In recent years there has been an increased focus on the role of education
and training, and on the effectiveness and efficiency of various instructional
design strategies. Some of the most important breakthroughs in this regard
have come from the discipline of Cognitive Science, which deals with the
mental processes of learning, memory and problem solving.
Cognitive load theory (e.g. Sweller, 1988; 1994) is an instructional
theory generated by this field of research. It describes learning structures
in terms of an information processing system involving long term memory,
which effectively stores all of our knowledge and skills on a more-or-less
permanent basis and working memory, which performs the intellectual tasks
associated with consciousness. Information may only be stored in long term
memory after first being attended to, and processed by, working memory. Working
memory, however, is extremely limited in both capacity and duration. These
limitations will, under some conditions, impede learning.
The fundamental tenet of cognitive load theory is that the quality of instructional
design will be raised if greater consideration is given to the role and limitations,
of working memory. Since its conception in the early 1980's, cognitive load theory
has been used to develop several instructional strategies which have been demonstrated
empirically to be superior to those used conventionally.
This paper outlines some of the basic principles of cognitive load theory. Examples
of the instructional design strategies generated by cognitive load theory are also
provided.
2: Memory
2.1 Remembering information
Some people believe that we remember information by 'capturing' it on something like
a video tape in our minds. This is not the case. What we see and remember depends
more on what we already know, than on what is actually presented.
Look at each of the following, and note what you see.
In the first example most people read 'THE CAT', even though the centre symbol in
each word is the same. The context of reading provides information which we use to
help interpret the symbols.
In the second example most people will read each symbol as an example of the letter
"a", even though no two symbols are identical. We can read an infinite range of symbols
as the letter "a", even most peoples' hand writing, although we have never seen their
handwriting before. We are able to do so because of our knowledge of what constitutes
the letter "a".
Similarly, we are also able to recognise literally millions of different trees, as
trees, even though no two are identical.
These examples demonstrate that we cannot help but to impose meaning on things that
we sense. Humans are able to behave and think in 'intelligent' ways because of their
ability to quickly identify meaning in presented stimuli.
Our knowledge and skills in activities as diverse as reading, driving, mathematics
and gardening all derive from the knowledge base which we hold more-or-less permanently
in long-term memory.
2.2 Chunking information
When presented a "large" set of elements to remember, it is often helpful to combine
the elements to form a smaller number of groups. Each of the groups is referred to
as a "chunk" of information.
For example, it is common practice to combine the digits of a phone number into two
or three chunks of several digits each, rather than listing all digits in one long
sequence. The phone number 3476 - 2980 may be easier to remember than the sequence
3 4 7 6 2 9 8 0.
Chunking does not need to be based upon any underlying meaning or logic that can be
identified within the elements of the to-be-learned information. However, if an underlying
meaning or logic can
be identified and is used to define the chunks, then remembering is greatly enhanced.
For example, remembering a shopping list where elements are chunked into like groups,
such as:
is much easier to remember than a list of identical elements which are chunked into
groups without any underlying structure, such as:
Look at each of the following statements in turn for just a few seconds, and try to
memorise the sequence of letters and spaces.
The first statement is difficult to memorise. The series of letters and spaces appears
to be random. If we are unable to identify any form of pattern or meaning then we
are reduced to a strategy of memorising individual letters in turn. If, however,
we are able to identify the "scrambled" meaning, then our strategy for remembering becomes
one of trying to remember the location of the spaces.
The second statement is easy to memorise because the spaces are located in a way that
promotes meaning. Consequently we need only memorise a few ideas (All fish, enjoy,
clean water).
When what we already know enables us to identify or impose meaning
on a new piece of information because it connects with information held in long-term
memory, then it is relatively easy for us to remember it because we can "build it
into" our existing knowledge base in a way that makes sense for us. The new information
becomes an integral part of our overall knowledge, held in long-term memory.
2.3 The modal model of memory
It is now widely accepted that we have, and use, more than one type of memory.
A modal model of memory distinguishes between three distinct memory types (modes).
These are sensory memory, working memory and long term memory.
Each mode has its own characteristics and limitations.
These three modes are integrated to define an information processing model of human
cognitive architecture.
2.4 Sensory memory
Sensory memory deals with incoming stimuli from our senses. These are sights, sounds,
smells, tastes and touches. A separate partition of sensory memory exists for each
of the senses.
Sensory memories extinguish extremely quickly. (About half a second for visual information,
3 seconds for auditory information). In that time, we must identify, classify and
assign meaning to the new information or it will be gone forever.
While looking at the picture below, quickly shut your eyes, and keep them shut for
a few seconds. Repeat this several times.
As soon as you shut your eyes you may have noticed an image of the picture remaining
for a split second "somewhere in your mind". This demonstrates the operation of the
partition of your sensory memory that deals with visual perceptions. This is not
restricted to blinking at pictures. Look at anything around you and it will still work.
2.5 Long term memory
Long term memory refers to the immense body of knowledge and skills that we hold in
a more-or-less permanently accessible form.
Our name, date of birth, the letters of the alphabet, how to read, how to write, how
to drive, swim, play chess, catch a ball and everything else that we "know" is all
held in our long term memory awaiting activation.
Activation will occur as a direct result of our working memory querying long term
memory for specific factual information (through our consciousness). Once a query
has been made activation (and the 'answer') is effectively instantaneous.
Knowledge and skills that are activated with extremely high regularity, such as walking
and talking, may be activated 'automatically' without the need for high levels of
conscious attention, even though the task itself may be a complex one. (Automation
is discussed further in Section 3.3.)
