Section B
Directions: In this section, you are going to read a passage with ten statements attached to it. Each statement contains information given in one of the paragraphs. Identify the paragraph from which the information is derived.
You may choose a paragraph more than once. Each paragraph is marked with a letter. Answer the questions by marking the corresponding letter on Answer Sheet 2.
What If You Could Learn Everything
A． Imagine every student has a tireless personal tutor, an artificially intelligent and inexhaustible companion that knows everything, knows the student, and helps her learn what she needs to know. "'You guys sound like you're from the future,'" Jose Ferreira, the CEO of the education technology startup Knewton, says. "That's the most common reaction we get from others in the industry."
B． Several million data points generated daily by each of 1 million students from elementary school through college, using Knewton's "adaptive learning" technology to study math, reading, and other fundamentals. Adaptive learning is an increasingly popular catchphrase denoting educational software that customizes its presentation of material from moment to moment based on the user's input. It's being hailed as a "revolution" by both venture capitalists and big, established education companies."
C． Ferreira started Knewton in 2008 with more or less the same vision he believes in today: to enable digital technology to transform learning for everyone and to build the company that dominates that transformation. "Look at what other industries the Internet has transformed," he once said."It laid waste to media and is rebuilding it. But for whatever reason, people don't see it with education. It is blindingly obvious to me that it will happen with education. All the content behind education is going to move online in the next 10 years. It's a great shift. And that is what Knewton is going to power."
D． The recommendation engine is a core technology of the Internet, and probably one you encounter every day. Google uses recommendations: other people who entered these search terms clicked on this page, so we'll show it to you first. Amazon uses them: other people who bought this book also bought that book. The more you use one of these websites, the more it knows about you--not just about your current behavior, but about all the other searches and clicks you've done. In theory, as you spend more time with a site its recommendations will become more personalized even as they also draw on everyone else's interactions within the platform.
E．Knewton, at base, is a recommendation engine but for learning. Rather than the set of all Web pages or all movies, the learning data set is, more or less, the universe of all facts. For example, a single piece of data in the engine might be the math fact that a Pythagorean triangle has sides in the ratio 3-4-5, and you can multiply those numbers by any whole number to get a new set of side lengths for this type of triangle. Another might be the function of "adversatives" such as "but," "however," or "on the other hand" in changing the meaning of an English sentence.
F．Ferreira calls these facts "atomic concepts," meaning that they're indivisible into smaller concepts--he clearly likes the physics reference.When a textbook publisher like Pearson loads its curriculum into Knewton's platform, each piece of content--it could be a video, a test question, or a paragraph of text--is tagged with the appropriate concept or concepts.
G．Let's say your school bought the Knewton-powered MyMathLab online system, using the specific curriculum, say, Lial's Basic College Mathematics Be. When a student logs on to the system, she first takes a simple placement test or pretest from the book, which has been tagged with the relevant "atomic concepts." As a student reads the text or watches the video and answers the questions, Knewton's system is "reading" the student as well--timing every second on task, tabulating (把…列成表格) every keystroke, and constructing a profile of learning style: hesitant or confident? Guessing blindly or taking her time?
H． Based on the student's answers, and what she did before getting the answer, "we can tell you to the percentile, for each concept: how fast they learned it, how well they know it, how long they'll retain it, and how likely they are to learn other similar concepts that well," says Ferreira. By watching as a student interacts with it, the platform deduces.
I．The platform forms a personalized study plan based on that information and decides what the student should work on next, feeding the student the appropriate new pieces of content and continuously checking the progress. A dashboard shows the student how many "mastery points" have been achieved and what to do next. Teachers, likewise, can see exactly which concepts the student is struggling with, and not only whether the homework problems have been done but also how many times each problem was attempted, how many hints were needed, and whether the student looked at the page or opened up the video with the relevant explanation. The more people use the system, the better it gets； and the more you use it, the better it gets for you.
J．In a traditional class, a teacher moves a group of students through a predetermined sequence of material at a single pace. Reactions are delayed--you don't get homework or pop quizzes back for a day or two. Some students are bored； some are confused. You can miss a key idea, fall behind, and never catch up. Software- enabled adaptive learning flips all of this on its head. Students can move at their own speed. They can get hints and instant feedback. Teachers, meanwhile, can spend class time targeting their help to individuals or small groups based on need.
K． Ferreira is able to work with competitors like Pearson and Wiley because his software can power anybody's educational content, the same way Amazon Web Services provides the servers for any website to be hosted in the cloud. But before it had any content partners, as a proof of concept, Knewton built its own remedial college math course using its software platform. Math Readiness was adopted starting in the summer of 2011 at Arizona State University； the University of Nevada, Las Vegas； and the University of Alabama.
L．At ASU, students worked through the computer material in Knewton's Math Readiness program on their own or in small groups, with instructors spending face-to-face time working on problem solving, critical thinking, and troubleshooting specific concepts. After two semesters of use, course withdrawal rates dropped by 56 percent and pass rates went from 64 percent to 75 percent. At Alabama, pass rates rose from 70 percent to 87 percent, and at UNLV, where entering students were given the chance to take the course online in the summer before they started college, the percentage who then qualified for college algebra went from 30 percent to 41 percent.
M．"Before this, I worked on the assumption that all students were at the same place. Now, because they progress at different rates, I meet them where they are," Irene Bloom, a math lecturer at ASU, told an education blog about the pilot program. "I have so much more information about what my students do (or don't do) outside of class. I can see where they are stuck, how fast they are progressing, and how much time and effort they are putting into leaming mathematics."
N． The Knewton system uses its analytics to keep students motivated. If it notices that you seem to have a confidence problem, because you too often blow questions that should be easy based on previous results, it will start you off with a few questions you're likely to get right. If you're stuck, choosing the wrong answer again and again, it will throw out broader and broader hints before just showing you the right answer. It knows when to drill you on multiplication and when to give you a fun animated video to watch.
O．It turns out that personalizing in this way can speed up learning. In the first year, 45 percent of ASU students in a 14-week course learned the material four weeks ahead of schedule. Better data is giving more options to the student who didn't succeed as well. Students may not yet know enough to pass the final exam, but a close read of their answers shows that they are making slow and steady progress. "In the past, those students would have dropped out of school," he says. In fact, the vast majority of students placed into remedial math at the nation's community colleges never get their degrees. "Instead, we were able to say, give them another semester and they'll get it. Their whole life has now changed."
46. Under the help of the platform, a teacher knows thoroughly about a certain student's study and can see it in detail.
47. The result of the Knewton system is that learning can be stimulated by customizing a student's learning style.
48. Knewton was founded on the belief that it would lead the industry which helps everyone leam by digital techniques.
49. Theoretically speaking, the more a person uses a site, the more individualized recommendations the site can give.
50. Adaptive learning casts away the single-paced traditional teaching schedule and help students personalize their study pace.
