2017年9月21日雅思阅读真题解析最新公布
2017年9月21日雅思阅读真题解析最新公布!历场雅思阅读考试中出现媒体类、植物类、艺术类、人文类、历史类题材的文章有很多，下面一起来详细看一下在2017年9月21日雅思考试中阅读部分出现了哪些考点以及题型。
| 2017年9月21日雅思考试真题及解析汇总 |
S1：
Becoming an Expert/ novice and expert
Expertise is commitment coupled with creativity. Specifically, it is the commitment of time, energy, and resources to a relatively narrow field of study and the creative energy necessary to generate new knowledge in that field. It takes a considerable amount of time and regular exposure to a large number of cases to become an expert.
A
An individual enters a field of study as a novice. The novice needs to learn the guiding principles and rules of a given task in order to perform that task. Concurrently, the novice needs to be exposed to specific cases, or instances, that test the boundaries of such heuristics. Generally, a novice will find a mentor to guide her through the process. A fairly simple example would be someone learning to play chess. The novice chess player seeks a mentor to teach her the object of the game, the number of spaces, the names of the pieces, the function of each piece, how each piece is moved, and the necessary conditions for winning or losing the game.
B
In time, and with much practice, the novice begins to recognize patterns of behavior within cases and. thus, becomes a journeyman. With more practice and exposure to increasingly complex cases, the journeyman finds patterns not only within cases but also between cases. More importantly, the journeyman learns that these patterns often repeat themselves over time. The journeyman still maintains regular contact with a mentor to solve specific problems and learn more complex strategies. Returning to the example of the chess player, the individual begins to learn patterns of opening moves, offensive and defensive game-playing strategies, and patterns of victory and defeat.
C
When a journeyman starts to make and test hypotheses about future behavior based on past experiences, she begins the next transition. Once she creatively generates knowledge, rather than simply matching superficial patterns, she becomes an expert. At this point, she is confident in her knowledge and no longer needs a mentor as a guide—she becomes responsible for her own knowledge. In the chess example, once a journeyman begins competing against experts, makes predictions based on patterns, and tests those predictions against actual behavior, she is generating new knowledge and a deeper understanding of the game. She is creating her own cases rather than relying on the cases of others.
D
The chess example is a rather short description of an apprenticeship model. Apprenticeship may seem like a restrictive 18th century mode of education, but it is still a standard method of training for many complex tasks. Academic doctoral programs are based on an apprenticeship model, as are fields like law, music, engineering, and medicine. Graduate students enter fields of study, find mentors, and begin the long process of becoming independent experts and generating new knowledge in their respective domains.
E
Psychologists and cognitive scientists agree that the time it takes to become an expert depends on the complexity of the task and the number of cases, or patterns, to which an individual is exposed. The more complex the task, the longer it takes to build expertise, or, more accurately, the longer it takes to experience and store a large number of cases or patterns.
F
The Power of Expertise
An expert perceives meaningful patterns in her domain better than non-experts. Where a novice perceives random or disconnected data points, an expert connects regular patterns within and between cases. This ability to identify patterns is not an innate perceptual skill; rather it reflects the organization of knowledge after exposure to and experience with thousands of cases. Experts have a deeper understanding of their domains than novices do, and utilize higher-order principles to solve problems. A novice, for example, might group objects together by color or size, whereas an expert would group the same objects according to their function or utility. Experts comprehend the meaning of data and weigh variables with different criteria within their domains better than novices. Experts recognize variables that have the largest influence on a particular problem and focus their attention on those variables.
G
Experts have better domain-specific short-term and long-term memory than novices do. Moreover, experts perform tasks in their domains faster than novices and commit fewer errors while problem solving. Interestingly, experts go about solving problems differently than novices. Experts spend more time thinking about a problem to fully understand it at the beginning of a task than do novices, who immediately seek to find a solution. Experts use their knowledge of previous cases as context for creating mental models to solve given problems.
H
Better at self-monitoring than novices, experts are more aware of instances where they have committed errors or failed to understand a problem. Experts check their solutions more often than novices and recognize when they are missing information necessary for solving a problem. Experts are aware of the limits of their domain knowledge and apply their domain's heuristics to solve problems that fall outside of their experience base.