Consider each of the following questions.
Question 1: What is your name?
You will be able to answer this quickly. It's no surprise since it is referred to
frequently and consists of only a few words. Note how quickly you can provide the
answer.
Question 2: What are the letters of the alphabet?
Again, you will be able to answer this quickly but this is a more interesting
question than the first. Here there are 26 items in the answer and virtually
everyone presents the 26 items in the same order. Our long term memory holds
the letters of the alphabet in alphabetical sequence. If you try to say the
letters of the alphabet in a random order, then you will find it an extremely
difficult, probably impossible task.
Question 3: Who won the lottery in 1992 at Wattle St., Sydney, Australia?
Most people will quickly realise that they do not know the answer to this question.
They recognise almost immediately that this is information that is not currently
held in their long term memory. Generally, people "know that they don't know".
2.6 Working memory
Working memory is the part of our mind that provides our consciousness. It is the
vehicle which enables us to think (both logically and creatively), to solve problems
and to be expressive.
Working memory is intimately related to where and how we direct our attention to "think
about something", or to process information.
The biggest limitation of working memory is its capacity to deal with no more than
about seven elements of information simultaneously (Miller, 1956).
Working memory capacity may be expanded slightly by mixing the senses
used to present information. That is, it is easier to attend to a body of
information when some of the information is presented visually and the remainder
of the information is presented auditorily than it is when all of the information
is presented through a single sense (either all visually or all auditorily).
If the capacity of working memory is exceeded while processing a body of information
then some, if not all, of that information will be lost.
Consider answering both of the following questions without using pencil and paper.
For most people Question 1 is quick and easy to solve as an example of mental arithmetic.
In many ways Question 2 is nothing more than a 'larger' version of Question 1, yet
it is almost impossible to solve mentally.
The role of long term memory is effectively the same for these two questions (to recall
the rules of addition).
The difference is that in Question 2 our working memory capacity is exceeded. It cannot
cope with the large number of elements (in this case the numerals) that need to be
attended to simultaneously in order to solve this problem.
The use of pen and paper aids solution to Question 2 because it effectively relieves
the burden placed upon working memory by giving us a means of recording elements
in a 'permanent' form once we have finished processing them.
3: Learning
3.1 Definition of learning
Learning may be defined as the encoding (storage) of knowledge and/or skills into
long term memory in such a way that the knowledge and skills may be recalled and
applied at a later time on demand.
Humans have a great capacity for learning and tend to spend their lives doing so.
They learn not only how to walk upright, but also how to talk, read and write. Many
people today learn how to drive a car, operate a microwave oven, and use a computer.
Some even learn how to perform a heart transplant operation. For all of these tasks (and
just about every other task you care to mention) the role, capacity and qualities
of sensory memory and working memory remain effectively unchanged. The driving force
behind all skilled performance is the knowledge base that has been acquired within long
term memory.
The capacity of our long term memory to acquire knowledge appears to be unlimited.
No-one ever "runs out of space", although with age there may be an overall deterioration
in the performance of our memory system.
It should also be noted that virtually everyone can
learn how to drive a car, operate a microwave oven, use a computer or even perform
a heart transplant operation, provided that they are given sufficient time and training
to enable them to acquire the necessary knowledge and skills.
The next diagram presents part of an information network for cars for you to complete,
or at least think about. There are no right or wrong answers.
Spend a few minutes writing down in point form some information about cars. Some ideas
have been included for you to work from but you are free to add anything you like.
For example, details about their use, cost, construction, road rules, impact on the
environment, history of development, principles of combustion engines, how to change
gears, how to replace spark plugs.....and so on.
Work quickly, writing down ideas as soon as they come to you. If you spend more than
a few seconds "stuck", then begin another branch.
Everyone living in modern society holds an enormous amount of knowledge regarding
cars, their use, road rules, and so on. This knowledge base is held in a well structured
information network which is itself connected to other networks. Networks such as
those for 'transport' or 'modern society' are higher order concepts, while networks for
'seat belt', 'spark plugs' and 'accelerator' are lower order concepts. Knowledge
about procedures is also held (for example, how to park and how to change gears).
These hierarchical information networks are referred to as "schemas". Schemas build
in detail and complexity as more extensive knowledge is acquired in a content area.
The network in the diagram above is part of your schema for cars held in your long-term
memory.
Individual differences exist in the nature and details of schemas. Someone who is
employed as a mechanic and spends their pastime rebuilding vintage cars will have
more detailed and complex schemas for cars than most people.
Schemas that are well learnt may be recalled and applied with relative
ease. For example, someone learning to drive a manual car needs to concentrate
intently on the knowledge and skills required to coordinate the movements
of the clutch, gear stick and accelerator, in order to change gears smoothly.
After several years of driving, however, most people are able to change
gears "automatically". As automation develops, there is a reduction in the
need for concentration.
3.2 Process of learning
The previous section (Section 3.1) argued that when we say that "something has been
learnt", we mean that is has been successfully encoded into long term memory and
can later be recalled on demand.
The next question to be considered is 'how does information become encoded
into long term memory?' While the factors which contribute to encoding may
vary from one situation to another, there is one factor that is always present.
To be encoded, information must first be attended to, and processed by, working memory
. If for any reason, working memory is unable to attend to a body of to-be-learnt
information, then learning will be ineffective.
This has important implications for instructional design because the limitations of
working memory may impede the learning process. This forms the basis of cognitive
load theory.
Try to learn the following rhyme without paying attention to it.
..........Twinkle twinkle little star, how I wonder what you are.
