What Can Technology Learn from the Brain?

Chapter

Abstract

Much of this book, like most writing on educational technology, focuses on what we can learn from technology. This chapter takes the opposite point of view: what technology can learn from us. We have chosen this contrarian route for several reasons. First, as educators who develop technology (both of us work at CAST, on educational research and development organization), we are always looking for ways to develop better learning technologies. At least for the present, there is no better learning (or teaching) technology than the human brain, so we are continually looking at how the brain goes about the tasks of learning and teaching. What can we, as educators who design technology, learn about better design from the ways in which our own brains are designed?

Keywords

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Much of this book, like most writing on educational technology, focuses on what we can learn from technology. This chapter takes the opposite point of view: what technology can learn from us. We have chosen this contrarian route for several reasons. First, as educators who develop technology (both of us work at CAST, on educational research and development organization), we are always looking for ways to develop better learning technologies. At least for the present, there is no better learning (or teaching) technology than the human brain, so we are continually looking at how the brain goes about the tasks of learning and teaching. What can we, as educators who design technology, learn about better design from the ways in which our own brains are designed?

We will hardly be exhaustive here; our purpose is only to illustrate several among the most obvious things about the ways that brains learn. We hope, nonetheless, to raise some issues of significance for our peers and for ourselves. We will begin with a striking syndrome that, in its anomaly, reveals several important things about the way the brain works.

A Disconnect: The Capgras Delusion

The Capgras delusion is one of the rarest and most colorful syndromes in neurology. The most striking feature of the disorder is that the patient – who is usually quite mentally lucid in other respects – comes to regard close acquaintances, typically either his parents, children, spouse, or siblings, as ‘imposters,’ i.e., he may claim that the person in question “looks like” or is even “identical to” his father, but really isn’t. (Hirstein & Ramachandran, 1997)

Individuals with Capgras syndrome are among the most striking of patients to show up at any psychiatrist’s office. Their problem sounds like a bad movie script: they report that an alien or imposter has replaced a loved one. The imposter or alien looks exactly the same as their loved one, but they are sure that it is not. The “illusion” is both vivid and persistent – and quite distressing to the loved one who is, of course, really just the same (Abumrad & Krulwich, 2010).

Formerly treated as a psychiatric disorder, modern researchers now recognize that individuals with Capgras syndrome have a neurological disorder: a lesion in their brain disturbs the connection between two normally connected regions of the brain (Hirstein & Ramachandran, 1997). The resulting disorder, for our purposes, is a vivid demonstration of an important aspect of the way the brain works and learns: the brain, at least the normal brain, typically has multiple ways of “knowing.” Under normal circumstances, these multiple ways of knowing are connected and integrated. What Capgras syndrome demonstrates is what happens in the odd circumstance when they are not. Let us explain.

The most obvious way that we recognize people is by their visual features. Many research studies have demonstrated that a specific region (often called the visual face form area or fusiform gyrus) in the temporal lobe learns to respond consistently to the distinctive features of individual faces (McCarthy, Puce, Gore, & Allison, 1997). That is, it recognizes them. But recent research has demonstrated that there are also other ways that our brains learn to respond distinctively to individual faces. One of the most interesting emerges in a different area of the brain, the limbic system, where the nervous system responds with emotion rather than vision. When a familiar face, one that evokes feelings of one type or another, is presented, this part of the brain responds with distinctive (although often subtle or unconscious) signs of emotional arousal in sweat glands, pupil dilation, heart rate, breathing, etc. Scientists are beginning to realize that we recognize individuals not only by their visual features but by the emotions they engender in us (Ellis & Lewis, 2001). We recognize someone in part by how they make us feel.

In someone with Capgras syndrome, the visual way of knowing is “disconnected” from the emotional, visceral way of knowing. As a result, they do not match up. The person looks like Tom, but does not “feel” like him.

What is most amazing is what the brain does next. Apparently when faced with two competing realities – someone who looks exactly like your wife but does not feel exactly like her – the brain seems to construct something entirely new, something that integrates the two realities into one. There are many other instances of this, the famous McGurk effect when there is a mismatch between what is seen with what is heard, visual illusions where the brain will see two different views, but only one at a time, etc. (McGurk & MacDonald, 1976).

