Download expertise within real-life situations

EDUC 5080
STUDY GUIDE
COGNITIVE LEARNING THEORY
PRODUCT : EDUC 5080 /SG / 01 / VER1
1
EDUC 5080
STUDY GUIDE
COGNITIVE LEARNING THEORY
Dr Greg Yates,
Magill Campus, University of South Australia
PRODUCT : EDUC 5080 /SG / 01 / VER1
2
Terminology
In line with international practice, the University of South Australia changed some of its academic
terminology. From 1 January, 2001:

Program replaced course

Course replaced subject

Unit replaced (credit) point
This publication reflects the new terminology.
Textbook
Bruning, R H; Schraw, G J ; Norby, M N; and Ronning, R R (2004). Cognitive psychology
and instruction. 4rd edition. Columbus, Ohio: Merrill.
© University of South Australia 2005
3
CONTENTS
MODULES AND READINGS
Module
Page
Topic
Textbook &
Readings
1
5
Introduction to cognitive psychology
Chapter 1
2
13
The working-memory system
Chapter 2
3
21
The long-term memory system
Chapter 3
4
29
Encoding processes
Chapter 4
5
37
Retrieval processes
Chapter 5
Yates &
Chandler
6
43
Problem solving
Chapter 8
ST1
53
Beliefs about the self
Chapter 6
ST2
57
Beliefs about knowledge
Chapter 7
ST3
61
Cognitive approaches to teaching science
Chapter 15
ST4
67
Learning via ICT
Chapter 10
Mayer
69
Appendices
86
References
4
5
MODULE 1
INTRODUCTION TO COGNITIVE PSYCHOLOGY
Reading
Text reading
Bruning et al, chapter 1.
Questions to ponder
Each week at this point we usually list a set of questions to ponder, but find this on page 8 for
this week.
Listing of key concepts
Each week at this point we list the significant terms or phrases from the readings, as a basic
vocabulary listing.
associationism
the stimulus-response
paradigm
use of animals to derive
elementary behavioural
laws
use of nonsense materials
to study verbal learning
learning as behavioural
conditioning
learning as a cognitive
process
learning as the
construction of meaning
structured knowledge
role of prior knowledge
and schemata
metacognition
learning as a function of
personal motivation and
self-beliefs
learning through social
interaction
thinking like an expert
Key points
Each week’s list of key points will reflect the major ideas to be covered. These key points will
frequently overlap with the summary provided within the textbook, so it will help to read the
key points in conjunction with the relevant chapter summaries.
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1
Although it has many forerunners, cognitive psychology, as an identifiable field of study
really emerged within the 1960s. One common view is that it emerged out of the relative
demise of associationism.
2
Associationism suggested that learning could be described in terms of associations
between stimuli and responses. In the period from about 1900 to perhaps the early 1970s,
psychologists were very much concerned with trying to describe universal laws of
learning that would apply to animals and humans alike.
3
To some extent associationism did specify a number of highly valid and significant
principles (for example, principles of reinforcement and behaviour modification). But as
an analysis of human mental behaviour (that is, what is actually going on within the
mind), associationism provided less than adequate explanations.
4
Virtually all contemporary psychologists now subscribe to cognitive psychology
principles. Your textbook is devoted to reviewing these principles in depth. But by way of
introduction, the following points (5 to 10 below) should be noted.
5
Learning is a process of meaningful construction. That is, we learn because we need to
understand things, and this can only be achieved by making active and effortful
responses.
6
Knowledge is structured or ‘packaged’ within the mind in several different ways, but
especially in the form of coherent and interlinked schemata. Schemata stem from prior
knowledge and are the basis for understanding new material. You will continue to meet
the term schemata throughout this course.
7
Learning is also related to the goals and beliefs that learners bring into a learning
situation. For example, people who believe that their intelligence is a fixed commodity
generally fare poorly when they find themselves failing to perform well on a set task,
whereas people who believe their intelligence is not a fixed entity do not expect to be
successful all the time, and may be relatively untroubled by failures.
8
We also recognise that much learning occurs within social situations in which students
can observe good models, then perform in ways that allow for peer interaction and
collaborations to serve as good learning and teaching environments.
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Cognitive psychology also stresses the contextual nature of much of our learning. For
example, learning typically takes place within a specific location and is often tied, in part,
to that location. The generalisation of a learned skill to other contexts will certainly occur,
but it is far less of an automatic process that might be expected. Indeed, a good deal of
human behaviour is highly discriminated (that is, we may behave differently in different
situations), and this facet applies strongly also to how and what we learn.
10 Cognitive psychology will provide for you with a new language which you will find
valuable in the analysis of both your own learning and that of your students. Overall, the
problem “why is learning so difficult’ will continue with us throughout, as many sensible
and meaningful answers, will emerge, but no final single resolution is possible.
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Reading notes
This section is intended to provide a guide to your reading. For the most part, the reading
will be a chapter from Bruning et al, your textbook.
Chapter 1 of Bruning et al provides a straightforward account of how cognitive psychology
grew out of earlier associationism. Some of the associationists would have been known as
‘behaviourists’, but it is unclear just what the word ‘behaviourist’ ever referred to. The
principles of behavioural conditioning described by researchers working in the 1920s and
1930s are as effective today as they were then. In the early years of last century, Pavlov
systematically described the laws of classical conditioning, and as a species, we are very
much subject to these principles. In the 1950s the noted behaviourist, B F Skinner extended
our knowledge to elements such as schedules of reinforcement. As far as they went, the laws
of associationism remain as important achievements within scientific psychology.
Effective behaviour modification therapies were also developed within this period, although
the clinical practitioners soon began to use cognitive learning principles within their work. If
you are ever in doubt as to the impact of reinforcement schedules on human behaviour, then
spend an evening watching people play poker machines. Within gambling parlours, people
are paid on a ‘lean’ (that is, intermittent) reinforcement schedule to reliably give money to the
machine’s proprietors.
In essence, this type of psychology (ie associationist, or S-R psychology) was overtaken by
cognitive psychology in the 1960s, not because it was wrong, but because it did not take
advanced human thinking processes into account. To describe thoughts as ‘operant actions’
was not especially meaningful.
After introducing the notion of cognitive psychology’s historical emergence, Bruning et al
review six major statements that emerge as fundamental starting points for the cognitive
analysis. Each of these statements will emerge within later chapters, so they are here mainly
to help you become oriented toward the field, before we move more deeply into memory
theory in week 2.
Also, by way of introduction to the field and its major concepts, you can read two brief case
studies in which the language of cognition is deliberately employed.
1
Firstly, a typical high school student, Kari, is featured on page 10 in Bruning.
2
Secondly, Mr Powell, a fire investigator, provides us with an example of high-level
expertise within a skills domain (see Appendix 1).
In both cases, notice the way that the situations the individuals face elicit active mental
operations within the individuals’ brains.
Active learning opportunities
This section includes activities and questions that can be used for revision purposes.
1
On page 2, Bruning et al note that cognitive psychology seeks to observe behaviour in
order to make inferences about the unobservable. What does this mean? Relate your
thinking to how we might interpret someone’s facial expressions, if we ask them to
memorise a number that is too long for their memory to cope with.
2
Does it mean, for example, that we are entitled to attach any meaning we want to another
person’s facial expressions?
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3
How do you know that another person is thinking? And how do you know what they are
thinking?
4
Associationist and conditioning learning principles were first described in animal research
around 1900 or earlier. But do they apply to human beings?
5
Why did cognitive psychology replace associationist psychology?
6
Write down at least five major assumptions that underpin modern cognitive psychology.
7
As a person becomes more expert within a field, how might his or her thinking processes
change? How long do you think this takes?
Questions to ponder
A decision in writing the Study Guide was to try to avoid quoting many websites at you.
There is a good deal of structured course reading already, and much of the web-based
material is of marginal information value. It is extremely hard for novices to distinguish
‘good’ from ‘bad’. There are some superb sites available specifically in the area of memory
strategies and some of these are given later in the topic.
One aspect of studying cognitive psychology you can note is the scepticism most professional
cognitive scientists have for most of what appears to take place in certain areas under labels
such as brain-based learning, learning styles, and allied aspects such as the famous “Mozart
effect”, and left-right brain hemispheres. Much of these activities are promoted through the
Internet. But there is a remarkable level of misinformation passed across as though it
represents actual research knowledge. When you enter the actual domain of cognitive
psychology, however, you discover that things such as ‘learning styles’ barely exist within
science at all, and have only marginal credibility. Most of the information about learning
styles stems from agencies that have no actual basis in research, but simply claim this
knowledge as though it was derived from research. Dr Yates wrote a book chapter on this
several years ago, so please email him, if you want a copy of this paper.
As a simple exercise in learning to become a sceptic about claims made in this area, log onto
the Internet and place “Mozart effect” into an engine such as Google. Can we warn you that
there is no actual data indicating that listening to Mozart increases student learning. But look
at what various sites claim. There is a huge site run by people promoting books and products,
and indeed it is necessary to view much of this area as being commercially-driven. Some
claim credibility by using a name within the Internet domain address itself. (Indeed, even
Nostradamus has his own website). The term “Mozart effect” is a registered commercial
trademark in the USA. Thankfully, if you keep searching on the Net you also find many sites
run by responsible people who counter the excessive claims made in the name of this effect.
For example, two sites (of many) giving polar opposite views see
http://www.mozarteffect.com and http://skepdic.com/mozart.html
Listening to ‘Sonata for two pianos’ is a wonderful and rich experience. But this will not
result in learning gains in other situations. So two questions to ponder are

How did this situation of such misinformation develop?

How can the average teacher be expected to tell genuine from pseudo-scientific
information?
But, to complete the introductory material, please consider the following case study:
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EXPERTISE WITHIN REAL-LIFE SITUATIONS
Adapted from the following
The Weekend Australian (2000, 4 November). ‘Bill Powell: after the flames, he sifts through
the mysteries of the ashes’, pages 9-10. [Weekend Magazine]
The newspaper, The Weekend Australian, regularly publishes human interest stories within its
magazine section. In 2000, the columnist Stephen Lacey interviewed Mr Bill Powell who has
served with the New South Wales Fire Service for 33 years. As a chief investigator with the
Fire Investigation Research Unit, he investigates and reports on around 150 fires per year,
gathering evidence for the courts and the state coroner. In short, Mr Powell is an expert within
the field of fire investigation.
The following statements are drawn directly from the published interview.
I always treat a fire as a jigsaw puzzle. I need to know what, how, and why.
We like to go to a hot fire, which means it is still burning. In that way we can interview the
first firefighters on the scene. Experienced firefighters can spot something unusual. If a fire
has been increased by an accelerant, most firefighters can tell. For a start, the fire is very
hard to put out. Secondly, when they put their hoses on it, the fire runs across the top of the
water.
Gathering factual information from eyewitnesses is an important part of the job.
We narrow down from the ‘area of origin’—a certain room for example—to the ‘point of
origin’, which might be over in a corner. At this point we’ll excavate debris in that location,
layer by layer, by hand so we don’t disturb the evidence.
When most people see a broken window, they assume it’s been caused by the fire. We look
closer. Maybe we’ll find some clean glass fragments. If they are unsooted, it indicates that the
glass was broken prior to the fire.
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This job has made me a suspicious bastard … You’ve got to be.
Absence of household goods sends me warning lights.
I hate having to record a fire as an undetermined cause … I like to know.
Within the interview, Mr Powell said a good deal more. For example he noted that, in his
experience, arsonists often may remain around their fires and then offer to become
eyewitnesses. They often seem to know more about the fire than would be expected. Many
arsonists actually hurt themselves, but when they go for medical treatment they tend do so as
far from the fire or their homes as they can. They may say it is a burn from a home BBQ
(barbecue).
Now, let us approach this interview from the perspective of cognitive psychology. We ask
‘What is going on within the mind?’. The above listing of statements from the interview is
especially interesting. Note how the investigator tells us that he approaches the problem with
an explicit mental focus. That is, he is suspicious, cautious and highly goal-oriented. He
knows that there has to be a cause, and that he has to proceed with considerable thought and
care. That is, he begins to employ an organised set of strategies. His knowledge of past fires
and the variables that cause them is so extensive that certain recognition processes will be
activated automatically. He knows that there are certain investigative steps to be taken while
the fire is still burning, and so he approaches the firefighters and witnesses with a planned
series of questions. The process of hypothesis testing has to take place, but the first crucial
thing is to gather the information that will eventually allow a specific hypothesis to be
supported more strongly than other possibilities.
Mr Powell does not react impulsively, but takes time to gather evidence. But note how
information gathering is not a random process. Indeed, he has to know what to look out for;
that is, how to identify and protect the evidence. He has to ensure that people do not destroy
the cues needed to arrive at a decision. For example, the disposition of broken glass is highly
important, and there could be any number of additional subtle cues left behind. He knows that
accelerants will leave some form of residue behind. For example, if anyone uses kerosene, it
leaves a smell and unburnt fluid. Mr Powell possesses a wide range of knowledge about how
fires can get started, how they develop, how quickly they spread, and what sequences to
expect as a fire takes hold. Note how such knowledge is actively embodied in schemata such
as ‘area of origin’ and ‘point of origin’. That is, even to use these terms entails a huge level of
knowledge about fire causes and sequences.
Mr Powell’s knowledge will be mentally filed within several different forms. Besides
declarative knowledge about fires, and schemas for such constructs as ‘point of origin’, he
will be able to draw upon case knowledge. Case knowledge involves memories of past events
and specific cases which bear upon the present. For example, he may find that the fire scene is
strangely devoid of furniture. This may cue him to recall a case he visited years ago, or read
about in the past, in which a criminal cleared a house of all valuable objects prior to setting a
building ablaze. Thus, recall of past cases helps by allowing him to perceive similarities
between different events.
Being able to read visual cues to locate areas such as the point of origin is a skill honed over
many years, perhaps a decade or more. To the rest of us a burnt house is a sad scene of
blackened desolation and hopeless loss. To Bill, it presents a clear mental challenge that
enables high-level detective skills to be activated and employed in the service of truth and
justice. Indeed, the newspaper interview touched on the theme of moral indignation; that is,
many fires are the work of people with criminal intent who seek to hurt others, to benefit
directly or cover up previous misdeeds.
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Besides using eyewitness reports and direct observation, the fire investigators proceed to seek
out additional information by procedures such as excavation. Additional data will be called
upon, such as the pattern of burns on any objects including human remains. That is, the
investigator has to take active steps to bring more data into the field, perhaps for a later stage
of analysis. While the scene is still fresh, Mr Powell will take scores of photographs, but even
knowing what to photograph is itself an acquired skill. The need for all this supplementary
evidence gathering will often be guided by the nature of the investigator’s preliminary
hypotheses, but he has to take care not to close off a viable cause too soon. A high level of
inference is demanded. That is, the evidence as it fell is matched to prior knowledge of
previous cases and to technical knowledge of fire sequence events. The specific presenting
case is matched to a more general case of ‘what would occur’ given all available knowledge
of situations and variables which resemble the current one.
And in addition to the tone of moral indignation (that is, the outrage he feels when people use
fire as a means of hurting others), note how Mr Powell, in common with many other experts,
sees the entire issue in terms of remarkably strong personal motivations. That is, his job is not
just to be there and fill out a mechanical report. He actually needs to know what caused the
fire. To not be able to do this would be a cause of intense discomfort. From the viewpoint of
the cognitive psychologist, this need to know is seen as a very powerful source of motivation.
We may surmise that people such as Mr Powell take great pride in their ability to solve
complex problems, and this is what makes his vocation personally rewarding.
Did you notice how we invoked a number of significant technical terms to analyse what
occurs within the mind of the investigator? Let’s look at these:

explicit mental focus

goal-orientation

strategies

knowledge (that is, declarative knowledge and case knowledge)

