# data[].attributes.body
Proactive Design

# data[].attributes.attachment_link.title
A Look Inside the Patriot Home for Wounded Warriors

# data[].attributes.attachment_link.description
Courtesy of Clark Realty Capital, a quick tour through the Patriot Home in Fort Belvoir, Virginia.

For more on Clark Realty and IDEO's involvement with the Wounded Warriors project, visit http://www.ideo.com/work/designing-the-ideal-home-for-wounded-warriors.

# data[].attributes.body
What works for those in the margins is often better for all!

# data[].attributes.body
The Brain and Learning

# data[].attributes.attachment_link.readable_text
Unable to find component with id 'glossword1' in [UdlXmlComponent [Component id = xmlContent]] Expected: 'xmlContent.glossword1'. Found with similar names: 'xmlContent:glossword' MarkupStream: [markup = org.apache.wicket.util.resource.StringResourceStream@1d374c1b:
The Brain and Learning

...modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

In the past, brain research focused on the anatomy of the brain and the functions of different regions. Whatever its value for other disciplines, such a static view of the brain has not been particularly useful in education, in part because it has little to say about how the brain learns. Now, instead of seeing the brain as a collection of discrete structures with specific functions, modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

This new view of neuroscience is deeply relevant to education, not only because it deals directly with issues of learning, but because it provides a foundation for understanding the nature and origins of learning variability. Here we provide a brief overview of how the brain actually learns. This forms the foundation for why and how we need to change our approach to teaching.

Returning to the study about the Raven's Matrices, why is it that individuals with autism differ in this particular area and not in a more distributed way? First, let's examine something fundamental about the brain that every learner shares.

Middle school teacher and mentor

...in this view of variability, students with disorders are part of the variability, rather than a separate group... more

Figure 3.3 shows that brain matter is made up of different kinds of tissue. Everywhere around the convoluted outer edge of the brain there is a thin band of gray tissue called cortex. In contrast, most of the rest of the brain-the massive interior-is dominated by white streaks that seem to crisscross everywhere. This is white matter, or connective tissue. The white color comes from myelin sheaths that are wrapped around the long connecting fibers, or axons, like insulation around a phone cable. These massive white regions that seem to crisscross the bulk of the brain are composed almost entirely of the trillions of interconnections between individual neurons.

These interconnections are being mapped with a wide variety of new imaging techniques. In Figure 3.4, MRI tractography techniques reveal the neural tracts between cortical and subcortical regions of the brain. What is most significant about our brains is not the thin band of neurons themselves but the astonishing interconnectivity between them. The human brain is, above all else, a dense, massively interconnected set of networks. The approximately one trillion neurons in cortex are linked by approximately 100 trillion connections. Just like computer networks, these multifaceted connections enable the individual components to communicate rapidly, flexibly, and along multiple pathways whether they are in close proximity or on opposite sides of the brain.

All of us share this basic architecture-lots of neurons connected in huge networks. And learning actually changes the brain: the neurons don't change but their interconnections do. The brain comprises massive numbers of networks large and small. Small networks are each specialized for performing certain kinds of tasks. When confronted with a specific task, such as the Raven's Progressive Matrices test, some networks are much more engaged and active than others and these active areas "light up" in images from brain scans. Because of their specialized functions, networks can be seen to engage with certain tasks reliably and predictably. Consider, for example, the location of the highlighted area in Figure 3.1. Because the Raven's test demands the ability to recognize and comprehend complex visual patterns, a neuroscientist would predict that those networks specialized for visual pattern recognition would "light up" on a brain scan, indicating that this part of the brain is very active when the subject is working on this test. Where is that part of the brain? Generally, networks specialized for perception are located toward the back, or posterior, of the brain. Within that area, the networks specialized to recognize complex visual patterns are just about where the yellow highlighting is shown in the figure. Not surprisingly, given the task, the difference in brain activity between autistic and non-autistic individuals shows up here.

Individual differences often show up in this way-as differences in the activity of small, specialized networks. But how many of these smaller networks can we discern? And how many do we need to consider in being able to think about individual differences in learning?

Dig Deeper

What does it mean to learn?
Neuroscientists have been chorusing "cells that fire together, wire together" since the late 1990s, meaning that if you perform a task or recall some information that causes different neurons to fire in concert, it strengthens the connections between those cells. Over time, these connections become thick, hardy road maps that link various parts of the brain -- and stimulating one neuron in the sequence is more likely to trigger the next one to fire. Thus, says Willis, "Practice makes permanent. The more times the network is stimulated, the stronger and more efficient it becomes." (Bernard, http://www.edutopia.org/neuroscience-brain-based-learning-neuroplasticity).

