0:00:01.623,0:00:04.633 So I'd like to talk about[br]the development of human potential, 0:00:04.633,0:00:09.747 and I'd like to start with maybe the most[br]impactful modern story of development. 0:00:09.747,0:00:13.275 Many of you here have probably heard[br]of the 10,000 hours rule. 0:00:13.551,0:00:15.724 Maybe you even model[br]your own life after it. 0:00:15.724,0:00:18.458 Basically it's the idea[br]that to become great in anything, 0:00:18.458,0:00:21.548 it takes 10,000 hours[br]of focused practice, 0:00:21.548,0:00:23.877 so you'd better get started[br]as early as possible. 0:00:23.925,0:00:27.290 The poster child[br]for this story is Tiger Woods. 0:00:27.688,0:00:30.950 His father famously gave him[br]a putter when he was seven months old. 0:00:31.440,0:00:34.544 At 10 months, he started[br]imitating his father's swing. 0:00:35.170,0:00:38.291 At two, you can go on YouTube[br]and see him on national television. 0:00:38.467,0:00:40.347 Fast-forward to the age of 21, 0:00:40.347,0:00:42.161 he's the greatest golfer in the world. 0:00:42.161,0:00:43.829 Quintessential 10,000 hours story. 0:00:43.829,0:00:46.233 Another that features a number[br]of bestselling books 0:00:46.233,0:00:48.186 is that of the three Polgar sisters, 0:00:48.186,0:00:51.344 whose father decided to teach[br]them chess in a very technical manner 0:00:51.344,0:00:52.619 from a very early age. 0:00:52.619,0:00:53.958 And really he wanted to show 0:00:53.958,0:00:56.000 that with a head start[br]in focused practice, 0:00:56.000,0:00:58.207 any child could become[br]a genius in anything. 0:00:58.529,0:00:59.528 And in fact, 0:00:59.528,0:01:02.577 two of his daughters went on[br]to become grandmaster chess players. 0:01:02.577,0:01:06.111 So when I became the science writer[br]at "Sports Illustrated" magazine, 0:01:06.111,0:01:07.360 I got curious. 0:01:07.452,0:01:09.386 If this 10,000 hours rule is correct, 0:01:09.386,0:01:11.980 then we should see that elite[br]athletes get a head start 0:01:11.980,0:01:13.797 in so-called "deliberate practice." 0:01:13.797,0:01:14.800 This is coached, 0:01:14.800,0:01:16.483 error-correction focused practice, 0:01:16.483,0:01:17.933 not just playing around. 0:01:17.933,0:01:18.933 And in fact, 0:01:18.933,0:01:20.700 when scientists study elite athletes, 0:01:20.700,0:01:23.612 they see that they spend[br]more time in deliberate practice ... 0:01:23.612,0:01:24.807 not a big surprise. 0:01:24.807,0:01:28.146 When they actually track athletes[br]over the course of their development, 0:01:28.146,0:01:29.485 the pattern looks like this: 0:01:29.485,0:01:33.125 the future elites actually spend[br]less time early on in deliberate practice 0:01:33.125,0:01:34.569 in their eventual sport. 0:01:34.713,0:01:37.979 They tend to have what scientists[br]call a "sampling period," 0:01:37.979,0:01:40.221 where they try a variety[br]of physical activities, 0:01:40.221,0:01:42.099 they gain broad, general skills, 0:01:42.099,0:01:44.266 they learn about[br]their interests and abilities 0:01:44.266,0:01:48.450 and delay specializing until later[br]than peers who plateau at lower levels. 0:01:49.057,0:01:51.057 And so when I saw that I said, 0:01:51.057,0:01:54.434 "Gosh, that doesn't really comport[br]with the 10,000 hours rule, does it?" 0:01:54.434,0:01:56.468 So I started to wonder about other domains 0:01:56.468,0:01:59.719 that we associate with obligatory,[br]early specialization, 0:01:59.719,0:02:00.758 like music. 0:02:00.989,0:02:02.915 Turns out the pattern's often similar. 