In all likelihood you had not proceeded past the word 'star' before you became aware
that you already knew this rhyme. Indeed, you could probably add a few more lines
of the rhyme without difficulty.
You were instructed to learn this rhyme without paying attention to it
. The fact that you "recognised" the rhyme as one that you already know, shows, however,
that you did attend to the information. Perhaps you feel that this is due to the
fact that a well known rhyme was used. Try the next one.
Try to learn the following rhyme without paying attention to it:
..........Emus and elephants into the stew, rub turns your until it tummy blue.
If you were aware of any grammatical problems with this statement, then you have again
been paying attention to it. Once again, you could not help yourself.
Working memory, as the embodiment of our consciousness, cannot be "turned off" or
"by passed" while we are
conscious.
3.3 What a novice needs to learn to become an expert
For any given cognitive domain (algebra, crosswords, astrophysics, chess, electronics)
we think of novices in that area as not knowing much and for their performance to
be slow and error prone. In contrast we view experts as knowing almost everything
and assume their performance to be quick and error free.
Contrary to popular belief, expertise does not appear to be due to anything as robust
as "intelligence". Nor does it appear that experts are more "thoughtful" than novices.
The only two distinguishing features of expertise are:
1
. the expansive schemas (information networks) that experts hold, and
2
. the high level of automation (ability to perform tasks without concentrating) that
experts exhibit.
Schemas and automation appear to explain all
other expert/ novice differences.
Experts, because of their expansive set of schemas, have effectively seen almost every
possible situation in the content domain before. Moreover, they have learnt what
response is required for each situation and can carry out the required responses
automatically, without the need for high levels of concentration. Experts are effectively
just going through a set of routine exercises. It is no surprise then that experts
are so fast and accurate in their performances.
Novices, on the other hand, have relatively few schemas. They have trouble recognising
anything but the most basic and common situations as ones that they have encountered
previously. Novices are presented with a "problem" almost every time they venture
into the content domain (problem being defined as not knowing what to do or how to do
it). Novices must "solve" almost every situation presented to them. To make matters
worse, even when they realise what response is required, they may have difficulties
in performing the response. They need to concentrate intently if they are to avoid making
errors.
Consider the task of reading this page of printed material. Presumably you may do
so with little effort. While you need to concentrate on the arguments
being presented, it is likely that you do not need to concentrate on the actual task
of reading, that is, on the interpretation of all these squiggles which represent
letters of the alphabet, which are sequenced to form words, which are sequenced to
form sentences, and so on.
The task of reading is incredibly complex yet we use written documents as an easy
and efficient way to communicate ideas.
All this changes, of course, if the reader is young (say five years old).
And what chance would a typical three year old have of reading even one line of this
page? None.
Many three year olds can recite the letters of the alphabet. They can also identify
many, if not all, of the letters in written form. However, lower case letters may
present some difficulties, and running writing may be virtually impossible for them
to handle. If a three year old can spell his or her name (or simple words like 'cat' or
'dog'), then the adults around the child express praise and encouragement.
By the age of five a child is likely to have developed more refined
schemas for letter recognition, and perhaps even for recognition of some
words (their name for example). However, there is a general absence of automation
in reading. Reading is slow, error prone, and needs high levels of concentration
(mental effort). It is likely that in "reading" the child will sometimes
sound out the letters of each word in a sentence, but not actually comprehend
the sentence as a whole. This is because their attention needs to be fully
focussed on each word in isolation.
Contrast this to your reading skills. You no longer need to attend to individual letters
or individual words. It is likely that you can process the text as quickly, if not
faster, than you can say the material aloud. The only times that you need to slow
your reading speed will be when reading becomes "difficult".
One source of difficulty lies in physical factors such as tiredness (low levels of
attention), loud music (distractions), tiny text or poor lighting (inability to discriminate).
The more interesting difficulties, however, arise when something presented on paper
fails to fit into your schemas and/or level of automation. Uncommon or technical
words such as 'einstellung' or 'xanthoma', or misspelt words such es thiis werrd
may cause your attention to be directed to the individual word, perhaps even the individual
letters.
The irony about tasks such as walking, talking and reading is that they are among
the most difficult that humans ever master, yet we are able to perform each of these
with extremely low levels of mental effort. Our schemas in these areas have become
so complete, and our level of automation so high, that we now find each of these tasks to
be almost trivially easy.
A well known proverb states that "familiarity breeds contempt". In the context of
education and training this should perhaps be modified to read "familiarity breeds
expertise".
4: Cognitive Load Theory
4.1 Definition of cognitive load
Cognitive load refers to the total amount of mental activity imposed on working memory
at an instance in time.
The major factor that contributes to cognitive load is the number of elements that
need to be attended to.
Look at each of the following statements in turn for just a few seconds, and try to
memorise the sequence of digits. Note that you do not need to remember all statements
at once. Give all of your attention to each statement in turn.
For this activity we may use the number of digits (the elements) to be remembered
as a simple measure of cognitive load. Consequently:
Note that the measure used for cognitive load does not equate mathematically to task
difficulty. That is, even though statement 2 has twice the number of digits as statement
1, it is almost as easy to remember.
In contrast, statement 4 has twice the number of digits as statement 3, yet seems
more than twice as difficult to remember. While statement 3 can be remembered with
effort, statement 4 is impossible for most people to remember without some form of
practice or memory aid.
4.2 Reasons why some material is difficult to learn
The previous activity (Activity 4.1) used a digit span task to demonstrate that human
working memory has a threshold
of somewhere between 4 and 10 elements.
