More on this book
Community
Kindle Notes & Highlights
“It’s an old joke among jazz musicians,” Cecchini said. “You ask, ‘Can you read music?’ And the guy says, ‘Not enough to hurt my playing.’”
breadth of training predicts breadth of transfer.
That is, the more contexts in which something is learned, the more the learner creates abstract models, and the less they rely on any particular example. Learners become better at applying their knowledge to a situation they’ve never seen before, which is the essence of creativity.
The parents with creative children made their opinions known after their kids did something they didn’t like, they just did not proscribe it beforehand. Their households were low on prior restraint.
(There is a specific Japanese word to describe chalkboard writing that tracks conceptual connections over the course of collective problem solving: bansho.)
When younger students bring home problems that force them to make connections, Richland told me, “parents are like, ‘Lemme show you, there’s a faster, easier way.’” If the teacher didn’t already turn the work into using-procedures practice, well-meaning parents will. They aren’t comfortable with bewildered kids, and they want understanding to come quickly and easily.
One of those desirable difficulties is known as the “generation effect.” Struggling to generate an answer on your own, even a wrong one, enhances subsequent learning. Socrates was apparently on to something when he forced pupils to generate answers rather than bestowing them. It
(Characterized by haughty scorn: Supercilious).
Being forced to generate answers improves subsequent learning even if the generated answer is wrong.
The more confident a learner is of their wrong answer, the better the information sticks when they subsequently learn the right answer. Tolerating big mistakes can create the best learning opportunities.*
The study conclusion was simple: “training with hints did not produce any lasting learning.”
Training without hints is slow and error-ridden. It is, essentially, what we normally think of as testing, except for the purpose of learning rather than evaluation—when
Used for learning, testing, including self-testing, is a very desirable difficulty.
Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful. The struggle is real, and really useful.
“spacing,” or distributed practice. It is what it sounds like—leaving time between practice sessions for the same material.
Short-term rehearsal gave purely short-term benefits.
Repetition, it turned out, was less important than struggle.
For a given amount of material, learning is most efficient in the long run when it is really inefficient in the short run.
Frustration is not a sign you are not learning, but ease is.
In 2007, the U.S. Department of Education published a report by six scientists and an accomplished teacher who were asked to identify learning strategies that truly have scientific backing. Spacing, testing, and using making-connections questions were on the extremely short list. All three impair performance in the short term.
it is difficult to accept that the best learning road is slow, and that doing poorly now is essential for better performance later.
“Professors who excel at promoting contemporaneous student achievement,” the economists wrote, “on average, harm the subsequent performance of their students in more advanced classes.” What looked like a head start evaporated.
The economists suggested that the professors who caused short-term struggle but long-term gains were facilitating “deep learning” by making connections. They “broaden the curriculum and produce students with a deeper understanding of the material.” It also made their courses more difficult and frustrating, as evidenced by both the students’ lower Calculus I exam scores and their harsher evaluations of their instructors.
Students evaluated their instructors based on how they performed on tests right now—a poor measure of how well the teachers set them up for later development—so they gave the best marks to professors who provided them with the least long-term benefit.
Teachers who guided students to overachievement in their own course were rated highly, and undermined student performance in the long run.
“Above all, the most basic message is that teachers and students must avoid interpreting current performance as learning. Good performance on a test during the learning process can indicate mastery, but learners and teachers need to be aware that such performance will often index, instead, fast but fleeting progress.”
nation’s report card,” have risen steadily since the 1970s. Unquestionably, students today have mastery of basic skills that is superior to students of the past. School has not gotten worse. The goals of education have just become loftier.
Focusing on “using procedures” problems worked well forty years ago when the world was flush with jobs that paid middle-class salaries for procedural tasks, like typing, filing, and working on an assembly line. “Increasingly,” according to Duncan, “jobs that pay well require employees to be able to solve unexpected problems, often while working in groups.
“blocked” practice. That is, practicing the same thing repeatedly, each problem employing the same procedure. It leads to excellent immediate performance, but for knowledge to be flexible, it should be learned under varied conditions, an approach called varied or mixed practice, or, to researchers, “interleaving.”
interleaving tends to fool learners about their own progress. In one of Kornell and Bjork’s interleaving studies, 80 percent of students were sure they had learned better with blocked than mixed practice, whereas 80 percent performed in a manner that proved the opposite.
The feeling of learning, it turns out, is based on before-your-eyes progress, while deep learning is not. “When your intuition says block,” Kornell told me, “you should probably interleave.”
Whether the task is mental or physical, interleaving improves the ability to match the right strategy to a problem. That happens to be a hallmark of expert problem solving. Whether chemists, physicists, or political scientists, the most successful problem solvers spend mental energy figuring out what type of problem they are facing before matching a strategy to it, rather than jumping in with memorized procedures.
Kind learning environment experts choose a strategy and then evaluate; experts in less repetitive environments evaluate and then choose.
Desirable difficulties like testing and spacing make knowledge stick. It becomes durable. Desirable difficulties like making connections and interleaving make knowledge flexible, useful for problems that never appeared in training. All slow down learning and make performance suffer, in the short term.
“far transfer.”
Galileo, the embodiment of bold truths, mocked him for the ridiculous idea of “the moon’s dominion over the waters.”
Deep analogical thinking is the practice of recognizing conceptual similarities in multiple domains or scenarios that may seem to have little in common on the surface.
“In my opinion,” Gentner told me, “our ability to think relationally is one of the reasons we’re running the planet. Relations are really hard for other species.”
Human intuition, it appears, is not very well engineered to make use of the best tools when faced with what the researchers called “ill-defined” problems. Our experience-based instincts are set up well for Tiger domains, the kind world Gentner described, where problems and solutions repeat.
If you’re asked to predict whether a particular horse will win a race or a particular politician will win an election, the more internal details you learn about any particular scenario—physical qualities of the specific horse, the background and strategy of the particular politician—the more likely you are to say that the scenario you are investigating will occur.
Focusing narrowly on many fine details specific to a problem at hand feels like the exact right thing to do, when it is often exactly wrong.
around 90 percent of major infrastructure projects worldwide go over budget (by an average of 28 percent) in part because managers focus on the details of their project and become overly optimistic.
Netflix came to a similar conclusion for improving its recommendation algorithm. Decoding movies’ traits to figure out what you like was very complex and less accurate than simply analogizing you to many other customers with similar viewing histories. Instead of predicting what you might like, they examine who you are like, and the complexity is captured therein.
Just being reminded to analogize widely made the business students more creative. Unfortunately, students also said that if they were to use analogy companies at all, they believed the best way to generate strategic options would be to focus on a single example in the same field.
Like the venture capitalists, their intuition was to use too few analogies, and to rely on those that were the most superficially similar.
“a problem well put is half-solved.”
Tycho Brahe’s observatory—so cutting edge at the time that it cost 1 percent of the national budget of Denmark.
the labs most likely to turn unexpected findings into new knowledge for humanity made a lot of analogies, and made them from a variety of base domains.
Whether it is the making-connections knowledge Lindsey Richland studied, or the broad concepts that Flynn tested, or the distant, deep structural analogical reasoning that Gentner assessed, there is often no entrenched interest fighting on the side of range, or of knowledge that must be slowly acquired. All forces align to incentivize a head start and early, narrow specialization, even if that is a poor long-term strategy. That is a problem, because another kind of knowledge, perhaps the most important of all, is necessarily slowly acquired—the kind that helps you match yourself to the right
...more
“Match quality” is a term economists use to describe the degree of fit between the work someone does and who they are—their abilities and proclivities.
