Andrew began his classroom life as a high-school English teacher in 1988, and has been working in or near schools ever since. In 2008, Andrew began exploring the practical application of psychology and neuroscience in his classroom. In 2011, he earned his M. Ed. from the “Mind, Brain, Education” program at Harvard University. As President of “Translate the Brain,” Andrew now works with teachers, students, administrators, and parents to make learning easier and teaching more effective. He has presented at schools and workshops across the country; he also serves as an adviser to several organizations, including “The People’s Science.”
Andrew is the author of "Learning Begins: The Science of Working Memory and Attention for the Classroom Teacher."
We frequently hear about the dangers of “smartphone addiction.” If you search those words in Google, you’ll find this juicy quotation in the second link:
The brain on “smartphone” is the same as the brain on cocaine: we get an instant high every time our screen lights up with a new notification.
“An instant high.” Like cocaine? Hmmmm.
You might even have heard that we’ve got research about the perils of such addictions. But: can we rely on it?
A recent study asked a simple question, and got an alarming answer.
How Do We Know What We Know About Phone Usage?
Studies about smartphones typically ask participants to rate their cell phone usage — number of minutes, number of texts, and so forth. They then correlate those data with some other harmful condition: perhaps depression.
Researchers in Britain wanted to know: how accurately do people rate their cellphone usage?
When they looked at Apple’s “Screen Time” application, they found that participants simply don’t do a good job of reporting their own usage.
In other words: depression might correlate with people’s reported screen time. But it doesn’t necessarily correlate with (and certainly doesn’t result from) their actual screen time.
In the modest language of research:
We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.
So much for that “instant high.” Like cocaine.
What Should Teachers Do
As I’ve written before, I think research into technology use is often too muddled and contradictory to give us good guidance right now.
Here’s what I wrote back in May:
For the time being, to preserve sanity, I’d keep these main points in mind:
First: don’t panic. The media LOVE to hype stories about this and that terrible result of technology. Most research I see doesn’t bear that out.
Second: don’t focus on averages. Focuses on the child, or the children, in front of you.
Is your teen not getting enough sleep? Try fixing that problem by limiting screen time. If she is getting enough sleep, no need to worry!
Is your student body managing their iPhones well? If yes, it’s all good! If no, then you can develop a policy to make things better.
Until we get clearer and more consistent research findings, I think we should respond — calmly — to the children right in front of us.
I still think that advice holds. If your child’s attachment to the cellphone seems unhealthy, then do something about it.
But if not, we shouldn’t let scary headlines drive us to extremes.
As teachers and school leaders, we see an obvious strategy. If we want people to stop thinking the wrong thing, we should teach them the right thing.
More information, more skill in processing that information, will save the day.
Or, Not…
Alas, we’ve got lots of research showing that this obvious strategy doesn’t work.
In fact, it produces “backfire effects.”
The more we talk about about all the facts that rebut the myth, the more familiar the myth seems. Our attempts to undo a myth turn out to reinforce it — simply because people hear so much about it.
Another problem: the more facts we use to rebut myths, the less mental processing space people have to consider them. As is so often the case: when trying to rebut myths, less information is more powerful.
A Handy Resource
Happily, John Cook and Stephan Lewandowsky have produced “The Debunking Handbook” to help us end myths without reinforcing them.
The focus on highly practical strategies:
Using best alternative explanations
Using graphics
Limiting contradictory information
With this handbook as a guide, you can help your colleagues get past the quaint falsehoods that interfere with learning.
And as a result, you’ll clear up time for the teaching strategies that truly help students flourish.
Whenever we put a lot of effort into a project, we really want to believe that it helped.
For that reason, we might somehow overlook the signs that our efforts fell short. Or, we might exaggerate skimpy data to suggest that we succeeded.
To overcome these all-too-human tendencies, we need well designed research to gather and analyze data. The only way to demonstrate success is to look hard for failure.
Taking It Up a Notch
To look especially hard for failure, we might look at two different kinds of evidence.
For example: does self-control training work?
To answer this question, let’s start by having 11-year-olds go through a self-control training program. At the same time, we’ll identify a control group that doesn’t get the training.
We can see if the training worked in two different ways.
First: several years later, have those children (now 25 years old!) provide information about their lives. Have they completed high school? College? Do they have a job? Have they been arrested? Do they frequently get in fights?
We can also have their parents fill out similar surveys. Oh, and we’ll have the control group fill out surveys as well.
