject matter. Strong positive affect made the critical difference in their learning outcomes.
Conversely, and as more commonly noted, affective problems also interfere with learning in various ways. One of the reasons students with severe affective disorders related to childhood depression or abuse are often vulnerable to reading failure is because strong affective influences can derail the work of recognition and strategic networks (see Gentile, Lamb, & Rivers, 1985; Kinard, 2001). Students preoccupied with emotional concerns may have little attention left over for schoolwork. In addition, students with a history of learning problems often become discouraged about their own abilities and withdraw effort from learning tasks. Still others may learn to associate negative feelings with certain subjects or media.
Understanding affective issues can help teachers support all learners more appropriately. Of the three learning networks, affective networks are perhaps intuitively the most essential for learning, yet they are given the least formal emphasis in the curriculum. All teachers know how important it is to engage students in the learning process, to help them to love learning, to enjoy challenges, to connect with subject matter, and to persist when things get tough. When students withdraw their effort and engagement, it is tempting to consider this a problem outside the core enterprise of teaching. We believe this is a mistake. Attending to affective issues when considering students' needs is an integral component of instruction, and it can increase teaching effectiveness significantly.
Consider these differences: Some students prefer to read in a quiet environment; others are comfortable reading in the middle of noisy activity. Some like the predictability of reading familiar stories multiple times, whereas others find rereading boring. Some students like the structure of being told what books to read and when to read them; others thrive on choice and independence. In addition, of course, there is huge variation in the type of content that interests different learners. All these preferences factor in to why students whose skills and achievement levels appear very similar on a test may react—and perform—very differently to particular assignments.
Although the students we've met so far in this chapter illustrate issues related primarily to recognition or strategic networks, each also raises an important affective angle. Sophia's love of music and poetry, Jamal's fascination with military tanks and subs, Charlie's deep concentration when playing Nintendo, and Paula's pleasure in riding bikes with other children all provide “hooks” for building engagement and learning. Positive emotion helps to motivate students just as negative emotion impedes progress. The teachers and students in the next two classroom examples illustrate both sides of the coin—how affective issues can sometimes be the root of both learning difficulties and learning solutions.
It is evident to Mr. O'Connell and Ms. Abrams that supporting the affective aspects of learning is as important as supporting recognition and strategy. Of course, Miguel's and Kamla's difficulties are not purely affective, but rather result from interactions between all three learning networks. Attention to all three networks is critical for understanding individual needs and strengths and for determining individually appropriate teaching methods and materials.
Meet Mr. O'Connell . . . and Miguel
Mr. O'Connell is a 4th grade teacher in a San Diego suburb. He is juggling a classroom full of diverse students facing all kinds of learning challenges, but he's particularly concerned about Miguel, for whom emotional factors have become a big issue.
Miguel has struggled with reading and math basics throughout his schooling, but with support from his family and adjustments to his assignments, he has progressed well. But recently Miguel's world has been thrown into turmoil by his parents' divorce and a grandparent's illness. Now his attention in class has started to wander, and his performance is on the decline. Mr. O'Connell recognizes that Miguel's academic problems are probably related to the boy's confusion and anxiety caused by events at home.
There is a bit of encouraging news: The art teacher reports that Miguel has started to produce detailed and skillful art projects and seems, in that subject, to be deeply engaged. Bearing this in mind, Mr. O'Connell plans to explore ways of bringing art into other subject areas to capitalize on the one area where Miguel shows interest and enthusiasm.
Meet Ms. Abrams . . . and Kamla
Ms. Abrams, an experienced 6th grade teacher in a mid-sized K–8 school in New York City, describes Kamla as a student who relates well to her peers, respects her teachers, and adores sports. But Kamla's long-term struggle with academics seems to have dampened her enthusiasm and energy for schoolwork. Two years ago, in the 4th grade, Kamla's decoding deficits led to her classification as a “slow reader,” although no specific disability was identified.
Now, in Ms. Abrams's classroom, Kamla continues to struggle with reading and writing. When asked to write an essay, she squirms in her chair, holding her pen awkwardly and moving her paper all over the desk. Reading is a similar struggle, although Ms. Abrams has noticed Kamla seems to enjoy articles and books about sports. Ms. Abrams suspects that this is partly because Kamla feels no pressure to complete this kind of reading within a time limit and partly because the sports topics feed Kamla's interests. Otherwise, Kamla's discomfort extends to most classroom assignments, and overall, she appears disengaged from learning.