I
The Paradox of Expertise
The strengths of expertise can also be weaknesses. Although one would expect experts to be good forecasters, they are not particularly good at making predictions about the future. Since the 1930s, researchers have been testing the ability of experts to make forecasts. The performance of experts has been tested against actuarial tables to determine if they are better at making predictions than simple statistical models. Seventy years later, with more than two hundred experiments in different domains, it is clear that the answer is no. If supplied with an equal amount of data about a particular case, an actuarial table is as good, or better, than an expert at making calls about the future. Even if an expert is given more specific case information than is available to the statistical model, the expert does not tend to outperform the actuarial table.
J
Theorists and researchers differ when trying to explain why experts are less accurate forecasters than statistical models. Some have argued that experts, like all humans, are inconsistent when using mental models to make predictions. A number of researchers point to human biases to explain unreliable expert predictions. During the last 30 years, researchers have categorized, experimented, and theorized about the cognitive aspects of forecasting. Despite such efforts, the literature shows little consensus regarding the causes or manifestations of human bias.
Questions 1-5
Complete the flow chart
Choose No More Than Three Words from the Reading Passage for each answer. Write your answers in boxes 1-5 on your answer sheet.
From a novice to an expert
Novice:
↓ need to study 1 under the guidance of a 2 3
↓ start to identify 4 for cases within or between study more 5 ways of doing things
Expert: create new knowledge perform task independently
Questions 6-10
Do the following statements agree with the information given in Reading Passage 1?
In boxes 6-10 on your answer sheet, write
TRUE if the statement is true
FALSE if the statement is false
NOT GIVEN if the information is not given in the passage
6. Novices and experts use the same system of knowledge to comprehend and classify objects.
7. The focus of novices' training is necessarily on long term memory
8. When working out the problems, novices want to solve them straight away.
9. When handling problems, experts are always more efficient than novices in their fields.
10. Expert tend to review more than novices on cases when flaws or limit on understanding took place.
Questions 11-13
Complete the following summary of the paragraphs of Reading Passage, using No More Than Two Words from the Reading Passage for each answer. Write your answers in boxes 11-13 on your answer sheet.
While experts outperform novices and machines in pattern recognition and problem solving, expert predictions of future behavior or events are seldom as accurate as simple actuarial tables. Why? Some have tried to explain that experts differ when using cognitive 11 to forecast. Researchers believe it is due to 12 . However attempting endeavor of finding answers did not yet produce 13 .
2017年9月21日雅思阅读真题解析最新公布
S2：化石数据库
1. ⅲ
2. ⅰ
3. ⅱ
4. ⅵ
5. ⅴ
6. ⅳ
7. B
8. D
9. C
10. B
11. D
12. B
13. C
Fossil Files—the Paleobiology Database
A
Are we now living through the sixth extinction as our own activities destroy ecosystems and wipe out diversity? That’s the doomsday scenario painted by many ecologists, and they may well be right. Thetrouble is we don’t know for sure because we don’t have a clear picture of how life changes between extinction events or what has happened in previous episodes. We don’t even know how many species are alive today, let alone the rate at which they are becoming extinct. A new project aims to fill some of the gaps. The Paleobiology Database aspires to be an online repository of information about every fossil ever dug up. It is a huge undertaking that has been described as biodiversity’s equivalent of the Human Genome Project. Its organizers hope that by recording the history of biodiversity they will gain an insight into how environmental changes have shaped life on Earth in thepast and how they might do so in the future. The database may even indicate whether life can rebound no matter what we throw at it, or whether a human induced extinction could be without parallel, changing the rules that have applied throughout the rest of the planet’s history.
B
But already the project is attracting harsh criticism. Some experts believe it to be seriously flawed. They point out that a database is only as good as the data fed into it, and that even if all the current fossil finds were catalogued, they would provide an incomplete inventory of life because we are far from discovering every fossilised species. They say that researchers should get up from their computers and get back into the dirt to dig up new fossils. Others are more sceptical still, arguing that we can never get the full picture because the fossil record is riddled with holes and biases.
