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CAST

CAST

2018

Abstract

What we know about the brain is central to the Universal Design for Learning Framework. Knowing key facts about the brain from a UDL perspective can help inform learning design to address learner variability. Find out more in this downloadable resource designed for educators.

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Cite As

CAST (2018). UDL and the learning brain. Wakefield, MA: Author. Retrieved from http://www.cast.org/products-services/resources/2018/udl-learning-brain-neuroscience

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What we know about the brain is central to the Universal Design for Learning Framework. Knowing key facts about the brain from a UDL perspective can help inform learning design to address learner variability. Find out more in this downloadable resource designed for educators.

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WICHE Cooperative for Educational Technologies

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Do you have much experience with Universal Design? Today we're thrilled to welcome a guest author who has not only implemented UDL on her own, but is assisting her colleagues in applying the principles in their classrooms as well. Tianhong Shi, instructional designer with Oregon State Extended Campus, joins WCET Frontiers today to tell us about her journey learning about UDL, applying the concepts in a variety of learning settings, and expanding the design across the OSU campus. Enjoy the read and enjoy your day,
Universal Design for Learning

Universal Design for Learning (UDL) is a practical tool for guiding course design to ensure that every learner succeeds, based on scientific insights into how humans learn (). There are three main principles of UDL:

1. multiple means of representations,

2. multiple means of engagement, and

3. multiple means of expressions.

UDL has roots in Inclusive pedagogy, which started around 1970s in the United States and as early as 1918 in the United Kingdom.

Why UDL?

With the multiple means of presentation, engagement and expressions, UDL strives to make content accessible to all learners, it stimulates interests and motivation for learning and it provides a pathway for every learner to succeed (; udlcenter.org).

By teaching students according to their individual needs, we make sure that every student succeeds and prepare them for the future (versus preparing for our own past by teaching them how we were taught) (Katie Novak,).

"You don't just get kids in the driver suit of learning; you get expert drivers once you use UDL" (Bill McGrath at UDL-IRN Summit 2018).

My Journey in UDL

In 2017, as part of the ID2ID program (a peer mentoring program for instructional designers organized by EDUCAUSE and Penn State University), my mentee Irene Knokh and I identified UDL as a learning topic. We read books such as Universal Design In Higher Education-Promising Practices, completed a free Canvas UDL training course (designed and taught by), browsed web resources (UDL at glance, ), and attended a webinar on Implementing UDL by Thomas Tobin.

Designing Courses Using UDL Principles: Removing Barriers

After intensive self-directed learning on UDL for several months, I started to promote UDL among online instructors at Oregon State University. My greatest gratitude goes to Oregon State University (OSU) online instructors Ted Paterson,, and Yvette Gibson who allowed me to share with them what I learned about UDL and started implementing the principles in their online courses in the Spring 2018 term. There are many applications of UDL design principles in some of OSU's Spring 2018 online courses.

During my meetings with Ted, Victor, and Yvette, we identified barriers and challenges to learning and designed courses to remove such barriers.

For Ted, the biggest challenge is the intensive reading and writing in managing ethics study. So, we added estimated average reading time for each reading assignment and created animated videos to explain complicated yet crucially important writing assignments.

For Victor, the challenge to his students was the abstractness of biophysics concepts in macromolecular structure study. To overcome this barrier, we designed simulation videos to explain the concepts and used graphics to illustrate other concepts. Students were assigned to use video, graphics, and texts to explain key concepts in meaningful ways.

For Yvette, the challenge was how to make learning meaningful and applicable in shrubland ecology study. We used learner-generated content as the main teaching strategy for the course and students were assigned to co-write portions of the textbook and create resources that will be published for public use upon instructor approval.

Examples of class activities in Ted's Managing Ethics course:

Text reading (Chapter 1 of textbook).

Listening to podcast (Ponzi Supernova podcast audio from Radio-lab).

Watching videos of instructor lectures.

Watching animated video explanation of a 7-page instruction for the term writing project.

Discussion forums: students post answers to prompts; students reply to peer classmates' posts.

Student developed Personal Ethical Action Plan (instructor provides feedback for students to incorporate prior to final due date).

Examples of class activities in Victor's Macromolecular Structure course and Yvette's Shrubland Ecology course:

Create a three-dimensional image.

Create a video to explain what "reciprocal space" means to the individual student.

Complete a literature search and review.

Write a letter to a relative to explain why the Fourier transform is so important to NMR spectroscopy.

Co-author part of course textbook.

UDL Implementation Success Tips

Promoting the implementation of UDL is bound to be a rough journey that is full of challenges. Why is it still worthwhile to do it? By adopting UDL principles, you will aid in a student's efforts to become expert learners.

Consider the following:

What challenges am I overcoming?

How could UDL add value to the learning design?

Implement one thing at a time (suggested by Thomas Tobin).

Consider UDL as operating system, not just as a framework (suggested by) in the days when you feel your UDL pioneering journey is becoming too rough: it is at work without you noticing it!

The College STAR program offers free access to UDL- Based teaching practices for faculty and staff members to implement in their college courses. College STAR also provides Incentive funding for faculty members to join Virtual Learning Communities and submit proposals which will be developed into online modules and case studies. Learn more about College STAR.

Here is a list of some tools that can help instructors implement UDL and several additional UDL resources for you to use and share.

Poll & Reflection Sharing

I would like to end this post with a simple poll (based on Katie Novak's presentation at UDL-IRN Summit 18) and a reflective question for us all to ponder:

"In your UDL journey for the next two years, what would you hope to be and how would you achieve that?"

Feel free to share your thoughts and ideas on this padlet wall.

Tianhong Shi
Instructional Designer
Extended Campus - Oregon State University
Tianhong.shi@oregonstate.edu
@tianhongshi

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Do you have much experience with Universal Design? Today we're thrilled to welcome a guest author who has not only implemented UDL on her own, but is assisting her colleagues in applying the principles in their classrooms as well. Tianhong Shi, instructional designer with Oregon State Extended Campus, joins WCET Frontiers today to tell us [...]

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Neuroplasticity is the brain's ability to change and adapt due to experience. It is an umbrella term referring to the brain's ability to change, reorganize, or grow neural networks. This can involve functional changes due to brain damage or structural changes due to learning.

Plasticity refers to the brain's malleability or ability to change; it does not imply that the brain is plastic. Neuro refers to neurons, the nerve cells that are the building blocks of the brain and nervous system. Thus, neuroplasticity allows nerve cells to change or adjust.

Types of Neuroplasticity

The human brain is composed of approximately 100 billion neurons. Early researchers believed that neurogenesis, or the creation of new neurons, stopped shortly after birth.

Today, it's understood that the brain's neuroplasticity allows it to reorganize pathways, create new connections, and, in some cases, even create new neurons.

There are two main types of neuroplasticity:

Functional plasticity is the brain's ability to move functions from a damaged area of the brain to other undamaged areas.

Structural plasticity is the brain's ability to actually change its physical structure as a result of learning.

How Neuroplasticity Works

The first few years of a child's life are a time of rapid brain growth. At birth, every neuron in the cerebral cortex has an estimated 2,500 synapses, or small gaps between neurons where nerve impulses are relayed. By the age of three, this number has grown to a whopping 15,000 synapses per neuron.

The average adult, however, only has about half that number of synapses. Why? Because as we gain new experiences, some connections are strengthened while others are eliminated. This process is known as synaptic pruning.

Neurons that are used frequently develop stronger connections. Those that are rarely or never used eventually die. By developing new connections and pruning away weak ones, the brain can adapt to the changing environment.

Benefits of Neuroplasticity

There are many benefits of brain neuroplasticity. Allowing your brain to adapt and change helps promote:

The ability to learn new things

The ability to enhance existing cognitive capabilities

Recovery from strokes and traumatic brain injuries

Strengthening areas where function is lost or has declined

Improvements that can boost brain fitness

Characteristics of Neuroplasticity

There are a few defining characteristics of neuroplasticity.

Age and Environment Play a Role

While plasticity occurs throughout the lifetime, certain types of changes are more predominant at specific ages. The brain tends to change a great deal during the early years of life, for example, as the immature brain grows and organizes itself.

Generally, young brains tend to be more sensitive and responsive to experiences than much older brains. But this does not mean that adult brains are not capable of adaptation.

Genetics can have an influence as well. The interaction between the environment and genetics also plays a role in shaping the brain's plasticity.

Neuroplasticity Is an Ongoing Process

Plasticity is ongoing throughout life and involves brain cells other than neurons, including glial and vascular cells. It can occur as a result of learning, experience, and memory formation, or as a result of damage to the brain.

In instances of damage to the brain, such as during a stroke, the areas of the brain associated with certain functions may be injured. Eventually, healthy parts of the brain may take over those functions and the abilities can be restored.

Brain Plasticity Has Limitations

It is important to note, however, that the brain is not infinitely malleable. Certain areas of the brain are largely responsible for certain actions. For example, there are areas of the brain that play critical roles in movement, language, speech, and cognition.

Damage to key areas of the brain can result in deficits in those areas because, while some recovery may be possible, other areas of the brain simply cannot fully take over those functions that were affected by the damage.

How to Improve Neuroplasticity

There are steps you can take to help encourage your brain to adapt and change, at any age.

Enrich Your Environment

Learning environments that offer plenty of opportunities for focused attention, novelty, and challenge have been shown to stimulate positive changes in the brain. This is particularly important during childhood and adolescence, but enriching your environment can continue to provide brain rewards well into adulthood.

Stimulating your brain might mean:

Learning a new language

Learning how to play an instrument

Traveling and exploring new places

Creating art and other creative pursuits

Get Plenty of Rest

Research has shown that sleep plays an important role in dendritic growth in the brain. Dendrites are the growths at the end of neurons that help transmit information from one neuron to the next. By strengthening these connections, you may be able to encourage greater brain plasticity.

Sleep has been shown to have important effects on both physical and mental health. Some researchers suggest that this is partly due to genetics and partly due to the makeup of the grey matter in the brain.

You can improve your sleep by practicing good sleep hygiene. This includes developing a consistent sleep schedule and creating an environment that contributes to good sleep.

Exercise Regularly

Regular physical activity has a number of brain benefits. Some research indicates that exercise might help prevent neuron loss in key areas of the hippocampus, a part of the brain involved in memory and other functions. Other studies suggest that exercise plays a role in new neuron formation in this same region.

A 2021 study adds that physical exercise also appears to boost brain plasticity through its impact on brain-derived neurotrophic factor (BDNF, a protein that impacts nerve growth), functional connectivity, and the basal ganglia-the part of the brain responsible for motor control and learning.

The U.S. Department of Health and Human Services recommends getting at least 150 minutes of moderate-intensity cardio exercises (such as walking, dancing, swimming, or cycling) per week and a minimum of two days of strength training exercises (lifting weights or doing bodyweight exercises).

Practice Mindfulness

Mindfulness entails completely immersing your mind in the present moment, without ruminating over the past or contemplating the future. Awareness of the sights, sounds, and sensations around you is key. Many studies have shown that cultivating and practicing mindfulness can foster the brain's neuroplasticity.

Games aren't just for kids: Studies show that playing board, card, video, and other games can improve your brain's neuroplasticity.

Problems With Brain Plasticity

Brain changes are often seen as improvements, but this is not always the case. In some instances, the brain's structure and function can be negatively influenced or changed.

For example, brain plasticity can be problematic when it allows detrimental changes caused by substance use, disease, or trauma (including brain injury or traumatic experiences that result in post-traumatic stress disorder or PTSD). Even lead poisoning can negatively impact brain plasticity.

There are also some medical conditions that can limit or hinder brain plasticity. Among them are a variety of pediatric neurological disorders such as epilepsy, cerebral palsy, tuberous sclerosis, and Fragile X syndrome.

How Neuroplasticity Was Discovered

Beliefs and theories about how the brain works have evolved substantially through the years. Early researchers believed that the brain was "fixed," while modern advances have shown that the brain is more flexible.

Early Theories

Up until the 1960s, researchers believed that changes in the brain could only take place during infancy and childhood. By early adulthood, it was believed that the brain's physical structure was mostly permanent.

In his 2007 book, "The Brain that Changes Itself: Stories of Personal Triumph From the Frontiers of Brain Science," which took a historical look at early theories, psychiatrist and psychoanalyst Norman Doidge suggested that this belief that the brain was incapable of change primarily stemmed from three major sources:

An ancient belief that the brain was much like an extraordinary machine, capable of astonishing things yet incapable of growth and change

The inability to actually observe the microscopic activities of the brain

The observation that people who had suffered serious brain damage were often unable to recover

Early on, psychologist William James suggested that the brain was perhaps not as unchanging as previously believed. Way back in 1890, in his book "The Principles of Psychology," he wrote, "Organic matter, especially nervous tissue, seems endowed with a very extraordinary degree of plasticity." However, this idea went largely ignored for many years.

Modern Theories

In the 1920s, researcher Karl Lashley found evidence of changes in neural pathways of rhesus monkeys. By the 1960s, researchers began to explore cases in which older adults who had suffered massive strokes were able to regain functioning, demonstrating that the brain was more malleable than previously believed. Modern researchers have also found evidence that the brain is able to rewire itself following damage.

Thanks to advances in technology, researchers are able to get a never-before-possible look at the brain's inner workings. As the study of modern neuroscience flourishes, research has demonstrated that people are not limited to the mental abilities they are born with and that damaged brains are often quite capable of remarkable change.

A Word From Verywell

The brain has an amazing ability to change throughout the course of our life, allowing us to learn new things or recover after sustaining a brain-based injury. Still, there are limits to how much the brain can adapt.

Constantly challenging ourselves, making sleep a priority, and getting regular exercise can also help improve brain plasticity. Avoiding certain substances is beneficial as well.

Frequently Asked Questions

Why is neuroplasticity important?

Without neuroplasticity, it would be difficult to learn or otherwise improve brain function. Neuroplasticity also aids in recovery from brain-based injuries and illnesses.

What is an example of neuroplasticity?

Research has found that children with blindness have increased connectivity and reorganized neurocircuits when compared with children without this condition. This suggests that the brain adapts to the inability to see by changing its structure and function, providing children with blindness a greater ability to use the information received from the other senses (such as hearing and touch).

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How Brain Neurons Change Over Time From Life Experience

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Neuroplasticity, also known as brain plasticity, is the brain's ability to change as a result of experience. Learn how it works and how the brain can change.

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Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing on JSTOR

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A three-level model of cognitive processing to account for complex monitoring when individuals are faced with ill-structured problems, i.e., problems on which o...

