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英語 高校生

教えてください😭

[2] あるクラスでディベートが行われています。 (1)~(3)はディベートの前半戦, (4)(5)は後半戦における発言の (1) 一部です。ディベートの流れを意識しながら、 進行役の先生のセリフ が流れよく成り立つよう、下線に当てはまるものを選択肢から選び, 記号で答えなさい。 [思•判・表] (2) (教科書 P.88~89, P.92~95 参照) (3) (1) Teacher Are you ready to start our class debate? (4) Our topic is here on the board: "Digital books are better than paper books." (5) Raise your hand if you have a reason to support for this statement. (2) Student A: With an eReader, we have access to all of the books that we own. We can read many on the train. We can't carry many books with us every day. Teacher: (5点x5) (3) Student B Paper books don't use electricity. It's important that we use as little electricity as possible to prevent global warming. Teacher: (4) Teacher: Next, we will refute the other side's opinions. Say which opinion you are refuting, give your refutation, then give an example. (5) Student C: They said that digital books are convenient, We don't always need all of our books with us. Teacher: but I don't think it's a big advantage. Just one book is enough. [選択肢] J. Good idea. "Good for the environment." . Great. "Digital books are convenient." t. Let's start with the negative side. 1. Let's start with the affirmative side. I. Very good. "Not a significant advantage."

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英語 高校生

四角IIの⑶なんでpressingなんですか 過去にしたことじゃなくないですか

EXERCISES UNIT7のまとめ いけないの。 (答 First Stage Second Stage Step 1 基本問題 別冊 p.28) Step 1 各文の( )内から最も適切なものを選びなさい。 1) I tried (to move, moving, move) that table, but it wouldn't 2) It's three o'clock now. What do you say (to take, to taken) a coffee break? 3) Please forgive me THOST First Sta move. taking, to be (not for attending, not to attend, for not attending) your lecture tomorrow. 2)に入る動詞を下の[]内から選び、適切な形にして入れなさい。 1) A: I'm really looking forward to ( B: Me, too. I can't wait. A: You should avoid )part in the race. )up late before an exam. B: I know. I'll go to bed early tonight. 3) A: This machine doesn't work. B: Try ( achine doesn the red button. 4) A: I must get up at six tomorrow morning. B: Remember ( ) the alarm clock before going to bed. [press/take/set/stay] JOJIW 3 日本文の意味に合うように,( )内に適語を入れなさい。 1)君のユニフォームはとても汚れているね。 洗濯が必要だ。 Your uniform is very dirty. It ( ) ( ) 2)昨日は折り返し電話できなくてごめんなさい。 O First Sta First Stage Second Stag I'm sorry for( ) ( ) ( ) you back yesterday. 3)妹は夜怖くて,一人でトイレに行けません。 My sister is afraid () ( ) to the bathroom alone at night. 日本文の意味に合うように,( の動詞は適切な形にすること。 )内の語句を並べかえなさい。 ただし、下線部 1)その映画はすばらしかった。 二度見る価値があるよ。 That movie was fantastic, It (twice, is, watch, worth). 2) 私たちはジュディが時間通りに来ないことに怒っていた。 We were angry at Judy (not, for, come) on time. 3)私は次に何をするべきかを言われるのは好きではない。 I don't (to, tell, do, what, like) next. First Stage AQUARIUM×ART atoa Let's write abou 230

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TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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