英語 IA 1A5 (=E1R86), 1L1 (=E1R05), 英語 IIA E2R40

(1)

2011-07-21

英語

IA 1A5 (=E1R86), 1L1 (=E1R05) ,

英語

IIA ^E2R40 , 2011

第

10

回

₍

^全

₁₀₊₁

^回

₎

黒田航 (非常勤) ^出口雅也⁽^非常勤⁾^の代理

(2)

講義資料の

Web

ページ

✤ URL

✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html

✤ The Feynman Lectures on Physics の音源ファイルや授業で使ったスライドはこのページから入手可能

✤ 予習や復習に使って下さい

✤ 解答もこのページから入手可能

✤ 京都工芸繊維大学で使っている教材（過去の分）もあるので，自習に使って良いです

(3)

期末ボーナス試験

✤ 7/28 (木) に試験をします

✤ この試験は任意参加のボーナス試験です

✤ 授業でやったのと同じ課題を行なう

✤ ハズレがアタリに

✤ アタリはアタリのまま

✤ 出題範囲

✤ L1, L2, L3 (繰り返し2回)

✤ L6 (繰り返しなし)

(4)

7/28

の試験が任意参加ではない方々

[7/21

確定

]

✤ 1A5 [6名]

✤ 脇田健史, 都築雅美, 弓場汐莉, 佐藤開, 本田貴大, 増尾貴裕

✤ 2R [14名]

✤ 大塚直通, 財前雄太, 乗竹剛志, 浦順貴, 大野遼, 長谷川栄貴, 小野原龍一, 松井孝憲, 三野春樹, 藤本瞭一, 福地崇洋, 原拓矢, 栗原拓也, 大月亮太

✤ 1L1 [5名]

✤ 窪田かすみ, 原祐太,, 松元大周, 川崎眞理子, 板野真衣

(5)

任意参加ではない方々

[

未確定

]

✤ 1A5

✤ 脇田健史

✤ 都築雅美, 弓場汐莉, 夏目知明, 藤本俊平, 佐藤開, 本田貴大

✤ 2R

✤ 大塚直通, 財前雄太, 乗竹剛志, 浦順貴, 大野遼, 長谷川栄貴, 小野原龍一, 松井孝憲, 三野春樹, 藤本瞭一, 福地崇洋, 原拓矢

✤ 栗原拓也, 大月亮太

✤ 1L1

✤ 窪田かすみ, 原祐太,, 松元大周, 川崎眞理子

✤ 岡田眞太郎, 藤貫裕, 宮本貴史

(6)

評価方法

✤ 得点獲得率 G で評価しています

✤ G = (S1 + S2 + ⋯ + Sn)/ n

✤ Si は i回目の試験の得点 (標準化されたもの)

✤ n の最大値は 9 (今ところ n = 7)

✤ 面倒な点

✤ 受けなかった試験を対象外にする (通常の意味での平均点)

✤ 受けなかった試験の得点 = 0 とする (上の場合)

✤ の二つで結果が違う

(7)

本日の予定

✤ 前半30分

1. L8の聞き取り課題の結果の報告

2. 正解の解説

✤ 休憩5分

✤ 後半45分

❖ 聞き取り訓練 L9

❖ Temple Grandin: The world needs all kinds of mindsの後半

❖ 自閉症 (autism), 創造性 (creativity), 脳科学 (brain science)

❖ 簡単なアンケート調査

(8)

L8

の結果

(Temple Grandin: The world

✤ 0.8が中心

✤ 0.9が中心

(15)

L8

の正答率分布

1A5

✤ 平均: 0.80; 標準偏差: 0.07

✤ 最高: 0.92; 最低: 0.60

✤ 0.8が中心

(16)

L8

の正答率分布

2R

✤ 平均: 0.78; 標準偏差: 0.08

✤ 最高: 0.92; 最低: 0.64

✤ 0.7後半が中心

✤ 0.8後半が中心

(17)

L8

の正答率分布

1L1

✤ 平均: 0.85; 標準偏差: 0.05

✤ 最高: 0.95; 最低: 0.76

✤ 0.85が中心

(18)