0:02:02.915,0:02:05.407 This is research from[br]a world-class music academy, 0:02:05.407,0:02:07.746 and what I want to draw[br]your attention to is this: 0:02:07.746,0:02:11.500 the exceptional musicians didn't start[br]spending more time in deliberate practice 0:02:11.500,0:02:12.756 than the average musicians 0:02:12.756,0:02:14.286 until their third instrument. 0:02:14.286,0:02:16.335 They, too, tended to have[br]a sampling period. 0:02:16.335,0:02:18.845 Even musicians we think of[br]as famously precocious, 0:02:18.845,0:02:19.945 like Yo-Yo Ma. 0:02:20.108,0:02:21.306 He had a sampling period, 0:02:21.306,0:02:24.132 he just went through it more rapidly[br]than most musicians do. 0:02:24.167,0:02:27.407 Nonetheless, this research[br]is almost entirely ignored, 0:02:27.407,0:02:28.670 and much more impactful 0:02:28.670,0:02:31.720 is the first page of the book [br]"Battle Hymn of the Tiger Mother," 0:02:31.720,0:02:34.640 where the author recounts [br]assigning her daughter violin. 0:02:34.864,0:02:37.240 Nobody seems to remember[br]the part later in the book 0:02:37.240,0:02:40.332 where her daughter turns to her[br]and says, "You picked it, not me," 0:02:40.332,0:02:41.331 and largely quits. 0:02:41.331,0:02:44.519 So having seen this sort of surprising[br]pattern in sports and music, 0:02:44.519,0:02:47.498 I started to wonder about domains[br]that affect even more people, 0:02:47.498,0:02:48.501 like education. 0:02:48.501,0:02:50.368 An economist found a natural experiment 0:02:50.368,0:02:52.738 in the higher-ed systems[br]of England and Scotland. 0:02:52.738,0:02:55.468 In the period he studied[br]the systems were very similar 0:02:55.468,0:02:56.470 except in England, 0:02:56.470,0:02:58.955 students had to specialize[br]in their mid-teen years 0:02:58.955,0:03:01.185 to pick a specific course[br]of study to apply to, 0:03:01.185,0:03:02.192 whereas in Scotland, 0:03:02.192,0:03:05.285 they could keep trying things[br]in the university if they wanted to. 0:03:05.285,0:03:06.312 And his question was: 0:03:06.312,0:03:07.969 who wins the trade-off, 0:03:07.969,0:03:09.997 the early of the late specializers? 0:03:09.997,0:03:13.468 And what he saw was that the early[br]specializers jump out to an income lead 0:03:13.468,0:03:15.606 because they have more[br]domain-specific skills. 0:03:15.606,0:03:18.181 The late specializers get to try[br]more different things, 0:03:18.181,0:03:19.230 and when they do pick, 0:03:19.230,0:03:20.230 they have better fit, 0:03:20.230,0:03:22.299 or what economists call "match quality." 0:03:22.299,0:03:24.933 And so their growth rates are faster. 0:03:24.933,0:03:26.125 By six years out, 0:03:26.125,0:03:27.410 they erase that income gap. 0:03:27.776,0:03:30.960 Meanwhile, the early specializers[br]start quitting their career tracks 0:03:30.960,0:03:32.109 in much higher numbers, 0:03:32.109,0:03:34.588 essentially because they were[br]made to choose so early 0:03:34.588,0:03:36.444 that they more often made poor choices. 0:03:36.444,0:03:38.644 So the late specializers lose[br]in the short term 0:03:38.644,0:03:39.644 and win the long run. 0:03:39.644,0:03:42.175 I think if we thought about[br]career choice like dating, 0:03:42.175,0:03:45.030 we might not pressure people[br]to settle down quite so quickly. 0:03:45.030,0:03:46.369 So this got me interested -- 0:03:46.369,0:03:47.710 seeing this pattern again -- 0:03:47.710,0:03:49.710 in exploring the developmental backgrounds 0:03:49.710,0:03:51.621 of people whose work I had long admired. 