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What If You Could Learn Everything A) Imagine every student has a tireless personal tutor, an artificially intelligent and i

admin2022-08-27  21
问题                                 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 8e. 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 learning 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."
According to Ferreira, some facts are too small to be divided into smaller concepts.
选项
答案D
解析 注意抓住题干中的关键词facts和divided。有关概念分解的内容出现在F段。该段第一句指出，费雷拉称这些事实为“原子概念”，这意味着它们不能再继续分割成更小的概念，显然他很喜欢使用物理术语。由此可见，题干与原文为同义转述，故答案是F。题干中的too…to be divided…与原文中的indivisible为同义转述。
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