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Neuroscience, Creativity & Innovation Toolbox

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Neuroscience, Creativity & Innovation Toolbox

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Carl Schoonover: How to look inside the brain

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As adult learners now make up the majority of U.S. students, it is more important than ever for educators who administer and teach in post-traditional programs to understand how the adult brain learns. This article examines the science of how the adult brain learns and offers suggestions for faculty in post-traditional programs to capitalize on this knowledge and maximize the effectiveness of their teaching. Both theoretical underpinnings and practical tips for brain-based teaching are offered

When I speak on this topic, as I frequently do, I almost always begin by asking my audience members, "How many of you in here genuinely care that your students learn?" I typically get a gentle wave of laughter and a sea of raised hands in response. Of course, we care that our students learn; that's why we do what we do! But how do we know learning is actually taking place? Many instructors simply do what they have done for years, or they do what feels right, or they choose an activity that they read about or saw somewhere. But if we genuinely care that our students learn, then it behooves us to make informed choices about our instructional strategies so we can rest assured learning is taking place.

I will elucidate on some of what we know about how the adult brain learns, through examination of one of the most intriguing concepts from the field of neuroandragogy, an emerging field born from the intersection of neuroscience and andragogy, a term popularized by Malcolm Knowles, the influential theorist of adult education whose book The Modern Practice of Adult Education still serves as a seminal text for educators. I will provide an illustrative example of the application of this knowledge to teaching practice. The science and the tips offered in this piece are applicable to both on-campus and online learning.
Andragogy
Most teachers of adult learners are likely familiar with Knowles's theory of andragogy by now. For those who aren't, the theory can be summarized in 5 simple words: adults learn differently than children. That is why Knowles employs the term andragogy for adults, as opposed to the term pedagogy, which applies to the learning of children. The Greek root "andra" means "man," or for Knowles's purpose, "adult," while the root "agogos" means "leading of." Andragogy, then, has to do with the leading or education of adults. This demarcation is present even between adults and young adults. Most cognitive scientists mark the "adult brain" as beginning at or around age 23-past the age of the traditional college student. Thus, if you teach adults in the same manner you do, have, or would teach traditional-age students, it is highly likely that learning is not, in fact, taking place.

More specifically, Knowles's andragogical theory is comprised of six principles:

1. Adults are self-directed learners

2. Adults accumulate experience that becomes an increasingly rich resource for learning

3. Readiness to learn is related to social roles

4. Adults want immediate application of knowledge (problem-centered orientation)

5. Adults tend to be internally motivated

6. Adults need to know the reason or learning something

Although all six of these principles are important, the second principle of prior experience is the one around which this article centers. The neuroandragogical concepts explained here support and illustrate Knowles's assumption that a learner's ever-growing reservoir of experience is a rich resource for learning.
Neuroandragogy
Neuroandragogy is a relatively new field that examines the intersection of neuroscience-how the brain works-and andragogy-the field of adult learning. According to Clive Wilson, author of No One is Too Old to Learn, "neuroandragogy investigates the rigorous research of scholars in brain studies, the scientific investigation of adult learning, and the evaluation of adult intelligence. Neuroandragogy is both the anatomical and physiological study of the adult brain and the cognitive functions of the systems of the brain that participate in intelligence, memory, recall, and learning" (2006, 3). Neuroandragogy is important to anyone who teaches adults because it focuses in particular on the function of and impact to the adult brain during the process of learning.

With a perfunctory understanding of andragogy and neuroandragogy, let us now examine the science of how the brain actually learns. I will then offer tips for maximizing this knowledge toward better teacher practice.
How the Brain Learns
The brain is comprised of 100 billion neurons, or brain cells. These cells contain nuclei, which make enzymes, proteins, and neurotransmitters-all of which are critical for the nerve cells in the brain to communicate with one another. Neurons have a single axon, which is a long tube that sends electrical impulses-called action potentials-to other cells. And neurons also have dendrites, which are more complicated structures (imagine little hands), which receive electrical impulses from the axon terminals of other neurons. A synapse is the specialized site at which that communication happens. All of these things together form what's called a neuronal network (Zull, 2002).

The most fascinating thing about the formation of a neuronal network is that each one represents a physical change in the brain. Each time we learn something new-not each time we encounter something new, but rather each time learning actually takes place through a synaptic connection-a new tiny "branch" forms in our brain. But this physical transformation can only take place by connecting new stimuli (information) to an existing neuron-hence the importance of connecting to prior experience in our learners. The process of learning is literally the physical act of growing our existing neuronal networks.

One can use myriad analogies to describe the brain, but for now, let's imagine the brain as a comprised of millions of tiny little storage cubbies-like the kind you used to store your mittens and toothbrush in in kindergarten. Each time we attend to a new stimulus, our brain processes the signal coming from that stimulus by comparing it immediately to the contents of those millions of cubbies to ask-as Taylor and Marienau put it: "How is this experience like what I already know?" (2016, p. 55). When the brain finds a connection to something we already know-something that exists in one of our storage cubbies-the brain processes that stimuli with a higher, stronger signal, which increases the likelihood that those fingerlike dendrites grab onto that stimulus and hold it. When our brain finds little connection between the stimulus and what we already know, the signal weakens, and little or no connection is made-in other words, no learning takes place.

Thus, to understand how the brain learns is to understand that teachers, trainers, and facilitators simply must find some way to connect new information to something the learner already knows. Without that connection, learning is simply not taking place.
Enriched environments
All of the information about the brain I've shared up to now is related to the notion that the adult brain has plasticity, which means that as long as it is stimulated, it is possible to continue to grow-to build new connections; in fact, Feyler (as cited in Wilson, 2006) specifically defines plasticity as the capacity for the brain to grow. But, as Taylor and Marienau (2016) point out, the role of plasticity vis-à-vis prior experience is more important that just growth; plasticity's critical role is in allowing the brain to adapt and change over time based on learning that comes from new experiences. Plasticity means that the connections made in the brain are "adapted, elaborated, and organized as new experiences strengthen or weaken existing synapses" (Taylor & Marienau, 2016, p. 28). In other words, there is a direct relationship between plasticity and prior experience. In fact, Ebner (1996) suggests that learning is defined as the direct result of experience combined with the storing of new information, and Turkington (as cited in Wilson, 2006) suggests that enriched environments have a critical effect on plasticity.

The most impactful way to leverage this relationship between prior experience and plasticity is to place the learner in an enriched environment-that is, one with multiple types of stimuli. Again, there is science behind these assertions. Turkish researchers Ozel et al. (2008) note that each stimulant coming from an environment increases the number of dendrites and, thus, the likelihood of a synaptic connection between the axon and dendrite. In other words, creating enriched environments increases the biochemical likelihood of the creation of a new neuronal network. Numerous research studies have found that mice placed or living in enriched environments (e.g., environments with water mazes, platforms, tunnels, multiple toys, wheels, etc.) saw positive impacts on various cognitive functions-e.g., increased spatial acquisition and retention, increased behavioral flexibility, increased long-term memory, and fewer errors on learning tests (Frick et al., 2003; Garthe, Roeder & Kempermann, 2016; Yuan et al., 2012). Wilson (2011) reports that similar results are now being found in adults age 50-70, though testing on adults is still quite nascent.

Let's move from the science of enriched environments to a metaphor to help drive this concept home. Robert Sylwester was a Professor of Education at the University of Oregon who focused on getting educators to easily understand the brain. He suggested that our brains are like a jazz quartet. If you're familiar with jazz, you know that the sound is constructed in a way that is random, miscellaneous-some might even call it discordant. But these discordant sounds come together to produce a congruent sound. Similarly, the brain needs enriched environments to learn because it processes things in a multi-modal fashion. You can think of like a jazz quartet-lots of layers come together to produce one congruent sound. Likewise, when learning, our brain produces a more congruent image when presented with stimuli from many different avenues. Things like pictures, charts, sounds, smells, vivid images, stories, colors, music, and poetry are all examples of ways multi-modal stimuli that help create enriched environments. Creating an enriched environment also increases the likelihood that you will connect with a higher percentage of your learners' prior experiences. If I tell a story that doesn't necessarily connect with a learner, but then I also show an image that does, then I've made a connection where I may not have with just the story!

To create an enriched environment in our classrooms (whether online or on campus), instructors should avoid what are called low-frequency activities, such as lectures, and utilize high-frequency activities-in other words those that utilize multiple modalities to increase the likelihood of strong synaptic connections. Basically, lots of stimuli and lots of engagement.

For example, I teach a graduate course called Adult Learning Strategies and Theories. This class is one in which I teach learners who want to become teachers or trainers how to be good teachers and trainers. One of the lessons in this course is the importance of understanding theory. I want my students to truly understand what theory offers us as teachers and trainers, and-even more importantly-how application of particular theories impacts the strategic and tactical choices we make in learning situations.

This is not a concept I can simply provide a textbook definition for-not if I really want learning to take place. I have to engage my students in truly learning-in creating new neuronal networks-I have to engage my students in multiple modes of interacting with this concept. I must create an enriched environment.

Therefore, when I teach this concept online, I engage in all of the following to teach this concept:

The students, of course, have reading.

I then ask students to watch a brief (5 to 7-minute lecture) in which I explain what theory is and how it serves as a lens through which they view the world, which, in turn, impacts decisions they make.

In my video, I put a pair of glasses on and I give this example:
Let's say I have a theory that animals deserve kindness and that anyone who does not treat animals with kindness is a bad person. This pair of glasses represents that theory. Thus, it literally represents how I see the world through that that lens. When I view the world through this lens, it impacts the choices I make. Because I think animals deserve kindness, I choose to be "mom" to several animals and treat them kindly. I choose to help animals in need by donating my time and money to animal organizations. Because my theory is that people who do not treat animals with kindness are bad people, that, too, impacts choices: I am more likely to choose to be friends with people who think like me (I don't hang around a lot of hunters). I lobby for harsher penalties for those who abuse animals. My theory about animal kindness impacts dozens of choices I make within my life in a given week. The same is true of any theory. If I view the world through an andragogical lens (I now put on a different pair of glasses) , I believe adults learn differently than children, and I believe that the experiences they bring to the table are critical to their learning. Thus, if I truly believe this theory, that must impact the strategy and tactics I choose as an instructor, yes?

Next, to add even more stimuli-i.e., enrichment-I ask my students to choose the theory that resonates with them the most, the one that likely represents their most common "lens," and to generate any type of analogy (metaphor, simile, story, parable, cliché, idiom) to explain or describe that theory. They must record their response and post a video on the class discussion board. I ask them to share the analogy they've crafted, but NOT to explain how or why that analogy illustrates their chosen theory. That work is done by their peers when they react and respond to the analogy-the peers must explain the link they see between the analogy and the theory. For example, I recall one student who immediately likened Constructivism to the book "The Boy Who Was Raised as a Dog." She merely shared this observation with her peer, who then had to explain the link he saw between the two.

I create many and varied stimuli with these activities. Moreover, not only have I crafted an enriched online environment, I have increased the opportunities for the new information (the theory or theories) to connect to my learners' prior experiences. My students have no choice but to tap into their prior experience to not only link a (new) theory to an (existing) analogy they already knew, but also to explain the connection they are seeing in their peers' analogies.

As another example, let's say I was teaching my students about constructivism-the theory that the process of learning takes place in the act of experiencing things and reflecting on those experiences. If I wanted to create an enriched environment, I might ask my students to choose one of their strongest personal mottos or beliefs (e.g., mine would be "better safe than sorry") and to reflect on what experiences in their lives led to the creation of that motto or belief. Then I would ask my students to construct a mind map, or perhaps a collage, of the various experiences, thoughts, and concepts that have led to the building of that belief. I might then ask my students to use their phones or webcams to record a "virtual tour" of their mind maps or collages. If I wanted to add even more enrichment, I might ask my students to listen to a podcast from Hidden Brain (the NPR radio show) about meaning making, or to find another podcast or video of their choosing. Or I could ask them to have an online discussion with a partner about how they anticipate this motto or belief will play out as they move farther into their professions.

The beauty of creating enriched environments is that using multiple stimuli increases the biochemical likelihood of connecting to a learner's prior experience. Therefore, my students are more likely to connect to-and thus remember and learn-this information because I have used multiple modalities to share it.
But Isn't This Just Multiple Intelligences?
I get this question a lot: isn't the concept of enriched environments the same thing as Howard Gardner's Theory of Multiple Intelligences? There is a great deal of overlap, to be sure, but they are not the same thing. Gardner's theory, which was groundbreaking at the time, was indeed based on research, but not specifically research about how the brain learns; rather, it was based, more broadly, on how the brain works or performs, or doesn't (Gardner, 1999, pgs. 33-46). And though the practice of addressing multiple intelligences and creating enriched environments may certainly (but not necessarily) look similar in practice, cognitively speaking, they are fundamentally different. The fact that Learner A is intelligent in a different way from Learner B is a fundamentally different assertion than the fact that both learners' brains produce a more congruent image when they are presented with multiple stimuli because both of their brains (assuming healthy, normal functioning) process stimuli in a multi-model fashion (again, like a jazz quartet).

Another way to illustrate the distinction between multiple intelligences and enriched environments is to imagine you are teaching only one student rather than a group of students. If you teach only one student and wish to address multiple intelligences, you would assess which intelligence that learner possesses most strongly and address that intelligence in order to teach her or him. That does not necessarily mean you would be creating an enriched environment-you may need only one kind of stimuli to address said intelligence. However, if you wish to ensure learning is taking place, you would be sure to create an enriched environment by utilizing multiple stimuli to teach that student (and if you want to ensure those stimuli address that learner's particular intelligence, all the better). But the reverse is not necessarily true: that is, regardless of how many intelligences a teacher addresses, if she or he does not connect the new information to something the learner already knows, no learning can take place. And creating an enriched environment makes it more likely this will happen.
Conclusion
This article has shared the science behind the importance of creating enriched environments when teaching and provided an example to help illustrate how you, too, can engage in the best possible practices to ensure learning is taking place in your classrooms whether online or on campus.

The field of neuroandragogy demonstrates that there is empirical evidence for the use of enriched environments, and that without them, your students may not be actually learning. To meet the needs of today's adult learner, we must utilize the science of how the brain learns to inform our instructional strategies if we truly care that our students learn.
References
Ebner, F. F. (1996). Teaching the brain to learn. Peabody Journal of Education, 71(4) 143-151.

Frick, K., Stearns, N., Pan, J., & Berger-Sweeney, J. (2003). Effects of environmental enrichment on spatial memory and neurochemistry in middle-aged mice. Learning and Memory, 10(3), 187-198.

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Allison Friederichs, Ph.D., is Associate Dean for Academic Affairs and assistant teaching professor at University College of the University of Denver. Allison engages in research and public speaking in academic and corporate sectors in the area of how the adult brain learns, and the implications of that knowledge on teaching, training, and curriculum development. She serves on UPCEA's Federal Policy Committee.

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How the Adult Brain Learns: The Importance of Creating Enriched Environments When Teaching | UNBOUND

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Introduction As adult learners now make up the majority of U.S. students, it is more important than ever for educators who administer and teach in post-traditional programs to understand how the adult brain learns.