Kamla's focus on the basketball court contrasts markedly with her lack of engagement in the classroom. She is a talented athlete who practices her sport diligently and enthusiastically. Her commitment in this area testifies to her ability to persist and to work hard in the service of something she loves to do.
Ms. Abrams puzzles over how to draw upon this affective strength to build a connection to academic learning. In personalizing instruction for Kamla, Ms. Abrams hopes to overcome the negative associations Kamla has formed with traditional academic tasks. She decides to bridge Kamla's interests in sports to academic tasks in hopes of generating some of the same enthusiasm, interest, and persistence so apparent on the basketball court.
Brain imaging technologies and neural networks are certainly not the first things that jump into most teachers' minds on the way to school each morning. But you do not need to have a degree in neuroscience to reap the benefits of understanding the learning brain. The fundamental nature of the recognition, strategic, and affective networks form a framework we can use to analyze our students' individual strengths and weaknesses and understand their individual differences.
One of the clearest and most important revelations stemming from brain research is that there are no “regular” students. The notion of broad categories of learners—smart, not smart, disabled, not disabled, regular, not regular—is a gross oversimplification that does not reflect reality. By categorizing students in this way, we miss many subtle and important qualities and focus instead on a single characteristic.
The modular organization of learning networks and the highly specialized subprocesses within networks mean that each student brings a unique assortment of strengths, weaknesses, and preferences to school. In our classroom examples so far, we have focused on characteristics within a single brain network in order to highlight the impact of issues within each network. This is not a recommendation to focus on one network alone for any given student. In fact, patterns of strength and weakness across all three networks interact with the teaching and learning environment in ways that can either bring about progress or frustration. Sometimes a problem in one area can receive so much attention that other issues are missed. For example, students with learning disabilities are often mistakenly thought to have problems only with recognizing words. But as our final classroom example of this chapter illustrates, most learning disabilities actually involve all three learning networks.
Use the three brain networks to analyze individual differences athttp://www.cast.org/TeachingEveryStudent/networks
New insights into the learning brain help educators understand how learners differ and give us ideas about how we might better promote their learning. UDL Classroom Template 1, available in the Appendix (p. 178) and online, will guide you through the development of your own Class Learning Profile—a compendium of your students' strengths, weaknesses, and interests across the three brain networks.
Meet Mr. Hernandez . . . and Patrick
Mr. Hernandez teaches 6th grade in a middle-class suburban neighborhood. One of his new students this year is Patrick, “a good kid,” albeit one who doesn't seem terribly invested in his schoolwork. According to Patrick's elementary and early middle school teachers, he is a “classic dyslexic, with atrocious spelling, missed vowels, and disjointed thoughts.” Despite tutoring and other special supports, Patrick continues to have difficulty reading and writing.
Mr. Hernandez spends the first few weeks of the semester identifying Patrick's strengths and weaknesses across all three networks. He notes that Patrick's learning issues are more complex than his individualized education plan (IEP) indicates. In addition to problems with recognition-based skills in reading and writing, Patrick demonstrates some strategic issues. Specifically, he's easily distracted and has difficulty self-monitoring, causing him to lose track of his goal midway through an activity. Further, Mr. Hernandez notes that although Patrick is generally cheerful, he has become accustomed to failure. In fact, Patrick's identity seems in some ways tied to being a poor student. From time to time, he jokes about his poor grades in a seemingly proud way and seems largely unwilling to invest effort in schoolwork. These behaviors clearly signal affective concerns that should be addressed.
Mr. Hernandez considers the best approach for supporting Patrick's learning based on his broader understanding of Patrick's strengths and weaknesses. He decides to address the affective side first, knowing that if Patrick is not motivated to achieve academically, his progress on all fronts will be limited. Mr. Hernandez will try capitalizing on Patrick's strong interest in baseball to fashion some early writing and math assignments around that subject. He also plans to support Patrick's reading and writing mechanics so that difficulties there don't interfere with his ability to produce good work.
The Class Learning Profile Template helps you evaluate learner needs and strengths in light of the three brain networks athttp://www.cast.org/TeachingEveryStudent/learnerneeds
Educators hoping to get the most accurate picture of students' capacities must also carefully consider the materials and tools available to them in the classroom. It is in the intersection of student characteristics and the tools they use that students' abilities are actually defined. In the next chapter, we examine the media and tools of teaching.
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