Conversely, when people stop practicing new things, the brain will eventually eliminate, or 'prune,' the connecting cells that formed the pathways. Like in a system of freeways connecting various cities, the more cars going to certain destination, the wider the road that carries them needs to be. The fewer cars traveling that way, however, the fewer lanes are needed. (Genesee (2000) http://www.cal.org/resources/digest/0012brain.html.)

The Brain and Learning

...modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

In the past, brain research focused on the anatomy of the brain and the functions of different regions. Whatever its value for other disciplines, such a static view of the brain has not been particularly useful in education, in part because it has little to say about how the brain learns. Now, instead of seeing the brain as a collection of discrete structures with specific functions, modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

This new view of neuroscience is deeply relevant to education, not only because it deals directly with issues of learning, but because it provides a foundation for understanding the nature and origins of learning variability. Here we provide a brief overview of how the brain actually learns. This forms the foundation for why and how we need to change our approach to teaching.

Returning to the study about the Raven's Matrices, why is it that individuals with autism differ in this particular area and not in a more distributed way? First, let's examine something fundamental about the brain that every learner shares.

Middle school teacher and mentor

...in this view of variability, students with disorders are part of the variability, rather than a separate group... more

Figure 3.3 shows that brain matter is made up of different kinds of tissue. Everywhere around the convoluted outer edge of the brain there is a thin band of gray tissue called cortex. In contrast, most of the rest of the brain-the massive interior-is dominated by white streaks that seem to crisscross everywhere. This is white matter, or connective tissue. The white color comes from myelin sheaths that are wrapped around the long connecting fibers, or axons, like insulation around a phone cable. These massive white regions that seem to crisscross the bulk of the brain are composed almost entirely of the trillions of interconnections between individual neurons.

These interconnections are being mapped with a wide variety of new imaging techniques. In Figure 3.4, MRI tractography techniques reveal the neural tracts between cortical and subcortical regions of the brain. What is most significant about our brains is not the thin band of neurons themselves but the astonishing interconnectivity between them. The human brain is, above all else, a dense, massively interconnected set of networks. The approximately one trillion neurons in cortex are linked by approximately 100 trillion connections. Just like computer networks, these multifaceted connections enable the individual components to communicate rapidly, flexibly, and along multiple pathways whether they are in close proximity or on opposite sides of the brain.

All of us share this basic architecture-lots of neurons connected in huge networks. And learning actually changes the brain: the neurons don't change but their interconnections do. The brain comprises massive numbers of networks large and small. Small networks are each specialized for performing certain kinds of tasks. When confronted with a specific task, such as the Raven's Progressive Matrices test, some networks are much more engaged and active than others and these active areas "light up" in images from brain scans. Because of their specialized functions, networks can be seen to engage with certain tasks reliably and predictably. Consider, for example, the location of the highlighted area in Figure 3.1. Because the Raven's test demands the ability to recognize and comprehend complex visual patterns, a neuroscientist would predict that those networks specialized for visual pattern recognition would "light up" on a brain scan, indicating that this part of the brain is very active when the subject is working on this test. Where is that part of the brain? Generally, networks specialized for perception are located toward the back, or posterior, of the brain. Within that area, the networks specialized to recognize complex visual patterns are just about where the yellow highlighting is shown in the figure. Not surprisingly, given the task, the difference in brain activity between autistic and non-autistic individuals shows up here.

Individual differences often show up in this way-as differences in the activity of small, specialized networks. But how many of these smaller networks can we discern? And how many do we need to consider in being able to think about individual differences in learning?

Dig Deeper

What does it mean to learn?
Neuroscientists have been chorusing "cells that fire together, wire together" since the late 1990s, meaning that if you perform a task or recall some information that causes different neurons to fire in concert, it strengthens the connections between those cells. Over time, these connections become thick, hardy road maps that link various parts of the brain -- and stimulating one neuron in the sequence is more likely to trigger the next one to fire. Thus, says Willis, "Practice makes permanent. The more times the network is stimulated, the stronger and more efficient it becomes." (Bernard, http://www.edutopia.org/neuroscience-brain-based-learning-neuroplasticity).

