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0:00
- My name is Barbara Oakley and I'm so happy to be able to give you some insight with this video about the neuroscience of learning and especially how these insights can inform educational policy. After all, perhaps the best way to move teaching and learning onto the 21st century is by building on the great insights that neuroscientists and cognitive psychologists are providing about the brain. We should begin by getting on the same page about reviewing what happens in the brain
0:34
as you're beginning to learn. If we focus on one of the brain's fundamental building blocks, that is, the neuron, we can understand some of the most important aspects of how we learn.
0:50
A neuron has these sort of legs that dangle off it. They're called dendrites. I show three dendrites here, but there actually, can be hundreds. And there are these little toes that emerge off of the dendrites. These toes are called dendritic spines. And then we have a sort of arm that comes up above. That's called an axon.
1:18
When you are learning, neurons are sending electrical signals to each other, making a little web of connections in long-term memory in the neocortex. A lot of times, you'll hear that you don't need to memorize anything because you can always look it up. But could you speak French, for example, if you were always looking it up on Google Translate? Could you take derivatives in math if you always had to look up how to do it? No, of course not. You need key aspects of whatever you're learning
1:52
in long-term memory for you to be able to truly have learned anything new and for you to gain expertise in any topic. Now learning often involves practice. Practice is what helps strengthen those neural connections, as you can see metaphorically here. So ultimately, those sets of links become sort of like this big thick nice necklace that you can easily pull into mind whenever you've learned something.
2:26
So this brings me to an important aspect of learning that sometimes people aren't aware of. And that is, what is the most important and powerful technique that people can use to learn most efficiently?
2:43
I'm going to present four different approaches here that are commonly used for learning. What I'd like you to do is just think, which of these four approaches that I'm going to present here is the approach that you use most often for your learning? The first technique I'll mention is that of rereading. Rereading would make sense as a potentially powerful technique for learning because it involves repetition. Then there's highlighting or underlining,
3:14
which involves motor neurons as you're, drawing and perhaps thus reinforcing, whatever is on the page. Or it could be retrieval practice or recall. For example, using a flashcard to check and see whether you know the information. Or it could be creating what's called a "concept map," such as the one I show here, where you write down key concepts of what you're learning and then see how they integrate together.
3:45
So hopefully of these techniques you see here, you have in mind which one you think is best. Which one is it? Well, as it turns out, it's retrieval practice. And indeed, hundreds of studies have shown that retrieval practice is the most effective technique for helping students learn. Not only because it helps students remember the material, but because it actually helps build a better understanding of the material. Why? Well, when a student is first learning something,
4:22
there are these weak links, as you see here, being created in long-term memory in the neocortex. But it is when you retrieve that memory over and over, over time, those sets of links, those are what help build the strong links, in the neocortex, of memory. Why does retrieval practice also help with understanding? Well, in some sense, it's because you're not only learning what something is. You're also learning what it is not.
5:00
Now of course, some students need more retrieval practice with the material to learn effectively. It all depends on a student's preexisting knowledge base, their motivation, and their innate neural underpinnings, all of which can differ substantially. But even though it can take some students longer to learn, those slower students, like me, can learn the material just as well and sometimes even better than faster students.
5:31
But what happens if for example, a student is very, very busy with all sorts of other activities and they just don't get around to retrieving and practicing with what they've learned?
5:44
Well, in that case, they're not using those particular dendritic spines that they used for the learning. And so their little synaptic janitor
5:56
sweeps those connections away. We can actually see this process going on in real life. You're all budding neuroscientists now, so you can recognize this as the leg of a neuron, the dendrite.
6:14
It's a light microscopy image and this image was taken before learning and before sleep. You see all these little toes, the dendritic spines, that are dangling off of it. Now, can you imagine what would this particular living neuron or living dendrite look like after learning and after sleep? We can actually see it. Here's an image of the same living neuron after learning and after sleep. Look here. You can see,
6:53
this lower image has a new dendritic spine that's emerged where there was none before.
7:02
So it's actually during sleep that neural connections are really formed and strengthened. This is why it's a good idea for students, of course, after they've learned the material during the day, to set aside maybe two to five minutes right before they go to sleep to remind themselves of what they really want to learn or remember. That will help their neurons to know, in some sense, the ideas, concepts, or techniques that are important to be practicing during sleep.
7:36
Indeed during sleep, those connections are used hundreds of times as the brain practices with those thoughts and activities during the sleeping process. So this can be why it's so important to space out learning over a number of days. Let's say a student has, say five hours for learning involving a given topic. They may wish to break that up into one hour per day for five days, rather than five hours all crammed in one day. This one day of cramming means that the connections
8:17
are actually weaker and their little synaptic janitor can more easily sweep them away. Whereas, for this spaced repetition in learning, one hour each day over these five days, you not only learn, but you also practice during sleep. So in some sense, you're getting twice the bang for the buck for your effort with spaced out learning as opposed to cramming. Metaphorically speaking, since I absolutely love metaphors, you can think of it as a little bit like a brick wall.