51. According to Ferreira, some facts are too small to be divided into smaller concepts.
52. When students are asked to do a placement test or a pretest, Knewton's sysem will analyze them and evaluate their study.
53. The Knewton system can analyze students well and thus know how to push them forward.
54. Before beginning their study in UNLV, more than 40% of students reached the necessary standard of college algebra by using Knewton's system.
55. The novelty of adaptive learning educational software lies in that the materials it presents to users vary according to their input.
Section B
【參考譯文】
如果你能學(xué)習(xí)所有知識(shí)會(huì)如何
A．想象一下，每一個(gè)學(xué)生都有個(gè)不知疲倦的個(gè)人導(dǎo)師，一個(gè)人工智能的、永不疲倦的伴侶，它無所不知，也了解學(xué)生，并幫助學(xué)生學(xué)習(xí)她需要了解的知識(shí)?！澳銈冞@些家伙聽起來像來自未來。”約瑟·費(fèi)雷拉，新創(chuàng)辦的教育技術(shù)企業(yè)Knewton的首席執(zhí)行官說，“這是業(yè)內(nèi)人士常見的反應(yīng)。”
B．每天都有一百萬名學(xué)生生成幾百萬個(gè)數(shù)據(jù)點(diǎn)，這些學(xué)生來自于從小學(xué)到大學(xué)的各個(gè)階段，但都使用Knewton的“適應(yīng)性學(xué)習(xí)”技術(shù)來學(xué)習(xí)數(shù)學(xué)、閱讀和其他基礎(chǔ)學(xué)科。[55]適應(yīng)性學(xué)習(xí)成為日趨流行的口號(hào)。說明了這款教育軟件時(shí)刻根據(jù)用戶的輸入為其呈現(xiàn)個(gè)性化定制材料的特征。無論是風(fēng)險(xiǎn)資本家還是的大型教育公司均將其譽(yù)為一場(chǎng)“革命”。
C．[48]費(fèi)雷拉于2008年創(chuàng)辦Kneton公司時(shí)的愿景或多或少與今日相同。即使數(shù)字技術(shù)能夠?yàn)樗腥烁淖儗W(xué)習(xí)方式，羞且該公司將統(tǒng)領(lǐng)這一改變?！翱纯椿ヂ?lián)網(wǎng)已經(jīng)改變了的其他行業(yè)，”他曾說，“它給媒介帶來了浪費(fèi)，但又將其重建。但是不管什么原因，人們看不到它與教育的聯(lián)系。我覺得很顯然互聯(lián)網(wǎng)也將用于教育。教育背后的所有內(nèi)容將在10年后出現(xiàn)在網(wǎng)絡(luò)中。這是一個(gè)偉大的轉(zhuǎn)變，而這正是Knewton要發(fā)力的地方。
D．推薦引擎是互聯(lián)網(wǎng)的一項(xiàng)核心技術(shù)，可能你每天都會(huì)用到。谷歌使用推薦引擎：其他人鍵入這些搜索條件后點(diǎn)擊的頁面。我們會(huì)首先為你提供。亞馬遜也在使用：購買了這本書的其他人也買了那本書。你使用這些網(wǎng)站的次數(shù)越多，這些網(wǎng)站就越了解你——不只是你目前的行為，而且對(duì)你做的所有其他搜索和點(diǎn)擊都了解。[49]從理論上講．你在一個(gè)網(wǎng)站上花的時(shí)間越長(zhǎng)。它給你的建議將變得更加個(gè)性化，他們甚至還會(huì)借鑒該平臺(tái)上其他人的互動(dòng)。
E．Knewton基本上是一個(gè)只針對(duì)學(xué)習(xí)的推薦引擎。它沒有囊括所有的網(wǎng)頁和電影，然而學(xué)習(xí)資料它幾乎無所不包。例如，該引擎中某項(xiàng)數(shù)據(jù)可能是個(gè)數(shù)學(xué)事實(shí)，如勾股三角形各個(gè)邊的比為3：4：5，你可以用任意一個(gè)整數(shù)乘以這些數(shù)字，就能得到該類型三角形的一組新邊長(zhǎng)。另外一項(xiàng)數(shù)據(jù)可能是關(guān)于“轉(zhuǎn)折性詞匯”的功能。如“但是”、“然而”或“另一方面”等，這些詞匯可以改變一個(gè)英文句子的意思。
F．[51]費(fèi)雷拉稱這些事實(shí)為“原子概念”，這意味著它們不能再繼續(xù)分割成更小的概念，顯然他很喜歡使用物理術(shù)語。如果像培生那樣的教科書出版社將其課程加載到Knewton的平臺(tái)，每一塊內(nèi)容——可以是一段視頻、一道測(cè)試題或一段文字——均會(huì)以一條或多條適當(dāng)?shù)母拍顦?biāo)記。
G．假設(shè)你們學(xué)校購買了Knewton推動(dòng)的MyMathLab在線系統(tǒng)，使用特定課程，比如Lial’s Basic CollegeMathematicsbe。學(xué)生登錄系統(tǒng)時(shí)，她首先需要做個(gè)簡(jiǎn)單的分級(jí)測(cè)試或預(yù)備測(cè)驗(yàn)，測(cè)試內(nèi)容都是書里的知識(shí)，而且都已被標(biāo)記為相關(guān)的“原子概念”。[52]當(dāng)學(xué)生讀課文、看視頻和回答問題時(shí)。Knewton系統(tǒng)也在“解讀”學(xué)生——計(jì)算他們用于每道題的時(shí)間，將每次鍵盤敲擊情況列成表格。構(gòu)建學(xué)生的學(xué)習(xí)類型：猶豫還是自信?盲目猜測(cè)還是不慌不忙?