For the previous activity this means that:
1.....when the total number of digits to be remembered is four or less then the task
is trivially easy for most people.
2..... when the total number of digits to be remembered is between five and nine then
the task is achievable for most people if they exert 'some' mental effort.
3.....when the total number of digits to be remembered is ten or more then the task
is difficult for most people.
In many ways, however, this task is artificial. People are rarely required to memorise
sequences of random digits. After all, even telephone numbers and post codes may
have an underlying logic.
Most of the information that we are required to learn in our lifetime is far more
complex than a simple sequence of objects (whether they be digits in a telephone
number, or items on a shopping list). Content areas such as mathematical calculus,
biochemistry and computer programming are considered to be "difficult" to master. One of the
reasons for this is undoubtedly the sheer volume of information that must be acquired
(and built into schemas) before an expert knowledge base is held in the area. But
there is another critically important quality that is evident in these content areas: that
of 'high element interactivity'.
Element interactivity is defined as the degree to which the elements of some to-be-learned
information can, or cannot, be understood in isolation. While the nature of element
interactivity is difficult (and often subtle) to comprehend, a simple example may assist in describing this concept.
Example 4.2 - Element Interactivity
Consider the task of learning a foreign language. Most people can quickly learn some
simple, everyday words, but will have difficulty in generating grammatically correct
sentences, even when all of the words used in the sentence are known.
Vocabulary is an example of low element interactive material. Although there may be
literally thousands of words to be learnt, most words may be learnt in isolation
to all of the other words.
To build sentences that are grammatically correct, however, one must attend to all of the words within the sentence at once
while also considering syntax, tense, verb endings and so on. Grammar is an example
of high element interactive material because to learn it, many elements must be considered
simultaneously.
Determine if either of the following statements could be true.
.....1. My fathers' brothers' grandfather is my grandfathers' brothers' son.
.....2. My fathers' brothers' grandfather is my grandfathers' brothers' father.
Although each of these statements requires only a few elements (people) to be considered,
the activity is extremely difficult because there is a need to also attend to the
relationships between
the elements. This is an example of "complex" information where elements interact
with each other. As a consequence of the high element interactivity, the cognitive
load induced exceeds the resources of working memory.
The cognitive load associated with this material can be greatly reduced if the information
is presented pictorially. Elements which interact with each other often have the
potential to be presented in pictorial form, where the picture itself holds (and
conveys) some of the information, reducing the need for it to be held in working memory.
The partial family tree presented below shows that statement 2 is logically possible.
4.3 Elements held in working memory are schemas
This paper has argued that the limited resources of working memory mean that only
a few elements of information may be attended to at any given time.
The previous section (Section 4.2) demonstrated that to-be-learned information
which has a high level of element interactivity imposes a cognitive load
over and above that imposed by the elements themselves, due to the need
to attend also to the relationships between elements. Consequently, high
element interactive material exacerbates the difficulties which result from
working memory limitations.
All of this begs the question "what is an element?" The short answer
is "that it depends". It depends on the schemas held by the person who is
required to attend to some body of to-be-learned information because generally,
elements are schemas. What is a single element consisting of a single schema
for an expert may be several elements consisting of sub-schemas for a novice.
Consider again the contrast between statements of the type represented by 1. and 2.
below.
The first statement presents itself as a random sequence of letters and spaces. It
is without meaning and consequently each letter and each space is a separate element
which working memory needs to attend to.
In contrast, the second statement contains obvious meaning. Each cluster of letters
forms a meaningful word, and the words combine to form a meaningful sentence. Here
the number of elements for an expert reader, who knows a little about the behaviour
of dogs and cats, may be as few as one. After all, it is a grammatically correct sentence,
and it is well known that dogs do chase cats.
Schemas not only provide the ability to combine 'many elements' into a single element.
They also have the capacity to incorporate the interactions between elements. This
means that information which consists of several elements, all of which interact
with one another, may be embodied into a single schema.
For example, a professional fibre glasser holds a schema for 'mixing resin' which
takes into account not only the ideal ratio of resin and catalyst that need to be
mixed, but also, automatically, considers interacting factors such as the air temperature,
air moisture, and purpose of the mixture. It is likely that a novice in this area would
not even know that if environmental factors such as temperature and moisture are
not taken into account, then a defective mixture may result.
4.4 Intrinsic and extraneous cognitive load
Intrinsic cognitive load
Intrinsic cognitive load is due solely to the intrinsic nature (difficulty) of some
to-be-learned content. Intrinsic cognitive load cannot be modified by instructional
design. For example, content which is high in element interactivity remains high
in element interactivity regardless of how it is presented.
Extraneous cognitive load
Extraneous cognitive load is due to the instructional materials used to present information
to students. Teaching materials addressing a concept such as continental drift, for
example, will be more effective if it makes an appropriate use of graphics rather than a text only presentation.
By changing the instructional materials presented to students, the level of extraneous
cognitive load may be modified. This may facilitate learning.
Demonstration 4.4
1.
When intrinsic cognitive load is low (simple content) sufficient mental resources
may remain to enable a learner to learn from "any" type of instructional material,
even that which imposes a high level of extraneous cognitive load.
2.
If the intrinsic cognitive load is high (difficult content) and the extraneous cognitive
load is also high, then total cognitive load will exceed mental resources and learning
may fail to occur.
3.
Modifying the instructional materials to engineer a lower level of extraneous cognitive
load will facilitate learning if the resulting total cognitive load falls to a level
that is within the bounds of mental resources.