8:57
You lay a layer of bricks and then mortar, bricks and mortar. But before you go too high, you let that layer of bricks dry, the mortar to dry. And if you take your time with learning like this, it'll create a very good foundation. If you don't however, you're going to get a structure for learning that looks more like this. A very poor foundation for learning. This is also irreverently sometimes referred to as the "meathead theory" of learning. As it turns out, both muscular tissue
9:36
and neural tissue are excitable tissues. So just as it takes time to build muscular structure, it takes time to build neural structure. Of course, however, some students can learn much more quickly while others much more slowly, just as there are natural athletes compared to natural klutzes like me.
10:02
Now to another aspect of learning. This relates to what is called working memory, which involves the limited set of ideas or thoughts that we can temporarily hold in mind and work with. So for example, if I gave you the numbers seven, four, three, and two, for you to remember, you would hold them in working memory by repeating them. Seven four three two. But if you stopped repeating, you'd forget. Long-term memory, on the other hand, can hold information for a long time,
10:37
but it can take much more effort to get information out of working memory and into long-term memory as it sets up links that form those memories.
10:49
I like to think of working memory as kind of like an octopus. Sometimes I call it the attentional octopus. Well, maybe you'd be better off calling it a quadropus because instead of having eight arms to hold information, your working memory has, on average, only about four arms.
11:11
Longterm memory, on the other hand, is quite different. It's more like a locker. And within that locker is stored all your sets of neural links. You can think of it like this. Your working memory works hard to create sets of links in longterm memory about whatever you're learning. Once working memory has created those sets of links and set them in longterm memory, it can then be easy for your working memory to reach out and grab those sets of links.
11:49
So this brings me to my younger daughter, Rachel.
11:55
Now not before this little video snippet was filmed that you're going to see, Rachel had learned how to back up a car. So I asked Rachel if she might model for us on video, what it felt like when she was first backing up a car. Here she is, modeling what it felt like. Watch her little face as she's trying to back up the car. Notice how she's looking to the side. She looks at front, she looks at us, she's looking behind her. Her working memory's overwhelmed. She doesn't really know what to do.
12:29
And then of course, she ends up heading off towards the ditch.
12:34
This gives us a sense of the process that occurs as people are first learning something, especially if that something is rather complex. Their working memory is trying to integrate all this different information. So you can think of it as their working memory having what's called a heavy cognitive load. And that means that the arms of their attentional octopus are already full. There's not room in their working memory for any other information. So if I was sitting beside Rachel,
13:09
I would not be asking her as she's learning how to back up the car. "Hey, by the way, what are you going to do today?" If she also tried to put some attentional octopus arms around what I was saying, she could ultimately become completely overwhelmed and maybe even hit another car as she was trying to back up.
13:32
But once Rachel learned how to back up a car, it became easy for her to do. All she had to do was think, "Hey, I wanna back up the car." She can then pull that memory, those sets of links of how to back up the car to mind. She has a light cognitive load. In other words, she's got attentional arms available for other thoughts, like "is my seatbelt fastened?" or "what's the song on the radio?" So let's turn our attention now to a student. Let's say that our student is sitting down
14:10
to take an exam and they practiced well for the exam. So they sit down, they take a look at the first problem and they go, "Hmm, this is a tough one." Since they practiced, they can quickly see that certain sets of links that they've already learned can easily connect with other links and still other links and boom, suddenly they've solved the problem. But on the other hand, if students haven't studied using retrieval practice, they can sit down to a test and what can happen is, surprise!
14:45
They realize that where they may have thought they knew the material, in reality, they didn't. In fact, when they start to work on the problem, they realize there are no sets of links in long-term memory for them. What they thought was in long-term memory was actually only in their working memory. They didn't really know the material.
15:08
What happens then is that these students can come to us as teachers and say, "You know, I suffer from test anxiety." But it often isn't test anxiety at all. It's the fact that they have not learned the material well. They haven't practiced and placed sets of links in longterm memory. Probably the biggest part of the challenge for our students is that we've never taught them how to study effectively. They think their lack of ability is due to their stress, when it actually relates to the fact
15:43
that they haven't learned the material properly. And we can make things worse for our students when we send them off to counseling, which also allows them to procrastinate, which is another frequent problem for students. All of this instead of teaching them how to effectively learn the material. The reality is the common facet of experts of all kinds is that they have lots and lots of well-practiced sets of links in longterm memory.
16:17
Now, an important idea for teachers is that working memory capacities can vary. So as I've mentioned, a typical working memory can hold about four pieces of information or chunks of information. A chunk can vary in size and content. It's something you know so well, you can easily pull it to mind as a single unit. Whether it's, for example, a section of a song you can play well on the piano or an equation you know how to work with, or a sentence in a foreign language.