H．根據(jù)學(xué)生的答案，以及她在得出答案之前所做的事情，“我們可以告訴你有關(guān)每個(gè)概念的百分率：他們學(xué)得有多快，他們對(duì)該概念的了解程度，他們能記住這一概念多久，以及他們學(xué)習(xí)其他類似概念的可能性?！辟M(fèi)雷拉說。通過觀察學(xué)生與平臺(tái)的互動(dòng)，平臺(tái)對(duì)學(xué)生進(jìn)行推斷。
I．基于該信息，平臺(tái)制定出個(gè)性化的學(xué)習(xí)計(jì)劃并確定學(xué)生下一步應(yīng)該努力學(xué)習(xí)的內(nèi)容，給學(xué)生適當(dāng)?shù)男聝?nèi)容。并不斷地檢查其取得的進(jìn)展。儀表板顯示學(xué)生已掌握了多少知識(shí)以及下一步該做什么。同樣，[46]教師也能清楚地看到學(xué)生學(xué)哪個(gè)概念比較吃力。教師不僅能看到學(xué)生是否完成了作業(yè)。還能看到每個(gè)問題是經(jīng)過幾次努力才解決的，學(xué)生需要多少暗示．學(xué)生是否看到了那一頁或是否打開了有相關(guān)解釋的那段視頻。人們?cè)绞褂迷撓到y(tǒng)，它就能變得更好；你越使用它，它就會(huì)給你更好的幫助。
J．在傳統(tǒng)的課堂上，老師按單一的速度通過一系列預(yù)定的材料來推動(dòng)一組學(xué)生的學(xué)習(xí)。反饋具有延遲性——你在一兩天后才能收回家庭作業(yè)或測(cè)驗(yàn)。在學(xué)習(xí)過程中，有些學(xué)生覺得無聊，有的還迷茫不解。你可能會(huì)錯(cuò)過一個(gè)重要概念，然后落于人后，且再也趕不上了。[50]基于軟件的適應(yīng)性學(xué)習(xí)改變了這一切。學(xué)生可以按照自己的速度推進(jìn)學(xué)習(xí)。他們可以得到暗示和即時(shí)反饋。與此同時(shí)，教師可以把課堂時(shí)間用于某個(gè)需要幫助的個(gè)人或小組。
K．費(fèi)雷拉能夠與培生和威利這樣的競(jìng)爭(zhēng)對(duì)手合作，因?yàn)樗能浖軌蚪o任何人的教育內(nèi)容提供動(dòng)力，就像亞馬遜網(wǎng)絡(luò)服務(wù)系統(tǒng)那樣，可以在云端提供服務(wù)器供其他任何網(wǎng)站使用。但在有任何內(nèi)容合作伙伴之前，作為對(duì)自身概念的證明，Knewton使用其軟件平臺(tái)建立了自己的大學(xué)數(shù)學(xué)補(bǔ)習(xí)課程?！皵?shù)學(xué)準(zhǔn)備就緒”已于2011年夏天分別被亞利桑那州立大學(xué)、拉斯維加斯的內(nèi)華達(dá)大學(xué)和阿拉巴馬大學(xué)使用。
L．在亞利桑那州立大學(xué)，學(xué)生自己或以小組形式，通過Knewton的“數(shù)學(xué)準(zhǔn)備就緒”程序提供的計(jì)算機(jī)材料來學(xué)習(xí)。與老師面對(duì)面的時(shí)間花在解決問題、批判性思考及解答具體的難懂概念上。經(jīng)過兩個(gè)學(xué)期的使用，退課率下降了56％，從64％上升到了75％。在阿拉巴馬大學(xué)，合格率從70％上升到了87％，[54]升入拉斯維加斯的內(nèi)華達(dá)大學(xué)的新生在開學(xué)前的暑假期間可以通過在線課程學(xué)習(xí)．達(dá)到大學(xué)幾何學(xué)習(xí)標(biāo)準(zhǔn)的百分比從30％升到了41％。
M．“在這之前，我以為所有的學(xué)生都處在同一個(gè)水平線上?，F(xiàn)在因?yàn)樗麄兊倪M(jìn)步速度不一，我要在他們需要幫助的時(shí)候給予幫助?！眮喞Ｄ侵萘⒋髮W(xué)的數(shù)學(xué)講師艾琳·布魯姆在一個(gè)教育博客上談?wù)撘豁?xiàng)試點(diǎn)計(jì)劃時(shí)說道。“關(guān)于我的學(xué)生在課堂外做(或不做)什么的信息我有很多。我可以看到他們卡在什么地方，他們的進(jìn)步速度如何以及他們?cè)跀?shù)學(xué)學(xué)習(xí)上投入了多少時(shí)間和精力?！?BR> N．[53]Knewton系統(tǒng)利用其分析來激勵(lì)學(xué)生學(xué)習(xí)。如果它注意到你似乎在信心方面有問題，因?yàn)槟愠３o法解答某些題目，而根據(jù)以前的結(jié)果，這些題目應(yīng)該并不難。它會(huì)讓你開始做幾個(gè)你很可能答對(duì)的問題。如果你卡住了，一遍一遍地選擇錯(cuò)誤答案，它會(huì)在告訴你正確答案之前，不斷地給你提示。它知道什么時(shí)候讓你多做題，什么時(shí)候讓你看段有趣的動(dòng)畫視頻。
O．[47]事實(shí)證明這樣的個(gè)性化可以加快快習(xí)進(jìn)程。在第一年，亞利桑那州立大學(xué)45％的學(xué)生花了十四周就學(xué)會(huì)了應(yīng)學(xué)會(huì)的材料，這比教學(xué)進(jìn)度提前了四周。更好的資料為那些沒學(xué)會(huì)的學(xué)生提供了更多的選擇。學(xué)生們所學(xué)的可能還不足以使其通過期末考試，但仔細(xì)閱讀他們的答案就會(huì)知道他們正在緩慢而穩(wěn)定地進(jìn)步。“要是在以前，這些學(xué)生就輟學(xué)了?！彼f。事實(shí)上，那些在全國(guó)的社區(qū)學(xué)院補(bǔ)習(xí)數(shù)學(xué)的學(xué)生，絕大多數(shù)沒有拿到學(xué)位?！跋喾矗覀兛梢哉f，再給他們一個(gè)學(xué)期，他們會(huì)得到學(xué)位。他們的整個(gè)人生現(xiàn)在已經(jīng)改變了。”
【答案解析】
46．I
解析：題干意為，在該平臺(tái)的幫助下，教師對(duì)某個(gè)學(xué)生的學(xué)習(xí)情況有了徹底了解并能看到該學(xué)生學(xué)習(xí)狀態(tài)的細(xì)節(jié)。注意抓住題干中的關(guān)鍵詞platform和teacher。關(guān)于平臺(tái)對(duì)教師的幫助的內(nèi)容在I段出現(xiàn)。該段第三句指出，教師也能清楚地看到學(xué)生學(xué)哪個(gè)概念比較吃力。教師不僅能看到學(xué)生完成了作業(yè)。還能看到每個(gè)問題是經(jīng)過幾次努力才解決的，學(xué)生需要多少暗示，學(xué)生是否看到了那一頁或是否打開了有相關(guān)解釋的那段視頻。由此可見，題干是對(duì)原文的簡(jiǎn)要概括，故答案是I。
47．O