0:03:51.621,0:03:52.618 Like Duke Ellington, 0:03:52.618,0:03:55.194 who shunned music lessons[br]as a kid to focus on baseball 0:03:55.194,0:03:56.445 and painting and drawing. 0:03:56.445,0:03:57.686 Or Maryam Mirzakhani, 0:03:57.686,0:03:59.642 who wasn't interested[br]in math as a girl -- 0:03:59.642,0:04:01.277 dreamed of becoming a novelist -- 0:04:01.277,0:04:03.758 and went on to become the first[br]and so far only woman 0:04:03.758,0:04:04.903 to win the Fields Medal, 0:04:04.903,0:04:07.146 the most prestigious prize[br]in the world in math. 0:04:07.146,0:04:08.196 Or Vincent Van Gogh -- 0:04:08.196,0:04:09.490 had five different careers, 0:04:09.490,0:04:11.360 each of which he deemed[br]his true calling 0:04:11.360,0:04:13.179 before flaming out spectacularly. 0:04:13.275,0:04:17.541 And in his late 20s picked up a book[br]called "The Guide to the ABCs of Drawing." 0:04:18.140,0:04:19.404 That worked out OK. 0:04:19.896,0:04:23.232 Claude Shannon was an electrical engineer[br]at the University of Michigan 0:04:23.232,0:04:26.310 who took a philosophy course[br]just to fulfill a requirement, 0:04:26.310,0:04:29.663 and in it, he learned about [br]a near century-old system of logic 0:04:29.663,0:04:32.801 by which true and false statements[br]could be coded as ones and zeros 0:04:32.801,0:04:34.630 and solved like math problems. 0:04:34.788,0:04:37.206 This led to the development[br]of binary code, 0:04:37.206,0:04:39.959 which underlies all of our[br]digital computers today. 0:04:40.199,0:04:41.919 Finally, my own sort of role model, 0:04:41.919,0:04:42.933 Frances Hesselbein -- 0:04:42.933,0:04:44.331 this is me with her -- 0:04:44.331,0:04:47.549 she took her first professional[br]job at the age of 54 0:04:47.549,0:04:49.881 and went on to become [br]the CEO of the Girl Scouts, 0:04:49.881,0:04:50.937 which she saved. 0:04:50.937,0:04:52.656 She tripled minority membership, 0:04:52.656,0:04:55.533 added 130,000 volunteers, 0:04:55.533,0:04:58.967 and this is one of the proficiency badges[br]that came out of her tenure -- 0:04:58.967,0:05:01.410 it's binary code for girls[br]learning about computers. 0:05:01.558,0:05:03.562 Today, Frances runs a leadership institute 0:05:03.562,0:05:05.988 where she works every[br]weekday in Manhattan, 0:05:05.988,0:05:07.420 and she's only 104, 0:05:07.420,0:05:09.230 so who knows what's next. 0:05:09.230,0:05:10.230 (Laughter) 0:05:10.754,0:05:13.564 We never really hear developmental[br]stories like this, do we? 0:05:13.564,0:05:15.087 We don't hear about the research 0:05:15.087,0:05:18.264 that found that Nobel laureate scientists[br]are 22 times more likely 0:05:18.264,0:05:19.817 to have a hobby outside of work 0:05:19.817,0:05:21.111 as are typical scientists. 0:05:21.111,0:05:22.110 We never hear that. 0:05:22.110,0:05:24.541 Even when the performers[br]or the work is very famous, 0:05:24.541,0:05:26.508 we don't hear these[br]developmental stories. 0:05:26.508,0:05:28.602 For example, here's[br]an athlete I've followed. 0:05:28.602,0:05:29.949 Here he is at age six, 0:05:29.949,0:05:31.335 wearing a Scottish rugby kit. 0:05:31.335,0:05:32.344 He tried some tennis, 0:05:32.344,0:05:33.339 some skiing, 0:05:33.339,0:05:34.338 wrestling. 0:05:34.338,0:05:37.530 His mother was actually a tennis coach[br]but she declined to coach him 0:05:37.530,0:05:40.161 because he wouldn't return balls normally. 0:05:40.161,0:05:42.348 He did some basketball,[br]table tennis, swimming. 