16:51
Some people have a lesser capacity working memory. For example, I've got a lesser capacity working memory. I'm lucky to be able to hold maybe three pieces of information in mind at once. But of course, some people have a greater capacity working memory than just four pieces of information. My colleague in teaching a popular course we both teach on Coursera, Learning How To Learn, is Terry Sejnowski, the Francis Crick Professor at the Salk Institute. Terry is one of only 12 living human beings
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who is simultaneously a member of the National Academy of Sciences, of Engineering, and of Medicine. Terry has an enormously large working memory capacity.
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The difference between us is that although I can learn what Terry learns, I am generally much slower than he is. Unless of course, it's something I have a lot of experience with by comparison to Terry.
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You might think, well, yes, but what about "growth mindset"? Maybe we just haven't improved our working memory capacity YET.
18:05
Of course, having the right attitude about what we're learning can make an enormous difference. It certainly did for me when I decided to change my mind back in my mid-20s about whether or not I could learn in math and science. But we have to be realistic about what growth mindset actually applies to. I mean, if we have two kids with different heights, growth mindset is not going to make a big difference in whether or not one kid can become taller. It's a physical characteristic.
18:41
Working memory capacity is also a physical characteristic. And it's one that a student can't really change, no matter how hard they try. So let's look at how working memory capacity changes as students mature. When a child is very young, it turns out their working memory capacity, no surprise, is very small.
19:07
They can forget what you tell them pretty much as fast as you can tell them.
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But as children mature, their working memory capacity gets larger until by the time they're about 15 years old. They can hold on average, well, anywhere between maybe three pieces to five pieces of information in mind at once.
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This means that a typical teacher is going to have a wide range of working memory capacities and learning speeds in their classroom. This is handled today by a technique known as "differentiation."
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Differentiation involves scaffolding information. That is, breaking information up into smaller chunks. Scaffolding allows students who grasp the material more slowly to still learn the material. Slower learners also benefit very greatly from more practice with the material.
20:14
Of course, this step by step approach with much more practice for those with lesser capacity working memory can take much more time. But what do we do in the classroom when we have someone like, for example, this who has a prodigiously large working memory capacity?
20:34
A student with such working memory capacity can move out of the realm of what a typical teacher can handle when they're trying to differentiate and help all the students in their classrooms. Thus, my colleague, Terry Sejnowski, was seen as a troublemaker in his high school because he asked a myriad of questions that his teachers were often simply unable to answer.
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This brings me to an important topic called the "expertise reversal effect."
21:08
It turns out that the more knowledgeable a student is about a topic, the less guidance they can need. Too much guidance in these situations actually impedes their learning. Think about how frustrated students can get if they knew as much or even far more than their teacher on a topic and yet we held them back. Instead of recognizing and honoring neurodiversity, in other words, we simply stick everyone in the same slot and say, "They must learn identically."
21:44
Instead of allowing students like Terry to flourish, we can frustrate these gifted students to such an extent that the allure of drugs or anything besides school can become overwhelming.
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Our school systems need to accommodate our gifted students like Terry, as well as those who benefit from slower learning with far more practice, like me. That's an important part of the educational process. Not only to reach those within typical standard deviations of how brains function, but even those who fall far outside the mean. Incidentally, if you're looking for the research citations involved in this topic, feel free to go to this URL.
22:32
So now we come to my favorite athlete of all time, Julius Yego. Julius is from Kenya. And if you know anything about Kenya, you know they're famous for their long distance runners. Well, if you look at Julius's arms, it's pretty clear that he's not a long distance runners. In fact, Julius always wanted to learn to throw the javelin. Sadly, he couldn't afford to go overseas and study. At that time, there weren't any great javelin throwing coaches in all of Kenya. So what did Julius do?
23:11
He began watching YouTube videos. As it turns out, by watching great instructors on YouTube and then going out and practicing, 98% of the time simply doing that, Julius became the world champion in throwing the javelin. This tells us a lot. For one thing, it tells us that we can learn things we never thought we could learn simply by going online.
23:40
Julius also shows us some very perceptive approaches to learning well that we can apply in our own learning and teaching. What do I mean by this?
23:50
Let's take a moment and look at the history of education. For thousands of years, a typical approach to teaching in school was just to stand up there and lecture for hours. And this made sense. It makes sense even right now. You're watching a lecture here as I speak. We can't sit around the table together, much as I would love to, and converse in person.
24:16
But in general, going on for many hours at a time is perhaps not as effective an approach as it could be in teaching. Teachers begin to realize, starting maybe 30 to 40 years ago, that active learning can be very, very beneficial in learning. So it became a popular notion, very nearly a fad, that students only learn the material when they're actively working on it. So all of a sudden, teaching became swept up in this idea that active learning was the only way that students learn.