Conversely, when people stop practicing new things, the brain will eventually eliminate, or 'prune,' the connecting cells that formed the pathways. Like in a system of freeways connecting various cities, the more cars going to certain destination, the wider the road that carries them needs to be. The fewer cars traveling that way, however, the fewer lanes are needed. (Genesee (2000) http://www.cal.org/resources/digest/0012brain.html.)

, index = 22, current = '' (line 0, column 0)]

Unable to find component with id 'glossword1' in [UdlXmlComponent [Component id = xmlContent]] Expected: 'xmlContent.glossword1'. Found with similar names: 'xmlContent:glossword' MarkupStream: [markup = org.apache.wicket.util.resource.StringResourceStream@1d374c1b:
The Brain and Learning

...modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

In the past, brain research focused on the anatomy of the brain and the functions of different regions. Whatever its value for other disciplines, such a static view of the brain has not been particularly useful in education, in part because it has little to say about how the brain learns. Now, instead of seeing the brain as a collection of discrete structures with specific functions, modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

This new view of neuroscience is deeply relevant to education, not only because it deals directly with issues of learning, but because it provides a foundation for understanding the nature and origins of learning variability. Here we provide a brief overview of how the brain actually learns. This forms the foundation for why and how we need to change our approach to teaching.

Returning to the study about the Raven's Matrices, why is it that individuals with autism differ in this particular area and not in a more distributed way? First, let's examine something fundamental about the brain that every learner shares.

Middle school teacher and mentor

...in this view of variability, students with disorders are part of the variability, rather than a separate group... more

Figure 3.3 shows that brain matter is made up of different kinds of tissue. Everywhere around the convoluted outer edge of the brain there is a thin band of gray tissue called cortex. In contrast, most of the rest of the brain-the massive interior-is dominated by white streaks that seem to crisscross everywhere. This is white matter, or connective tissue. The white color comes from myelin sheaths that are wrapped around the long connecting fibers, or axons, like insulation around a phone cable. These massive white regions that seem to crisscross the bulk of the brain are composed almost entirely of the trillions of interconnections between individual neurons.

These interconnections are being mapped with a wide variety of new imaging techniques. In Figure 3.4, MRI tractography techniques reveal the neural tracts between cortical and subcortical regions of the brain. What is most significant about our brains is not the thin band of neurons themselves but the astonishing interconnectivity between them. The human brain is, above all else, a dense, massively interconnected set of networks. The approximately one trillion neurons in cortex are linked by approximately 100 trillion connections. Just like computer networks, these multifaceted connections enable the individual components to communicate rapidly, flexibly, and along multiple pathways whether they are in close proximity or on opposite sides of the brain.

All of us share this basic architecture-lots of neurons connected in huge networks. And learning actually changes the brain: the neurons don't change but their interconnections do. The brain comprises massive numbers of networks large and small. Small networks are each specialized for performing certain kinds of tasks. When confronted with a specific task, such as the Raven's Progressive Matrices test, some networks are much more engaged and active than others and these active areas "light up" in images from brain scans. Because of their specialized functions, networks can be seen to engage with certain tasks reliably and predictably. Consider, for example, the location of the highlighted area in Figure 3.1. Because the Raven's test demands the ability to recognize and comprehend complex visual patterns, a neuroscientist would predict that those networks specialized for visual pattern recognition would "light up" on a brain scan, indicating that this part of the brain is very active when the subject is working on this test. Where is that part of the brain? Generally, networks specialized for perception are located toward the back, or posterior, of the brain. Within that area, the networks specialized to recognize complex visual patterns are just about where the yellow highlighting is shown in the figure. Not surprisingly, given the task, the difference in brain activity between autistic and non-autistic individuals shows up here.

Individual differences often show up in this way-as differences in the activity of small, specialized networks. But how many of these smaller networks can we discern? And how many do we need to consider in being able to think about individual differences in learning?

Dig Deeper

What does it mean to learn?
Neuroscientists have been chorusing "cells that fire together, wire together" since the late 1990s, meaning that if you perform a task or recall some information that causes different neurons to fire in concert, it strengthens the connections between those cells. Over time, these connections become thick, hardy road maps that link various parts of the brain -- and stimulating one neuron in the sequence is more likely to trigger the next one to fire. Thus, says Willis, "Practice makes permanent. The more times the network is stimulated, the stronger and more efficient it becomes." (Bernard, http://www.edutopia.org/neuroscience-brain-based-learning-neuroplasticity).