4.5 Principles of cognitive load theory
Cognitive load theory focuses on the role of working memory in the learning process.
The fundamental principles of cognitive load theory rest upon the following argument.
1.
Working memory is extremely limited.
2.
Long term memory is essentially unlimited.
3.
The process of learning requires working memory to be actively engaged in the comprehension
(and processing) of instructional material to encode to-be-learned information into
long term memory.
4.
If the resources of working memory are exceeded then learning will be ineffective.
4.6 Applying cognitive load theory to instructional design
The fundamental principles of applying cognitive load theory to instructional design
rest upon the following argument.
1.
Excessively high levels of cognitive load may result directly from the instructional
materials presented to students.
2.
Redesigning instructional materials to reduce the levels of extraneous cognitive
load may enhance learning.
3.
Content areas that are most likely to demonstrate beneficial results from improved
instructional design are those that deal with "complex" information where the elements
of to-be-learned information interact with one another (therefore imposing a high
level of intrinsic cognitive load).
Summary 4.6 - Applying cognitive load theory to instructional design
Cognitive load theory states that learning will be maximised by ensuring that as much
of a learners' working memory as possible is free to attend solely to encoding to-be-learned
information.
5: Effects Generated by Cognitive Load Theory
5.1 The different effects
Cognitive load theory has been used successfully to develop several instructional
techniques which facilitate learning.
These include:
.....the goal free effect
.....the worked example and problem completion effect
.....the split attention effect
.....the redundancy effect
.....the modality effect.
5.2 Benefits for learning
Each of the effects listed above in Section 5.1 has been shown empirically to provide
strong benefits to learners when used appropriately. In each case the benefits include
all
of the following:
.....reduced training time
.....enhanced performance* on test problems (similar to those seen during training)
.....enhanced performance* on transfer problems (those which are dissimilar to problems seen during training
but requiring the same rules for solution).
Note
:
Enhanced performance* means both shorter times to complete problems, and fewer errors.
The fact that students spend less time learning, yet return superior performances
when tested, is a powerful finding that has considerable implications for education
and training.
Of special importance is the increased performance on transfer problems. This shows
that the learning which results from each of these effects is at a level of true
understanding that enables students to solve a wider range of problems than those
students taught using "conventional" instructional materials.
5.3 Generating a measurable effect
Each effect has been developed by the argument that engineering a cognitive load which
falls within the limitations of working memory facilitates learning.
The specifics which determine when and how each of the effects operate for a given
set of learners on a given set of to-be-learned content, may be found in the original
research papers.
Of particular importance to the successful generation of these effects is the expertise
of the learner relative to the to-be-learned information.
When learners hold high levels of expertise in the content area then the elements
which their working memory may attend to are each, in and of themselves
, large complex knowledge networks (high level schemas). Consequently, their working
memory need only consider a few elements in order to hold all of the to-be-learned
information in mind. Ample cognitive resources thus remain for the process of learning.
Instructional design manipulations for this group of learners will be ineffective
because their working memory capacity is not being exceeded.
In contrast, when learners hold a low level of expertise in the content
area then only simple elements (low level schemas) have been acquired (perhaps
almost none in the case of true novices). Consequently, working memory needs
to attend to many elements in order to hold all of the to-be-learned information
in mind. Here, cognitive resources are stretched beyond their capacity and
insufficient cognitive resources remain for the process of learning. Instructional
design manipulations for this group of learners will be effective if the
reduction of cognitive load results in a level that is within the capacity
of working memory.
The dynamics of generating any of the effects thus depends on obtaining
a group of students whose relative level of expertise to content difficulty
is ripe for instructional design manipulations. (See Cooper & Sweller,
1987, for details on how student ability impacts upon the generation of a
measurable effect.)
5.4 Conventional problems
Before presenting information detailing the effects generated by cognitive load theory
a brief overview describing conventional problems, and the process by which novices
solve conventional problems, will be presented. This is because both the goal free
effect and the worked example effect are based upon the finding that the method employed
by novices to solve conventional problems (means-ends analysis, which is discussed
in the next section) imposes a relatively high level of cognitive load (Sweller,
1988).
Conventional problems are those which present students with a set of
given data (the known information) and a well defined goal (specifies what
needs to be found). Moreover, the answer may be objectively determined to
be correct or incorrect by applying rules (such as formulae) in an algorithm
based sequence.
Conventional problems are typically found in all topic areas of mathematics and science,
and in all subject areas that make use of mathematical principles (for example engineering,
accountancy and computer programming).
Example 5.4.a
If y = x + 6, x = z + 3, and z = 6, find the value of y
.
Example 5.4.b
A particle starts from rest and is accelerated at 12 m/s2 for 4.5 seconds.
What is its terminal velocity?
Example 5.4.c
For the right triangle shown, determine the length of the hypotenuse
.
5.5 Using means-ends analysis to solve problems
Means-ends analysis is a problem solving heuristic (strategy) which is widely used
to solve conventional problems by people who are not highly familiar with the specific
problem type (Larkin, McDermott, Simon & Simon, 1980; Simon & Simon, 1978).
Means-ends analysis is based upon the principle of reducing differences between the
current problem state (which begins at the problem givens) and the goal state. In
practice, this procedure often results in a problem solver working backwards from
the goal to the problem givens, before then working forwards from the givens to the goal.
While this strategy is very effective in obtaining answers (assigning a value to a
goal state) it has a necessary consequence of inducing very high levels of cognitive
load. This is because the nature of the strategy requires attention to be directed
simultaneously to the current state, the goal state, differences between them, procedures
to reduce those differences and any possible subgoals that may lead to solution.