Of what significance is this bizarre condition to educators or technology designers? Actually, there are many important things that Capgras syndrome reveals about the brain. For our purposes, we will emphasize only three. First, for constructivists like us, it is one of the more vivid demonstrations of how much the brain “constructs” the reality in which it lives, rather than simply perceiving it. Designers who think their job is merely to transfer information from the environment into a receptive, and passive, brain should take note.

Second, the Capgras syndrome illustrates how important emotion and affect is in what we know and learn. Many educational designers think of the brain as merely an information processor and their task as informational design. But that neglects the lesson of Capgras. The brain is always, constantly and pervasively, evaluating the significance or value of any information. The brain is not really an information-processing device; the Capgras delusion, and our own emotions, reveal that much of it is a values-processing device (Damasio, 1999; LeDoux, 1996).

But the larger point we want to illustrate with Capgras is that the brain has ­multiple ways of knowing. Usually, these ways of knowing are congruent and ­integrated – normally we construct, and live in, a single universe. It takes an unusual anomaly, like the Capgras delusion, to reveal the underlying diversity in the ways that we know our world.

But how many ways of knowing are there? To a neuroscientist, there are many, many ways of knowing: it has been often estimated that there are at least 30 different ways of knowing within the visual system alone (Banich, 2004). In the next section, we look at some very simple anatomy with an eye to identifying the most general ways of knowing that are distinguished in our brains.

Something Different: The Spinal Cord

One of the most obvious things about the brain is that it has many different parts.

Even the most cursory comparison of those different parts – for example, a comparison of the thalamus, the cortex, and the amygdala – under a microscope shows that they are each composed of very different and distinctively shaped neurons and those are in turn connected by very different wiring patterns. On the face of it, it seems very unlikely that each of these different parts would perform in the same way, or learn in the same way. But most of the brain is composed of three highly general components (Cytowic, 1996; Rose & Meyer, 2002). To illustrate them, we would like to take a quick look at the spinal cord, where they are easy to see.

In Fig. 1, a diagram of the circuitry of the spinal cord, you can see that there are three primary components or types of neurons: a sensory neuron, a motor neuron, and an interneuron. This is as simple as the nervous system circuitry gets. One other aspect of the circuitry is important to note: the location of each of the three types of neurons (this will be helpful later). The sensory neurons are always in the back of the nervous system – here in the back of the spinal cord. The motor neurons, in contrast, are always in the front of the nervous system (here the spinal cord). The interneurons, finally, are in the center or core of the nervous system (Stiles, 2008).
Fig. 1

Circuitry of the spinal cord

As educators, we do not usually work directly with the spinal cord or its neurons. We only introduced it here to illustrate a simple framework around which the whole brain is organized. Now we will move to a much more interesting part of the brain that educators should work directly with – the cerebral cortex. While the cerebral cortex is much more complicated than the spinal cord, it is basically organized in the same way.

We all recognize the cerebral cortex as the massive crinkled lobes of our brain that are mostly visible on the surface and that are the most “human” of the brain’s many structures. Even within that one type of brain tissue, however, there are many distinctly specialized regions. While the specializations often seem complicated to the novice, at the most basic level they follow the same pattern that we just saw in the spinal cord. Let us elaborate on three broad types of cerebral cortex and the roles they play in learning.

Recognition Networks

First, consider the large expanse of cerebral cortex in the rear of the brain (most of what is known as parietal, occipital, and temporal lobes). That entire region of cortex is specialized for gathering, comparing, and interpreting information that comes from the senses (note the parallel to the spinal cord where sensory neurons are always found in the rear as well). For convenience, we call these regions recognition networks (For more information on recognition networks, see: Banich, 2004; Cabeza & Kingstone, 2001; Farah, 2000; Martin, 2007; Mountcastle, 1998).

At any given moment, we see, hear, smell, taste, and touch countless patterns – patterns of light, sound, chemicals, touch – in our environment. The posterior regions of cortex – recognition networks – are specialized for learning to perceive and understand those patterns. With time and experience, they learn to recognize the differing patterns of the smell of gasoline or coffee and make good choices about which one to have for breakfast and which one to put in the lawnmower. Learning to recognize things – to build useable knowledge about the world in which you live – is one very powerful type of learning in the brain. But there are two more.