Conversely, when people stop practicing new things, the brain will eventually eliminate, or 'prune,' the connecting cells that formed the pathways. Like in a system of freeways connecting various cities, the more cars going to certain destination, the wider the road that carries them needs to be. The fewer cars traveling that way, however, the fewer lanes are needed. (Genesee (2000) http://www.cal.org/resources/digest/0012brain.html.)

The Brain and Learning

...modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

In the past, brain research focused on the anatomy of the brain and the functions of different regions. Whatever its value for other disciplines, such a static view of the brain has not been particularly useful in education, in part because it has little to say about how the brain learns. Now, instead of seeing the brain as a collection of discrete structures with specific functions, modern neuroscience neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks.

This new view of neuroscience is deeply relevant to education, not only because it deals directly with issues of learning, but because it provides a foundation for understanding the nature and origins of learning variability. Here we provide a brief overview of how the brain actually learns. This forms the foundation for why and how we need to change our approach to teaching.

Returning to the study about the Raven's Matrices, why is it that individuals with autism differ in this particular area and not in a more distributed way? First, let's examine something fundamental about the brain that every learner shares.

Middle school teacher and mentor

...in this view of variability, students with disorders are part of the variability, rather than a separate group... more

Figure 3.3 shows that brain matter is made up of different kinds of tissue. Everywhere around the convoluted outer edge of the brain there is a thin band of gray tissue called cortex. In contrast, most of the rest of the brain-the massive interior-is dominated by white streaks that seem to crisscross everywhere. This is white matter, or connective tissue. The white color comes from myelin sheaths that are wrapped around the long connecting fibers, or axons, like insulation around a phone cable. These massive white regions that seem to crisscross the bulk of the brain are composed almost entirely of the trillions of interconnections between individual neurons.

These interconnections are being mapped with a wide variety of new imaging techniques. In Figure 3.4, MRI tractography techniques reveal the neural tracts between cortical and subcortical regions of the brain. What is most significant about our brains is not the thin band of neurons themselves but the astonishing interconnectivity between them. The human brain is, above all else, a dense, massively interconnected set of networks. The approximately one trillion neurons in cortex are linked by approximately 100 trillion connections. Just like computer networks, these multifaceted connections enable the individual components to communicate rapidly, flexibly, and along multiple pathways whether they are in close proximity or on opposite sides of the brain.

All of us share this basic architecture-lots of neurons connected in huge networks. And learning actually changes the brain: the neurons don't change but their interconnections do. The brain comprises massive numbers of networks large and small. Small networks are each specialized for performing certain kinds of tasks. When confronted with a specific task, such as the Raven's Progressive Matrices test, some networks are much more engaged and active than others and these active areas "light up" in images from brain scans. Because of their specialized functions, networks can be seen to engage with certain tasks reliably and predictably. Consider, for example, the location of the highlighted area in Figure 3.1. Because the Raven's test demands the ability to recognize and comprehend complex visual patterns, a neuroscientist would predict that those networks specialized for visual pattern recognition would "light up" on a brain scan, indicating that this part of the brain is very active when the subject is working on this test. Where is that part of the brain? Generally, networks specialized for perception are located toward the back, or posterior, of the brain. Within that area, the networks specialized to recognize complex visual patterns are just about where the yellow highlighting is shown in the figure. Not surprisingly, given the task, the difference in brain activity between autistic and non-autistic individuals shows up here.

Individual differences often show up in this way-as differences in the activity of small, specialized networks. But how many of these smaller networks can we discern? And how many do we need to consider in being able to think about individual differences in learning?

Dig Deeper

What does it mean to learn?
Neuroscientists have been chorusing "cells that fire together, wire together" since the late 1990s, meaning that if you perform a task or recall some information that causes different neurons to fire in concert, it strengthens the connections between those cells. Over time, these connections become thick, hardy road maps that link various parts of the brain -- and stimulating one neuron in the sequence is more likely to trigger the next one to fire. Thus, says Willis, "Practice makes permanent. The more times the network is stimulated, the stronger and more efficient it becomes." (Bernard, http://www.edutopia.org/neuroscience-brain-based-learning-neuroplasticity).

Conversely, when people stop practicing new things, the brain will eventually eliminate, or 'prune,' the connecting cells that formed the pathways. Like in a system of freeways connecting various cities, the more cars going to certain destination, the wider the road that carries them needs to be. The fewer cars traveling that way, however, the fewer lanes are needed. (Genesee (2000) http://www.cal.org/resources/digest/0012brain.html.)

