Handbook of
Research on Educational Communications and Technology.
Chapter 5.
Cognitive Perspectives in Psychology.
William Winn,
412 Miller,
(206) 543-1847
billwinn@u.washington.edu
Running Head: COGNITIVE
PERSPECTIVES.
I. INTRODUCTION
Caveat Lector.
This is a
revision of the chapter on the same topic that appeared in the first edition of
the Handbook, published in 1995. In the intervening years, a great many
changes have occurred in cognitive theory, and its perceived relevance to
Education has been challenged. As a participant in, and
This chapter
consists of the same content, updated and slightly abbreviated, that was in the
first edition of the Handbook, focusing research in cognitive theory up
until the mid 'nineties. I have added sections that present and discuss the
reasons for current dissatisfaction, among some educators, with these
traditional views of cognition. And I have added sections that describe recent
views, particularly of mental representation and cognitive processing, which
are different from the more traditional views. There are three reasons for my
decision. First, the reader of a Handbook like this needs to consider the
historical context within which current theory has developed, even when that
theory has emerged from the rejection, not the extension, of some earlier
ideas. Second, recent collaborations with colleagues in Cognitive Psychology,
Computer Science and Cognitive Neuroscience have confirmed for me that these
disciplines, which I remain convinced are centrally relevant to research in Educational
Technology, still operate largely within the more traditional view of
cognition. Third, a great deal of the research and of the practice of
Educational Technology continues to operate within the traditional framework,
and continues to benefit from it. I also note that other chapters in the Handbook
deal more thoroughly, and more ably, with the newer views. So, if readers find
this chapter somewhat "old fashioned" in places, I am nonetheless
confident that within the view of our discipline offered by the Handbook
in its entirety, this chapter still has
an important place.
Basic Issues.
Over the last few years, education scholars have grown increasingly dissatisfied with the "standard view" of cognitive theory. The "standard view" is that people represent information in their minds as single or aggregated sets of symbols, and that cognitive activity consists of operating on these symbols by applying to them learned plans, or algorithms. This view reflects the analogy that the brain works in the same way as a computer (Boden, 1988; Johnson-Laird, 1988), a view that inspired, and was perpetuated by, several decades of research and development in artificial intelligence.
This
computational view of cognition is based on several assumptions: 1) That there
is some direct relationship, or "mapping", between internal
representations and the world outside, and that this mapping includes
representations that are analogous to objects and events in the real world,
i.e. mental images look to the mind's eye like the perceived phenomena from
which they were first created (Kosslyn, 1985). 2) There is both a physical and
phenomenological separation between the mental and the physical world, i.e.
perception of the world translates objects and events into representations that
mental operations can work on, and the altered
representations are in turn translated into behaviors and their outcomes that
are observable in the external world. 3) This separation applies to the timing
as well as to the location of cognitive action.
Some scholars' dissatisfaction with the computational view of cognition arose from evidence that suggested these assumptions might be wrong. 1) Evidence from Biology and the Neurosciences, which we will examine in more detail later, shows that the central nervous system is informationally closed, and that cognitive activity is prompted by perturbations in the environment that are not represented in any analogous way in the mind (Maturana & Varela, 1980, 1992; Bickhard, 2000). 2) There is evidence that cognitive activity is not separate from the context in which it occurs (Lave, 1988; Suchman, 1987). Thinking, learning and acting are embedded in an environment to which we are tightly and dynamically coupled and which has a profound influence on what we think and do. What is more, evidence from the study of how we use language (Lakoff & Johnson, 1980) and our bodies (Clark, 1997; Varela et al., 1991) suggests that cognitive activity extends beyond our brains to the rest of our bodies, not just to the environment. Many metaphorical expressions in our language make reference to our bodies. We "have a hand" in an activity. We "look up to" someone. Our gestures help us think (see the review by Roth [2002]) and the proprioceptive feedback we get from immediate interaction with the environment is an important part of thinking and learning. 3) Scholars have argued that cognitive activity results from the dynamic interaction between two complex systems – a person and the environment. Indeed, it is sometimes useful to think of the two (person and environment) acting as one tightly-coupled system rather than as two interacting but separate entities (Beer, 1995; Roth, 1999). The dynamics of the activity are crucial to an understanding of cognitive processes, which can be described using the tools of Dynamical System Theory (Port & Van Gelder, 1995a). 4) Finally, scholars have made persuasive arguments that the value of the knowledge we build lies not in its closeness to any ideal or correct understanding of the external world, but to how it suits our own individual needs and guides our own individual actions. This pragmatic view of what is called "Constructivism" finds its clearest expression in accounts of individual (Winn & Windschitl, 2002) and situated (Lave & Wenger, 1991) problem solving. (The danger that this way of thinking leads inevitably to solipsism is effectively dispelled by Maturana & Varela [1992], pp. 133-137).
The constructivists were among the first to propose an alternative conceptual framework to the computational view of cognition. For educational technologists, the issues involved are clearly laid out by Duffy & Jonassen (1992) & Duffy, Lowyck & Jonassen (1993). Applications of constructivist ideas to learning that is supported by technology are provided by many authors, including Cognition and Technology Group at Vanderbilt (2000), Jonassen (2000), and White & Frederiksen (1998). Briefly, understanding is constructed by students, not received in messages from the outside simply to be encoded, remembered and recalled. How knowledge is constructed and with what results depends far more on a student's history of adaptations to the environment (Maturana & Varela, 1989) than on particular environmental events. Therefore, learning is best explained in terms of the student's evolved understanding and valued on that criterion rather than on the basis of objective tests.
However, constructivism, in its most radical forms, has been challenged in its turn for being unscientific (Sokal & Briquemont, 1998), even anti-intellectual (Cromer, 1997; Dawkins, 1997). There is indeed an attitude of "anything goes" in some post-modern educational research. If you start from the premise that anything that the student constructs must be valued, then conceptions of how the world works may be created that are so egregious as to do the student intellectual harm. It appears that, for some, the move away from the computational view of cognition has also been away from learning and cognition, as the central focus of educational research, in any form. This is understandable. If the knowledge we construct depends almost entirely on our unique personal experiences with the environment, then it is natural to try to explain learning and to prescribe learning strategies by focusing on the environmental factors that influence learning, rather than on the mechanisms of learning themselves. Skimming the tables of contents of educational books and journals over the last fifteen years will show a decline in the number of articles devoted to the mechanisms of learning and an increase in the number devoted to environmental factors, such as poverty, ethnicity, the quality of schools, and so on. This research has made an important contribution to our understanding and to the practice of education. However, the neglect of cognition has left a gap at the core that must be filled. This need has been recognized, to some extent, in a recent report from the National Research Council (Shavelson & Towne, 2002), which argues that education must be based on good science.
There are, of course, frameworks other than constructivism, that are more centrally focused on cognition, within which to study and describe learning. These are becoming visible now in the literature. What is more, some provide persuasive new accounts of mental representation and cognitive processes. Our conceptual frameworks for research in educational technology must make room for these accounts. For convenience, I will place them into four categories: systems theoretical frameworks, biological frameworks, approaches based on cognitive neuroscience, and neural networks. Of course, the distinctions among these categories often blur. For example, neuroscientists sometimes use system theory to describe cognition.
System theory. System theory has served educational technology for a long time and in different guises (Heinich, 1970; Pask, 1975, 1984; Scott, 2001; Winn, 1975). It offers a way to describe learning that is more focused on cognition while avoiding some of the problems confronting those seeking biological or neurological accounts that, until recently, appeared largely intractable. A system-theoretic view of cognition is based on the assumption that both learners and learning environments are complex collections of interacting variables. The learner and the environment have mutual influences on each other. The interactions are dynamic, and do not stand still for scrutiny by researchers. And to complicate matters, the interactions are often nonlinear This means that effects cannot be described by simple addition of causes, since what is cause and what is effect is not always clear. Changes in learners and their environments can be expressed by applying the mathematical techniques of Dynamics (see relevant chapters in Port & Van Gelder, 1995b). In practice, the systems of differential equations that describe these interactions are often unsolvable. However, graphical methods (Abraham & Shaw, 1992) provide techniques for side-stepping the calculus and allow researchers to gain considerable insight about these interacting systems. The accounts of cognition that arise from Dynamical System Theory are still abstractions from direct accounts, such as those from biology or cognitive neuroscience. However, they are closer to a description of systemic changes in understanding and in the processes that bring understanding about than accounts based on the computational or constructivist views.
Biological frameworks. Thinking about cognition from the standpoint of Biology reminds us that we are, after all, living beings who obey biological laws and operate through biological processes. I know this position is offensive to some. However, I find the arguments on this point, put forward by Dawkins (1989), Dennett (1995) and Pinker (1997), among others, to be compelling and highly relevant. This approach to our topic raises three important points. First, what we call "mind" is an emergent property of our physical brains, not something that has divine or magical provenance and properties. This opens the way for making a strong case that neuroscience is relevant to education. Second, cognition is embodied in our physical forms (Varela et al., 1991; Clark, 1997; Kelso, 1999). This implies two further things. What we can perceive directly about the environment, without the assistance of devices that augment our perceptual capacities, and therefore the understanding we can construct directly from it, are very limited – to visible light, to a small range of audio frequencies, and so on (Nagel, 1974; Winn & Windschitl, 2001b). Also, we use our bodies as tools for thinking – from counting on our fingers to using bodily movement in virtual environments to help us solve problems (Dede et al., 1996; Gabert, 2001). Third, and perhaps most important, the biological view helps us think of learning as adaptation to an environment (Holland, 1992, 1995). Technology has advanced to the point where we can construct complete environments within which students can learn. This important idea is developed later.
Cognitive neuroscience. The human brain has been called the most complex object in the universe. Only recently have we been able to announce, with any confidence, that some day we will understand how it works (although Pinker [1997] holds a less optimistic position). In the meantime, we are getting closer to the point where we will be able to explain, in general terms, how learning takes place. Such phenomena as memory (Baddeley, 2000; Tulving, 2000), imagery (Farah, 2001; Kosslyn & Thompson., 2000), vision (Hubel, 2000), implicit learning (Knowlton & Squire, 1996; Liu, 2002), and many aspects of language (Berninger & Richards, 2002) are now routinely discussed in terms of neurological processes. While much of the research in cognitive neuroscience is based on clinical work, meaning that data come from people with abnormal or damaged brains, recent developments in non-intrusive brain-monitoring technologies, such as fMRI, are beginning to produce data from normal brains. This recent work is relevant to cognitive theory in two ways. First, it lets us reject, once and for all, the unfounded and often rather odd views about the brain that have found their way into educational literature and practice. For example, there is no evidence from neuroscience that some people are "right brained", and some "left brained". Nor is there neurological evidence for the existence of learning styles (Berninger & Richards, 2002). These may be metaphors for observed human behaviors. But they are erroneously attributed to basic neural mechanisms. Second, research in cognitive neuroscience provides credible and empirically-validated accounts of how cognition, and the behavior it engenders, change as a result of a person's interaction with the environment. Learning causes detectable physical changes to the central nervous system that result from adaptation to the environment, and that change the ways in which we adapt to it in the future (Markowitsch, 2000. See also Cisek [1999] pp. 132-134 for an account of how the brain exerts control over a person's state in their environment).
