英語 IA 1A5 (=E1R86), 1L1 (=E1R05) , 英語 IIA E2R40 , 2011 第 6 回 ( 全 10 回 ) 黒田 航 ( 非常勤 ) 出口雅也 ( 非常勤 ) の代理 2011-06-23 Thursday, June 23, 2011
で，自習に使って良いです 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 [mp3 フォー マット ] 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 Thursday, June 23, 2011
期末ボーナス試験 ✤ 7/28 ( 木 ) に試験をします ✤ 試験をしつつ， 4 回分の補講をするのは無理 ✤ 補講は期間外にはできないそうです ✤ 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 Thursday, June 23, 2011
本日の予定 ✤ 前半 30 分 1. L4 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 40 分 ❖ 聞き取り訓練 L5 Thursday, June 23, 2011
L4 の結果 ( Deb Roy: The birth of a word, Part 1) Thursday, June 23, 2011
L4 の得点分布 1A5, 2R, 1L1 ✤ 参加者 : 71 人 ✤ 平均 : 79.67; 標準偏差 : 9.62 ✤ 最高 : 98.33; 最低 : 51.67 ✤ 得点グループ ✤ 75 点が中心のグループ ✤ 85 点が中心のグループ Thursday, June 23, 2011
ループ L4 の得点分布 1A5 ✤ 受講者数 : 23 ✤ 平均 : 23.48/ n [78.26] 点 ✤ 標準偏差 : 3.02/ n [10.08] 点 ✤ 最高 : 29.50/ n [98.33] 点 ✤ 最低 : 18.00/ n [61.67] 点 ✤ n = 30 ✤ 得点グループ ✤ 65 点 , 75 点 , 85 点 , 100 点が中心のグ Thursday, June 23, 2011
L4 の得点分布 2R ✤ 受講者数 : 15 ✤ 平均 : 23.37/ n [77.89] 点 ✤ 標準偏差 : 3.25/ n [10.83] 点 ✤ 最高 : 28.00/ n [93.33] 点 ✤ 最低 : 15.50/ n [51.67] 点 ✤ n = 30 ✤ 得点グループ ✤ 70 点 , 85 点 , 95 点が中心のグループ Thursday, June 23, 2011
L4 の得点分布 1L1 ✤ 受講者数 : 33 ✤ 平均 : 24.44/ n [81.46] 点 ✤ 標準偏差 : 2.60/ n [ 8.67] 点 ✤ 最高 : 27.00/ n [95.00] 点 ✤ 最低 : 16.00/ n [65.00] 点 ✤ n = 30 ✤ 得点グループ ✤ 75 点 , 95 点が中心のグループ Thursday, June 23, 2011
得点の変遷 (L4 まで ) Thursday, June 23, 2011
L4 の正解率分布 1A5, 2R, 1L1 ✤ 参加者 : 71 人 ✤ 平均値 : 0.88 ✤ 最高値 : 0.95; 最低値 : 0.50 ✤ 標準偏差 : 0.07 ✤ 正答率のグループ ✤ 0.8 辺りが中心のグループ Thursday, June 23, 2011
L4 の正答率分布 1A5 ✤ 参加者 : 23 人 ✤ 平均 : 0.88; 標準偏差 : 0.05 ✤ 最高 : 0.98; 最低 : 0.79 ✤ 正答率のグループ ✤ 0.85 が中心のグループ Thursday, June 23, 2011
L4 の正答率分布 2R ✤ 参加者 : 15 人 ✤ 平均 : 0.88; 標準偏差 : 0.04 ✤ 最高 : 0.95; 最低 : 0.82 ✤ 正答率のグループ ✤ 0.95 が中心 ? Thursday, June 23, 2011
L4 の正答率分布 1L1 ✤ 参加者 : 33 人 ✤ 平均 : 0.89; 標準偏差 : 0.04 ✤ 最高 : 0.96; 最低 : 0.81 ✤ 正答率のグループ ✤ 0.95 が中心 ? Thursday, June 23, 2011
正答率の変遷 (L4 まで ) Thursday, June 23, 2011
全体の評価 ✤ 得点と正答率のいずれでも， 1A5, 2R, 1L1 の全クラスで ✤ 過去最高 ✤ おそらく問題が簡単すぎた ✤ 1L1 の伸長が顕著 ✤ 2R のみ単調に増加 ✤ 出席者の選抜の影響 ? Thursday, June 23, 2011
L4 の解答 (FLP) Thursday, June 23, 2011
01/11 ✤ Imagine if you could [ 1. record ] your life —everything you said, everything you did, available in a perfect memory store at your fingertips, so you could go back and find memorable [ 2. moments ] and relive them, or sift through traces of time and discover patterns in your own life that previously had gone undiscovered. Well that’s exactly the journey that my family began five and a half years ago. This is my wife and collaborator, Rupal. And on this day, at this moment, we [ 3. walked ] into the house with our first child, our beautiful baby boy. And we walked into a house with a very special home video recording system. Thursday, June 23, 2011
02/11 ✤ This moment and thousands of other moments special for us, were captured [ 4. in ] our home because in every room in the house, if you looked up, it’s your camera and a microphone, and if you looked down, you’d get this bird’s-eye [ 5. view ] of the room. Here’s our living room, the baby bedroom, kitchen, dining room and the rest of the house. And all of these fed into a disc array that was designed for a continuous [ 6. capture ]. So here we are flying through a day in our home as we move from sunlit morning through incandescent evening and, finally, [ 7. lights ] out for the day. Thursday, June 23, 2011
