【易伯华出品】雅思阅读机经真题解析-Novice and Expert

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You should spend about 20 minutes on Question 14-26 which are based on

Reading Passage below.

Becoming an 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.

EPsychologists 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.

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