Are the children who got self-control training likelier to have more education and a job? Less likely to harm themselves and others? If yes, those differences suggests that they used those self-control strategies well.
Second: we can look at their brains.
In particular, we have decades of research showing the importance of a particular brain region for self control.
Roughly speaking, we want self-control regions of the brain — the prefrontal cortex (PFC) — to communicate well with the emotional drivers of human behavior. Better PFC communication means better self-control.
That brain region is in the middle (medial) part of the underside (ventral) of the PFC. So, we call it the ventromedial prefrontal cortex: vm-PFC. (Important note: neuroscience is fantastically complicated. This summary is a very streamlined version of a wildly intricate web of brain connectivity.)
So, after we survey the students who went through self-control training, we can have them hang out in a functional magnetic resonance imaging (fMRI) gizmo.
Our hypothesis: trained students should have better vm-PFC connectivity between the PFC and brain regions that process emotions.
Today’s Research
A team of 14 researchers have in fact done all that.
An organization in rural Georgia called “Strong African American Families” wanted to improve the prospects of children living in poverty. They developed a program that included training for parents, and for their 11-year-old children.
Parents learned about “emotional support, [and] high levels of monitoring and control.”
The children “focused on forming goals for the future and making plans to attain them.” They also learned about strategies to use when encountering racism.
14 years later (!!), the researchers gathered both kinds of data described above. That is: the children (now 25) filled out surveys. And the had an fMRI scan to measure vm-PFC connectivity.
Sure enough, both measures suggested that the training made a real difference.
That is: the children who had the training did better on measures of adult self-control. And, they had higher levels of vm-PFC connectivity.
Reasonable Conclusions
The program run by Strong African American Families was tailored to the circumstances of its participants. We should not, in other words, conclude that their program will work for everyone.
But: we have quite persuasive evidence that their program had the effects it intended — poor children grew up as more responsible adults than un-trained peers.
And: we have a good neurobiological explanation for the different behavior — their altered life trajectory included developmental differences in the vm-PFC.
All these findings give us hope that well designed self-control programs can indeed have the effect that we want them to. That’s not just wishful thinking.
You didn’t have to think very hard to decide that this statement is false.
Why? Because “canary” and “an hour” belong in different mental categories. One is a physical object; the other is a unit of time.
Unless you’re Emily Dickinson, they can’t be the same thing.
Over at 3-Star Learning Experiences, Mirjam Neelen and Paul A. Kirschner want us to think about our students as they learn new concepts.
In particular, students often have ideas in the wrong categories. When that happens, these “prior misconceptions” make correct understanding extremely difficult.
To help them learn new concepts, therefore, we don’t simply need to ply them with more information. Instead, we need to help them rethink prior misconceptions.
In other words, we need to help them reshelve old ideas in new mental categories.
For Example…
In my classroom, students struggle with the idea that The Scarlet Letter is a romance.
Why? Because they already have a very clear concept of the word “romance.” Their pre-existing definition doesn’t include … well … anything that happens in Puritan Boston.
Could anything be less romantic than, say, Hester and Dimmesdale meeting in the woods with Pearl?
My teacherly mission: help students build a new concept of “romance.” Once they think about romance as Hawthorne did, they’ll have a new category of knowledge. And, that category quite comfortably fits all the oddities that make Scarlet Letter so strange and wonderful.
For further thoughts on this process, check out Neelen and Kirschner’s post. Me: I’m looking forward to Part II!
Classrooms should do more than simply house our students. We want them to welcome students. To set an encouraging and academic tone. To reflect the values our schools champion.
That’s a lot of work for one classroom to do.
As a result, our rooms sometimes end up looking like the nearby image: a busy tumult of color and stuff.
Does this level of decoration have the desired result? Does it make students feel welcome, valued, and academic? Realistically, might it also distract them?
Two researchers in Portugal wanted to find out.
Today’s Research
Several people have studied the effect of classroom decoration on learning. (In perhaps the best-know study, Fisher, Godwin and Seltman showed that kindergarteners learned less in a highly decorated classroom.)
Rodrigues and Pandeirada wanted to know exactly which mental functions were disrupted by all that decoration. Their study design couldn’t be simpler.
These researchers created two study environments.
The first looks basically like a library carrel with a dull white finish.
The second added lots of lively, upbeat photos to that carrel.
The result isn’t as garish as the photo above, but it’s certainly quite busy. (You can see photographs of these two environments on page 9 if you click the link above.)
Rodrigues and Pandeirada then had 8-12 year-olds try tests of visual attention and memory.