C
Fans of the Paleobiology Database acknowledge that the fossil record will always be incomplete. But they see value in looking for global patterns that show relative changes in biodiversity. “The fossilrecord is the best tool we have for understanding how diversity and extinction work in normal times,” says John Alroy from the National Center for Ecological Analysis and Synthesis in Santa Barbara. “Having a background extinction estimate gives us a benchmark for understanding the mass extinction that’s currently under way. It allows us to say just how bad it is in relative terms.”
D
To this end, the Paleobiology Database aims to be the most thorough attempt yet to come up with good global diversity curves. Every day between 10 and 15 scientists around the world add information about fossil finds to the database. Since it got up and running in 1998, scientists have entered almost 340,000 specimens, ranging from plants to whales to insects to dinosaurs to sea urchins. Overall totals are updated hourly at www.paleodb.org. Anyone can download data from thepublic part of the site and play with the numbers to their heart’s content. Already, the database has thrown up some surprising results. Looking at the big picture, Alroy and his colleagues believe they have found evidence that biodiversity reached a plateau long ago, contrary to the received wisdom that species numbers have increased continuously between extinction events. “The traditional view is that diversity has gone up and up and up,” he says. “Our research is showing that diversity limits were approached many tens of millions of years before the dinosaurs evolved, much less suffered extinction.” This suggests that only a certain number of species can live on Earth at a time, filling a prescribed number of niches like spaces in a multi-storey car park. Once it’s full, no more new species can squeeze in, until extinctions free up new spaces or something rare and catastrophic adds a new floor to the car park.
E
Alroy has also used the database to reassess the accuracy of species names. His findings suggest that irregularities in classification inflate the overall number of species in the fossil record by between 32 and 44 per cent. Single species often end up with several names, he says, due to misidentification or poor communication between taxonomists in different countries. Repetition like this can distort diversity curves. “If you have really bad taxonomy in one short interval, it will look like a diversity spike~a big diversification followed by a big extinction—when all that has happened is a change in thequality of names,” says Alroy. For example, his statistical analysis indicates that of the 4861 North American fossil mammal species catalogued in the database, between 24 and 31 per cent will eventually prove to be duplicates.
F
Of course, the fossil record is undeniably patchy (adj. 不协调的). Some laces and times have left behind more fossil-filled rocks than others. Some have been sampled more thoroughly. And certain kinds of creatures— those with hard parts that lived in oceans, for example— are more likely to leave a record behind, while others, like jellyfish, will always remain a mystery. Alroy has also tried to account for this. He estimates, for example, that only 41 per cent of North American mammals that have ever lived are known from fossils, and he suspects that similar proportion of fossils are missing from other groups, such as fungi and insects .
G
Not everyone is impressed with such mathematical wizardry (n. 魔法). onathan Adrain from theUniversity of Iowa in Iowa City points out that statistical wrangling ( 争吵) has been known to create mass extinctions where none occurred. It is easy to misinterpret data. For example, changes in sea level or inconsistent sampling methods can mimic major changes in biodiversity. Indeed, a recent and thorough examination of the literature on marine bivalve fossils has convinced David Jablonsky fromthe University of Chicago and his colleagues that their diversity has increased steadily over the past 5 million years .
H
Adrain believes that fancy analytical techniques are no substitute for hard evidence, but he has also seen how inadequate historical collections can be. When he started his ongoing study of North American fossils from the Early Ordovician, about 500 million years ago, the literature described one genus and four species of trilobites, lust by going back to the fossil beds and sampling more thoroughly, Adrain found 11 genera and 39 species. “Looking inward has maybe taken us as far as it’s going to take us,” he says. “There’s an awful lot more out there than is in the historical record.” The only way to really get at the history of biodiversity, say Adrain and an increasingly vocal group of scientists, is to get back out in the field and collect new data.
I
With an inventory of all living species, ecologists could start to put the current biodiversity crisis in historical perspective. Although creating such a list would be a task to rival even the PalaeobiologyDatabase, it is exactly what the San Francisco-based ALL Species Foundation hopes to achieve in thenext 25 years. The effort is essential, says Harvard biologist Edward O. Wilson, who is alarmed by current rates of extinction. “There is a crisis. We’ve begun to measure it, and it’s very high,” Wilson says. “We need this kind of information in much more detail to protect all of biodiversity, not just the ones we know well.” Let the counting continue.
S3：巧克力的历史