recall and recognition

prototypical features

automaticity

schemata

information gathering

hypothesis testing

high level of inference

mental challenge, and the need to know
These terms are all basic to the cognitive analysis of mental functioning. Within this course,
we review these and many other terms in more depth, looking at the research basis behind
their usage. Note how they help us to describe the processes the human brain actively uses in
order to solve problems, not just in solving crimes, but in all areas of our psychological
functioning, and especially when the situations in front of us demand effortful thought.
ADDENDUM: We contacted Mr Powell directly, and asked him to read these pages prior to
our citing him in this way. He declared himself amazed and fascinated by what was written,
and we believe these pages were used in training sessions, for the NSW Fire Service.
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13
MODULE 2
THE WORKING-MEMORY SYSTEM
Reading
Text reading
Bruning et al, chapter 2.
Questions to ponder
1
How do we learn? What actual process has to occur within the mind?
2
What are the three distinct memory components described in the modal model?
3
Psychologists used to talk about short-term memory (STM), but now seem to talk more
about working memory (WM). So what has changed?
4
What are the normal limits of the working memory, and how do these limits affect our
normal functioning?
5
How can teachers avoid over-reaching their students’ processing limits and still make
learning highly meaningful?
6
What does it really mean, in the mind, to ‘pay attention’?
14
Listing of key concepts
Information processing
models (including the
modal model)
sensory memory (that is,
the sensory register)
encoding and retrieval
pattern recognition
storage
assignment of meaning to
a stimulus
the visual and auditory
registers
templates
prototypes
feature analysis
the structural description
approach
knowledge as schemata
attention as a process of
selection
attention as a process of
attenuated processing
automaticity
short-term memory
capacity
chunks
the working-memory
system
cognitive load theory
Key points
1
Cognitive psychologists agree that the modal memory theory represents, in broad outline,
an accurate picture of what the mind does in situations where input information has to be
mentally processed. This theory describes the relationship between three memory stores.
2
Sensory memories (or iconic memories) are brief. Within the visual system, they may last
for perhaps 0.5 of a second if no further processing is activated. Within the auditory
register, the sensory memories (or echoes) possibly last around 3 seconds. However,
information picked up at this level may be then further processed; that is, perception and
pattern recognition processes may become active.
3
Elementary perception operates through four different mechanisms. Templates are
applied, especially in identification of individual letters. Prototypes represent the ideal or
‘best fit’ pattern when it comes to whole images. Feature analysis applies to when we
apprehend the critical features that determine the identification of an object (for example,
identifying a cat from the shape of its ears ). A structural description involves a more
verbal process wherein we apply statements and words to what we see, and so make a
slower but reasoned judgement as to what the stimulus is.
4
Once perception is achieved, the next process will involve the knowledge store in some
way. This will be covered in more depth in chapter 3 of the text, but at this point note that
the mind is engaged with activating prior knowledge to assist the processes involved in
perception and the assignment of meaning.
5
The process of paying attention is a little mysterious, in that some forms of selective
switches are involved; that is, the mind appears able to switch from one input to another,
and virtually shut off some input sources. Experimental studies have established that the
mind does possess the tendency to follow meaningful inputs, almost irrespective of
changes across sources; hence, a high level of input processing must be occurring outside
of normal consciousness.
15
6
As skill within an area increases, there is a corresponding increase in the automaticity for
basic cognitive processing. This proceduralisation allows the process of attention to shift
from lower level concerns to higher level meaningful goals.
7
Short-term memory capacity is thought to be of around seven to eight basic chunks, and
for perhaps 10 to 20 seconds.
8
The working memory is not just a fixed capacity system. It involves a system of
information management. Active thinking (that is, ‘consciousness’) is said to occur within
the executive aspect, but this is served by a visual–spatial sketch pad, and a verbal
articulatory loop.
Reading notes
These present notes may go a little beyond the chapter in the textbook in some places, but link
in with information in the next module. Across the two modules we are focused upon how we
the mind is constructed. That is, there are systems that operate, and it is important to learn a
set of technical terms that can be used to describe these processes. This week we are
concerned more with how the mind stores experiences at the level of the active working
memory, whereas in Module 3 we describe how the long-term memory operates. In future
weeks we return to these topics, looking at how the mind acquires knowledge and lays down
memories, but at the outset we need to review the basic terminology.
Chapter 2 of the text conveys the basic information-processing model that has dominated
theory and research in cognitive psychology over the past 30 years. This model sees learning
as the ability to transfer information into the long-term memory, but how it gets there and
how it becomes accessible are extremely complex problems..
One clear theme that emerges is that the human mind possesses very definite limitations. At
times these are expressed as limitations in capacity; that is, how much information can be
processed at one moment in time. For example, on page 26 of the text, it is suggested that the
short-term span is perhaps seven or eight chunks of data. But if the task in front of you
involves some relatively unknown notions, such as the names of unfamiliar towns in a
country you have never been to, or the biological names of plants, then the short-term
memory capacity is likely to drop to around four items. Generally, when we say that the
short-term memory capacity is around seven items, it assumes that these are somewhat
familiar items.
Chapter 2 reviews our basic knowledge about the phenomena of sensory memory. Most
people do not even realise that there actually is a very short memory system associated with
each input modality. Visual icons last for about 0.5 of a second, but auditory echoes seem to
last a few seconds. These are sensory memories, in that they are specific to the modality
system. However, they provide information that can be apprehended at the next level; that is,
the process of perception.
The processes involved in basic perception and pattern recognition are reviewed briefly
within the text. A template is a type of standard that you fit onto the incoming stimulus. For
example, all of us can read letters, irrespective of the printing font. That is, all fonts fall
within our templates for individual letters. Even if a font looks very strange at first, it takes
16
only a minute or so to adjust our mental templates to be able to read it. You can think of a
prototype as being a kind of statistical average of what we think something ought to look
like. We can ask people to draw things such as ‘car’ or ‘chair’ or ‘dog’. But their drawings
may not actually correspond to any one real car, chair or dog. That is, when people make such
drawings, they tend to make idealised pictures, not because they are poor at drawing, but
because they access idealised prototypes of these things within their minds.
The process of perception is extended to another level through feature analysis. This is
where the brain picks up particular features of the object, and ‘knows’ that these features
enable a specific decision to be made. For example, those distinctive ears on figure 2-5 in the
text tell you that the hidden animal is a cat. Another example could be that if you see a dog
with its ears flattened back, then this feature will tell you the dog is angry.
The highest level of basic perception occurs with what is called the structural description.
This is generally where there is a verbal-like definition being applied, possibly in a thoughtful
manner, rather than as an automatic template. This is illustrated via the notion of a ‘strait’, but
other examples could be that of ‘mountain peak’, a ‘magnetic field’, an ‘electrical circuit’, a
‘defensive fielding position’ in the game of cricket, a ‘checkmate’ position within chess, or
even the ‘point of origin’ within the field of fire investigation (see week 1). In engineering,
for example, notions such as ‘arc’, ‘trajectory’ and ‘balance point’ could all be seen in terms
of structural descriptions; that is, patterns that people may still ‘see’ even though there may be
no specific object, point or pattern that allows feature analysis to occur. Although structural
definitions often resemble concepts, please realise that a structural definition refers more to
what you see. That is, when you stare at a strange thing and then work out what it is, you see
it as a ‘chair’, not as a ‘piece of furniture’.
Our perceptions are steered by our schemata, and we meet this notion again next week.
Schemata represent the way in which knowledge is organised within the mind. It is
impossible to say where structural descriptions stop and schemata start. The very notion of
schemata will subsume the more basic elements of perception. For example, the notion of tree
within your mind includes an awareness of a perceptual prototype (an idealised tree-like
shape). In addition, you can note certain crucial features that discriminate trees from other
things (for example, a central trunk, then branches which radiate out, etc). Also, there will be
a structural definition of ‘tree’ within your mind (even though it will not be a verbal or
conscious one), which you can apply to any object in front of you to decide if this thing is a
tree or not. Finally, ‘tree’ will exist within your mind as a schema. That is, you have access to
prior knowledge about the properties of trees: what they are composed of, what we can do
with them, where they grow, what different forms they take, how they can get their roots into
drains, etc.
At this stage it may help you to realise that although we describe all of these mental processes
almost as a sequence, and the diagram of the modal model (Figure 2-1, page 16) seems to
indicate a sequence, please realise that the sequence is occurring within split seconds. As a
general principle, mental activity takes place within fast time frames. Sometimes it may take
almost a second to answer an unusual question (for example, ‘Does a tree have feathers?’),
but the point to note here is that neural processes and basic perceptual reactions can be
occurring within frames of around a quarter of a second.
17
From page 23 there is a long discussion about theories of attention. You can skim through
this without being worried about understanding everything there. There has been a long
debate within experimental psychology as to what happens to sensory inputs that do not
appear to be directly attended towards. The modern view appears to be that we can and do
monitor more than one input channel, fairly successfully, as many tasks in our lives simply do
not need huge amounts of continuous attention. But this ability is far less effective when we
focus on a central task which demands high levels of intense concentration (called ‘resourcelimited tasks’ in the textbook).
The notion of automaticity is reviewed on page 25. The ability to transfer control of a skill to
a more automatic level is one key aspect in skill development. To read these words, you are
using automaticity within the domain of text processing. You no longer need to employ effort
to decode individual letters and words. Instead, you have been able to shift control of reading
to an ‘automatic pilot’. This has tremendous mental benefits. For one thing, you are now free
to actually think as you read.
Chapter 2 concludes with a good discussion about the short-term memory and the working
memory. The term ‘short-term memory’ originates from the field of psychological testing (for
example, IQ and brain-damage assessments) and was used to describe the amount of
information that can be held for short periods in the absence of any additional processing. It
was thought of as a store about the size of a telephone number. It was shown to be highly
subject to interference effects. That is, as new material comes in, it bumps out the old.
Within the past 20 years, the term short-term memory has generally been overtaken by the
notion of working memory. This notion suggests that the system is far more active than just
being a storage bay or ‘temporary holding room’ for information passing into and out of the
long-term memory (as implied by the modal model; see Figure 2-1). One way to think about
these issues is to use the term ‘short-term memory’ when it’s a question of seeing how much
capacity can be held by the system (that is, how many digits can be recalled, etc). But when
there are decisions to be made, or there is material to be mulled over, then you are activating
your working-memory system. One interesting notion of the working-memory theory, that
now appears soundly validated, is that the human mind has separate ‘stores’ for verbal and
visual information, and that our mental work can be effectively distributed (that is, shared
across) both of these stores.
The chapter ends by listing seven principles as implications for instruction.
Active learning opportunities
1 Your sensory memory
The sensory memory is one that we are barely aware of, or perhaps we simply do not think of
it as a separate store as such. One way of trying to ‘see’ this effect is to close your eyes, turn
your head, open your eyes very briefly, then with eyes closed again, ‘look’ at the image that
remains there. It will go within a second. (Although there may be a longer afterimage effect,
especially if you were looking at a bright light). Notice how within that second you can ‘read’
some things, but not much.
18
If you wish to see how some of the classic experiments into visual iconic memory were
actually done, there is a web site
http://www.mtsu.edu/~sschmidt/Cognitive/sensory_store/sensory.html
which allows you to download demonstration protocols using the QuickTime movie system.
You can also download demonstrations on iconic memory and memory span from:
http://courses.smsu.edu/tab293f/mem/mydemos.html
2 The short-term memory store (See Appendix 1)
Try testing your friends on how many digits (numbers) they can hold within their short-term
memory.

Tell them that you are deliberately trying to overload their short-term memories. Since
this is a test of mental capacity, you need to warn them that the goal is to deliberately
overload them, to the point they fail.

You simply prepare a list of random numbers.
–
Tell them how long the string will be on each trial, beginning with four.
–
You need to tell them how many digits are coming up, and you need to give them a
cue as to when to pay attention.
So you would say something like, ‘The next one is of four numbers. Ready? … 6 … 9 …
4 … 7’

You say them one at a time, about every half second. We normally allow about a half
second per digit, but some people may want you to slow down or to speed up, and you
should try to do what they ask on this point.

Ask them to repeat back the numbers in the order they heard them.

They can have more than one trial, as sometimes they may simply fail to pay attention to
the numbers.

If they get four correct, then tell them the next trial is at five numbers, and so on. They
will begin to fail, but allow them a couple of fails per level before deciding to stop.
Most adults should be able to recall around eight digits. But realise that some people may fare
better with words than numbers.
If people actually do better than eight then ask them how they do it. Do you suppose the
reason is their large STM capacity, or are they using some specific tactic (a strategy) which
enables them to cope better?
3 Engaging working memory
For variations on short-term memory capacity tests, try repeating the above experiment, but:

Ask them to ‘add one’ to their replies. That is, you say ‘3 … 7 … 9’, and they respond ‘4
… 8 … 10’.

If they seem to cope with this, make it even harder by asking them to ‘subtract one’.
Note how these tasks invoke operations at the level of the working memory; that is, ‘take
something in, do something with it, hold it in the rehearsal buffer, locate the next input, do
something with this, etc’. (See Appendix 1 at the back of this guide.)
19
4 Memory for words
For yet another variation on short-term memory tests, try using a word list (see Appendix 1 at
the back of this guide). This test hinges upon:

showing friends the list, within the matrix array, and

allowing friends to read it through, but not to revise it. They will to take about 90 seconds
to read these.

Then they have to write the words down on a blank page. Note that for word tests, the
order of recall does not matter.
In general, adults will do better on words than numbers, recalling perhaps nine or ten words.
This may be because they can use strategies such as imagery and linking words together. The
ability to use chunking and elaboration on the stimuli is often much better with words than
numbers. However, if the words are actually not known words, then it is far harder to use
such strategies (see the ‘Nonword test’ in Appendix 1 at the back of this guide). On tests for
nonword memory, many people score around 4 or 5. Why is this?
5 Internet connections
You will find that there are many Internet resources available with cognitive psychology,
although many of them are not particularly useful. However, some sites are worth visiting
now and in the future. The following two sites feature good information on memory strategies
and mnemonics:
http://www.exploratorium.edu/memory/index.html
http://www.mindtools.com/
In searching with Google we located other sites, which covered much the same territory, and
there is a danger of just going over the same material. However, if you find sites that you feel
are especially valuable, or useful for students (e.g., students learning for examinations), then
please let us know about this.
6 Cognitive load
Professor John Sweller at the University of New South Wales in Australia has proposed a
specific version of information-processing theory known as cognitive load theory (see page
30 of the text). An essay, written by one of Dr Sweller’s colleagues, as an introduction to this
approach can be found at:
http://education.arts.unsw.edu.au/CLT_NET_Aug_97.HTML
Although this essay goes well beyond our immediate needs, it will become a valuable
resource for future weeks, especially for thinking about educational implications.
7 Attention and mindfulness limits
As human beings, we routinely attend to sensory inputs from several sources, but that, as a
central task becomes more demanding, then our ability to monitor other things drops away.
Note that this applies also to how experts can function; that is, a highly skilful person may
appear to attend to two things at once. But when you are a novice, or learning something new,
then you are in a resource-limited mode (see page 24), which means that you are likely to be
20
highly ‘mindful’ in order to survive. This can apply also when you are under stress, such as
driving in bad traffic. Many car accidents are linked to drivers’ attention being diverted by
listening to the car radio. When you are under such potentially stressful conditions, what steps
can you take to safeguard your level of performance? What advice can we give to students
about to be confronted with attention-demanding situations (such as reading, studying or
memorising)? What advice can we give the novice driver?
Indeed, there is an elementary principle at stake here: We can learn from what we pay
attention towards, and we fail to learn when we are unable to attend. This is plainly a common
sense principle, but it is quite amazing how often we may act in violation of this principle,
both as learners and as teachers in a position to help others learn. Please recall a famous
quotation from Voltaire: that one basic problem with common sense it that it was not very
common.
21
Module 3
THE LONG-TERM MEMORY SYSTEM
Reading
Text reading
Bruning et al, chapter 3.
Questions to ponder
1
How does the information we receive get organised, stored and retrieved?
2
Why is it important for teachers to distinguish between declarative and procedural
knowledge?
3
How does the perceptual system convert stimulus features into meaningful concepts?
4
How does the mind generate ideas out of words, such as in reading text?
5
What sort of mental organisation can we identify that operates at a level higher than a
concept or a proposition?
6
Does the mind somehow store images as well as words?
22
Listing of key concepts
long-term memory
distinction between
declarative and procedural
knowledge
distinction between
semantic and episodic
memory
concepts, propositions and
schemata represent
declarative knowledge
productions and scripts
represent procedural
knowledge
three theories of concept
learning (as defined rules,
as fuzzy prototypes, and
as probabilistic entities)
propositions and the
propositional network
schemata and instantiation
productions and the
production system
scripts
dual coding theory
spreading activation
theory
the ACT model
connectionist models
mental models (see
reading notes below on
this concept)
Key points
1
The long-term memory (LTM) is the permanent repository of a lifetime of information.
This is the type of memory that enables us to recognise things we have seen before, to
recall the appropriate past experience, and to produce the correct action when we come
across familiar situations. This type of memory system allows us to do things such as pass
exams, tell jokes, drive cars and pay our bills on time.
2
Declarative knowledge refers to information stored mainly in verbal form (although it
could be via imagery). Procedural knowledge refers to our knowledge of how to string a
series of actions together to accomplish a goal. Whereas declarative refers to ‘knowing
that’, procedural refers to ‘knowing how’. The distinction is very important, as the two
types of knowledge serve different functions, and are acquired under different
circumstances.
3
We also distinguish semantic memory (that is, the general case, for example, that dogs
bark) from episodic memory that features personal experience (for example, that my dog
barked last night).
4
There are three major theories about how we form concepts:

Rule-based theory says that the mind learns to apply a set of definitive rules (for
example, ‘that anything that one can sit on can be a called chair’).

Prototype theory suggests that we learn concepts by virtue of closeness or similarity
to certain typical features (for example, ‘it looks like a chair’).

A third approach, probabilistic theory, is a variation on prototype theory, and suggests
the mind adds together the number of features (for example, ‘its got enough
characteristics to be a chair’).
23
5
At the next level up, the mind uses propositions to store information. Propositions are
akin to ideas. A proposition represents the most basic unit of meaning, which can be
judged true or false. Many sentences embody multiple propositions. As you assimilate
propositions, you begin to store them within a network of associated ideas.
6
Schemata are regarded as the fundamental units, however, by which the mind is able to
keep its LTM knowledge store in order. Although difficult to describe, we think of
schemata as higher level organisational units which serve to provide meaning and context
to our experiences.
7
Moving now to procedural knowledge, we regard productions as basic IF/THEN rules
which are used to guide actions. If a certain condition is fulfilled, then an action ensues.
For example, IF the computer screen freezes, THEN press the reset button. But note that
IF/THEN sequences can develop into very long and complex productions that require
years of learning and refining. This is, of course, the underpinning of expertise.
8
The notion of script is used to refer to procedures which we learn as coherent and
recurring patterns. Thus, we all possess, within our heads, scripts for events such as going
to a movie; departing from an aeroplane; going to church, mosque or temple etc.
9
There is solid evidence for the notion that the human brain possesses a non-verbal storage
system. The imaginal system appears to hold information in the form of visual images,
and also sounds.
10 Mental network theories suggest that information is held within the LTM in logical and
hierarchical groupings, or nodes. Memories are accessed then through the process of
spreading activation along neural links to the nodes.
11 Cognitive psychology originally assumed that mental operations occurred as a sequence
or series of neural events, much like a computer executing a sequence of instructions.
However, the more modern view is that the brain is set up for parallel mental processing.
This is sometimes called the connectionist model; that is, the assumption that complex
actions are in part derived from patterns of neural firings in which multiple lower-level
decisions come together to form meaningful perceptions.
Reading notes
Chapter 3 of the text is attempting to give you an appreciation of the storage systems inherent
within the LTM. Some writers refer to these aspects as the ‘architecture’ of the mind. It is
important to realise that these notions are an extension of the words used to describe
perceptual processes in week 2 (prototypes, feature analysis, structural descriptions), and so
week 3 completes our initial review of how the mind is constructed.
However, a basic and fundamental distinction to be made is between declarative and
procedural knowledge (see page 37 of the text). These two knowledge types are acquired in
different ways and are probably stored within different parts of the brain. Declarative
knowledge represents more verbal aspects as indexed by our ability to talk about events, or
describe how we might do something. We might be able to describe, for example, what we
can do to save money. To the extent that we get it correct, then, we can be said to possess
declarative knowledge.
Procedural knowledge refers more to actions and events within sequences. This is the ‘how to
do it’ knowledge, where the index of knowledge is the action itself. Do you know how to
24
change a tyre on a car? Most people would be able to access the correct declarative
knowledge. But do they know how to do this in real life? The ACT model of long-term
memory (see page 57) actually suggests that our skills begin as declarative verbal knowledge
which then is used in real situations to develop into procedural skill. We should note that this
basic distinction is crucial to educators, since the conditions of learning associated with
declarative and procedural knowledge are very different.
Pages 42ff there is a good discussion on how we learn concepts. Remember that concepts are
the next level of complexity up from structural descriptions. Concepts can become abstract,
such as ‘vehicle’, ‘furniture’, ‘the economy’ or ‘having a relationship’. Within teaching
situations, we know that people acquire concepts through learning what is, and what is not,
within the classification. This is called the juxtaposition of examples and non-examples. Over
the past 40 years we assumed that concepts were best acquired through learning the
boundaries of whatever concept we were trying to teach. This was what was called the
classical theory of concept learning which the textbook refers to as ‘rule-governed theory’.
Within the past few years, however, we have begun to appreciate that the mind very happily
learns concepts also from typical features (as distinct from the boundary demarcations). The
mind is not bothered by fuzziness. When we learn a concept such as furniture, it does not
really matter if ‘carpet’ is included within the concept or not. What seems to happen is that
the mind readily forms a central notion as a type of prototype of what ‘furniture’ means.
Hence, things such as sofas and tables are generally seen as being central to this idea, but
other things become less central (for example, vase, clock, radio (see table 3-1, page 45)).
Following on from concepts, propositions are discussed on pages 47 and 48. The proposition
is the lowest unit by which an assertion can be made. And it is possible to make many of them
very quickly. For example, if you read that ‘Compared to green ones, red tomatoes are very
easy to eat on hot summer nights’ then there are six clear propositions embedded within the
one sentence (that is, some tomatoes are red, some are green etc) as well as some possible
unclear ones (for example, is the sentence telling us that green tomatoes are hard to eat?). The
point here is that propositions are abstracted by the brain out of the sentences. The sentences
are the surface structure which serves to activate the propositions which represent deep
structure. One classic finding in this area is that people simply do not store sentences within
their brains. Instead, sentences are converted immediately to propositions, and we very
quickly ‘forget’ the words used to convey those propositions. Also note that different people
may abstract different propositions out of the same sentences.
The notion of schemata is reviewed on pages 48 to 51. Read these pages carefully. To
‘understand’ literally means to use pre-existing schemata to interpret some new information.
Try to think of schemata not as passive entities, but as things that the mind actually uses.
Thus, when you read ‘Piggo’, the schema for piggy bank is quickly activated and then used.
So as new propositions enter your encoding, they are immediately understood in relation to
what you know about ‘piggy banks’. That is, the schema enables you to ‘go beyond the
information given’ to make sense of those words. But this can only apply if you actually
know what a piggy bank is at the outset. Many people will not have a pre-existing schema for
piggy banks, and hence comprehension of this passage will be severely constrained.
25
You could perhaps think of schema as a high-level concept enabling you to classify things.
But the phenomena we are reviewing at this stage within the textbook are more than this. It is
preferable to think of schemas as having multiple facets. For example, even the notion of
piggy bank seems to have at least 12 elemental propositions associated with it (see page 49).
Being able to activate a schema enables you to organise your thinking in such a way as to
make sense out of scores of minor propositions and allied concepts. For example, think of a
schema for ‘arriving at airport in foreign country’. This, of course, involves a script (see page
53) since a sequence of events has to occur. However, having the appropriate schema in your
head enables you to correctly interpret information that is given to you, and so navigate your
way successfully through a very complex process, even though you have never been there
before, and no-one helps you.
Procedural knowledge, in the form of productions, is reviewed on pages 51 and 52. Although
IF/THEN sequences may seem quite logical and simple to you, please appreciate that they are
the basis of high-level expertise. That is, production systems take years of development, can
become extremely complex, and hinge upon very subtle effects. Think of the high-level
cognition and physical skill required to fly an aeroplane or drive a racing car. Such actions are
readily described within terms of IF/THEN rules, but require many years of practice to
achieve proficiency.
Knowledge can also be stored within images. That is, we can literally recall how things
‘look’, where the memories appear not to be embedded within words, but in a way that
somehow mimics some aspects or dimensions of the original perception. Thus, you might
draw a picture of a house you lived in ten years ago, where you seem to be using imagery
rather than words to make this drawing. Possibly the perspective in the drawing relates to how
the house looked from your gate. If so, you are constructing an image in your mind.
The material from pages 54 to 62 of the text can be skimmed through fairly quickly, as it
tends to be concerned with highly theoretical notions of mental storage and long-term
memory structures. However, educational implications of long-term memory concepts are
reviewed on pages 62 and 63.
Finally, to complete the notions of mental storage, we want to suggest another notion that is
not reviewed within your textbook. Some psychologists have suggested that all of the
information you have reviewed in this chapter comes together in the notion of a mental
model. The idea of mental model is that people develop their own theories of how the world
operates. A good example would lie in very complex notions such as ‘evolution’. It is
possible to have many different notions of what this means, and the model built up by a 15year-old will be very different from the mental model possessed by a biological scientist.
So is ‘evolution’ just a like another schema? Well, yes, in so far as it provides a way of
encoding information and seeing relationships that might otherwise go undetected. But
evolution is more than this:

it represents a major way by which huge areas of knowledge can be assimilated into
coherency;

the theory of evolution informs us what happened in the past;

it provides us with operating knowledge that resembles IF/THEN procedures;
26

it can incorporate graphic imagery or at least can be expressed via imagery;