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Harvard University Extension School, Cambridge, MA, United States

Faculty for Latin American Social Science Research, Quito, Ecuador

The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences. To determine why knowledge from the learning sciences has not had a greater impact on educational policy, this study considered evidence from the learning sciences through a previously published systematic review of the literature followed by a Delphi panel of experts on the learning science ( Tokuhama-Espinosa, 2017). This was compared with a literature review of the trends in leadership decision-making models (data-based; context; distributed; transformative; goal-orientated, results-oriented) (Appendix A in Supplementary Material). This review found 30 current educational policies that contradict evidence from the learning sciences, suggesting a disconnect between educational science and other learning sciences. While there are some initiatives underway in a small number of educational leadership sectors to incorporate more learning science data into decision-making, it is not a norm. The current business-oriented model in educational policy design may explain this divide. The analysis of these results considers how switching from a business to a learning science model may result in different educational priorities. Such a vision offers a distinct and possibly more universally acceptable measure of "quality" education, detached from the immediate social and political goals and independent of the historical times in which they are taken. This paper suggests further research into this new learning sciences evidence-based framework on which educators base policy decisions.

Introduction

Educational Leadership

Public expectations of educational leaders have changed over the past 50 years ( Hallinger, 2011). Educational administration has given way to a broader personal role for educational leaders though it continues to maintain the universal objective of free and equal education for all ( Shields, 2017). This vision evolved in the United States from the 1960s and 1970s as schools sought out people with "courage, initiative and imagination," ( Broudy, 1962, p. 132) to the 1980s−2000s when the goal was to be caring ( Beck, 1994), moral ( Hodgkinson, 1991), and ethical ( Beck and Murphy, 1994). Starting in the early 2000s educational leadership added on elements of social justice ( Bogotch, 2002) and equity in leadership roles ( Grogan and Shakeshaft, 2010). Beginning at the turn of the twenty-first. century, technology ( Picciano, 2002) and innovation ( Jackson and Kelley, 2002) were added to the growing list of educational policy objectives, as was a new value in international comparisons of school leadership and decision-making (e.g., Darling-Hammond et al., 2007).

In a 2019 analysis of competency-based standards for school principals in Canada, Australia and the United States, Lambert and Bouchamma found that 85% of the measures that improved student success were similar or identical across countries, suggesting a shared vision of good leadership. These included 20 "professional behavioral standards," only three of which came directly from the learning sciences ("knowledge of pedagogy/programmes [sic], "learning practices and strategies" and "needs of the students"). Ten of the 20 standards came directly from business models (laws and policies; material and financial resources; human resource management; organizational climate; improvement and innovation; collaboration, shared leadership and empowerment; supervision; decisions and responsibilities; mission and vision formation; and data use and management). The last seven standards can be found in both the learning sciences and business models of educational leadership (balanced personal and professional life [self and others]; professional development [self and others]; technology [ICT]; communication; adaptation to change and feedback; community partnership and outreach initiatives; cultural diversity [inclusion]) ( Lambert and Bouchamma, 2019, p. 61). Other international comparisons similarly place educational leadership skills and behaviors into similar categories and suggest both a humanization of leaders in which personal investment into group dynamics is valued, as well a deeper reliance on data for decision-making (e.g., Amanchukwu et al., 2015).

Whereas, it was once thought that the role of principals and department heads was to manage ( Grace, 2005), effective leaders are now asked to inspire and transform ( Anderson, 2018). The current literature on educational leaders reflects their ever-growing areas of responsibility. On one end of the spectrum this spans from "instructional leadership" (e.g., Hallinger et al., 2015) in which school leaders are meant to drive better teaching initiatives (e.g., Smylie et al., 2016), social-emotional learning and wellness programs (e.g., Bryant et al., 2016), to the other end of the spectrum where they are also responsible for the design and maintenance of sustainable buildings (e.g., Veronese, 2012), and community outreach initiatives (e.g., Epstein, 2018).

To achieve this broad band of responsibilities special competencies are required. Some have researched how these competencies impact success through the effectiveness of different personality types or behaviors (e.g., Judge et al., 2002; Amanchukwu et al., 2015), or how theoretical models of leadership transpose onto school environments ( Wang et al., 2017; Gumus et al., 2018). Only a handful of studies have considered the broad range of responsibilities and the difficulties of prioritizing them (e.g., Cheong Cheng and Ming Tam, 1997; Scheerens, 2012). The challenges of being a good educational leader grows with every new competency, which continue to be added to the profile over time and as the role of schools expands. That is, what was valued 50 years remains valued today, but along with those competencies are newly added responsibilities.

The complex nature of educational leadership can make it tempting to go "back to the basics" of good administrative skills and core management concepts. It can be argued that managing numbers is easier than managing people ( Pfeffer and Veiga, 1999). Therefore, many find the decision of how to be a good educational leader easier when they lean toward the business and management side of their role than the educational side, meaning many schools are run more like businesses than places of learning ( Senge et al., 2012; Onorato, 2013). The business or economic model of quality education celebrates the achievement of cost-effective practices ( Levin and Belfield, 2015). As a public good that is almost always underfunded ( Ikpa, 2016), many educational leaders feel that their role is to stretch each dollar as far as possible, which in many cases means favoring decisions in which budget items that provide a high level of impact at low cost are favored. Standardized testing is an example of an attractive decision which promises a lot with a relatively low investment ( Dolton and Marcenaro-Gutierrez, 2016). Other researchers suggest, however, that education is not actually underfunded, but rather poorly managed ( James, 2015), adding to the pressure on educational leaders to spend wisely. An international comparative study found that there is a global trend is the use of "market terminology in educational settings" and "increased accountability and responsibility for school leaders," ( Townsend, 2011, p. 93). There are many, however, who also see the importance and potential gains of embracing more complex models of educational leadership.

Pont, for example ( Pont, 2013), describes both professional as well as behavioral standards of principals. In the professional standards he suggests five basic groupings: (a) prioritize guidance (e.g., mission, vision, innovation); (b) establish organizational conditions (e.g., material and financial resources); (c) develop self and others (e.g., professional development and human resource management); (d) exercise pedagogical management (e.g., use of relevant data, technology); (e) create harmony within the school (e.g., communication, organizational climate). In the behavioral standards, he suggests four groupings: (a) flexible management of change (e.g., adaptation to change and feedback); (b) communication; (c) values (e.g., decisions and responsibilities, cultural diversity); and (d) theory linked to practice (e.g., use of relevant data, results, and research). Pont's detailed work suggests that many educators are actively involved in trying to adapt to the growing list of responsibilities and several have managed to systematize the understanding of this range of tasks.

The Learning Sciences

The goal of the learning sciences is to, among other things, study how people learn best and under which conditions ( Sawyer, 2005a). Originally called the "science of learning" and changed to the "learning sciences" after the Cambridge Handbook of the Learning Sciences was revised in 2006, this field is really a collection of more than a dozen fields that share common foci on problems ( Sawyer, 2008). The learning sciences include a wide range of fields such as neuroscience, psychology and education ( Fischer et al., 2018), which grow independently but also collectively.

The new field of Mind, Brain, and Education science, founded in 2004 at the Harvard University School of Education, seeks to elevate teacher practice through greater professionalization ( Fischer et al., 2007). In part, this is done by helping educators see their roles are equally important in resolving school challenges as that of neuroscientists and psychologists. Mind, Brain, and Education science is a sub-field of the greater learning sciences and responds to challenges on topics of equal concern to neuroscientists, psychologists and educators. For example, "motivation," is studied by multiple learning sciences, but each with its own methods and units of analysis (neuroscientists study the brain, psychologist study the mind and educators study learners in classroom contexts). Mind, Brain, and Education science attempts to unify the collective knowledge on any one of these topics through translational research and/or unique methodologies. The logic of this approach is that it is more likely that a transdisciplinary vision of a problem in education will yield a better solution than a single discipline perspective ( Tokuhama-Espinosa, 2008).

There have been international recommendations, such as the OECD's expert panel on Pedagogical Knowledge and the Changing Nature of Teaching ( Guerriero, 2017), and counsel from academia pushing educational leaders to consider "How Can the Learning Sciences (Better) Impact Policy and Practice?" ( McKenney, 2018). Additionally, there have been two international Delphi expert panels which sought to gain consensus by learning scientists around the world about how educational leaders' work and teachers' new pedagogical knowledge should be shaped by a learning sciences knowledge base ( Tokuhama-Espinosa, 2008, 2017). These studies point to a consistent base of knowledge that should inform educational decisions and included a list of research, practice and policy goals that should guide educational decision-making processes ( Tokuhama-Espinosa, 2015, 2017). Only a small number of teacher colleges and policy makers, such as the Deans for Impact (2015), have actually incorporated learning sciences documentation into their decision-making practices, however. To better understand the slow uptake of information from the learning sciences into educational practice, this study sought to compare educational decision-making with evidence from the learning sciences to see where they differed and why.

Research Question

The primary research question of this study was How and to what extent do educational leaders take into consideration information from other learning sciences when making policy decisions?

Once answered, a new question emerged which was considered in the Conclusions: Can the learning sciences catalyze a change in educational policy priorities?

Materials and Methods

Materials

This study reports on the fourth of four stages of research, which is a comparative review of the literature from educational leadership and the learning sciences. The literature review specifically studied how decisions in favor of quality education are made from the educational leadership perspective as compared with the learning sciences perspective. The first stage of this research was a triangulated mixed-methods design including a grounded theory meta-analysis of the literature using open coding, followed by an international Delphi panel on Mind, Brain and Education science conducted by the author for her doctoral thesis ( Tokuhama-Espinosa, 2008). The second stage was conducted in 2015 which consisted of identifying 217 indicators that have been used to measure quality education over the past 40 years in 34 OECD nations ( Tokuhama-Espinosa, 2015) and how countries prioritized them based on individual choices including long- vs. short-term investments, budgetary restraints and political promises. This prioritization was used to identify key areas for educational leadership decision-making that were analyzed from both educational administrator perspectives and learning scientists' viewpoints. The third stage was a 10-year follow-up to the international Delphi panel on Mind, Brain, and Education science, proceeded by a meta-analysis of the literature 2007-2017 ( Tokuhama-Espinosa, 2017), the goal of which was to confirm learning science principles, tenets, neuromyths and research, practice and policy goals. This study along with the first Delphi in 2008 were used to establish the learning scientists' perspective. These background studies provided the platform upon which a comparison of the learning sciences could be made with educational leadership.

The literature review of this paper explores educational leadership research to determine areas of responsibility (Appendix A in Supplementary Material) based on key words used in meta-analyses over the past decade. Research was limited to studies for which there was free or open access or available through the Harvard Online Library (HOLLIS) and published between 2000 and 2019. This list was used to (a) appreciate the spectrum of responsibilities of educational leaders and (b) and compare it with the types of problems learning scientists research.

Methods

Backward Design

This literature review study applied Backward Design planning (objectives, evaluation, activities) ( Wiggins and McTighe, 1998) to compare and contrast the ways educational leaders and learning scientists articulate problems, frame research questions and identify objectives. One hypothesis was that heuristics was responsible for both actors' decision-making frameworks.

Backward Design or Understanding By Design from the book title ( Wiggins and McTighe, 1998) means beginning with the end in mind, which points to a clear path to success by marked stages of advancement. Backward Design is a planning mentality that was first introduced in the 1940s in business contexts ( Tyler, 1949) and since the late 1990s in educational models ( Wiggins and McTighe, 1998). In Backwards Design, users are prompted to first identify the objective. Second, they choose the evaluation method(s) that will best offer evidence of advancement toward that objective. Third and last, users are asked to select the activities which would most likely be able to generate the indicators for evaluation.

Wiggins and McTighe note that the most complicated aspect of Backwards Design is appropriately identifying the objective. It is for this reason that this study undertook a review of multiple literary sources to identify the collective agreement on objectives in education, rather than use a single perspective. Wiggins and McTighe note that identifying objectives individually can be deceptively illusive to educators. It is not uncommon to hear teachers declare an activity as an objective ("We will play a crossword puzzle" rather than "Students will improve their vocabulary"). Policy makers also mistakenly label global policy expectations as objectives ("Improve education") without understanding that the precision with which they can identify the objectives, the easier it will be to both measure and plan activities around it. It is for this reason that meta-analyses and systematic literature reviews were used to identify the objectives of learning scientists and objectives of educational leaders.

The reference to "backwards" is that many educators and educational leaders complete this planning process in the opposite order. That is, it is not uncommon to find that teachers and educational policy makers execute activities/policies and then evaluate them, sometimes without ever considering how closely the objective matched the activity or the evaluation tool.

In business, leaders set a target objective (for sales, or growth in market share, for example). They then choose indicators for success (e.g., earned income or brand affiliation testimonies). Lastly, they select the best activities to reach the objective(s) (e.g., investing in advertising, interviews with product users, and so on). In education, leaders do much the same. Educational leaders identify objectives (e.g., equitable timetabling for teachers or improved test scores), select an evaluation method (e.g., satisfaction rates of teachers and number of hours assigned per person or additional time for student test preparation), then choose activities that help them reach their goals (e.g., buy a timetabling software and review prior years' planning or devise test preparation activities, and so on).

Figure 1. Educational leadership requirements.

Figure 2. The learning sciences.

Figure 3. Backward design literature review parameters.

Determining the objective in Backwards design is complicated mainly due to the "framing" created by word choice ( Nelson, 2011) and influenced by heuristics ( Lockton et al., 2013). The precise articulation of objectives permits a clear and logical decision about the choice of evaluation tool and activities.

In summary, the current study consisted of a literature review of educational leaders' areas of responsibility based on publications between 2000 and 2019, which was compared with learning scientists' areas of responsibilities based on two Delphi panels on the learning sciences ( Tokuhama-Espinosa, 2008, 2017).

The evaluation of educational leader responsibilities was gauged by using the 217 indicators used to measure quality education by OECD member countries between 1975 and 2015 ( Tokuhama-Espinosa, 2015). The evaluation of the learning scientists' responsibilities was measured by using the second Delphi panel's stated research, practice and policy goals ( Tokuhama-Espinosa, 2017).

The results permitted the comparison and contrasting of the ways educational leaders and learning scientists approach the objective of quality education. This comparison identified 30 current educational policies that contradict evidence from neuroscience, psychology and other learning sciences, suggesting a disconnect between educational science and other learning sciences.

Theoretical Frameworks

This paper examined the contrasting views of educational leaders and learning scientists in their articulation and response to important topics in education including school start times, learning modalities, curriculum priorities, evaluation, teacher formation among others. To do so, two distinct theoretical frameworks were used: (a) Heck and Hallinger's Educational Leadership perspective; and (b) Sawyer's construct of the Learning Sciences, each of which is briefly described below.

Educational Leadership

First, today there is less agreement about the significant problems that scholars should address than in past years. Second, scholarly directions seem to be changing, as an increasing number of scholars are approaching educational leadership and management as a humanistic and moral endeavor rather than a scientific one. Third, although there are more diverse and robust methodological tools available for inquiry, programs of sustained empirical research are few in number. Fourth, a reluctance to evaluate the worth of contrasting conceptual and methodological approaches according to an accepted set of scholarly criteria leaves researchers, policy-makers and practitioners to fall back upon individual judgments of what is useful and valid knowledge. Finally, a lack of empirical rigor in the field continues to impact the development of a future generation of researchers (p. 229).