解析：題干意為，Knewton系統(tǒng)的結(jié)果是可以通過定制學(xué)生的學(xué)習(xí)方式以促進(jìn)其學(xué)習(xí)。注意抓住題千中的關(guān)鍵詞stimulated和customizing。關(guān)于定制學(xué)習(xí)方式可以促進(jìn)學(xué)習(xí)的內(nèi)容出現(xiàn)在。段中。該段第一句指出，事實(shí)證明這樣的個(gè)性化學(xué)習(xí)能夠加快學(xué)習(xí)進(jìn)程。由此可見，題干是原文的同義轉(zhuǎn)述，故答案為O。題干中的customizing與原文中的personalizing為同義轉(zhuǎn)述，stimulated則與speed up對(duì)應(yīng)。
48．C
解析：題干意為，Knewton建立時(shí)的信念就是該公司將成為幫助所有人運(yùn)用數(shù)字技術(shù)進(jìn)行學(xué)習(xí)的行業(yè)的。注意抓住關(guān)鍵詞Knewton和digital techniques。關(guān)于Knewton建立時(shí)之愿景的內(nèi)容出現(xiàn)在C段。該段第一句指出，費(fèi)雷拉于2008年創(chuàng)辦Knewton公司時(shí)的愿景或多或少與今日相同，即，使數(shù)字技術(shù)能夠?yàn)樗腥烁淖儗W(xué)習(xí)方式，并且該公司將統(tǒng)領(lǐng)這一改變。由此可見，題干與原文是同義轉(zhuǎn)述，故答案是C。題干中的belief與原文中的vision意思相當(dāng)，題干中的lead與原文中的dominates相對(duì)應(yīng)。
49．D
解析：題干意為，從理論上講，用戶使用某個(gè)網(wǎng)站越多，該網(wǎng)站就越能給出個(gè)性化的推薦。注意抓住題干中的關(guān)鍵詞theoretically和a site。關(guān)于網(wǎng)站和用戶關(guān)系的內(nèi)容出現(xiàn)在D段。該段后一句指出，從理論上講，你在一個(gè)網(wǎng)站上花的時(shí)間越長(zhǎng)，該網(wǎng)站給你的建議就越個(gè)性化。由此可見，題干與原文是同義轉(zhuǎn)述，故答案是D。題干中的individualized與原文中的personalized是同義轉(zhuǎn)換。
50．J
解析：題干意為，適應(yīng)性學(xué)習(xí)摒棄了傳統(tǒng)教學(xué)中單一進(jìn)度的模式，幫助學(xué)生為自己的學(xué)習(xí)進(jìn)度進(jìn)行個(gè)性化定制。注意抓住題干中的關(guān)鍵詞pace。關(guān)于學(xué)習(xí)進(jìn)度的內(nèi)容在J段出現(xiàn)。該段第五、六句指出，基于軟件的適應(yīng)性學(xué)習(xí)改變了這一切。學(xué)生可以按照自己的學(xué)習(xí)速度推進(jìn)學(xué)習(xí)。由此可見，題干是對(duì)原文的同義轉(zhuǎn)述，故答案是J。題干中的casts away與原文中的flip Oil its head意思相當(dāng)，pace與原文中的speed是同義轉(zhuǎn)換。
51．F
解析：題干意為，按照費(fèi)雷拉的說法，有些信息的概念已經(jīng)很小，無法再進(jìn)一步分解。注意抓住題干中的關(guān)鍵詞facts和divided。有關(guān)概念分解的內(nèi)容出現(xiàn)在F段。該段第一句指出，費(fèi)雷拉稱這些事實(shí)為“原子概念”，這意味著它們不能再繼續(xù)分割成更小的概念，顯然他很喜歡使用物理術(shù)語。由此可見，題干與原文為同義轉(zhuǎn)述，故答案是F。題干中的too…tohedivided…與原文中的indivisible為同義轉(zhuǎn)述。
52．G
解析：題干意為，當(dāng)學(xué)生被要求完成分級(jí)測(cè)試或預(yù)備測(cè)驗(yàn)時(shí)，Knewton系統(tǒng)將會(huì)對(duì)他們做出分析并評(píng)估他們的學(xué)習(xí)情況。注意抓住關(guān)鍵詞placementtest和pretest。關(guān)于學(xué)生做分級(jí)測(cè)試及預(yù)備測(cè)驗(yàn)的內(nèi)容出現(xiàn)在G段。該段第二句提及了這兩個(gè)概念，接著第三、四句指出，當(dāng)學(xué)生讀課文、看視頻和回答問題時(shí)，Knewton系統(tǒng)也在“解讀”學(xué)生——計(jì)算他們用于每道題的時(shí)間，將每次鍵盤敲擊情況列成表格，構(gòu)建學(xué)生的學(xué)習(xí)類型：猶豫還是自信?盲目猜測(cè)還是不慌不忙?由此可見，題干是對(duì)原文的概述，故答案是G。
53．N
解析：題干意為，Knewton系統(tǒng)能夠很好地對(duì)學(xué)生做出分析，因此知道如何推動(dòng)他們的學(xué)習(xí)。注意抓住題干中的關(guān)鍵詞analyze。關(guān)于系統(tǒng)分析學(xué)生的內(nèi)容出現(xiàn)在N段。該段第一句指出，Knewton系統(tǒng)利用其分析來激勵(lì)學(xué)生學(xué)習(xí)。由此可見，題干是原文的同義轉(zhuǎn)述。
54．L
解析：題干意為，新生進(jìn)入內(nèi)華達(dá)大學(xué)學(xué)習(xí)之前，就有40％多的學(xué)生通過使用Knewton系統(tǒng)達(dá)到了大學(xué)幾何的學(xué)習(xí)要求。注抓住題干中的關(guān)鍵詞UNLV和college algebra。關(guān)于UNLV大學(xué)新生使用Knewton系統(tǒng)的情況出現(xiàn)在L段。該段后一句指出，升入內(nèi)華達(dá)大學(xué)的新生在開學(xué)前的暑假期間可以通過在線課程學(xué)習(xí)，達(dá)到大學(xué)幾何學(xué)習(xí)標(biāo)準(zhǔn)的百分比從30％升到了41％。由此可見，題干與原文是同義轉(zhuǎn)述。故答案是L。題干中的reached the necessary standard是原文中qualified的同義轉(zhuǎn)述。
55．B
解析：題干意為，適應(yīng)性學(xué)習(xí)教育軟件的新奇之處在于它給用戶呈現(xiàn)的材料根據(jù)其輸入而變化。注意抓住題干中的關(guān)鍵詞educational software和input。關(guān)于這款軟件新奇之處的介紹出現(xiàn)在B段。該段第二句提出,適應(yīng)性學(xué)習(xí)成為日趨流行的口號(hào)，說明了這款教育軟件時(shí)刻根據(jù)用戶的輸入為其呈現(xiàn)個(gè)性化定制材料的特征。由此可見，題干是原文的同義轉(zhuǎn)述，故答案是B。
Directions: In this section, you are going to read a passage with ten statements attached to it. Each statement contains information given in one of the paragraphs. Identify the paragraph from which the information is derived.