Neural networks. This fourth
framework within which to think about cognition crosses several of these
categories. Neural networks are implemented as computer programs which,
like people, can learn through iterative adaptation to input and can solve
novel problems by recognizing their similarity to problems they already know
how to solve. Neural network theory takes its primary metaphor from
neuroscience – that even the most complex cognitive
activity is an emergent property of the coordinated activation of networks of
many atomic units (neurons) that can exist in only two states, on or off. (See McClelland & Rumelhart [1986,
1988], Rumelhart & McClelland [1986] for
conceptual and technical accounts.) The complexity and dynamics of
networks reflects many of the characteristics of system theory, and research
into networks borrows from systems analysis techniques. Neural networks also
transcend the representation-computation distinction, which is fundamental to
some views of cognition and to which we return later. Networks represent
information through the way their units are connected. But the changes in these
connections are themselves the processes by which learning takes place. What is
known and the ways knowledge is changed are one and the same. Neural networks
have been most successful at emulating low-level cognitive processes, such as
letter and word recognition. Higher level operations require more abstract,
more symbolic, modes of operation, and symbols are now thought to be compatible
with network architectures (Holyoak & Hummel,
2000).
What has
all this go to do with cognition and, particularly, with its relationship to
educational technology? The rest of this chapter seeks answers to this
question. It begins with a brief history of the precursors of cognitive theory
and a short account of cognitive theory's ascendancy. It then presents examples
of research and theory from the traditional cognitive perspective. This view is
still quite pervasive, and the most recent research suggests that it might not
be as far off the mark as we recently suspected. The chapter therefore examines
traditional research on mental representation and mental processes. In each of
these two sections, it presents the major findings from research and the key
objections to the traditional tenets of cognitive theory. It then discusses
recent alternative views, based roughly on the four frameworks we have just
examined. The chapter concludes by looking more closely at how traditional and
more recent views of cognition can inform and guide educational technology
research and practice.
II. HISTORICAL OVERVIEW.
Most readers will already know that cognitive theory came into
its own as an extension of (some would say a replacement of) behavioral theory.
However, many of the tenets of cognitive theory are not new and date back to
the very beginnings of psychology as an autonomous discipline in the late
nineteenth century. We therefore begin with a brief discussion of the new
science of mind and of Gestalt theory before turning to the story of cognitive
psychology's reaction to behaviorism.
The beginnings: A science of mind.
One of the major forces that helped Psychology emerge as a
discipline distinct from Philosophy, at the end of the nineteenth century, was
the work of the German psychologist, Wundt (Boring,
1950). Wundt made two significant contributions, one
conceptual and the other methodological. First, he clarified the boundaries of
the new discipline. Psychology was the study of the inner world, not the outer
world, which was the domain of Physics. And the study of the inner world was to
be the study of thought, or mind, not of the physical body, which was the
domain of Physiology. Wundt's methodological
contribution was the development of introspection as a means for studying the
mind. Physics and Physiology deal with phenomena that are objectively present
and therefore directly observable and measurable. Thought is both highly
subjective and intangible. Therefore, Wundt proposed,
the only access to it was through the direct examination of one's own thoughts
through introspection. Wundt developed a program of
research that extended over many decades and attracted adherents from
laboratories in many countries. Typically, his experimental tasks were simple
-- pressing buttons, watching displays and the like. The data of greatest
interest were the descriptions his subjects gave of what they were thinking as
they performed the tasks.
On the face of it, Wundt's approach
was very sensible. You learn best about things by studying them directly. The
only direct route to thought is via a subject's description of his own
thinking. There is a problem, however. Introspection lacks objectivity. Does the act of thinking about thinking interfere
with and change the thinking that one is interested in studying? Perhaps. But the same general access route to cognitive
processes is used today in developing think-aloud protocols (Ericsson &
Simon, 1984), obtained while subjects perform natural or experimental tasks.
The method is respected, judged to be valid if properly applied, and essential
to the study of thought and behavior in the real world or in simulations of it.
Gestalt psychology
The word "Gestalt" is a German noun, meaning both
"shape" or "form" and "entity" or
"individual" (Hartmann, 1935).
Gestalt psychology is the study of how people see and understand the
relation of the whole to the parts that make it up. Unlike much of science,
which analyzes wholes to seek explanations about how they work in their parts, gestalt psychology looks at the parts in terms of the wholes
that contain them. Thus, wholes are greater than the sum of their parts, and
the nature of parts is determined by the wholes to which they belong Wertheimer (1924). Gestalt psychologists therefore
account for behavior in terms of complete phenomena, which they explain as
arising from such mechanisms as "insight". We see our world in large
phenomenological units and act accordingly.
One of the best illustrations of the whole being different from
the sum of the parts is provided in a musical example. If a melody is played on
an instrument, it may be learned and later recognized. If the melody is played again, but this time
in another key, it is still recognizable.
However, if the same notes are played in a different sequence, the
listener will not detect any similarity between the first and the second
melody. Based on the ability of a person
to recognize and even reproduce a melody (whole Gestalt) in a key different from
the original one, and on their inability to recognize the individual notes (parts)
in a different sequence, it is clear that, "The totals themselves, then,
must be different entities than the sums of their parts. In other words, the 'Gestaltqualität'
('form quality') or whole has been reproduced: the elements or parts have
not." (Hartmann, 1935).
The central tenet of Gestalt theory -- that our perception and
understanding of objects and events in the world depends upon the appearance
and actions of whole objects not of their individual parts -- has had some
influence on research in Educational Technology. The key to that influence are
the well-known Gestalt laws of perceptual organization, codified by Wertheimer
(1938). These include the principles of "good figure", "figure-ground
separation" and "continuity". These laws formed the basis for a
considerable number of
message design principles (Fleming & Levie,
1978, 1993), in which Gestalt theory about how we perceive and organize
information that we see is used in prescriptive recommendations about how to present
information on the page or screen. A similar approach to what we hear is taken
by Hereford & Winn, (1994).
More broadly, the influence of Gestalt theory is evident in much
of what has been written about visual literacy. In this regard, Arnheim's book Visual Thinking (1969) is a key work. It was
widely read and cited by scholars of visual literacy and proved influential in
the development of that field.
Finally, it is important to note a more recent renewal of
interest in Gestalt theory (Henle, 1987; Epstein,
1988). The Gestalt psychologists provided little empirical evidence for their
laws of perceptual organization beyond everyday experience of their effects. Using
newer techniques that allow experimental study of perceptual organization, researchers
(Pomerantz, 1986; Rock, 1986) have provided
explanations for how Gestalt principles work. The effects of such stimulus
features as symmetry on perceptual organization have been explained in terms of
the "emergent properties" (Rock, 1986) of what we see in the world
around us. We see a triangle as a triangle not as three lines and three angles.
This experience arises from the closeness (indeed the connection) of the ends
of the three sides of the triangle. Emergent properties are the same as the Gestaltist's "whole" that has features all its
own that are, indeed, greater than the sum of the parts.
The rise of cognitive psychology
Behavioral theory is described in detail elsewhere in this
Handbook. Suffice it to say here that behaviorism embodies two of the key
principles of positivism -- that our knowledge of the
world can only evolve from the observation of objective facts and phenomena;
and that theory can only be built by applying this observation in experiments
where the experimenter manipulates only one or two factors at a time. The first
of these principles therefore banned from behavioral psychology unobservable
mental states, images, insights and Gestalts. The second principle banned
research methods that involved the subjective techniques of introspection,
phenomenology and the drawing of inferences from observation rather than from
objective measurement. Ryle's (1949) relegation of the concept of "mind" to the status
of "the ghost in the machine", both unbidden and unnecessary for a
scientific account of human activity, captures the behaviorist ethos
exceptionally well.
Behaviorism's reaction against the suspect subjectivity of
introspection and the non-experimental methods of Gestalt psychology was necessary
at the time if Psychology was to become a scientific discipline. However, the
imposition of the rigid standards of objectivism and positivism excluded from
accounts of human behavior many of those experiences with which we are
extremely familiar. We all experience mental images,
feelings, insight, and a whole host of other unobservable and unmeasurable phenomena. To deny their importance is
to deny much of what it means to be human (Searle, 1992). Cognitive psychology
has been somewhat cautious in acknowledging the ability or even the need to
study such phenomena, often dismissing them as "folk psychology"
(Bruner, 1990). Only recently, this time as a reaction against the inadequacies
of cognitive rather than behavioral theory, do we find serious consideration of
subjective experiences. (These are discussed in Bruner, 1991, Clancey, 1993, Dennett, 1991, Edelman, 1992, Pinker, 1997, Searle,
1992, Varela, Thompson & Rosch, 1991, among
others. They are also addressed elsewhere in this Handbook.)
Cognitive psychology's reaction against the inability of
behaviorism to account for much human activity arose mainly from a concern that
the link between a stimulus and a response was not straightforward, that there
were mechanisms that intervened to reduce the predictability of a response to a
given stimulus, and that stimulus-response accounts of complex behavior unique
to humans, like the acquisition and use of language, were extremely complex and
contrived. (Chomsky's [1964] review of Skinner's [1957] S-R
account of language acquisition is a classic example of this point of view and
is still well worth reading.) Cognitive psychology therefore shifted
focus to mental processes that operate on stimuli presented to the perceptual
and cognitive systems, and which usually contribute significantly to whether or
not a response is made, when it is made, and what it is. Whereas behaviorists
claim that such processes cannot be studied because they are not directly
observable and measurable, cognitive psychologists claim that they must be
studied because they alone can explain how people think and act the way they
do. Somewhat ironically, cognitive neuroscience reveals that the mechanisms
that intervene between stimulus and response are, after all, chains of internal
stimuli and responses, of neurons activating and changing other neurons, though
in very complex sequences and networks. Markowitsh
(2000) discusses some of these topics, mentioning that the successful
acquisition of information is accompanied by changes in neuronal morphology and
long-term potentiation of inter-neuron connections.
Let me give two examples of the transition from behavioral to
cognitive theory. The first concerns memory, the second mental imagery.
