Is this related to "learning
styles?" My daughter was tested on some such test and it was
supposed to reveal her "natural" way of learning. I thought that
it was horse hockey at the time, but hey, maybe they were on to
something.
David
Scientific American
In the 1960s, the legendary psychologist Albert
Bandura rejected the view that learning is passive.
Instead he emphasized the importance of the active use of
learning strategies. Today, Bandura’s legacy lives on, and
has been extended in exciting new directions.
Grounded in
Bandura’s pioneering research, in 1986 Barry Zimmerman and
Martinez Pons published
a paper that helped spur an entire new field of study
on self-regulated learning strategies. Zimmerman and Pons
interviewed 40 tenth-grade students who were on a “high
achievement track†and compared their responses against
those of 40 tenth-graders who were in “lower achievement
tracks.†Specifically, they asked the students about the
learning strategies they used to participate in class,
study, and complete their assignments. Through the course of
their interviews, they identified fourteen self-regulated
learning strategies. They found that the high-achieving
students differed from the low-achieving students in regard
to whether they used these strategies, how much they used
the strategies, and their consistency in using the
strategies.
Over the
past few decades there have been multiple
studies showing the effectiveness of the
self-regulated learning strategies approach using a
variety of methodologies (e.g., think-aloud protocols,
diaries, observation). In one recent
large review, John Dunlosky and colleagues
evaluated the relative utility of ten learning
strategies. While some of the learning strategies
(e.g., highlighting, rereading) were found to have low
utility in benefitting learning outcomes, the
following strategies were assessed as having moderate
to high utility: practice testing (high), distributed
practice (high), elaborative interrogation (medium),
self-explanation (medium), and interleaved practice
(medium). Practice testing had the most
evidence supporting its benefits for learning across
context and over time.
Researchers
have also recently begun to integrate the learning
strategies approach with the expert
performance approach. A plethora of research
shows that a very deliberate type of practice
involving the active use of strategies to maximize
performance and overcome limitations is essential
to greatness
across many domains, including the arts,
sciences, and sports. Excitedly, recent research
suggests that the expert performance approach can also
be applied to increase our understanding of the
acquisition of school-based knowledge.
In one study,
Kiruthiga Nandagopal and K. Anders Ericsson
investigated the use of self-regulated learning
strategies among advanced undergraduate bioscience
majors. Because these students “made active
decisions to embark on the road to acquiring expertise
in the biological sciences,†they met the expert
performance approach criteria. Adopting one of the key
methodologies of the expert performance approach, they
analyzed student diaries over the course of three
weeks, estimating the presence, frequency, and
duration (in terms of total number of hours) of
self-regulated learning strategies. They grouped
fourteen self-regulated learning strategies into six
main categories: self-regulating
(self-assessing, goal-setting, planning, and so on), organizing,
seeking information, mnemonic usage,
seeking social assistance (for instance,
seeking assistance from peers, tutors, and
professors), and reviewing (reviewing prior
problems, notes, textbook, and such). Then they
compared the diary responses among the following three
groups of achievers based on their GPA before entering
the course: high-achieving students (GPA > 3.7),
average-achieving students (GPA ≥ 3) and
low-achieving students (GPA < 3).
Comparing the diary responses of the different groups
of achievers, they found that the high-achieving
students reported employing a larger number of
different strategies. The high-achieving students were
particularly more likely to engage in organizing and
transforming, seeking information, and reviewing
strategies compared to the low-achieving
students. Timing was also critical. While students
engaged in organizing, transforming, and reviewing
notes more frequently and for longer stretches of time
during the midterm week than other weeks, high-achieving
students sought more assistance from their peers and
spent more time studying during midterm
weeks compared to low-achieving students. In
contrast, low-achieving students engaged in these
strategies more than average-achieving students toward
the end of the semester. High-achieving students also
spent more time overall in study-related activities
earlier in the semester compared to average and
low-achieving students, whereas there was no such
difference between the groups later on in the
semester.
The most
important learning strategies for predicting
end-of-semester GPA were (1) seeking information, (2)
reviewing the textbook, and (3) seeking assistance
from peers during the midterm week. While the
correlation between prior SAT scores and semester GPA
was significant, once the most predictive
learning strategies were considered, prior SAT
scores didn’t explain any additional variation in
end of semester GPA. Considering IQ
scores (which are highly
correlated with SAT scores) are known to be
excellent predictors of academic achievement,
this finding is actually quite striking! This suggests
that one of the crucial reasons why those with higher
general cognitive ability tend to do so well across so
many learning situations is due, in large part, to
their use of efficient learning strategies that
maximize learning outcomes.
This
idea is consistent with a fascinating
study conducted by Nandagopal, Roy
Roring, and Jeanette Taylor. They had twins
think aloud while they were taking three
cognitive tests that are significantly
correlated with IQ– associative
learning, working memory, and processing
speed. After analyzing the thought processes
of the participants, the researchers found
that performance on all three cognitive tests
was heavily influenced by cognitive strategies
(e.g., mnemonic encoding techniques). Most
compellingly, differences in strategy use on
the associative learning task (which was most
amenable to the use of strategies) explained a
significant amount of the genetic influences
on performance. While there certainly needs to
be more research on the development of
learning strategies, this study is the
first to demonstrate that the heritability
of performance on cognitive tasks is due, in
part, to the use of specific cognitive
strategies.
Another recent
study further supports the importance of learning
strategies for predicting long-term growth and achievement.
Kou Murayama and colleagues investigated the simultaneous
prediction of motivation, learning strategies and IQ for
explaining the long-term growth in mathematics achievement
from Grades 5 to 10 among a sample of German students. Their
measure of math achievement tested competencies such as
arithmetic, algebra, and geometry. At the start of their
study, IQ, motivation, and learning strategies significantly
predicted math performance, with motivation and learning
strategies adding additional prediction above IQ.
A different story emerged, however, once they looked at the
predictors of long-term growth. IQ was not related
to growth in mathematics achievement after taking into
account demographic information. In contrast,
perceived control (e.g., “When doing math, the harder
I try, the better I performâ€), intrinsic motivation
(e.g., “I invest a lot of effort in math, because I am
interested in the subjectâ€), and deep learning
strategies (e.g., “When I study for exams, I try to
make connections with other areas of mathâ€),
significantly predicted growth of mathematics knowledge.
What’s more, surface learning strategies (“For some
math problems I memorize the steps to the correct solutionâ€)
negatively predicted mathematics growth.
The
researchers related their findings to The
Matthew Effect: those with high intrinsic
motivation and effective learning strategies will tend to
increase their ability, while those without those
characteristics will tend to decrease their ability. Over
time, the
gap between those with higher ability and those with lower
ability will widen. Which is all the more
reason why we ought to set up the right conditions for
active engagement for everyone, and teach people the
proper strategies for success.
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