You may choose a paragraph more than once. Each paragraph is marked with a letter. Answer the questions by marking the corresponding letter on Answer Sheet 2.
What If You Could Learn Everything
A． Imagine every student has a tireless personal tutor, an artificially intelligent and inexhaustible companion that knows everything, knows the student, and helps her learn what she needs to know. "'You guys sound like you're from the future,'" Jose Ferreira, the CEO of the education technology startup Knewton, says. "That's the most common reaction we get from others in the industry."
B． Several million data points generated daily by each of 1 million students from elementary school through college, using Knewton's "adaptive learning" technology to study math, reading, and other fundamentals. Adaptive learning is an increasingly popular catchphrase denoting educational software that customizes its presentation of material from moment to moment based on the user's input. It's being hailed as a "revolution" by both venture capitalists and big, established education companies."
C． Ferreira started Knewton in 2008 with more or less the same vision he believes in today: to enable digital technology to transform learning for everyone and to build the company that dominates that transformation. "Look at what other industries the Internet has transformed," he once said."It laid waste to media and is rebuilding it. But for whatever reason, people don't see it with education. It is blindingly obvious to me that it will happen with education. All the content behind education is going to move online in the next 10 years. It's a great shift. And that is what Knewton is going to power."
D． The recommendation engine is a core technology of the Internet, and probably one you encounter every day. Google uses recommendations: other people who entered these search terms clicked on this page, so we'll show it to you first. Amazon uses them: other people who bought this book also bought that book. The more you use one of these websites, the more it knows about you--not just about your current behavior, but about all the other searches and clicks you've done. In theory, as you spend more time with a site its recommendations will become more personalized even as they also draw on everyone else's interactions within the platform.
E．Knewton, at base, is a recommendation engine but for learning. Rather than the set of all Web pages or all movies, the learning data set is, more or less, the universe of all facts. For example, a single piece of data in the engine might be the math fact that a Pythagorean triangle has sides in the ratio 3-4-5, and you can multiply those numbers by any whole number to get a new set of side lengths for this type of triangle. Another might be the function of "adversatives" such as "but," "however," or "on the other hand" in changing the meaning of an English sentence.
F．Ferreira calls these facts "atomic concepts," meaning that they're indivisible into smaller concepts--he clearly likes the physics reference.When a textbook publisher like Pearson loads its curriculum into Knewton's platform, each piece of content--it could be a video, a test question, or a paragraph of text--is tagged with the appropriate concept or concepts.
G．Let's say your school bought the Knewton-powered MyMathLab online system, using the specific curriculum, say, Lial's Basic College Mathematics Be. When a student logs on to the system, she first takes a simple placement test or pretest from the book, which has been tagged with the relevant "atomic concepts." As a student reads the text or watches the video and answers the questions, Knewton's system is "reading" the student as well--timing every second on task, tabulating (把…列成表格) every keystroke, and constructing a profile of learning style: hesitant or confident? Guessing blindly or taking her time?
H． Based on the student's answers, and what she did before getting the answer, "we can tell you to the percentile, for each concept: how fast they learned it, how well they know it, how long they'll retain it, and how likely they are to learn other similar concepts that well," says Ferreira. By watching as a student interacts with it, the platform deduces.
I．The platform forms a personalized study plan based on that information and decides what the student should work on next, feeding the student the appropriate new pieces of content and continuously checking the progress. A dashboard shows the student how many "mastery points" have been achieved and what to do next. Teachers, likewise, can see exactly which concepts the student is struggling with, and not only whether the homework problems have been done but also how many times each problem was attempted, how many hints were needed, and whether the student looked at the page or opened up the video with the relevant explanation. The more people use the system, the better it gets； and the more you use it, the better it gets for you.
J．In a traditional class, a teacher moves a group of students through a predetermined sequence of material at a single pace. Reactions are delayed--you don't get homework or pop quizzes back for a day or two. Some students are bored； some are confused. You can miss a key idea, fall behind, and never catch up. Software- enabled adaptive learning flips all of this on its head. Students can move at their own speed. They can get hints and instant feedback. Teachers, meanwhile, can spend class time targeting their help to individuals or small groups based on need.
K． Ferreira is able to work with competitors like Pearson and Wiley because his software can power anybody's educational content, the same way Amazon Web Services provides the servers for any website to be hosted in the cloud. But before it had any content partners, as a proof of concept, Knewton built its own remedial college math course using its software platform. Math Readiness was adopted starting in the summer of 2011 at Arizona State University； the University of Nevada, Las Vegas； and the University of Alabama.
L．At ASU, students worked through the computer material in Knewton's Math Readiness program on their own or in small groups, with instructors spending face-to-face time working on problem solving, critical thinking, and troubleshooting specific concepts. After two semesters of use, course withdrawal rates dropped by 56 percent and pass rates went from 64 percent to 75 percent. At Alabama, pass rates rose from 70 percent to 87 percent, and at UNLV, where entering students were given the chance to take the course online in the summer before they started college, the percentage who then qualified for college algebra went from 30 percent to 41 percent.
M．"Before this, I worked on the assumption that all students were at the same place. Now, because they progress at different rates, I meet them where they are," Irene Bloom, a math lecturer at ASU, told an education blog about the pilot program. "I have so much more information about what my students do (or don't do) outside of class. I can see where they are stuck, how fast they are progressing, and how much time and effort they are putting into leaming mathematics."
N． The Knewton system uses its analytics to keep students motivated. If it notices that you seem to have a confidence problem, because you too often blow questions that should be easy based on previous results, it will start you off with a few questions you're likely to get right. If you're stuck, choosing the wrong answer again and again, it will throw out broader and broader hints before just showing you the right answer. It knows when to drill you on multiplication and when to give you a fun animated video to watch.
O．It turns out that personalizing in this way can speed up learning. In the first year, 45 percent of ASU students in a 14-week course learned the material four weeks ahead of schedule. Better data is giving more options to the student who didn't succeed as well. Students may not yet know enough to pass the final exam, but a close read of their answers shows that they are making slow and steady progress. "In the past, those students would have dropped out of school," he says. In fact, the vast majority of students placed into remedial math at the nation's community colleges never get their degrees. "Instead, we were able to say, give them another semester and they'll get it. Their whole life has now changed."
46. Under the help of the platform, a teacher knows thoroughly about a certain student's study and can see it in detail.
47. The result of the Knewton system is that learning can be stimulated by customizing a student's learning style.
48. Knewton was founded on the belief that it would lead the industry which helps everyone leam by digital techniques.
49. Theoretically speaking, the more a person uses a site, the more individualized recommendations the site can give.
50. Adaptive learning casts away the single-paced traditional teaching schedule and help students personalize their study pace.