it tells us what is likely to happen in the future; and

it specifies the existence of likely mechanisms and processes that are found within the
‘real world’.
So, for example, reading about how deadly bacteria are being selectively bred as a result of
antibiotic usage can be interpreted in terms of evolution; that is, a fundamental understanding
of how life on earth develops.
It is possible to suggest then that notions such as evolution represent a ‘model’ of how the
world is constructed; that is, a model within the mind. Although it begins as a simple concept,
it develops into a multifaceted schema. With increasing development, the notion becomes a
major explanatory device, to the point at which we can suggest that the person has
constructed a mental model that serves to organise and integrate a huge area of knowledge. In
other possible examples, we might suggest that Mr Powell (remember from week 1) has
developed a complex mental model for fires that you and I simply do not have. Also, your car
mechanic has developed a mental model for how the car operates. That is, he/she does not just
fix little bits, but possesses a complex notion of how all the various systems of the motor car
work together. Please note that people may take years, even decades progressively
accumulating, developing and refining their mental models.
Active learning opportunities
1 Declarative and procedural knowledge
The difference between declarative and procedural forms of knowledge is a crucial distinction
within cognitive psychology, as the conditions for learning associated with each type of
knowledge are very different. At this stage, can you say roughly how these conditions will be
different?
Procedural knowledge is shown most clearly within recurring activities. For example, a
person working at the counter of a fast-food restaurant may use the same procedures in
securing food orders from customers perhaps 500 times a working day. Most companies in
service industries use carefully prescribed training programs to ensure that staff acquire sound
procedures. When such companies pay respect to the abilities of their staff, in effect it is a
tribute to the human capacity to master procedural routines. Not only are such routines
necessary in maintaining the system, but employees can use them to override personality
variations. For example, when handling an abusive customer, it is most sensible to smile and
ask if he/she would like to order anything else. To appreciate such human capacities, we
suggest you observe people working in areas such as fast-food counters. Watch their actions
carefully, and begin to analyse what they are doing in terms of IF/THEN procedural
knowledge. You will gain fresh insight into (and perhaps admiration for?) human skill
acquisition principles.
2 Skills acquisition experiences
Think back to those situations where you first learnt a new and complex skill. This could be
learning to drive, or operate a difficult machine, or word processing, or skiing etc. Do you
recall trying to use declarative knowledge to drive your actions. If so, note the very heavy
27
memory strain involved in this process. The present writer (GY) recalls one moment when
teaching his 16-year-old daughter to drive. She was driving along and called out, ‘Hey Dad,
tell me again, which pedal is the brake’. Concentrating hard on the road ahead, her attention
was so mindful, she had forgotten what her feet were supposed to be doing. Can you recall
similar situations when you are were acquiring new skills? What was the mind doing in these
situations?
3 The issue of music and attention
Concerning the problem of attention: have you ever come across people who claim to listen to
music as they study? There is good evidence that certain types of pleasant music can help you
relax, place you into a pleasant mood and perhaps focus on a task (ie ‘Mozart Effects’). But
the effect is terribly overrated. Real problems emerge when you focus on the music rather
than any other input material you are trying to learn. In terms of normal information
processing, listening to music or the radio will naturally slow down your reaction times,
sometimes by up to about a second. For example, it is a very bad practice to listen to the radio
if you are driving in intense traffic. Traffic accidents are linked to listening to music. Overall,
it is more important to control your attention by removing, attenuating or just stopping those
things that cause distractions. The notion that music can somehow directly stimulate your
brain to learn other things better is not truly validated (recall Mozart effect research, from
Module 1), and people such as high-school students are better off not listening to music as
they study. People often report that they listen to music as they study. It may be possible to
review known material, by way of checking, as they listen to music. But, for most people, this
is not an efficient or sensible way to focus on learning new material.
4 Imagery
Concerning imagery: ask friends how many windows they have within their main room at
home. Note how some people do this by closing their eyes and counting. If so, what exactly
are they counting? It’s not the windows themselves, but their images of those windows. When
we do this in class, we get some people who seem to close their eyes and even turn their
heads as though to face their imaginary windows. We know from studies into this that people
take longer when there are more windows to count.
Other variations on the imagery tasks are as follows:

Ask someone to take an imaginary walk around their house, and tell you what they see.

Ask someone to close his/her eyes and imagine an object such as a flower. Now ask what
colour it is. Most people can tell you what colour it is, since images of flowers invoke
colours, even though you did not ask for this.
It does not indicate this within the textbook, but there is some evidence that children are often
able to ‘hang onto’ images in their heads for longer than adults.
5 Prototypes
To try to tap into prototypes, ask a friend to draw a picture of a flower. (You can do this for
any other common object, such as a chair, bird, car etc.) Note how such drawings often seem
to represent typical features of objects, rather than being any one such example taken from the
class of objects.
28
6 Eyewitnesses
You have read how memory for actual sentences is remarkably poor, as people strive to
encode what they hear into propositions that they understand within their minds. Note how
this tendency can give rise to some level of confusion, as people may actively disagree on
their memories for specific social interactions, and the nature of words that were uttered.
Have you ever seen examples of this; that is, where two people witness the same event but
then produce remarkably different interpretations of what took place, and what actual words
were used?
7 Free association
One practical way to try to illustrate the notion of spreading activation is to play word
association games, either by yourself or with others. For example, ask a friend to free
associate for a minute beginning on a word such as ‘frog’. One person said ‘frog … swamp …
mud … unknown … dark … streets … buildings … lamps … fires … detective work’.
The interesting point about such free associations is that each local link can be highly
meaningful. Activation is spreading from one item (or node) to the next, in an orderly and
understandable way. One cannot predict where the mind is going to go, but the process is not
random. We can suggest that each word is serving as a node (see pages 63 to 66 of the text);
that is, a point from which activation can spread onto the next. The fact that the human mind
is able to play such association games so easily will help you to think of the mind as a
network.
Another notable point about such networks is that the associations are likely to have a basis in
unique personal experiences, rather than any simple logical process. Mental networks are not
logical, since they are not derived from sets of logical rules. For instance, in the above
example, the person associated ‘dark’ onto ‘mud’. Why? Well, it was because when he looks
into a mud puddle he does not know how deep it is, so to walk there would be a step into the
unknown. Such linkages are not inherently logical, but within context of the person’s life and
unique historical memories, they are psychologically meaningful.
8 Teaching strategies
Please think about the issues of teaching concepts and schemata. Then examine the list we
have constructed on these as possible strategies for teachers (see Appendix 2).
9 How people learn
http://books.nap.edu/html/howpeople1/
Note the existence of the monograph, “How people learn” which was constructed by a group
of distinguished cognitive psychologists, as a commissioned project for the National Research
Council in the United States. The book is published on-line. Notably, there is an excellent
chapter on expertise development (chapter 2), which will overlap directly with material we
cover within this course and EDUC 5090. The book provides tight little summaries which can
be read quickly, and dips into much contemporary cognitive research in a deliberately nontechnical manner.
29
MODULE 4
ENCODING PROCESSES
Reading
Text reading
Bruning et al, chapter 4.
Questions to ponder
1
Why do we need to develop encoding strategies? What role do they play and do they
develop as we grow?
2
What is metacognition? How is an understanding of this idea valuable within the
classroom?
3
How effectively can strategies be taught to people who do not appear to use them? Will
such training be durable?
4
Can the mind be trained to learn better or faster? Should I purchase an expensive
memory-training package?
5
How can teachers help students to retain their knowledge? What teaching strategies
promote both initial encoding and then long-term retention?
30
Listing of key concepts
encoding
storage: retrieval
rehearsal
maintenance rehearsal
elaborative rehearsal
mediation
imagery
mnemonics
keyword method
advance organisers
having schemata
activated
shallow vs deep
processing levels
distinctiveness of encoding
elaboration
metacognition
(declarative, procedural
and conditional
knowledge elements)
good strategy usage
(components of this)
strategy instruction
inert knowledge
Key points
1
The processes though which the mind attempts to place information into the long-term
memory are referred to as encoding (that is, coding something into some other form).
2
A basic mechanism available for encoding is rehearsal. When this involves merely
repeating as it comes in, the process is called maintenance rehearsal. But when it involves
changing the input experience in some way then we call it elaborative rehearsal.
3
One general principle is that people will exhibit more effective memory recall when they
use active encoding strategies at the moment of learning. This could be, for example, the
deliberate use of imagery and mnemonics.
4
There are many different types of mnemonics. Some are widely known and used (for
example, FACE for notes in music), but others are less obvious. For example, the
‘pegword’ method (see the textbook) can be used to rapidly learn lists of up to 10 familiar
objects.
5
Different mnemonics are suited to different task demands. In the case of learning
vocabulary items and foreign languages, the ‘keyword’ method has established an
excellent research record.
6
Before people are asked to learn complex material they benefit from the provision of
advance organisers; that is, short packages or summaries of information which help them
to organise the incoming information. The psychological process that underlies this effect
is now referred to as ‘schema activation’.
7
One way to increase attention towards complex material is to arrange for questions to be
asked and answered. Questions can be used before, during and after formal lessons.
8
It is known that memory recall is greater when people encode in such a manner as to
encourage deep processing. Although this is true, we often do not actually know what
‘deep processing’ is in any one learning situation.
9
Memorability is also tied to how distinctive material is at the time of encoding. That is, if
a stimulus is made more difficult to discern, then it becomes more memorable.
10 One major way to enhance learning is to encourage people to elaborate on an input
experience. In this context, elaboration means to relate a current stimulus to your prior
knowledge.
31
11 Metacognition refers to thinking about thinking. Knowledge of thought processes can be
seen as having declarative aspects and procedural aspects. A third component—that is,
knowledge of where a specific strategy would work (for example, in knowing that the
keyword method can be used to improve your vocabulary)—is called ‘conditional
knowledge’.
12 Several psychologists have suggested that students within all learning contexts have the
problem of managing their mental resources. They describe this ‘job’ in terms of the good
strategy user construct.
13 A sizeable body of research suggests that strategies can be taught to students, but they do
not readily generalise to situations beyond the original training. Generalisation is
possible, but hinges upon several factors being in operation.
Reading notes
Chapter 4 of the text is really about how knowledge is acquired. It begins by describing a
basic learning strategy we all use from time to time. When we need to hold a limited piece of
information within the head for a short time frame we are inclined to use rehearsal; that is,
simply repeating the information in a type of reiterating loop. Maintenance rehearsal is really
holding the information active within the short-term memory where it stays there through
active repetition. This is highly adaptive for some things, but as you know from Module 2, the
STM system is fragile and limited. So another form of rehearsal, called elaborative rehearsal,
is often used. Elaborative rehearsal can take different forms, but the term elaborative is used
as it involves the person doing something other than repeating the stimulus, and typically
involves things such as reorganising the input, dividing it into chunks, seeing a pattern, or
using prior knowledge.
The concept of imagery returns on page 68. Note how the emphasis now is on using imagery
as a mnemonic tool. Mnemonics are discussed in some depth. The word mnemonic really
refers to anything that the mind can do to help with memory recall. That is, mnemonics refers
to a class of mental activities occurring at the encoding level. In order to recall something in
the future, it is vital to do something in the present, and hence the notion of mnemonics
implies a very definite goal orientation.
We generally use the term mnemonics to refer to activities that assist people to learn things
that would otherwise be an arbitrary listing. All commercial memory-training programs are
based on teaching mnemonics, and as such they can have a certain validity, provided that
realistic goals are set. However, you should note that there are no secrets in this field. All the
information you need to try acquiring new mnemonic skills is readily available. For example,
all of the techniques described from pages 69 to 74 in the text are thoroughly validated, and
you can find a good deal more about memory tricks and strategies on the Internet pages cited
earlier.
Mnemonic skills are very useful and worthwhile. But do not gain the impression that
acquiring a few memory tricks will suddenly improve your overall ability to do things such as
read better, study more effectively, or begin to pass courses at a high level. Indeed, one
possibly disappointing finding to emerge out of the research has been that when a person
develops a memory skill in one area, it seems to have little impact on memory skills in a
different area. (Also, you can read more on some of the research into exceptional memory
32
skills on pages 288 to 290 in the paper by Ericsson and Lehmann that is supplied within the
Readings for EDUC 5090).
But back to Bruning et al, note that from page 74 to 80 they offer a good but brief discussion
about how the mind tackles more complex and meaningful information. They review several
mechanisms that are known to come into play when we try to learn complex information,
especially when it comes at us in a verbal form. We will benefit from any information that
enables us to organise new information (that is, the notion of the advance organiser). We
benefit from any information that enables us to recall what we already know, and this is
expressed as schema activation. Another way to engage active learning is to try to answer
questions about what we are learning. Questions appear to work in several ways, firstly to
help us direct attention, and then to encourage us to integrate information across different
sources. Questions help us to use our knowledge in new and active ways.
We also benefit from anything that forces us to make an enhanced effort toward
understanding the new information. This can, at times, come about through manipulating the
distinctiveness of the stimuli (see page 78). Sometimes this can take the form of increasing
the level of elaborative processing that an item may elicit (see page 79). This may entail
ensuring that the new information links in well with old information, and that a rich supply of
concrete and varied examples are embedded within the new information.
Metacognition, within the context of strategy instruction, is discussed from pages 81 to 86.
Metacognition is a difficult word to define, but it involves knowing about how the mind
operates, and being able to use this knowledge to achieve a goal. In some situations it
translates to a matter of monitoring to see how well you are doing, then using this information
to alter your strategies. But it also covers aspects such as knowing what your memory span is
likely to be (that is, a declarative aspect), knowing which strategy to use when learning a
vocabulary list (that is, another declarative aspect), and actually being able to carry this
strategy out (a procedural aspect). Knowing exactly when and where to use a specific strategy
is another type of metacognitive knowledge we call conditional knowledge (that is, being
aware of the conditions under which a strategy can work).
One fascinating finding that keeps emerging within the research into metacognition is that
people are often somewhat overconfident about their own skills or ability to learn. This
appears true especially of young children and people who are fairly unfamiliar with an area.
When you encounter a new knowledge domain area it is all too easy to underestimate how
difficult it is to do well within the area. That is, with increasing experience and maturity, we
become more accurate in assessments of our own competency levels.
The concept of the good strategy user is one that has been promoted especially by noted
psychologist, Michael Pressley. This position suggests that the key to adaptive learning
involves two levels of self-control:
1
the cognitive level which involves strategies, knowledge, and metacognition, and
2
the emotional/motivational level which involves focusing attention, controlling
distractions, maintaining your goals, and controlling destructive emotions such as anxiety
and helplessness.
33
Research on strategy instruction is reviewed on pages 84 to 86 of the text. The authors
advance five specific conclusions stemming from the major reviews of the field:
1
Strategies can be taught, and most students do benefit from strategy instruction.
2
Strategy instruction most clearly helps younger and lower-achieving students.
3
The most effective programs aim to teach several different strategies.
4
It is important to help students acquire metacognitive (that is, conditional) knowledge.
5
Generalisation of strategy usage is far slower than we might have hoped for. Quickly
taught strategies will be quickly abandoned. Steps must be taken to encourage
generalisation.
Instructional implications are reviewed from pages 86 to 90. These revolve around several
key matters including:

stimulating deep processing such as in helping students to activate schemata and use their
prior knowledge;

directly teaching strategies as means of attacking problems;

stimulating conditional knowledge via means such as checklists that students can use to
help them become more organised and strategic within their learning; and