Full details of how means-ends analysis operates, and its consequences for working
memory, are presented in Sweller (1988).
Example 5.5
If y = x + 6, x = z + 3, and z = 6, find the value of y
.
A novice problem solver (using means-ends analysis) would first focus on the goal
state (find the value of y).
Rereading the question s/he would note that the value of "y" is provided by the equation
"y = x + 6", so finding the value of "x" becomes a subgoal.
Similarly, a further rereading of the question would show that the value of "x" is
provided by the equation "x = z + 3", so finding the value of "z" becomes a subgoal
also.
Rereading the question yet again s/he would identify that the value of z is provided
as given information (z = 6). This value may now be substituted into the equation
"x = z + 3" to obtain the value "x = 9".
A true novice at this point may forget why the value of "x" was required. After all,
their working memory has been heavily taxed attending to many elements of the problem.
Nevertheless, he or she will eventually identify that the value of "x" was calculated
so that it could be substituted into the equation "y = x + 6". Doing so yields the
value of "y = 15", which is the goal state.
As can be seen by this example (and this is just a simple problem),
means-ends analysis is very cumbersome, and requires large amounts of cognitive
resources for the strategy to be implemented successfully. Problem solvers
using means-ends analysis may successfully solve "many" problems of an identical
type, yet effectively learn nothing from the activity (Sweller & Levine,
1982)
5.6 The goal free effect
Means-ends analysis operates on the principle of reducing differences between the
goal state and problem givens. Consequently, means-ends analysis may be rendered
inoperable by redefining the problem goal so that no obvious goal exists (for example,
"find what you can"). This is the principle behind the generation of goal free problems.
If problems are "goal free" then a problem solver has little option but to focus on
the information provided (the given data) and to use it where ever possible. This
automatically induces a forwards working solution path similar to that generated
by expert problem solvers. Such forward working solutions impose very low levels of cognitive
load and facilitate learning (Owen and Sweller, 1985; Ayres, 1993)
Example 5.6.a
If y = x + 6, x = z + 3, and z = 6, find what you can.
Attention would focus on "z = 6" as this is the only variable specified as a numerical
value.
Rereading the question it would be identified that the value of "z = 6" can be substituted
into the equation "x = z + 3". Doing so provides "x = 9".
Rereading the question it would now be identified that the value of "x =9" can be
substituted into the equation "y = x + 6". Doing so provides "y = 15".
Nothing else remains to be found.
It can be seen that this solution path is far simpler than that generated by means-ends
analysis in Example 5.5.
Example 5.6.b
A particle starts from rest and is accelerated at 12 m/s2 for 4.5 seconds.
Find what you can.
Example 5.6.c
For the right triangle shown,
Find what you can
.
5.7 The worked example and problem completion effect
Historically subjects such as mathematics and science have been taught using the following
general technique:
Step 1
:Introduce a new topic. Present background knowledge, principles and rules.
Step 2
:Demonstrate, using a few
worked examples, how to apply the principles and rules.
Step 3
:Have the students "practice" how to apply the principles and rules by solving many
, conventional, goal specific problems.
Section 5.5 described how the use of means-ends analysis to solve conventional problems
imposes high levels of cognitive load, and thus impedes learning. It is therefore
likely that the emphasis given to "practice problems" described above will not result
in efficient learning.
While the use of goal free problems provides an effective alternative to conventional
problem solving its application is limited to situations where the problem space
is "small". As the size of the problem space becomes "large" the increasing number
of alternatives faced at each step in a solution render the technique impractical for teaching
purposes.
An alternative technique may be found in reconsidering the nature and
purpose of worked examples. Worked examples are presented to students to
show them directly, step by step, the procedures required to solve different
problem types. Worked examples contain explicit information that equates
to schemas and automation.
That is, worked examples promote the acquisition of knowledge and skills required
to:
.....identify problems as being of a particular type,
.....recall the steps (in sequence) needed to solve each particular type, and
.....perform each step without error.
Studying worked examples imposes a low level of cognitive load because attention need
only be given to two problem states at a time and the transformation (rule operator)
that links them.
A successful method for placing emphasis on worked examples is to present them with
conventional problems in an alternating sequence (example type A, problem type A,
example type B, problem type B and so on). Students are informed of the paired nature
of the material and instructed to study each example closely because they will not be
allowed to look back at it once they begin the associated problem.
Students thus focus their attention on the problem type and the associated steps to
solution (the schemas). In solving the associated conventional problem they are testing
themselves to determine if they have learnt the procedure. This may be a more genuine form of "practice problem solving".
Example 5.7 - A worked example format for teaching algebra.
Following the numbered sequence, first study the worked example, then cover it, and
attempt to solve the associated problem.
For each of the following, solve for 'a'.
The problem completion procedure has a similar rationale
and effect to the use of worked examples (see Paas, 1992; Van Merrienboer
and Krammer, 1987). Instead of providing an entire worked example followed
by a problem, students are just provided with partially completed worked
examples. For instance, in example 1 above, they may be provided with the
first two lines and required to complete the third line themselves.
Discussion 5.7
The specific details regarding the number of example-problem pairings or completion
problems to present, the range of examples to present, the rate at which the orbit
of problem type is increased and so on, depends on the complexity of the material
relative to the expertise of the learners. The greater the relative expertise, the quicker
the pace of increase in problem types.
Worked example techniques have been demonstrated to be highly effective at facilitating
learning across a wide range of mathematically based content (see Cooper and Sweller,
1987; Zhu and Simon,1987; Pass and Van Merrienboer, 1994).