Strategic Networks

Just as the recognition networks are specialized for gathering information from the senses, the strategic networks are specialized for action, for movement. (Again, note the parallel to spinal cord where motor neurons are in the front.) At any given moment, there are many possible courses of action an individual might take. Strategic networks are specialized for choosing what to do (setting a goal), formulating a plan or strategy for doing it, and then activating the right sequence of muscle movements to actually take action. None of those abilities come easily; the brain must learn how to set realistic goals, how to choose effective plans of action, and how to monitor progress – what are called “executive functions.” And the development of those executive functions depends upon the prior mastery of many “lower level” skills and abilities which are necessary for carrying them out – learning to be fluent and automatized with millions of movements and actions (including very complicated expressive acts like speaking and writing) that can be combined and recombined again and again (For more information on strategic networks, see: Dawson & Guare, 2010; Goldberg, 2002; Jeannerod, 1997; Meltzer, 2007; Rothi & Heilman, 1997; Stuss & Knight, 2002).

Affective Networks

The third major division of the brain is not devoted to recognizing information or generating actions but to setting priorities. Since we are constantly receiving information and have many possible courses of action, we are constantly assigning values and significance to each of them, whether negative or positive. When a stranger approaches us, we immediately (and largely unconsciously) evaluate their significance: are they enticing, boring, frightening? That evaluation is critical in determining our priorities – will we ignore them (to do something else of higher priority), attend to them cautiously, approach them warmly, or run. Affective networks are critical in making that determination. To do so they combine information about the “external” environment (e.g., “Who is that approaching me?” and “What experience have I had with them or people like them in the past,”) with information about our own “internal” environment (e.g., “What are my priorities right now? How hungry am I? How anxious or frightened am I from when I was mugged last year?”). Affective networks are the important part of our brain for “coloring” our experience, for giving it value and importance, for setting our priorities. We experience the work of the affective networks as motivation and emotion. Over time and experience, affective networks learn to attach motivation and emotion to the experiences of our lives (For more information on affective networks, see: Barsalou, Breazeal, & Smith, 2007; Coch, Dawson, & Fischer, 2007; Damásio, 1994; Davidson, Scherer, & Goldsmith, 2003; Easton & Emery, 2005; Lane, Nadel, & Ahern, 2000; Levesque et al., 2004; Lewis & Stieben, 2004; Rolls, 1999).

At this point, it is useful to return to the Capgras delusion as a summary of where we have been. Now, it is easy to see that Capgras results from a separation between two kinds of knowledge: what the recognition system knows and what the affective system knows. When we recognize faces, we certainly use visual cortex to do so. But we also use affective cortex to recognize how we feel about those faces, what significance they have for us. Knowing about the three basic networks, one should prompt us, however, to ask whether the third component – strategic systems – also has any role in face recognition. Good question!

Yes they do. And hopefully you will not be surprised to find that their role is focused on action and strategy rather than sensation or affect. In brief, strategic cortex knows a face by how it looks at it. To recognize a face requires more than a single global glance: it requires a careful, deliberate inspection of the most distinctive features (Farah, 2000). Even though this feels automatic to us, eye movement studies reveal how strategically and skillfully the eye investigates the distinctive features and relationships of the face. And, not surprisingly, strategic systems move the eyes to concentrate not only on the features that are optimal for recognizing the face (who it is), but also on the features that are optimal for recognizing the emotion in that face (what she/he means to me) (Hirstein & Ramachandran, 1997).

Why is this tripartite brain important to the work of educational technology designers? Some readers will recognize that these three brain systems underlie the three principles of universal design for learning (UDL) (Rose & Meyer, 2002). But we shall have more to say about that later. For now it is important simply to recognize one conclusion: you can never really teach (or learn) one thing in isolation. The brain is inevitably learning – all the time – in all three of the ways we have been describing. Although technology developers may think they are teaching one thing – the causes of the Civil War, say – learners are actually learning multiple things. When shown a historical paragraph, they are not only learning to recognize its meaning, they are learning strategies for how to examine future historical tracts, and they are learning how they feel about this content (and probably about themselves, about historical inquiry more generally, and many other things). They are learning what its personal significance is, so that they will know how to engage or disengage in the future.

This is important for many reasons, not the least of which is related to the relationship between affect and other kinds of learning. Most designers recognize the value of engagement and expend considerable effort in designing a learning environment that attracts and sustains attention. Fewer recognize, however, the pernicious effects that such designs may have long-term, when they are unconnected – or wrongly connected to actual learning goals (Lepper & Greene, 1975; Lepper, Corpus, & Iyengar, 2005). Providing external rewards and attractions to engage and sustain effort can appear to improve performance in the short run but can actually decrease the long-term motivation to learn in the relevant domain. Fabulously engaging games can boost phonics skills, but students may be learning nothing about the joy of reading and may actually read less as a result.

