The following article tells about the how
children Succeed
Which matters more, cognitive ability or motivation?
Angela Duckworth, a psychologist at the University of
Pennsylvania, has made it her life’s work to analyze which children succeed and
why. She says she finds it useful to divide the mechanics of achievement into
two separate dimensions: motivation and volition. Each one, she says, is
necessary to achieve long-term goals, but neither is sufficient alone. Most of
us are familiar with the experience of possessing motivation but lacking
volition: You can be extremely motivated to lose weight, for example, but
unless you have the volition—the willpower, the self-control—to put down the
cherry Danish and pick up the free weights, you’re not going to succeed. If
children are highly motivated, self-control techniques and exercises—things
like learning how to distract themselves from temptations or to think about
their goals abstractly—might be very helpful. But what if students just aren’t
motivated to achieve the goals their teachers or parents want them to achieve?
Then, Duckworth acknowledges, all the self-control tricks in the world aren’t
going to help.
But that doesn’t mean it’s impossible to shift a person’s
motivation. In the short term, in fact, it can be surprisingly easy. Consider a
couple of experiments done decades ago involving IQ and M&M’s. In the first
test, conducted in Northern California in the late 1960s, a researcher named
Calvin Edlund selected 79 children between the ages of 5 and 7, all from
“low-middle class and lower-class homes.” The children were randomly divided
into an experimental group and a control group. First, they all took a standard
version of the Stanford-Binet IQ test. Seven weeks later, they took a similar
test, but this time the kids in the experimental group were given one M&M
for each correct answer. On the first test, the two groups were evenly matched
on IQ. On the second test, the IQ of the M&M group went up an average of 12
points—a huge leap.
A few years later, two researchers from the University of
South Florida elaborated on Edlund’s experiment. This time, after the first,
candy-less IQ test, they divided the children into three groups according to
their scores on the first test. The high-IQ group had an average IQ score on
the first test of about 119. The medium-IQ group averaged about 101, and the
low-IQ group averaged about 79. On the second test, the researchers offered
half the children in each IQ category an M&M for each right answer, just as
Edlund had; the others in each group received no reward. The medium-IQ and
high-IQ kids who got candy didn’t improve their scores at all on the second
test. But the low-IQ children who were given M&M’s for each correct answer
raised their IQ scores to about 97, almost erasing the gap with the medium-IQ
group.
The M&M studies were a major blow to the conventional
wisdom about intelligence, which held that IQ tests measured something real and
permanent—something that couldn’t be changed drastically with a few
candy-covered chocolates. They also raised an important and puzzling question
about the supposedly low-IQ children: Did they actually have low IQs or not?
Which number was the true measure of their intelligence: 79 or 97?
This is the kind of frustrating but tantalizing puzzle
that teachers face on a regular basis, especially teachers in high-poverty
schools. You’re convinced that your students are smarter than they appear, and
you know that if they would only apply themselves, they would do much better.
But how do you get them to apply themselves? Should you just give them
M&M’s for every correct answer for the rest of their lives? That doesn’t
seem like a very practical solution. And the reality is that for low-income
middle-school students, there are already tremendous rewards for doing well on
tests—not immediately and for each individual correct answer, but in the long
term. If a student’s test scores and GPA through middle and high school reflect
an applied IQ of 97 instead of 79, he is much more likely to graduate from high
school and then college and then to get a good job—at which point he can buy as
many bags of M&M’s as he wants.
But as every middle-school teacher knows, convincing
students of that logic is a lot harder than it seems. Motivation, it turns out,
is quite complex, and rewards sometimes backfire. In their book Freakonomics,
Steven Levitt and Stephen Dubner recount the story of a study researchers
undertook in the 1970s to see if giving blood donors a small financial stipend
might increase blood donations. The result was actually that fewer people gave
blood, not more.
And while the M&M test suggests that giving kids
material incentives to succeed should make a big difference, in practice, it often
doesn’t work that way. In recent years, the Harvard economist Roland Fryer has
essentially tried to extend the M&M experiment to the scale of a
metropolitan school system. He tested several different incentive programs in
public schools—offering bonuses to teachers if they improved their classes’
test results; offering incentives like cellphone minutes to students if they
improved their own test results; offering families financial incentives if
their children did better. The experiments were painstaking and carefully
run—and the results have been almost uniformly disappointing. There are a
couple of bright spots in the data—in Dallas, a program that paid young kids
for each book they read seems to have contributed to better reading scores for
English-speaking students. But for the most part, the programs were a bust. The
biggest experiment, which offered incentives to teachers in New York City, cost
$75 million and took three years to conduct. And in the spring of 2011, Fryer
reported that it had produced no positive results at all.
