Expertise 5

Write a 1,050- to 1,200-word instruction paper on the processes involved with attaining expertise, reference the chapter in your text titled, “Expertise”. Anderson, J.R. (2009). Cognitive psychology and its implications (7th Ed.). New York, NY: Worth Publishers

 

Include the following salient points in your work:

1. Outline the stages in the development of expertise.

2. Outline the dimensions involved in the development of expertise.

3. Discuss how obtaining skills makes changes to the brain

4. EXAMPLE OF PAPER BELOW DO NOT COPY Plag FREE COPY ONLY

•The Nature of Expertise

So far in this chapter, we have considered some of the phenomena associated

with skill acquisition. An understanding of the mechanisms behind these phenomena

has come from examining the nature of expertise in various fields of

endeavor. Since the mid-1970s, there has been a great deal of research looking

at expertise in such domains as mathematics, chess, computer programming,

and physics. This research compares people at various levels of development of

their expertise. Sometimes this research is truly longitudinal and follows students

from their introduction to a field to their development of some expertise.

More typically, such research samples people at different levels of expertise. For

instance, research on medical expertise might look at students just beginning

medical school, residents, and doctors with many years of medical practice.

This research has begun to identify some of the ways that problem solving

becomes more effective with experience. Let us consider some of these dimensions

of the development of expertise.

.

Tactical Learning

As students practice problems, they come to learn the sequences of actions

required to solve a problem or parts of the problem. Learning to execute such

sequences of actions is called tactical learning. A tactic refers to a method that

accomplishes a particular goal. For instance, Greeno (1974) found that it took

only about four repetitions of the hobbits and orcs problem (see discussion

surrounding Figure 8.7) before participants could solve the problem perfectly.

In this experiment, participants were learning the sequence of moves to get the

creatures across the river. Once they had learned the sequence, they could simply

recall it and did not have to figure it out.

Logan (1988) argued that a general mechanism of skill acquisition involves

learning to recall solutions to problems that formerly had to be figured out. A

nice illustration of this mechanism is from a domain called alpha-arithmetic. It

entails solving problems such as F _ 3, in which the participant is supposed to

say the letter that is the number of letters forward in the alphabet—in this case,

F _ 3 _ I. Logan and Klapp (1991) performed an

experiment in which they gave participants problems

that included addends from 2 (e.g., C _ 2) through 5

(e.g., G _ 5). Figure 9.9 shows the time taken by participants

to answer these problems initially and then

after 12 sessions of practice. Initially, participants

took 1.5 s longer on the 5-addend problems than on

the 2-addend problems, because it takes longer to

count five letters forward in the alphabet than two

letters forward. However, the problems were repeated

again and again across the sessions. With repeated,

continued practice, participants became faster on all

problems, reaching the point where they could solve

the 5-addend problems as quickly as the 2-addend

problems. They had memorized the answers to these

problems and were not going through the procedure

of solving the problems by counting.1

There is evidence that, as people become more

practiced at a task and shift from computation to

retrieval, brain activation shifts from the prefrontal

cortex to more posterior areas of the cortex. For

instance, Jenkins, Brooks, Nixon, Frackowiak, and

Passingham (1994) looked at participants learning to key out various sequences

of finger presses such as “ring, index, middle, little, middle, index, ring, index.”

They compared participants initially learning these sequences with participants

practiced in these sequences. They used PET imaging studies and found that

there was more activation in frontal areas early in learning than late in learning.2

On the other hand, later in learning, there was more activation in the hippocampus,

which is a structure associated with memory. Such results indicate that, early

in a task, there is significant involvement of the anterior cingulate in organizing

the behavior but that, late in learning, participants are just recalling the answers

from memory. Thus, these neurophysiological data are consistent with Logan’s

proposal.

Tactical learning refers to a process by which people learn specific procedures

for solving specific problems.

Strategic Learning

The preceding subsection on tactical learning was concerned with how students

learn tactics by memorizing sequences of actions to solve problems. Many small

problems repeat so often that we can solve them this way. However, large and

complex problems do not repeat exactly, but they still have

similar structures, and one can learn how to organize one’s

solution to the overall problem. Learning how to organize

one’s problem solving to capitalize on the general structure of

a class of problems is referred to as strategic learning. The

contrast between strategic and tactical learning in skill acquisition

is analogous to the distinction between tactics and strategy

in the military. In the military, tactics refers to smaller-scale

battlefield maneuvers, whereas strategy refers to higher-level

organization of a military campaign. Similarly, tactical learning

involves learning new pieces of skill, whereas strategic learning

is concerned with putting them together.

