Rote Learning
Rote learning
Rote learning is a
memorization technique based on repetition. The idea is that one will be able
to quickly recall the meaning of the material the more one repeats it. Some of
the alternatives to rote learning include meaningful learning, associative
learning, and active learning.
Versus Critical Thinking
Rote methods are routinely
used when fast memorization is required, such as learning one's lines in a play
or memorizing a telephone number.
Rote learning is widely used
in the mastery of foundational knowledge. Examples of school topics where rote
learning is frequently used include phonics in reading, the periodic table in
chemistry, multiplication tables in mathematics, anatomy in medicine, cases or
statutes in law, basic formulae in any science, etc. By definition, rote
learning eschews comprehension, so by itself it is an ineffective tool in
mastering any complex subject at an advanced level. For instance, one
illustration of rote learning can be observed in preparing quickly for exams, a
technique which may be colloquially referred to as "cramming".
Rote learning is sometimes
disparaged with the derogative terms parrot fashion, regurgitation, cramming,
or mugging because one who engages in rote learning may give the wrong
impression of having understood what they have written or said. It is strongly
discouraged by many new curriculum standards. For example, science and
mathematics standards in the United States specifically emphasize the
importance of deep understanding over the mere recall of facts, which is seen
to be less important. The National Council of Teachers of Mathematics stated:
"More than ever,
mathematics must include the mastery of concepts instead of mere memorization
and the following of procedures. More than ever, school mathematics must
include an understanding of how to use technology to arrive meaningfully at
solutions to problems instead of endless attention to increasingly outdated
computational tedium."
However, advocates of
traditional education have criticized the new American standards as slighting
learning basic facts and elementary arithmetic, and replacing content with
process-based skills. In math and science, rote methods are often used, for
example to memorize formulas. There is greater understanding if students commit
a formula to memory through exercises that use the formula rather than through
rote repetition of the formula. Newer standards often recommend that students
derive formulas themselves to achieve the best understanding. Nothing is faster
than rote learning if a formula must be learned quickly for an imminent test
and rote methods can be helpful for committing an understood fact to memory.
However, students who learn with understanding are able to transfer their
knowledge to tasks requiring problem-solving with greater success than those
who learn only by rote.
On the other side, those who
disagree with the inquiry-based philosophy maintain that students must first
develop computational skills before they can understand concepts of
mathematics. These people would argue that time is better spent practicing
skills rather than in investigations inventing alternatives, or justifying more
than one correct answer or method. In this view, estimating answers is
insufficient and, in fact, is considered to be dependent on strong foundational
skills. Learning abstract concepts of mathematics is perceived to depend on a
solid base of knowledge of the tools of the subject. Thus, these people believe
that rote learning is an important part of the learning process.
In Computer Science
Rote learning is also used
to describe a simple learning pattern used in machine learning, although it
does not involve repetition, unlike the usual meaning of rote learning. The
machine is programmed to keep a history of calculations and compare new input
against its history of inputs and outputs, retrieving the stored output if
present. This pattern requires that the machine can be modeled as a pure
function — always producing same output for same input — and can be formally
described as follows:
See also Memorization.
References:
From Wikipedia, the free
encyclopedia