Educational designers typically focus too much on what recognition systems do and too little on teaching the strategies that students need for future learning. They also pay too little attention to the affective domain, that is, on designs that engage and build motivation for future learning. Game designers usually do the opposite. They focus primarily on amplifying the engagement – some would say addiction – of the environment (Gentile, 2009). They may build strategies or skills but often in domains that have little transfer to real life. The informational domain (i.e., the recognition network) is usually attended to the least. What we need are educational environments that are focused on all three: developing valuable knowledge, skills, and emotions.

It is time to take a more specific look at what the brain might teach us about the actual art or science of teaching. To do that, we want to look more closely at two important findings within the strategic networks specifically.

A Reflection of Purpose: Mirror Neurons

One of the most striking, and revolutionary, discoveries about the brain during the last decade has been the discovery of “mirror neurons.” A recent scholarly review by Brass and Rüschemeyer (2010) accurately captures the importance of their discovery for many neuroscientists and cognitive psychologists. When mirror neurons were first discovered, scientists were studying how the brain controls voluntary movement. They inserted tiny electrodes deep into motor cortex (part of the strategic networks described above) to measure the activity of single motor neurons. They quickly found neurons that emitted a burst of firing whenever the monkey made a specific purposeful movement – like taking a sip from a straw. What was more dramatic, and much more surprising, was that the same neuron would also exhibit a burst of firing when the monkey merely observed another monkey making the same action. In that sense, these neurons seemed to “mirror” the behavior performed by another.

Many studies have been conducted since, which speculate on the meaning of this neural “mirroring.” Recent research has shown, for example, that mirror neurons do not just reflect simple actions; they reflect their purpose. That is, a mirror neuron that emits a burst of activity when the monkey observes another monkey reaching out to grab a raisin, does not emit that same burst when the same monkey reaches out (in the same way) to turn a knob (Rizzolatte & Sinigaglia, 2007). Mirror neurons thus seem to reflect not only the physical actions of others, but also their goals and intentions.

With these kinds of properties, scientists have indulged in considerable speculation about the role of mirror neurons. Many have speculated, for example, that this mirroring capacity is essential for understanding the actions of others (Rizzolatte & Sinigaglia, 2007). Individuals understand the actions of others because they are able to “simulate” or mirror those actions in their own heads. That is how the meaning of actions is recognized, assimilated, and understood.

Not surprisingly, many scientists have concluded that this mirroring functionality is also the basis for imitation. With the ability to mirror actions produced by others, it is possible not only to understand them but also to imitate or copy them. This is not a trivial matter for any brain. While monkeys, and humans, are skilled at learning by imitation, most animals do not in fact have that capacity. As many neuroscientists see it, the functionality of mirror neurons is one of the essential substrates for learning by imitation (Iacoboni & Dapretto, 2006).

For “altricial” species – like humans and primates – that depend for their survival on learning rather than inherited fixed action patterns, there is a premium on “social” learning, the ability to learn from the experience of others. The protracted dependency of these species on their caregivers – in contrast to “precocial” species that are independent almost from birth – provides both the opportunity and necessity for the advantages of imitation. For many scientists, mirror neurons are one of the brain’s best mechanisms for taking advantage of what others have already learned.

There is one more dramatic development in the last few years of research on mirror neurons that is important for this discussion. While mirror neurons were discovered in motor cortex, recent research has found this same mirroring capacity in many other areas of the human brain – including all three of the major networks we have discussed earlier. Recently, the scientists who originally discovered mirror neurons in motor cortex have published a book with a remarkably more expansive title that reflects the wider findings: Mirrors in the Brain: How Our Minds Share Actions, Emotions, and Experience (Rizzolatte & Sinigaglia, 2007). Note the close resemblance, with slight name changes, to the three networks as outlined in this chapter.

In this new, expanded view of mirror neurons, scientists believe that the mirroring functionality is not only the key for understanding motor action and imitation, but also for understanding the highest forms of human cognition and social behavior. Through these capacities – resident in affective and recognition cortex rather than just in motor cortex – humans gain the power for understanding emotions, for “theory of mind,” for empathy, and for compassion (Rizzolatte & Sinigaglia, 2007). All of these depend on the ability to mirror or simulate not only what another person is doing, but also what they are feeling, what they are thinking, and what they know about or care about.