This is the problem with trying to motivate people: No
one really knows how to do it well. It is precisely why we have such a booming
industry in inspirational posters and self-help books and motivational
speakers: What motivates us is often hard to explain and hard to measure.
Part of the complexity is that different personality
types respond to different motivations. We know this because of a series of
experiments undertaken in 2006 by Carmit Segal, then a postdoctoral student in
the Harvard economics department and now a professor at a university in Zurich.
Segal wanted to test how personality and incentives interacted, and she chose
as her vehicle one of the easiest tests imaginable, an evaluation of basic
clerical skills called the coding-speed test. It is a very straightforward
test. First, participants are given an answer key in which a variety of simple
words are each assigned a four-digit identifying number. The list looks
something like this:
And then a little lower on the page is a multiple-choice
test that offers five four-digit numbers as the potential correct answer for
each word.
All you have to do is find the right number from the key
above and then check that box (1C, 2A, 3C, etc.). It’s a snap, if a somewhat
mind-numbing one.
Segal located two large pools of data that included
scores from thousands of young people on both the coding-speed test and a
standard cognitive-skills test. One pool was the National Longitudinal Survey
of Youth, or NLSY, a huge survey that began tracking a cohort of more than
12,000 young people in 1979. The other was a group of military recruits who took
the coding exam as part of a range of tests they had to pass in order to be
accepted into the U.S. Armed Forces. The high-school and college students who
were part of the NLSY had no real incentive to exert themselves on the
tests—the scores were for research purposes only and didn’t have any bearing on
their academic records. For the recruits, though, the tests mattered very much;
bad scores could keep them out of the military.
When Segal compared the scores of the two groups on each
test, she found that on average, the high-school and college kids did better
than the recruits on the cognitive tests. But on the coding-speed test, it was
the recruits who did better. Now, that might have been because the kind of
young person who chose to enlist in the armed forces was naturally gifted at
matching numbers with words, but that didn’t seem too likely. What the
coding-speed test really measured, Segal realized, was something more
fundamental than clerical skill: the test takers’ inclination and ability to
force themselves to care about the world’s most boring test. The recruits, who
had more at stake, put more effort into the coding test than the NLSY kids did,
and on such a simple test, that extra level of exertion was enough for them to
beat out their more-educated peers.
Now, remember that the NLSY wasn’t just a one-shot test;
it tracked young people’s progress afterward for many years. So next Segal went
back to the NLSY data, looked at each student’s cognitive-skills score and
coding-speed score in 1979, and then compared those two scores with the
student’s earnings two decades later, when the student was about 40.
Predictably, the kids who did better on the cognitive-skills tests were making
more money. But so were the kids who did better on the super-simple coding
test. In fact, when Segal looked only at NLSY participants who didn’t graduate
from college, their coding-test scores were every bit as reliable a predictor
of their adult wages as their cognitive-test scores. The high scorers on the coding
test were earning thousands of dollars a year more than the low scorers.
And why? Does the modern American labor market really put
such a high value on being able to compare mindless lists of words and numbers?
Of course not. And in fact, Segal didn’t believe that the students who did
better on the coding test actually had better coding skills than the other
students. They did better for a simple reason: They tried harder. And what the
labor market does value is the kind of internal motivation required to try hard
on a test even when there is no external reward for doing well. Without anyone
realizing it, the coding test was measuring a critical noncognitive skill that
mattered a lot in the grown-up world.
Segal’s findings give us a new way of thinking about the
so-called low-IQ kids who took part in the M&M experiment in south Florida.
Remember, they scored poorly on the first IQ test and then did much better on
the second test, the one with the M&M incentive. So the question was: What
was the real IQ of an average “low-IQ” student? Was it 79 or 97? Well, you
could certainly make the case that his or her true IQ must be 97. You’re
supposed to try hard on IQ tests, and when the low-IQ kids had the M&M’s to
motivate them, they tried hard. It’s not as if the M&M’s magically gave
them the intelligence to figure out the answers; they must have already
possessed it. So in fact, they weren’t low-IQ at all. Their IQs were about
average.
But what Segal’s experiment suggests is that it was
actually their first score, the 79, that was more relevant to their future
prospects. That was their equivalent of the coding-test score, the low-stakes,
low-reward test that predicts how well someone is going to do in life. They may
not have been low in IQ, but they were low in whatever quality it is that makes
a person try hard on an IQ test without any obvious incentive. And what Segal’s
research shows is that that is a very valuable quality to possess.
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