51. According to Ferreira, some facts are too small to be divided into smaller concepts.
52. When students are asked to do a placement test or a pretest, Knewton's sysem will analyze them and evaluate their study.
53. The Knewton system can analyze students well and thus know how to push them forward.
54. Before beginning their study in UNLV, more than 40% of students reached the necessary standard of college algebra by using Knewton's system.
55. The novelty of adaptive learning educational software lies in that the materials it presents to users vary according to their input.
Section B
【參考譯文】
如果你能學(xué)習(xí)所有知識(shí)會(huì)如何
A．想象一下，每一個(gè)學(xué)生都有個(gè)不知疲倦的個(gè)人導(dǎo)師，一個(gè)人工智能的、永不疲倦的伴侶，它無所不知，也了解學(xué)生，并幫助學(xué)生學(xué)習(xí)她需要了解的知識(shí)?！澳銈冞@些家伙聽起來像來自未來。”約瑟·費(fèi)雷拉，新創(chuàng)辦的教育技術(shù)企業(yè)Knewton的首席執(zhí)行官說，“這是業(yè)內(nèi)人士常見的反應(yīng)。”
B．每天都有一百萬名學(xué)生生成幾百萬個(gè)數(shù)據(jù)點(diǎn)，這些學(xué)生來自于從小學(xué)到大學(xué)的各個(gè)階段，但都使用Knewton的“適應(yīng)性學(xué)習(xí)”技術(shù)來學(xué)習(xí)數(shù)學(xué)、閱讀和其他基礎(chǔ)學(xué)科。[55]適應(yīng)性學(xué)習(xí)成為日趨流行的口號(hào)。說明了這款教育軟件時(shí)刻根據(jù)用戶的輸入為其呈現(xiàn)個(gè)性化定制材料的特征。無論是風(fēng)險(xiǎn)資本家還是的大型教育公司均將其譽(yù)為一場(chǎng)“革命”。
C．[48]費(fèi)雷拉于2008年創(chuàng)辦Kneton公司時(shí)的愿景或多或少與今日相同。即使數(shù)字技術(shù)能夠?yàn)樗腥烁淖儗W(xué)習(xí)方式，羞且該公司將統(tǒng)領(lǐng)這一改變?！翱纯椿ヂ?lián)網(wǎng)已經(jīng)改變了的其他行業(yè)，”他曾說，“它給媒介帶來了浪費(fèi)，但又將其重建。但是不管什么原因，人們看不到它與教育的聯(lián)系。我覺得很顯然互聯(lián)網(wǎng)也將用于教育。教育背后的所有內(nèi)容將在10年后出現(xiàn)在網(wǎng)絡(luò)中。這是一個(gè)偉大的轉(zhuǎn)變，而這正是Knewton要發(fā)力的地方。
D．推薦引擎是互聯(lián)網(wǎng)的一項(xiàng)核心技術(shù)，可能你每天都會(huì)用到。谷歌使用推薦引擎：其他人鍵入這些搜索條件后點(diǎn)擊的頁面。我們會(huì)首先為你提供。亞馬遜也在使用：購買了這本書的其他人也買了那本書。你使用這些網(wǎng)站的次數(shù)越多，這些網(wǎng)站就越了解你——不只是你目前的行為，而且對(duì)你做的所有其他搜索和點(diǎn)擊都了解。[49]從理論上講．你在一個(gè)網(wǎng)站上花的時(shí)間越長(zhǎng)。它給你的建議將變得更加個(gè)性化，他們甚至還會(huì)借鑒該平臺(tái)上其他人的互動(dòng)。
E．Knewton基本上是一個(gè)只針對(duì)學(xué)習(xí)的推薦引擎。它沒有囊括所有的網(wǎng)頁和電影，然而學(xué)習(xí)資料它幾乎無所不包。例如，該引擎中某項(xiàng)數(shù)據(jù)可能是個(gè)數(shù)學(xué)事實(shí)，如勾股三角形各個(gè)邊的比為3：4：5，你可以用任意一個(gè)整數(shù)乘以這些數(shù)字，就能得到該類型三角形的一組新邊長(zhǎng)。另外一項(xiàng)數(shù)據(jù)可能是關(guān)于“轉(zhuǎn)折性詞匯”的功能。如“但是”、“然而”或“另一方面”等，這些詞匯可以改變一個(gè)英文句子的意思。
F．[51]費(fèi)雷拉稱這些事實(shí)為“原子概念”，這意味著它們不能再繼續(xù)分割成更小的概念，顯然他很喜歡使用物理術(shù)語。如果像培生那樣的教科書出版社將其課程加載到Knewton的平臺(tái)，每一塊內(nèi)容——可以是一段視頻、一道測(cè)試題或一段文字——均會(huì)以一條或多條適當(dāng)?shù)母拍顦?biāo)記。
G．假設(shè)你們學(xué)校購買了Knewton推動(dòng)的MyMathLab在線系統(tǒng)，使用特定課程，比如Lial’s Basic CollegeMathematicsbe。學(xué)生登錄系統(tǒng)時(shí)，她首先需要做個(gè)簡(jiǎn)單的分級(jí)測(cè)試或預(yù)備測(cè)驗(yàn)，測(cè)試內(nèi)容都是書里的知識(shí)，而且都已被標(biāo)記為相關(guān)的“原子概念”。[52]當(dāng)學(xué)生讀課文、看視頻和回答問題時(shí)。Knewton系統(tǒng)也在“解讀”學(xué)生——計(jì)算他們用于每道題的時(shí)間，將每次鍵盤敲擊情況列成表格。構(gòu)建學(xué)生的學(xué)習(xí)類型：猶豫還是自信?盲目猜測(cè)還是不慌不忙?