prompting and encouraging students to reflect upon how the knowledge and strategies
they learn in one context can be applied within another.
Active learning opportunities
1 Web resources
Take some more time to revisit the web pages we noted in week 2, especially the pages
devoted to memory and memory improvement. Note how these sites feature mnemonics a
good deal. You can place mnemonics into a search engine and then obtain many hundreds of
hits. Note how there are many different types of mnemonics, and different writers may use
slightly different versions of the same one. For example, Bruning et al describe the rhyming
pegword. Note that in relation to ‘Nine is a pine’ (see page 70), the writer of this Study guide
sometimes uses this pegword for a memory aid in shopping but says ‘Nine is wine’. Do you
think these variations are important?
2 Special mnemonics
Note that mnemonics are often developed for learning specific topics. For example, a
mathematics student may use a mnemonic to recall the value of pi.
A wonderful collection of medical ones can be now found at
http://www.medicalmnemonics.com/. Medical students are expected to memorise large
amounts of knowledge, and so they often devise highly creative memory tricks to assist them.
For example, the set of cranial bones are called occipital, parietal, frontal, temporal, ethmoid
and sphenoid. To learn these as a list, the student may say ‘Old people from Texas eat
spiders’ as a first letter mnemonic. Such mnemonics are very useful, but they hinge upon the
34
student actually knowing all these bones to begin with; that is, mnemonics are useful but they
cannot substitute for meaningful learning acquisition.
Another interesting medical site is http://www.md4sure.com/resources.asp, but this one seems
less amenable to non-medical people than the other site.
3 Encoding as a slow process
Have you ever wondered why your brain seems to fail sometimes when you want to learn but
just cannot? One possible factor is that of overload. Consider the following. As a means of
inputting data, the normal lecturing and speaking pace is about 150 words per minute or 30
propositions per minute. This may exceed human information-processing levels by a factor of
perhaps 500%. This is not intended as a joke. It has been known for about 90 years that
people listening to a lecture normally will retain up to around 20% of the actual lecture
information within their LTM. If every item entering LTM requires at least 10 seconds
processing time, then we might learn only about six propositions per minute, not 30.
There is even a strange paradox (called the Feynman effect after the noted physicist) that
lectures that are ‘easy’ to listen to are more enjoyable, but may have lower retention levels.
This is because the audience does not need to concentrate strongly on the content information.
The Feynman effect gives rise to a cognitive illusion, in that you believe you learn more from
the lecture because you liked it, whereas in objective terms you retain less.
There is also another well-known effect in the literature called the Dr Fox effect, where a
lecturer presents material well, or appears to do so, even though the content is inherently
nonsensical and full of contradictions. Dr Fox, however, still receives glowing evaluations
from his audience.
What do we make of all this? Well, it tells us that merely attending a lecture is only the
starting point for actual learning. No one claims that listening to another person is, within
itself, an effective learning experience. However, there are many reasons why lecturing and
other forms of didactic teaching still are very valuable teaching tools. From our perspective
we note that time assumes a vital role in any situation where learning is meant to occur. Most
of us (and we include teachers in this) have only poor notions of how long it takes someone to
master a new skill, or acquire a new piece of knowledge. We fail to appreciate how slow the
knowledge-acquisition process really is, and we are fooled by the ease by which a learner
might regurgitate back a few trite points that he or she has just heard. We fail to realise that
novices require extended practice opportunities without pressure or harassment.
4 Personal encoding experiences
Many people report upon personal encoding tendencies which may intrude upon interactions.
For example, people who are conscious about their weight may judge everyone they meet in
terms of how overweight they are; people who are especially religious may continually stress
morals; and people who are obsessed with money may always be judging others by their
wealth. It is possible to suggest that these are subtle schemata operating. Can you identify any
such tendencies within your own life adjustments?
35
The author of this Study guide experienced the strange effect of becoming gutter-schematic
several years ago when he had to renew the rain gutters on his house. He undertook a good
deal of research into the problem and discovered there are many types of gutters, varying in
aspects such as materials, contour shape, size, strength and colour. Reading all this
information and doing the actual handiwork turned him into a type of gutter expert. Following
on from this experience, he found that every time he ventured close to any house he began
analysing the gutters on the roof. This carried on for six months before he was finally and
thankfully able to stop. Other people report similar experiences in areas such as furniture,
carpeting, and looking at your host’s crockery. That is, once you acquire detailed knowledge,
this becomes an unwitting, and often unwanted, basis for reacting to the world. This can be
especially true when it invokes implicit evaluations. Such experiences are tied in with the
notion of automaticity, which is covered in depth in EDUC 5090.
5 Metacognition and memory limits
If someone tells you that they can recall about eight numbers, they are displaying
metacognition. That is, they know, in advance, limits of the STM. This is one aspect of
metacognitive declarative knowledge. If you have access to children, then try asking them
how many numbers they can hold in their STM. In general, it is not until around 8 years will
they give accurate answers. The author of this Study guide tested metacognition for memory
span in preschoolers several years ago, and was impressed with the optimistic responses of
some children. For example ‘About a hundred’, ‘Thirty’, or ‘Mummy remembers it all, so I
don’t have to’. In the study, we demonstrated to one young man how he could actually
remember about four numbers. So we asked him again and he continued to say ‘12’. When
we pressured the point he simply snapped back ‘OK, I didn’t do it then, but I can still do it’.
If you have access to children, try asking them such questions. One method is to show an
array of picture cards, and keep adding a card at a time, asking the child to stop when they
think they would forget any if the cards were covered by a cloth. Our experience was that
most young children fail to know where to stop.
6 Teaching strategies
How should we teach for memory improvement and for enhanced metacognition? Please
think about these issues, then examine the list of teaching strategies we have constructed:
‘helping with memory’ and ‘helping with metacognition’ (see Appendix 3).
36
37
Module 5
RETRIEVAL PROCESSES
Text reading
Bruning et al, chapter 5.
Reading 1
Yates, G and Chandler, M (1994). ‘Prior knowledge and how it influences classroom
learning: what does the research tell us?’ Set: Research Information for Teachers, issue 2,
item 6, pages 1-8.
Questions to ponder
1
What is the distinction between recall and recognition?
2
Getting students to answer questions at the time of learning seems to be a most effective
means of helping them retain information better. Why?
3
It seems that we can often recall things much better when we are prompted in certain
ways. This is called encoding specificity. What is meant by such a strange term?
4
What factors cause errors in memory recall? Should we trust eyewitnesses to events?
Listing of key concepts
retrieval
encoding specificity
generation effect
elaborative interrogation effect
state-dependent learning
recall and recognition: the threshold
hypothesis
the dual process model
reconstruction in memory
flashbulb memories
relearning
massed practice and distributed practice
38
Key points
1
The principle of encoding specificity says that memory performance is greater the extent
to which the conditions under which recall is expected match conditions present at the
time of the original learning.
2
The generation effect refers to fact that recall will be enhanced when people are induced
to generate an active response to an input at the time of learning.
3
One highly effective way to make meaningful material more memorable is to get students
to answer ‘why’ questions at the time of learning. This is referred to as the elaborative
interrogation effect.
4
A specific case of encoding specificity is called state-dependent learning. This refers to
the match between psychological states at the time of encoding and retrieval. This match
has been shown to account for memory effects.
5
People are generally able to recognise far more information than they are able to recall.
6
The older theory was that recall meant that memories had to have strength to overcome a
threshold, and that this threshold was set lower in the case of recognition performances.
The newer theory is that the recall and recognition operate in essentially the same way,
but that recall simply entails a more thorough (that is, deeper) memory search through the
associative networks of the mind.
7
Modern psychology stresses the constructive nature of much memory work. That is,
unwittingly we make errors in our recall protocols, and often these are related to idealised
or schematised notions. Often our minds will ‘fill in the gaps’, and we do not realise that
this natural process is occurring.
8
In general terms, the research indicates that eyewitness testimony, as may occur in law
courts, is remarkably subject to biases.
9
It is possible for people to exhibit significant levels of relearning. Relearning means to
learn material for the second time, after appearing to forget the initial learning. Although
hard to demonstrate through experiments, it is possible that relearning effects are very
strong in real life contexts.
11 In general, massed practice will result in far less retention of information over time that
distributed or spaced practice sessions.
Reading notes
This is a short but significant chapter in that it reviews a good deal of what has been
discovered about long-term retention effects. The most obvious point to make is that longterm retention depends a good deal upon exactly how the original information was learnt.
Hence, there is a good deal of overlapping content between this chapter and the previous one
on encoding processes.
The term encoding specificity is a rather complex notion for what is essentially a simple
effect. When we try to remember things we benefit from having access to anything that
appears linked to events that were present at the time of originally laying the memory down.
In commonsense language, we may suggest that the mind can be ‘triggered’ by anything that
is linked to the original learning context.
39
At times this phenomena can take strange forms, such as the notion that students’ memory in
exams may be slightly better if they are tested within the same room as they did the original
learning (a context effect), or that memories we have within certain psychological states can
be somewhat specific to that state. There are several studies, for example, showing mood
effects in memory. If people learn material within a happy frame of mind, for example, their
memories of this material is better when they are tested in a happy mood again. Such effects
are known to be implicated in clinical depression; that is, when someone is depressed, their
memories of unhappy events seems to become more available to the mind.
But such state dependent memory may be a subtle rather than a strong effect. The notion,
for example, of totally ‘forgetting’ what you did while drunk is a false one. The encoding
specificity hypothesis does not suggest that recall across diverse contexts is impossible, only
that we need to recognise the fact that the context in which you try to recall information does
have an active role in helping the mind to access memory. In essence, people benefit strongly
from any stimuli available that can trigger their minds. Recall is partially dependent upon
being provided with good retrieval cues.
On page 94 there is a citation to a significant notion: elaborative interrogation. Once again,
please do not get fooled by the language. This is one of the most effective strategies we know
about to help people to learn meaningful material. The requirement is for people to answer
‘why’ questions. This should take place at the time of original learning; that is, it is
technically an encoding strategy. The need to answer ‘why’ questions at the time of learning
ensures that the degree of effort needed increases. But, more specifically, it forces the learner
to activate prior knowledge. How might this work?
Suppose you tell someone that ‘Japan imports iron ore from Australia’. Then you ask ‘why’.
The student generates a reason for this fact, which is consistent with his or her prior
knowledge. (For example, ‘Because Japan has industries which need raw materials, and there
are huge ore deposits in Western Australia’). When it comes to a recall test, the mind is more
able to access data which was associated with a greater spread of activation at the time of
learning. The fact that Japan imports ore from Australia can be accessed by a greater range of
retrieval cues. That is, instead of just remembering a single proposition, the knowledge is tied
into a network involving multiple propositions, any one of which can be used as a basis for a
possible retrieval cue. That is the above knowledge may be accessed from any of the
following questions: ‘What does Australia export?’, ‘Does Japan need to import anything?’,
‘Where does iron ore come from?’, ‘What is the relationship between Western Australia and
Tokyo?’, and ‘What sort of industries does Japan rely upon?’ That is, each one of these now
contains a viable retrieval cue that would be far less effective than if the teacher had not asked
the question ‘why’ in the first place.
Several published studies now indicate that high ability students do tend to use interrogative
elaboration on a spontaneous basis. That is, they may automatically ask themselves ‘why’
when they receive new information. The findings from laboratory studies now show that that
this is a strategy that can be easily taught, even though it will work only if the ‘why’ question
can actually be answered in a meaningful way. This strategy is of considerable use in helping
people to use their knowledge most effectively.
40
The distinction between recall and recognition is discussed in depth from pages 97 to 100. In
our undergraduate classes we often demonstrate that after seeing a list of 30 words for a
minute, most people can recall perhaps 10 of them. But if we ask them to simply pick out
those 30 words from a larger list, then people get about 20 correct. So how many did they
really remember, 10 or 20? Well, it just depends on how we define remembering at that
moment. The older theory of recognition suggested that it is easier to recognise an item than
recall it as the threshold of memory strength was set lower. To perform the recognition, you
basically had to exert less effort, and the material could be located, even though the trace still
did not have enough strength to trigger a full recall.
This older theory has now been replaced by mental network theory. This theory suggests that
recall is difficult, not because the memory is weak, but because it may not be appropriately
cued. On the other hand, a test of recognition provides you with an abundance of cues that
may trigger the appropriate response. From the network perspective, both recall and
recognition are essentially the same mental process, but the stimuli used to trigger the
memories are different. That is, the relative accessibility of information is the issue, not the
strength of the memory trace as such.
On pages 100 to 104 of the text, the classic work on memory distortions is briefly cited. The
topic of eyewitness reliability emerges from this work and has been handled in depth by
experts such as Ceci and Bruck (1995). For our purpose, we need to note that massive
changes are typically found between material people read or witness, and their recall
protocols. The existence of such changes can be described in terms such as loss of detail,
abbreviations, loss of sequencing, simplifications and magnifications. In modern research we
tend to emphasise how constructed memories are changed by the schemata activated at the
time of learning and by the nature of available retrieval cues at moment of recall. We can note
the interesting research finding that people are, in general, not able to detect their memory
distortions. Hence, confidence in recall is not any guide to its accuracy. In general, people are
overly confident in their ability to recall accurately (see page 105), and, indeed, humans tend
to be a little overly confident in many of their actual competencies and abilities.
The topic of relearning is handled on page 105. This is an especially interesting phenomenon
in that it occurs without people realising what is happening. Relearning refers to the
advantage that occurs when learning something for the second time. The original learning
appears to be unavailable. OK, but even though this appears ‘forgotten’, in fact the second
learning occurs very rapidly. We are aware of several cases in which people appear to have
forgotten a language they learnt as a child, but then relearned it within a few days. It is
possible that the relearning phenomenon is far more general than is obvious, since it occurs
outside of awareness. Your ability to ‘pick up’ new material may be substantially a function
of how much old material is locked away within the mind, even though this material is not
consciously available.
Various educational implications of these findings are discussed from pages 122 to 124.
Active learning opportunities
By this stage of the course you have accomplished a good deal. You have worked through
110 pages of fairly intense cognitive psychology, learning a relatively new, interesting, but
nevertheless difficult language. At times your enjoyment of the material and the insights it
41
brings will have been muted by the need to just move on, and the need to keep thinking about
your own expertise project. By way of consolidation, we have included two essays into the
materials at this point. These two essays (see points 1 and 2 below) should contain little by
way of new information, but each will serve as a device to help you organise your
information. As you read each one, think about how it covers material you know about
already.
1 Undergraduate module
We use an essay in undergraduate educational psychology courses at the time of introducing
information-processing theory. It covers basic principles and can be used as an overview of
the field, suitable for introductory purposes (see appendix 5).
2 Article on prior knowledge
The Yates and Chandler article (Reading 1) attempts to provide a coherent synthesis of a large
body of findings in the area of prior knowledge effects. We trust you will find this material
interesting and pertinent. Prior knowledge theory uses schema theory in describing memory
effects. This article was written for teachers who would have minimal background within
psychology. Pay special attention to the notion of the Matthew effect which has massive
educational implications.
3 Why does abstract language fail?
In the Yates and Chandler article (Reading 1), note the material on ‘Making text
incomprehensible’ (page 6). Use your new knowledge in cognitive psychology to say why the
abstract statement fails to achieve any significant learning, whereas the listing of examples
becomes far more user-friendly. Note also how this illustrates rule-example-rule principles.
4 Examples of retrieval problems
Here are five illustrative problems:
1
What is the 12th letter of the alphabet?
2
Is there a four-letter word in the English language ending in -eny?
3
What relationship to you is your mother’s brothers’ wife’s daughter?
4
Are there more words in English that start with R or have R as the third letter?
5
What were you doing on the morning of 25 September last year? Be totally sure on this.
Problem 1 In the first problem you really do know the 12th letter, but to identify it you may
have to dump the alphabet out of the file where it is stored as a linear sequence, and then start
to count the products of this dump. The task takes a few moments, simply because the
question does not relate to how the alphabet is normally stored. Hence, direct retrieval is
impossible, although you immediately know that all the information is actually in your head,
and you have sufficient metacognitive capacity to carry through some basic steps to produce
an outcome. A variation on this same problem is to ask people to recite the alphabet
backwards. People can do this, but its highly mindful at first.
42
Problem 2 The second problem seems to have the following effect: about half the people we
try it on pronounce -eny as thought it was -enny, and then fail to realise that the 4th letter of
the alphabet actually solves the matter. The problem illustrates another example of a possible
encoding/retrieval mismatch, as most simply will people read (that is, encode) the stimulus in
a way that makes retrieval difficult.
Problem 3 The relationship problem is one of overload in that three steps are involved.
These problems can be made harder simply by increasing the number of steps to exceed
natural WM limits. When under such pressure one active step is to convert the premises to a
different representation by writing it down. This relieves considerable pressure on the WM,
and hence retrieval becomes more accurate and easier.
Problem 4 Most people will say the R appears more at beginning of English words than in
the number three spot, but this would not be correct. The effect here is called availability;
that is, when you think of words with R, then those that begin with R are simply more
accessible or available in the mind than those that have R in a different spot. The same also
applies to V and K. One variation on this problem is to ask would more words in English have
seven letters and end in -ing, or have seven letters and have N as the second to last letter?
Obviously, there have to be more with N as the second to last letter, but many people are
fooled by the availability of the -ing word ending. Please realise that we have a principle
operating (or heuristic) here: Other things being equal, human decision making will be biased
in favour of stimuli that are more currently available to the mind, in contrast to other
information which is only potentially available.
Problem 5 To rediscover and be totally sure of what you were doing on the morning of 25
September, you may need to begin an active memory search in which you orient through a
number of checks such as what day of the week, where is my diary, which city was I in, who
was I with, did anything different occur, and so on. That is, rather than rely on your current
memory contents, you need to take steps to obtain more information and so secure the
retrieval onto a reality basis. The process can involve use of external aids such as diaries,
computer records, bank statements or even other people’s accounts. However, you have to
keep track of the information and be cautious of premature closure, such as in deciding you
were at work, when in fact you may have been sick that day. It seems strange that you need to
undertake such steps, since it is your own life. But the mind simply does not lay down
memories in such a way as to definitely answer that given question. This is yet another
example of a mismatch between encoding tendencies and retrieval demands. That is, you had
not forgotten what you were doing on the 25 September, but as you lived that day you simply
did not keep saying to yourself ‘I must remember this in case they ask me later’.
5 Using photographs as retrieval cues
You can try the following personal experiment. Do you know of any photos stored away in
which you are a member of a past group. This could be from your school days or past work.
Try to remember as many people as you can that should be present in that photo, by writing
names down. Now locate the photo and see if there are people there who you did not recall
initially, but whose name can now be recalled by looking at the photograph. What has
happened is that you have tried to recall under two conditions, where the photo provides a
much stronger retrieval cue than memory alone. This is called cued recall; that is, the person’s
name was in your brain all the time, but it took a strong cue to bring it into consciousness.
Note that looking at the photo is not a recognition test, as we are not asking if this person was
in the group or not.
43
MODULE 6
PROBLEM SOLVING
Text reading
Bruning et al, generally chapter 8, but detailed reading of pages 162 to 180.
Questions to ponder
1
Which problem-solving strategies would be of most benefit to students?
2
Does problem solving occur in an instant flash, or can we describe the process as a series
of steps?
3
As a teacher, how can you assist a student to be adaptable in transferring their problemsolving skills across situations?
4
What does it mean to be an expert in an area? Do experts behave and think differently
from novices?
5
How does one develop high levels of skill and expertise? Does high achievement depend
upon early talent?
6
Does the act of solving problems help us to learn? (Discussion on this found later within
this week’s materials, but answer the question now, and then see if your answer matches
the information given).
44
Listing of key concepts
problems as well
defined or ill
defined
trial and error
Dewey’s steps:
Gestalt approach
functional
fixedness
identifying the
problem
overcoming
obstacles to
problem
identification
representing the
problem
the problem space
initial state, goal
state and operators
recognising deep
structures
selection of
strategies
algorithms
heuristics
means-end analysis
strategy
implementation
evaluation of
solutions
domain knowledge
general knowledge
seven
characteristics of
expertise
deliberate practice
talent
facilitating transfer of problem-solving
skills
Key points
1
In human life it is important to distinguish well-defined problems (which have clear
solutions) from ill-defined problems (which have no universally agreed-upon solutions),
and to help our students grapple with both types.
2
The first psychologists to systematically investigate problem solving were associationists
(for example, Thorndike) who stressed the nature of ‘trial and error’. That is, they saw
problem solving as a graduated process of learning from feedback.
3
However, John Dewey challenged associationist views by suggesting that we solve
problems by working through a series of mental steps. And another approach, called
Gestalt psychology, stressed the role of sudden perceptions, or insight, as the base process
involved.
4
It is often difficult to identify exactly where a problem is located. That is, problems need
to be defined carefully, and sometimes this does not occur, or people may not give
themselves sufficient time to really assimilate the dimensions of the problem.
5
The next step is that solutions may be hard to arrive at if the problem is not adequately
represented. This means to categorise its elements, to define the goal state, and be able to
articulate the crucial operators (rules) and constraints that bear on the context.
6
Several general problem-solving strategies have been described, including trial and error,
means-end analysis, working forwards and working backwards.
7
Once strategies are selected, they are implemented. Experts seem to implement their
strategies with far less mindful fuss than do novices.
8
Even after problems appear solved, the learner needs to engage processes of evaluation to
fully learn from the experience.
9
As we interact with the world, we develop significant levels of domain-specific
knowledge, and this is crucial in any real-life problem-solving context.
45
10 The study of expertise across many different domains has converged on several central
notions, including the awareness that human expertise develops through deliberate
practice over large slabs of time, perhaps 10 years.
11 Across many domains, it has been possible to characterise expertise in terms of seven key
attributes.
12 The research into how high-achieving individuals develop their expertise over time
indicates that early talent markers have little if any predictive validity.
13 People appear to be able to transfer their problem-solving skills across situations by
mechanisms such as automation, analogies, studying worked examples and identifying
deep structures.
Reading notes
Our interest as educators is certainly in the whole chapter of Bruning et al. But for study
purposes at this point, we suggest you read for depth up to page 180. This covers a large area;
that is, the psychology of human problem solving. Note how the material from pages 171 and
following is directly relevant to the course project.
Let’s begin with a little history (page 162ff). The early associationist psychologists knew that
problem solving in humans could take place very quickly, but they saw this as the result of a
significant history of individual learning. People could apply what they learnt beforehand
within a new situation. Thus, the speed of problem solving was a type of illusion where the
long history of trial and error was not taken into account. What is interesting today is that this
thinking also underlies what is now known as expertise; that is, when people acquire
significant levels of expertise they do not need to exert much effort in problem solving, as
they rely very much on recognition processes. Strangely enough, this position was espoused
by associationists a hundred year ago. However, within today’s psychology, there is far less
emphasis on trial and error as the vehicle for either acquiring knowledge or attacking
problems.
The next significant development within this field was the series of steps proposed by John
Dewey (see page 163). This step sequence has been widely used within psychology for 90
years, and of course it forms the natural basis for the review in the textbook, pages 164 to
171.. The steps have certain validity, yes, but we cannot see them as occurring within real
time frames. That is, one does not spend a half second identifying a problem, a half second
representing the problem, a second selecting a strategy and so on. The steps’ major use is as a
means of:

reviewing what we know about the mental processes that have to be involved as we solve
problems, and

analysing why some people do not or cannot find adequate solutions.
Lets pick up on some of the themes that you come across within the reading. The Gestalt
approach is cited on page 163. This position has problem solving as the result of seeing
events in a new way. That is, the processes of perceptual reorganisation and pattern
recognition are seen as critical. One reason why we, at times, fail to solve problems is that we
become too fixed and habitual in the manner in which we perceive the world and the objects
within it. Hence, when circumstances change, we fail to see the unusual uses certain objects
might serve. This is called functional fixedness (see page 164).
46
From pages 164 to 166 there is an excellent discussion of problem identification; that is, the
initial facet of recognising that a problem actually exists. This facet is not obvious, as indeed
when we even talk of ‘problem solving’ the assumption normally is that the problems are
known ones. But there is an earlier stage in which people need to invest some time
assimilating the fact that a problem actually does exist, and what exactly is its true nature. Is it
a problem that demands attention, or not? Should I spend some time just identifying the
parameters of the problem, or can I assume that I already know where the trouble lies?
Virtually all the research into this issue tells us that good problem solvers are very cautious
about identifying problems. They take time to think about the problem before it arrives; they
plan ahead, do not dish out quick impulsive responses, and may even identify the problem in
strangely divergent ways that others had not thought about.
Once the nature of the problem has been identified, the next step is representation. This
refers to how the mind plays out the problem within its mental space. One notable way that
many students approach school-type problems is to change the given representations to a form
that is easier to process. The classic example of this is shown in the monk problem on page
167. By converting a verbal representation to a graphical representation, the solution suddenly
becomes available. One excellent strategy found in many good problem solvers is to vary the
problem representations in as many ways as is possible; for example, verbally, pictorially,
graphically, algebraically, as objects or models to move around, as icons on a computer
screen, using CAD etc.
Many individuals, for example, feel the need to solve problems using diagrams, sketches,
figures, or just writing words down using paper and pencil. If you look at what they are doing,
it is often a case of converting information from one input (for example, someone else’s
words) to another representation (that is, a form that they can deal with and respond towards
far more comfortably). Once they have represented the problem in a form they cope with,
people need to recognise or discover the fundamental operators (see page 168) that apply
within that knowledge domain. This is another way of saying that people have to respect the
rules of what is possible, and what is not possible. For example, someone who assumes they
can solve the problem of paying the rent by gambling demonstrates a very poor grasp on
fundamental operators in their life.
From page 168 to 170 there is a good discussion on the next two steps: strategy selection and
implementation. Note the term algorithm. This is most useful term, used in many areas. It
refers to rules and procedures that serve specific functions very effectively. For example, you
acquired algorithms at a young age which enable you to do computational mathematics
relatively quickly and error-free. Hence, as you read a problem in mathematics, you may
represent it in your mind as a ‘ratio’ problem, and as such activate algorithms for calculating
proportions and fractions.
The next construct is that of heuristic. Whereas algorithms generate precise answers,
heuristics refers to generalised tendencies. We met the heuristic of availability in the reading
notes last week (tendency to pick things that are more immediately available in your
consciousness). But in this week’s text reading, the two heuristics cited are trial and error and
means-end thinking. To these we will add working forwards and working backwards.
47
Trial and error is a tactic people use when they have no other known way of proceeding.
This may occur because the situation is new, or perhaps they have simply run out of good
ideas. Means-end analysis can often be a very effective strategy. It implies fixing upon what
an end state (goal) should look like. Then progress towards this is checked regularly, step by
step. Working forwards is very similar to this, but the implication is that the person knows
already what the goal state is like, and so the need for continuous monitoring is not present;
that is, the person appears to ‘plough on’ without actually stopping to check on anything.
Working backwards is an especially interesting strategy, especially when used in
combination with other tactics. Backwards working means to look at the goal state and
change it to a state that is far easier to reach. For example, many problems in mathematics can
be solved by working from the finishing point. In one sense this is similar to problem
identification step we discussed earlier; that is, if a person is able to redefine the problem in a
creative way, then divergent solutions come more easily into view.
From page 172ff the topic of expertise is reviewed. We have met this notion many times
before, and a professional level readings on this topic are supplied with this course, and set
for Week 11. The text reading for this current week serves as an excellent overview. The
topic of expertise is now one of the most thoroughly researched topics within modern
psychology, and a number of striking findings have emerged. One of the major themes that
you will see emerging is the role of content knowledge; that is, in order to become an expert,
a person accumulates an incredible level of knowledge which he or she uses to advantage in
any problem-solving context. Rather than rely on raw strategies such as means-end, or
brainstorming, or hypothesis testing, experts call upon their knowledge base using mainly
recognition processes to identify complex patterns that are seldom detected by novices. This
can give rise to an interesting paradox: experts typically will solve problems within their
domains, not by activating general problem-solving strategies at all. Instead, it is the
characteristic of the novice to have to activate effective problem-solving strategies. Why?
Because they cannot ‘see’ what is painfully obvious to the more knowledgeable expert.
The section, as far as we need to take it, reviews some of the data on deliberate practice and
transfer of training. One surprising finding to emerge from the deliberate practice research
was that indices of initial talent within specific domains have some validity to begin with, but
then have little meaning once children begin to accumulate levels of practice. We used to
believe that children have to have ‘talent’ to succeed within an area of achievement. But it
now has been found, across many studies, that deliberate practice rapidly outweighs talent.
Many high achievers emerge from the ranks of children who score around average on early
talent measures.
Active learning opportunities
1 Watch expertise on film
By way of direct enrichment however, we can suggest something rather different. Try to have
a look at specific examples of expertise on film, video or TV. You can look at many sports
performances in terms of what the participants are actually doing. Many of these could be
highly practised routines involving little variation. Such examples might be in gymnastics,
ice-skating and diving. In other sports the participants are responding to stimuli that are more
dynamic and so continuous adjustments are being made to musculature. Developments in
48
camera work have provided remarkable views of, for example, cricket balls coming straight
down the pitch, and close-ups of racing car drivers correcting a dangerous skid.
It is instructive to video record such examples, and then replay the action frame by frame on a
home video, paying especial attention to what the expert is actually doing, where his/her eyes
are directed, what the hands are doing etc. This activity can be very productive in viewing
championship tennis, where it is possible to note, via slow motion, the actual point at which
the player begins to adjust limbs for the return volley.
While on the topic of films, we recommend certain commercial films available on video.