For instance: students had to tap blocks in a certain order. (Like the game Simon from when I was a kid.) Or, they had to recreate a complex drawing.
Crucially, these 8-12 year-olds did these tasks in both environments. Researchers wanted to know: did the visual environment make a difference in their performance?
It certainly did. On all four tests — both visual attention and memory — students did worse.
In short: when the visual environment is too busy, thinking gets harder. (By the way, visual distraction is not a “desirable difficulty.” It results in less learning.)
Two Sensible Questions
When I discuss this kind of research with teachers, they often have two very reasonable questions.
Question #1: how much is “too much”? More specifically, is my classroom “too much”?
Here’s my suggestion. Invite a non-teacher friend into your classroom. Don’t explain why. Notice their reaction.
If you get comments on the decoration — even polite comments — then it’s probably over-decorated.
“What a wonderfully colorful room!” sounds like a compliment. But, if your students see a “wonderfully colorful room” every day, they might be more distracted than energized.
Question #2: Won’t students get used to the busy decoration? My classroom might look over-decorated now, but once you’ve been here for a while, it will feel like home.
This question has not, as far as I know, been studied directly. But, the short answer is “probably not.”
The Fisher et al. study cited above lasted two weeks. Even with that much time to “get used to the decoration,” students still did worse in the highly-decorated classroom.
More broadly, Barrett et al. looked at data for 150+ classrooms in 27 schools. They arrived at several conclusions. The pertinent headline here is: moderate levels of decoration (“complexity”) resulted in the most learning.
In other words: students might get used to visual complexity. But: the research in the field isn’t (as far as I know) giving us reason to think so.
Summer Thoughts
Here’s the key take-away from Rodrigues and Pandeirada’s research: we should take some time this summer to think realistically about our classroom’s decoration.
We want our spaces to be welcoming and informative. And, we want them to promote — not distract from — learning.
Research can point us in the right direction. We teachers will figure out how best to apply that research to our classrooms, for our students.
A final note: I’ve chatted by email with the study’s authors. They are, appropriately, hesitant to extrapolate too much from their library-carrel to real classrooms.
They show, persuasively, that visual distractions can interfere with attention and memory. But: they didn’t measure what happens in a classroom with other students, and teachers, and so forth.
I think the conclusions above are reasonable applications of these research findings; but, they are my own, and not part of the study itself.
Design thinking invites students to approach learning with an engineer’s perspective.
Students begin with a problem, and think their way towards several possible solutions. Each design thinking framework includes its own particulars, but all include variations of these steps:
deliberately explore the problem,
brainstorm several possible solutions,
create those solutions,
repeat these steps as necessary (with healthy doses of metacognition).
Here, for instance, is a 1-pager from Harvard’s Graduate School of Education that summarizes key design-thinking ideas and protocols.
To be confident that this approach has merit, we should ask ourselves two hard questions:
First: do students who learn design thinking apply it in new circumstances? If not, then the method might help students solve a specific problem — but not help them think differently about problems in general.
Second: when students apply design thinking to novel problems, do they learn more than others who don’t? If not, then this new way of thinking doesn’t seem to have made much of a difference.
So: how might we answer these tough questions?
Researchers at Stanford’s School of Education wanted to give it a try…
The Research Plan
A large research team worked with 6th graders in a California public school. They had students practice two distinct design thinking systems.
One group practiced a system that urged them to seek out corrective feedback. That is: they got in the habit of looking for constructive criticism.
A second group practiced a different design-thinking system that emphasized creating several different prototype models before deciding on which one to pursue.
Helpfully, the study design insured that students learned and used these 2 systems in different classes.
Math class (2 weeks)
Social Studies (1 week)
Science (1 week)
A week later, students took a test gave them the chance to apply those skills.
However — and this is the key point — the test didn’t resemble any of the previous design thinking work that they had done. For this reason, the test let researchers answer this question:
“Do students who practice design thinking for a full month spontaneously apply those strategies when facing new, not-obviously-related problems?”
And, given how well they did on this test, it let them answer a second question:
“Do these design thinking strategies help students solve problems more effectively?”
That is: this study design let researchers answer the two hard questions we asked ourselves at the beginning of this post.
Two Answers
This study, I suspect, will be something of a Rorschach test for people who look at its conclusions.
Skeptics — and, by the way, I myself am often in the “skeptic” category — may focus on the most straightforward finding: “there was no stand-alone effect of treatment.”
In other words: the training didn’t have a statistically measurable effect.