In summary, many now believe that mirroring capacity underlies much of what makes us human. Indeed, our very culture (and certainly our entertainment) depends upon the ability to effectively mirror and understand the social and emotional behavior of other humans.

It is not hard to see the relevance of mirroring for educational designers. At the very least, it encourages all of us to take advantage of what mirror neurons can do. That is to say, to maximize the opportunities for students to learn not by trial and error, nor even by independent exploration and discovery (although some of that is very good), but by taking advantage of the capacity for imitation.

Clearly, imitation has been a critical aspect of most forms of mentoring and apprenticeships over the span of human history. The arrival of “book learning” altered the landscape profoundly, and privileged a different method of learning – one based on the transfer of information. While there is value in that kind of learning, the drastic reduction in active apprenticeships – with lots of opportunities for modeling and imitation – fails to take advantage of the mirroring that our brains can do.

New technologies provide a much better platform for taking advantage of modeling and imitating than textbooks (Dalton & Proctor, 2007; Rose & Dalton, 2009). Although real, live skillful teachers would be better under most circumstances than anything computers can do, however, real, live skillful teachers are only intermittently available to their students. The problems of time sharing with 20–30 students simultaneously are obvious.

The popularity of “How to” videos on YouTube is testimony to how much more effective this medium can be for mentoring and modeling than the printed word. More importantly, many research studies have investigated the advantages of providing “just in time” modeling by human mentors on video or by avatars created on computers. Game designers have essentially abandoned instruction manuals or written descriptions of rules of play because the ability of new media to model intended behavior is so much more powerful and direct (Gee, 2007). Instructional designers who are using modern technologies should take full advantage of both the technology’s capability for modeling and the brains capability for mirroring. They should also take care to consider modeling that addresses all three of the networks – modeling of affective skills and effective strategies for managing frustration, for instance, is as important as modeling skills for finding the lowest common denominator.

A Key to Learning: Pervasive and Reciprocal Feedback

Most descriptions or drawings of the motor systems in the brain emphasize the giant motor neurons in motor cortex that travel all the way down the spinal cord to where they synapse on “lower” motor neurons (Stiles, 2008). From those lower motor neurons, a long axon snakes far out of the spinal cord to connect to actual muscles in the arms and legs. The emphasis on motor neurons makes sense because they are the active link between our brain and our ability to move and act upon the world. But anatomists, those who study the brain’s biological structure, typically note something else about the motor system: the overwhelming pervasiveness of mechanisms for “feedback.”

The nervous system is not composed primarily of simple one-way connections from brain to muscles. Instead, the connections between brain and muscle are highly reciprocal. Indeed, the majority of the connections in the motor system are reciprocal: they are not merely conveying impulses from the brain to the muscles but are carrying information from muscles (and other neurons and parts of the body) back to the brain (Banich, 2004; Jeannerod, 1997). From the architecture, it is clear that the brain does not merely issue orders to move muscles, making actions possible: it collects information about the status of those muscles and the effects of its own manipulation of them. The brain is constantly monitoring the effects of its own activity (Dawson & Guare, 2010; Goldberg, 2002; Rothi & Heilman, 1997; Stuss & Knight, 2002). While the brain’s motor neurons are often the most highlighted aspects of the motor system, the anatomy suggests how important feedback is to its success.

Observation of the anatomy and physiology reveals something else about feedback. In the discussion so far, we have highlighted only the motor feedback, the feedback that is localized within the motor systems themselves. But the wiring of the nervous system reveals other feedback channels as well, feedback from very different parts of the nervous system. Indeed, both of the large network systems described earlier – recognition networks and affective networks – provide extensive and continuous feedback to the strategic motor systems. A few words about the nature of their feedback is warranted.

Recognition networks are wired to provide feedback from the senses, not from muscles. That feedback – from effects on the environment that can be seen, heard, touched, tasted – is critical in determining not just whether an action was successfully launched, but whether it achieved its intended results (Banich, 2004; Cabeza & Kingstone, 2001; Farah, 2000; Martin, 2007; Mountcastle, 1998). The information from the senses does not tell us about whether muscles properly contracted or flexed but rather whether the pounding of the hammer actually hit the nail, whether the cup actually reach the lips, whether the pitched ball was a strike, and whether the beating of the drum was forceful enough to be heard above the orchestra.