Behavioral accounts of how we remember lists of items are usually associationist. Memory in such cases is accomplished by
learning S-R associations among pairs of items in a set and is improved through
practice (Gagné,
1965; Underwood, 1964). However, we now know that this is not the whole story
and that mechanisms intervene between the stimulus and the response that affect
how well we remember. The first of these is the collapsing of items to be
remembered into a single "chunk". Chunking is imposed by the limits
of short-term memory to roughly seven items (Miller, 1956). Without chunking,
we would never be able to remember more than seven things at once. When we have
to remember more than this limited number of items, we tend to learn them in
groups that are manageable in short-term memory, and then to store each group
as a single unit. At recall, we "unpack" (
A second mechanism that intervenes between a stimulus and
response to promote memory for items is interactive mental imagery. When people
are asked to remember pairs of items and recall is cued with one item of the
pair, performance is improved if they form a mental image in which the two
items appear to interact (Paivio, 1971, 1983; Bower,
1970). For example, it is easier for you to remember the pair "Whale - Cigar"
if you imagine a whale smoking a cigar. The use of interactive imagery to
facilitate memory has been developed into a sophisticated instructional
technique by Levin and his colleagues (Morrison & Levin, 1987; Peters &
Levin, 1986). The considerable literature on the role of imagery in
paired-associate and other kinds of learning is summarized by Paivio (1971, 1983; Clark & Paivio,
1991).
The importance of these memory mechanisms to the development of
cognitive psychology is that, once understood, they make it very clear that a
person's ability to remember items is improved if the items are meaningfully
related to each other or to the person's existing knowledge. The key word here
is "meaningful". For now, we shall simply assert that what is
meaningful to a person is determined by what they can remember of what they
have already learned. This implies a circular relationship among learning,
meaning and memory -- that what we learn is affected by how meaningful it is,
that meaning is determined by what we remember, and that memory is affected by
what we learn. However, this circle is not a vicious one. The reciprocal
relationship between learning and memory, between environment and knowledge, is
the driving force behind established theories of cognitive development (Piaget,
1968) and of cognition generally (Neisser, 1976). It
is also worth noting that Ausubel's (1963) important
book on meaningful verbal learning proposed that learning is most effective
when memory structures appropriate to what is about to be learned are created
or activated through advance organizers. More generally, then, cognitive
psychology is concerned with meaning, or semantics, while behavioral psychology
is not.
The most recent research suggests that the activities that
connect memory and the environment are not circular but concurrent.
Mental imagery provides another interesting example of the
differences between behavioral and cognitive psychology. Imagery was so far
beyond the behaviorist pale that one key article that re-introduced the topic
was subtitled, "The return of the ostracized". Images were, of
course, central to Gestalt theory, as we have seen. But
because they could not be observed, and because the only route to them was
through introspection and self-report, they had no place in behavioral theory.
Yet we can all, to some degree, conjure up mental images. We can
also deliberately manipulate them. Kosslyn, Ball
& Reiser (1978) trained their subjects to
"zoom" in and out of images of familiar objects and found that the
"distance" between the subject and the imagined object constrained
the subject's ability to describe the object. To discover the number of claws
on an imaged cat, for example, the subject had to move closer to it in the
mind's eye.
This ability to manipulate images is useful in some kinds of
learning. The method of "Loci" (Kosslyn,
1985; Yates, 1966), for example, requires a person to create a mental image of
a familiar place in the mind's eye and to place in that location images of
objects that are to be remembered. Recall consists of mentally walking through
the place and describing the objects you find. The effectiveness of this
technique, which was known to the orators of ancient
Mental imagery will be discussed in more detail later. For now,
we will draw attention to two methodological issues that are raised by its
study. First, some studies of imagery are symptomatic of a conservative color
to some cognitive research. As
The second methodological issue is exemplified by Kosslyn's (1985) use of introspection and self-report by
subjects to obtain his data on mental images. The scientific tradition that
established the methodology of behavioral psychology considered subjective data
to be biased, tainted and therefore unreliable. This precept has carried over
into the mainstream of cognitive research. Yet, in his invited address to the
1976 AERA conference, the sociologist Uri Bronfenbrenner
(1976) expressed surprise, indeed dismay, that
educational researchers did not ask subjects their opinions about the
experimental tasks they carry out, nor about whether they performed the tasks
as instructed or in some other way. Certainly, this stricture has eased in much
of the educational research that has been conducted since 1976, and
non-experimental methodology, ranging from ethnography to participant
observation to a variety of phenomenologically-based
approaches to inquiry are the norm for certain types of educational research
(see, for example, the many articles that appeared in the mid-'eighties, among
them Baker, 1984; Eisner, 1984; Howe, 1983; Phillips, 1983). Nonetheless,
strict cognitive psychology has tended, even recently, to adhere to
experimental methodology, based on positivism, which makes research such as Kosslyn's on imagery somewhat suspect to some.
Cognitive science
Inevitably, cognitive psychology has come face to face with the
computer. This is not merely a result of the times in which the discipline has
developed, but emerges from the intractability of many of the problems
cognitive psychologists seek to solve. The necessity for cognitive researchers
to build theory by inference rather than from direct measurement has always
been problematic.
One way around this problem is to build theoretical models of
cognitive activity, to write computer simulations that predict what behaviors
are likely to occur if the model is an accurate instantiation of cognitive activity, and to compare the behavior predicted by the model
-- the output from the program -- to the behavior observed in subjects. Examples of this approach is found in the work of Marr
(1982) on vision, and in connectionist models of language learning (Pinker,
1999, pp. 103-117). We look at Marr's work in more detail.
Marr began with the assumption that the mechanisms of human
vision are too complex to understand at the neurological level. Instead, he set
out to describe the functions that these mechanisms need to perform as what is
seen by the eye moves from the retina to the visual cortex and is interpreted
by the viewer. The functions Marr developed were mathematical models of such
processes as edge detection, the perception of shapes at different scales, and stereopsis (Marr & Nishihara, 1978). The electrical
activity observed in certain types of cell in the visual system matched the
activity predicted by the model almost exactly (Marr & Ullman,
1981).
Marr's work has had implications that go far beyond his
important research on vision, and as such serves as a paradigmatic case of
cognitive science. Cognitive science is not called that because of its close
association with the computer but because it adopts the functional or
computational approach to psychology that is so much in evidence in Marr's
work. By "functional" (see Pylyshyn, 1984),
we mean that it is concerned with the functions the cognitive system must
perform not with the devices through which cognitive processes are implemented.
A commonly-used analogy is that cognitive science is concerned with cognitive
software not hardware. By "computational" (Arbib
& Hanson, 1987; Richards, 1988) we mean that the models of cognitive
science take information that a learner encounters, perform logical or
mathematical operations on it, and describe the outcomes of those operations.
The computer is the tool that allows the functions to be tested, the
computations to be performed. In a recent extensive exposition of a new theory
of science, Wolfram (2002) goes so far as to claim that every action, whether natural or man-made, including all cognitive
activity, is a "program" that can be recreated and run on a computer.
Wolfram's theory is
The tendency in cognitive science to create theory around
computational rather than biological mechanisms points to another
characteristic of the discipline. Cognitive scientists conceive of cognitive
theory at different levels of description. The level that comes closest to the
brain mechanisms that create cognitive activity is obviously biological.
However, as Marr presumed, this level was at the time virtually inaccessible to
cognitive researchers, consequently requiring the construction of more abstract
functional models. The number, nature and names of the levels of cognitive
theory vary from theory to theory and from researcher to researcher.
The computer has assumed two additional roles in cognitive
science beyond that of a tool for testing models. First, some have concluded
that, because computer programs written to test cognitive theory accurately
predict observable behavior that results from cognitive activity, cognitive
activity must itself be computer-like. Cognitive scientists have proposed
numerous theories of cognition that embody the information-processing
principles and even the mechanisms of computer science (Boden,
1988; Johnson-Laird, 1988). Thus we find reference in the cognitive science
literature to input and output, data structures, information processing,
production systems, and so on. More significantly, we find descriptions of
cognition in terms of the logical processing of symbols (Larkin & Simon,
1987; Salomon, 1979; Winn, 1982). Second, cognitive science has provided both
the theory and the impetus to create computer programs that "think"
just as we do. Research in artificial intelligence blossomed during the 'eighties,
and was particularly successful when it produced intelligent tutoring systems (Anderson
& Lebiere, 1998; Anderson & Reiser, 1985; Anderson, Boyle & Yost, 1985; Wenger,
1987) and expert systems (Forsyth, 1984). The former are characterized by the
ability to understand and react to the progress a student makes working through
a computer-based tutorial program. The latter are smart
"consultants", usually to professionals whose jobs require them to
make complicated decisions from large amounts of data.
Its successes notwithstanding, AI has shown up the weaknesses of
many of the assumptions that underlie cognitive science, especially the
assumption that cognition consists in the logical mental manipulation of
symbols. Scholars (Bickhard, 2000; Clancey, 1993; Clark, 1997; Dreyfus, 1979; Dreyfus &
Dreyfus, 1986; Edelman, 1992; Freeman & Nuñez, 1999; Searle, 1992) have criticized this and other assumptions of
cognitive science as well as of computational theory and, more basically,
functionalism. The critics imply that cognitive scientists have lost sight of
the metaphorical origins of the levels of cognitive theory and have assumed
that the brain really does compute the answer to problems by symbol
manipulation. Searle's comment sets the tone, "If you are tempted to
functionalism, we believe you do not need refutation, you need help."
(1992, p. 9).
Section summary
We have traced the development of cognitive theory up to the
point where, in the 1980s, it emerged preeminent among psychological theories
of learning and understanding. Although many of the ideas in this section will
be developed in what follows, it is useful at this point to provide a short
summary of the ideas presented so far. We have seen that cognitive psychology
returned to center stage largely because stimulus-response theory did not
adequately or efficiently account for many aspects of human behavior that we
all observe from day to day. The research on memory and mental imagery that we
briefly described indicated that psychological processes and prior knowledge
intervene between the stimulus and the response making the latter less
predictable. We have also seen that non-experimental and non-objective
methodology is now deemed appropriate for certain types of research. However,
it is possible to detect a degree of conservatism in main-stream cognitive
psychology that still insists on the objectivity and quantifiability
of data.
Cognitive science, emerging from the confluence of cognitive
psychology and computer science, has developed its own set of assumptions, not
least among which are computer models of cognition. These have served well, at
different levels of abstraction, to guide cognitive research, leading to such
applications as intelligent tutors and expert systems. However, the computational
theory and functionalism that underlie these assumptions have been the source
of recent criticism and their role in research in Education needs to be
reassessed.
The implications of all of this for research and practice in
Educational Technology will be dealt with in section V. I would nonetheless
like to anticipate three aspects of that discussion. First, educational
technology research, and particularly main-stream instructional design
practice, needs to catch up with developments in psychological theory. As I
have suggested elsewhere (Winn, 1989), it is not sufficient simply to
substitute cognitive objectives for behavioral objectives and to tweak our
assessment techniques to gain access to knowledge schemata rather than just to observable behaviors. More fundamental changes are
required including, now, those required by demonstrable limitations to
cognitive theory itself.