Optimists, however, might well have a different take.
To explore their results in greater detail, Chin & Co. analyzed data for the students based on their prior academic accomplishment.
For students in the high-achieving group, and the middle-achieving group, the design thinking training had no statistically measurable effect.
However, for those in the low-achieving group, it certainly did.
An optimist’s summary might go like this.
“Mid- and high-achieving students are ALREADY doing what design thinking teaches. That is, those student ALREADY seek out constructive feedback, and try different models before they decide on one.
The design-thinking training helped low-achieving students behave more like their mid- and high-achieving peers.
That’s great!”
If, in fact, a design thinking curriculum can help some students develop the good learning habits that other students already have, that is in fact great news.
The best way to use design thinking will clearly depend on your own school’s culture and demographics. This study gives us some hope that — used the right way with the right students — it can help students learn.
The short version is: “listening to Mozart makes you smarter!” (Translation: “Parents: run right out and by Mozart recordings for your children!”)
The longer version is: “in one study, children who listened to Mozart before they took a spatial reasoning test did better than those who didn’t. The effect lasted, at most, fifteen minutes.”
That initial study turned into several books, and several extravagant claims. In 1998, the governor of Georgia wanted the state budget to buy every child a classical music recording.
Plausible Extrapolation?
If listening to Mozart before a spatial reasoning test improves performance, then … just maybe … listening to music while I do my schoolwork will help me think better.
I know LOTS of teenagers who insist that this is true. Whenever I talk about brain research at schools, high-schoolers assure me quite passionately that they learn more with their music playing.
That’s a plausible claim. Let’s research it.
Perham and Currie tested this claim quite simply. They had adults take a reading comprehension test adapted from the SAT. Over headphones, they heard either…
…music they chose because they liked it (Frank Ocean, Katy Perry),
…music they didn’t like (thrash metal),
…music that didn’t have lyrics, or
…silence
What Perham and Currie find?
Quite clearly, these learners did their best thinking in silence.
More specifically, when they answered reading comprehension questions in silence, they averaged 61%. Listening to music without lyrics, they averaged a 55%. Music with lyrics — either likable-Katy Perry or disliked-thrash metal — led to a 38% average.
The drop from a 61% to a 38% should get everyone’s attention.
Here’s a straightforward summary for our students.
Would you like to increase your reading comprehension 20%?
TURN OFF THE MUSIC and read in silence.
Asking the Right (Narrow) Question
To sum up:
Perham and Currie’s study strongly suggests that listening to music with lyrics interferes with reading comprehension.
This study strongly suggests that listening to music during a task interferes with students’ creativity.
But, this study suggests that listening to upbeat music before a task increases creativity.
And, this study might — or might not — suggest that students who join band classes in high school improve in their ability to process language sounds … which might (or might not) have beneficial academic effects.
In other words: to understand the relationship between music and learning, we need to ask narrow, precise questions.
When students say “I study better with music because, Mozart Effect,” we can say:
a) we’ve got good research showing that’s not true,
and
b) we can’t extrapolate from very tentative Mozart findings to your homework.
One final point deserves emphasis.
I understand the desire to say: “students should study music because it helps them do this other thing better.”
I’d rather say: “everyone should make music, because it connects us to our humanity and to each other.”
Mozart or Frank Ocean or Thrash Metal. Bring it on…
To answer this important and practical question, Blasiman’s team first had students watch an online lecture undistracted. They took a test on that lecture, to see how much they typically learn online with undivided attention.
Team Blasiman then had students watch 2 more online lectures, each one with a distractor present.
Some students had a casual conversation while watching. Others played a simple video game. And, yes, others watched a fencing scene from Princess Bride.
Did these distractions influence their ability to learn?
On average, these distractions lowered test scores by 25%.
That is: undistracted students averaged an 87% on post-video quizzes. Distracted students averaged a 62%.
Conversation and The Princess Bride were most distracting (they lowered scores by ~30%). The nature video was least distracting — but still lowered scores by 15%.
In case you’re wondering: men and women were equally muddled by these distractions.
Teaching Implications
In this case, knowledge may well help us win the battle.
Blasiman & Co. sensibly recommend that teachers share this study with their students, to emphasize the importance of working in a distraction-free environment.
And, they encourage students to make concrete plans to create — and to work in — those environments.
(This post, on “implementation intentions,” offers highly effective ways to encourage students to do so.)
I also think it’s helpful to think about this study in reverse. The BAD news is that distractions clearly hinder learning.