H．根據(jù)學(xué)生的答案，以及她在得出答案之前所做的事情，“我們可以告訴你有關(guān)每個(gè)概念的百分率：他們學(xué)得有多快，他們對(duì)該概念的了解程度，他們能記住這一概念多久，以及他們學(xué)習(xí)其他類似概念的可能性?！辟M(fèi)雷拉說。通過觀察學(xué)生與平臺(tái)的互動(dòng)，平臺(tái)對(duì)學(xué)生進(jìn)行推斷。
I．基于該信息，平臺(tái)制定出個(gè)性化的學(xué)習(xí)計(jì)劃并確定學(xué)生下一步應(yīng)該努力學(xué)習(xí)的內(nèi)容，給學(xué)生適當(dāng)?shù)男聝?nèi)容。并不斷地檢查其取得的進(jìn)展。儀表板顯示學(xué)生已掌握了多少知識(shí)以及下一步該做什么。同樣，[46]教師也能清楚地看到學(xué)生學(xué)哪個(gè)概念比較吃力。教師不僅能看到學(xué)生是否完成了作業(yè)。還能看到每個(gè)問題是經(jīng)過幾次努力才解決的，學(xué)生需要多少暗示．學(xué)生是否看到了那一頁或是否打開了有相關(guān)解釋的那段視頻。人們?cè)绞褂迷撓到y(tǒng)，它就能變得更好；你越使用它，它就會(huì)給你更好的幫助。
J．在傳統(tǒng)的課堂上，老師按單一的速度通過一系列預(yù)定的材料來推動(dòng)一組學(xué)生的學(xué)習(xí)。反饋具有延遲性——你在一兩天后才能收回家庭作業(yè)或測(cè)驗(yàn)。在學(xué)習(xí)過程中，有些學(xué)生覺得無聊，有的還迷茫不解。你可能會(huì)錯(cuò)過一個(gè)重要概念，然后落于人后，且再也趕不上了。[50]基于軟件的適應(yīng)性學(xué)習(xí)改變了這一切。學(xué)生可以按照自己的速度推進(jìn)學(xué)習(xí)。他們可以得到暗示和即時(shí)反饋。與此同時(shí)，教師可以把課堂時(shí)間用于某個(gè)需要幫助的個(gè)人或小組。
K．費(fèi)雷拉能夠與培生和威利這樣的競(jìng)爭(zhēng)對(duì)手合作，因?yàn)樗能浖軌蚪o任何人的教育內(nèi)容提供動(dòng)力，就像亞馬遜網(wǎng)絡(luò)服務(wù)系統(tǒng)那樣，可以在云端提供服務(wù)器供其他任何網(wǎng)站使用。但在有任何內(nèi)容合作伙伴之前，作為對(duì)自身概念的證明，Knewton使用其軟件平臺(tái)建立了自己的大學(xué)數(shù)學(xué)補(bǔ)習(xí)課程?！皵?shù)學(xué)準(zhǔn)備就緒”已于2011年夏天分別被亞利桑那州立大學(xué)、拉斯維加斯的內(nèi)華達(dá)大學(xué)和阿拉巴馬大學(xué)使用。
L．在亞利桑那州立大學(xué)，學(xué)生自己或以小組形式，通過Knewton的“數(shù)學(xué)準(zhǔn)備就緒”程序提供的計(jì)算機(jī)材料來學(xué)習(xí)。與老師面對(duì)面的時(shí)間花在解決問題、批判性思考及解答具體的難懂概念上。經(jīng)過兩個(gè)學(xué)期的使用，退課率下降了56％，從64％上升到了75％。在阿拉巴馬大學(xué)，合格率從70％上升到了87％，[54]升入拉斯維加斯的內(nèi)華達(dá)大學(xué)的新生在開學(xué)前的暑假期間可以通過在線課程學(xué)習(xí)．達(dá)到大學(xué)幾何學(xué)習(xí)標(biāo)準(zhǔn)的百分比從30％升到了41％。
M．“在這之前，我以為所有的學(xué)生都處在同一個(gè)水平線上?，F(xiàn)在因?yàn)樗麄兊倪M(jìn)步速度不一，我要在他們需要幫助的時(shí)候給予幫助?！眮喞Ｄ侵萘⒋髮W(xué)的數(shù)學(xué)講師艾琳·布魯姆在一個(gè)教育博客上談?wù)撘豁?xiàng)試點(diǎn)計(jì)劃時(shí)說道。“關(guān)于我的學(xué)生在課堂外做(或不做)什么的信息我有很多。我可以看到他們卡在什么地方，他們的進(jìn)步速度如何以及他們?cè)跀?shù)學(xué)學(xué)習(xí)上投入了多少時(shí)間和精力?！?BR> N．[53]Knewton系統(tǒng)利用其分析來激勵(lì)學(xué)生學(xué)習(xí)。如果它注意到你似乎在信心方面有問題，因?yàn)槟愠３o法解答某些題目，而根據(jù)以前的結(jié)果，這些題目應(yīng)該并不難。它會(huì)讓你開始做幾個(gè)你很可能答對(duì)的問題。如果你卡住了，一遍一遍地選擇錯(cuò)誤答案，它會(huì)在告訴你正確答案之前，不斷地給你提示。它知道什么時(shí)候讓你多做題，什么時(shí)候讓你看段有趣的動(dòng)畫視頻。
O．[47]事實(shí)證明這樣的個(gè)性化可以加快快習(xí)進(jìn)程。在第一年，亞利桑那州立大學(xué)45％的學(xué)生花了十四周就學(xué)會(huì)了應(yīng)學(xué)會(huì)的材料，這比教學(xué)進(jìn)度提前了四周。更好的資料為那些沒學(xué)會(huì)的學(xué)生提供了更多的選擇。學(xué)生們所學(xué)的可能還不足以使其通過期末考試，但仔細(xì)閱讀他們的答案就會(huì)知道他們正在緩慢而穩(wěn)定地進(jìn)步。“要是在以前，這些學(xué)生就輟學(xué)了?！彼f。事實(shí)上，那些在全國(guó)的社區(qū)學(xué)院補(bǔ)習(xí)數(shù)學(xué)的學(xué)生，絕大多數(shù)沒有拿到學(xué)位?！跋喾矗覀兛梢哉f，再給他們一個(gè)學(xué)期，他們會(huì)得到學(xué)位。他們的整個(gè)人生現(xiàn)在已經(jīng)改變了。”
【答案解析】
46．I
解析：題干意為，在該平臺(tái)的幫助下，教師對(duì)某個(gè)學(xué)生的學(xué)習(xí)情況有了徹底了解并能看到該學(xué)生學(xué)習(xí)狀態(tài)的細(xì)節(jié)。注意抓住題干中的關(guān)鍵詞platform和teacher。關(guān)于平臺(tái)對(duì)教師的幫助的內(nèi)容在I段出現(xiàn)。該段第三句指出，教師也能清楚地看到學(xué)生學(xué)哪個(gè)概念比較吃力。教師不僅能看到學(xué)生完成了作業(yè)。還能看到每個(gè)問題是經(jīng)過幾次努力才解決的，學(xué)生需要多少暗示，學(xué)生是否看到了那一頁或是否打開了有相關(guān)解釋的那段視頻。由此可見，題干是對(duì)原文的簡(jiǎn)要概括，故答案是I。
47．O