0:05:42.348,0:05:45.676 When his coaches wanted to move[br]him up a level to play with older boys, 0:05:45.676,0:05:48.576 he declined because he just wanted[br]to talk about pro wrestling 0:05:48.576,0:05:50.103 after practice with his friends. 0:05:50.103,0:05:51.585 And he kept trying more sports: 0:05:51.585,0:05:54.294 handball, volleyball, soccer,[br]badminton, skateboarding -- 0:05:54.294,0:05:56.273 so who is this dabbler? 0:05:56.674,0:05:58.368 This is Roger Federer. 0:05:58.624,0:06:01.984 Every bit as famous[br]as an adult as Tiger Woods, 0:06:01.984,0:06:05.085 and yet even tennis enthusiasts[br]don't usually know anything 0:06:05.085,0:06:06.694 about his developmental story. 0:06:06.694,0:06:08.985 Why is that even though it's the norm? 0:06:09.293,0:06:12.148 I think it's partly because the Tiger[br]story is very dramatic. 0:06:12.488,0:06:14.923 But also because it seems[br]like this tidy narrative 0:06:14.923,0:06:16.605 that we can extrapolate to anything 0:06:16.605,0:06:19.008 that we want to be good[br]at in our own lives. 0:06:19.265,0:06:20.965 But that I think is a problem, 0:06:20.965,0:06:24.340 because it turns out that in many ways[br]golf is a uniquely horrible model 0:06:24.340,0:06:26.552 of almost everything[br]that humans want to learn. 0:06:26.552,0:06:27.854 (Laughter) 0:06:28.108,0:06:31.152 Golf is the epitome of what[br]the psychologist Robin Hogarth called 0:06:31.152,0:06:32.585 a "kind" learning environment. 0:06:32.585,0:06:35.784 Kind learning environments[br]have next steps and goals that are clear, 0:06:35.784,0:06:37.838 rules that are clear and never change -- 0:06:37.838,0:06:41.021 when you do something you get feedback[br]that is quick and accurate; 0:06:41.021,0:06:43.179 work next year will look[br]like work last year. 0:06:43.363,0:06:44.413 Chess: 0:06:44.413,0:06:46.010 also a kind learning environment. 0:06:46.010,0:06:49.648 The grandmasters' advantage is based[br]on knowledge of recurring patterns, 0:06:49.648,0:06:51.694 which is also why[br]it's so easy to automate. 0:06:51.694,0:06:54.921 On the other end of the spectrum[br]are "wicked" learning environments, 0:06:54.921,0:06:57.035 where next steps and goals[br]may not be clear. 0:06:57.300,0:06:58.690 Rules may change. 0:06:58.925,0:07:01.451 You may or may not get feedback[br]when you do something. 0:07:01.451,0:07:02.447 It may be delayed, 0:07:02.447,0:07:03.464 it may be inaccurate 0:07:03.464,0:07:06.059 and work next year may not[br]look like work last year. 0:07:06.165,0:07:08.207 So which one of these[br]sounds like the world 0:07:08.207,0:07:10.173 we're increasingly living in? 0:07:10.503,0:07:12.927 In fact, our need to think[br]in an adaptable manner 0:07:12.927,0:07:15.003 and to keep track of interconnecting parts 0:07:15.003,0:07:17.467 has fundamentally changed our perception 0:07:17.467,0:07:19.289 so that when you look at this diagram, 0:07:19.289,0:07:22.575 the central circle on the right[br]probably looks larger to you 0:07:22.575,0:07:25.382 because your brain is drawn[br]to the relationship of the parts 0:07:25.382,0:07:26.422 in the whole, 0:07:26.422,0:07:28.986 whereas someone who hasn't been[br]exposed to modern work 0:07:28.986,0:07:31.563 with its requirement for adaptable,[br]conceptual thought, 0:07:31.563,0:07:34.731 will see correctly that the central[br]circles are the same size. 0:07:35.200,0:07:38.169 So here we are in the wicked work world, 0:07:38.169,0:07:41.584 and there, sometimes[br]hyperspecialization can backfire badly. 