The GOOD news: in a distraction-free environment, students can indeed start to learn a good deal of information.
(Researchers didn’t measure how much they remembered a week or a month later, so we don’t know for sure. But: we’ve got confidence they had some initial success in encoding information.)
In other words: online classes might not be a panacea. But, under the right conditions, they might indeed benefit students who would not otherwise have an opportunity to learn.
I’ve just learned that both of Dr. Blasiman’s co-authors on this study were undergraduates at the time they did the work. That’s quite unusual in research world, and very admirable! [6-11-19]
For me, individually, you might say it began when I first attended preschool. But, truthfully, learning began well before then.
I learned how to walk and speak, and to do (a very few of) the things my parents told me to do.
In the womb, I even learned to recognize sounds – like my mother’s voice.
But, let’s go much further back.
When did our species start learning? Or, before then, great apes? Or, even earlier, mammals?
Did dinosaurs learn?
How about those little one-celled organisms that developed when life began, over 3.5 billion years ago? Did they do anything we could meaningfully call “learning”?
Paul Howard-Jones answers that question with a resounding yes. And, most intriguingly, the biological mechanisms that allowed them to learn still help us to do so…all these billions of years later.
As Howard-Jones writes, learning “changes not just our mental world but also our biological form.” The basic biological and chemical mechanisms necessary for the earliest kinds of learning still help us learn today.
The Story Begins
Let’s start with E. coli. This single cellular organism has a bad rep, but we’ve got lots of very useful E. coli in our guts. And, they can – in a manner of speaking – learn.
In order to eat, E. coli have to move. And, they have two options for movement. If they’re successfully getting nutrition as they move, they want to keep going straight. If they’re not, they want to move randomly about – until they stumble into a better path to follow. Once they do, they start going straight again.
To accomplish this goal, E coli need to “remember” how much nutrition they were getting a few seconds ago, and compare that level to the current intake. Remembering, of course, is a kind of learning.
Howard-Jones helpfully describes the cellular mechanism that allows this memory comparison to happen. It’s a little complicated: think “methyl groups” and “receptors.” But, this clever and efficient system allows cells to remember, and thereby to eat and flourish. (Check out pages 24-5 for a full version of this story.)
Learning gets even cooler from there.
As evolution brought single-cellular organisms together into eukaryotes – from which sprang reptiles and amphibians and mammals and you – it produced ever-more-intricate systems for learning.
For instance, neurons evolved to ensure that multi-cellular organisms could coordinate their movements. (If each cell did its own thing, then we’d get no benefits from having all those cells.)
And, of course, neurons now form the biological basis of learning that happens in our brains.
Vertebrates and Primates
As evolution led to the development of more-and-more complex organisms, so too it produced increasingly complex kinds of learning: the ability to organize information by association, for example, or to recall something that happened yesterday.
The Evolution of the Learning Brain, devotes considerable time to primate development. In particular, it asks this question: since most evolutionary developments favor specialization, why did our species prove so successful? After all, our brains allow for great cognitive flexibility – the ability to be generalists, not specialists.
Howard-Jones answers this question by looking at the extraordinary climatic and geological upheaval at the time of our evolution.
Primates developed cognitive complexity – probably – in order to keep track of larger and larger social networks.
For instance, female vervet monkeys recognize their own offsprings’ cries. When they hear their children cry, unsurprisingly, they look at the child. When they hear someone else’s child cry, amazingly, they look at that child’s mother.
The story gets even more complicated when we look at chimpanzee dominance networks.
At the same time, later primates developed basic “theory of mind”: the ability to think about what others are thinking.
In one astonishing study, chimpanzees preferred to steal back food when researchers weren’t present – or when the container from which they stole the food was opaque. That is, chimps can think about what others can see, and behave accordingly.
All this complexity – social intelligence, theory of mind – proved especially important during the opening of the Great Rift in Africa: geological changes that led to rapidly changing climate and terrain. In this unusual set of circumstances, a species (like, say, Homo sapiens) with extra cognitive complexity was in a better position to manage upheavals.
As Howard-Jones writes:
The unique geology of the Rift Valley …is thought to have produced extreme climate variability with cycles lasting 400,000 or 800,000 years. […]
This inconsistent environment provided a novel genetic testing ground in which different hominin species were pursuing different approaches to survival, including generalizing vs. specializing. […]
Rather than evolving to fit one change, [Homo sapiens] evolved greater ability to respond to change itself.
Wow.
Classroom Implications
How should this understanding of evolution and learning shape our classroom practice?