Based on Heck and Hallinger's work, this paper begins with five premises. First, there are diverse problems in education require leadership attention, and there is not always agreement on the priorities. Second, education is both a humanistic as well as scientific endeavor. Third, more diverse and robust methodological tools should be used in educational inquiry. Fourth, individual judgement is valid but should be informed by data. And fifth, empirical rigor should be improved in educational policy decisions.

The Learning Sciences

The theoretical framework for the learning sciences is derived from Sawyer's work ( Sawyer, 2005a, b, 2008) as he has been one of the emergent field's best chronologists and has been featured as a main author in this field by the OECD and the Cambridge Handbook of the Learning Sciences. Sawyer suggests that

Learning sciences is an interdisciplinary field that studies teaching and learning. Learning scientists study learning in a variety of settings - not only the more formal learning of school classrooms, but also the more informal learning that takes place at home, on the job, and among peers. The goal of the learning sciences is to better understand the cognitive and social processes that result in the most effective learning, and to use this knowledge to redesign classrooms and other learning environments so that people learn more deeply and more effectively...This new science is called the learning sciences because it is an interdisciplinary science; the collaboration among these disciplines has resulted in new ideas, new methodologies, and new ways of thinking about learning [bold by author] ( Sawyer, 2008, p. 1).

A premise of this paper derived from Sawyer's work is on the role of researchers, teacher-practitioners and educational leaders as people who can leverage the new knowledge from the learning sciences to redesign learning.

Analysis

To analyze the comparison of the educational leadership objectives with learning science objectives, Tokuhama-Espinosa's decision-making process for the evaluation of quality education ( Tokuhama-Espinosa, 2015) was compared with the Delphi panel's 2017 delineation of goals for quality education ( Tokuhama-Espinosa, 2017).

The Evaluation of "Quality" in Education

A quote attributed to many people including Cameron (1963) is apropos in analyzing findings: "Not everything that counts can be counted, and not everything that can be counted counts." There is a temptation in education to measure what is easily measurable, rather than identifying what may be the most important ( Buros, 1977; Madaus and Stufflebeam, 1988; Coe and Fitz-Gibbon, 1998; Harvey and Newton, 2004; Ozga et al., 2011; Belfield and Brooks Bowden, 2019). This paper considers how educational decision-making often falls into the temptation of low-cost initiatives rather than framing decision-making around stated objectives using the learning sciences. This slant in favor of a business model of education is easily justified in the realm of economic planning, but becomes less defensible when framed in light of student learning objectives.

As Tokuhama-Espinosa found in a 2015 review of 40 years of literature on the evaluation of quality education, decision-makers "may resort to measuring easily available or less costly data, as opposed to appropriate data for their objectives" ( Tokuhama-Espinosa, 2015, p. 115). The 271 indicators used by OECD countries to measure quality indicators were divided into 11 categories, which were similar to the areas of responsibility mentioned by educational leaders: (a) Coverage; (b) Equality and Equity; (c) Retention, Completion and Staying Rates; (d) Standards; (e) Academic Achievement; (f) Teachers and Teaching; (g) Evaluation; (h) Finance; (i) Governance; (j) Family and Community; (k) Context (Culture, Legal, Demographic). Findings from the study showed that the "common or standard approach" of those responsible for educational quality was to identify quality education indicators by appraising "what is easily accessible, less costly, politically weighty, or recommended by large ...organizations," ( Tokuhama-Espinosa, 2015, p. 115) rather than to "find systematic, recurring reflective processes that respond to the many distinct aspects of indicator choice," (p. 115). In the case of educational decision-makers at the school level, pressure to use evaluation tools is compounded by market forces ( Burch, 2006) which sometime reveal a circular relationship between textbook publishers and testing companies ( Saltman, 2016) and may all but ignore indicators for student success that do not lend themselves to a multiple-choice test ( Duckworth et al., 2012). It is notable that educational leaders who prioritize student learning over economic thrift often have long-term payoffs at both the individual student level as well as at a macro level of regional, state or country benefits ( Hanushek and Woessmann, 2010, 2015).

Results

Two important findings were made. First, priorities in educational leadership are not the same as priorities in the learning sciences as determined by the way each group frames educational problems or decisions. Second, what is deemed important by society, educational leaders, parents and students related to education may vary and is not always easy to measure.

Problem Framing and Articulation

Educational leaders and learning scientists often see similar problems but articulate their research questions differently, leading to distinct ways of both measuring progress toward objectives as well as the choice of appropriate activities. For example, both learning scientists as well as educational leaders are concerned about the curriculum (e.g., Glatthorn et al., 2018; Tokuhama-Espinosa, 2019), the school calendar (e.g., Farbman et al., 2015; Finnie et al., 2018), and teacher training (e.g., Allen and Penuel, 2015; Darling-Hammond et al., 2017), but they approach these issues differently based on the words they use to state their objectives. The literature suggests at least 30 questions for which both educational leaders and learning scientists have conducted research and sought answers (see Table 1). Their approaches, however, were influenced by word choice and heuristics in how they framed the problems.

Table 1. How decisions in education would look if mind, brain, and education scientists ran schools.

Word Choice: Framing Questions to Identify Objectives

Table 1 explains 30 different topics in education broken down into six groupings: (a) time; (b) space; (c) content; (d) order; (e) the meaning of "success"; and (f) teachers and teaching. Each table identifies the general topic, followed by a question or problem statement. After this question or problem statement there are two columns, one each for educational leaders and learning scientists. Below each is a question based on actual research studies that may run through the mind of each coupled with its matching objective.

The ways problems are articulated are shaped by at least three factors: (a) vocabulary ( Dove, 2014), (b) heuristics and bias ( De Martino et al., 2006), and (c) the professional vocabulary of one's field of formation ( Loewenstein, 2014). Fairhurst points out that communication style and problem articulation are not merely surface decisions, but rather are deeply seeded in the ontological foundations of different types of thinkers. Because educational leaders and learning scientists frame problems differently, they see different objectives within each category. This results in different priorities in educational improvement initiative, which will be explored in the discussion.

Identifying Important Problems in Education: Measurable vs. Important

Measurable

A second interesting result was that the literature review revealed that the 217 indicators mentioned in the 2015 study by Tokuhama-Espinosa was an accumulative list of all of the indicators used over a 40-year period by 34 OECD member countries. This means it reported what had been used but did not factor in potential question or problem statements posed by the larger community nor those posed by learning scientists. This suggests a disconnect between educational leaders' decision-making priorities and what both the general public and learning scientists consider worthy of resources.

While the "Top 10 Issues in Education" ( Knowles, 2018; Chen, 2019; Rich, 2019) reported in the popular press are for the most part included in the indicators used by the OECD countries to measure quality-including hiring the right people to be teachers in the first place and supporting them to find professional success-some important indicators with a high potential to change educational outcomes were not measured by the OECD countries-such as how early bilingualism improved overall cognition or how later school day start times influenced learning outcomes.

The strong focus on measurable outcomes is rooted in data driven decision-making which prioritizes the quantitative (often short-term) aspects of quality education and often overlooks the qualitative (often long-term) aspects of quality education, which imbalances the evaluation of multiple indicators ( Schintler and Kulkarni, 2014). Cost is often a factor in determining measurement indicators; budget restraints may push educational leaders to opt for evaluation tools that that thrifty rather than thorough or important.

Important

A third example comes from early childhood education. It has been known for the past 60 years that high quality early childhood education has a lasting impact on student learning outcomes ( Pianta et al., 2016). Good early childhood programs also improve self-regulation skills ( Montroy et al., 2016), which have greater impact than innate intelligence on academic success ( Moffitt et al., 2011). It is also clear that strong early childhood education programs shape good nutritional habits ( Schwartz et al., 2011), sleep habits ( Cespedes et al., 2014), and social skills ( Jones et al., 2015), among others.

A fourth and final example comes from bilingual education. Early biliteracy skills have been shown to mitigate the effects of poverty ( Petitto and Dunbar, 2009), yet foreign language and bilingual programs are some of the first courses (along with physical education and the arts) to be cut from educational curricula to make room for more first language and math in order to respond to standardized tests ( Crocco and Costigan, 2007). It is ironic that the enhanced executive functions from early bilingualism ( Bialystok, 2018) which have a spillover effect in all other subject areas, is eliminated from some schools in order to try and improve learning which would naturally be enhanced by bilingual education.

The decision to choose indicators which are easily measurable over those that may be more important and potentially yield better educational outcomes will be explored in the Discussion section.

Discussion

This suggests that the way problems are framed influences perspective-taking changes primarily in one of two ways. Either the understanding and articulation of the problem are changed by the heuristics of the end user (leader or scientist), or the choice of available evaluation tools drives the objective and not the other way around. This implies that there are four possible reasons why educational science has not yet considered itself a learning science: (a) Problem articulation, (b) priorities from the learning sciences not being on educational leaders' agendas, (c) the need to nudge educational leaders into the learning sciences, and (d) no natural access to learning science information on the part of educators. Each will be explained in more detail below.

Why Don't Educational Leaders Think Like Learning Scientists?

The findings suggests that the indicators currently used to make educational decisions by school leaders may be incomplete as they ignore much of the data from the learning sciences. Given the gravity of the stakes-quality education-it is important educational leaders take advantage of all of the information possible to make the best decisions in all areas of responsibility. This means that educational leaders may improve outcomes if they think more like learning scientists. Evidence from the literature indicates that there are at least four contributors as to why educational leaders do not normally consider these high impact decisions. Each of these reasons is followed by a hypothetical solution.

Problem Articulation

Contribution 1

An initial hypothesis as to why educational leaders do not think like learning scientists suggests that the problem is not only systemic in terms of professional access, but that it may also be a problem of semantics. The first contribution is that educational leaders and learning scientists articulate problems differently. That is, when asked, what is the objective? In Backward Design thinking, they would phrase and focus questions and problems with the lenses of their professions, blinding them to different perspectives.

While the use and the steps in Backward Design are the same for business leaders as for educational leaders, business leaders are often facilitated in their objectives by the quantitative nature of problem conception. That is, business goals tend to be less "messy" as they broadly focus on "clean" numbers ( Mandinach and Jackson, 2012), whereas educational goals are complicated by multiple human variables. Educational leaders walk a fine line between the business objectives of efficiency, balanced budgets, satisfied workers, and so on, and educational objectives or quality teaching and learning and the human side of education ( Daly, 2012).

The professional schizophrenia between efficiency and expediency on the one side and caring human investment on the other also explains why some educational leaders confound variables in decision making. For example, learning scientists know that test scores do not measure intelligence but rather sub-elements of limited aspects of intelligence. The complexities of human intelligence cannot be measured in a single test, no matter how comprehensive ( Tokuhama-Espinosa, 2018). Ever since "intelligence" tests were conceived in the 1920s, the objective of improved "student learning outcomes" has used test scores, primarily in math and language, as a proxy for intelligence. Despite multiple detailed arguments to the contrary, the objective (learning) and the evaluation tool (multiple choice tests) do not align as Backward Design would suggest. However, it is very expedient to use a single tool such as a standardized test. Tests are also less costly, which explains why many educational leaders may be convinced of standardized test utility ( Gipps, 2011). Mistakes stemming from the articulation of the problem explain why educational leaders and learning scientists reach different conclusions about standardized testing and other key decisions in school settings.

Not on the Radar

Contribution 2

Second, it has been suggested that the lack of attention toward some of the information from the other learning sciences, such as neuroscientific data about school start times or social-anthropological data about school calendars, is because these are not recurrent or annual leadership decisions ( Robinson et al., 2008) and are simply not "on the agenda" or "the radar" due to time constraints ( Maule et al., 2000). That is, the general day-to-day demands and the wide spectrum of responsibilities of educational leaders does not permit them the time to add in additional decisions that are not regularly attended to.

This could potentially be resolved by calling attention to the importance of these topics and their relatively weighty impact on student learning outcomes, including significant effect sizes reported in recent work by Hattie and Yates (2013) and others. Despite decades of evidence, however, the National Academies of Sciences, Engineering and Medicine suggests in their consensus study report, How People Learn II (2018) that educational leaders may be aware, they just do not prioritize these findings. This leads to a third hypothesis which is at the heart of leadership.

The Need for a Nudge

Contribution 3

Most humans conform to the status quo unless otherwise nudged ( Thaler and Sunstein, 2009). Leaders tend to be the people who can do the nudging; however, this can go against some leaders' human nature. Some suggest that the lack of attention to these types of decisions is due to heuristics ( Busenitz and Barney, 1997; Hafenbrädl et al., 2016). Humans do not pay attention or question school start times, school calendars, early childhood education policies or bilingual education because they presume if there was an important decision to be made, "everyone" would be talking about doing it ( Kim and Gambino, 2016). It takes a truly bold leader to break from the pack and the risk-aversion that accompanies change. Most people are uncomfortable with "rocking the boat" or creating dissonance in the community, and therefore, safe inertia is prized over unstable change ( Hayes, 2018).

To compound this challenge, some educational leaders may know about the evidence from the learning sciences but either are unwilling or unable to communicate it to their constituencies or do not have a built-in context through which to facilitate this data. This means that educational inertia is both a sin of inactivity as well as inability. In the first case, the leader is culpable of taking the path of least resistance and ignoring the information. In the second, however, leaders may be willing to try and nudge for change, but they may either not be good enough communicators of the message, or they may be faced with a more articulate resistance. For example, many educational leaders report that their attempts to change school start times or academic calendars was met with push-back from the community who "resisted change." Rather than "power through" toward successful implementation, they bow to the pressure of a less informed community. Or, if they attempt to rebut the resistance and fail, it may be due to their lack of persuasive dialogue. Leadership requires perseverance as well as good communication ( Marques and Dhiman, 2016), characteristics not always present in administrator profiles.

No Natural Access to Information

Contribution 4

A final contribution as to why educational leaders do not think about high-impact decisions as seen by learning scientists is that they are simply unaware they exist. Many from the learning science community believe educational leaders do not access the information or data they need to make decisions ( Thomas et al., 2019), possibly because information from the learning sciences is not naturally shared with those in educational leadership ( Morris and Sah, 2016).

Information on the learning sciences has slowly become more available to educators, however. Access has improved over the past two decades with the emergence of more frequent high-quality opportunities for professional development such as the International Mind, Brain, and Education conference, the Learning & the Brain conference and educational leaders themselves who seek out professional advice and consulting from learning scientists. The Deans for Impact, a consortium of teacher colleges in the U.S., published a basic guidebook on the learning sciences and motivates educational leader participation in academic programs that bridge administration and knowledge from the learning sciences (2015). New online communities, such as Nature Partner Journal's Science of Learning community and the journals Learning Sciences, Science of Learning, Mind, Brain and Education, and others seek to merge knowledge from educational sciences and other learning sciences such as neuroscience and psychology, for better decision making.