0:07:41.704,0:07:42.706 For example, 0:07:42.706,0:07:44.234 in research in a dozen countries 0:07:44.234,0:07:46.948 that matched people[br]for their parent's years of education, 0:07:46.948,0:07:48.054 their test scores, 0:07:48.054,0:07:49.522 their own years of education, 0:07:49.522,0:07:52.329 the difference was some got[br]career-focused education 0:07:52.329,0:07:54.515 and some got broader, general education. 0:07:54.515,0:07:57.230 The pattern was those who got[br]the career-focused education 0:07:57.230,0:07:59.566 are more likely to be hired[br]right out of training, 0:07:59.566,0:08:01.565 more likely to make more money right away, 0:08:01.565,0:08:03.949 but so much less adaptable[br]in a changing work world 0:08:03.949,0:08:06.884 that they spend so much less time[br]in the workforce overall 0:08:06.884,0:08:09.545 that they win in the short term[br]and lose in the long run. 0:08:09.810,0:08:13.346 Or consider a famous,[br]20-year study of experts 0:08:13.346,0:08:15.900 making geopolitical[br]and economic predictions. 0:08:16.037,0:08:19.769 The worst forecasters[br]were the most specialized experts. 0:08:20.245,0:08:23.386 Those who'd spent their entire careers[br]studying one or two problems 0:08:23.386,0:08:26.430 and came to see the whole world[br]through one lens or mental model. 0:08:26.430,0:08:27.926 Some of them actually got worse 0:08:27.926,0:08:30.116 as they accumulated[br]experience and credentials. 0:08:30.594,0:08:35.289 The best forecasters were simply[br]bright people with wide-ranging interests. 0:08:35.862,0:08:36.906 Now in some domains, 0:08:36.906,0:08:37.968 like medicine, 0:08:37.968,0:08:41.060 increasing specialization has been[br]both inevitable and beneficial. 0:08:41.060,0:08:42.064 No question about it. 0:08:42.064,0:08:43.920 And yet it's been a double-edged sword. 0:08:43.920,0:08:44.916 A few years ago, 0:08:44.916,0:08:47.726 one of the most popular surgeries[br]in the world for knee pain 0:08:47.726,0:08:49.681 was tested in a placebo-controlled trial. 0:08:49.681,0:08:51.851 Some of the patients got sham surgery. 0:08:51.851,0:08:53.804 That means the surgeons make an incision, 0:08:53.804,0:08:55.943 they bang around like[br]they're doing something, 0:08:55.943,0:08:57.565 then they sew the patient back up. 0:08:57.565,0:08:59.004 That performed just as a well. 0:08:59.004,0:09:02.116 And yet surgeons who specialize[br]in the procedure continue to do it 0:09:02.116,0:09:03.242 by the millions. 0:09:04.065,0:09:08.183 So if hyperspecialization isn't always[br]the trick in a wicked world, what is? 0:09:08.332,0:09:09.996 That can be difficult to talk about 0:09:09.996,0:09:12.146 because it doesn't[br]always look like this path. 0:09:12.146,0:09:14.381 Sometimes it looks like[br]meandering or zigzagging 0:09:14.381,0:09:15.651 or keeping a broader view. 0:09:15.651,0:09:17.273 It can look like getting behind. 0:09:17.416,0:09:20.230 But I want to talk about what[br]some of those tricks might be. 0:09:20.230,0:09:22.610 If we look at research[br]on technological innovation, 0:09:22.610,0:09:23.901 it shows that increasingly, 0:09:23.901,0:09:26.610 the most impactful patents[br]are not authored by individuals 0:09:26.610,0:09:29.491 who drill deeper, deeper, deeper[br]into one area of technology, 0:09:29.491,0:09:31.532 as classified by the US Patent Office, 0:09:31.532,0:09:34.726 but rather by teams[br]that include individuals 0:09:34.726,0:09:38.016 who have worked across a large number[br]of different technology classes 0:09:38.016,0:09:40.162 and often merge things[br]from different domains. 