03/11 ✤ Over the course of three years, we recorded eight to 10 hours a day, amassing roughly a quarter-million hours of multi-track audio and video. So you’re looking at a piece of what is by far the largest home video collection ever made. (Laughter) ✤ And what this [ 8. data ] represents for our family at a personal level, the, the, the impact has already been immense, and we’re still learning its value. Countless moments of unsolicited natural moments, not posed [ 9. moments ], are captured there, and we’re starting to learn how to discover them and find them. Thursday, June 23, 2011
04/11 ✤ But there’s also a scientific reason that drove this project, which was to use this kind of natural longitudinal data to [ 10. understand ] the process of how a child learns language— that child being my son. And so with many privacy provisions put in place to protect everyone [ 11. who ] was recorded in the data, we made elements of the data available to my trusted research team at MIT. So we could start teasing apart patterns in this massive data set, trying to understand the influence of [ 12. social ] environments on language acquisition. Thursday, June 23, 2011
05/11 ✤ So we’re looking here at one of the first things we started to do. This is my wife and I cooking breakfast in the kitchen. And as [ 13. we ] move through space and through time, a very everyday pattern of life in the kitchen. ✤ In order to convert [ 14. this ] opaque, 90,000 hours of video into something that we could start to see, we use motion analysis to pull out, as we move through space and through time, what we call space-time worms. Thursday, June 23, 2011
06/11 ✤ And this has become part of our toolkit for being able to [ 15. look ] and see where the activities are in the data, and with it, trace the pattern of, in particular, where my son moved throughout the home, so that we could focus our transcription efforts, all of the speech environment around my son —all of the [ 16. words ] that he heard from myself, my wife, our nanny, and over time, the words he began to produce. So with that technology and that data and the ability to, with machine assistance, transcribe [ 17. speech ], we’ve now transcribed well over seven million words of our home transcripts. And with that, let me take you now for a first tour into the data. Thursday, June 23, 2011
07/11 ✤ So you’ve all, I’m sure, seen time-lapse videos where a flower will blossom as you accelerate time. I’d like you to now [ 18. experience ] the blossoming of a speech form. My son, soon after his first birthday, would say “gaga” to mean “water.” And over the course of the next half-year, he slowly learned to approximate the [ 19. proper ] adult form, “water.” So we’re going to cruise through half a year in about 40 seconds. No video here, so you can focus on the sound, the acoustics, of a new [ 20. kind ] of trajectory: “gaga” to “water.” Thursday, June 23, 2011
08/11 ✤ Gagagagagaga Gaga gaga gaga guga guga guga wada gaga gaga guga gaga wader guga guga water water water water water water water water water. ✤ He [ 21. sure ] nailed it, didn’t he? Thursday, June 23, 2011
09/11 ✤ So he didn’t just learn water. Over the course of the 24 months, the first two years, that we really focused on, this is a [ 22. map ] of every word he learned in chronological order. And because we have full transcripts, we’ve identified each of the 503 words that he learned to produce by his second birthday. He was an early [ 23. talker ]. And so we started to analyze why. Why were certain words born before others? This is one of the first results that came out of our study a little over a year ago that really surprised us. The way to interpret this apparently simple graph is on the vertical is an [ 24. indication ] of how complex caregiver utterances are based on the length of utterances. And the vertical axis is time. Thursday, June 23, 2011
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