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  Reading: Build Knowledge

THE SCIENCE OF LEARNING TO LEARN


READING: BUILD KNOWLEDGE

When it comes to the process of learning, we need to target our learning for a very important reason: Knowledge. We understand things through the prism of what we know, and anything that we want to learn is based on what we’ve already learned. In other words, we’re going to look beyond the role of short-term memory and better understand how long-term memory shapes the development of expertise.  

Every few months or so, I’ll be sitting at my computer trying to resolve some sort of technical issue it’s having. Maybe I can’t print out some documents, or perhaps I can’t find the external drive on the office’s network. I’ve already rebooted the computer, hunted around online for a solution, and maybe even watched a YouTube video or three. Nothing helps.

Finally, I reach someone in my organization’s tech department. More often than not, it’s a desktop support technician named Horace Payne, who walks me through the solution, showing me how I need to use a certain set of commands, or maybe he’ll explain the best way to fix some software. Besides the obvious technical support,  Payne is also providing a very basic form of tutoring—or one-on-one instruction—a powerful and proven method of teaching.

It’s hard to argue against the overwhelming body of evidence supporting the effectiveness of this practice. Some decades ago, psychologist Benjamin Bloom argued that tutoring was twice as effective as any other form of education. One U.S. Department of Education document dubbed it “the most effective form of instruction known.” Whether it’s tech help or French lessons or marketing strategy, pairing a student with an instructor is one of the most effective ways to learn.

Many organizations have come to this realization over the years. Some computer firms, for instance, now offer one-on-one tech services at their stores. Similarly, consider the concierge at many high-end hotels: It’s a type of tutoring for travelers. The rub, of course, is that this sort of tutoring is pricey. It requires a lot of people power. This is why computer companies like Apple limit the length of their on-one-one tech appointments—and why most low-budget hotels don’t even offer a concierge service.

A more important point to consider is figuring out why exactly tutoring is so effective. A few reasons seem pretty clear. When people get one-on-one attention, they get a lot of feedback. It’s also easier to motivate students—tutors know what things you find meaningful.

And then there’s the fact that tutoring is tailored to a student’s level of knowledge. It’s highly focused. Payne, for instance, the tech guy in my office, is aware of what I know about the technology. He asks questions tailored to my knowledge level: What exactly has gone wrong? What did you do to try to fix it? Did you update the programs?

This is typical in tutoring. If someone in a one-on-one class has a misconception about fractions, for instance, the teacher will typically stop and explain the issue. Don’t know about yeast but want to bake bread? The teacher will unpack the topic for you. Land in a new town but don’t know the language? The concierge will outline a few basic words for you.

Tutoring works, then, because it builds on what we know. The instructor, in real time, is adapting the information to what we understand. In the Introduction, I described this idea as the Knowledge Effect. It boils down to the fact that it’s hard to learn something if you don’t know anything about it.

This idea holds true for every field—math, art, woodcarving. There is no learning without some prior knowledge. Facts and figures are the first step to richer forms of thought, as cognitive scientist Dan Willingham has argued in making the case that we need background knowledge to understand just about anything.

As an example, consider a phrase like this: Haben Sie heute gefrühstückt? The text doesn’t make sense if you don’t know German. Or take the following sentence: “An improved dispersion strengthened lead-tin alloy solder is provided in which there is dispersed in the solder up to about 5 percent of small particles.” Again, the text is nearly impossible to understand without some previous knowledge of materials science.

Think of knowledge as the central building block of learning. It’s the brick and mortar of understanding—and one of the best predictors of learning. There are countless illustrations of this idea. Mastery of long division helps people get better at algebra. Expertise in construction fosters architectural skills. If people have a better understanding of basic Civil War facts, they’re better able to grapple with the causes of Southern secession.

This happens because our brains create mental templates in order to store experiences in long-term memory. More exactly, our brains will “bundle” new information with previous information, using old knowledge to help us make meaning out of new knowledge. So after we receive information in short-term memory, it’s shipped to long-term memory, where it rests within a broader context of understanding.