0:09:40.162,0:09:41.653 Someone whose work I've admired 0:09:41.653,0:09:43.657 who was sort of[br]on the forefront of this -- 0:09:43.657,0:09:45.605 a Japanese man named Gunpei Yokoi. 0:09:45.605,0:09:48.405 Yokoi didn't score well[br]on his electronics exams at school, 0:09:48.405,0:09:51.687 so he had to settle for a low-tier job[br]as a machine maintenance worker 0:09:51.687,0:09:53.491 at a playing card company in Kyoto. 0:09:53.676,0:09:56.640 He realized he wasn't equipped[br]to work on the cutting edge, 0:09:56.640,0:09:59.702 but that there was so much[br]information easily available 0:09:59.702,0:10:02.657 that maybe he could combine things[br]that were already well-known 0:10:02.657,0:10:05.030 in ways that specialists[br]were too narrow to see. 0:10:05.370,0:10:08.856 So he combined some well-known technology[br]from the calculator industry 0:10:08.856,0:10:11.745 with some well-known technology[br]from the credit card industry 0:10:11.745,0:10:13.197 and made handheld games. 0:10:13.197,0:10:14.486 And they were a hit. 0:10:14.486,0:10:16.790 And it turned this playing card company, 0:10:16.790,0:10:20.327 which was founded in a wooden[br]storefront in the 19th century, 0:10:20.327,0:10:22.047 into a toy and game operation. 0:10:22.047,0:10:23.242 You may have heard of it; 0:10:23.242,0:10:24.445 It's called Nintendo. 0:10:24.445,0:10:26.374 Yokoi's creative philosophy translated 0:10:26.374,0:10:29.110 to "lateral thinking [br]with withered technology;" 0:10:29.110,0:10:31.715 taking well-known technology[br]and using it in new ways. 0:10:31.836,0:10:33.978 And his magnum opus was this: 0:10:33.978,0:10:35.073 the Game Boy. 0:10:35.073,0:10:37.390 Technological joke in every way. 0:10:37.559,0:10:41.441 And it came out at the same time[br]as color competitors from Saga and Atari 0:10:41.441,0:10:43.130 and it blew them away 0:10:43.130,0:10:46.417 because Yokoi knew what his[br]customers cared about wasn't color. 0:10:46.417,0:10:47.813 It was durability, 0:10:47.813,0:10:48.942 portability, 0:10:48.942,0:10:50.081 affordability, 0:10:50.081,0:10:51.078 battery life -- 0:10:51.078,0:10:52.133 game selection. 0:10:52.136,0:10:54.613 This is mine that I found[br]in my parents' basement -- 0:10:54.613,0:10:55.797 (Laughter) 0:10:55.797,0:10:57.239 seen better days. 0:10:57.375,0:10:59.088 But you can see the red light is on. 0:10:59.088,0:11:00.948 I flipped it on and played some Tetris, 0:11:00.948,0:11:02.905 which I thought was especially impressive 0:11:02.905,0:11:05.288 because the batteries had expired[br]in 2007 and 2013. 0:11:05.288,0:11:06.652 (Laughter) 0:11:07.601,0:11:11.213 So this breadth advantage holds[br]in more subjective realms as well. 0:11:11.213,0:11:14.598 In a fascinating study of what leads[br]some comic book creators 0:11:14.598,0:11:17.462 to be more likely to make[br]blockbuster comics, 0:11:17.462,0:11:18.755 a pair of researchers found 0:11:18.755,0:11:22.434 that it was neither the number of years[br]of experience in the field 0:11:22.434,0:11:25.423 nor the resources of the publisher, 0:11:25.423,0:11:27.526 nor the number of previous comics made. 0:11:27.662,0:11:31.973 It was the number of different genres[br]that a creator had worked across. 0:11:31.973,0:11:33.387 And interestingly, 0:11:33.387,0:11:37.108 a broad individual[br]could not be entirely replaced 0:11:37.108,0:11:38.786 by a team of specialists. 