The existence of such resources, however, does not guarantee that educational leaders use them. This means that educational leaders should be more pro-active in exploring the other learning sciences until a natural conduit of information to and from each of the learning sciences is facilitated.

What Would Happen if Educational Leaders Thought More Like Learning Scientists?

These reflections beg the final question: What would happen if educational leaders could be more conscious of their heuristics and how they articulate problems, and consequently attempt to view problems within schools from a learning science perspective?

There have been many shifts in the role of educational leaders over the past 50 years. None perhaps is as challenging as bridging knowledge across disciplines. The slow uptake of information produced in the learning sciences into schools is testament to the complexity of transdisciplinary thinking ( Fam et al., 2018). The mental reframing of problems to reflect a broader use of data from all of the learning sciences has the potential to significantly improve education and should, therefore, be considered by educational leaders.

This suggests that educational leaders must ask themselves whether they can see the forest for the trees. That is, what is the real objective of education? For many decades, the cost-effective model ( Levin and Belfield, 2015), which has now been fueled by the data-driven model ( Meyer et al., 2016), has steered leadership decisions in favor short-term economic indicators rather than long-term learning as the ultimate measure of "quality" and often without considering the roles of all actors involved ( Levin and Datnow, 2012). If education is meant to produce human cogs in a societal wheel of progress in an efficient way, then we may already have the perfect system. However, if formal education conducted in schools is meant to maximize the potential of all students to learn, then perhaps we should start thinking more like learning scientists.

Conclusions

This study centered around the research question, How and to what extent does educational science take into consideration information from other learning sciences when making policy decisions? It was determined that policy decisions by educational leaders are complex, broad in their scope and have been studied by many researchers over the years in multiple context and with varying lenses (e.g., Darling-Hammond et al., 2007). Aside from the recommendations from the Deans for Impact (2015), however, the literature review of this study did not reveal consistent or systematic use of data from other learning sciences such as neuroscience by educational leaders. This means that the answer to the original research question is that many educational leaders do not take into consideration much of the information available from the other learning sciences when they make policy decisions, despite its availability. This is due to numerous factors, including problem articulation (semantics, heuristics), not being on educational leaders' radars due to other priority issues which are more familiar, the need for a nudge into the unfamiliar territory of the learning sciences, and that education about the learning sciences is not a "natural" part of educational leaders' typical formation. This clearly points to an explanation as to why educational science has lagged behind other learning sciences, and creates an interesting invitation to do so now.

After considering the results, this study suggests a new research question: Can the learning sciences catalyze a change in educational policy priorities? Which is explained below.

Can the learning sciences catalyze a change in educational policy priorities?

Educational leaders and learning scientists both contribute to the improvement of educational objectives in society but do so from distinct perspectives due to their academic formations and accompanying heuristics. Educational science is a sub-field of the learning sciences, yet few educators consider themselves scientists in this respect. The results of this study suggest that if educational leaders thought more like other learning scientists, priorities in educational policy decisions would change to privilege the important quality indicators in education rather than easily measurable or less costly indicators of student learning outcomes.

Some promising examples have been seen in international schools which have the luxury of applying learning science findings into real school settings. Punahou School's educational leadership team in Hawaii, for example, has made school policies on daily start-times, physical space and ergonomics, transdisciplinary thinking and mastery learning goals, and the planning of social-emotional integration policies a priority (McCaren, personal conversation May 2018) thanks to their learning science decision-making. The International School in Geneva has modified school policies in hopes of ensuring all education contributes to a life-long love of learning by articulating the ways "school" and "work" can and should be integrated (Hawley and David, personal conversation, June 2028). The International School of Bangkok has recently tried "flipping" their leadership meetings (sending video summaries before actually meeting) to leverage time for deeper and more personal exchanges and to make policy decisions after taking advantage of the social aspects of learning (Simmers, personal conversation, October 2019). A recent meta-analysis of K-12 schools that have moved to year-round schooling show modest but positively improved results ( Fitzpatrick and Burns, 2019). Glen Whitman at the Center For Transformative Teaching has been training teacher leaders to understand the principles of learning science and to incorporate them into real classroom settings. His school, St. Andrew's Episcopal, assures that "100% of their teachers are trained in Mind, Brain, and Education science" before working with students. The Kamehameha Schools in Hawaii have also recently undergone a 2-year induction into the learning sciences and are now basing policy decisions on the learning sciences (Wong, personal conversation September 2019), including the decision to incorporate early bilingual education (Fields, personal conversation, October 2019).

While these examples are of privileged school systems, they nonetheless point to encouraging examples of better learning science decision making into educational leadership practice. Many of the low to no cost policy changes which rely on new teacher training for professional development possibilities have already been adopted by some teacher colleges ( Deans for Impact, 2015) and thousands more educational leaders in the public school systems have been trained in conference formats such as Learning & the Brain and Mind, Brain, and Education ( Kelleher and Whitman, 2018) and through programs like "BrainU" at the University of Minnesota ( Dubinsky et al., 2013).

To reach the stage where educators begin to think more like learning scientists, several barriers will have to be overcome. Most importantly, access and integration of data from the learning scientists needs to be shared in a more systematic way with educators. Once available, this data must be communicated well to all actors in formal education. This in turn is dependent on broadening educational leaders' perspectives to include the semantics and heuristics of learning scientists, a choice left solely to the leaders themselves. It is also important to remember that the integration of the learning sciences alone will not cure all of the ills facing education. To respond more agilely to the challenges in education, a broader look across not only the learning sciences is needed, but also visionary perspectives from social and economic sciences as well.

Additional research will help advance the discussion around educational leaders in the learning science context. First, more research should be conducted to directly query actors about their views. How do educational leaders feel about the utility of the information coming from the other learning sciences? Do educational leaders consider that data from the learning sciences has a role to play in their decision-making processes, and if so, what is it? Second, there should be additional research as to the specific heuristics and semantics used in the different learning sciences to better understand the historical evolution of communication challenges. Third, more research is needed to identify additional areas that are of mutual concern to educational leaders and to learning scientists. Fourth, it is recommended that collaborative research efforts by educational leaders and learning scientists be conducted in order to cross-pollinate ideas for problem resolution. Such steps can potentially launch a shift in educational leadership and decision-making that is informed by the learning sciences, which would benefit both individuals and their communities.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material.

Author Contributions

TT-E design, applied, summarized, analyzed and wrote the study, and its findings.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author wishes to thank the following people for their time and energy toward the generation of this study, the lively debate and the on-going commitment to better educational practices. Thanks to the following for participating in the Delphi survey, offering to review findings, recommending panelists and/or for words of encouragement: Doris Alvarez, Eliot Berkman, Virginia Berninger, Jane Bernstein, Jeffrey Bowers, John T. Bruer, David Daniel, Bert DeSmedt, Stanislas Dehaene, Phil Fisher, Uta Frith, Howard Gardner, Walter Glannon, Ezequiel Gleichgerrcht, Usha Goswami, Ronald H. Grabner, Sonia Guerreiero, Mariale Hardinman, Takao Hensch, Paul Howard-Jones, Mary Helen Immordino-Yang, Jelle Jolles, Mary Layne Kalbfleisch, Johannes König, Iveta Kovalčíková, Abigail Larrison, Marilyn Leaske, Gigi Luk, Susan Magsamen, Pedro Maldonado, Richard Mayer, Bruce McCandliss, Renata Meneces, John Morrison, Eric Pakulak, Michael Posner, Sidarta Ribeiro, Vanessa Rodriguez, Todd Rose, Pankaj Sah, Daniel Schwartz, Jack Shonkoff, Beate Sodian, Elsbeth Stern, Michael Thomas, Nienke van Atteveldt, Judy Willis, and Patricia Wolfe. Thanks to Thomas Gorham, Julia Volkman, Juad Masters, Drew Nelson, Robert Murphy, and Curtis Kelly for their enthusiasm for this project and to Dee Rutgers for keeping me on a writing schedule.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2019.00136/full#supplementary-material

References

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The Learning Sciences Framework in Educational Leadership

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The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences.

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Patricia Furstenberg, Writer of Historical Fiction, Children's Books - Travel via Stories

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The Six Principles That Move Mind, Brain, Education, MBE - Patricia Furstenberg, Writer of Historical Fiction, Children’s Books

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The Six Principles That Move Mind, Brain, Education, MBE, ITSI SA. Education, the limits of the human brain. What are the limits of the human brain? How do we learn best? How can we aid and support our children in reaching their full potential as students? Throughout the centuries, great teachers have been guided by

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

Reenvisioning the UDL Guidelines

Because the UDL Guidelines are meant to be informed by feedback from the field as well as new research, they have been updated several times in the past. We are in the process of updating the Guidelines once again in our UDL Rising to Equity initiative.

This update will focus specifically on addressing systemic barriers that result in inequitable learning opportunities and outcomes. CAST aims to develop a transparent, inclusive, and community-driven process. If you are interested in collaborating and staying updated on our progress, we invite you to complete a brief survey.

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The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

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Disclaimer

The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here.

This model highlights just a few of the key brain regions involved in different memory functions. Since no part of the brain exists in isolation, most types of memory include more areas than the ones listed here. The big idea to take away is that different areas of the brain have different functions.

If you wish to delve more deeply into the types of memory or the brain anatomy involved, we encourage you to explore the references linked at the bottom of the page.

When we learn, our brain takes in information about the world that causes us to change our behavior in some way.

For example, a young baby doesn't understand that fire can hurt them. But thanks to learning, if they touch a candle flame even one time, they will probably not do it again. The information that feeds into the brain-candle, flame, hot, pain-is physically recorded in the connections between cells. And those connections influence our future behavior-don't touch!

As the brain takes in any information and stores it, there are physical changes to the connections between brain cells. And those connections influence our behavior.

Neurons that fire together wire together

The brain is an enormous interconnected network of specialized cells called neurons and supporting cells called glia. To transmit information in the brain, neurons pass signals to one another. This passing of signals can cause physical changes to the connections between cells.

When we learn a skill (like riding a bike) or new information (like multiplication tables), we are building brain connections. The more we use those brain connections, the stronger they get. It's the reason "practice makes perfect."

In the activity above, you can read about how injuries to certain brain areas cause certain memory functions to stop working. But just as important are the connections between them. If two brain areas can no longer communicate, then learning and memory can break down.

Like trails through the wilderness, brain connections get stronger the more they're used

Learning is a process that relies on long-term memory systems in the brain. People who study learning typically divide it into 4 steps: attention, storage, consolidation, and retrieval. These steps dovetail with the Types of Memory described above, and they provide a slightly different framework for thinking about what happens when one or more of these steps go wrong.

Attention - Our sensory systems are constantly bombarded with information, yet most of it fades before we are even aware of it. Attention is like a filter that lets important information into the brain while keeping the irrelevant information out. Without attention, we would remember very little.

Encoding - As sensory information comes in, the brain processes it. Strings of sounds become words, flashes of light and color become images. The brain begins connecting this new information with what it already knows about the world. Some information that is encoded will fade, and some will stick around.

Storage - Long-term storage of information requires new brain connections. The brain has its own way of rehearsing experiences (there's evidence that this is what dreaming is all about), which helps strengthen brain connections. But we can also consciously repeat information or skills to help the brain form strong memories.

Retrieval - Retrieval is the process of accessing stored memories. We use it when we recall a fact or reimagine an experience. But we also use it when we take in new information. It's part of what helps us connect new information to old.

Even when memory systems function normally, memory is imperfect. To learn more about the fallibility of memory, visit The Seven Sins of Memory.

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The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here. This model highlights just a few of the key brain regions involved in different memory functions.

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Smithsonian Science Education Center (SSEC)

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Good Thinking! — That’s so Meta(cognitive)!

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Cognitive Load Theory (Cognitive Load Theory - the idea that working memory is limi... Abbreviated to CLT, the idea that working memory is limited ... More ) has recently become 'The Next Big Thing' in teaching. Dylan Wiliam tweeted on 26 January 2017 that he had 'come to the conclusion Sweller's Cognitive Load Theory More is the single most important thing for teachers to know.' This is an emphatic statement and it is important to consider the implications. As teachers, there are huge demands on our time, so when considering a new strategy it is essential to evaluate the evidence.

CLT, first researched by Sweller (Sweller, 1998) towards in the late 1980s, is based around the idea that our working memory - the part of our mind that processes what we are currently doing - can only deal with a limited amount of information at one time. Reif's (Reif, 2010) description of cognitive load is extremely useful: 'The cognitive load involved in a task is the cognitive effort (or amount of information processing) required by a person to perform this task.' There are a number of excellent resources freely available online that explain CLT (see Paas et al. (Paas et al., 2003) for a useful overview), so we will only touch on the foundations of the theory here that will be useful for the rest of the article.

The theory identifies three different forms of cognitive load:

Intrinsic cognitive load: the inherent difficulty of the material itself, which can be influenced by prior knowledge of the topic

Extraneous cognitive load: the load generated by the way the material is presented and which does not aid learning

Germane cognitive load: the elements that aid information processing and contribute to the development of 'schemas'.

CLT suggests that if the cognitive load exceeds our processing capacity, we will struggle to complete the activity successfully. In summarising CLT, De Jong (De Jong, 2010) states that 'cognitive load theory asserts that learning is hampered when working memory capacity is exceeded in a learning task'.

Working memory should be seen as short term and finite, whereas long-term memory can be seen as infinite. The aim should be to move knowledge to long-term memory because when a student is exposed to new material, they can draw on this previous knowledge and the cognitive load is reduced. However, if subject knowledge is incomplete, the student is unable to fall back on the long-term memory and the working memory becomes overloaded, leading to working memory failures. According to Gathercole and Alloway (Gathercole and Alloway, 2007), indications of working memory failures include:

incomplete recall

failing to follow instructions

place-keeping errors

task abandonment.

Of course, there are many other reasons for these that are not related to CLT; however, if teachers understand how this theory applies to their classroom, they can plan their lessons in a way that takes into account cognitive load.

Reducing cognitive load

Intrinsic cognitive load can be reduced by breaking down the subject content, sequencing the delivery so that sub-tasks are taught individually before being explained together as a whole. The idea is to not overwhelm a student too early on in the introduction of new work.

Extraneous cognitive load can be reduced by the way in which instructions are presented. We make sense of new material by referencing schema or mental models of pre-existing knowledge. Lack of clarity in instruction puts too high a load on the working memory, and so too much time is spent problem-solving the instructions as opposed to new schema formation. For example, lessons that use PowerPoint with excessive writing and the teacher talking at the same time, can inadvertently generate excessive cognitive load and lead to working memory failures. Chandler and Sweller (Chandler and Sweller, 1991) write that 'Cognitive Load Theory suggests that effective instructional material facilitates learning by directing cognitive resources towards activities that are relevant to learning.'