5.8 The split attention effect
Many instructional materials require both a pictorial component and a textual component
of information. Conventionally a graphic has been presented with the associated text
above, below, or at the side. Such instructional presentations introduce a split
attention effect where the student needs to attend to both the graphic and the text.
Neither the graphic, nor the text, alone, provide sufficient information to enable
understanding. The instructional material can only be understood after the student
has mentally integrated the multiple sources of information. The portion of working memory
that needs to be used in integrating the graphic and text is unavailable for the
learning process. Consequently learning is ineffective.
Consider this conventional mathematics based example, taken from Sweller, Chandler,
Tierney & Cooper (1990).
Example 5.8.a - Split instructional format for teaching co-ordinate geometry.
The presentation may be restructured to improve learning by physically integrating
the solution into the graphic
to produce a single source of instructional information. This eliminates the need
to split attention between the graphic and the text. The association between the
text and the graphic is clearly indicated.
Example 5.8.b - Integrated instructional format for co-ordinate geometry.
The split attention effect is not limited to worked examples in mathematics. It is
demonstratable in all contexts where a graphical and a textual presentation are both
necessary to impart meaning.
Consider the instructional material presented below dealing with electrical testing.
(Taken from Chandler & Sweller, 1991)
Example 5.8.c -Split instructional format for teaching a procedure. INSULATION RESISTANCE TESTS
a) CONDUCTORS IN PERMANENT WIRING
Test : To test Insulation Resistance from conductors to earth.
How conducted : i ) Disconnect appliances and busways during these tests. Make sure
mainswitch is "on" and all fuses are "in". Remove main earth from neutral bar and
set meter to read insulation. Connect one lead to earth wire at MEN bar and take
first measure by connecting the other lead to the active. Take next measure by connecting
the lead to the neutral.ii) If resistance is not high enough in either of the two
tests in i) then measure each circuit separately.
Results required :
i) At least One Megaohm
ii) Same result as i) above
Again, by reformatting the material so that the instructions are integrated into the
graphic, learning is enhanced. In fact, in this study, evidence indicated better
performance resulted on both theoretical and
practical tests.
Example 5.8.d - Integrated instructional format for teaching a procedure.
INSULATION RESISTANCE TESTS
a) CONDUCTORS IN PERMANENT WIRING
5.9 Sources of split attention
The examples presented in Section 5.8 (the split attention effect) focussed on the
need to eliminate split attention effects which result from separate textual and
graphical components of instructional materials. Appropriately integrating the text
into the graphic facilitates learning.
Split attention, however, will result whenever
a learner needs to simultaneously attend to two or more sources of instruction or
activities.
Multiple sources of purely text based instructional materials will induce a split
attention effect if two or more sources must be considered simultaneously. For example,
this is likely to occur when cross referencing documents, or even cross referencing
within a single document.
Chandler and Sweller (1992) provided evidence that a split attention effect occurs
when reading conventional experimental papers because the results section and the
discussion section are reported separately, yet need to be considered simultaneously
to understand the complex of results and their implications. Here the split attention effect
may be eliminated and intelligibility increased, by restructuring experimental papers
to integrate the results and discussion sections.
A split attention effect may also result from mixing activities. For example, when
learning to use a software package it is common practice for the learner to simultaneously
refer to a hard copy tutorial (or manual) and
the computer. The tutorial provides step-by-step instructions for performing each
task and the learner attempts to carry out each step on the computer. While this
may seem to be an obvious way of learning a software package, experimental investigations
have shown that far more effective learning strategies are available.
The simplest modification is to eliminate the use of the computer in
the learning phase and replace it by appropriate pictures and diagrams.
Provided the manual contains all of the relevant information, then students
who study the manual alone outperform students who perform each step in
sequence on the computer based upon the manual instructions. The irony here
is that the manual-only-group complete their "training" without ever having
used the software package, yet in testing, on a computer with the real software,
they perform better than the group who has already spent time using the software
package. See Chandler and Sweller (1996) for details.
Another alternative is to develop a computer based training package which integrates
text based instructions into a computer simulation of the target computer package.
When this is done the manual may be eliminated from the training process, leaving
students to focus their attention wholly on the computer screen. This eliminates split attention
and facilitates learning. See Cerpa, Chandler & Sweller (1996).
Summary 5.9 - Sources of split attention
Split attention occurs whenever a learner needs to attend to more than one source
of information, or more than one activity. A common source of split attention is
the need for a learner to perform a search. Searching a graphic to locate a component,
searching a document to find a reference and searching software pull-down menus to find
a function referred to in a manual are all examples of split attention.
Redesigning instructional materials to eliminate search and other sources of split
attention facilitates learning.
5.10 The redundancy effect
Sections 5.8 and 5.9 described the benefits which result from integrating mutually
referring textual and graphical sources of instruction.
Caution needs to be exercised, however, to ensure that both sources of instruction
truly are necessary for the to-be-learned information to be intelligible.
In situations where a source of textual instruction, or a source of graphical instruction
alone
provides full intelligibility then only one source of instruction should be used
(either the textual or the graphical), and the other source, which is redundant,
should be removed completely from the instructional materials. In these contexts
a single source of instruction returns higher levels of learning than either an integrated format
(text integrated into the graphic), or a dual format (both text and graphic presented
in parallel).
Cognitive load theory explains this result by focussing on the levels of cognitive
load imposed upon the learner who needs to process the varying instructional materials.