24:53
But what is active learning really? Often, it involves only a superficial notion of how student brains actually learn. I mean, you've already seen here that retrieval practice, which is generally and rightly considered a part of active learning, is very important. But a student has to get those sets of neural links at least weakly in place before they have anything to retrieve. In other words, guidance from a teacher from pages of a book or the pixels of a video
25:30
are still necessary to get students started in their initial placement of sets of links involving the material into the brain. So at least some explicit explanation from teachers along with active practice by students are necessary for learning. But what's the best mixture? I mean, should it be a lot of explicit instruction with a little practice? Or a little lecture with lots of active learning practice? Or perhaps we also might have something that is a mixture of lecture,
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equally distributed lecture and active learning? Frankly, researchers really have little idea of what the appropriate mixture is of active learning with lecture. It depends on what your student's backgrounds are, their individual traits and capacities. But what we do know based on plenty of research evidence, is that a mixture of explanation and active practice is the best way to go. This explanation-active practice mixture is called "direct instruction."
26:51
Doesn't this make sense? Isn't this what most teachers do, mix up an explanation with letting students work actively on the material? And indeed, this is exactly what Julius Yego was doing when he was learning to become a world champion in throwing the javelin. He would pay attention to the video and then he would go out and practice. Watch and practice, watch and practice. It was explicit instruction mixed with active practice. And that's how he became an expert in throwing the javelin.
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But what does this all mean from a neuroscientific perspective?
27:39
I've mentioned that we create sets of links in long-term memory when we learn anything. But what I didn't mention was that there are two different pathways that these sets of links can be deposited into long-term memory.
27:57
The first pathway involves taking the information we receive from working memory as it passes through the hippocampus into long-term memory. This is called the "declarative pathway." It's called "declarative" because we can "declare" it. We're consciously aware of it. This passes through the hippocampus.
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There's a completely different pathway though, called the "procedural pathway." And this goes through the basal ganglia. We're often not conscious of things we've learned procedurally,. For example, you may make use of the patterns of your native language without even being conscious of them because you've learned them procedurally. As it turns out, both declarative and procedural sets of links are what allow us to learn well and excel in what we're learning. What do I mean by that?
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I mean that the links deposited by each system can have very, very different characteristics and you need both types of links to be able to be fast, intuitive, and flexible in whatever you or your students are learning. Now declarative learning, as it turns out, can develop through conscious explanation. So you're conscious, for example, of what I'm explaining here right now. Procedural learning on the other hand, develops through lots of practice.
29:35
Practice solving theorems, practice tying your shoe laces, practice playing a song on the piano, skateboarding, or speaking a new language. Procedurally learned information is unusual in that you can't explain it very easily. If you know how to solve a Rubik's cube, for example, you may or may not be able to explain how you can solve it, but you could still solve it. And indeed, many children can learn really well procedurally, even though they can't learn that well declaratively.
30:12
So for example, those on the autistic spectrum can sometimes learn incredibly well and swiftly by learning procedurally. But if you ask them to stop and explain every step declaratively, well, that's not the way they learned. You can actually kill these students' interest and desire in learning by making them stop and use only the declarative approach to explain whatever they're learning, because that's not how they learned. Again, it's the expert reversal effect
30:49
where walking back through what you already know well can kill desire and motivation for learning.
30:56
The procedural system is often thought of as only involving that awful "rote" learning. But in reality, it's at the heart of our ability to understand and work with incredibly complex patterns, like that of your native language, which you learned procedurally. The beauty of the procedural system is that once you've learned something through it, no matter how complex it is, you don't need to worry about it anymore because your procedural system's thinking
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and problem solving is all done below the level of conscious awareness. One can't help but wonder that when it came to Fermat's Last Theorem, a to the n plus b to the n is equal to c to the n, Fermat saw in a procedural-system-intuitive-glance the way to solve the problem.
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But it took 250 years for the equation to be proved declaratively, step-by-step. It's generally very slow to learn procedurally and it can take a lot of practice. It takes a long time to learn to type, for example, but it's very fast once you've learned to type.
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But procedural learning is also inflexible. So for example, if my mischievous husband went and rewired the different keys on the keyboard, it would take me a long time to relearn how to type.
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Each neural pathway, declarative as well as procedural, has its advantages and disadvantages in the types of links it lays into place. But both types of links can underpin good learning. Keep in mind however, that if we try to fit every student into a box where they must learn, for example, declaratively, by parroting back the rote explanations we want them to feed back to us, we can kill students' interest and desire in learning. In fact, we can erroneously give a high grade
33:08
to a student who doesn't understand how to do a problem at all, just because the student can parrot back an explanation we want them to give. Or on the other hand, we may give of low marks to a student who knows how to solve problems intuitively and correctly, but who can't parrot back the declarative explanation a teacher might demand.
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Interestingly, there's evidence that some students can have an inclination to learn more declaratively, while other students can have an inclination to learn more procedurally. For example, it seems that those with dyslexia can have a strong tendency to learn declaratively because there may be challenge with some aspects of their procedural learning system. On the other hand, some students on the autistic spectrum may have particularly pronounced and talented procedural systems.
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In other words, they learn best procedurally. So perhaps these sorts of differences are what teachers are observing when they feel that students have different styles of learning. Now, each system may provide distinct gifts when it becomes particularly strengthened, as it is in some students with seeming disorders, such as dyslexia and autistic spectrum. In fact, this may be why some Nobel Prize winners with dyslexia are able to achieve their extraordinary breakthroughs.