Searching for Bobby Fischer is an American film made in the mid 1990s starring the
noted actor Ben Kingsley. Although based on a true story, the plot is a bit sentimental.
But the film has excellent shots of junior chess championships. From a psychology view,
the film shows training conditions, apprenticeship factors, social modelling, and highlevel pattern-recognition skills.

Another high quality film depicting exceptional abilities is Rain Man starring Dustin
Hoffman. Although a fictional story, the character was based on the idiot savant
syndrome; that is, expertise in narrow areas found in some disabled individuals. Mr
Hoffman studied several such individuals before acting the role. The psychologist
Michael Howe (1990) has made a special study of the savant syndrome.

Another most remarkable film, beautifully made and strongly recommended for what it
shows about the development of skills, is Hiliary and Jackie, based on the life of cellist
Jaqueline Du Pre.
2 Expertise in yourself: two key aspects
By way of reflection, we want to alert you to two key aspects about expertise.
Firstly, as you know from your project, it is not a rarefied phenomenon. It is found
everywhere in human behaviour; that is, within any context where people engage repeatedly
in activities over extended periods of time. To be assimilating these pages, for example,
implies you have acquired considerable expertise in the act of reading, perhaps over decades.
You possibly have personal expertise, for example, in controlling a car, and almost certainly
in your most treasured life pursuit, be it painting, writing poetry, gardening, making money,
managing your children, or teaching.
Secondly, a subtle point is that it is misleading to assume that expertise is a status one
somehow acquires. The definitions of ‘novice’ and ‘expert’ are convenient tags for purposes
of experimental design. In their book, Surpassing ourselves, Bereiter and Scardamalia (1993)
are at pains to stress that within cognitive psychology, expertise is viewed as a process of
mind rather than a social status. Expertise is evident once an individual has mastered basic
elements such that attention (that is, the WM) is no longer focused upon immediate
requirements. Attention can then be reinvested into higher level problem-solving activities
based upon confidence of successful task mastery.
3 Toward an understanding of automaticity
We often say that experts use automaticity. However, this is not a closing down of
consciousness, but a shift of attention to a higher level. Experts frequently exhibit highly
49
mindful activities involving planning, organisation and self-monitoring (see page 297 in
Ericsson and Lehman, Readings Booklet for EDUC 5090). Certainly, experts can lose insight
into the difficulty and complexity of basic skill acquisition, as automaticity takes over and
makes task performance ‘easy’. But this does not imply mindlessness per se, only a shift in
mental focus. Interestingly, the emergence of skill automaticity can easily make the experts
into terrible teachers for that particular skill. Can you say why?
4 Why do we behave stupidly?
Why do smart people often act so stupidly? This is a fascinating issue that goes far beyond
this Study Guide, and indeed is the subject of a recent book, “Why smart people can be so
stupid”, edited by Sternberg (2003). There are many answers as to why people frequently
perform in ways that are dumb, in relation to their actual competencies. You will meet other
explanations later on, (eg, misconceptions, in Week 12). And those of you who move onto
another topic in this University award called “Problem Solving” will encounter many more
explanations. However one possible explanation (of perhaps a dozen offered by cognitive
scientists) is the simple observation that people frequently experience overload in their mental
adjustments.
The parent slaps his child for a minor indiscretion. The teacher loses her temper with the
class. The driving instructor shouts at the student for exceeding 60 kph. The student throws a
tantrum when he is given more homework to do. The priest finds himself being rude to
someone who came for help. You take a silly risk on the road. You regret buying those
expensive sheets as soon as you get home from shopping. And so on. In such cases the person
may reflect “Why was I so stupid?” It is possible that many people fail to perform at an
optimal level due to their minds being at a point of overload. Indeed, sometimes overload will
trigger automatic or mindless responding, which appears to be the case of a procedural
knowledge element overriding a controllable response which ‘should’ have been guided by
more adaptively appropriate declarative knowledge.
Some factors implicated in overload are cited in the essay attached to this Study Guide,
Appendix 5. But for purposes of this present chapter, try to think of times when overload has
occurred in your mind, thus preventing sensible problem solving, and even ‘dumb’ behaviour.
5 Encouraging transfer of training
A deceptively simple question: what are the major variables likely to determine the extent to
which a skill taught in situation A will be evident in a potential transfer context which we call
situation B? Perhaps one of the most difficult educational goals is transfer of training. The
enemy of transfer is inert knowledge. Knowledge is said to be inert if it is potentially
available but fails to be activated at the appropriate time. This may be evident in the failure to
use existing knowledge to solve problems even though the solution appears ‘obvious’ once
known. All of us plainly know more than is often apparent, and the cry ‘Why didn’t I think of
that. I knew that, but just didn’t think about it’ appears to be a very common human
experience.
One fear expressed by many educators is that some forms of classroom teaching may
encourage inert knowledge, useful in answering questions on paper and pencil tests, but
50
failing to generalise to real life (or authentic) applications. We know of science teachers who
tell amusing stories of how their students may learn of a basic principle in laboratory classes,
but fail to realise it applies at home or in the ‘real world’ as well. Note how even the
vocabulary of many school-taught subjects is very different from the vocabulary used in
everyday life, so this in itself represents an obstacle.
The inert knowledge factor is not a major problem in its own right, once we begin to analyse
it in terms of insufficient instructional provisions. For example, there could be failure to
identify schematic elements, failure to instate an IF/THEN production system, or failure to
develop skill beyond stage 1 of the automation process. It is important to realise that much
human learning is context-specific (recall this discussion from week 1). So rather than see
inert knowledge as a ‘major issue’, try seeing it as the natural state of how the human mind
works. That is, transfer is something that has to be learnt, rather than assuming it will occur
spontaneously and automatically.
All this points to the need to broaden teaching and training environments to include the range
of cues and task demands that can be expected to occur within more natural contexts, rather
than rely on purely verbal forms of teaching and assessment. And of course, it may point to
the need to devote more time and practice to specific topics within the curriculum. Training
strategies such as elaboration and question asking assist in making the original learning more
accessible and retrievable across a greater range of situations. From this perspective, inert
knowledge is seen as a natural, understandable and surmountable problem of instruction and
application, and not as a human failing or an indictment of an educational system.
Now, following on from this discussion, try to list things teachers can do to assist with
transfer effects. Once you have devised such a list, compare it with one we prepared on the
same problem (see Appendix 6).
6 An strange problem: Do we really learn through problem solving?
In recent years, a chorus of educators worldwide have espoused a pedagogy generally referred
to as problem-solving approaches to education. This has reached a high point in terms of the
tertiary level problem-based curriculum movement which has attracted a good deal of
attention in fields such as business and medical training, and is often known by its mnemonic,
PBL.
While not questioning the value of this approach, we note the existence of an assumption very
often conveyed by promoters and advocates within this area: That people learn effectively
through solving problems. Such an assertion implies that effective learning is a direct result
of successfully solving a problem; that is, that the process of problem solving itself creates
new and deep learning. But within scientific cognitive psychology, such an assertion has
never been adequately validated, and is seen as seductive, but potentially misleading.
Within an information-processing view, problem solving is the natural result of being able to
apply what has been learnt, rather than the factor that accounts for the learning itself. By no
means is this a trivial or a semantic point. Professor John Sweller (University of New South
Wales) has documented the fact that some students within high-school mathematics lessons
may solve some specific problems but still be well deficient in terms of the schema
acquisition process. That is, the existence of a successful resolution to one problem does not
in itself guarantee successful and generalisable learning. After all, why should it?
Dr Sweller has found that students benefit most from a systematic exploration of conditions
that induce direct schema learning, such as carefully reviewing worked examples of
completed problems, or being in a position to focus adequate attention upon crucial elements.
51
But getting students to work on problems before necessary schemata are secured in place has
been shown to be an ineffective use of instructional time.
Dr Sweller has tied his findings to the theory of cognitive load. Asking people to learn as they
solve problems places high to excessive levels of cognitive strain onto the mind. Hence, as a
direct consequence they fail to learn to maximum advantage. On the other hand, when
educators take steps to teach schemata more directly, rather than giving people ‘problems’
and expecting them to discern underlying patterns unaided, then learning effects (especially as
defined in cost/benefit terms) will surface most strongly. This argument does not ridicule the
problem-based curriculum, it merely provides a soundly validated analysis of when and how
learning takes place most effectively.
We would hesitate to say that problem-based learning involves ‘bad pedagogy’, but it does
worry cognitive scientists that PBL can be driven by a faulty notion of learning processes. It
is more likely that the value of the problem-solving approaches to education, including the
problem-based learning movement, lies more in the following two aspects:
1
high levels of motivation and engagement; that is, students are provided with strong
reasons to learn, and
2
high levels of self-efficacy (confidence) in ability to solve significant and realistic
problems within a credible professional context.
Since these are valuable educational goals themselves, the additional assertion that problemsolving activities automatically create significant learning effects appears unnecessary and
mistaken. Indeed, Dr Sweller’s own work suggests that students’ schemata benefit from a
careful analysis of past case studies, and these methods are easily brought into problem-based
curricula.
So, to recap on this strange issue: The notion that one learns through solving problems
invokes a flawed assumption. Learning is learning. When one engages in problem solving it is
likely to be based on prior learning. But to assume that one activity produces another however
may be untrue and even false if the act of solving problems overloads the mind (high
cognitive load) and thus prevents adequate learning from taking place. Many of us might
solve problems without fully understanding what it is that ‘worked’, or what the underlying
schemata and fundamental operators actually were at the time. This problem can be made far
worse when solutions to problems appear as known or defined options (such as in multiple
choice test), and hence response selections rather than generated declarative knowledge are all
that is required.
If you can read these pages, and think “Hey, this is all beginning to make some sense”, then
be assured you are beginning to think like a cognitive psychologist. Poor you… there can be
no turning back.
THE SPECIAL TOPICS
From this point on, we feel you have covered the basic elements of cognitive learning theory,
and we move onto additional or supplementary information, under what we call “Special
topics”. These can be treated, for course purposes, more as options in that we do not
necessarily expect all students to be equally interested in each of the topics. We have prepared
four such Special Topics: (a) Self-beliefs, (b) Knowledge-related beliefs, (c) Cognitive
approaches in teaching science, and (d) Cognitive principles in ICT.
52
53
SPECIAL TOPIC 1
BELIEFS ABOUT THE SELF
Reading
Text reading
Bruning et al, chapter 6.
Questions to ponder
1
We all agree that confidence is an important aspect to consider in human learning, but
how can we incorporate this into the theory of cognitive learning?
2
Do people actually think about the causes of their own actions?
3
Do teachers signal cues to students about what they think of the students’ abilities?
4
Should teachers strive to give students a sense of personal autonomy?
5
Is it possible to misapply rewards?
Listing of key concepts
outcome expectancies
self-efficacy
enactive learning
vicarious learning
modelling
goal setting
cognitive modelling
self-regulation
attributions
attributional dimensions
attributional style
attributional retraining
intrinsic motivation
autonomy vs external
control
classroom-controlling
conditions
sense of personal control
evaluation via norms or
objective criteria
the over-reinforcement
effect
Key points
1
Self-efficacy refers to an individual’s confidence when faced with a real situation. It is a
personal judgement which mobilises motivation and effort in the face of challenge.
54
2
Self-efficacy is built up through actual experience and positive feedback. But it is also
influenced by observing good models, by accepting persuasion attempts, and by taking
factors such as physiology and mood into account.
3
One significant means through which people learn complex skills is through cognitive
modelling; that is, where words and actions are correlated within a model’s performance.
4
It is inevitable that students arrive at personal explanations or attributions for virtually
everything that occurs to them.
5
Attributions have motivating properties. The attributional pattern a person uses to account
for the past determines how that person reacts to the future.
6
It is possible to identify poor, unhealthy attributional tendencies and seek to modify these
through attributional retraining procedures.
7
A large body of data indicates that students are highly sensitive to classroom conditions
that signal levels of control in operation. In general, motivational conditions are healthiest
when teachers de-emphasise external controls and allow students a sense of autonomy.
8
Indeed it is possible to overuse rewards within the classroom, and, under some conditions,
this can lead to reductions in intrinsic motivation levels.
Reading notes
This week we move into the area of motivational psychology; that is, the study of conditions
under which students are most likely to exert high levels of goal-directed effort. What has
become obvious over past 25 years of research is that motivational psychology has moved to
a very strong cognitive orientation. The dynamics of effort exertion are now seen to implicate
the thought processes of the individual learner. When you attempt to understand things, for
example, not only do you have concepts and schemata being activated, but also you have
thoughts and feelings about what you are doing, and why you are doing it.
Chapter 6 of the text reviews the major advances in this area. The first thing to notice is that
the chapter says little about the notion of self-esteem. This is because self-esteem is not
especially relevant in most learning situations. People can learn material perfectly well
whether they have low or high self-esteem. The notion of self-esteem is frequently overrated,
and most psychologists today would view self-esteem more as the end result of successful
experiences rather than a determinant of that success.
What has been discovered over past years is that confidence, as a human attribute, is not well
articulated as an overall trait. Instead, it is better seen as a task-specific measure. This is why
Bandura coined a new term, self-efficacy, to refer to judgements people make as they size up
the dimensions of a real task put before them. In this sense, self-efficacy is not something to
carry around, or something one somehow possesses. Instead, it is typically expressed as ‘Yes,
I can do this’. But this is a personal assessment based on realistic indices rather than nonspecific impressions. To arrive at a self-efficacy assessment probably takes a second or so of
a person’s time. But the outcome of this process then determines the level of motivation a
person is prepared to exert on this task.
After reviewing Bandura’s social learning approach, the chapter goes on to attribution theory
(pages 119 to 126). The underlying notion here is that all of us make personal attributions,
virtually all the time, which serve to explain things as they occur within our experience. A
55
well-documented finding is that if students begin to blame their learning problems on their
lack of ability, then motivational problems follow quickly. The studies cited between pages
123 and 125 lead into what is called learned helplessness. This topic will be looked at again
next week. It has been found that many students react poorly to failures, whether real or
anticipated. With careful training, however, it is possible to realign attributional patterns in
the direction of more adaptive patterns.
The chapter then moves on to discuss the topic of intrinsic motivation from the perspective of
self-determination theory (Deci). This view stresses the role of student perceptions as
mediators of motivated effort. The basic notion is that students are more strongly motivated
when they feel more in control of their own learning. It has been shown that students do rate
(that is, encode) the basic climate of their school and classrooms in accordance with the
extent to which they feel their autonomy is either supported or thwarted.
The final fascinating issue taken up in the chapter concerns the misuse of rewards. Rewards
can be administered as blatant controlling gestures, for example. One recurring finding has
been that when rewards are over-used and then withdrawn, students may express lower levels
of intrinsic motivation than previously expressed. Such findings do not suggest that rewards
should not be used. It’s a question of how rewards are applied, what they mean to the student
within the specific context. There are other studies (not cited within the chapter) that indicate
that social rewards can stimulate intrinsic motives well, especially when such rewards are for
clear goals set at a high, attainable and non-competitive level.
Active learning opportunities
1 Self-esteem measures
If you want to see what a reasonable self-esteem test looks like, have a look at the Queendom
website. You can do this test on the net and get a score on self-esteem level, in contrast to
others who have done this test. Note how the questions deal with generalised notions. This is
perhaps why self-esteem is not particularly important within most realistic learning situations.
Within the psychology domain, there is lot to offer on the queendom main site:
http://www.queendom.com/.
However, we do suggest that you do not take some of the tests there too seriously, as this site
is really oriented to general interests and entertainment rather than serious academic study.
2 Self-efficacy web site
Professor Frank Pajares has contributed a huge and very rich web site devoted exclusively to
the construct of self-efficacy. This includes several hundred links to his own work and that of
others. However, be warned that it is possible to spend hours exploring this site. Of special
note is that some links are to the actual questionnaires used within some of the classic studies
in this area. The URL is http://www.emory.edu/EDUCATION/mfp/self-efficacy.html
56
3 Self-efficacy as a calibrated assessment
One easily misunderstood aspect of self-efficacy is that it represents a judgement whereby a
person matches an envisaged task to his or her perceived capabilities. Hence, it is possible to
identify a kind of ‘false self-efficacy’ whereby a person makes a declaration such as ‘I can do
Task X’, but without genuinely apprehending what Task X actually is. Such declarations may
take the form of impulsive responding, carelessness, inflated aggrandisement or simply lying.
Within the published research, this factor of inflated ratings is controlled by making people
accountable for the judgements they are asked to make. That is, they are asked to judge their
ability to do Task X, but know that they might be asked to do Task X within a few minutes.
Hence, self-efficacy has to be seen as a personal calibration. Set this too low and you will
undersell yourself, underestimate your worth, pull back from challenge and fail to develop
skills. Low self-efficacy is a recipe for low levels of learning, performance and development.
But set self-efficacy too high, and there is danger of over-exertion, overload, inadequate
performance levels and unfavourable cost/benefit ratios. Further, if this occurs within a public
area, there is the added threat of possible shame following on from social ridicule.
So should your self-efficacy level be set at exactly the same level as your actual
competencies? The answer is ‘No’. Bandura has been most explicit on this point.
Psychological development and effort exertion hinges in part upon feeling free to set selfefficacy levels slightly ahead of actual levels. This provides a level of motivation and
inducement. Since levels of effort have to be activated to enable performance to rise to this
new level, successful learning can now be attributed to personal factors. In essence, this
model views psychological development in terms of iterative cycles of self-efficacy
assessments, goal setting, effort exertion, skill development, and revised self-efficacy
assessments. This is one possible mechanism for Matthew effects.
4 Are there gender differences in self-efficacy?
Do boys and girls differ on measures of self-efficacy? The evidence is mixed in that such sexdifference effects simply are not found universally. However, when they do appear it is more
often the case that boys will claim slightly greater levels of self-efficacy than girls. This effect
seems to hinge upon what method of self-efficacy assessment is used. Girls and boys appear
equally confident when asked to rate themselves in relation to others, to their position in class,
or to their overall skill level. But it does seem to be the case that girls are more accurate in
predicting what they cannot do. Several studies now suggest that boys tend to be overly
confident even when they perform poorly. On the other hand, girls tend to know their limits
with greater accuracy, and are less inclined to over-estimate their competency levels.
The meanings behind these effects are unknown. It is too easy to over-emphasise these
findings, which are subtle statistical trends rather than clear-cut differences. One possibility is
that boys’ overall sense of competency is, in relative terms, less influenced by immediate
setbacks. In such a scenario, there is no need to reduce confidence levels just because ‘It
didn’t work the first time’.
57
SPECIAL TOPIC 2
BELIEFS ABOUT KNOWLEDGE
Reading
Text reading
Bruning et al, chapter 7.
Questions to ponder
1
Can people significantly improve their level of intelligence, or is this a relatively fixed
trait?
2
If a person was to fair poorly in a learning situation, what cognitions are most likely to
determine appropriate remedial activities?
3
Is it better to focus on displaying existing performances, or on developing new skills?
4
Is the world describable in terms of ‘black-and-white’ clear-cut issues?
5
Has doing this course made you think any differently about what it means to learn and
acquire knowledge? Specifically, what changes have you experienced?
6
Has you attitude to students altered?
Listing of key concepts
implicit theories
incremental theory
entity theory
learning vs performance
goals
learned helplessness
epistemological beliefs
dualism vs relativism
simple knowledge
certain knowledge
fixed ability
quick learning
stages of reflective
judgement
hope
58
Key points
1
Inevitably, we hold and defend sets of beliefs concerning the nature of human learning,
achievement, intelligence and knowledge. These beliefs may be not formalised, but exist
on a tacit, unspoken or implicit level.
2
We use the term entity theorist to describe people who tend to express belief in the fixed
nature of human intelligence. On the other hand, incremental theorists are people who
retain faith in the inherently changeable aspects of human nature and intelligence.
3
Entity theorists are inclined to approach most learning situations harbouring an
underlying performance goal. This is also called ego-orientation.
4
Incremental theorists approach learning situations in a different frame of mind. They
stress learning goals (such as, ‘What will I learn from this experience?’). This is also
known as task-involvement.
5
Learned helplessness represents the extreme case where entity theorists fail and then
withdraw effort because of their activated self-beliefs.
6
Epistemological beliefs concern the way in which students think about the nature of
knowledge in the abstract.
7
A well-known distinction is made between dualism (‘black-and-white thinking’) and
relativism. As people move through university life, there is a general tendency towards
relativism.
8
Schommer has postulated four dimensions of beliefs:
9