, index = 22, current = '' (line 0, column 0)]

org.apache.wicket.markup.MarkupStream.throwMarkupException(MarkupStream.java:526)

org.apache.wicket.MarkupContainer.renderNext(MarkupContainer.java:1473)

org.apache.wicket.MarkupContainer.renderAll(MarkupContainer.java:1592)

org.apache.wicket.MarkupContainer.renderComponentTagBody(MarkupContainer.java:1567)

org.apache.wicket.MarkupContainer.renderAssociatedMarkup(MarkupContainer.java:689)

org.apache.wicket.markup.html.panel.AssociatedMarkupSourcingStrategy.renderAssociatedMarkup(AssociatedMarkupSourcingStrategy.java:76)

org.apache.wicket.markup.html.panel.PanelMarkupSourcingStrategy.onComponentTagBody(PanelMarkupSourcingStrategy.java:112)

org.apache.wicket.Component.internalRenderComponent(Component.java:2559)

org.apache.wicket.MarkupContainer.onRender(MarkupContainer.java:1530)

org.apache.wicket.Component.internalRender(Component.java:2389)

org.apache.wicket.Component.render(Component.java:2317)

org.apache.wicket.MarkupContainer.renderNext(MarkupContainer.java:1428)

org.apache.wicket.MarkupContainer.renderAll(MarkupContainer.java:1592)

org.apache.wicket.MarkupContainer.renderComponentTagBody(MarkupContainer.java:1567)

org.apache.wicket.MarkupContainer.onComponentTagBody(MarkupContainer.java:1521)

org.apache.wicket.markup.html.panel.DefaultMarkupSourcingStrategy.onComponentTagBody(DefaultMarkupSourcingStrategy.java:72)

org.apache.wicket.Component.internalRenderComponent(Component.java:2559)

org.apache.wicket.MarkupContainer.onRender(MarkupContainer.java:1530)

org.apache.wicket.Component.internalRender(Component.java:2389)

org.apache.wicket.Component.render(Component.java:2317)

org.apache.wicket.MarkupContainer.renderNext(MarkupContainer.java:1428)

org.apache.wicket.MarkupContainer.renderAll(MarkupContainer.java:1592)

org.apache.wicket.MarkupContainer.renderComponentTagBody(MarkupContainer.java:1567)

org.apache.wicket.MarkupContainer.onComponentTagBody(MarkupContainer.java:1521)

org.apache.wicket.markup.html.panel.DefaultMarkupSourcingStrategy.onComponentTagBody(DefaultMarkupSourcingStrategy.java:72)

org.apache.wicket.Component.internalRenderComponent(Component.java:2559)

org.apache.wicket.MarkupContainer.onRender(MarkupContainer.java:1530)

org.apache.wicket.Component.internalRender(Component.java:2389)

org.apache.wicket.Component.render(Component.java:2317)

org.apache.wicket.MarkupContainer.renderNext(MarkupContainer.java:1428)

org.apache.wicket.MarkupContainer.renderAll(MarkupContainer.java:1592)

org.apache.wicket.MarkupContainer.renderComponentTagBody(MarkupContainer.java:1567)

org.apache.wicket.MarkupContainer.onComponentTagBody(MarkupContainer.java:1521)

org.apache.wicket.markup.html.panel.DefaultMarkupSourcingStrategy.onComponentTagBody(DefaultMarkupSourcingStrategy.java:72)

org.apache.wicket.Component.internalRenderComponent(Component.java:2559)

org.apache.wicket.MarkupContainer.onRender(MarkupContainer.java:1530)

org.apache.wicket.Component.internalRender(Component.java:2389)

org.apache.wicket.Component.render(Component.java:2317)

org.apache.wicket.MarkupContainer.renderNext(MarkupContainer.java:1428)

org.apache.wicket.MarkupContainer.renderAll(MarkupContainer.java:1592)

org.apache.wicket.Page.onRender(Page.java:907)

org.apache.wicket.markup.html.WebPage.onRender(WebPage.java:140)

org.apache.wicket.Component.internalRender(Component.java:2389)

org.apache.wicket.Component.render(Component.java:2317)

org.apache.wicket.Page.renderPage(Page.java:1035)

org.apache.wicket.request.handler.render.WebPageRenderer.renderPage(WebPageRenderer.java:118)