正答率の変遷

(L8

まで

)

(19)

L8

の解答

(Temple Grandin: The world

needs all kinds of minds)

(20)

誤りの傾向

✤ 1. exactly ⇒ exact, about

✤ 2. continuum ⇒ continue

✤ 3. spectrum ⇒ espetrum

✤ 4. ways

✤ 5. pick

✤ 6. ignores

✤ 7. concerned ⇒ cerned

✤ 8. balk ⇒ book, block, brok

✤ 9. thought ⇒ felt, found said

✤ 10. noticing ⇒ knowing

✤ 11. geek

✤ 12. thought ⇒ saw, felt

✤ 13. see

✤ 14. like

✤ 15. improving ⇒ proving, prooving

✤ 16. run ⇒ one

✤ 17. used

✤ 18. worries ⇒ worrys

✤ 19. belong ⇒

along, alone, born, worn, wrong

✤ 20. showed

✤ 21. emphasize ⇒ enfasize

✤ 22. cortex

✤ 23. got ⇒ join, enjoy

✤ 24. showing

✤ 25. where ⇒ one, more

✤ 26. pattern

✤ 27. reading

✤ 28. issues ⇒

access, aces, dishes, ages, assit

✤ 29. have ⇒ are, some, set, at, by, bothered

✤ 30. thinks

✤ 31. insight ⇒ insite, insights

✤ 32. puts ⇒ put

✤ 33. beat ⇒ be, builds, beet

✤ 34. want

✤ 35. find

✤ 36. equipment ⇒ quick, quickment

✤ 37. People

✤ 38. safer ⇒ safe

✤ 39. guess ⇒ is, yes, yet

✤ 40. pulling ⇒ pouring, pooling

(21)

01/15

✤ I think I’ll start out and just talk a little bit about what [1.

exactly] autism is. Autism is a very big continuum that goes from very severe, the child remains non-verbal, all the way up to brilliant scientists and engineers. And I actually feel at

home here, because there is a lot of autism genetics here. You wouldn’t have any, um ... (Applause)

✤ It’s a [2. continuum] of traits. When does a nerd turn into, you know, uh Asperger, which is just mild autism? I mean Einstein and Mozart and Tesla, would all be probably

diagnosed as autistic [3. spectrum] today. And one of the

things that is really gonna concern me is getting these kids to, to be the ones that are going to invent the next energy things.

Now, that Bill Gates talked about this morning.

(22)

02/15

✤ Okay. Now, if you want to understand autism, animals.

And I want to talk to you now about different [4. ways]

of thinking. You have to get away from verbal language. I think in pictures. I don’t think in language.

✤ Now, the thing about the autistic mind is— it attends to details. Okay, this is a test where you either have to [5.

pick] out the big letters, or pick out the little letters. And the autistic mind picks out the little letters more quickly.

And the thing is, the normal brain [6. ignores] the

details. Well, if you’re building a bridge, details are pretty important because it will fall down if you ignore the

details.

(23)

03/15

✤ And one of my big concerns with a lot of policy things today is things are getting too abstract. People are getting away from doing hands-on stuff. I’m really [7. concerned]

that a lot of schools have taken out the hands-on classes, because art, and classes like that, those are the classes

where I excelled.

✤ Okay, in my work with cattle, I noticed a lot of little things that most people don’t notice would make the cattle [8.

balk]. Like, for example, this flag waving, right in front of the veterinary facility. This feed yard was gonna tear down their whole veterinary facility, all they needed to do was

move the flag; rapid movement, contrast.

(24)

04/15

✤ In the early ’70s when I started, I got right down in the chutes to see what cattle were seeing. People [9. thought]

that was crazy. A coat on a fence would make them balk.

Shadows would make them balk, a hose on the floor.

People weren’t [10. noticing] these things, a chain

hanging down, and that’s shown very very nicely in the movie. In fact I loved the movie how they duplicated all my projects. That’s the [11. geek] side. My drawings got to star in the movie, too. And actually it’s called Temple Grandin, not Thinking in Pictures.