simple knowledge

certain knowledge

fixed ability

quick learning
Kitchener and King have described seven steps in the development of reflective
judgement.
10 Although epistemological beliefs do change as people mature, it is not clear just how
educators ought to actively try to change such beliefs at the level of the individual. There
is some suggestive evidence that thinking skills can be shaped by the type of training
experienced within university studies.
Reading notes
The text chapter for this week continues on from last week, and there is much overlap in
content and ideas. The focus of this chapter, however, is on the role of generalised beliefs,
rather than self-oriented beliefs, as was the concern last week.
Carol Dweck (see pp 139 to 144) has contributed a major program of research into the
consequences of harbouring implicit theories about intelligence. Belief in fixed intelligence is
fine, provided one continues to succeed. But if you are failing and believe that the task you
are on is a fair reflection of your true ability, then you are faced with a type of existential
problem: how can you increase your competency if you have run out of ability? People who
think this way are ‘entity theorists’, and they tend to perform well when they know they can
succeed. But the onset of obstacles suggests that the limits of their capabilities are coming in
fast, and hence the disposition to keep maintain exertion is seriously impaired.
59
It is appropriate to think of these dimensions in terms of what the mind is doing. Two students
sitting next to each other within a difficult mathematics test could be activating totally
divergent mental frames. One child (entity theorist) could be interpreting the questions as
valid indices of her competency level, and experiencing huge levels of helplessness, knowing
that she cannot escape, that she will get a low score, and that her score will tell her and others
that she is a poor student. Her friend next door (incremental theorist) knows that she cannot
do questions 7, 9, and 10. But so what? They are difficult but interesting problems, and she
will find out how to do them next week. She may get a low score on this test, but this has no
implications for her capabilities or intelligence. In fact, its rather good to get such challenging
questions as one learns from them and increases the personal knowledge base.
Epistemological beliefs are then handled from page 145 to the end of the chapter. Where does
knowledge come from? And what is the relationship between your knowledge and the world?
What is the nature of knowledge? Can it be acquired through amassing scores of facts?
Questions such as these tap into the epistemological belief system. Within psychology there is
a level of debate as to what are the appropriate dimensions to measure in this area, and most
of us feel that the area has yet to ‘gel’ in terms of generating securely validated research
findings.
Nevertheless, some interesting work has been carried out. Pioneering work was done by Perry
who found that as students progressed through their undergraduate degrees increasingly they
tended to endorse views representing a relativistic worldview. This work was done mainly
through interviews, but Marlene Schommer developed a questionnaire scale which she was
able to use to monitor four dimensions of belief and show some interesting developmental
patterns. The work by Kitchener and King is interesting, but some of the subtle points
between the seven levels are very hard to appreciate, and we suggest you skim read some of
this material.
From pages 152 to 155, Bruning et al cite several studies which indicate that the content of
your education influences epistemological beliefs and some reasoning skills. For example,
graduate students from medicine and psychology seem to acquire greater skills in reasoning in
terms of statistical probabilities. Schommer found that science graduates apparently reduced
their level of belief in certain knowledge. These studies imply a connection between
education and changing beliefs.
The chapter finishes with a brief discussion about the possibility of teachers holding different
beliefs about learning and knowledge. Exactly how such beliefs may impact upon how they
treat their students is a topic as yet not well researched. The assumption would be that
teachers actually teach in a manner consistent with their stated epistemological views. Several
studies indicate consistencies between such views and how lessons are planned.
Active learning opportunities
1 Dweck’s research programe
Over the past 20 years, Carol Dweck, her colleagues and students, have published many
spectacular studies into the dynamics of implicit theories of intelligence. Her work is
summarised in a highly readable book Self-theories: their role in motivation, personality, and
development (Dweck, 2000). Her work reveals that Western society is overly concerned with
displays of ability, where ability is defined within terms of unalterable and stable personal
qualities. This world view gives arise to the following four conclusions about the
development of what she refers to as mastery-oriented qualities such as engaging effort,
persisting, overcoming obstacles:
60
1
Students with high ability are more likely to display mastery-oriented qualities.
2
Success in school directly fosters mastery-oriented qualities.
3
Praising a student’s intelligence encourages master-oriented qualities.
4
Students’ confidence in their intelligence is the key to mastery-oriented qualities.
Do you agree with any of these statements? Well, Dr Dweck’s research indicates each one of
these notions embodies a falsehood; that is, the opposite of what her empirical data actually
indicate.
2 New directions in the psychology of hope and optimism
Dr Snyder’s research into generalised hope is briefly cited on pages 155 and 156. Another
stimulating research program is evident in the work into optimism and pessimism. Several
research groups around the world are concerned with these problems, and many fascinating
findings have emerged. Of especial note is the work of Martin Seligman who has published
many books in this area, but his early paperback Learned optimism (1990) remains an all-time
classic. There is much spectacular work carried out in this area, and this is not the place to
attempt to review it. However, we might note that Seligman has convened a group of leading
researchers who are currently engaged in a push for what they refer to as positive psychology.
These are people who are among the most creative psychologists working today. The group
uses Dr Seligman’s web site at:
http://www.psych.upenn.edu/seligman/
Also, if you place ‘Martin Seligman’ into a search engine, you will find many links to his
work in the public domain. Among the first products of this group was the January 2000 issue
of the leading journal American Psychologist, which was edited by Dr Seligman and Dr
Csiksentmihaly (available per your online library access).
3 A point of orientation: what is cognitive psychology?
It has been our experience that students, when encountering cognitive psychology for perhaps
the first time, are a little overwhelmed by the arrangement of topics that define the world of
cognitive psychology. In one tutorial discussion a student said to us that she felt very
perplexed. One week it was heavy on schemata, retrieval strategies and encoding specificity.
But then the next week it was the nature and dynamics of human motivation and optimism.
The simple answer is that cognitive psychology has a very broad sweep, and within any
training course we have try to survey as much of the field as is realistic.
So is there any intrinsic connection between topics such LTM organisation and human hope?
One answer is that the notion of schemata underlies both. When we are optimistic, or
depressed, we are using selective cognitive filters that enable us to structure psychological
space within manageable parameters. As an extreme example, when we are depressed we
have black thoughts appearing with a clear level of automaticity. Under depression we have
immediate access to negative sentiments and will interpret the world using schemata that
reflect the permanent and pervasive attributes of negative experiences. For example, you are
depressed and the car breaks down. Naturally, you see that this event is a part of the pervasive
and continuing pattern of negative experiences that continue to assail you. You know this
because the mind accesses other negative events and these are chunked together forming a
category of prominent negative events that can be recalled, rehearsed and ruminated over.
Under such prominence it is hard to maintain positive thoughts.
61
SPECIAL TOPIC 3
COGNITIVE APPROACHES TO TEACHING SCIENCE
Reading
Text reading
Bruning et al, chapter 15.
Questions to ponder
1
What is the level of basic scientific knowledge within our community? What roadblocks
to scientific literacy can be identified?
2
Why do people rigidly retain ideas that are wrong?
3
What does a teacher have to do to encourage conceptual change?
4
Does knowledge of the scientific processes (that is, data, theories, experiments etc) come
naturally, or does it have to be taught?
5
What factors predict how well students perform on tests of science achievement?
Listing of key concepts
naive beliefs:
misconceptions
intuitive physics
conceptual change
conditions (dissatisfaction,
intelligibility, plausibility,
and fruitfulness)
weak restructuring
radical restructuring
exposing misconceptions
creating conflict
supporting new
information
distinguishing theory from
data
commonsense vs critical
views of theory
obstacles to scientific
literacy
extended experimentation
the determinants of achievement
62
Key points
1
Even within groups of educated people the level of misconceptions in basic scientific
knowledge is surprisingly high. To solve scientific problems, most people use their own
naive conceptions.
2
There is debate within the field as to whether faulty beliefs are fragmentary and
incomplete, or represent a coherent and integrated worldview.
3
Whichever view is correct, it is apparent that people adhere to their faulty beliefs with
tenacity. Thus, science educators are faced with the challenge of conceptual change.
4
To achieve conceptual change, misconceptions need to be articulated, challenged, and
students need much support in adopting the new position.
5
Young children and unsophisticated adults often fail to distinguish theory from data.
Primitive (commonsense) notions of ‘theory’ are that it as a collection of facts based on
solid data. The more sophisticated (critical) view of theory is that it a humanly
constructed approximation of reality that needs continual data support.
6
It has been widely established that many roadblocks to scientific literacy exist, including
lack of knowledge, lack of teacher training, inadequate reading strategies and insufficient
time allocated to the curriculum.
Reading notes
From our biased perspective we feel that the science curriculum is often given insufficient
attention within our schools. Many students exit from the educational system with a poor
grasp of scientific concepts and of the processes involved in carrying though procedures in a
scientific manner. We tend to subscribe to the view that science is not the continuing
accumulation of facts to be memorised, but a means of generating:

a rational and coherent view of the world, and

procedures that help us, and the rest of humanity, make balanced and sensible decisions.
In short, helping students learn the rules and procedures of the scientific method is itself a
higher level educational goal; that is, over and above whatever content has to be assimilated.
Chapter 15 of the text opens with a review of the well-documented findings that many people
hold notions of natural phenomena that are at variance with recognised scientific principles.
That is, rather than having ‘no idea’, it is often the case that people do have ideas that are
wrong. Misconceptions have been located within many knowledge areas besides the ones
cited in the textbook. They seem to stem from natural observations and from other people.
Wrong ideas and false beliefs appear to abound within the commonsense world, and our
students bring these notions into the classroom.
The classic studies in this area, however, tell us that students do not or cannot shed their old
ideas. Not only does faulty knowledge never correct itself, but it often stays there and can
become a base point for additional misconceptions. Just telling students they are wrong is not
an adequate pedagogical position, although it is where one has to start. The chapter reviews
several methods that can be used to try to undermine faulty knowledge. This involves schema
acquisition and refinement. Note how it is crucial to support and reinforce students as they
change their views (see page 346).
63
Note how it is really not feasible to teach science from a perspective of ‘Here are the facts.
Now, learn them to get ready for the test’. Instead, it is vital to get students to:

be active in seeing relationships across different phenomena

use theories to generate hypotheses

carry out the procedures that yield that data

discuss the findings in the light of the results
Such teaching can be loosely called the inquiry-based method (see page 350). But this should
never be interpreted as meaning that students are somehow free to learn what they want to, or
have to ‘discover’ everything for themselves.
In fact, the science curriculum embodies necessarily high levels of direct teaching. The use of
procedures such as experimentation and discussion are often highly structured and goaldriven. To put it bluntly, it is a waste of time and effort for students to engage in inquiry
methods if they are looking at the wrong things, using the wrong measures, not implementing
appropriate experimental controls, not reading the data properly, or failing to interpret the
data correctly.
Beginning on page 348, Bruning et al discuss some of the work of noted science educator
Deanna Kuhn, whose work was also cited in chapter 7. She has documented that a large
percentage of the population has difficulty identifying the nature of the data needed to support
a theory. This applies even to theories that people purport to believe in themselves. Very
often, people will confuse theory and data. People typically fail to adjust their personal
theories even in the face of disconfirming data. Reading some of her adult studies tends to
leave one a little pessimistic about the possibilities for achieving a rational society. But then
such data indicate how vital it is to teach the scientific method to people while still at school.
In one fascinating study, Roth (see pages 351 to 352) found that only a minority of middleschool students changed their misconceptions through reading disconfirming materials. She
identified several strategies that students use to cope, including one remarkable strategy she
labelled ‘overreliance on prior knowledge’. To this point within this course, we have tended
to stress how prior knowledge helps current learning. But prior knowledge can also be a
source of misconception. And in her study Roth found some students were able to activate
what has been called the “know-it-all effect”. A know-it-all effect is when students fail to
engage with materials on the assumption that they know it already. Have you ever been faced
with something you have read too quickly? Perhaps you failed to pick up a subtle point
because you thought you knew all about it anyway? If so, that would be a natural know-it-all
effect.
Kuhn’s other studies in helping young teens learn the scientific methods are cited on page
356. Students successfully did learn scientific procedures for decision making, but we must
stress that such learning took several weeks, and seems best to involve extended
experimentations. The notion of extended experimentations means to keep working on the
same problems over a long period, rather than trying to cover a broad range of topics. As a
generalisation, successful teaching in this area often involves continuing to work with the
same group of students on very similar problems over several weeks and in this way the depth
of students’ knowledge increases along with their intrinsic motivation. Science teaching can
64
be a great and rewarding experience for teachers and students alike if sufficient time is
devoted to concept development.
The notion of instructional time emerges in the research cited on page 357 to 358. This
project was one of several carried out within the United States concerned with the
determinants of achievement and educational productivity. This entailed collecting data on
student test scores, and correlating such data against sets of predictive variables such as
ratings on home backgrounds, parents income levels, the type of school attended etc. This is
difficult research to do in that it hinges upon getting hold of appropriate measures, and
engaging in massive number crunching to reveal that worthwhile patterns. The specific study
cited was based on a survey of over 5000 students at middle-school and early high-school
levels.
The data can then be expressed as a type of picture called a path analysis in which factors are
seen to flow on from each other in a coherent manner. Note that the largest influence on
achievement scores turned out to be past achievement (path coefficient of 0.73, a massive
effect; see Figure 15-5, page 357). But other influences are also significant. Instructional time
had a coefficient of 0.32—that is, around one half the power of past attainment—but this is
still a remarkably strong effect. Finally, instructional quality was seen to have a low effect
(0.1). But in this type of research there are always doubts as to whether this type of variable
really was measured adequately by the questionnaires used. The fact that it was done on 5000
students means that the measures used have to be easily applied.
Note how this type of research leads to interesting relationships being uncovered. For
example, the students’ home background was seen to impact upon past achievement, but not
current achievement. That is, the variable was already accounted for on the earlier test, so did
not figure again on the test of current achievement. Home effects are explained by the fact
that this factor has already entered the equation at an earlier point in time. Such path models
are highly valuable devices in uncovering statistical relationships, but they hinge upon having
adequate data in the first place. Also the mechanics of actually carrying out the analysis are
very complex.
Active learning opportunities
1 Misconceptions
It is virtually impossible to reflect upon the nature of any misconceptions that you yourself
hold, until you actually encounter information that seems to directly contradict what you
believe. Has this experience occurred to you recently? Perhaps it has even occurred during the
tenure of this course. For example, several weeks ago we made the assertion, in these reading
notes, that self-esteem has virtually no relationship with the ability to learn. Our experience
with in-house tutorial groups is that whenever we make such a statement, a number of
students will say they simply disagree with us.
65
When this occurs we use conceptual change methods by querying whether or not this
statement is, or is not, a matter for personal opinion. Should you hold personal opinions that
seem to fit your life experiences to date, but which are at variance with other data sources?
One common fallacy even in well-educated people is the person–who argument. This is the
use of statements such as ‘I know a person who … ‘ as data for a general statement about the
world or humanity. For example, ‘Smoking does not kill people as I know a 95-year-old man
who smokes like a chimney’, ‘You do not need a university education as lots of successful
people left school before graduating’, and ‘Anyone can win on poker machines. My friend
Jonnie went in with $10 last night and came out with $500’. These are all example of the
person–who argument which is a remarkably common defence when people peddle
misconceptions. Have you ever seen people use this type of argument, and have you found
yourself using it?
2 Experimental controls
Do you know how to conduct a controlled experiment? One of the favourite tasks used by
researchers is the pendulum problem. Suppose you were given materials such as string,
scissors and plasticine (ie modelling clay or playdough) and asked to construct a pendulum.
Now you are asked what factor or factors determine the frequency of the oscillations of your
pendulum. What factors could they be? You think quickly and name weight, height of drop,
length of string and whether or not you give it a push to start it off. OK, these are possible
hypotheses, but they may work in interaction. How do you decide what to do to answer the
question?. A variation on this type of problem is often used in primary science classrooms
when students are asked to discover factors that make seeds grow more rapidly.
Many adults fail on this type of problem. The pendulum problem was used by the great Swiss
researcher Jean Piaget as an index of formal operational thought, as people move into
adolescence. However, it became apparent that many highly educated individuals, who
clearly have attained formal operations in other areas, have difficulty in aspects such as the
need to control variables one at a time. The notion that we all have an inherent sense of
experimental procedures and know how to isolate and control for different possible variables
is simply untrue.
3 Do you believe in UFOs?
The paranormal continues to attract people, despite this being an age of supposed
enlightenment and high tech applications. A few people claim to have seen UFOs. But, far
more people appear to believe that UFOs buzz around the globe than actually claim to have
seen one. In a survey of students at an Australian university, we (Yates & Chandler) found
that 46% actually admitted belief in UFOs, 47% said they believed in séances, and the belief
level for Nostradamus’ predictions was 39%. A report on this was published in the journal
Research in Science Education, volume 30, but a conference paper version is on
http://www.unisanet.unisa.edu.au/edpsych/research/ (the file is NewAge.rtf).
We are drawn to the conclusion that much intelligent discussion can be centred around some
very shaky notions. Beliefs in paranormal events and processes are easy to sustain, provided
they are held within a community of like-minded people. If you do harbour beliefs that you
suspect people like us would challenge, please ask yourself ‘What evidence really sustains
this belief?’, and ‘Is this evidence genuine?’. We are not asking people to abandon their
beliefs, only to query the basis upon which such beliefs are held. On a humorous level, there
66
is the story of a lady who was asked whether she believed in angels. She replied ‘No, not
personally. But that doesn’t mean that they don’t exist’. There is a subtle logic to this.
4 How important is time on task?
At this point we want to draw attention to some common threads. Note how from the Ryan
and Walberg data (Figure 15-5, page 357), it is possible to say that one major factor affecting
student mastery in science is the amount of class time that their teachers devote to science.
The more science that students get exposed to, the better their test score. Note how this is
consistent with all that we know about the impact of factors such as deliberate practice and
skill development over time. That is, note how this time factor emerged as a relatively strong
variable outweighing other factors, as it seems to also in the talent research data (see page 176
in Brunning, and also the reading by Ericsson used in EDUC 5090)
Furthermore, there could be a Matthew effect lurking within the Ryan and Walberg data. We
do not know for sure, as the necessary analyses were not reported. But the powerful role of
prior achievement is clearly seen there. A Matthew effect would be evident if the differences
in achievement from time 1 to time 2 actually expanded rather than contracted. This is what
could be occurring: students who are good at science move to classes where they do more
science. Because they do more science, they get better at it. Dynamics similar to these are
played out within the real world all the time. But note how time enters these relationships as
an active factor, helping to explain why some students emerge with higher scores than others.
To help you relate to this type of study and its significance, it will help you to realise that with
5000 students to assess, the achievement test used was similar to what would appear within
national examinations for purposes such as university entrance.
5 Matthew effects: Are they everywhere?
Recall that in the article by Yates and Chandler (Reading 1), the Matthew effect was
described within the appendix using the rule-example-rule teaching principle. Although not
described formally within your textbook, some of the data supporting the existence of
Matthew effects are clearly cited within the book. For example, within the present chapter
(15) on science, note how Roth (pages 351ff) reported on a minority of students in middle
school who used sophisticated reading strategies when confronted with material discrepant
from their existing beliefs. These students exhibited conceptual change strategies and so
benefited from an experience that left others relatively unaffected. Upon reflection, note that
this is likely to define yet another Matthew effect. That is, to those who already possess a
positive trait (in this case, sophisticated reading skills), the educational experience provides
the most benefit.
Although we will not be reviewing the chapters on reading skills within this current course,
we note that the Matthew effects abound within this specific area and can be documented on
several different levels. For example, the whole language and language experience programs
are of most value to children who can already read. Further, as a child begins to read,
automaticity kicks in, and then the experience of reading serves to enhance aspects such as
vocabulary development and knowledge acquisition. These traits, in turn, stimulate greater
skill in reading.
Some of the research data into vocabulary and knowledge effects within reading are
summarised from page 269 to around 277 in Bruning et al. They may not use the term
Matthew effect, but note that within your mind a schema for the Matthew effect is
developing as a direct consequence of your participation within this course and through being
an excellent reader. Can we suggest that you will begin to ‘see’ such effects occurring within
your own teaching location?
67
Special Topic 4
COGNITIVE LEARNING PRINCIPLES IN ICT
Reading
Text reading
Bruning et al, chapter 10.
Reading 7
Mayer, R E (2003). The promise of multimedia learning: using the same instructional design
methods across different media, Learning and Instruction, 13, 125-139.
Reading notes
Once again, we look on this topic as course enrichment, rather than presenting you with any
new material. The principles of knowledge acquisition do not change across environments,
and hence there is nothing dramatically ‘special’ about learning from information
communication technology (ICT) sources. By ICT we refer generally to machine-mediated
learning, of course most notably the impact of the modern PC facility. Computers bring great
promise and high levels of expectation. But how can such promise be realised?
There has been a fascinating debate going on for perhaps 60 years about whether or not any
one learning source or medium possesses any real advantages. There is no final agreement of
such issues, but one generally held position, consistent with virtually all learning theories, is
that humans learn well when learning sources are varied, when the mind can integrate
information from different data sources, using different modalities, and using diverse media.
A modern version of this view is found in the work of R E Clark (see page 213) whose
writings stress the point that learning depends far more on the quality of the educational
experience, rather than the medium used in its delivery. Hence there is nothing ‘special’ about
ICT, other than the fact that certain learning opportunities may be organised and managed
with greater levels of precision than possibly afforded by alternative means. For example,
computers can track individual responses, success and failures on attempted items, in a way
that far exceeds the information processing capacity of any human classroom teacher. The
68
debate over the role of different media as learning resources is covered in depth in the book,
Learning from media: Arguments, evidence, and analysis, edited by Clark (2001).
Within the ICT world, instructional designers have shown a keen appreciation of ways in
which cognitive learning theories can assist them in their goals. This chapter, together with
the Mayer reading will provide a sound introduction to this area, and show how these two
fields (cognitive psychology, and IT development) converge on a set of meaningful
principles. Richard Mayer is a major professor at the Santa Barbara UCLA, and his work is
also covered in depth in his book, Multi-media learning (2001) which is readily available (eg
try the Internet second hand book people, around $20, American).(Note: If the reading from
Mayer does not accompany this booklet, please contact Dr Yates directly).
Bruning et al. again provide us with a clear highly readable overview of the entire field. After
a general introduction, they move more into the psychology of learning with an excellent
discussion on cognitive load theory, page 219ff. Of course, you will have met this before,
briefly, in Module 2. Professor John Sweller, from University of New South Wales, is a
leading figure in this area, and his work dovetails neatly alongside that of Richard Mayer.
Mayer’s work is discussed on page 221, and you can then note how the Readings article
expands greatly on these points.
One curious thing about all this work on defining the cognitive principles behind learning
from ICT is that it may seem initially so ‘obvious’. But this is an illusion. It seems obvious in
that it all makes such great sense AFTER one has read it (ie what is called the hindsight bias).
In fact, whenever we look at most learning programs available as educational software, we
find they violate many such ‘obvious’ principles. This is not the place to debate this aspect,
but the point to appreciate is that designing good software for educational purposes is so
remarkably difficult. This area has experienced many false starts and false promises over the
past two decades, as initial expectations were set far too high.
Some examples of high quality American-based packages are given in the text, notably the
Jasper Woodbury mathematics discs. This one has a rather interesting website that’s worth a
visit if you are interested in mathematics teaching.
http://peabody.vanderbilt.edu/projects/funded/jasper/Jasperhome.html
Please realise of course that such programs have a clear commercial basis, and will naturally
engage in much self promotion. However, this series is of strong interest to us in that they
specifically used a team of distinguished cognitive researchers and mathematics teachers from
an American university (Vanderbuilt) in the development stage of the materials. Many sound
principles of learning and motivation are incorporated into the program (see pp 227- 228).
Overall, this is a clever and impressive package, especially useful in stimulating young people
to think of mathematics as an authentic problem solving tool. The evaluations did indicate
that students (middle school level) found the discs stimulating to use, and this resulted in
positive attitudes to mathematics, as well as viable cognitive learning gains. This is not meant
here as any ‘advertisement’ for Jasper, only the realisation that such well designed programs,
put together with expertise and strong team development, can become effective tools for
teachers to use.
69
APPENDIX 1
SHORT-TERM MEMORY
Short-term memory for digits: digit span test and plus
one digit test
Digit span test
The digit span test taps short-term memory for numbers, presented visually below.