The feedback from affective networks serves a very different function. The feedback is not about whether movements achieved the physical results intended but whether they achieved the emotional or affective results desired (Barsalou et al., 2007; Coch et al., 2007; Damásio, 1994). Did the hammering of the nail bring pain (perhaps because you were hammering your thumb) rather than satisfaction, did the cup of coffee taste good, did the sound of the drum bring pleasure? This affective kind of feedback is essential, especially to learning, because it motivates and prioritizes future actions. Where other feedback compares results to what was intended, this kind of feedback compares results to what is valued, to the goals and priorities the individual holds. Such feedback helps to determine whether actions are valued – either positively or negatively – enough to be repeated, avoided, prioritized, ­practiced, or even obsessed about. Much cognitive neuroscience research about ­memory, attention, and persistence has emphasized the critical role of emotion and affective feedback in facilitating (or inhibiting) learning (Kensinger, 2004; Levine & Pizarro, 2004).

What significance does all of this – the enormous and diversified investment of neural architecture to feedback – have for the work of educational designers? What it suggests to us is how important feedback is to successful learning. The brain, essentially wired for learning, is demonstrably wired for feedback. In comparison, most educational environments seem grossly impoverished in the quality, density, immediacy, and variety of feedback they provide. The core procedures and activities of most classrooms provide little feedback (to either teacher or student) or provide feedback that is too infrequent, too late, or too uninformative (Blackwell, Trzesniewski, & Dweck, 2007; Cimpian, Arce, Markman, & Dweck, 2007). For example, textbooks are completely disabled in this regard. They are presentational (feed-forward) only. As a result, tests or exercises are added to supplement the reading, but those are usually summative rather than formative, neither timely nor informative enough to guide instruction or learning. They simply do not provide the feedback that the brain seems eager and prepared to receive.

The new technologies of learning provide a better, or at least more promising, platform. Unlike textbooks, modern technology has the capacity to be dynamic, interactive, and responsive. As such, with proper design, new learning technologies can provide feedback that is plentiful, varied, and timely. But too often new technologies are designed more like textbooks, with only limited options for feedback, options that are far narrower than the nervous system is prepared for.

As a guide to what kinds of feedback should be considered, it is useful to consider each of the three networks. First, consider the strategic networks and especially their motor capabilities. We are all aware of the advances in the design of information technologies so that they provide basic sensory–motor feedback. Computer keyboards, automated teller machines, cell phones all tend to give immediate feedback – a tactile click, a physical depression, a beep, a visual cue – to let us know that our action was registered. Designers have long ago learned how frustrated and lost customers feel when they do not get that feedback.

But of course that kind of feedback is hardly enough. Knowing that a key or button was successfully pushed is necessary but not sufficient. We also need feedback on whether our motor acts achieved the results on the environment we intended – did we actually type the password with letters in the right order, did we choose the multiple choice answer that was correct, did we generate a good ­synthesis of the data from our experiment, did our essay or e-mail convey a proper tone of sarcasm. All of this kind of feedback requires recognition cortex – the ­ability to perceive the results of our actions and make sense of them, as well as the ability to compare our actual effects on the environment (including whatever we create) to what we intended.

Most new learning technologies do not provide enough of this kind of feedback. But there are excellent models available. Many “smart” games, of course, provide this kind of feedback consistently and continuously. In fact, many cognitive psychologists have surmised that one of the most important reasons that games are so addictive and motivating is that they are rich and immediate with their feedback (Gee, 2007; Shaffer, 2006). Some well-designed educational programs take similar advantage of the power of technology to provide pervasive feedback, but their purposes and techniques are much more instructional. That is, they track what students actually do, provide helpful feedback – to both students and designers – based on the kinds of errors that student’s exhibit, and modify instruction on the basis of that feedback. For examples, see the chapter on adaptive assessments by Russell (this volume).

Finally, let us consider feedback in terms of the affective networks. The work of Carol Dweck, Deci, and many others have demonstrated how powerful the right kind of emotional feedback can be for motivating learning, and how motivationally unproductive the wrong kind can be (Blackwell et al., 2007; Cimpian et al., 2007; Deci & Moller, 2005; Dweck, 2000). Much of what passes for educational technology rewards students in the ways that researchers have demonstrated to be unproductive, an easy thing to fix. More challenging is to design educational technologies so that they can not only monitor actions and their results, but also their affective consequences as well.