Attending to both textual and graphical sources of instruction requires more mental
resources than attending to a single source. Attending to both textual and graphical
sources of instruction, therefore, results in a reduced portion of working memory
being available for the process of learning.
Maps, whether their purpose is to locate countries (an atlas), indicate
the steepness of terrain (a topographic map) or to show the way to get from
A to B (a street directory) are examples of graphically based sources of
instruction that are fully self contained. Provided the user has the skills
to read and interpret a map, then there is no need for any associated body
of textual information.
Similarly, many instances of textual instruction have no need for graphics.
Arguments of litigation, analysis of history and the use of a dictionary
or thesaurus are fully intelligible in a text-only format. The use of graphics
in these situations actually reduces the level of learning that results from
the use of these documents.
Example 5.10.a - Redundant textual information in a dual format
Example 5.10.b - Redundant textual information in an integrated format
This example, dealing with the functions of the heart, is taken from Chandler and
Sweller (1991). Note
The graphic contains labels to indicate parts of the heart, and arrows to indicate
the flow of blood.
The textual statements which are integrated into the graphic do nothing other than
restate the parts and the flow of blood. On this basis the textual statements are
redundant and should be deleted from the instructional material.
Students presented a graphic only instructional format learn more than students presented
either an integrated format or a dual format.
5.11 The modality effect
All of the effects discussed so far in this paper have emphasised the need to reduce
cognitive load because of the limitations of working memory.
While information processing models of learning have historically emphasised
the "fixed" limits of working memory, there is evidence (Pavio, 1990; Baddeley,
1992) that under some conditions, an expansion of working memory may be
achieved.
Consequently, rather than attempting to reduce cognitive load, an alternative strategy,
that of expanding working memory, may be pursued as a means of facilitating learning.
The work by Pavio and Baddeley indicates that at least some portions
of working memory appear to be sensory mode specific. That is, some portion
of working memory is dedicated to attending to visual information only (especially
diagrammatic information) and some other portion of working memory is dedicated
to attending to aural information only (especially verbal information).
(Note, however, that the majority of working memory appears to be in the
form of a central resource which may be allocated to any type of sensory
information.)
Partitioning to-be-learned information so that some information, such as graphics,
is presented visually, while other information, such as text, is presented auditorily
enhances learning (see Mousavi, Low and Sweller, 1995; Jeung, Chandler and Sweller,
1997; Tindall-Ford, Chandler and Sweller, 1997). The modality effect holds the
potential to impact upon the multi media industry.
Example 5.11 - Mixed mode instructional format
This example is taken from Jeung, Chandler and Sweller (1997). Notes:
1. The graphic is presented visually but the text is only
presented auditorily.
2. Screen highlights (flashing) were used to identify the components of the graphic
referred to by each auditory statement to eliminate screen search.
When two parallel lines intersect with a third line, four pairs of corresponding angles
are equal. In the diagram, two parallel lines, AB and CD, intersect with a third
line, XY. The following four pairs of angles are corresponding angles:
Section 6: Summary and Discussion
Cognitive load theory displays strong consistencies with current knowledge regarding
memory, thought, learning and problem solving.
It is a theory which views the limitations of working memory to be the
primary impediment to learning. Reducing total cognitive load imposed by
a body of to-be-learned information increases the portion of working memory
which is available to attend to the learning process. This may only be achieved
by engineering reduced levels of extraneous cognitive load through instructional
design.
It is interesting (and important) to note that the effects generated by cognitive
load theory often "fly in the face" of standard practices. This attests to the strength
of the theory. The table below outlines this observation.
The effects generated by cognitive load theory should be viewed as "rules of thumb"
rather than absolute "laws of instruction". The bottom line, according to cognitive
load theory, will always be the need to reduce total cognitive load, and the need
to maximise cognitive resources available to be utilised in the learning process. If for
some reason cognitive load increases rather than decreases, then learning will be
inhibited.
For example, the worked example effect will not occur if the examples used actually increase, rather
than decrease, extraneous cognitive load. This is the case for examples which impose
a split attention effect. Redesigning the format of the examples to eliminate split
attention returns the educational benefit of the use of the worked examples.
The success of cognitive load theory in developing strategies and techniques which
result in both reduced training times and enhanced performance is of paramount importance
to the education and training industries.
Any fears that the application of instructional design techniques generated
by cognitive load theory may result in a "poorer" quality of student or
worker who is less able to think and act independently in unusual or unforeseen
situations are totally unfounded. Over the last ten years a large body of
evidence has been acquired to show that students taught using cognitive
load generated materials are actually more able to deal with such unusual
or unforeseen situations as attested to by their superior performances on
transfer problems (those that differ to problems seen during training, but
requiring similar rules for their solution).
It should also be noted that current research projects have provided preliminary evidence
for four additional effects generated by the application of cognitive load theory.
These are (1) the procedural learning effect, (2) the imagination effect, (3) the
colour coding effect and (4) the interaction effect. These effects are not discussed
in the current paper as the results are not yet published (at December 1998). However,
the effects appear to be real and promise to deliver further strategies for instructional design.
Suggested readings
If you wish to pursue further readings in cognitive load theory then try:
1. Sweller (1991), a very short, non technical description of how educational practice
is often based upon myths rather than empirical research, and then
2. Sweller (1994) which presents a more detailed (though still non-technical) review
of cognitive load theory and the effects generated.
3. Sweller, Van Merrienboer & Paas (1998) which presents a detailed (and partially technical) summary
of human cognitive architecture and the implications for instructional design.
After that you may wish to go to the original journal papers. Happy readings.
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