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Likewise, some on the autistic spectrum have been able to achieve their Nobel Prize winning breakthroughs because of the strength of the procedural system. Learning through one system, as it turns out, can inhibit learning through the other. So for example, insisting that students learn by being able to declaratively, step by step explain whatever they're learning, may impede their ability to learn procedurally. This means it can be difficult for the them to be fast
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and intuitive in their handling of mathematical calculations and procedures, for example, if they've not practiced in ways that develop their procedural sets of links.
35:35
Also, especially for students with dyslexia, it can be difficult for them to learn math if they're presented with a lot of verbiage and walls of text that may seem like a blurred mess. Dyslexia, in fact, has a 40% overlap with dyscalculia.
35:58
Let's take a look at a few extraordinary people who can enhance our thinking about neural differences. So here for example, we have the extraordinary Marie Curie who was thought to be on the autistic spectrum. And here is my favorite writer growing up, Octavia Butler, who was bullied because of her dyslexia. In fact, director Steven Spielberg attributes his movie-making prowess in part to his dyslexia, which slows his reading considerably and allows him to simultaneously consider visuals
36:36
in a way that the neurotypical person cannot. Then there's Jack Horner, one of the world's greatest paleontologists and the basis for the main character in the movie "Jurassic Park." Warner flunked out the university seven times due to his dyslexia. He never did finish. Then there's Albert Einstein, also thought to be on the autistic spectrum. And Muhammad Ali, dyslexia. Scientist James Lovelock and creator of the Gaia Hypothesis, that living creatures helped create the Earth's atmosphere,
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had dyslexia. And Elon Musk is on the autistic spectrum.
37:22
Can you imagine Elon Musk, already thinking at a graduate school level while still in elementary school, being forced to think in lockstep with all the other fifth grade children, unable to reach anything like his actual capabilities because of hidebound regulatory processes that insist, despite profound neuroscientific evidence to the contrary, that students do not have natural gifts and talents?
37:55
But what do all these differences in learning approaches and abilities mean for teachers? It means it's not a good idea to put all students in one teaching box and say they must all learn the same way. They DON'T learn in the same way. It's incumbent on us as a society and for us as teachers to make sure we're reaching and teaching neurally diverse students and allowing them all to flourish.
38:26
Now, how can we best teach students? Well, retrieval practice is great for teaching students to learn through both declarative and procedural pathways. Spaced repetition also helps students learn through both pathways. Straight explanation or lecture helps with declarative learning. But on the other hand, varied practice and especially interleaving help with procedural learning. What is interleaving? It means taking and mixing closely related ideas together
39:03
so that students can begin detecting the differences, which are sometimes subtle, as they're practicing. Let's say for example, that you have problems that use the geometric distribution, the binomial distribution, and the negative binomial distribution. Typically, students might do 10 of each type of problem before moving on to explore the next type of problem. This is called "blocking."
39:34
But if, when students first get the hang of how to solve a certain type of problem, they start mixing in problems of a different type and then yet another type. This helps them to start to understand the differences between the different types of problems and when to use the different problem-solving techniques. What good does it do a student after all, if they know how to do the geometric distribution and how to do the binomial distribution, but they don't know when to use one approach
40:08
versus the other? Off they can go on a final examination correctly using the WRONG technique. Unfortunately, textbooks often supply their problems using a blocked approach. Whereas, students actually learn much better using interleaving.
40:27
So a good teaching approach is to try to ensure that despite the way that material is presented in textbooks, that students are presented with interleaved problem sets in whatever they're learning.
40:42
It's important to remember that when you eliminate seeming rote approaches to learning- disparaging these type of approaches by using phrases such as "drill and kill"- you are actually eliminating the easiest, best way the brain has to handle routine learning tasks. Instead, it's best to think of procedural learning as "drill to SKILL." I'd like to turn now briefly to touch on a topic, thAT may seem to have little to do with how the brain works and how it learns.
41:19
But what I'm going to talk about involves such a common- and commonly misapplied-approach to teaching that I think it's important to talk about it. And that relates to the problem of collaborative and team learning.
41:35
So Veronique Mintz is a high school student- or was a high school student-who wrote an op-ed in the New York Times that describes the common challenges students incur in today's teaching. She estimates that 80% of her learning at school took place in collaborative teams. The consequence was that she grew to dread school because of her teachers' inability to manage students. Responsibility for controlling student behavior was being foisted off on students who had little ability or training
42:12
to deal with the complex issues involved in motivating their peers, who often had no interest in learning.
42:20
At the heart of the problem of group learning in school, in fact, is the idea that group learning is a panacea that generally allows for excitement, engagement, and creativity. Au contraire. Let me give you an example. When I was going through engineering school, I was on a team that was tasked with designing a high efficiency heat exchanger. The three guys on my team pooh-poohed my idea, instead devising ideas that, it turned out, were virtually identical to the other ideas in the class.