org.apache.wicket.request.handler.render.WebPageRenderer.respond(WebPageRenderer.java:246)

org.apache.wicket.request.handler.RenderPageRequestHandler.respond(RenderPageRequestHandler.java:167)

org.apache.wicket.request.cycle.RequestCycle$HandlerExecutor.respond(RequestCycle.java:784)

org.apache.wicket.request.RequestHandlerStack.execute(RequestHandlerStack.java:64)

org.apache.wicket.request.cycle.RequestCycle.execute(RequestCycle.java:255)

org.apache.wicket.request.cycle.RequestCycle.processRequest(RequestCycle.java:212)

org.apache.wicket.request.cycle.RequestCycle.processRequestAndDetach(RequestCycle.java:283)

org.apache.wicket.protocol.http.WicketFilter.processRequest(WicketFilter.java:188)

org.apache.wicket.protocol.http.WicketFilter.doFilter(WicketFilter.java:244)

org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:193)

org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:166)

org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:199)

org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:96)

org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:493)

org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:137)

org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:81)

org.apache.catalina.valves.AbstractAccessLogValve.invoke(AbstractAccessLogValve.java:660)

org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:87)

org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:343)

org.apache.coyote.http11.Http11Processor.service(Http11Processor.java:798)

org.apache.coyote.AbstractProcessorLight.process(AbstractProcessorLight.java:66)

org.apache.coyote.AbstractProtocol$ConnectionHandler.process(AbstractProtocol.java:808)

org.apache.tomcat.util.net.NioEndpoint$SocketProcessor.doRun(NioEndpoint.java:1498)

org.apache.tomcat.util.net.SocketProcessorBase.run(SocketProcessorBase.java:49)

java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)

java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)

org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)

java.lang.Thread.run(Thread.java:748)

# data[].attributes.attachment_link.description
modern neuroscience views the brain as a complex web of integrated and overlapping networks. And learning is seen as changes in the connections within and between these networks. In the past, brain research focused on the anatomy of the brain and the functions of different regions.

# data[].attributes.body
Three Primary Classes of Networks for Learning

# data[].attributes.attachment_link.title
Read Universal Design for Learning: Theory and Practice for Free in Clusive! - CAST

# data[].attributes.attachment_link.description
Our flagship publication is now available at no cost on our flexible and customizable digital reading tool.

# data[].attributes.attachment_link.title
Read Universal Design for Learning: Theory and Practice for Free in Clusive! - CAST

# data[].attributes.attachment_link.description
Our flagship publication is now available at no cost on our flexible and customizable digital reading tool.

# data[].attributes.attachment_link.title
Read Universal Design for Learning: Theory and Practice for Free in Clusive! - CAST

# data[].attributes.attachment_link.description
Our flagship publication is now available at no cost on our flexible and customizable digital reading tool.

# data[].attributes.attachment_link.title
Read Universal Design for Learning: Theory and Practice for Free in Clusive! - CAST

# data[].attributes.attachment_link.description
Our flagship publication is now available at no cost on our flexible and customizable digital reading tool.

# data[].attributes.headline
UDL Excerpt National Educational Technology Plan (2010)

# data[].attributes.subject
UDL Excerpt National Educational Technology Plan (2010)

# data[].attributes.attachment_link.description
The page you’re seeking may have been moved or may no longer exist. Try going to our home page, using our website navigation, or by using our search at the top of the page to find what you’re looking for.

# data[].attributes.attachment_link.readable_text
About the National Educational Technology Plan

The U.S. Department of Education released the 2024 National Educational Technology Plan (NETP): A Call to Action for Closing the Digital Access, Design and Use Divides. First released in fulfillment of Improving America's Schools Act of 1994, NETP has been updated multiple times since its original release, most recently in 2016. While past NETPs have largely served as surveys of the state of the field, the Department of Education's 2024 NETP frames three key divides limiting the transformational potential of educational technology to support teaching and learning, including:

The Digital Use Divide, addressing opportunities to improve how students use technology to enhance their learning, including dynamic applications of technology to explore, create, and engage in critical analysis of academic content and knowledge;

The Digital Design Divide, addressing opportunities for educators to expand their professional learning and build the capacities necessary to design learning experiences enabled by technology; and

The Digital Access Divide, addressing opportunities for students and educators to gain equitable access to educational technology, including connectivity, devices, and digital content. This also includes accessibility and digital health, safety, and citizenship as key elements of digital access.