Van Merriënboer et al. (Van Merriënboer et al., 2003) recommend using simple-to-complex sequencing to try to reduce cognitive load. They advise starting with worked-out examples (where a full solution is shown, which students then have to apply to a new question), then moving into completion assignments (where a partial solution is given and they have to complete it themselves), and then moving to conventional tasks, where they are simply given the question. This acts as a form of Progressively introducing students to new concepts to suppor... scaffolding More , which helps students to learn independently, without necessarily needing the help of their teacher for each stage.

Renkl and Atkinson (Renkl and Atkinson, 2003)further investigated this fading form of scaffolding. They suggestedthat moving through activities sequentially could reduce intrinsic load, as learners will have already mastered some of the knowledge they need to work out a solution in an earlier skill stage. Therefore, their research recommends beginning with a model (a complete example), gradually removing completed steps, which the learner will have to complete independently, and finally leaving just the to-be-solved problem.

These principles can be readily applied in the classroom by beginning with a model answer, then providing a writing frame/structure with a lot of information, followed by a writing frame/structure with less information, then finally a question that learners must complete independently without a writing frame. It is worth, though, being aware of the 'expertise reversal effect' suggested by Kalyuga et al. (Kalyuga et al., 2003), whereby if you continue to provide worked-out examples for experts, their usefulness is significantly reduced. Cognitive load theorists suggest this is because worked-out examples contain information that an expert could work out for themselves, making it redundant and therefore extraneous cognitive load rather than useful germane cognitive load.

Presenting information to minimise cognitive load

Chandler and Sweller (Chandler and Sweller, 1992) found evidence of the split-attention effect. This occurs when different sources of information discussing the same topic are separated by time or space, such as a diagram with a key that corresponds to separate text next to it. When information is presented in this way, it is left to the learner to attempt to amalgamate it, which generates extraneous cognitive load. Therefore, it is recommended that if one of the sources adds nothing new, it should be eliminated. However, if it is essential to include both sources, they should ideally be physically integrated (e.g. texts and diagrams combined). This way, extraneous cognitive load is reduced and working memory capacity can be used for intrinsic and germane cognitive load instead.

A word of caution

There are, of course, issues with CLT. Reif (Reif, 2010) writes that if cognitive load is reduced too much, 'the entire learning process would consist of too many small steps - and would thus become unduly fragmented and long'. There are also issues to do with the hypothesis being unfalsifiable. Doug Holton (Holton, 2009) points out that it is difficult to measure cognitive load, and therefore difficult to generate evidence to prove the theory.

An important question, though, is whether it is useful in the classroom. Ashman (Ashman, 2017) has explained that an understanding of CLT changed his maths teaching, and offers the following four examples:

1. I don't read out my slides - avoid simultaneous oral and text presentation

2. Break it down, further - pause for practice between individual problem types (this leads directly into number 3)

3. Example-problem pairs - give a worked example alongside an almost identical question

4. Stop after five minutes - advise students never to spend more than five minutes trying to solve a homework problem

So is CLT the single most important thing for a teacher to know? Perhaps not - it is a bold claim. But, if used correctly, it can improve teacher instruction, which is an important variable in the complex classroom environment.

KEY TAKEAWAYS

Break down subject content when introducing new topics and pause regularly to check understanding.

Present instructions clearly without using too many sources of information at the same time.

Be wary of reducing cognitive load too much - the learning process should be manageable, but not overly fragmented.

Dominic Shibli is Senior Lecturer in the School of Education at the University of Hertfordshire. Prior to that he was Head of Science and SLE for the North Herts Teaching Alliance.

Rachel West has a BSc in psychology and has been teaching psychology for three years. She is interested in raising awareness of the ways in which psychological theory can be applied to teaching practice.

References

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Cognitive Load Theory and its application in the classroom

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Cognitive Load Theory (CLT) has recently become 'The Next Big Thing' in teaching. Dylan Wiliam tweeted on 26 January 2017 that he had 'come to the conclusion Sweller's Cognitive Load Theory is the single most important thing for teachers to know.' This is an emphatic statement and it is important to consider the implications.

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Learning Sciences: Cognitive Load Theory 

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Learning Sciences: Cognitive Load Theory 

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Teaching Strategies: Cognitive Load Theory

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As adult learners now make up the majority of U.S. students, it is more important than ever for educators who administer and teach in post-traditional programs to understand how the adult brain learns. This article examines the science of how the adult brain learns and offers suggestions for faculty in post-traditional programs to capitalize on this knowledge and maximize the effectiveness of their teaching. Both theoretical underpinnings and practical tips for brain-based teaching are offered

When I speak on this topic, as I frequently do, I almost always begin by asking my audience members, "How many of you in here genuinely care that your students learn?" I typically get a gentle wave of laughter and a sea of raised hands in response. Of course, we care that our students learn; that's why we do what we do! But how do we know learning is actually taking place? Many instructors simply do what they have done for years, or they do what feels right, or they choose an activity that they read about or saw somewhere. But if we genuinely care that our students learn, then it behooves us to make informed choices about our instructional strategies so we can rest assured learning is taking place.

I will elucidate on some of what we know about how the adult brain learns, through examination of one of the most intriguing concepts from the field of neuroandragogy, an emerging field born from the intersection of neuroscience and andragogy, a term popularized by Malcolm Knowles, the influential theorist of adult education whose book The Modern Practice of Adult Education still serves as a seminal text for educators. I will provide an illustrative example of the application of this knowledge to teaching practice. The science and the tips offered in this piece are applicable to both on-campus and online learning.
Andragogy
Most teachers of adult learners are likely familiar with Knowles's theory of andragogy by now. For those who aren't, the theory can be summarized in 5 simple words: adults learn differently than children. That is why Knowles employs the term andragogy for adults, as opposed to the term pedagogy, which applies to the learning of children. The Greek root "andra" means "man," or for Knowles's purpose, "adult," while the root "agogos" means "leading of." Andragogy, then, has to do with the leading or education of adults. This demarcation is present even between adults and young adults. Most cognitive scientists mark the "adult brain" as beginning at or around age 23-past the age of the traditional college student. Thus, if you teach adults in the same manner you do, have, or would teach traditional-age students, it is highly likely that learning is not, in fact, taking place.

More specifically, Knowles's andragogical theory is comprised of six principles:

1. Adults are self-directed learners

2. Adults accumulate experience that becomes an increasingly rich resource for learning

3. Readiness to learn is related to social roles

4. Adults want immediate application of knowledge (problem-centered orientation)

5. Adults tend to be internally motivated

6. Adults need to know the reason or learning something

Although all six of these principles are important, the second principle of prior experience is the one around which this article centers. The neuroandragogical concepts explained here support and illustrate Knowles's assumption that a learner's ever-growing reservoir of experience is a rich resource for learning.
Neuroandragogy
Neuroandragogy is a relatively new field that examines the intersection of neuroscience-how the brain works-and andragogy-the field of adult learning. According to Clive Wilson, author of No One is Too Old to Learn, "neuroandragogy investigates the rigorous research of scholars in brain studies, the scientific investigation of adult learning, and the evaluation of adult intelligence. Neuroandragogy is both the anatomical and physiological study of the adult brain and the cognitive functions of the systems of the brain that participate in intelligence, memory, recall, and learning" (2006, 3). Neuroandragogy is important to anyone who teaches adults because it focuses in particular on the function of and impact to the adult brain during the process of learning.

With a perfunctory understanding of andragogy and neuroandragogy, let us now examine the science of how the brain actually learns. I will then offer tips for maximizing this knowledge toward better teacher practice.
How the Brain Learns
The brain is comprised of 100 billion neurons, or brain cells. These cells contain nuclei, which make enzymes, proteins, and neurotransmitters-all of which are critical for the nerve cells in the brain to communicate with one another. Neurons have a single axon, which is a long tube that sends electrical impulses-called action potentials-to other cells. And neurons also have dendrites, which are more complicated structures (imagine little hands), which receive electrical impulses from the axon terminals of other neurons. A synapse is the specialized site at which that communication happens. All of these things together form what's called a neuronal network (Zull, 2002).

The most fascinating thing about the formation of a neuronal network is that each one represents a physical change in the brain. Each time we learn something new-not each time we encounter something new, but rather each time learning actually takes place through a synaptic connection-a new tiny "branch" forms in our brain. But this physical transformation can only take place by connecting new stimuli (information) to an existing neuron-hence the importance of connecting to prior experience in our learners. The process of learning is literally the physical act of growing our existing neuronal networks.

One can use myriad analogies to describe the brain, but for now, let's imagine the brain as a comprised of millions of tiny little storage cubbies-like the kind you used to store your mittens and toothbrush in in kindergarten. Each time we attend to a new stimulus, our brain processes the signal coming from that stimulus by comparing it immediately to the contents of those millions of cubbies to ask-as Taylor and Marienau put it: "How is this experience like what I already know?" (2016, p. 55). When the brain finds a connection to something we already know-something that exists in one of our storage cubbies-the brain processes that stimuli with a higher, stronger signal, which increases the likelihood that those fingerlike dendrites grab onto that stimulus and hold it. When our brain finds little connection between the stimulus and what we already know, the signal weakens, and little or no connection is made-in other words, no learning takes place.

Thus, to understand how the brain learns is to understand that teachers, trainers, and facilitators simply must find some way to connect new information to something the learner already knows. Without that connection, learning is simply not taking place.
Enriched environments
All of the information about the brain I've shared up to now is related to the notion that the adult brain has plasticity, which means that as long as it is stimulated, it is possible to continue to grow-to build new connections; in fact, Feyler (as cited in Wilson, 2006) specifically defines plasticity as the capacity for the brain to grow. But, as Taylor and Marienau (2016) point out, the role of plasticity vis-à-vis prior experience is more important that just growth; plasticity's critical role is in allowing the brain to adapt and change over time based on learning that comes from new experiences. Plasticity means that the connections made in the brain are "adapted, elaborated, and organized as new experiences strengthen or weaken existing synapses" (Taylor & Marienau, 2016, p. 28). In other words, there is a direct relationship between plasticity and prior experience. In fact, Ebner (1996) suggests that learning is defined as the direct result of experience combined with the storing of new information, and Turkington (as cited in Wilson, 2006) suggests that enriched environments have a critical effect on plasticity.

The most impactful way to leverage this relationship between prior experience and plasticity is to place the learner in an enriched environment-that is, one with multiple types of stimuli. Again, there is science behind these assertions. Turkish researchers Ozel et al. (2008) note that each stimulant coming from an environment increases the number of dendrites and, thus, the likelihood of a synaptic connection between the axon and dendrite. In other words, creating enriched environments increases the biochemical likelihood of the creation of a new neuronal network. Numerous research studies have found that mice placed or living in enriched environments (e.g., environments with water mazes, platforms, tunnels, multiple toys, wheels, etc.) saw positive impacts on various cognitive functions-e.g., increased spatial acquisition and retention, increased behavioral flexibility, increased long-term memory, and fewer errors on learning tests (Frick et al., 2003; Garthe, Roeder & Kempermann, 2016; Yuan et al., 2012). Wilson (2011) reports that similar results are now being found in adults age 50-70, though testing on adults is still quite nascent.

Let's move from the science of enriched environments to a metaphor to help drive this concept home. Robert Sylwester was a Professor of Education at the University of Oregon who focused on getting educators to easily understand the brain. He suggested that our brains are like a jazz quartet. If you're familiar with jazz, you know that the sound is constructed in a way that is random, miscellaneous-some might even call it discordant. But these discordant sounds come together to produce a congruent sound. Similarly, the brain needs enriched environments to learn because it processes things in a multi-modal fashion. You can think of like a jazz quartet-lots of layers come together to produce one congruent sound. Likewise, when learning, our brain produces a more congruent image when presented with stimuli from many different avenues. Things like pictures, charts, sounds, smells, vivid images, stories, colors, music, and poetry are all examples of ways multi-modal stimuli that help create enriched environments. Creating an enriched environment also increases the likelihood that you will connect with a higher percentage of your learners' prior experiences. If I tell a story that doesn't necessarily connect with a learner, but then I also show an image that does, then I've made a connection where I may not have with just the story!

To create an enriched environment in our classrooms (whether online or on campus), instructors should avoid what are called low-frequency activities, such as lectures, and utilize high-frequency activities-in other words those that utilize multiple modalities to increase the likelihood of strong synaptic connections. Basically, lots of stimuli and lots of engagement.

For example, I teach a graduate course called Adult Learning Strategies and Theories. This class is one in which I teach learners who want to become teachers or trainers how to be good teachers and trainers. One of the lessons in this course is the importance of understanding theory. I want my students to truly understand what theory offers us as teachers and trainers, and-even more importantly-how application of particular theories impacts the strategic and tactical choices we make in learning situations.

This is not a concept I can simply provide a textbook definition for-not if I really want learning to take place. I have to engage my students in truly learning-in creating new neuronal networks-I have to engage my students in multiple modes of interacting with this concept. I must create an enriched environment.

Therefore, when I teach this concept online, I engage in all of the following to teach this concept:

The students, of course, have reading.

I then ask students to watch a brief (5 to 7-minute lecture) in which I explain what theory is and how it serves as a lens through which they view the world, which, in turn, impacts decisions they make.

In my video, I put a pair of glasses on and I give this example:
Let's say I have a theory that animals deserve kindness and that anyone who does not treat animals with kindness is a bad person. This pair of glasses represents that theory. Thus, it literally represents how I see the world through that that lens. When I view the world through this lens, it impacts the choices I make. Because I think animals deserve kindness, I choose to be "mom" to several animals and treat them kindly. I choose to help animals in need by donating my time and money to animal organizations. Because my theory is that people who do not treat animals with kindness are bad people, that, too, impacts choices: I am more likely to choose to be friends with people who think like me (I don't hang around a lot of hunters). I lobby for harsher penalties for those who abuse animals. My theory about animal kindness impacts dozens of choices I make within my life in a given week. The same is true of any theory. If I view the world through an andragogical lens (I now put on a different pair of glasses) , I believe adults learn differently than children, and I believe that the experiences they bring to the table are critical to their learning. Thus, if I truly believe this theory, that must impact the strategy and tactics I choose as an instructor, yes?

Next, to add even more stimuli-i.e., enrichment-I ask my students to choose the theory that resonates with them the most, the one that likely represents their most common "lens," and to generate any type of analogy (metaphor, simile, story, parable, cliché, idiom) to explain or describe that theory. They must record their response and post a video on the class discussion board. I ask them to share the analogy they've crafted, but NOT to explain how or why that analogy illustrates their chosen theory. That work is done by their peers when they react and respond to the analogy-the peers must explain the link they see between the analogy and the theory. For example, I recall one student who immediately likened Constructivism to the book "The Boy Who Was Raised as a Dog." She merely shared this observation with her peer, who then had to explain the link he saw between the two.

I create many and varied stimuli with these activities. Moreover, not only have I crafted an enriched online environment, I have increased the opportunities for the new information (the theory or theories) to connect to my learners' prior experiences. My students have no choice but to tap into their prior experience to not only link a (new) theory to an (existing) analogy they already knew, but also to explain the connection they are seeing in their peers' analogies.