✤ So, what is thinking in pictures? It’s literally movies in your head. My mind works like Google for images.

(25)

05/15

✤ Now, when I was a young kid I didn’t know my thinking was different. I [12. thought] everybody thought in pictures. And then when I did my book, Thinking in Pictures, I start

interviewing people about how they think. And I was shocked to find out that my thinking was quite different. Like if I say,

“Think about a church steeple” most people get this sort of generalized generic one. Now, maybe that’s not true in this room, but it’s gonna be true in a lot of different places.

✤ I [13. see] only specific pictures. They flash up into my memory, just like Google for pictures. And in the movie,

they’ve got a great scene in there, where the word “shoe” is said, and a whole bunch of ’50s and ’60s shoes pop into my imagination.

(26)

06/15

✤ Okay, there is my childhood church. That’s specific. There is some more, Fort Collins. Okay how about famous ones? And they just kind of come up, kind of [14. like] this. Just really quickly, like Google for pictures. And they come up one at a time. And then I think, okay well maybe we can have it snow, or we can have a thunderstorm, and I can hold it there and turn them into videos.

✤ Now, visual thinking was a tremendous asset in my work designing cattle handling facilities. And I’ve worked really hard on [15. improving] um how cattle are treated at the slaughter plant. I’m not gonna go into any gucky slaughter

slides. I’ve got that stuff up on YouTube if you want to look at it.

(27)

07/15

✤ But, one of the things that I was able to do in my design work is I could actually test [16. run] a piece of

equipment in my mind, just like a virtual reality computer system. And this is an aerial view of a

recreation of one of my projects that was [17. used] in the movie. That was like just so super cool. And there were a lot of kind of Asperger types, and ah autism

types, working out there on the movie set, too. (Laughter)

✤ But one of the things that really [18. worries] me, is

where is the younger version of those kids going today.

They are not ending up in Silicon Valley, where they [19.

belong]. (Laughter)

(28)

08/15

✤ Now, (Applause) one of the things I learned very early on because I wasn’t that social, is, I had to sell my work, and not myself. And the way I sold livestock jobs is, I showed off my drawings, I [20. showed] off pictures of things.

Another thing that helped me, as a little kid, is, boy, in the ’50s you were taught manners. You were taught you can’t pull the merchandise off the shelves in the store and throw it around.

✤ Now, when kids get to be in third or fourth grade, you might see that this kid is gonna be a visual thinker,

drawing in perspective. Now, I want to [21. emphasize]

that not every autistic kid is gonna be a visual thinker.

(29)

09/15

✤ Now, I had this brain scan done several years ago, and I used to joke around about having a gigantic internet

trunk line going deep into my visual [22. cortex]. This is tensor imaging. And my great big internet trunk line is twice as big as the control’s. The red lines there are me, and the blue lines are the sex and age matched control.

And there I [23. got] a gigantic one, and the control over there, the blue one, has got a really small one.

✤ And some of the research now is [24. showing] is that people on the spectrum actually think with primary

visual cortex. Now, the thing is, the visual thinker is just one kind of mind.

(30)

10/15

✤ You see, the autistic mind tends to be a specialist mind: good at one thing, bad at something else. And [25. where] I was bad was algebra. And I was never allowed to take geometry or trig:

gigantic mistake. I’m finding a lot of kids who need to skip algebra, go right to geometry and trig.

✤ Now, another kind of mind is the pattern thinker. More abstract. These are your engineers, your computer pro-

grammers. Now, this is [26. pattern] thinking. That praying

mantis is made from a single sheet of paper, no scotch tape, no cuts. And there in the background is the pattern for folding it.

✤ Here are the types of thinking, photo realistic visual thinkers, like me. Pattern thinkers, music and math minds.

(31)

11/15

✤ Some of these often have problems with [27. reading]. You also will see these kind of problems with um, with kids that are dyslexic. You’ll see these different kinds of minds.

✤ And then there is a verbal mind. They know every fact about everything.