Second, shifts in the technology itself away from rather prosaic
and ponderous computer-assisted programmed instruction to highly interactive
multimedia environments permit educational technologists to develop serious
alternatives to didactic instruction (Winn, 2002). We can now use technology to
do more than direct teaching. We can use it to help students construct meaning
for themselves through experience in ways proposed by constructivist theory and
practice described elsewhere in this Handbook and by Duffy & Jonassen (1992), Duffy, Jonassen
& Lowyck (1993), Winn & Windschitl
(2001a) and others.
Third, the proposed alternatives to computer models of
cognition, that explain first-person experience, non-symbolic thinking and
learning, and reflection-free cognition, lay the conceptual foundation for
educational developments of virtual realities (Winn & Windschitl,
2001a). The full realization of these new concepts and technologies lies in the
future. However, we need to get ahead of the game and prepare for when these
eventualities become a reality.
III. MENTAL REPRESENTATION
The previous section showed the historical origins of the two major
aspects of cognitive psychology that are addressed in this and the next
section. These have been and continue to be mental representation and mental
processes. Our example of representation was the mental image, and passing
reference was made to memory structures and hierarchical chunks of information.
We also talked generally about the input, processing and output functions of
the cognitive system, and paid particular attention to Marr's account of the
processes of vision. In this section we look at traditional and emerging views
of mental representation.
The nature of mental representation and how to study it lie at
the heart of traditional approaches to cognitive psychology. Yet, as we have
seen, the nature, indeed the very existence, of mental representation
are not without controversy. It merits consideration here, however,
because it is still pervasive in educational technology research and theory, because
it has, in spite of shortcomings, contributed to our understanding of learning,
and because it is currently regaining some of its lost status as a result of
research in several disciplines. This section therefore deals with cognitive
theories of mental representation. How we store information in memory,
represent it in our mind's eye, or manipulate it through the processes of
reasoning has always seemed relevant to researchers in educational technology.
Our field has sometimes supposed that the way in which we represent information
mentally is a direct mapping of what we see and hear about us in the world (see
Knowlton, 1966; Cassidy & Knowlton, 1983; Sless,
1981). Educational technologists have paid a considerable amount of attention
to how visual presentations of different levels of abstraction affect our
ability to reason literally and analogically (Winn, 1982). Since the earliest
days of our discipline (Dale, 1946), we have been intrigued by the idea that
the degree of realism with which we present information to students determines
how well they learn. More recently (Salomon, 1979), we have come to believe
that our thinking uses various symbol systems as tools, enabling us both to
learn and to develop skills in different symbolic modalities. How mental
representation is affected by what a student encounters in the environment has
become inextricably bound up with the part of our field we call "message
design" (Fleming & Levie, 1993; Rieber, 1994, chapter 7).
Schema theory.
The concept of "schema" is central to early cognitive
theories of representation. There are many descriptions of what schemata are.
All descriptions concur that a schema has the following characteristics: 1) It
is an organized structure that exists in memory and, in aggregate with all
other schemata, contains the sum of our knowledge of the world (Paivio, 1974), 2) It exists at a higher level of
generality, or abstraction, than our immediate experience with the world, 3) It
consists of concepts that are linked together in propositions, 4) It is
dynamic, amenable to change by general experience or through instruction, 5) It
provides a context for interpreting new knowledge as well as a structure to
hold it. Each of these features requires comment.
Schema as memory structure. The idea that memory is organized in
structures goes back to the work of
Schema as abstraction. A schema is a more abstract representation
than a direct perceptual experience. When we look at a cat, we observe its
color, the length of its fur, its size, its breed if that is discernible and
any unique features it might have, such as a torn ear or unusual eye color.
However, the schema that we have constructed from experience to represent
"cat" in our memory, and by means of which we are able to identify
any cat, does not contain these details. Instead, our "cat" schema
will tell us that it has eyes, four legs, raised ears, a particular shape and
habits. However, it leaves those features that vary among cats, like eye color
and length of fur, unspecified. In the language of schema theory, these are
"place-holders", "slots", or "variables" to be
"instantiated" through recall or recognition (
It is this abstraction, or generality, that makes schemata
useful. If memory required that we encode every feature of every experience
that we had, without stripping away variable details, recall would require us
to match every experience against templates in order to identify objects and
events, a suggestion that has long since been discredited for its unrealistic
demands on memory capacity and cognitive processing resources (Pinker, 1985).
On rare occasions, the generality of schemata may prevent us from identifying
something. For example, we may misidentify a penguin because, superficially, it
has few features of a bird. As we shall see below, learning requires the
modification of schemata so that they can accurately accommodate unusual
instances, like penguins, while still maintaining a level of specificity that
makes them useful.
Schema as dynamic structure. A schema is not immutable. As we learn new
information, either from instruction or from day-to-day interaction with the
environment, our memory and understanding of our world will change. Schema
theory proposes that our knowledge of the world is constantly interpreting new
experience and adapting to it. These processes, which Piaget (1968) has called
"assimilation" and "accommodation", and which Thorndyke & Hayes-Roth (1979) have called "bottom
up" and "top down" processing, interact dynamically in an
attempt to achieve cognitive equilibrium without which the world would be a
tangled blur of meaningless experiences. The process works like this: 1) When we encounter a new object, experience or piece of
information, we attempt to match its features and structure (nodes and links)
to a schema in memory (bottom-up). On the basis of the success of this first
attempt at matching, we construct a hypothesis about the identity of the
object, experience or information, on the basis of which we look for further
evidence to confirm our identification (top-down). If further evidence confirms
our hypothesis we assimilate the experience to the schema. If it does not, we
revise our hypothesis, thus accommodating to the experience.
Learning takes place as schemata change when they accommodate to
new information in the environment and as new information is assimilated by
them. Rumelhart and Norman (1981) discuss important
differences in the extent to which these changes take place. Learning takes
place by accretion, by schema tuning, or by schema creation. In the case of
accretion, the match between new information and schemata is so good that the
new information is simply added to an existing schema with almost no accommodation
of the schema at all. A hiker might learn to recognize a golden eagle simply by
matching it to an already-familiar bald eagle schema noting only the absence of
the former's white head and tail.
Schema tuning results in more radical changes in a schema. A
child raised in the inner city might have formed a "bird" schema on
the basis of seeing only sparrows and pigeons. The features of this schema
might be: a size of between 3 and 10 inches; flying by flapping wings; found
around and on buildings. This child's first sighting of an eagle would probably
be confusing, and might lead to a mis-identification
as an airplane, which is bigger than ten inches long and does not flap its
wings. Learning, perhaps through instruction, that this creature was indeed a
bird would lead to changes in the "bird" schema, to include soaring
as a means of getting around, large size and mountain habitat. Rumelhart & Norman describe schema creation as occurring
by analogy. Stretching the bird example to the limits of credibility, imagine
someone from a country that has no birds but lots of bats for whom a
"bird" schema does not exist. The creation of a bird schema could
take place by temporarily substituting the features birds have in common with
bats and then specifically teaching the differences. The danger, of course, is
that a significant residue of bat features could persist in the bird schema, in
spite of careful instruction. Analogies can therefore be misleading (Spiro, Feltovich, Coulson &
Anderson, 1989) if they are not used with extreme care.
More recently, research in conceptual change (Posner et al.,
1982; Vosniadou, 1994; Windschitl
& André, 1998) has extended our understanding of schema change in
important ways. Since this work concerns cognitive processes, we will deal with
it in the next major section. Suffice it note, for now, that it aims to explain
more of the mechanisms of change, lead to practical applications in teaching
and learning, particular in Science, and more often than not involves
technology.
Schema as context. Not only does a schema serve as a
repository of experiences. It provides a context that affects how we interpret
new experiences and even directs our attention to particular sources of
experience and information. From the time of
The research design for these studies requires the activation of
a well-developed schema to set a context, the presentation of a text, that is
often deliberately ambiguous, and a comprehension posttest. For example, Bransford and Johnson (1972) had subjects study a text that
was so ambiguous as to be meaningless without the presence of an accompanying
picture. Anderson, Reynolds, Schallert & Goetz
(1977) presented ambiguous stories to different groups of people. A story that
could have been about weight lifting or a prison break was interpreted to be about
weight-lifting by students in a weight-lifting class, but in other ways by
other students. Musicians interpreted a story that could have been about
playing cards or playing music as if it were about music.
Finally, recent research on priming (Schachter
& Buckner, 1998; Squire & Knowlton, 1995) is beginning to identify
mechanisms that might eventually
account for schema activation, whether conscious or implicit. After all, both
perceptual and semantic priming predispose people to perform subsequent cognitive
tasks in particular ways, and produce effects that are not unlike the
contextualizing effects of schemata. However, given that the experimental tasks
used in this priming research are far simpler and implicate more basic
cognitive mechanisms than those used in the study of how schemata are activated
to provide contexts for learning, linking these two bodies of research is
currently risky, if not unwarranted. Yet, the possibility that research on
priming could eventually explain some aspects of schema theory is too
intriguing to ignore completely.
Schema theory and
Educational Technology.
Schema theory has influenced educational technology in a variety
of ways. For instance, the notion of activating a schema in order to provide a
relevant context for learning finds a close parallel in Gagné, Briggs &
Wager's (1988) third instructional "event", "stimulating recall
of prerequisite learning". Reigeluth's (Reigeluth & Stein, 1983) "Elaboration theory"
of instruction consists of, among other things, prescriptions for the
progressive refinement of schemata. The notion of a "generality", that has persisted through the many stages of Merrill's
instructional theory (Merrill, 1983, 1988; Merrill, Li & Jones, 1991),
is close to a schema.
There are however three particular ways in which educational
technology research has used schema theory (or at least some of the ideas it
embodies, in common with other cognitive theories of representation). The first
concerns the assumption, and attempts to support it, that
schemata can be more effectively built and activated if the material
that students encounter is somehow isomorphic to the putative structure of the
schema. This line of research extends into the realm of cognitive theory
earlier attempts to propose and validate a theory of audiovisual (usually more
visual than audio) education and concerns the role of pictorial and graphic
illustration in instruction (Dale, 1946; Carpenter, 1953; Dwyer, 1972, 1978,
1987). The second way
in which educational technology has used schema theory has been to develop and
apply techniques for students to use to impose structure on what they learn and
thus make it more memorable. These techniques are referred to, collectively, by
the term "information mapping".
The third line of research consists of attempts to use schemata
to represent information in a computer and thereby to enable the machine to
interact with information in ways analogous to human assimilation and accommodation.