We can use this mental habit to help us learn. Say, for example, you want to recall the number 1,945. One way to improve your memory of the number is to think of the year that World War II ended, 1945. If you’re like most people, you will find it easier to recall the number 1,945 because it has become attached to a long-term memory.

For another example, imagine if I wanted to recall the name of my boss’s three young daughters—Kiera, Beatrice, Penny. I would recall them a lot more easily if I linked the names to something that I know well, if I bundled the data into long-term memory. In this case, I might think of some of my favorite basketball teams—the Knicks, the Bulls, and the Pistons—and use the first letters of the team names to help me recall the names of the three young women.

Similarly, take the classic mnemonic My Very Educated Mother Just Served Us Nine Pizzas, which stands for the order of the planets in our solar system (Mercury, Venus, Earth, etc.). As a learning device, mnemonics are effective because of the nature of long-term memory: They hang new knowledge on old knowledge, even if it’s just a phrase about pizza.

Humor can work, too, because we remember things if they're outlandish for much the same reason. “There are always ways to make connections. What does what I’m learning have to do with my life, what does it remind me of,” argues learning expert Paul Rivas. “The sillier and funnier, the better.”

There’s more when it comes to knowledge and long-term memory, though. Because it turns out that facts are not just some form of intellectual fuel for our ruminating engines. Rather, knowledge and thinking are mixed together within the structure of our brains, as Willingham suggests. Content and cognition turn out to support each other within our neural constructs. “Memory is the residue of thought,” as Willingham writes.

There’s a rich-get-richer aspect to this idea, in that a network of knowledge makes it easier to add to the network. In other words, if you want to learn more statistics, the best thing to know is some statistics. If you want to improve your Spanish, the best thing is to know is some Spanish.

The converse of this idea is true, too. If you don’t know any Spanish, it’s best to start with learning some basics, maybe frequently used words like “hombre” and “quatro.” If you’re starting to learn guitar, you can gain a lot by mastering some of the fundamentals like chord progressions.

For individuals, this starts with identifying what knowledge is necessary for expertise. Sometimes this is obvious. It’s hard to learn how to cannonball into a pool if you don’t know how to swim. But more typically, it’s subtle. So ask yourself: What knowledge do I need to know to become an expert? What background skills do I need to acquire before I can advance? Does this field of study have some foundational concepts that I need to master first?

As we gain expertise, we also need to know how to organize our knowledge. Indeed, in many ways, this is the hallmark of an expert. They understand the systems within an area of expertise. In this regard, we have to keep in mind that expertise—and memory—aren’t linear sorts of things but rather they function more like sprawling networks, a system of nodes and links.  

Take Bror Saxberg as an example. He knows this idea as well as anyone. One of the best learners I’ve known, Saxberg has a medical degree from Harvard University along with a PhD in engineering from the Massachusetts Institute of Technology. Saxberg has also landed a master’s degree in mathematics from Oxford along with two undergraduate degrees, and currently works as the Chief Learning Officer at the education firm Kaplan.

It was early in his career that Saxberg first noticed that experts organize their understanding very differently than amateurs do. Back then, Saxberg was a medical school student at Harvard, working with a team on a difficult case, a patient with a painful illness. Together with the group of students, Saxberg ran down the basics on the patient—blood pressure, lab results—without any luck in coming to a diagnosis.

Then Saxberg and his team began to hunt for more unusual illnesses, reading textbooks and hunting around in different medical manuals. They ordered more tests and exams. Again, no clear diagnosis. So the team called in one of the most senior doctors in the hospital—let’s call him Doctor Wildenstein.

A serious man with a long white lab coat, Wildenstein walked into the patient’s room and declared a diagnosis within a few moments. In fact, it took Wildenstein less than a minute to figure out what was wrong with the patient, detail a path to recovery, and then stride out of the room.  

For Saxberg, the Wildenstein story offers a clear lesson. While Saxberg and his team had a collection of isolated facts, Wildenstein had a systematized type of expertise. The experienced doctor knew the concepts—and the connections—and so he had a much easier time figuring out what was wrong. Without studying each lab result, or consulting any sort of textbook, Wildenstein made a link between his expertise and the symptoms. As Saxberg argues, Wildenstein was a “walking data analyzer” because he had “pattern recognition to quickly realize what was important and what was not.”