解析：題干意為，Knewton系統(tǒng)的結(jié)果是可以通過定制學(xué)生的學(xué)習(xí)方式以促進(jìn)其學(xué)習(xí)。注意抓住題千中的關(guān)鍵詞stimulated和customizing。關(guān)于定制學(xué)習(xí)方式可以促進(jìn)學(xué)習(xí)的內(nèi)容出現(xiàn)在。段中。該段第一句指出，事實(shí)證明這樣的個(gè)性化學(xué)習(xí)能夠加快學(xué)習(xí)進(jìn)程。由此可見，題干是原文的同義轉(zhuǎn)述，故答案為O。題干中的customizing與原文中的personalizing為同義轉(zhuǎn)述，stimulated則與speed up對(duì)應(yīng)。
48．C
解析：題干意為，Knewton建立時(shí)的信念就是該公司將成為幫助所有人運(yùn)用數(shù)字技術(shù)進(jìn)行學(xué)習(xí)的行業(yè)的。注意抓住關(guān)鍵詞Knewton和digital techniques。關(guān)于Knewton建立時(shí)之愿景的內(nèi)容出現(xiàn)在C段。該段第一句指出，費(fèi)雷拉于2008年創(chuàng)辦Knewton公司時(shí)的愿景或多或少與今日相同，即，使數(shù)字技術(shù)能夠?yàn)樗腥烁淖儗W(xué)習(xí)方式，并且該公司將統(tǒng)領(lǐng)這一改變。由此可見，題干與原文是同義轉(zhuǎn)述，故答案是C。題干中的belief與原文中的vision意思相當(dāng)，題干中的lead與原文中的dominates相對(duì)應(yīng)。
49．D
解析：題干意為，從理論上講，用戶使用某個(gè)網(wǎng)站越多，該網(wǎng)站就越能給出個(gè)性化的推薦。注意抓住題干中的關(guān)鍵詞theoretically和a site。關(guān)于網(wǎng)站和用戶關(guān)系的內(nèi)容出現(xiàn)在D段。該段后一句指出，從理論上講，你在一個(gè)網(wǎng)站上花的時(shí)間越長(zhǎng)，該網(wǎng)站給你的建議就越個(gè)性化。由此可見，題干與原文是同義轉(zhuǎn)述，故答案是D。題干中的individualized與原文中的personalized是同義轉(zhuǎn)換。
50．J
解析：題干意為，適應(yīng)性學(xué)習(xí)摒棄了傳統(tǒng)教學(xué)中單一進(jìn)度的模式，幫助學(xué)生為自己的學(xué)習(xí)進(jìn)度進(jìn)行個(gè)性化定制。注意抓住題干中的關(guān)鍵詞pace。關(guān)于學(xué)習(xí)進(jìn)度的內(nèi)容在J段出現(xiàn)。該段第五、六句指出，基于軟件的適應(yīng)性學(xué)習(xí)改變了這一切。學(xué)生可以按照自己的學(xué)習(xí)速度推進(jìn)學(xué)習(xí)。由此可見，題干是對(duì)原文的同義轉(zhuǎn)述，故答案是J。題干中的casts away與原文中的flip Oil its head意思相當(dāng)，pace與原文中的speed是同義轉(zhuǎn)換。
51．F
解析：題干意為，按照費(fèi)雷拉的說法，有些信息的概念已經(jīng)很小，無法再進(jìn)一步分解。注意抓住題干中的關(guān)鍵詞facts和divided。有關(guān)概念分解的內(nèi)容出現(xiàn)在F段。該段第一句指出，費(fèi)雷拉稱這些事實(shí)為“原子概念”，這意味著它們不能再繼續(xù)分割成更小的概念，顯然他很喜歡使用物理術(shù)語。由此可見，題干與原文為同義轉(zhuǎn)述，故答案是F。題干中的too…tohedivided…與原文中的indivisible為同義轉(zhuǎn)述。
52．G
解析：題干意為，當(dāng)學(xué)生被要求完成分級(jí)測(cè)試或預(yù)備測(cè)驗(yàn)時(shí)，Knewton系統(tǒng)將會(huì)對(duì)他們做出分析并評(píng)估他們的學(xué)習(xí)情況。注意抓住關(guān)鍵詞placementtest和pretest。關(guān)于學(xué)生做分級(jí)測(cè)試及預(yù)備測(cè)驗(yàn)的內(nèi)容出現(xiàn)在G段。該段第二句提及了這兩個(gè)概念，接著第三、四句指出，當(dāng)學(xué)生讀課文、看視頻和回答問題時(shí)，Knewton系統(tǒng)也在“解讀”學(xué)生——計(jì)算他們用于每道題的時(shí)間，將每次鍵盤敲擊情況列成表格，構(gòu)建學(xué)生的學(xué)習(xí)類型：猶豫還是自信?盲目猜測(cè)還是不慌不忙?由此可見，題干是對(duì)原文的概述，故答案是G。
53．N
解析：題干意為，Knewton系統(tǒng)能夠很好地對(duì)學(xué)生做出分析，因此知道如何推動(dòng)他們的學(xué)習(xí)。注意抓住題干中的關(guān)鍵詞analyze。關(guān)于系統(tǒng)分析學(xué)生的內(nèi)容出現(xiàn)在N段。該段第一句指出，Knewton系統(tǒng)利用其分析來激勵(lì)學(xué)生學(xué)習(xí)。由此可見，題干是原文的同義轉(zhuǎn)述。
54．L
解析：題干意為，新生進(jìn)入內(nèi)華達(dá)大學(xué)學(xué)習(xí)之前，就有40％多的學(xué)生通過使用Knewton系統(tǒng)達(dá)到了大學(xué)幾何的學(xué)習(xí)要求。注抓住題干中的關(guān)鍵詞UNLV和college algebra。關(guān)于UNLV大學(xué)新生使用Knewton系統(tǒng)的情況出現(xiàn)在L段。該段后一句指出，升入內(nèi)華達(dá)大學(xué)的新生在開學(xué)前的暑假期間可以通過在線課程學(xué)習(xí)，達(dá)到大學(xué)幾何學(xué)習(xí)標(biāo)準(zhǔn)的百分比從30％升到了41％。由此可見，題干與原文是同義轉(zhuǎn)述。故答案是L。題干中的reached the necessary standard是原文中qualified的同義轉(zhuǎn)述。
55．B
解析：題干意為，適應(yīng)性學(xué)習(xí)教育軟件的新奇之處在于它給用戶呈現(xiàn)的材料根據(jù)其輸入而變化。注意抓住題干中的關(guān)鍵詞educational software和input。關(guān)于這款軟件新奇之處的介紹出現(xiàn)在B段。該段第二句提出,適應(yīng)性學(xué)習(xí)成為日趨流行的口號(hào)，說明了這款教育軟件時(shí)刻根據(jù)用戶的輸入為其呈現(xiàn)個(gè)性化定制材料的特征。由此可見，題干是原文的同義轉(zhuǎn)述，故答案是B。