This brings us to a consideration of the role of schemata, or
"scripts" (Schank & Abelson,
1977) or "plans" (Minsky, 1975) in AI and
"intelligent" instructional systems. The next sections examine these
lines of research.
Schema-message isomorphism: Imaginal
encoding.
There are two ways in which pictures and graphics can affect how
information is encoded in schemata. Some research suggests that a picture is
encoded directly as a mental image. This means that encoding leads to a schema
that retains many of the properties of the message that the student saw, such
as its spatial structure and the appearance of its features. Other research
suggests that the picture or graphic imposes a structure on information first
and that propositions about this structure rather than the structure itself are
encoded. The schema therefore does not contain a mental image but information
that allows an image to be created in the mind's eye when the schema becomes
active. This and the next section examine these two possibilities.
Research into imaginal encoding is
typically conducted within the framework of theories that propose two (at
least) separate, though connected, memory systems. Paivio's
(1983, Clark & Paivio, 1992) "dual
coding" theory and Kulhavy's (Kulhavy, Lee & Caterino,
1985; Kulhavy, Stock & Caterino,
1994) "conjoint retention" theory are typical. Both theories assume
that people can encode information as language-like propositions or as
picture-like mental images. This research has provided evidence that 1)
pictures and graphics contain information that is not contained in text and 2)
that information shown in pictures and graphics is easier to recall because it
is encoded in both memory systems, as propositions and as images, rather than
just as propositions which is the case when students read text. As an example,
Schwartz and Kulhavy (1981) had subjects study a map
while listening to a narrative describing the territory. Map subjects recalled
more spatial information related to map features than non-map subjects, while
there was no difference between recall of the two groups on information not
related to map features. In another study, Abel & Kulhavy
(1989) found that subjects who saw maps of a territory recalled more details
than subjects who read a corresponding text suggesting that the map provided
"second stratum cues" that made it easier to recall information.
Schema-message isomorphism: Structural encoding.
Evidence for the claim that graphics help students organize
content by determining the structure of the schema in which it is encoded comes
from studies that have examined the relationship between spatial presentations
and cued or free recall. The assumption is that the spatial structure of the
information on the page reflects the semantic structure of the information that
gets encoded. For example, Winn (1980) used text with or without a block
diagram to teach about a typical food web to high-school subjects. Estimates of
subjects' semantic structures representing the content were obtained from their
free associations to words naming key concepts in the food web (e.g. "consumer",
"herbivore"). It was found that the diagram significantly improved
the closeness of the structure the students acquired to the structure of the
content.
McNamara, Hardy and Hirtle (1989) had
subjects learn spatial layouts of common objects. Ordered trees, constructed
from free recall data, revealed hierarchical clusters of items that formed the
basis for organizing the information in memory. A recognition test, in which
targeted items were primed by items either within or outside the same cluster,
produced response latencies that were faster for same-cluster items than for
different-item clusters. The placement of an item in one cluster or another was
determined, for the most part, by the spatial proximity of the items in the
original layout. In another study, McNamara (1986) had subjects study the
layout of real objects placed in an area on the floor. The area was divided by
low barriers into four quadrants of equal size. Primed recall produced response
latencies suggesting
that the physical boundaries imposed categories on the objects when they were
encoded that overrode the effect of absolute spatial proximity. For example,
recall Reponses were slower to items physically close but separated by a
boundary than two items further apart but within the same boundary. The results
of studies like these have been the basis for recommendations about when and
how to use pictures and graphics in instructional materials (Levin, Anglin & Carney, 1987; Winn, 1989b).
Schemata and Information
Mapping.
Strategies exploiting the structural isomorphism of graphics and
knowledge schemata have also formed the basis for a variety of text- and
information-mapping schemes aimed at improving comprehension (Armbruster &
The assumptions underlying all information-mapping strategies
are that if information is well-organized in memory it will be better
remembered and more easily associated with new information, and that students
can be taught techniques exploiting the spatial organization of information on
the page that make what they learn better organized in memory. We have already
given examples of research that bears out the first of these assumptions. We
turn now to research on the effectiveness of information-mapping techniques.
All information-mapping strategies (reviewed and summarized by
Hughes, 1989) require students to learn ways to represent information, usually
text, in spatially constructed diagrams. With these techniques, they construct
diagrams that represent the concepts they are to learn as verbal labels often
in boxes and that show interconcept relations as
lines or arrows. The most obvious characteristic of these techniques is that
students construct the information maps for themselves rather than studying
diagrams created by someone else. In this way, the maps require students to
process the information they contain in an effortful manner while allowing a
certain measure of idiosyncrasy in the ideas are shown, both of which are
attributes of effective learning strategies.
Some mapping techniques are radial, with the key concept in the
center of the diagram and related concepts on arms reaching out from the center
(Hughes, 1989). Other schemes are more hierarchical with concepts placed on
branches of a tree (Johnson, Pittelman &
Heimlich, 1986). Still others maintain the roughly linear format of sentences
but use special symbols to encode inter-concept relations, like equals signs or
different kinds of boxes (Armbruster & Anderson,
1984). Some computer-based systems provide more flexibility by allowing "zooming"
in or out on concepts to reveal subconcepts within
them and by allowing users to introduce pictures and graphics from other
sources (Fisher et al., 1990).
The burgeoning of the World Wide Web has given rise to a new way
to look at information mapping. Like many of today's teachers, Malarney (2000) had her students construct web pages to
display their knowledge of a subject, in this case "Ocean Science". Malarney's insight was that the students' web pages were in
fact concept maps, in which ideas were illustrated and connected to other ideas
through layout and hyperlinks. Carefully used, the Web can serve both as a way
to represent "maps" of content, and also as tools to assess what
students know about something, using tools described, for example, by Novak
(1998).
Regardless of format, information mapping has been shown to be
effective. In some cases, information mapping techniques have formed part of study skills
curricula (Holley & Dansereau, 1984; Schewel, 1989). In other cases, the technique has been used
to improve reading comprehension (Ruddell &
Boyle, 1989) or for review at the end of a course (Fisher et al., 1990).
Information mapping has been shown to be useful for helping students write
about what they have read (Sinatra, Stahl-Gemake
& Morgan, 1986) and works with disabled readers as well as with normal ones
(Sinatra, Stahl-Gemake & Borg, 1986). Information
mapping has proved to be a successful technique in all of these tasks and
contexts, showing it to be remarkable robust.
Information mapping can, of course, be used by instructional
designers (Jonassen, 1990, 1991; Suzuki, 1987). In
this case, the technique is used not so much to improve comprehension as to
help designers understand the relations among concepts in the material they are
working with. Often, understanding such relations makes strategy selection more
effective. For example, a radial outline based on the concept "zebra"
(Hughes, 1989) shows, among other things, that a zebra is a member of the horse
family and also that it lives in
All of this seems to suggest that imagery-based and
information-structuring strategies based on graphics have been extremely useful
in practice. Tversky (2001) provides a summary and
analysis of research into graphical techniques that exploit both the analog
(imagery-based) and metaphorical (information-organizing) properties of all
manner of images. Her summary shows that they can be effective. Vekiri (2002) provides a broader summary of research into the
effectiveness of graphics for learning that includes several studies concerned
with mental representation. However, the whole idea of isomorphism between an
information display outside the learner and the structure and content of a
memory schema implies that information in the environment is mapped fairly
directly into memory. As we have seen, this basic assumption of much of
cognitive theory is currently being seriously challenged. For example, Bickhard (2000) asks, "What's wrong with 'encodingism'?", his term for direct mapping to mental
schemata. The extent to which this challenge threatens the usefulness of using
pictures and graphics in instruction remains to be seen.
Schemata and AI.
Another way in which theories of representation have been used
in educational technology is to suggest ways in which computer programs
designed to "think" like people might represent information. Clearly,
this application embodies the "computer models of mind" assumption
that we looked at above (Boden, 1988).
The structural nature of schemata make
them particularly attractive to cognitive scientists working in the area of
artificial intelligence. The reason for this is that they can be described
using the same "language" that is used by computers and therefore
provide a convenient link between human and artificial thought. The best
examples are to be found in the work of Minsky (1975) and of Schank and his associates (Schank
& Abelson, 1977). Here, schemata provide
constraints on the meaning of information that the computer and the user share
that make the interaction between them more manageable and useful. The
constraints arise from only allowing what typically happens in a given
situation to be considered. For example, certain actions and verbal exchanges
commonly take place in a restaurant. You enter. Someone shows you to your
table. Someone brings you a menu. After a while, they come back and you order
your meal. Your food is brought to you in a predictable sequence. You eat it in
a predictable way. When you have finished, someone brings you the bill, which
you pay. You leave. It is not likely (though not impossible, of course) that
someone will bring you a basketball rather than the food you ordered. Usually,
you will eat your food rather than sing to it. You use cash or a credit card to
pay for your meal rather than offering a giraffe. In this way, the almost
infinite number of things that can occur in the world
are constrained to relatively few, which means that the machine has a better
chance of figuring out what your words or actions mean.
Even so, schemata (or "scripts" as Schank
[1984] calls them) cannot contend with every eventuality. This is because the
assumptions about the world that are implicit in our schemata, and therefore
often escape our awareness, have to be made explicit in scripts that are used
in AI. Schank
(1984) provides examples as he describes the difficulties encountered by
TALE-SPIN, a program designed to write stories in the style of Aesop's fables.
"One day Joe Bear was hungry. He asked his friend Irving
Bird where some honey was.
"This was not the story that TALE-SPIN set out to tell.
[...] Had TALE-SPIN found a way for Henry to call to Bill for help, this would
have caused Bill to try to save him. But the program had a rule that said that
being in water prevents speech. Bill was not asked a direct question, and there
was no way for any character to just happen to notice something. Henry drowned
because the program knew that that's what happens when a character that can't
swim is immersed in water." (1984, p. 84).
The rules that the program followed, leading to the sad demise
of Henry, are rules that normally apply. People do not usually talk when they're
swimming. However, in this case, a second rule should have applied, as we who
understand a calling-for-help-while-drowning schema are well aware of.
The more general issue that arises from these examples is that
people have extensive knowledge of the world that goes beyond any single set of
circumstances that might be defined in a script. And human intelligence rests
on the judicious use of this general knowledge. Thus, on the rare occasion that
we do encounter someone singing to their food in a restaurant, we have
knowledge from beyond the immediate context that lets us conclude the person
has had too much to drink, or is preparing to sing a role at the local opera
and is therefore not really singing to her food at all, or belongs to a cult
for whom praising the food about to be eaten in song is an accepted ritual. The
problem for the AI designer is therefore how much of this general knowledge to
allow the program to have? Too little, and the correct inferences cannot be
made about what has happened when there are even small deviations from the norm.
Too much, and the task of building a production system that embodies all the
possible reasons for something to occur becomes impossibly complex.