Howard-Jones remains helpfully modest in answering this question. As he writes:
Evolution cannot tell us how to teach and learn, but it can help us frame and understand this research.
In his closing chapters, therefore, Howard-Jones encourages us to think about teaching with this perspective.
He suggests several insights about a) engagement, b) building of knowledge, and c) consolidation of learning that have evolutionary and neuro-biological grounding.
For instance: engagement. How can we help students pay attention?
Teachers have long known that novelty helps students focus. (Evolution helps explain why. Anything new could be a threat. Or, it could be food!)
Howard-Jones points out that shared attention is itself motivating:
Our strong motivation to share attention is a uniquely human characteristic that may have played a key role in our ancient cultural accumulation of knowledge, as it does today. When self-initiated, this capturing of shared attention also leads to reward-related brain activation.
In other words: schooling works because we invite our students to look with us, and to look with each other.
Another practical application: embodied cognition. Howard-Jones details several studies where a particular kind of movement helps students learn particular content.
He also explains why numbers and reading – more cultural practices than evolved cognitive capabilities – prove an enduring challenge to our students.
In Sum
Howard-Jones brings together many disciplines and a few billion years of history to tell this story.
Some readers might wish for more immediate, concrete teaching strategies. Some specialists, no doubt, disagree with his interpretation of the evidence.
I recommend this book so highly not because it tells us to do particular things, but because it helps us think in new and fresh ways about the work we have to do.
If we understand the evolutionary and neuro-biological sources of our difficulties and our enormous potential, we can think more realistically about avenues of success in schools.
In the words of Howard-Jones’s subtitle, we’ll understand how we got to be so smart. We might even understand how to get smarter still.
Over on Twitter, Blake Harvard has started a lively discussion about the persistence of Learning Styles Theory.
Given that the theory has been so widely debunked, why does it — zombie-like — stagger on?
To answer this question, Harvard checked out the websites of the top ten schools of education in the US. He searched for “learning styles,” and explored the first page of the search results.
One school (Columbia Teachers College) included a link that debunked the myth.
Two schools had no relevant mentions.
The remaining seven (including the ed school I attended!) included links to a neutral or positive description of learning styles.
Frying Pan, Meet Fire
Since posting his results, Harvard has raised the stakes.
In this twitter post, he invited readers to check out their own ed schools’ pages to see what they found.
The grim responses will dismay you. I haven’t tabulated the numbers — the list keeps growing — but the “roughly 70%” level seems plausible.
Imagine, for instance, that medical schools were still teaching miasma theory to explain cholera. Or that law schools were teaching that it’s unconstitutional for women to vote in federal elections.
In effect, that’s happening in today’s schools of education.
An Alternative, Additional Explanation
Perhaps this myth persists because our professional schools teach it. (Or, fail to root it out.)
Perhaps it persists because — you might want to sit down for this one — it’s mostly false but partly true.
For example: we do learn material better if we have many different ways to access a memory.
If I say: “I’m thinking of an actor from Australia,” you might or might not know who I’m talking about.
But:
If I say: “I’m thinking about an actress. She’s from Australia. She was in that movie about Virginia Woolf. And she was married to Tom Cruise,” you’re MUCH likelier to figure out that I’m talking about Nicole Kidman.
Instead of calling up 2 neural networks (profession, people from Australia), I’ve called up 5 (profession, gender, people from Australia, Virginia Woolf movies, people married to Tom Cruise.)
So, too, if you believe learning styles theory, you’re likely to teach everything multiple ways (visually, auditorily, kinesthetically). That is, you’re giving students three distinct cues to access a particular memory.
This strategy WILL help students learn better, but NOT because some are visual learners and others are auditory learners.
If this explanation is true, then we have to go beyond “the theory is wrong.” We need to say “this part of the theory works for this reason, and that part works for that reason. But, the theory itself isn’t correct.”
That message requires more nuance, but might be more effective in persuading teachers — and schools of education — to update their understandings of teaching and learning.
A note on credit. I believe that this “mostly false but partly true” hypothesis comes from The Learning Scientists’ blog. Alas, I haven’t been able to locate the precise source. Credit for this idea shouldn’t go to me, but … at the moment of writing this post … I can’t determine who really gets it. I’ll update the post once I find out.
[Updated 6-7-19] Yana Weinstein-Jones has helpfully pointed me to the Learning Scientists source. It is this blog post, by Carolina Kuepper-Tetzel. I recommend it highly.
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