42:58
So when the professor happened to stop by, I mentioned my idea. He said, "Hmm, that's very interesting and hasn't been done before. You should try it." Grudgingly, my team tried the idea. Our heat exchanger turned out to have the highest efficiency heat exchanger of any exchanger that had been designed in the 20 years that the professor had been teaching that class. I'm not saying this to blow my own horn. I'm saying this because, in fact, research has shown
43:35
that it is individuals, rather than groups, who are by far the most creative when it comes to scientific or technological breakthroughs. One massive review of tens of millions of patents and papers found that it was one person, perhaps two or, at most, three individuals working together who made truly creative breakthroughs. Larger groups could accomplish large tasks, but not truly creative tasks. So if you are planning to have your students work collaboratively,
44:11
keep in mind that collaboration is not a cure for creative work. In fact, too much can diminish creativity.
44:22
But there's more. There's the biggest challenge in all of teamwork: And that is, that there will always be some team members who do not pull their weight.
44:34
To understand this, we should step back and look at an article in the Chronicle of Higher Education by Harvard Professor Robert Coles. Coles told the story of a young student who was working her way through Harvard by cleaning dorm rooms. One student, let's call him "Michael," was a pre-med student who was already a successful journalist. Michael would rudely and crudely proposition this young woman whenever she cleaned his room. What was worse was that both she and the student
45:14
had taken classes in morality and ethics together and Michael apparently had excelled. So here was this star student, a seeming paragon of society at Harvard- and the reality was that he had learned nothing in his ethics and morality classes despite his top grades. In fact, as this young woman pointed out, worldwide there are many philosophers, scientists, poets, and politicians who have experienced plenty of morality training through their early lives
45:52
and have gone on to support despicable causes and regimes.
45:58
In the end, this woman resigned from what she called "fancy, phony Cambridge." With her departure, Harvard lost a guiding light in morality while instead, with Michael, graduating an ethically repulsive character.
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It's important to remember that there will always be a few students like Michael amongst us and amongst our teams. Such students will parrot back the line of morality even as they can take deep advantage of those they're working with. To deal with these kinds of situations and also with situations where students may be overworked, overwhelmed-and still willing to take advantage if necessary-you might give students the tools that they need through life through using a case study.
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Here's a good example case study that may be of help. This is "How to Manage Yourself on a Collaborative Team." This brief essay describes a typical student who takes advantage of others, and tells students what to do if they find themselves in a team with this kind of situation.
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This form, as well as the others I'll be mentioning, can be found with research citations that I pointed out earlier.
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Social emotional learning is exceptionally important. Talking through a case study like this one allows students to understand one of the most important aspects of social emotional learning- one that is typically neglected- and that is the importance of saying "no" when necessary. Learning to put up boundaries is exceptionally important, especially for young girls who are often socialized to be empathetic and agreeable. Think about it. If you always teach that empathy
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is extraordinarily important, who's going to listen to and heed this message? Well, it's those who are already empathetic. In fact, by over-emphasis on empathy without concomitant training on how to say no and set limits in the behavior that we accept from others, we can set students, especially kind and empathetic students, up for codependency. That is, the situation where a person can put up with almost any kind of mistreatment because they think they're helping others.
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There are other ways beyond the case study that teachers can use forms and checklists to help them better manage teams in their classroom. For example, this form provides "Policies for Team Assignments" so students know exactly what role to play on their team. Whether it's coordinator, monitor, or checker.
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This "Team Expectations Agreement" can help students to sign off on their expectations. Signing off together on expectations prevent situations where a student can claim to have not known what they were supposed to be doing or how they can contact other students on the team. This form, "Evaluation of Progress Towards Effective Team Functioning," allows students to get a sense of how an optimal team should be functioning, including checks to see if their team is meeting on time,
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whether they're quickly getting on task, and whether they're listening to one another. This "Peer Rating of Team Members" form allows teams to evaluate one another. Of course, this form, once it's filled out is for your eyes only. You might be surprised to learn that one of the most useful aspects of this form is that it allows you to see how some students are exceptionally helpful behind the scenes where you can't see.
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Above all, it's important to remember, as my own work on pathological altruism has shown, that over-emphasis on the value of empathy can end up reaching only the already empathic. This can lead to these sensitive individuals being taken advantage of and bullied and also lead these already kind students towards lives of codependency.
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Let's turn now to an exceptionally important idea, which as cognitive psychologist Dan Willingham, notes with his book by that name, "When Can You Trust the Experts? How can you tell good science from bad in education?"
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As Willingham notes, educators fall primarily into two groups. Those who understand the world through REASON and those who understand the world through EXPERIENCE.
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If we look carefully, these ideas seem to relate to declarative learning as opposed to experiential, procedural kind of learning. In fact, if we look more closely, we could say that Willingham's objective reason, that is, declarative learning is a little bit like this blue box here. It's going through the hippocampus. Remember, we're at least partly conscious of what's going on in working memory and onward through the hippocampus, as declarative learning proceeds logically,
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step-by-step through the hippocampus. Whereas, experience, that is, procedural learning, is something like this: This gray box of the basal ganglia. Procedural learning then, is sort of a misty fog. we're not conscious of its influence, no matter how profound that influence might be.