In many ways, this ability is the hallmark of mastery. Just about every professional has developed these “pattern recognition” skills. From airline pilots to architects, from baseball players to musicians, experts think in more connected, more relational ways. Their long-term memory is rooted in links instead of features, in systems instead of facts. Like a diviner, like a “walking data analyzer,” they can look past the surface features of a problem and identify core issues.

A number of experiments back up this idea. Cognitive scientist Art Graesser once pulled a group of people together into his lab where the subjects learned about different devices—a toaster oven, a cylinder bolt, a dishwasher. Then Graesser gave the subjects various ways in which the household items could break down to see how they reacted. It turned out that the subjects who understood the device better asked better queries about what could have gone wrong. By seeing connections, by knowing relationships, they could more easily come up with “plausible faults” with the device.

It takes a long time to develop this sort of networked expertise, this improved form of thinking. In the next chapter, we will look more closely at the issue of how to practice in ways that support this form of mastery. But there’s an important lesson for people who are just starting to learn something—we need to target the underlying logic that ties together an area of understanding.

One approach is to write down what you know about a topic before you learn something new about that topic. So if I’m honing my grilling skills, I might note things like: Choose steaks with a bit of fat. High heat works best. Use tongs, not a fork, so meat stays juicy. If I’m learning about the Electoral College, I might write: The political process that helps get a president elected.

According to experts like Robert Marzano, the benefit of this approach is that it helps people focus on linkages rather than isolated facts. By writing down what we know, we’re preparing our mind to make more connections within that body of expertise, creating a more systematized form of thinking—and understanding.

Another tool to better network our learning at this stage is low-stakes tests. Part of the benefit of tests is the obvious—they provide clarification. Tests help us understand what exactly we don’t know. When we do poorly on an exam on microbiology, we know that we need to get better at microbiology.

But just as important, informal quizzes can help us better systematize our expertise. Exams help people develop more subtle ties within an area of knowledge. For an illustration, ask yourself a question like—why is Aaron Burr important? or why do people use crampons while climbing?—and inevitably you’ll start thinking about related facts and ideas.

So, for the Aaron Burr query, you might think about how Vice President Burr oversaw the first impeachment trial, and then you make conceptual ties to modern impeachment trials. As for crampons, you might think of them as hooves but for climbing boots.

Indeed, people who gain skills and knowledge effectively are often engaging in a type of quizzing in their minds. They’ll ask themselves questions as they learn: Why is this true? How does this link to other ideas? In Graesser’s study of the learning of household items, for instance, subjects who asked “why” and “how” questions showed much richer understanding of the items than those who didn’t.

Bror Saxberg—the chief learning officer at Kaplan—does this in conversation, too. As we talked recently, Saxberg ended just about every other sentence with the word “right?” and a little pause. In essence, Saxberg asked: Why are we talking about this? How well do you understand this?

Saxberg has seen the value of helping people make connections at Kaplan, too. With Saxberg’s help, the company has begun using a new approach as part of its LSAT prep classes. In the past, the LSAT classes on reasoning had been taught with a video in which a professor excitedly lectures to students about how to solve a specific type of problem. But the firm recently developed a set of learning tools that presented the complex ideas in a more focused, more networked fashion, with specific examples that directly walked students through the skill set. 

The outcomes were tremendous. Students performed far better in the post-test. What’s more, it took students only nine minutes to master the topic. In contrast, the video-based lecture required students to learn for some ninety minutes. The issue wasn’t that the video was bad—or that the professor was weak. The issue was that the examples provided a more robust way to spot connections. It broke down the material in a more coherent way. It made the system of knowledge easier to learn. 

The understanding of facts makes gaining expertise much more effective. Speed reading is a good case in point. There’s little evidence that speed reading works. You’re much better off having some knowledge about what you’re reading. If you know the background facts in an article or book or website that you’re aim to read, you’ll be able to gain understanding at a much faster rate. In the end, knowledge really is power. Content is more than king. It’s also learning itself.

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