Keep the numbers covered.

Expose only one line at a time.

Begin with four digits (that is, level 4) which you can look at and read, but only very
briefly, around four or five seconds, to place them into your short-term memory.

These are then covered up, and you write the numbers down on paper.
You must get every number in the correct order to be scored correct.
Once you have done four numbers, you move onto five numbers (that is, level 5), using the
same procedure. And so on, until you can no longer do it correctly.
Note:

You can have up to two attempts per level, on the assumption that the first ‘fail’ is a lapse
in concentration.

As the numbers increase, you are allowed a little more reading time, up to one second per
digit.
70
LINE BELOW HAS FOUR DIGITS (Keep covered until ready to begin.)
2 8 3 5
SECOND TRY AT FOUR DIGITS (Skip this ‘second try’ if first one correct.)
4 0 5 3
THE LINE BELOW HAS FIVE DIGITS
7 4 9 3 5
SECOND TRY
1 2 9 8 5
THE LINE BELOW HAS SIX DIGITS
9 6 7 4 8 6
SECOND TRY
4 9 7 3 1 6
THE LINE BELOW HAS SEVEN DIGITS
6 0 9 6 8 3 5
SECOND TRY
3 2 1 2 6 0 7
THE LINE BELOW HAS EIGHT DIGITS
7 5 4 5 6 2 80
SECOND TRY
3 0 1 8 2 0 2 9
THE LINE BELOW HAS NINE DIGITS
0 6 0 1 7 3 6 5 4
SECOND TRY
5 3 4 0 7 5 0 6 5
THE LINE BELOW HAS TEN DIGITS
5 8 9 4 3 2 0 7 9 1
SECOND TRY
8 1 0 7 4 0 3 7 2 5
THE LINE BELOW HAS ELEVEN DIGITS
1 7 0 2 3 9 5 9 0 1 4
SECOND TRY
4 1 7 0 7 9 7 3 8 5 1
THE LINE BELOW HAS TWELVE DIGITS
3 1 0 8 1 4 6 0 5 3 0 7
SECOND TRY
6 2 4 1 0 5 8 4 0 7 1 5
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Plus one digit test
This test is similar to the digit span test except that instead of repeating back the same digits you see on
the page, you now add one to each of the numbers. If you see ‘6 9 7’ write down ‘7 10 8’.
THE LINE BELOW HAS FOUR DIGITS
8 2 6 7
SECOND TRY
5 0 8 3
THE LINE BELOW HAS FIVE DIGITS
7 1 8 3 6
SECOND TRY
8 9 2 1 7
THE LINE BELOW HAS SIX DIGITS
8 4 7 6 9 3
SECOND TRY
6 4 9 7 3 1
THE LINE BELOW HAS SEVEN DIGITS
3 8 6 9 0 6 2
SECOND TRY
0 7 3 2 1 4 6
THE LINE BELOW HAS EIGHT DIGITS
2 0 8 1 0 3 4 7
SECOND TRY
1 3 8 2 4 1 6 3
THE LINE BELOW HAS NINE DIGITS
6 1 5 4 1 7 3 6 9
SECOND TRY
6 0 2 9 8 4 1 5 3
THE LINE BELOW HAS TEN DIGITS
2 0 7 9 1 8 9 4 3 1
SECOND TRY
0 7 2 5 4 0 3 7 8 3
THE LINE BELOW HAS ELEVEN DIGITS
6 1 4 3 9 5 9 1 7 0 2
SECOND TRY
4 1 7 8 5 1 7 3 9 3 6
THE LINE BELOW HAS TWELVE DIGITS
4 6 0 5 3 3 1 0 8 0 7 5
SECOND TRY
7 3 8 2 5 2 8 9 4 0 7 1
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Short-term memory for words
This is another test of short-term memory capacity, but for words this time. Also the
procedure is different in that you will see a large number (that is, 20) of four-letter words,
which you try to read through, taking up to a minute (that is, 60 seconds). Then try to write
down as many as you can, without looking.
Although you will see them arranged in a table of five columns, the order or position of the
words does not matter. Just try to cram as many of them into your memory as you can, then
write them out anyway you feel.
FARM
CONE
RENT
KILN
DENT
MOOD
STAR
BIKE
WANT
FIST
FOOT
SILK
FULL
LAMP
ZINC
PORT
LAST
JINX
NORM
SOAP
Short-term memory for nonwords
This is yet another test of short-term memory capacity. Nonwords are sometimes called
nonsense words in that they look like genuine words but are not currently used within the
English language. For example, SALM is a nonword in that you can easily say the word, but
it has no immediate meaning. In this test:
(a) You will see a large number (that is, 20) of four-letter nonwords;
(b) You can try to read through once, taking up to a minute (60 seconds).
(c) You then write down, without looking, as many as you can recall.
Although you will see them arranged in a table of five columns, the order or position of the
words does not matter. Just try to cram as many of them as you can into your memory as you
can, then write them out anyway you feel. The order is not important in this type of free-recall
exercise.
BRAL
LONK
REXT
TRAS
YILD
DREM
MURT
KRUL
SPOD
HARG
POOM
DIRB
FROJ
TRIB
GEAL
NARP
WURT
FRUK
TROR
LART
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APPENDIX 2
HOW TO TEACH CONCEPTS AND SCHEMATA
Helping students form concepts
1
Use the rule-example-rule strategy. This can involve six steps:

Define the concept or rule.

Clarify the terms in the definition.

Give examples to illustrate the key features or characteristics.

Restate the rule.

Provide additional examples.

Have students generate their own examples of the concepts.
2
Numerous and varied positive instances help to illustrate a concept. Through
encountering many instances of concepts, students can form prototypes of these concepts.
Give students some experiences in prototype matching. Think of different concepts, and
then ask students what are the general features of these.
3
Negative instances are crucial in demonstrating what a concept is not. Negative instances,
particularly when they are near misses to the concept, are helpful in defining the
concept’s limits and in preventing overgeneralisation. For example, in learning about
mammals, students can be shown similar-looking non-mammals such as frogs and lizards.
4
Positive and negative instances are more effective when presented simultaneously.
Students tend to learn concepts more easily when they see examples and non-examples
simultaneously rather than sequentially. As far as possible, also try to vary the manner of
presentation. For example, try to use different representations or multi-modal inputs such
as pictures, real objects, models, video, graphs, readings etc.
5
Make concepts as clear as possible and give concrete examples. Spend some time
thinking about the best way to present a new concept, especially an abstract one. Assess
students understanding of a concept by asking them to classify new examples. If you want
students to come up with the concept ‘vehicle,’ ask them to come up with examples of it.
They probably will say ‘car’ and maybe ‘truck’ or ‘bus’. Show them photographs of other
vehicles, such as a sled and a boat, to illustrate the breadth of the concept.
6
Help students relate new concepts to concepts they already know. For example, a student
might know what gold and silver are but not be aware of what platinum and plutonium
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are. In this case, build on their knowledge of gold and silver to teach the concepts of
platinum and plutonium.
7
Encourage students to create concept maps. Getting students to visually map out the
hierarchical organisation of a concept can help them learn it. The hierarchical arranging
can be used to help students understand the concept’s characteristics from more general to
more specific. Hierarchical organisation benefits memory.
8
Ask students to generate hypotheses about a concept. Generating hypotheses encourages
students to think and develop strategies. Work with students on developing the most
efficient strategies for determining what a strategy is.
9
Motivate students to apply the concept to other contexts. Get them to expand their
knowledge of the concept and elaborate on it by assigning further reading about the
concept. Ask the students how the concept can be applied in different contexts. For
example, in learning the concept of fairness, ask students how fairness can make life
smoother, not only at school, but also at play and at home.
Teaching for schemata development
The principles for teaching concepts and schemata will substantially overlap. However, note
the following points:
1
Provide students with practice to organise a body of knowledge about a specific topic.
Assist children in focusing their attention on information that is likely to be important and
to ignore what is probably unimportant.
2
Encourage activities in which students can form schemas about events. These event
schemas are called scripts. For example, what happens when we have an art lesson?
Students put on a smock, get the art paper and paints from the cupboard, clean the brushes
when they are finished, and so on. Therefore a student who comes in late to the art class is
more likely to know much of what to do because he has an art activity script.
3
Provide students with scenarios, short stories and descriptions of events and then get
students to make a list of the facts. Analyse this list observing which information is
actually evident and what information has been ‘filled in’. Discuss how information is
sometimes assumed to be true from prior knowledge.
4
Recognise that the starting point of learning is what students already know.
Brainstorming at the outset of an activity is one strategy. Students understand what they
read, hear and see through the filters of their experiences in their families and cultures.
This needs to be the starting point of instruction.
5
Help students activate their current knowledge. Having relevant knowledge is one thing;
using it in new learning is another. New information needs to be instantiated within
students’ schemata. Teachers need to ensure that students have activated relevant
knowledge and take maximum advantage of the relationship between prior knowledge
and new knowledge. Among useful activities that will assist in encoding and elaboration
processes are:

stimulating students’ recall of related information

providing analogies and ‘schema activation’ experiences

probing students’ intellectual and emotional reactions to materials.
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6
Expose students to examples that vary widely on irrelevant attribute values. This prevents
an irrelevant attribute value from being encoded as part of the schema. Careful selection
of examples and non-examples is crucial.
7
Provide students with concept maps and get them to create their own maps.
8
Use visual examples along with a verbal description to assist with higher-level schema
formation. For example, teaching students to recognise a triangle using both a visual
example and a verbal definition of a triangle, with two examples of a triangle that vary on
irrelevant attribute values, encourages the student to form a schema for triangle that
contains both propositional and perceptual information about triangles. Since the student
can combine propositional and perceptual information, activation of one type of
knowledge is likely to lead to activation of the other type. Thus, if a student sees a new
triangle, the image of a triangle will be activated and this will give access to the label
‘triangle’ and definitional information about triangles.
9
Provide experiences to encourage students to learn when to apply steps in problem
solving rather than purely memorising them. Engage students in cooperative learning
groups or computer-simulation activities to promote knowledge that is easily transferred.
Problem-recognition activities are highly effective and focus students on refinement of
schemas.
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APPENDIX 3
HELPING WITH MEMORY AND METACOGNITION
Helping with memory
1
Meaningful learning is more effective than rote learning. Students will remember
information better over the long term if they understand the information rather than just
passively rehearsing and memorising it. Rehearsal works well for encoding information
into short-term memory, or for storing small parcels of information into the LTM. But to
be meaningful in the long term, it is much less efficient. Encourage students to:

understand information

give information meaning

elaborate on information

personalise information
Associating new information with things that have already been learned promotes
effective storage and more successful retrieval.
2
Assist students in organising what they put in their memory. Students will remember
information better if they organise it hierarchically. Give them some practice on arranging
and reworking material that requires restructuring.
3
Teach students some mnemonic strategies as memory aids for remembering information.
Mnemonic strategies can involve imagery and words.
4

Method of loci: developing images of items to be remembered and mentally storing
them in familiar locations.

Rhymes: examples include, ‘Thirty days hath September, April, June and November
… ‘ (to remember months of the year), and the song to learn the English alphabet.

Acronyms: involves creating a word from the first letters of items to be remembered.

Keyword method: vivid imagery is attached to important words.
Encourage students to distribute their learning over a longer period rather than cramming
for the test at the last minute. Cramming tends to produce short-term memory that is
processed in a shallow rather than deep manner.
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5
Get students to ask themselves questions: ‘What is the meaning of what I just read? Why
is this important?’ This helps the elaboration process; that is, to expand the number of
associations they make with the information they need to retrieve.
6
Help students to take good notes, not just brief, disorganised or verbatim notes. Strategies
include:

Summarising: listen for a few minutes and then write down the main idea.

Outlining: similar to chapters and subheadings in books. This will need to be
modelled.