It has been claimed that AI has failed (Dreyfus & Dreyfus,
1986) because "intelligent" machines do not have the breadth of
knowledge that permits human reasoning. A continuing project called "Cyc" (Guha & Lenat, 1991; Lenat, Guha, Pittman, Pratt, & Shepherd, 1990) has as its goal
to imbue a machine with precisely the breadth of knowledge that humans have.
Over a period of years, programmers will have worked away at encoding an
impressive number of facts about the world. If this project is successful, it
will be testimony to the usefulness of general knowledge of the world for
problem-solving and will confirm the severe limits of a "schema" or "script"
approach to AI. It may also suggest that the schema metaphor is misleading.
Maybe people do not organize their knowledge of the world in clearly delineated
structures. A lot of thinking is "fuzzy", and the boundaries among schemata are
permeable and indistinct.
Mental Models
Another way in which theories of representation have influenced
research in educational technology is through psychological and human factors
research on mental models. A mental model, like a schema, is a putative
structure that contains knowledge of the world. For some, mental models and
schemata are synonymous. However, there are two properties of mental models
that make them somewhat different from schemata. Mayer (1992, p. 431)
identifies these as 1) representations of objects in whatever the model
describes and 2) descriptions of how changes in one object effect changes in
another. Roughly speaking, a mental model is broader in conception than a
schema because it specifies causal actions among objects that take place within
it. However, you will find any number of people who disagree with this distinction.
The term "envisionment" is often applied to
the representation of both
the objects and the causal relations in a mental model (DeKleer & Brown, 1981; Strittmatter
& Seel, 1989). This term draws attention to the
visual metaphors that often accompany discussion of mental models. When we use
a mental model, we "see" a representation of it in our "mind's
eye". This representation has spatial properties akin to those we notice
with our biological eye. Some objects are "closer to" some than to
others. And from seeing changes in our mind's eye in one object occurring
simultaneously with changes in another, we infer causality between them. This
is especially true when we consciously bring about a change in one object
ourselves. For example, Sternberg and Weil (1980) gave subjects problems to
solve of the kind "If A is bigger than B and C is bigger than A, who is
the smallest?" Subjects who changed the representation of the problem by
placing the objects A, B and C in a line from tallest to shortest were most
successful in solving the problem because envisioning it in this way allowed
them simply to "see" the answer. Likewise, envisioning what happens
in an electrical circuit that includes an electric bell (DeKleer
& Brown, 1981) allows someone to come to understand how it works. In short,
a mental model can be "run" like a film or computer program and
watched in the mind's eye while it is running. You may have observed
world-class skiers "running" their model of a slalom course, eyes
closed, body leaning into each gate, before they make their run.
The greatest interest in mental models by educational
technologists lies in ways of getting learners to create good ones. This
implies, as in the case of schema creation, that instructional materials and
events act with what learners already understand in order to construct a mental
model that the student can use to develop understanding. Just how instruction
affects mental models has been the subject of considerable research, summarized
by Gentner & Stevens (1983), Mayer (1989a), Rouse
& Morris (1986) among others. At the end of his review, Mayer lists seven
criteria that instructional materials should meet for them to induce mental
models that are likely to improve understanding. (Mayer refers to the
materials, typically illustrations and text, as "conceptual models"
that describe in graphic form the objects and causal relations among them.) A
good model is: Complete -- it contains all the objects, states and actions of
the system; Concise -- it contains just enough detail; Coherent -- it makes "intuitive
sense"; Concrete -- it is presented at an appropriate level of
familiarity; Conceptual -- it is potentially meaningful; Correct -- the objects
and relations in it correspond to actual objects and events; Considerate -- it
uses appropriate vocabulary and organization. If these criteria are met, then
instruction can lead to the creation of models that help students understand
systems and solve problems arising from the way the systems work. For example,
Mayer (1989b) and Mayer & Gallini (1990) have demonstrated that materials,
conforming to these criteria, in which graphics and text work together to
illustrate both the objects and causal relations in systems (hydraulic drum
brakes, bicycle pumps) were effective at promoting understanding. Subjects were
able to answer questions requiring them to draw inferences from their mental
models of the system using information they had not been explicitly taught. For
instance, the answer (not explicitly taught) to the question, "Why do
brakes get hot?" can only be found in an understanding of the causal
relations among the pieces of a brake system. A correct answer implies that an accurate
mental model has been constructed.
A second area of research on mental models in which educational
technologists are now engaging arises from a belief that interactive multimedia
systems are effective tools for model-building (Hueyching
& Reeves, 1992; Kozma, Russell, Jones, Marx &
Davis,1993; Seel & Dörr,
1994; Windschitl & André, 1998). For the first time, we are able, with reasonable ease,
to build instructional materials that are both interactive and that, through
animation, can represent the changes of state and causal actions of physical
systems. Kozma et al. (1993) describe a computer
system that allows students to carry out simulated chemistry experiments. The
graphic component of the system (which certainly meets Mayer's criteria for
building a good model) presents information about changes of state and
causality within a molecular system. It "corresponds to the
molecular-level mental models that chemists have of such systems" (Kozma et al., 1993, p. 16). Analysis of constructed student
responses and of think-aloud protocols have demonstrated the effectiveness of
this system for helping students construct good mental models of chemical
reactions. Byrne, Furness & Winn (1995) described a virtual environment in
which students learn about atomic and molecular structure by building atoms
from their sub-atomic components. The most successful treatment for building
mental models was a highly interactive one. Winn & Windschitl
(2002) examined videotapes of students working in an immersive virtual environment
that simulated processes on physical oceanography. They found that students who
constructed and then used causal models solved problems more effectively than
those who did not. Winn, Windschitl, Fruland & Lee (2002) give examples of students connecting
concepts together to form causal principles as they constructed a mental model
of ocean processes while working with the same simulation.
Mental Representation and
the Development of Expertise.
The knowledge we represent as schemata or mental models changes
as we work with it over time. It becomes much more readily accessible and
useable, requiring less conscious effort to use it effectively. At the same
time, its own structure becomes more robust and it is increasingly internalized
and automatized. The result is that its application
becomes relatively straightforward and automatic, and frequently occurs without
our conscious attention. When we drive home after work, we do not have to think
hard about what to do or where we are going. It is important in the research
that we shall examine below that this process of "knowledge compilation and translation"
(
Out of the behavioral tradition that continues to dominate a
great deal of thinking in educational technology comes
the assumption that it is possible for mastery to result from instruction. In
mastery learning, the only instructional variable is the time required to learn
something. Therefore, given enough time, anyone can learn anything. The
evidence that this is the case is compelling (Bloom, 1984, 1987; Kulik, 1990a,b). However, "enough
time" typically comes to mean the length of a unit, module or semester and
"mastery" means mastery of performance not of high-level skills such
as problem-solving.
There is a considerable body of opinion that expertise arises
from a much longer exposure to content in a learning environment than that
implied in the case of mastery learning. Labouvie-Vief
(1990) has suggested that wisdom arises during adulthood from processes that
represent a fourth "stage" of human development, beyond Piaget's
traditional three. Achieving a high level of expertise in chess (Chase &
Simon, 1973) or in the professions (Schon, 1983,
1987) takes many years of learning and applying what one has learned. This
implies that learners move through stages on their way from novice-hood to
expertise, and that, as in the case of cognitive development (Piaget & Inhelder, 1969), each stage is a necessary prerequisite for
the next and cannot be skipped. In this case, expertise does not arise directly
from instruction. It may start with some instruction, but only develops fully
with maturity and experience on the job (Lave & Wenger, 1991).
An illustrative account of the stages a person goes through on
the way to expertise is provided by Dreyfus & Dreyfus (1986). The stages
are: Novice, advanced beginner, competence, proficiency and expertise. Dreyfus
and Dreyfus' examples are exceptionally useful in clarifying the differences
between stages. The following few paragraphs are therefore based on their
narrative (1986, pp. 21-35).
Novices learn objective and unambiguous facts and rules about
the area that they are beginning to study. These facts and rules are typically
learned out of context. For example, beginning nurses learn how to take a
patient's blood pressure and are taught rules about what to do if the reading
is normal, high or very high. However, they do not yet necessarily understand what blood
pressure really indicates nor why the actions specified in the rules are
necessary nor how they affect the patient's recovery. In a sense, the knowledge
they acquire is "inert" (Cognition and Technology Group at
Vanderbilt, 1990) in that, though it can be applied, it is applied blindly and
without a context or rationale.
Advanced beginners continue to learn more objective facts and
rules. However, with their increased practical experience, they also begin to
develop a sense of the larger context in which their developing knowledge and
skill operate. Within that context, they begin to associate the objective rules
and facts they have learned with particular situations they encounter on the
job. Their knowledge becomes "situational" or "contextualized".
For example, student nurses begin to recognize patients' symptoms by means that
cannot be expressed in objective, context-free rules. The way a particular
patient's breathing sounds may be sufficient to indicate that a particular
action is necessary. However, the sound itself cannot be described objectively,
nor can recognizing it be learned anywhere except on the job.
As the student moves into competence and develops further
sensitivity to information in the working environment, the number of
context-free and situational facts and rules begins to overwhelm the student.
The situation can only be managed when the student learns effective
decision-making strategies. Student nurses at this stage often appear to be unable to make
decisions. They are still keenly aware of the things they have been taught to
look out for and the procedures to follow in the maternity ward. However, they
are also now sensitive to situations in the ward that require them to change
the rules and procedures. They begin to realize that the baby screaming its
head off requires immediate attention even if to give that attention is not
something set down in the rules. They are torn between doing what they have
been taught to do and doing what they sense is more important at that moment.
And often they dither, as Dreyfus & Dreyfus put it, "...like a mule
between two bales of hay." (1986, p. 24).
Proficiency is characterized by quick, effective and often
unconscious decision-making. Unlike the merely competent student, who has to
think hard about what to do when the situation is at variance with objective
rules and prescribed procedures, the proficient student easily grasps what is
going on in any situation and acts, as it were, automatically to deal with
whatever arises. The proficient nurse simply notices that a patient is psychologically
ready for surgery, without consciously weighing the evidence.
With expertise comes the complete fusion of decision-making and
action. So completely is the expert immersed in the task, and so complete is
the expert's mastery
of the task and of the situations in which it is necessary to act, that "...
When things are proceeding normally, experts don't solve problems and don't
make decisions; they do what normally works." (Dreyfus
& Dreyfus, 1986, 30-31). Clearly, such a state of affairs can only
arise after extensive experience on the job. With such experience comes the
expert's ability to act quickly and correctly from information without needing
to analyze it into components. Expert radiologists can perform accurate
diagnoses from x-rays by matching the pattern formed by light and dark areas on
the film to patterns they have learned over the years to be symptomatic of
particular conditions. They act on what they see as a whole and do not attend
to each feature separately. Similarly, early research on expertise in chess
(Chase & Simon, 1973) revealed that grand masters rely on the recognition
of patterns of pieces on the chessboard to guide their play and engage in less
in-depth analysis of situations than merely proficient players. Expert nurses
sometimes sense that a patient's situation has become critical without there
being any objective evidence and, although they cannot explain why, they are
usually correct.