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But there is a difference between these two approaches, which involves the complex nature of the basal ganglia. Now I shouldn't imply that the hippocampus, this hippocampus here is simple. But we're conscious of our thinking when we're using declarative [that is] hippocampal thinking. And in this sense, it's simpler. So if we're thinking along here, we can think logically step-by-step and we're conscious of what we're thinking. But with this basal ganglia procedural learning, on the other hand,
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we are not conscious of this extremely complex system.
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This type of learning through the basal ganglia develops through plenty of practice over and over again. So that's how you learn to hit a ball or how to solve a Rubik's cube, how you speak your native language. Whatever you're learning that is processed through the basal ganglia generally involves non-conscious procedural learning. Now also, this procedural system develops what's called a "value function." This value function develops around what is ultimately found to be most rewarding.
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In other words, the value function of the procedural system is like a nest of hidden thinking about what is deemed optimal for you in the long run, based on thousands and thousands of previous experiences, inputs, what you've seen, what you've done, what you've heard, everything. All of this is wrapped up inside that procedural value function. And this procedural value function subconsciously biases your conscious thinking. You may THINK you are thinking completely logically, but you're not.
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Your conscious declarative thinking emerging from your working memory and hippocampus is biased by your procedural system's value function.
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Let's say that you give a logical, step-by-step argument to someone that you think conclusively proves your case.
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You think that that person is following your argument with their conscious declarative system. And they may be. But their procedural system is also influencing what they're thinking-
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and the person is completely unconscious of this bias. In fact, this is the source of a very common bias, which is the, "I'm not biased" bias. Let's say for example, you have two equally-trained statisticians and you give them a paper on a controversial topic of your choice. One is pro this controversial topic and the other is against it. As you might guess, when the statisticians read the paper, they will almost always come up with quite different conclusions.
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If the paper supports their bias, they will be like, "Oh, obviously, it's a very objective well-designed study." But if the paper goes against their preexisting bias, they will find ways to poke holes in it and undermine, instead of accepting, its credibility. These different views on the same exact paper are due to the influence of each statistician's value function, which the statisticians themselves are oblivious of. Or, if they're aware of their bias,
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they're so completely convinced, oftentimes, that their bias is right, that they think it's objective thinking.
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Our procedural systems can be shaped not only by our experiences, but also by the training we might receive from our education, the type of media we read, or people that we're surrounded by. For example, if we learn to associate certain seemingly benign ideas with evil or somehow sinful behavior,
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that is our thoughts on this topic have been associated with negativity via the amygdala, well, that's going to bias our procedural thinking. As a consequence, when we encounter that topic, we can't think objectively and logically about it because of our preexisting bias and because of how our dopamine system is set up. We can even experience pleasure and joy every time we might sort of "hate on" the ideas or people we've learned to associate with negativity.
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It can become natural for some less flexible and more dogmatic individuals to block out ideas that are contradictory because it can be reinforcing and pleasurable as they reaffirm how correct they are in their thinking even as they denigrate any conflicting ideas.
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In this way, individuals can find themselves no longer being informed by a broad spectrum of insights. Instead, they can become more and more deeply convinced that only their own approach is correct. This phenomenon, as it turns out, is surprisingly common in very intelligent people. In fact, as Tolstoy once said, "The simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already without a shadow of a doubt what is laid before him."
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This inflexibility of intelligent people is part of why research itself has shown that science does indeed seem to advance funeral by funeral.
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In fact, it may surprise you to learn that key researchers-in fact, entire groups of prominent researchers- can become inflexibly fixed and block progress in an area.
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For example, Alzheimer's research has been blocked for years because there was a monolithic feeling that one approach, involving the elimination of amyloid plaques, was correct. Journal editors, major researchers, grant providers, and other leading figures were all convinced that they knew the right approach, but their approach was wrong. Amyloid plaques were a symptom, not a cause of Alzheimer's. It was like researchers combining forces to learn how to reduce a person's high temperature
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when they have pneumonia, instead of actually tackling the pneumonia itself. This scientific group think ended up holding back researchers in Alzheimer's for years.
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Let's take a tiny mental break here and go someplace together that can inform us about what's going on in education.
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Where is that somewhere? The South Pole Station in Antarctica. It's a bit chilly there. I know because that's me. I used to work there. Actually, that's where I met my husband [Phil Oakley]. So I always like to say, I had to go to the end of the earth to meet that man.
1:00:01
In fact, there's some interesting and relevant ideas for education that we can gain from polar research. To frame this, I'd like to ask you, which of the three things that I'm going to show you is not like the other? Is it lemons, limes, or polar bears?
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Well, it's actually the limes.
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Why is that? To understand the answer to that question, we need to look back at the history of scurvy. In the mid-1700s, James Lund came up with a cure for scurvy.