0:11:39.240,0:11:42.148 We probably don't make as many[br]of those people as we could 0:11:42.148,0:11:43.409 because early on, 0:11:43.409,0:11:45.057 they just look like they're behind 0:11:45.057,0:11:48.673 and we don't tend to incentivize anything[br]that doesn't look like a head start 0:11:48.673,0:11:49.816 or specialization. 0:11:49.816,0:11:52.807 In fact, I think in the well-meaning[br]drive for a head start, 0:11:52.807,0:11:56.158 we often even counterproductively[br]short circuit even the way we learn 0:11:56.158,0:11:58.364 new material at a fundamental level. 0:11:58.579,0:11:59.611 In a study last year, 0:11:59.611,0:12:02.514 seventh-grade math classrooms in the US 0:12:02.514,0:12:05.413 were randomly assigned[br]to different types of learning. 0:12:05.413,0:12:07.928 Some got what's called "blocked practice." 0:12:08.051,0:12:09.811 That's like, you get problem type A, 0:12:09.811,0:12:10.931 AAAAA, 0:12:10.931,0:12:11.932 BBBBB, 0:12:11.835,0:12:12.835 and so on. 0:12:12.835,0:12:14.232 Progress is fast; 0:12:14.232,0:12:15.427 kids are happy; 0:12:15.427,0:12:16.460 everything's great. 0:12:16.460,0:12:20.210 Other classrooms got assigned[br]to what's called "interleaved practice." 0:12:20.545,0:12:22.642 That's like if you took[br]all the problem types 0:12:22.642,0:12:23.742 and threw them in a hat 0:12:23.742,0:12:25.074 and drew them out at random. 0:12:25.074,0:12:26.300 Progress is slower; 0:12:26.300,0:12:28.029 kids are more frustrated. 0:12:28.274,0:12:30.975 But instead of learning[br]how to execute procedures, 0:12:30.975,0:12:34.401 they're learning how to match[br]a strategy to a type of problem. 0:12:34.645,0:12:36.369 And when the test comes around, 0:12:36.369,0:12:39.697 the interleaved group blew[br]the block practice group away. 0:12:40.006,0:12:41.233 It wasn't even close. 0:12:41.825,0:12:45.070 Now I found a lot of this research[br]deeply counterintuitive. 0:12:45.419,0:12:46.778 The idea that a head start, 0:12:46.778,0:12:49.027 whether in picking a career[br]or a course of study 0:12:49.027,0:12:50.713 or just in learning new material, 0:12:50.713,0:12:53.579 can sometimes undermine[br]long-term development. 0:12:53.579,0:12:57.704 And naturally, I think there are as many[br]ways to succeed as there are people, 0:12:57.704,0:13:02.086 but I think we tend only to incentivize[br]and encourage the Tiger path, 0:13:02.086,0:13:03.805 when increasingly in a wicked world, 0:13:03.805,0:13:06.590 we need people who travel[br]the Roger path as well. 0:13:06.713,0:13:09.295 Or as the eminent physicist[br]and mathematician 0:13:09.295,0:13:12.842 and wonderful writer[br]Freeman Dyson put it -- 0:13:12.842,0:13:15.845 and Dyson passed away yesterday, 0:13:15.845,0:13:18.240 so I hope I'm doing[br]his words honor here. 0:13:18.247,0:13:19.248 As he said, 0:13:19.248,0:13:20.574 "For a healthy ecosystem, 0:13:20.574,0:13:22.565 we need both birds and frogs. 0:13:23.020,0:13:24.256 Frogs are down in the mud, 0:13:24.256,0:13:26.175 seeing all the granular details. 0:13:26.399,0:13:29.108 The birds are soaring up above[br]not seeing those details, 0:13:29.108,0:13:31.157 but integrating[br]the knowledge of the frogs." 0:13:31.157,0:13:32.461 And we need both. 0:13:32.515,0:13:34.400 The problem, Dyson said, 0:13:34.400,0:13:36.996 is that we're telling[br]everyone to become frogs. 0:13:36.996,0:13:38.159 And I think, 0:13:38.159,0:13:39.680 in a wicked world, 0:13:39.680,0:13:41.753 that's increasingly shortsighted. 0:13:42.014,0:13:43.174 Thank you very much. 0:13:43.357,0:13:46.317 (Applause)