# data[].attributes.attachment_link.title
National Educational Technology Plan - Office of Educational Technology

# data[].attributes.attachment_link.description
The 2024 NETP frames key divides limiting the potential of educational technology, including: Digital Use, Digital Design, and Digital Access.

# data[].attributes.body
Todd Rose, 2012

# data[].attributes.body
Advocacy

# data[].attributes.body
Implementation

# data[].attributes.attachment_link.readable_text
CAST works with organizations and stakeholders in a number of fields to find ways of improving learning opportunities in today's rapidly changing world. We regularly partner with organizations in the public and private sectors to explore challenging questions in education through our consultation and curriculum design services. Our staff includes experts in human development, neuropsychology, instructional design, literacy instruction, learning analytics, technology design, accessibility, and more.
Looking for something tailored just for you?
Curriculum Consultation

CAST offers consultation on the design of curriculum units to ensure that they proactively address the variability of learners. We consult on individual lessons, instructional materials and methods, and/or assessments across content areas (ELA, Math, Science, Social Studies, etc.), grade levels (pre-K - post-secondary), and settings (online, classroom, or blended).

The following services are available:

General overview: Includes an introduction of how to align the UDL framework in relation to unit design and lesson templates (such as UBD).

Pre-design: Includes support of the development of a unit or lesson template that embeds UDL principles to the content.

Review ('UDL audit'): Includes UDL analysis of already designed units or lessons with recommendations for aligning to the UDL framework.

Contact Madeline Reggiannini to learn more about our curriculum consultation services.
UDL Implementation

CAST's Implementation services enable any educational organization to work closely with CAST UDL experts to identify needs and challenges, develop goals and action plans for sustainable change, build capacity for systemic change for UDL implementation, and infuse UDL into classrooms in a systematic and data-driven way. Using our implementation guide and resources, CAST guides educator teams in the process of UDL implementation.

CAST's implementation services consist of the following, customized to fit your school or districts' needs:

Development of a school-based implementation team

Consultation that assists in the development of an action plan for UDL implementation and capacity building at the organizational level

Online resources to support learning about UDL and onsite workshops that offer blended learning opportunities

Facilitation by a CAST implementation specialist to support the UDL school-based team in integrating UDL into educational practices

A focus on the CAST UDL implementation five-phase process including Explore (Pre-Phase), Prepare (Phase 1), Integrate (Phase 2), Scale (Phase 3), and Optimize (Phase 4)

Contact Madeline Reggiannini for more information about our UDL Implementation Services,
Customized Solutions

CAST's Customized Solutions enable any educational organization to work closely with CAST UDL experts to identify issues and challenges; develop goals and action plans for sustainable change; facilitate discussions around how to build consensus among teams and cohorts; and implement professional learning solutions that make a difference. Through our customized solutions, we guide educators as they consider how to benefit from the UDL framework, whether you are developing awareness of UDL or are ready to implement.

CAST's customized solutions are designed to address the needs that educational organizations face in meeting the challenges of learner variability. We work with educational leaders and collaboratively plan the right combination of services that may include:

Online courses, webinars, and onsite workshops that offer blended learning opportunities

Consultation that assists in the development of an action plan for UDL implementation and capacity building at the organizational level

Coaching that is facilitated by CAST professional learning experts to support the integration of UDL into educational practices

Curriculum review focusing on aligning the goals, assessments, methods, and materials to the UDL framework and other standards.

UDL Implementation services using CAST's five-phase process including Explore, Prepare, Integrate, Scale, and Optimize.

For more information about our Customized Solutions or to book your consultation, contact Madeline Reggiannini.
Please note: CAST staff are available for onsite services if all participants are masked. This policy will be monitored and revised as the global health situation changes.

# data[].attributes.attachment_link.description
CAST works with organizations and stakeholders in a number of fields to find ways of improving learning opportunities in today's rapidly changing world. We regularly partner with organizations in the public and private sectors to explore challenging questions in education through our consultation and curriculum design services.

# data[].attributes.body
UDL Guidelines

# data[].attributes.body
Low Tech representation of the guidelines

# data[].attributes.body
An "oldie" but a goodie....

# data[].attributes.attachment_link.title
Bach, Toccata and Fugue in D minor, 10-year anniversary edition

# data[].attributes.body
http://olms.cte.jhu.edu/olms2/simulator/web/simulations/69#!/scene/148