One of the clearest demonstrations of such strategic changes is in the domain

of physics problem solving. Researchers have compared novice and expert solutions

to problems like the one depicted in Figure 9.10. A block is sliding down an

inclined plane of length l, and u is the angle between the plane and the horizontal.

The coefficient of friction is m. The participant’s task is to find the velocity of the

block when it reaches the bottom of the plane. The typical novices in these studies

are beginning college students and the typical experts are their teachers.

In one study comparing novices and experts, Larkin (1981) found a difference

in how they approached the problem.

The novice’s solution typifies the reasoning backward method, which starts with

the unknown—in this case, the velocity v. Then the novice finds an equation for

calculating v. However, to calculate v by this equation, it is necessary to calculate a,

the acceleration. So the novice finds an equation for calculating a; and the novice

chains backward until a set of equations is found for solving the problem.

The expert, on the other hand, uses similar equations but in the completely

opposite order. The expert starts with quantities that can be directly computed,

such as gravitational force, and works toward the desired velocity. It is also apparent

that the expert is speaking a bit like the physics teacher that he is, leaving

the final substitutions for the student.

Another study by Priest and Lindsay (1992) failed to find a difference in

problem-solving direction between novices and experts. Their study included

British university students rather than American students, and they found that

both novices and experts predominantly reasoned forward. However, their

experts were much more successful in doing so. Priest and Lindsay suggest that

the experts have the necessary experience to know which forward inferences are

appropriate for a problem. It seems that novices have two choices—reason forward,

but fail (Priest & Lindsay’s students) or reason backward, which is hard

(Larkin’s students)

Reasoning backward is hard because it requires setting goals and subgoals

and keeping track of them. For instance, a student must remember that he

or she is calculating F so that a can be calculated and hence so that v can be

calculated. Thus, reasoning backward puts a severe strain on working memory

and this can lead to errors. Reasoning forward eliminates the need to keep

track of subgoals.

However, to successfully reason forward, one must know

which of the many possible forward inferences are relevant to the final solution,

which is what an expert learns with experience. He or she learns to associate

various inferences with various patterns of features in the problems. The

novices in Larkin’s study seemed to prefer to struggle with backward reasoning,

whereas the novices in Priest and Lindsay’s study tried forward reasoning

without success.

Not all domains show this advantage for forward problem solving. A good counterexample is computer programming (Anderson, Farrell, & Sauers, 1984; Jeffries, Turner, Polson, & Atwood, 1981; Rist, 1989). Both novice and expert programmers develop programs in what is called a top-down manner; that is, they

work from the statement of the problem to sub problems to sub-sub problems, and so on, until they solve the problem. This top-down development is basically the same as what is called reasoning backward in the context of geometry or physics. There are differences between expert programmers and novice programmers, however. Experts tend to develop problem solutions breadth first, whereas novices develop their solutions depth first. Physics and geometry problems have a rich set of givens that are more predictive of solutions than is the goal. In contrast, nothing in the typical statement of a programming

problem would guide a working forward or bottom-up solution. The typical problem statement only describes the goal and often does so with information that will guide a top-down solution. Thus, we see that expertise in different domains requires the adoption of those approaches that will be successful for

those particular domains. In summary, the transition from novices to experts does not entail the same

changes in strategy in all domains. Different problem domains have different structures that make different strategies optimal. Physics experts learn to reason forward; programming experts learn breadth-first expansion. Strategic learning refers to a process by which people learn to organize their

problem solving.

Problem Perception

As they acquire expertise problem solvers learn to perceive problems in ways

that enable more effective problem-solving procedures to apply. This dimension

can be nicely demonstrated in the domain of physics. Physics, being an intellectually

deep subject, has principles that are only implicit in the surface features

of a physics problem. Experts learn to see these implicit principles and represent

problems in terms of them.

Chi, Feltovich, and Glaser (1981) asked participants to classify a large set of

problems into similar categories. Figure 9.11 shows sets of problems that

novices thought were similar and the novices’ explanations for the similarity

groupings. As can be seen, the novices chose surface features, such as rotations

or inclined planes, as their bases for classification. Being a physics novice myself,

I have to admit that these seem very intuitive bases for similarity. Contrast

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