Good teachers are constantly monitoring their student’s affect and motivation in order to make optimal instructional decisions and to provide the right kind of feedback. They continually monitor a student’s level of interest, frustration, boredom, anticipation, anxiety, to decide when to push harder, when to modify the difficulty, when to congratulate, when to take a break. So far, modern learning technologies are drastically less capable of this kind of affective monitoring than are experienced teachers, but interesting work is being done that demands attention (Picard, 2010; Woolf et al., 2009).

This last point bears emphasis. Most cognitive psychologists and noneducators think of teaching as “informational work” – the work inherent in dispensing information or teaching specific skills. But experienced educators – and neuroscientists, if they think about it – know that teaching is as much or more “emotional work.” Effective teaching requires the ability to understand exactly where students are in their learning – not only what information and skills they have but also the frustration, boredom, anticipation, wonder, and passion they are feeling. Effectively optimizing the emotional conditions for learning is the most important challenge of teaching. Educational technologies have a lot to learn in this area.

Anxiety and Individual Differences

One of the most obvious things about human brains is how much they all look alike. The overall shape and fissured lobes of the cerebral cortex looks pretty much the same from one to another. But modern techniques for imaging the brain have made it possible to vividly illuminate the microscopic anatomy of the living brain and even to watch the microphysiology and chemistry of its dynamic activity. What is equally obvious is that each brain is strikingly unique and individual (Hariri, 2009; Leonard, Eckert, Given, Virginia, & Eden, 2006). No two are alike.

How are they different? In almost every way one can measure: in gross anatomy (i.e., the relative size and shape of various regions), in fine structural anatomy (the detailed pattern of connections between cells), in physiology, and in chemistry.

These individual differences in the brain’s anatomy, physiology, and chemistry are as distinctive as the differences in individual fingerprints. But the effects of those differences are much greater. Researchers repeatedly discover dramatic relationships between the anatomical and physiological differences in the brain and many aspects of behavior, from the simplest to the most complex (Hariri, 2009).

Consider just one example: individual differences in anxiety. Whether measured behaviorally (e.g., by observation or self-report) or physiologically (e.g., blood pressure, heart rate, skin conductance, etc.), individuals vary significantly in both their chronic level of anxiety and in their reactivity to potentially stressful situations. Some people are consistently much more anxious than others. What is interesting is that those behavioral differences can be predicted on the basis of measurable differences in the brain’s anatomy and physiology (van Reekum et al., 2007). For example, researchers have found that the volume of brain tissue in the amygdala (but not most other areas of the brain) is correlated with the level of trait anxiety reported by the individual – the smaller the volume of the amygdala on the left side of the brain, the more anxious the individual (Spampinato, Wood, De Simone, & Grafman, 2009).

It is clear that this one difference would have profound effects on learning: some children will be too anxious for the social and cognitive demands of learning in almost any classroom situation, some will not be nearly anxious enough. Others will be profoundly affected (either positively or negatively) by unexpected stressors at home or school.

Anxiety is but one dimension of individual differences that have been linked to brain differences – there are hundreds of others. By the time kids go to school, they have brains that are really different from one another – differences that are founded in biology and continually reshaped by the environment. These differences are not subtle or ephemeral – they reflect substantial differences in who the learner is.

What is the lesson from all this and what are the implications for educational designers? First, it is important to recognize that most publishers and educational technology developers do not design as if their users differ significantly one from the other. For the most part, educational designs are almost completely uniform, with minor modifications (occasionally) for individuals with disabilities. Given the brain’s great interindividual differences, it seems that technology developers might take more advantage of the flexibility of technology to differentiate along the lines of individual differences. But what kinds of differences are worth designing for?

The UDL Guidelines

The field of UDL has grown up around a common framework for recognizing the full extent of individual differences and for addressing them in the design of curricula. The UDL guidelines demonstrate what might be done to improve the one-size-fits-all curriculum that has been traditionally used in schools (Rose & Gravel, 2009). These guidelines are organized into three principles that directly correlate to the three brain networks described in this chapter. The principles help to articulate the kinds of options that are important to consider in designing a curriculum that is effective when students, as they always are, are diverse. (For a detailed description of the guidelines, please visit http://www.udlcenter.org/.) Here are the three principles with a brief orientation to them.