As another example, let's say I was teaching my students about constructivism-the theory that the process of learning takes place in the act of experiencing things and reflecting on those experiences. If I wanted to create an enriched environment, I might ask my students to choose one of their strongest personal mottos or beliefs (e.g., mine would be "better safe than sorry") and to reflect on what experiences in their lives led to the creation of that motto or belief. Then I would ask my students to construct a mind map, or perhaps a collage, of the various experiences, thoughts, and concepts that have led to the building of that belief. I might then ask my students to use their phones or webcams to record a "virtual tour" of their mind maps or collages. If I wanted to add even more enrichment, I might ask my students to listen to a podcast from Hidden Brain (the NPR radio show) about meaning making, or to find another podcast or video of their choosing. Or I could ask them to have an online discussion with a partner about how they anticipate this motto or belief will play out as they move farther into their professions.

The beauty of creating enriched environments is that using multiple stimuli increases the biochemical likelihood of connecting to a learner's prior experience. Therefore, my students are more likely to connect to-and thus remember and learn-this information because I have used multiple modalities to share it.
But Isn't This Just Multiple Intelligences?
I get this question a lot: isn't the concept of enriched environments the same thing as Howard Gardner's Theory of Multiple Intelligences? There is a great deal of overlap, to be sure, but they are not the same thing. Gardner's theory, which was groundbreaking at the time, was indeed based on research, but not specifically research about how the brain learns; rather, it was based, more broadly, on how the brain works or performs, or doesn't (Gardner, 1999, pgs. 33-46). And though the practice of addressing multiple intelligences and creating enriched environments may certainly (but not necessarily) look similar in practice, cognitively speaking, they are fundamentally different. The fact that Learner A is intelligent in a different way from Learner B is a fundamentally different assertion than the fact that both learners' brains produce a more congruent image when they are presented with multiple stimuli because both of their brains (assuming healthy, normal functioning) process stimuli in a multi-model fashion (again, like a jazz quartet).

Another way to illustrate the distinction between multiple intelligences and enriched environments is to imagine you are teaching only one student rather than a group of students. If you teach only one student and wish to address multiple intelligences, you would assess which intelligence that learner possesses most strongly and address that intelligence in order to teach her or him. That does not necessarily mean you would be creating an enriched environment-you may need only one kind of stimuli to address said intelligence. However, if you wish to ensure learning is taking place, you would be sure to create an enriched environment by utilizing multiple stimuli to teach that student (and if you want to ensure those stimuli address that learner's particular intelligence, all the better). But the reverse is not necessarily true: that is, regardless of how many intelligences a teacher addresses, if she or he does not connect the new information to something the learner already knows, no learning can take place. And creating an enriched environment makes it more likely this will happen.
Conclusion
This article has shared the science behind the importance of creating enriched environments when teaching and provided an example to help illustrate how you, too, can engage in the best possible practices to ensure learning is taking place in your classrooms whether online or on campus.

The field of neuroandragogy demonstrates that there is empirical evidence for the use of enriched environments, and that without them, your students may not be actually learning. To meet the needs of today's adult learner, we must utilize the science of how the brain learns to inform our instructional strategies if we truly care that our students learn.
References
Ebner, F. F. (1996). Teaching the brain to learn. Peabody Journal of Education, 71(4) 143-151.

Frick, K., Stearns, N., Pan, J., & Berger-Sweeney, J. (2003). Effects of environmental enrichment on spatial memory and neurochemistry in middle-aged mice. Learning and Memory, 10(3), 187-198.

Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York: Basic Books.

Garthe, A., Roeder, I., & Kempermann, G. (2016). Mice in an enriched environment learn more flexibly because of adult hippocampal neurogenesis. Hippocampus, 26(2) 261-171.

Knowles, M. (1988). The modern practice of adult education: From pedagogy to andragogy. Englewood Cliffs, NJ: Cambridge.

Ozel, A., Byindir, N., Ozel, E., & Ciftciohlu, I. (2008). "Brain-based Learning and student-centrism on curriculum." Ekev Academic Review, 12(35), 343-350.

Taylor, K. & Marienau, C. (2016). Facilitating learning with the adult brain in mind. San Francisco, CA: Jossey-Bass.

Turkington (1996). The brain encyclopedia. Checkmark Books.

Wilson, C. (2011). Neuroandragogy: Making the case for a link with andragogy and brain-based learning. Paper presented at the meeting of the Midwest Research-to-Practice Conference in Adult, Continuing, Community, and Extension Education, Lindenwood University, St. Charles, MO.

Wilson, C. (2006). No one is too old to learn (Neuroandragogy: A theoretical perspective on adult brain functions and adult learning). Lincoln, NE: iUniverse.

Yuan, Z., Wang, M., Yan, B., Gu, P., Jiang, X., Yang, X. & Cui, D. (2012). An enriched environment improves cognitive performance in mice from the senescence-accelerated prone mouse 8 strain: Role of upregulated neurotrophic factor expression in the hippocampus. Neural Regeneration Research, 7(23), 1797-1804.

Zull, J. (2002). The art of changing the brain. Sterling, VA: Stylus.
Allison Friederichs, Ph.D., is Associate Dean for Academic Affairs and assistant teaching professor at University College of the University of Denver. Allison engages in research and public speaking in academic and corporate sectors in the area of how the adult brain learns, and the implications of that knowledge on teaching, training, and curriculum development. She serves on UPCEA's Federal Policy Committee.

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How the Adult Brain Learns: The Importance of Creating Enriched Environments When Teaching | UNBOUND

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Introduction As adult learners now make up the majority of U.S. students, it is more important than ever for educators who administer and teach in post-traditional programs to understand how the adult brain learns.

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What is Understanding by Design? Interview with Jay McTighe

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Jay McTighe: What questions should we consider for a modern education?

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Jay McTighe: What questions should we consider for a modern education?

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Learn how to open and use the Accessibility Checker to find issues that cause difficulties for people with disabilities.

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(Image credit: Unsplash / Steinar Engeland)

The best text-to-speech software makes your written content more accessible by reading it aloud.

Text-to-voice apps are a great way to bring on-screen words to life - whether you're focused on boosting productivity and accessibility, creating artificial voice-overs for videos, or just want to hear your own work out-loud. How often, after all, has a typo gone unnoticed until you heard your copy spoken?

Unlike speech-to-text apps and dictation software, TTS apps convert text documents to audio. The process is streamlined, and each is equipped with an unrivalled toolkit. You can even download audio files, if you're looking for the best text-to-speech software for YouTube videos or similar.

Some office programs, like Microsoft Word and Google Docs, offer basic text-to-speech (or TTS) tools. In the main, they work well - accuracy is good and seems to be consistently improving. But beyond a limited set of extras, such as accent and language, these word processors lack the full suite of functions found in TTS software.

To help you identify the right tool for the written and spoken word, TechRadar Pro tested the best text-to-speech software across the user-experience, performance, output, and pricing.

If you're working to a tighter budget, explore the best free text-to-speech software.

The best text-to-speech software of 2023 in full

Best text-to-speech software for home and work

Reasons to buy

If you're looking for a cloud-based speech synthesis application, you should definitely check out NaturalReader. Aimed more at personal use, the solution allows you to convert written text such as Word and PDF documents, ebooks and web pages into human-like speech.

Because the software is underpinned by cloud technology, you're able to access it from wherever you go via a smartphone, tablet or computer. And just like Capti Voice, you can upload documents from cloud storage lockers such as Google Drive, Dropbox and OneDrive.

Currently, you can access 56 natural-sounding voices in nine different languages, including American English, British English, French, Spanish, German, Swedish, Italian, Portuguese and Dutch. The software supports PDF, TXT, DOC(X), ODT, PNG, JPG, plus non-DRM EPUB files and much more, along with MP3 audio streams.

There are three different products: online, software, and commercial. Both the online and software products have a free tier.

Best for super-realistic voices

Reasons to buy

Specializing in voice synthesis technology, Murf uses AI to generate realistic voiceovers for a range of uses, from e-learning to corporate presentations.

Murf comes with a comprehensive suite of AI tools that are easy to use and straightforward to locate and access. There's even a Voice Changer feature that allows you to record something before it is transformed into an AI-generated voice- perfect if you don't think you have the right tone or accent for a piece of audio content but would rather not enlist the help of a voice actor. Other features include Voice Editing, Time Syncing, and a Grammar Assistant.

The solution comes with three pricing plans to choose from: Basic, Pro and Enterprise. The latter of these options may be pricey but some with added collaboration and account management features that larger companies may need access to. The Basic plan starts at around $19 / £17 / AU$28 per month but if you set up a yearly plan that will drop to around $13 / £12 / AU$20 per month. You can also try the service out for free for up to 10 minutes, without downloads.

3. Amazon Polly

Best text-to-speech system for developers

Reasons to buy

Alexa isn't the only artificial intelligence tool created by tech giant Amazon as it also offers an intelligent text-to-speech system called Amazon Polly. Employing advanced deep learning techniques, the software turns text into lifelike speech. Developers can use the software to create speech-enabled products and apps.

It sports an API that lets you easily integrate speech synthesis capabilities into ebooks, articles and other media. What's great is that Polly is so easy to use. To get text converted into speech, you just have to send it through the API, and it'll send an audio stream straight back to your application.

You can also store audio streams as MP3, Vorbis and PCM file formats, and there's support for a range of international languages and dialects. These include British English, American English, Australian English, French, German, Italian, Spanish, Dutch, Danish and Russian.

Polly is available as an API on its own, as well as a feature of the AWS Management Console and command-line interface. In terms of pricing, you're charged based on the number of text characters you convert into speech. This is charged at approximately $16 / £14 / AU$24 per1 million characters , but there is a free tier for the first year.

Best AI voice generator for podcaster

Reasons to buy

In terms of its library of voice options, it's hard to beat Play.ht as one of the best text-to-speech software tools. With almost 600 AI-generated voices available in over 60 languages, it's likely you'll be able to find a voice to suit your needs.

Although the platform isn't the easiest to use, there is a detailed video tutorial to help users if they encounter any difficulties. All the usual features are available, including Voice Generation and Audio Analytics.

In terms of pricing, Play.ht comes with four plans: Personal, Professional, Growth, and Business. These range widely in price, but it depends if you need things like commercial rights and affects the number of words you can generate each month.

5. Voice Dream Reader

Best text-to-speech app for macOS and iOS

Reasons to buy

There are also plenty of great text-to-speech applications available for mobile devices, and Voice Dream Reader is an excellent example. It can convert documents, web articles and ebooks into natural-sounding speech.

The app comes with 186 built-in voices across 30 languages, including English, Arabic, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, Finnish, French, German, Greek, Hebrew, Hungarian, Italian, Japanese and Korean.

You can get the software to read a list of articles while you drive, work or exercise, and there are auto-scrolling, full-screen and distraction-free modes to help you focus. Voice Dream Reader can be used with cloud solutions like Dropbox, Google Drive, iCloud Drive, Pocket, Instapaper and Evernote.

Text-to-speech software buying advice

How to choose the best text-to-speech software

When deciding which text-to-speech software is best for you, it depends on a number of factors and preferences. For example, whether you're happy to join the ecosystem of big companies like Amazon in exchange for quality assurance, if you prefer realistic voices, and how much budget you're playing with. It's worth noting that the paid services we recommend, while reliable, are often subscription services, with software hosted via websites, rather than one-time purchase desktop apps.

Also, remember that the latest versions of Microsoft Word and Google Docs feature basic text-to-speech as standard, as well as most popular browsers. So, if you have access to that software and all you're looking for is a quick fix, that may suit your needs well enough.

Read more on how we test, rate, and review products on TechRadar (opens in new tab).

What is the best text-to-speech software for YouTube?

What's the difference between web TTS services and TTS software?

Do I need a text-to-speech subscription?

How can I incorporate text-to-speech as part of my business tech stack?

How we test the best text-to-speech software

We test for various use cases, including suitability for use with accessibility issues, such as visual impairment, and for multi-tasking. Both of these require easy access and near instantaneous processing. Where possible, we look for integration across the entirety of an operating system, and for fair usage allowances across free and paid subscription models.

At a minimum, we expect an intuitive interface and intuitive software. We like bells and whistles such as realistic voices, but we also appreciate that there is a place for products that simply get the job done. Here, the question that we ask can be as simple as "does this piece of software do what it's expected to do when asked?"

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Best text-to-speech software (February 2023)

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(Image credit: Unsplash / Steinar Engeland) The best text-to-speech software makes your written content more accessible by reading it aloud. Text-to-voice apps are a great way to bring on-screen words to life - whether you're focused on boosting productivity and accessibility, creating artificial voice-overs for videos, or just want to hear your own work out-loud.

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If you really want to see how innovative a school is, inquire about its thinking and practices regarding assessment. For the students, does the mere thought of assessment trigger stress? Do the teachers rely heavily on high-stakes, multiple-choice, Bell Curve-generating tests? Or do the students seem relaxed and engaged as teachers experiment with new forms of assessment designed to support deep and lasting learning?

Clearly, there continues to be a place for traditional assessments of learning that prepare students for standardized tests such as ERBs, AP exams, SATs, and college midterm exams. However, we believe that independent schools, unrestrained from the chains of public education policies, can take on an important leadership role in transforming K-12 assessments from a mere measure of short-term learning to a crucial component of the teaching and learning process. In this system of assessment, learning becomes personalized, encouraging students to adopt a mindset focused on discovery and engagement rather than grades and test scores. Most important, in such a system, the sharp lines between teaching, learning, and testing diminish.

This shift in thinking about assessment is a central issue for all schools. As the research strongly suggests, when students focus on mastery of learning rather than on their performance on tests, they significantly increase their intrinsic motivation for learning.

New forms of assessment can be found in an increasing number of independent schools today. One example is in a 10th-grade United States History class at St. Andrew's Episcopal School (Maryland) taught by coauthor Glenn Whitman. Instead of offering students a typical final exam, Glenn offers them a choice: "What type of final exam would help you to best demonstrate what you have learned in this class?" Based on their reflection, students select the format of their final exam. The more traditional and familiar option allows students to sit for two hours and complete an array of multiple-choice, document-analysis, historical-geography, and free-response questions. The alternative option is to complete what Glenn calls the "Historian's Head" ¹ - a collage of images and scholarly narratives that respond to the essential questions of the course.

In any given year, 60 to 80 percent of the students choose the assessment that asks them to demonstrate their understanding through images and scholarly narratives - even though this alternative is more demanding when it comes to higher-order thinking and executive functioning skills. It is also more time intensive. So why do so many students choose this option?

Here is what a few representative students have said:

I function better on projects than on tests because I do not work well under time constraints. In a two-hour exam, I get too stressed and I take too long on one answer. With the project, I am not timed and therefore I will not be stressed.

My primary reason for creating a "head" over a traditional exam is because it will allow me to be creative and use my knowledge in a different way besides memorizing what I have to know. Also I will be able to dedicate more time to it than I would to studying for an exam.