A number of things are immediately clear from his account of the
development of expertise. The first is that any student must start by learning
explicitly-taught facts and rules even if the ultimate goal is to become an
expert who apparently functions perfectly well without using them at all. Spiro
et al. (1992) claims that learning by allowing students to construct knowledge
only works for "advanced knowledge" which assumes the basics have
already been mastered.
Second, though, is the observation that students begin to learn
situational knowledge and skills as early as the "advanced beginner"
stage. This means that the abilities that appear intuitive, even magical, in
experts are already present in embryonic form at a relatively early stage in a
student's development. The implication is that instruction should foster the
development of situational, non-objective knowledge and skill as early as
possible in a student's education. This conclusion is corroborated by the study
of situated
learning (Brown, Collins and Duguid, 1989) and
apprenticeships (Lave & Wenger, 1991) in which education is situated in
real-world contexts from the start.
Third is the observation that as students becomes more expert,
they are less able to rationalize and articulate the reasons for their
understanding of a situation and for their solutions to problems. Instructional
designers and knowledge engineers generally are acutely aware of the difficulty
of deriving a systematic and objective description of knowledge and skills from
an expert as they go about content or task analyses. Experts just do things that work and do not
engage in specific or describable problem-solving. This also means that
assessment of what
students learn as they acquire expertise
becomes increasingly difficult and eventually impossible by traditional
means, such as tests. Tacit knowledge (Polanyi, 1962)
is extremely difficult to measure.
Finally, we can observe that what educational technologists
spend most of their time doing -- developing explicit and measurable
instruction -- is only relevant to the earliest step in the process of
acquiring expertise. There are two implications of this. First, we have, until
recently, ignored the potential of technology to help people learn anything
except objective facts and rules. And these, in the scheme of things we have
just described, though necessary, are intended to be quickly superceded by
other kinds of knowledge and skills that allow us to work effectively in the
world. We might conclude that instructional design, as traditionally conceived,
has concentrated on creating nothing more than training wheels for learning and
acting that are to be jettisoned for more important knowledge and skills as
quickly as possible. The second implication is that by basing instruction on
the knowledge and skills of experts, we have completely ignored the protracted
development that has led up to that state. The student must go through a number
of qualitatively
different stages that come between novice-hood and expertise, and can no more
jump directly from stage 1 to stage 5 than a child can go from Piaget's
pre-operational stage of development to formal operations without passing
through the intervening developmental steps. If we try to teach the skills of
the expert directly to novices, we shall surely fail.
The Dreyfus & Dreyfus account is by no means the only description
of how people become experts. Nor is it to any great extent given in terms of
the underlying psychological processes that enable it to develop. In the next
paragraphs, we look briefly at more specific accounts of how expertise is
acquired, focusing on two cognitive processes: automaticity
and knowledge organization.
Automaticity. From all
accounts of expertise, it is clear that experts still do the things they
learned to do as novices, but more often than not they do them without thinking
about them. The automatization
of cognitive and motor skills is a step along the way to expertise that occurs
in just about every explanation of the process. By enabling experts to function
without deliberate attention to what they are doing, automaticity
frees up cognitive resources that the expert can then bring to bear on problems
that arise from unexpected and hitherto unexperienced
events as well as allowing more attention to be paid to the more mundane though
particular characteristics of the situation. This has been reported to be the
case for such diverse skills as: learning psychomotor skills (Romiszowski, 1993), developing skill as a teacher (Leinhart, 1987), typing (Larochelle,
1982) and the interpretation of x-rays (Lesgold et
al., 1988).
Automaticity occurs as a result of overlearning (Shiffrin & Schneider, 1977). Under the mastery learning
model (Bloom, 1984), a student keeps practicing and receiving feedback,
iteratively, until some predetermined criterion has been achieved. At that
point, the student is taught and practices the next task. In the case of overlearning, the student continues to practice after attaining mastery, even if the
achieved criterion is 100 percent performance. The more students practice using
knowledge and skill beyond just mastery, the more fluid and automatic their
skill will become. This is because practice leads to discrete pieces of
knowledge and discrete steps in a skill becoming fused into larger pieces, or "chunks".
Knowledge organization. We saw briefly above that experts appear
to solve problems by recognizing and interpreting the patterns in bodies of
information, not by breaking down the information into its constituent parts.
If automaticity corresponds to the "cognitive
process" side of expertise, then knowledge organization is the equivalent
of "mental representation" of knowledge by experts. There is
considerable evidence that experts organize knowledge in qualitatively
different ways from novices. It appears that the chunking of information that
is characteristic of experts' knowledge leads them to consider patterns of
information when they are required to solve problems rather than improving the
way they search through what they know to find an answer. For example, chess
Masters are far less effected by time pressure than
lesser players (Calderwood, Klein & Crandall,
1988). Requiring players to increase the number of moves they make in a minute
will obviously reduce the amount of time they have to search through what they
know about the relative success of potential moves. However, pattern
recognition is a much more instantaneous process and will therefore not be as
affected by increasing the number of moves per minute. Since Masters were less
affected than less expert players by increasing the speed of a game of chess,
it seems that they use pattern recognition rather than search as their main
strategy.
Charness (1989) reported changes in a chess player's strategies over a
period of nine years. There was little change in the player's skill at
searching through potential moves. However, there were noticeable changes in
recall of board positions, evaluation of the state of the game, and chunking of
information, all of which, Charness claims, are
pattern-related rather than search-related skills. Moreover, Saariluoma (1990) reported, from protocol analysis, that
strong chess players in fact engaged in less extensive search than intermediate
players, concluding that what is searched is more important than how deeply the
search is conducted.
It is important to note that some researchers (Patel & Groen, 1991) explicitly discount pattern recognition as the
primary means by which some experts solve problems. Also, in a study of expert
X-ray diagnosticians, Lesgold et al. (1988) propose
that experts' knowledge schemata are developed through "deeper"
generalization and discrimination than novices'. Goldstone et al. (2000) cite evidence for
this kind of heightened perceptual discrimination in expert radiologists, beer
tasters and chick sexers. There is also evidence that
the exposure to environmental stimuli that leads to heightened sensory discrimination
brings about measurable changes in the auditory (Weinberger, 1993) and visual (Logothetis, Pauls & Poggio, 1995) cortex.
Internal and external representation.
Two assumptions underlie
this traditional view of mental representation. First, we assume that schemata, mental models and so on change in response to
experience with an environment. The mind is plastic, the environment fixed. Second,
the changes make the internal representations somehow more like the
environment. These assumptions are now seen to be problematic.
First, arguments from
biological accounts of cognition, notably Maturana
& Varela (1980, 1992), explain cognition and
conceptual change in terms of adaptation to perturbations in an environment.
The model is basically
Second, since the
bandwidth of our senses is very limited, we only experience a small number of
the environment's properties (Nagel, 1974; Winn & Windschitl,
2001b). The environment we know directly is therefore a very incomplete and
distorted version, and it is this impoverished view that we represent
internally. The German word "Umwelt", which
means "environment", has come to refer to this limited, direct view
of the environment (Roth, 1999). "Umwelt"
was first used in this sense by the German biologist, Von Uexküll
(1934), in a speculative and whimsical description of what the world might look
like to creatures, such as bees and scallops. The drawings accompanying the
account were reconstructions from what was known about the organisms' sensory
systems. The important point is that each creature's Umwelt
is quite different from another's. Both our physical and cognitive interactions
with external phenomena are, by nature, with our Umwelt,
not the larger environment that science explores by extending the human senses
through instrumentation. This means that the
knowable environment (Umwelt) actually changes as we
come to understand it. Inuit really do see many different types of snow. And
as we saw above, advanced levels of expertise, built through extensive
interaction with the environment, lead to heightened sensory discrimination ability
(Goldstone et al., 2000).
This conclusion has
profound consequences for theories of mental representation (and for theories
of cognitive processes, as we shall see in the next section). Among them is the
dependence of mental representation on concurrent
interactions with the environment. One example is the reliance of our memories
on objects present in the environment when we need to recall something. Often,
we place them there deliberately, such as putting a post-it note on the mirror
–
These newer views of the
nature of mental representation do not necessarily mean we must throw out the
old ones. But they do require us to consider two things. First, in the
continuing absence of complete
accounts of cognitive activity based on research in neuroscience, we must
consider mental images and mental models as metaphorical rather than direct explanations
of behavior. In other words, we can say that people act as if they represented phenomena as mental models, but not that
they have models actually in their heads. This has implications for
instructional practices that rely on the format of messages to
Summary.
In this section we have seen that theories of mental representation have influenced
research in educational technology in a number of ways. Schema theory, or
something very much like it, is basic to just about all cognitive research on
representation. And schema theory is centrally implicated in what we call
message design. Establishing predictability and control over how what appears in
instructional materials and how the depicted information is represented has
been high on the research agenda. So it has been of prime
importance to discover a) the nature of mental schemata and b) how changing
messages affects how schemata change or are created.
Mental representation is also the key to information mapping
techniques that have proven to help students understand and remember what they
read. Here, however, the emphasis is on how the relations among objects and
events are encoded and stored in memory and less on how the objects and events
are shown. Also, these interconcept relations are
often metaphorical. Within the graphical conventions of information maps --
hierarchies, radial outlines and so on -- "above", "below",
"close to" and "far from" use the metaphor of space to
convey semantic, not spatial structure (see Winn & Solomon, 1993, for
research on these "metaphorical" conventions). Nonetheless,
the supposition that representing these relations in some kind of structure in
memory improves comprehension and recall.
The construction of schemata as the basis for computer reasoning
has not been entirely successful. This is largely because computers are
literal-minded and cannot draw on general knowledge of the world outside the
scripts they are programmed to follow. The results of this, for story-writing
at least, are often whimsical and humorous. However, some would claim that the
broader implication is that AI is impossible to attain.
Mental model theory has a lot in common with schema theory.
However, studies of comprehension and transfer of changes of state and
causality in physical systems suggest that well-developed mental models can be "envisioned"
and "run" as students seek answers to questions. The ability of
multimedia computer systems to show the dynamic interactions of components
suggests that this technology has the potential for helping students develop
models that represent the world in accurate and accessible ways.
The way in which mental representation changes with the development
of expertise has perhaps received less attention from educational technologists
than it should. This is partly because instructional prescriptions and
instructional design procedures (particularly the techniques of task analysis)
have not taken into account the stages a novice must go through on the way to
expertise, each of which requires the development of qualitatively different
forms of knowledge. This is an area to which educational technologists could
profitably devote more of their attention.