✤ Now, another thing is the sensory [28. issues]. I was really

concerned about having to wear this gadget on my face. And you guy came in half an hour beforehand so I could have it put on and kind of get used to it. And they got it bent so it’s not hitting my chin. But sensory is an issue. Some kids are bothered by fluorescent lights; others [29. have] problems with sound sensitivity. You know, um, it’s gonna be variable.

(32)

12/15

✤ Now, visual thinking gave me a whole lot of insight into the animal mind. Because —think about it— an animal is a sensory based thinker, not verbal; thinks in pictures;

thinks in sounds; [30. thinks] in smells.

✤ Think about how much information there is there on the local fire hydrant. He knows who’s been there, when

they were there, are they friend or foe, is there anybody he can go mate with. There is a ton of information on that fire hydrant. It’s all very detailed information. And, looking at these kind of details gave me a lot of [31.

insight] into animals.

(33)

13/15

✤ Now, the animal mind, and also my mind, [32. puts] sensory based information into categories. Man on a horse, and a

man on the ground, that is viewed as two totally different

things. You could have a horse that’s been abused by a rider.

They will be absolutely fine with the veterinarian, and with the horse shoer, but you can’t ride him. You have another horse, where maybe the horse shoer [33. beat] him up, and he’ll be terrible for anything on the ground, with the

veterinarian, but, um, a person can ride him.

✤ Cattle are the same way. Man on a horse, a man on foot,

they are two different things. You see, it’s a different picture.

See, I [34. want] you to think about just how specific this is.

(34)

14/15

✤ Now, this ability to put information into categories, I [35.

find] a lot of people are not very good at this. Like when I’m out troubleshooting equipment or problems with

something in a plant, they don’t seem to be able to figure out, “Do I have a training people issue? Or do I have

something wrong with the equipment?” In other words, categorize [36. equipment] problem, from a people

problem. I find a lot of people have difficulty doing that.

✤ Now, let’s say I figure out it’s an equipment problem. Is it a minor problem, with something simple I can fix? Or is the whole design of the system wrong? [37. People] have a hard time figuring that out.

(35)

15/15

✤ Let’s just look at something like, you know, solving problems with making airlines [38. safer]. Yeah, I’m a million mile flier. I do lots and lots of flying, and, um, you know, like if I was at the FAA, what would I be, eh, doing a lot of direct observation of ? It would be their airplane tails. You know, five fatal wrecks in the last 20 years, the tail either came off or co-steering stuff inside the tail broke in some way. Ah, it’s tails, pure and simple.

✤ And when the pilots walk around the plane, [39. guess] what?

They can’t see that stuff inside the tail. You know, now as I think about that, I’m [40. pulling] up all of that, you know, specific information. It’s specific. So, you—, my thinking is bottom-up. I take all the little pieces and I put the pieces together like a puzzle.

(36)

TED

を使った聞き取り

L9

✤ Temple Grandin: The world needs all kinds of minds の後半

✤ 今日の課題の長さ: 5分まで

✤ 穴埋め方式

✤ 長い目のユニットごとに2回反復

✤ ユニットの間に答えを書く時間を作ります

✤ 最後に簡単なアンケート

✤ 来学期も担当するかわかりませんが，今後のための情報収集

英語 IA 1A5 (=E1R86), 1L1 (=E1R05), 英語 IIA E2R40

IA 1A5 (=E1R86), 1L1 (=E1R05) ,

IIA E2R40 , 2011

10

(

10+1

)

Web

7/28

[7/21

]

[

]

L8

(Temple Grandin: The world

needs all kinds of minds, Part 1)

L8

1A5, 2R, 1L1

L8

1A5

L8

2R

L8

1L1

(L8

)

L8

1A5, 2R, 1L1

L8

1A5

L8

2R

L8

1L1

(L8

)

L8

(Temple Grandin: The world

needs all kinds of minds)

01/15

02/15

03/15

04/15

05/15

06/15

07/15

08/15

09/15

10/15

11/15

12/15

13/15

14/15

15/15

TED

L9

IIA ^E2R40 , 2011

₍

₁₀₊₁

₎