I chose this option over the regular exam because it has an artistic element to it and we are able to spend more time on it that enables a process of deeper thinking in contrast to the quick memorization that would be necessary for the traditional fi nal.

The journey to designing this assessment has been long. Not surprising, when Glenn first began his independent school teaching career in 1991, his assessments looked a lot like those he experienced as a student. And while instinct and intuition, and student feedback, suggested that there must be a better way to measure student understanding, ultimately the growing body of research in educational neuroscience became the game changer for his 10th-grade history final exam.

The field of educational neuroscience is still quite young - converging developmental psychology, cognitive science, and education. While some of its initial findings merely support what educators have intuitively believed, it is also challenging many assumptions and providing new insight into best educational practices, especially regarding assessment.

What we do know is that a plethora of assessment-related research has shed light not just on the importance of students' mindsets, but also on the importance of continual feedback and how active retrieval of information (memorization recall), in carefully spaced intervals, can produce long-lasting learning. Research also shows that providing students with assessment choice enhances attention and engagement - and confirms that the arts can help deepen long-term memory consolidation. ² These latter points were instrumental in driving the changes to Glenn's 10th-grade history final exam.

Specifically, the following research is helping to change our understanding of the correlation between teaching and learning - and altering our approaches to student assessment.

Mindsets: Performance vs. Mastery Goals

Research on the connection between motivation and learning has focused on two types of mindsets that students develop, based on the kind of experiences (including assessments) we present them with in school. Students tend to develop either performance-related goals or mastery goals. ³

Performance-related goals are those that are linked to more traditional types of assessments. Students become motivated by the grades they achieve, their rankings compared with other students, and extrinsic rewards such as honor rolls or school awards. In contrast, students who develop mastery goals are motivated by the actual learning experiences. Their rewards arise from the challenges of acquiring and applying new knowledge and skills.

While students may possess a combination of both types of goals, those motivated primarily by performance goals tend to lose motivation and confidence when faced with difficult academic challenges or when set back by failures. In contrast, students who are motivated by mastery goals are more likely to persevere in the face of such challenges. Difficult tasks or setbacks do not diminish their motivation or self-esteem. ⁴ Students with a mastery mindset, similar to those students who described their experiences in the history class at St. Andrew's, are more likely to choose more difficult but rewarding ways to demonstrate learning.

Feedback on Performance

Another focal point of research is the timeliness and method of providing feedback to students. Timely feedback has been shown to deepen one's memory for the material assessed. ⁵ Moreover, in a study comparing immediate versus delayed feedback, the mere anticipation of timely feedback produced better performance. Students who knew that they would get immediate feedback performed better on a task than those who were told that feedback would be delayed. ⁶ Finally, studies suggest that marking answers right or wrong (as in multiple-choice tests) has little effect on learning. However, providing the correct response only after a student has spent time "struggling" to find the correct answer significantly increases retention of the material. ⁷

Researchers have also discovered that the spacing of assessments and feedback produces more effective learning. If students revisit content over carefully spaced intervals, they retain information longer than if presented with information once and then only assessed immediately after initial (short-term) mastery. ⁸

These findings, among others, show us the reciprocal relationship between assessment and learning. Providing timely and effective feedback can improve students' mastery of the content and produce more efficient - and satisfying - learning experiences.

Active Retrieval of Information

A growing body of research suggests that actively retrieving information (self-testing) produces significant long-term benefits for learning compared with passive studying (merely reading one's class notes). ⁹ While any assessment requires some type of active retrieval, having students reconstruct what they know through alternative assessments (as in Glenn's U.S. history class) leads to deeper understanding and consolidates learning in more powerful ways than traditional testing. ¹⁰

Active retrieval is also crucial as students prepare for assessments. Too often, when we ask students to reflect on their study strategies, they say they simply reread class notes. Training students to build regular self-testing into their study strategies will help them embed material into their long-term memory. ¹¹

Students with a mastery mindset... are more likely to choose more difficult but rewarding ways to demonstrate learning.

From Theory to Practice

Moving neuroscientific research on assessment into classroom practice faces two barriers. First, despite the fact that the work of educators targets the organ of learning, the brain, most teachers and school leaders have little understanding of the architecture of the brain and how it receives, filters, and applies information. Second, work that falls under the various versions of "brain-based" is too often believed to be of greatest benefit to struggling learners, those who historically might be called "learning-challenged." Such thinking is shortsighted and fails to recognize the importance of research in educational neuroscience for all students.

Also, it seems that too many parents do not want to believe what K-12 teachers intuitively know: that every student needs help and guidance to meet his or her peak potential. A parent of an Advanced Placement History student recently asked Glenn, "What can educational neuroscience do for my son? He is already getting all A's." This question came at a time in the school year when hundreds of thousands of students were gearing up for AP exams and/or final course exams. These cumulative assessments of learning place intense demands and stress on each student - stress that, research tells us, inhibits optimal learning and demonstration of understanding.

Glenn's response to this AP student's parent came in the form of a question: "What if I told you that the lessons from educational neuroscience research could potentially reduce the number of hours your son spends studying?" This question should pique any parent's interest. Research on multitasking challenges the way most students study today - especially the transaction cost of switching between social media and academic responsibilities. Additionally, research also highlights the importance of active versus passive studying and how crucial sleep is for both resting and learning.

What teachers assess should be what they want students to know, not just for an upcoming test, but also for the long term. And how we assess students has important implications for whether students will retain content or procedural knowledge for the short or long term. Far too often, as neuropsychology professor Tracey Tokuhama-Espinosa points out, "While students manage to keep enough dates, facts, and formulas in their head to pass the test, this knowledge never made it to long-term declarative memory, it was never truly learned at all (only memorized in the short term)." ¹² Research in educational neuroscience, therefore, should not only inform the types of assessments teachers give students but also help shape the strategies students use to prepare for assessments, or to work through projects, which Glenn prefers to call "learning opportunities."

Assessment Tic-Tac-Toe

The following assessment options can be used with middle school and high school students. Assessment Tic-Tac-Toe is designed to offer students options that both play to their strengths and encourage the development of new skills.

During the school year, each student must score tic-tac-toe by choosing a row, column, or diagonal assessment "chain" to be used as a summative assessment for three different units. Students are responsible for creating a grading rubric for each assessment they select as part of their assessment choice.

Podcast

3-D Medium (trifold poster,
diorama)

PowerPoint/Prezi/Keynote
Presentation

Public
Speaking/Oral
Presentation

Free Space (What else is
available that your teacher
has not learned about yet?)

2-D Medium (painting,
drawing, PowerPoint
poster)

How can all this research impact assessment at your school? Think of this as a challenge to teachers to embed richness in assessments - to think about how to foster optimal long-term learning when designing assessments for their classes or for individual students. We recognize that students - all students - have areas of evolving strength and weakness, passion and disinterest. When we get the richness right, we both challenge and support all students. What is important to understand about the human brain is that it changes with experience, a process often referred to as plasticity. When we create rich assessments that require deep thinking and problem solving, teachers are expanding students' cognitive capacities. How exciting to think of teachers as "brain changers."

Every year, we need to assess every student in multiple, developmentally appropriate ways (see sidebar on Assessment Tic-Tac-Toe). This is what differentiated assessment means. Some assessments will play to a student's strengths while others will pose significant challenges; some assessments will, wonderfully, do both.

Another central value of alternative assessments is that they help students learn essential skills for success in today's world - such as critical thinking, problem solving, communication, collaboration, resiliency, and grit. These skills are best developed through a variety of alternative assessments, particularly through project-based learning and design challenges in which "failing forward" - mistakes or failures that lead to breakthroughs, understanding, and student growth - is part of the assessment process. ¹³

Projects enhance student engagement. We also know that when students can own their learning choices, and make an emotional connection to the material, their learning is enhanced. Moreover, when we challenge students to demonstrate their learning in an authentic, purposeful way, they become more engaged in the process and actually learn more. As University of Virginia psychology professor Daniel Willingham points out, "A teacher's goal should always be to get students to think about meaning." ¹⁴ Alternative assessments do just that.

New Thinking at St. Andrew's

Here are some other important, research-informed changes in the ways St. Andrew's teachers think about assessment for their students. Such translation of research to practice further extends the school's highest-achieving students and improves student achievement overall.

Most units begin with a pretest to assess prior knowledge and to be able to measure individual student growth from the beginning to the end of a unit.

Teachers use more formative assessments, such as ungraded (thus low-stress) surprise quizzes, as a self-reflective process for students. Frequent formative assessments, which can be as short as one question, allow students to practice recalling knowledge from their long- and short-term memory. Frequent retrieval of information significantly enhances recall ability. This is called the "testing effect." It is a great way to have students begin or exit a class. And there are technology tools, such as Socrative, designed specifically to help with this process.

Teachers provide students with test-correction opportunities. Delaying or "scaffolding" feedback, and having students struggle with finding the correct answer, leads to better retention than does simply providing correct answers.

In order to help students decide appropriate study strategies, teachers ask them to reflect on what demands - attention, memory, visual thinking, executive functioning, language, neuromotor function, social or higher-order cognition - a particular assessment might be placing on their brains. This process includes providing students an opportunity to reflect - think metacognitively - on their assessment performance, particularly on how well their study strategies worked and how they might study differently in the future.

Teachers share research into the transaction costs associated with multitasking and how limiting the toggling between websites or social media channels while studying can enhance memory consolidation (certainly no easy request of today's students).¹⁵

As designers of their classes, teachers decide the appropriate assessments for a certain body of knowledge or skill. But what research regarding choice and engagement shows is that teachers should also respect the ability of students to make decisions about how they can best demonstrate their understanding (as well as their confusion). That is why it is essential that teachers and educational leaders have ongoing professional development about how the brain learns. The Center for Transformative Teaching & Learning (www.thecttl.org) at St. Andrew's was established to do just this - enhance teacher quality through training in the principles and strategies of neuroeducation.

Recent research on teacher efficacy points to the importance of deepening each teacher's toolkit with a neuroscientific lens into curriculum design and working with every student. ¹⁶ Such research supports the notion that when teachers understand the principles from educational neuroscience, they expand their teaching repertoire, including the ways they measure a student's knowledge, skills, and understanding. Assessments today, therefore, are much broader than what most of us adults experienced in our own academic journeys. And that is a good thing.

Notes

1. The final exam idea was adapted from Jim Percoco's "Historical Head" project in A Passion for the Past: Creative Teaching of U.S. History. Portsmouth, NH: Heinemann, 1998.

2. See Mariale Hardiman, The Brain-Targeted Teaching Model for 21st-Century Schools. Thousand Oaks: Corwin Press, 2012.

3. Carole Ames and Jennifer Archer, "Achievement Goals in the Classroom: Students' Learning Strategies and Motivation Process," Journal of Educational Psychology, 80(3) 260-267 (1988).

4. Paul R. Pintrich, "Multiple Goals, Multiple Pathways: The Role of Goal Orientation in Learning and Achievement," Journal of Educational Psychology, 92(3), 544-555 (2000); Heidi Grant and Carol Dweck, "Clarifying Achievement Goals and Their Impact," Journal of Personality and Social Psychology, 85(3), 541-553 (2003).

5. Harold Pashler, Nicholas J. Cepeda, John T. Wixted, and Doug Rohrer, "When Does Feedback Facilitate Learning of Words?" Journal of Experimental Psychology, Learning, Memory and Cognition, 31(1), 3-8 (2005).

6. Keri L. Kettle and Gerald Häubl, "Motivation by Anticipation: Expecting Rapid Feedback Enhances Performance," Psychological Science, 21(4), 545-547 (2010).

7. Lisa K. Fazio, Barbie J. Huelser, Aaron Johnson, and Elizabeth J. Marsh, "Receiving Right/Wrong Feedback: Consequences for Learning," Memory, 18(3), 335-350 (2010).

8. See, for example, Nate Kornell, Alan D. Castel, Teal S. Eich, and Robert A. Bjork, "Spacing as the Friend of Both Memory and Induction in Young and Older Adults," Psychology and Aging, 25(2), 498-503 (2010).

9. See, for example, Jeffrey D. Karpicke and Henry L. Roediger, "The Critical Importance of Retrieval for Learning," Science, 319(5865), 966-968 (2008).

10. Jeffrey D. Karpicke and Janell R. Blunt, "Retrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping," Science, doi:10.1126/science.1199327 (2011).

11. Nate Kornell and Lisa K. Son, "Learners' Choices and Beliefs About Self-Testing," Memory 17(5), 493-501 (2009).

12. Tracey Tokuhama-Espinosa, Mind, Brain, and Education Science: A Comprehensive Guide to the New Brain-Based Teaching. New York: W. W. Norton (2010).

13. See Paul Tough, How Children Succeed: Grit, Curiosity, and the Hidden Power of Character. New York: Houghton Mifflin Harcourt (2013).

14. Daniel Willingham, Why Don't Students Like School? A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom. San Francisco: Jossey-Bass, 61 (2009).

15. Eyal Ophir, Clifford Nass, and Anthony D. Wagner, "Cognitive Control in Media Multitaskers," Proceedings of the National Academy of Science (2009).

16. Mariale Hardiman, Luke Rinne, R. John Bull, Emma Gregory, Julia Yarmolinskaya (in review), "How Knowledge of Neuro- and Cognitive Sciences Influences Teaching Practices, Attitudes, and Efficacy Beliefs." Manuscript submitted for publication. R. John Bull, Mariale Hardiman, Luke Rinne, "Professional Development Effects on Teacher Efficacy: Exploring How Knowledge of Neuro- and Cognitive Sciences Changes Beliefs and Practice." Paper presented at the American Educational Research Association conference, San Francisco (2013).
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If you really want to see how innovative a school is, inquire about its thinking and practices regarding assessment. For the students, does the mere thought of assessment trigger stress? Do the teachers rely heavily on high-stakes, multiple-choice, Bell Curve-generating tests?

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References

(1) Benjamin, A. S., & Tullis, J. (2010). What makes distributed practice effective? Cognitive Psychology, 61, 228-247.

(2) Roediger, H. L., Putnam, A. L., & Smith, M. A. (2011). Ten benefits of testing and their applications to educational practic e. In J. Mestre & B. Ross (Eds.), Psychology of learning and motivation: Cognition in education, (pp. 1-36). Oxford: Elsevier.

(3) McDaniel, M. A., & Donnelly, C. M. (1996). Learning with analogy and elaborative interrogation. Journal of Educational Psychology, 88, 508-519.

(4) Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24, 355-367.

(5) Rawson, K. A., Thomas, R. C., & Jacoby, L. L. (2014). The power of examples: Illustrative examples enhance conceptual learning of declarative concepts. Educational Psychology Review, 27, 483-504.

(6) Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 4, 444-452.

(7) Pomerance, L., Greenberg, J., & Walsh, K. (2016, January). Learning about learning: What every teacher needs to know. Retrieved from http://www.nctq.org/dmsView/Learning_About_Learning_Report

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