Principle I: Provide Multiple Means of Representation

This principle addresses the diversity that would be typically associated with recognition networks of the brain. It is important to provide options for students for perception, language and symbols, and comprehension. Perception is the most basic level of this principle. Students need to have physical access to the information. This could mean customizing the display by increasing text size or providing students with a text to speech option. Simply providing an unsupported text or an audio recording is not enough for most students to really comprehend the information presented. Consideration must be given to the diversity of preferences and limitations between learners.

The next step in providing multiple means of representation is to ensure that all students can access the language and symbols that are being used. This step recognizes that not all students have the same linguistic backgrounds. Beyond just language, students differ in their understanding of vocabulary, fluency, language structure, and mathematical symbols. Some suggestions in this area include: preteaching vocabulary, making relationships between symbols explicit, and providing glossaries with pronunciation guides.

Finally, to truly include all students, options must be given for comprehension. Students do not all understand in the same way. This is because each individual brings a unique set of knowledge and experience; so do not learn in the same ways. This option includes: activating background knowledge, highlighting relationships, guiding information processing, and supporting memory and transfer. Some ways to do these things are activating background knowledge, using graphic organizers, scaffolding instruction, and giving sufficient time for thinking.

Principle II: Provide Multiple Means of Action and Expression

This is the “how” of learning, and falls into the strategic networks. It involves providing options for physical action, expressive skills and fluency, and executive functions. The simplest level to provide options for is physical action. It goes without saying that students need physical access to the curriculum. This could include things like ensuring there are multiple ways to respond (not just typing or handwriting) or allowing students to use navigations tools or assistive technologies.

The next level is providing options for expressive skills and fluency. All students differ in their proficiency in particular media. Some might be very familiar with using a computer, while some have very limited experiences. Options should be provided for learners in regards to what type of media they use, the tools they can use to help themselves, and how to scaffold their practice. This could include allowing students to use spell check or giving appropriate feedback to students.

The most important level in this principle is providing options for executive functions. As previously noted in the section on strategic networks, executive functions are vital to learning. Learners need to set a goal, make a plan, execute the plan, and evaluate whether they were successful or not. This involves a great deal of organization and planning, something not everyone can do easily. It is therefore important to support each of the aforementioned aspects of executive functioning. To do this one might use models, scaffolding, checklists, embedded prompts, mentors, and a variety of other strategies.

Principle III: Provide Multiple Means of Engagement

In this principle, the “why” of learning is addressed through the affective networks. One must provide options for recruiting interest, sustaining effort and persistence, and self-regulation. All of these options are more difficult to implement because they involve accounting for students emotions. The physical environment can be changed easily, but this is more difficult to accomplish. However, it is affect that regulates all learning.

Options should be provided for recruiting interest. This might include something as simple as giving students choice and removing potential distractions to allowing students to develop their own goals. It is also important that goals and purposes are genuine. Most likely students will not be overly excited about doing busy work. They want meaning and purpose to be varied. Worksheets become trite and ordinary when only worksheets are used.

It is equally important to ensure that all students are persistent and effortful in their work. This means that goals must be made clear, demands and resources should be varied to optimize challenge, collaboration and community should be fostered, and feedback should be provided. Students need to understand what they are doing, and if what they are doing is accurate or not. Again, they need meaning and purpose.

Finally, options need to be provided for self-regulation. In many ways, this is the ultimate goal of all education. Learners will leave teachers and gain independence. Teachers cannot always be there to give support and make accommodations. Options need to be given to allow students to develop their own goals and expectations, coping skills and strategies, and reflection and assessment skills. Many of these goals revolve around helping students, through scaffolding and modeling, understand their strengths and weaknesses as learners. With this knowledge, they can better know how to support themselves without the help of a teacher.

Conclusion

The human brain’s capacity – and its design – for learning are unique, easily distinguishable from any other learning device in the animal kingdom or the world of new technology. In this paper, we have highlighted a few notable aspects of the way the human brain learns, aspects that we think merit consideration by all those who develop learning technologies. Our list is hardly exhaustive, and there is much to learn.

We wish to end, however, with a different observation. While we can train animals and computers to do astonishingly complex tasks, to play chess, for example, we have not been able to successfully train either animals or computers to be competent teachers (we can program them to do teacherly things, but the underlying pedagogy and technology has inevitably been designed by a human who is actually doing the teaching by proxy). It may well be that human brains differ more profoundly from other brains in their power to teach than in their power to learn. It is this unmistakably human capacity – teaching – that needs more attention in both neuroscience and education. That attention will have much to teach us about making better teaching technologies.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.CASTWakefieldUSA

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