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He found that lemons seemed to do the trick as you can see from this rather overly prettified painting of people with scurvy.
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It took about 50 years for James Lund's findings to make their way through common policy into the Royal Navy.
1:01:00
They administered lemon juice to all sailors and it cured scurvy. Easy peasy.
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But there was a problem.
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In the early 1900s, polar explorers started experiencing scurvy. It turned out that researchers had THOUGHT they understood how to cure scurvy, but the reality was they didn't really understand the underlying cause of scurvy- that it involved a deficiency in vitamin C. Here's Robert Falcon Scott who led the first team to the South Pole in 1912.
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Scott died not long after this picture was taken while he was trying to get back to the edge of Antarctica.
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Scott did a lot of things wrong on his expedition, but one of his biggest challenges may have been that he and his team came down with scurvy. But it's so easy to cure scurvy! How on earth 150 years after the cure for scurvy was found could people forget how to cure scurvy?
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Simple. The cure for scurvy was forgotten due to technological ADVANCES. Ships started to go much faster, so sailors weren't stuck out at sea for many months at a time. This meant that through the 1800s, as the source of citrus was changed from high vitamin C lemons from the Mediterranean to low vitamin C limes from the Caribbean, and copper pipes began to be used that destroyed what remaining vitamin C there was, well, no one noticed. Sailors just weren't out at sea long enough to get scurvy
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due to the much faster ships. In fact, pretty much only polar explorers who were away from the fresh fruits for long periods of time began to get scurvy. Especially Antarctic explores because polar bears in the Arctic like lemons, but unlike limes, are potent sources of vitamin C.
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The real problem was that technological advances in the form of faster ships caused problems with the reappearance of scurvy. This was because researchers hadn't truly understood the cause of scurvy. Lund's previous superficial understanding of scurvy's cure helped provide a remedy in the form of Mediterranean lemons. But the real cause of scurvy wasn't understood and technological advances masked understanding when scurvy was incipient.
1:03:53
Wrongheaded practices to cure the disease consequently began to be increasingly used. So how is the problem of technological advances in some areas that set us back in other areas relevant for us today? Let's take a look here at calculators. Wow, we've got a calculator here. Isn't this cool? You don't have to learn your multiplication tables anymore because calculators will do it all for you. In fact, it can seem intuitively obvious that students should just use calculators,
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saving them the time and struggle of learning the multiplication tables. This can sound great to those who don't really understand how the brain learns and how procedural system develops an intuition about patterns like those of mathematics and language. More than that, let's just use search engines because, you know, we can always just look it up. Well, the reality is that you CAN'T always just look it up. In fact, as we mentioned, the common factor of experts of all kinds
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is that they have lots and lots of links in their longterm memory about the area of their expertise, whether it's chess, math, language, music, art, or what have you. But the real challenge is that adults, for example, really don't remember what it was like for them as a child to learn math. To them, just using a calculator seems to make sense because they often don't understand how the brain acquires the patterns of math, that the acquisition of expertise can involve practice,
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especially retrieval practice, along with spaced repetition. Likewise, as an adult, your experience as a reader is very different than it was when you were a child when you were first learning how to read. If you don't understand how students' brains actually learn to read, you can think that "joyous whole language" approaches, where kids avoid the repetition of phonics,is the way to go. You can believe that no matter what research says, because it's your lived EXPERIENCE as an adult-
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the value function of your procedural system can bias you to close your mind. So it doesn't really matter what kind of objective reason might be put to you.
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In fact, the technological breakthroughs of search engines and calculators, which are fantastic, have also misled us because we have not previously understood at a deep level, what's going on in the brain when we're actually learning things. It turns out that not acquiring the neural patterns needed for mastery, patterns which can involve plenty of practice and yes, some rote learning, can students' abilities to be successful longterm in learning a subject.
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I must close by referring to my favorite scientist, Santiago Ramón y Cajal.
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Growing up, Cajal was basically a terrible student.
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But this former loser of a student made such extraordinary scientific breakthroughs that he was awarded the Nobel Prize. But even more impressive, Cajal is now known as the "Father of Modern Neuroscience." He was asked, "How did you succeed?" And he said, "Well, I was persistent." In other words, he practiced a lot. But the second thing he said was that he was flexible.
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As he said-I have worked with many geniuses
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and I was no genius. And he was not. But he said, the problem with geniuses is that they can jump to conclusions and then when they're wrong, they can't change their minds. They simply use their intellects to find a way to justify why they must have been right after all. Over and over again, we are seeing the technological advances in some areas such as calculators and search engines are actually hindering progress in education. Educators without a solid understanding
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of how the brain actually learns can inadvertently and sometimes inflexibly promote approaches that can hinder rather than help students' abilities to learn. But you, on the other hand, have watched this talk all the way through, I hope with an open mind and a willingness to bring solid recent insights from neuroscience and cognitive psychology into education.
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It is YOU who will help bring our students to the future. You have my deepest thanks, and the thanks of like-minded educators, and the thanks of the many students you will impact in the years to come.
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