Concept maps: visually portraying information in a spider-like format.
Helping students use metacognition in the classroom
1
Recognise that strategies are a key aspect of solving problems. Monitor students’
knowledge and awareness of strategies for effective learning outcomes. Many students do
not use or are unaware that good strategies can help them learn. Sometimes, they may get
too fixated on less effective strategies.
2
Model effective strategies for students. While doing this, verbalise the steps in the
strategy.
3
Scaffold students’ initial attempts at using new strategies, gradually phasing out the
scaffolding as students become more proficient. Give students many opportunities to
practice the strategy. As students practice the strategies, provide guidance and support to
the students. Give them feedback until they can use the strategies independently. As part
of your feedback, inform them about where and when the strategies are most useful.
4
Encourage students to monitor the effectiveness of their new strategy in comparison to
the effectiveness of old strategies. This helps the students to see the benefits of using the
new strategy.
5
Remember that it takes students a considerable amount of time to learn how to use an
effective strategy independently. Keep encouraging students to use the strategy over and
over again, until they can use it automatically.
6
Understand that students need to be motivated to use the strategies. They are not always
going to be motivated to use the strategies. Especially important to students’ motivation is
their expectations that the strategies will lead to successful learning outcomes.
7
Encourage students to use multiple strategies: finding out what works well, when and
where. Students should learn a wide variety of strategies, as well as the situations in
which each one is appropriate.
8
Students can often learn effective strategies by working cooperatively with their
classmates. Teach students to use elaborative interrogation, (questions along the line of
‘Why is it that such and such is true?’). This technique appears to promote better recall
for facts and increased integration of ideas, by encouraging students to draw on their prior
knowledge to understand new information.
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APPERNDIX 4
HELPING STUDENTS TRANSFER INFORMATION AND AVOID
THE INERT KNOWLEDGE EFFECT
1
Set expectations: simply alert learners to occasions where they can apply what they are
learning directly, without transformation or adjustment. For example, ‘Remember, you’ll
be asked to use these pronouns correctly in the essay due at the end of the week’.
2
Match: adjust the learning to make it almost the same experience as the ultimate
applications. For example, in sports, play practice games; in drama, full-dress rehearsals.
3
Simulate: use simulation, role-playing, acting out, to approximate the ultimate
applications.
4
Model: show and demonstrate rather than just describe.
5
Use problem-based learning: give students many opportunities for real-world learning.
6
Anticipate applications: ask students to predict possible applications remote from the
learning context: ‘Where might you use this or adapt it?’
7
Generalise concepts: ask students to generalise from their experience to produce widely
applicable principles, rules and ideas.
8
Use analogies: engage students in finding and elaborating an analogy between a topic
under study and something rather different.
9
Use parallel problem solving: engage students in solving problems with parallel structure
in two different areas, to gain an appreciation for the similarities and contrasts. Help them
to see underlying patterns.
10 Metacognitive reflection: prompt and support students in planning, monitoring and
evaluating their own thinking: ‘What went well, what was hard, and how could I handle
what was hard better next time?’
11 Deliberately incorporate occasional obstacles into instructional sequences and lessons.
Teach your students to overcome obstacles. This principle is seen in things such as
computer games in which the going gets harder, once a basic skill is acquired. Everyone
enjoys overcoming a challenge, and there is no reason why learning always has to occur
under perfect conditions. So occasionally do things deliberately to make learning more
challenging. Use encouraging statements such as ‘OK, this is problem. But I know you
can all cope with it’.
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APPENDIX 5: Material as used in undergraduate topics on the Magill Campus
INTRODUCTION TO THE PSYCHOLOGY OF LEARNING
AND BASIC INFORMATION PROCESSING
(This version intended for students in EDUC 1019. October 2004. Dr Greg Yates, UNISA Magill)
How do we learn? What actual process has to occur within the mind? How can we make our
learning more meaningful? What can we do to prevent memory loss? Can the mind ever get
“full up”? Can the mind be trained to learn better or faster? Will my knowledge of psychology
help me to pass university exams? Questions such as these can be addressed in the light of
information covered within this topic.
These pages will provide you with an introductory commentary focussing upon a
specific way of looking at human learning. The perspective adopted by contemporary
psychologists is referred to in general as information processing theory. Your textbook will
take you more deeply into this theory and its educational implications.
We begin this commentary by reviewing several elementary principles of learning.
Some of these will be very familiar to you, but you may never before have appreciated such
principles as formal statements about human learning. In this module, we will quickly review
three topics: Viz, (a) principles of acquisition, (b) principles of memorisation, and (c) principles
of mental overload. Following on from this review we will quickly cover the multi-store theory
of human information processing.
Principles of Acquisition
1. Role of time, effort, and motivation
Human learning is a slow process, typically indexed over months and years rather
than hours or days. The necessary concomitants of the process are (a) time, (b) goalorientation, (c) frequent review, and (d) accumulated successful practice. Notions such as
“instant expertise”, superfast learning, speed-reading, and other magic-like programmes are
seen as faddish quackery, and stand in violation of known principles of human learning.
Most humans will appear to learn specific small-scale behaviours, bits of knowledge,
or low-level objectives within only a few minutes. But this impression of quick learning is
highly deceptive for a variety of reasons. Unless the material is very strongly meaningful, it is
subject to very rapid forgetting. Even if it is retained, it may be difficult to retrieve within an
appropriate context. To acquire any level of domain competency will take a bare minimum of
100 hours practice, and genuine expertise takes perhaps 5 to 10 years of consistent skill
development.
2. Concentration span
Adult humans have a natural attention or concentration span, of around 15 to 20
minutes, before significant levels of mind wandering will occur. Well-motivated learners may
then refocus their mental activities back onto a task, but will still need short breaks in order to
avoid overload effects. If you need to teach anyone some new information, then, it is most
helpful to do so within 15 minutes, or else you will “have lost them”. Note how university
lectures may exceed the span by a factor of three, and of course the lecture room violates the
notion that learners should make active responses. Also note how attending to music may
actually harm attention focussing and resultant learning.
3. Spacing effect/ Distributed practice
To try to learn material within a single block of time often turns out to be far less
effective than if the same duration of time was broken into shorter periods spaced over
several days or weeks. This is especially true when learning new skills. For example, if you
were to learn to drive a car, you would benefit far more from 6 sessions of 20 minutes each
spaced over a week, than from a single block session of 2 hours. In most human learning
situations, blocks of 20-30 minutes are most effective in cost-benefit terms. The concept of
distributed practice is also used to refer to the spacing effect.
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4. Prior knowledge effect
In any one context, the major determinant of knowledge acquisition will be what the
mind already knows. It is far easier to build on existing knowledge (which we call schemata
or schemas) than to learn new material. Input information that cannot be related to one’s
existing knowledge is quickly shed. In sheer power, prior knowledge effects are far stronger
than other variables influencing learning. Prior knowledge effects, for example readily
outweigh effects due to IQ or ‘learning styles’ which have only fairly weak effects on learning.
When your prior knowledge is faulty, however, it creates a huge obstacle, an effect called
interference, which we discuss in more depth within the next section on memory effects
5. Principles of information structuring
The mind does not relate well to unstructured data. We find it extremely taxing to
learn random lists or otherwise cope with unrelated materials. We need to find organisation,
structure, and meaning in what we learn. Very often, meaningfulness stems directly from
prior knowledge, but we also benefit enormously from being shown how to group information,
how to find patterns, how to use orderings, or how to schematise and summarise. In teaching
situations, good teachers often provide overviews of what we are to learn, and these are
referred to as advance organisers.
6. The mind responds well to multi-media (ie multi-modality) input
From time to time you will come across people describing ‘visual learners’, or ‘tactile learners’,
or whatever. The basic experimental data, however, indicates that people are far more similar
than they are different in such ‘learning styles’. In fact, we all are visual learners, not just
some of us. Studies reveal that we all learn well when the inputs we receive are multi-modal
or via several different media. That is, the brain is set up, rather beautifully, as a device which
integrates information from different sources, from different inputs, or from different
modalities. Strong learning occurs when words and images are combined. Claims such that
‘some students learn from words, but others from images’ are incorrect as students learn
most effectively through the contiguity (ie mental associations) of words and images.
Principles of memory retention
1. Recall is hard: recognition is easy
To recognise means to indicate that the material is known, often by signalling in
some coherent way, such as ticking a box on a multiple-choice test. But recall means to
produce, regurgitate, reconstruct, rebuild, etc. Measures of recognition are sensitive in that
they may pick up partial knowledge very easily. Recall measures are far more severe and are
typically insensitive to partial knowledge. Hence, in terms of items “remembered”, a recall test
yields much lower scores. Indeed, part of the art of constructing high quality multiple-choice
tests is to devise items that cannot be answered by simple, direct recognition but which
involve deeper levels of processing.
2. Primacy and recency effects
These two effects are not widely known to lay people. However, they are well
established in the research literature. As a learner, the individual copes inevitably with
sequences of information. The human brain is a type of linear processor, and recall is often
subject to what are called serial position effects. Obviously some information entering the
mind is more important than other information, and this facet will dominate the individual’s
attention. But the sequencing of data also has been shown to influence learning in that the
information that enters the mind first within a sequence is often recalled more readily. This is
called primacy.
The recency effect occurs when the information that enters the mind last has an
advantage in mental processing. For example, you may listen to a lecture and recall the
beginning and end bits, but the middle somehow gets forgotten. Recency effects can be
strong immediately after a learning experience. However, the primacy effect often tends to be
stronger than recency when recall is attempted a period of time after the original experience.
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3. Meaningfulness determines retention level
It is possible to learn meaningless material. Lists of nonsense words, or columns of
random numbers can be committed to memory. But the retention level for this type of rote
learning is very low, possibly around 20% after a day. Rote acquisition results in rapid
forgetting, and this appears to occur within minutes or hours of the original learning. If such
material is to stay within the head, it must be constantly rehearsed or otherwise some sort of
pattern must be perceived. Various mnemonic devices might be activated to try to aid
retention levels, and these are often useful due to the relative meaninglessness of the input.
4. There are different rates of forgetting over time
The rate of forgetting can depend on the type of original learning. For example,
once mastered, motor actions are typically retained virtually for the life of the individual. A fit
senior citizen might be able to ride a bicycle even after not riding on one for 50 years. Also,
the retention level for words is very high in humans, at least within one’s native language. But
moderate levels of decay over time will occur for most intellectual-type skills, especially if the
skill hinges upon detailed knowledge of specific operations, facts, or arbitrary numbers. The
mind will shed isolated facts very rapidly, and we all have great difficulty holding onto arbitrary
items such as telephone numbers, bank account numbers, etc, even when such things might
be seen as important things to try to recall.
5. Memory is a constructive process.
It is tempting to think of the memory as “play-back video recorder”, but this metaphor
is misleading. Memory is highly constructive in that it hinges upon the brain making sense of
partial cues and imprecise information. Memory is dependent upon the focus of attention at
the time of learning. But what two people focus upon, given the same experience, could be
very, very different. Human beings are notoriously unreliable as eyewitnesses to objective
events. Memory for aspects such as time estimates, vocal emphasis, specific words spoken,
causal sequences, and even actor-action associations can vary dramatically between
witnesses. Interpretations vary in accord with prior expectations and other sense-making
strategies.
The act of recall must be seen as a person’s attempt to find meaningful patterns in
what otherwise is unprocessed chaos. Hence, our memories are subject to many different
types of error such as oversimplifications, abbreviations, schematizing, distortions, and
intrusions. An intrusion is where a person recalls some aspect that was not part of the
original learning experience, but which is possible, feasible, likely, or just something that
seems to “make sense”. It should be noted that people generally are not aware when their
memory plays such tricks upon them. We all fall into the trap of believing that our memories
correlate perfectly with reality. Indeed, confidence in memory accuracy has often been shown
to be unrelated to actual objective indices. This has been documented both in laboratory type
experiments and naturalistic studies. Put bluntly, an eyewitness’s confidence level is a flawed
predictor of this person’s actual accuracy level.
At times, memory reports can be biased by factors such as stereotypes, prejudices
and faulty expectations. As intelligent people, and teachers responsible for managing student
welfare, we must be alert to such sources of distortion in what people report. It is important to
realise that human interactions are actually very difficult things to recall accurately.
6. The principle of savings (ie relearning)
Suppose you learnt a foreign language 20 years ago, but appear to have forgotten it
completely. Well, this is unlikely to be the full story. Studies have shown that we can learn
material the second time very rapidly, even when the original learning appears lost and
inaccessible. We know about this principle because of the huge time advantage people have
when they relearn material. In such situations, people are largely unaware of the power of
this effect, and may not realise that substantial savings are being made. All they know is that
they seem to be “picking it up fast”. This effect is quite dramatic when a person visits a
country after not having spoken a specific language there for 20 years and then “picks it up
again” within 3 or 4 days of arriving in the country. Often there can be ‘hidden reasons’ why
some people learn quickly, notably when prior knowledge can serve as an unconscious
source of memory savings.
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7. Memories are subject to interference.
Interference refers to natural memory loss due to experiences either before or after
the original experience. For example, if you learn a list of 20 Spanish vocabulary words and
then a list of 20 French words, there is a very strong chance that your recall of the Spanish
words is inhibited by learning the French list . This is called retroactive interference. But
similarly, your recall of the French list is reduced by the fact that you had earlier learnt the
Spanish list, and this form of interference is called proactive interference. These are
genuine memory effects, not merely the result of fatigue or overload.
In school situations these effects can operate in subtle ways. Although we hold that
one’s prior knowledge generally will help learning, there are times when prior knowledge can
become a clear source of proactive interference. For example, within the Science curriculum
words such as “force”, “matter”, “vector”, “ratio”, “space”, and “living” all have technical
definitions that can be very hard for students to assimilate precisely because such words also
have common sense meanings. (Note, this relates to the notion of misconceptions, which
are often discussed in connection with Science teaching)
Principles of handling overload
At times people find themselves in situations where they are overloaded. The efficiency and
organisation of their actions is threatened simply because there is too much going on within
the mind. Overload is implicated in a multitude of human pathologies and miseries, and is
one reason why people at times appear to act against their goals and self-interest. For
example, under provocation and stress, a teacher may strike a student despite being well
aware that such physical gestures are illegal. The explanation for many forms of violence is
that the actors were overloaded. Some basic principles follow.
1. Learning is an unpleasant experience.
This principle may surprise many of you, as the overall results of learning can often
bring high levels of reward and personal satisfaction. But, seriously look at this more closely.
You may find that positive emotions tend to be correlated with (a) planning and goal setting in
the first place, and then with (b) achieving your goals. For the most part the actual learning is
NOT enjoyable, even though it is helpful to tell yourself otherwise. It is enjoyable to have skill,
to display prowess, and to envisage what one can do. That is, it is pleasurable to perform, or
dream about performance benefits. But the actual process of learning (ie the moment when
learning takes place) is far more likely to be stressful and loaded with emotions of uncertainty
which quickly shift into negative feelings.
One factor implicated in this principle is that humans possess a natural tendency
towards overconfidence in being able to learn. That is, we tend to be optimists and believe
we can perform better than we really can in most learning situations. Similarly, we tend to
underestimate the amount of time and practice it takes to master a new skill. Please realise
these natural tendencies are neither “good” nor “bad”, but they should be recognised by all
instructors, teachers, and parents. The overconfidence effect is especially strong before
people receive objective feedback about performances. Feedback may force a person to
radically alter such assessments.
2. Learning places great stress on mental resources
The learner is vulnerable. She has to maintain adaptive composure in the face of
often unpredictable consequences. It is necessary to mobilise high levels of effort and
vigilance, and so be prepared to respond to input experiences in a variety of ways. The
learner may not know how the world is going to react towards an action she has initiated. She
may not know of the appropriate stimuli to pay attention toward. She may not know how to
match the intensity of her response to the immediate input, or how to pull back if she has
optimistically overstated her current capabilities. In short, mental resources are stretched,
possibly to a point of non-optimal processing we refer to as overload.
3. Every person has to develop coping strategies.
We all develop ways of coping with stressful learning contexts. We can do things
such as pay attention, work slowly, increase the level of practice, reread the materials, or find
a good teacher. Our coping strategies must apply to two fundamental aspects: (a)
increasing our opportunities to learn, and (b) managing our emotional responses. It is
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necessary for every learner to develop a wide range of possible coping methods. Failure to
learn coping skills renders the learner passive in the face of inevitable overload. Incidentally, it
has been found that there is some level of consistency in the way individuals will tend to
respond to quite different sources of stress.
4. Overload factors can be identified
There is no one single cause of overload, and some people will cope with such stress
better than others. However, at the level of the individual learner, it is possible to specify that
overload can be linked to ANY ONE of the following:
(a) Low levels of prior knowledge.
(b) Deficient use of mental strategies.
(c) Unrealistic expectations (eg overconfidence, or goals set too high, or are immutable).
(d) Poor instruction, inadequate teaching, or failure to engage with learning material.
(e) Unfavourable learning conditions (eg study facilities, presence of distractions, etc).
(f)
Assessment apprehension (eg unfair tests, competition, emotional/motivational
problems).
Multi-store theory
In this section we introduce the major theory of memory that has held sway over the past 30
years. This theory accounts for many (but not all) of the principles listed above, and is widely
accepted within cognitive psychology today. The theory says that the human memory system
consists of at least three levels of memory, which can loosely be called stores. They are the
iconic store, the short term memory, and long term memory. There has been debate within
academic psychology as to whether these stores should be seen are separate entities, or part
of a single process, but for purposes of this essay we will treat them separately.
1. Iconic Memory
This form of memory is also known as sensory memory, and as ultra-short term
memory. Iconic memory relates to input experiences and perceptions within a sensory
modality. Within the visual system, for example, experiments reveal that a large amount of
data can be stored for around a second. In a laboratory study, for example, you might be
asked to look at a screen where an image appears for one twentieth of a second. Your visual
system takes it in and then has perhaps up to a second to “read” from the image until it fades
from your mind. The auditory system appears to have longer duration sensory images
possibly 2 or 3 seconds duration.
2. Short term memory (STM) or working memory (WM)
For our purposes we will regard these two terms as virtually synonymous. However,
many psychologists now avoid the term “short term memory” and prefer “working
memory”. This is to signify that this form of memory represents the amount of space available
for current mental processing. Metaphorically, it is the working area or the workbench of the
mind. But it is a system that has to stay active, lest items drop off the workbench. Indeed, it is
a limited capacity workbench.
The WM system has two basic problems. Firstly, the amount of information it easily
holds is limited to only a few items at a time, and secondly, information is lost quickly from the
system. How much information can be held? Answer: only around four items if they are
unfamiliar ones, but around eight if they are relatively familiar items such as numbers, letters
or simple words. How long does information stay within the system? Answer: somewhere
between five and twenty seconds.
For example, you find a telephone number in the White Pages. But someone speaks
to you in between reading the number and being able to dial. This interaction destroys your
mental rehearsal and your ability to get the number correct is lost after five seconds. And after
a 20-second interruption, you may recall not even one of the original numbers. To retain such
information you need to rehearse in order to maintain it within an active buffer. This is called
the articulatory loop. Laboratory studies have shown that humans posses a natural
articulatory loop of one and a half seconds. That is, it is “easy” to retain as much as you can
say to yourself within 1 ½ seconds, and do so indefinitely. The other way to retain information
is to transfer it to long term memory.
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3. Long term memory (LTM)
Metaphorically, this is the archival library store where data are filed for retrieval at a
later time. It is held that this system holds information in permanent storage form. The term
permanent memory is favoured by some writers. Certainly, LTM storage will be affected
dramatically by disease or brain trauma, but this system is not subject to the same decay
processes that beset one’s STM efficiency. In short, the passage of time alone does not dim
this system.
There are still many problems with storage within the LTM. One major issue is that
the system does not possess anything like the FTP download capability of a computer. There
is no human equivalent to transferring information from one’s floppy disk to one’s hard drive. It
is often noted that the major cause of apparent forgetting in humans is actually forgetting to
learn properly in the first place. As we noted earlier, humans tend to be overly confident in
their learning ability, and underestimate the time and effort required to achieve skills.
It is assumed that there are no natural spatial limits on LTM, ie we have yet to come
across any healthy human being whose LTM capacity has reached a top limit. Indeed, the
principle of prior knowledge even suggests the opposite, ie the more knowledge you have the
easier it becomes to learn even more. In the course of aging, the mind may lose often certain
agilities, notably those to do with fast accessing, but the volume of material stored primarily is
not affected.
The major problems of the LTM system hinge around three aspects: Viz, (a) the
sheer difficulty of loading information into the system, (b) the use of efficient encoding
strategies which enable inputs to be fully processed and so interpreted in such a way as to
relate to what the head already knows, and (c) the use of efficient retrieval strategies which
enable recovery of the stored data. Each of these three can involve high levels of conscious
effort and thinking, consistent with what is referred to as the “good information processing”
model of human cognition. Within contemporary psychology we find a large number of
concepts used to describe how the LTM operates. These concepts and their research findings
are treated in depth in chapter 6 of your textbook
4. The nature of learning: The need for strategies
The multi-store theory portraits learning in terms of transfer of information across the
memory banks within the mind. Whereas the sensory store appears to take in a good deal,
the process of attention ensures that only a few items are transferred into the STM. In this
sense, attention operates as a filter keeping a high level of information out of consciousness.
Within the STM information may be held for brief periods, but unless immediately refreshed it
fades quickly, and is lost forever. On the other hand, the learner can use strategies to moves
the data into LTM. This entails some form of active responding in that the mind has to “do
something with this stuff before it disappears”. But what?
The mind could try a bit of CRIME: ie, chunking, rehearsal, imagery, mnemonics,
and elaboration. Chunking is involved whenever the mind groups items together that did not
necessarily come together in direct experience. Chunking can mean to group, sort, organise
or classify. The central idea is that the mind is able to reduce mental load by arranging related
items into a meaningful pattern. These patterns stem from prior knowledge.
Rehearsal means literally to repeat oneself, ie to refresh the data. This can be done
subvocally, within a verbal buffer. When one rehearses aloud it is called recitation. The mind
is working on the theory that repetition will make the memory trace more permanent. When
this practice is applied to data that cannot be linked to existing knowledge we may use the
term “rote learning”. In the early childhood years this strategy generally takes the form basic
labelling (ie naming whatever stimulus is present within immediate view). In later childhood,
rehearsals may take the form of a list that can be quietly repeated to oneself. By adolescence,
rehearsal can take the form of a cumulative rehearsal-fast finish, a much more
sophisticated form.
Imagery is another way to respond to an input experience, which is described in
depth in chapter 3. Literally, this means to “picture it” within the mind, a skill that some people
report they use very naturally. We encounter some people, for example who claim that they
even recall telephone numbers not by rehearsing them subvocally, but by imagining what the
numbers actually look like written down. This writer once worked with a person who claimed
to recall telephone numbers by “projecting” the number onto a blank wall.
Mnemonics is really a general word that can be used to refer to any memory device,
but we may tend to use the term more readily to refer to temporary tricks such as ROYGBIV,
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or Every Boy Deserves Fruit, or even CRIME. As a student you will know of specific
mnemonics which relate to problems within your areas of knowledge. Such tricks exist, for
example, for being able to memorise the value of pi, the periodic tables, the positions of
planets, the nerves of the body, etc.
Elaboration means to process information by adding to it in meaningful ways, ie to
use the input information as a trigger for bringing other data into consciousness, and so fusing
the new with the old to create a more durable and accessible memory trace. Let us try to
illustrate this: (a) you have to learn a number, 7812815, and realise that you were born in
1978, in the month of December, so you “pretend” that it was quarter past 8 in the morning,
and (b) you read the word “Taipan” in the TV programme, and your mind crosses back to
when you were in Queensland and the farmers told you to watch out for deadly snakes hiding
in the cane fields. In both instances your memory for the inputs (the number and the TV
programme) is enhanced simply because you elaborated on them at the moment of initial
exposure. Such elaborations may be either involuntary, or quite deliberately employed as a
conscious learning strategy in which prior knowledge associations are used to advantage.
(Note: virtually all the memory training schemes or books base themselves around the
principles of elaboration)
5. Final note
This concludes our introductory tutorial on information processing theory. The story will be
taken up in depth in McInerney and McInerney. Also, you may find it helpful to also consult
other texts that are designed for the undergraduate student. Indeed, any basic textbook within
psychology or educational psychology will have at least one chapter on basic information
processing and cognitive learning theory, and most texts generally have good use of visuals
such as flow diagrams.
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REFERENCES
The following were cited in this Study guide, but are not part of the reading program, and are
not cited within the text or readings used within the current course.
Ceci, S. & Bruck, M. (1995). Jeopardy in the courtroom: a scientific analysis of children’s testimony.
Washington: American Psychological Association.
Clark, R. E. (2001). Learning from media: Arguments, evidence, and analysis. Greenwich, CT:
Information Age Publishing.
Dweck, C. (2000). Self-theories: their role in motivation, personality, and development. Phil, PA:
Psychology Press.
Howe, M. J.A. (1990). The origins of exceptional abilities. Oxford: Blackwell.
Howe, M.J.A. (1999). Genius explained. Cambridge: Cambridge University Press
Mayer, R E (2001). Multi-media learning. Cambridge: Cambridge University Press.
McInerney, D. M. & McInerney, V. (2002). Educational psychology: Constructing learning, 3rd
Edition. Frenchs Forest, NSW: Prentice Hall.
Seligman, M E P (1990). Learned optimism. New York: Alfred Knopf. (Published within Australia by
Random House).
Sternberg, R J (2003) Why smart people can be so stupid. New Haven: Yale University Press.
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