Finally, we looked at more recent views of mental representation
that require us to treat schemata, images, mental models and so on as
metaphors, not literal accounts of representation. What is more, mental
representations are of a limited and impoverished slice of the external world
and vary enormously from person to person. The role of concurrent interaction
with the environment was also seen to be a determining factor in the nature and
function of mental representations. All of this requires us to modify, but not
to reject entirely, cognitive views of mental representation.
IV. MENTAL PROCESSES.
The second major body of research in cognitive science has
sought to explain the mental processes that operate on the representations we
construct of our knowledge of the world. Of course, it is not possible to
separate our understanding, nor our discussion, of representations and
processes. Indeed, the sections on mental models and expertise made this
abundantly clear. However, a body of research exists that has tended to focus
more on process than representation. It is to this that we now turn.
Information Processing Accounts of Cognition
As we have seen, one of the basic tenets of cognitive theory is
that information that is present in an instructional stimulus is acted upon by
a variety of mediating variables before the student produces a response.
Information-processing accounts of cognition describe stages that information
moves through in the cognitive system and suggests processes that operate at
each step. We therefore begin this section with a general account of human
information processing. This account sets the stage for our consideration of
cognition as symbol manipulation and as knowledge construction.
Although the rise of information-processing accounts of
cognition cannot be ascribed uniquely to the development of the computer, the
early cognitive psychologists' descriptions of human thinking use distinctly
computer-like terms. Like computers, people were supposed to take information
from the environment into "buffers", to "process" it before
"storing it in memory". Information-processing models describe the
nature and function of putative "units" within the human perceptual
and cognitive systems, and how they interact. They trace their origins to
Atkinson & Shiffrin's (1968) model of memory,
which was the first to suggest that memory consisted
of a sensory register, a long-term and a short-term store. According to
Atkinson & Shiffrin's account, information is
registered by the senses and then placed into a short-term storage area. Here,
unless it is worked with in a "rehearsal buffer", it decays after
about fifteen seconds. If information in the short-term store is rehearsed to
any significant extent, it stands a chance of being placed into the long-term store
where it remains more or less permanently. With no more than minor changes,
this model of human information processing has persisted in the instructional
technology literature (R. Gagné, 1974; E. Gagné, 1985) and in
recent ideas about long-term and short-term, or working memory (Gagné
& Glaser, 1987). The importance that every instructional designer gives to
practice stems from the belief that rehearsal improves the chance of
information passing into long-term memory.
A major problem that this approach to explaining human cognition
pointed to was the relative inefficiency of humans at information processing. This
is to be a result of the limited capacity of working memory to roughly seven
(Miller, 1956) or five (Simon, 1974) pieces of information at one time. (E. Gagné
[1985, p. 13] makes an interesting comparison between a computer's and a person's
capacity to process information. The computer wins handily. However, humans' capacity to be creative, to imagine and to solve complex problems
do not enter into the equation.) It therefore became necessary to modify the basic
model to account for these observations. One modification arose from studies
like those of Shiffrin & Schneider (1977) and
Schneider & Shiffrin (1977). In a series of
memory experiments, these researchers demonstrated that with sufficient
rehearsal people automatize what they have learned so
that what was originally a number of discrete items become one single "chunk"
of information. With what is referred to as "overlearning",
the limitations of working memory can be overcome. The notion of chunking
information in order to make it possible for people to remember collections of
more than five things has become quite prevalent in the information-processing
literature (see
Another problem with the basic information-processing account
arose from research on memory for text in which it was demonstrated that people
remembered the ideas of passages rather than the text itself (Bransford & Franks, 1971; Bransford
& Johnson, 1972). This suggested that what was passed from working memory
to long-term memory was not a direct representation of the information in
short-term memory but a more abstract representation of its meaning. These
abstract representations are, of course, schemata, which we discussed at some
length earlier. Schema theory added a whole new dimension to ideas about
information processing. So far, information-processing theory assumed that the
driving force of cognition was the information that was registered by the
sensory buffers -- that cognition was data-driven, or bottom up. Schema theory
proposed that information was, at least in part, top down. This meant,
according to Neisser (1976), that cognition is driven
as much as by what we know as by the information we take in at a given moment.
In other words, the contents of long-term memory play a large part in the
processing of information that passes through working memory. For instructional
designers, it became apparent that strategies were required that guided
top-down processing by activating relevant schemata and aided retrieval by
providing the correct context for recall. The Elaboration Theory of instruction
(Reigeluth & Stein, 1983; Reigeluth
& Curtis, 1987) achieves both of these ends. Presenting an epitome of the
content at the beginning of instruction activates relevant schemata. Providing
synthesizers at strategic points during instruction helps students remember,
and integrate, what they have learned up to that point.
Bottom up information processing approaches have recently
regained ground in cognitive theory as the result of the recognition of the
importance of preattentive perceptual processes
(Marr, 1982; Arbib & Hanson, 1987; Boden, 1988; Treisman, 1988; Pomerantz, Pristach &
Carlson, 1989). Our overview of cognitive science, above,
described computational approaches to cognition. In this return to a
bottom up approach, however, we can see marked differences from the bottom-up
information-processing approaches of the 'sixties and 'seventies. Bottom-up
processes are now clearly confined within the barrier of what Pylyshyn (1984) called "cognitive impenetrability".
These are processes over which we can have no attentive, conscious, effortful
control. Nonetheless, they impose a considerable amount of organization on the
information we receive from the world. In vision, for example, it is likely
that all information about the organization of a scene, except for some depth
cues, is determined preattentively (Marr, 1982). What
is more, preattentive perceptual structure
predisposes us to make particular interpretations of information, top down
(Owens, 1985a, 1985b; Duong, 1994). In other words, the way our perception
processes information determines how our cognitive system will process it.
Subliminal advertising works!
Related is research into implicit learning (Knowlton &
Squire, 1996; Reber & Squire, 1994). Implicit
learning occurs, not through the agency of preattentive
processes, but in the absence of awareness that learning has occurred, at any
level within the cognitive system. For example, after exposure to
"sentences" consisting of letter sequences that do or do not conform to
the rules of an artificial grammar, subjects are able to discriminate,
significantly above chance, grammatical from non-grammatical sentences they
have not seen before, even though they are not aware of the rules of the
grammar, deny that they have learned anything and typically report that they
are guessing (Reber, 1989). Liu (2002) has replicated
this effect using artificial grammars that determine the structure of color
patterns as well as letter sequences. The fact that learning can occur without
people being aware of it is, in hindsight, not surprising. But while this
finding has, to date, escaped the attention of mainstream cognitive psychology,
its implications are wide-reaching for teaching and learning, with or without
the support of technology.
Although we still talk rather glibly about short-term and
long-term memory and use rather loosely other terms that come from
information-processing models of cognition, information-processing theories
have matured considerably since they first appeared in the late 'fifties. The
balance between bottom-up and top-down theories, achieved largely within the
framework of computational theories of cognition, offers researchers a good
conceptual framework within which to design and conduct studies. More
important, these views have developed into full-blown theories of conceptual
change and adaptation to learning environments that are currently providing far
more complete accounts of learning than their predecessors.
Cognition as symbol
manipulation.
How is information that is processed by the cognitive system
represented by it? One answer is "as symbols". This notion lies close
to the heart of traditional cognitive science and, as we saw in the very first
section of this chapter, it is also the source of some of the most virulent
attacks on cognitive theory (Bickhard, 2000; Clancey, 1993). The idea is that we think by mentally
manipulating symbols that are representations, in our mind's eye, of referents
in the real world, and that there is a direct mapping between objects and
actions in the external world and the symbols we use internally to represent
them. Our manipulation of these symbols places them into new relationships with
each other, allowing new insights into objects and phenomena. Our ability to
reverse the process by means of which the world was originally encoded as
symbols therefore allows us to act on the real world in new and potentially
more effective ways.
We need to consider both how well people can manipulate symbols
mentally and what happens as a result.
The clearest evidence for people's ability to manipulate symbols in their "mind's
eye" comes from Kosslyn's (1985) studies of
mental imagery. Kosslyn's basic research paradigm was
to have his subjects create a mental image and then to instruct them directly
to change it in some way, usually by "zooming" in and out on it.
Evidence for the success of his subjects at doing this was found in their
ability to answer questions about properties of the imaged objects that could
only be inspected as a result of such manipulation.
The work of Shepard and his colleagues (Shepard & Cooper,
1982) represents another "classical" case of our ability to
manipulate images in our mind's eye. The best known of Shepard's experimental
methods is as follows. Subjects are shown two three-dimensional solid figures
seen from different angles. The figures may be the same or different. The
subjects are asked to judge whether the figures are the same or different. In
order to make the judgment, it is necessary to mentally rotate one of the
figures in three dimensions in an attempt to orient it to the same position as
the target so that a direct comparison may be made. Shepard consistently found
that the time it took to make the judgment was almost perfectly correlated with
the number of degrees through which the figure had to be rotated, suggesting
that the subject was rotating it in real time in the mind's eye.
Finally, Salomon (1979) speaks more generally of "symbol
systems" and of people's ability to internalize them and use them as "tools
for thought". In an early experiment (Salomon, 1974), he had subjects
study paintings in one of the following three conditions: a) A film showed the
entire picture, zoomed in on a detail, and zoomed out again, for a total of
eighty times, b) the film cut from the whole picture directly to the detail
without the transitional zooming, c) the film showed just the whole picture. In
a posttest of cue attendance, in which subjects were asked to write down as
many details as they could from a slide of another picture, low-ability
subjects performed better if they were in the "zooming" group.
High-ability subjects did better if they just saw the entire picture. Salomon
concluded that zooming in and out on details, which is a symbolic element in
the symbol system of film, television and any form of motion picture, modeled
for the low-ability subjects a strategy for cue attendance that they could
execute for themselves cognitively. This was not necessary for the high ability
subjects. Indeed, there was evidence that modeling the zooming strategy reduced
performance of high-ability subjects because it got in the way of mental
processes that were activated without prompting. Bovy
(1983) found results similar to Salomon's using "irising"
rather than zooming. A similar interaction between ability and modeling was
reported by Winn (1986) for serial and parallel pattern recall tasks.
Salomon continued to develop the notion of internalized symbol
systems serving as cognitive tools. Educational technologists have been
particularly interested in his research on how the symbolic systems of
computers can "become cognitive", as he put it (Salomon, 1988). The
internalization of the symbolic operations of computers led to the development
of a word-processor, called the "Writing Partner" (Salomon, Perkins,
& Globerson, 1991), that
helped students write. The results of a number of experiments showed that
interacting with the computer led the users to internalize a number of its ways
of processing which led to improved metacognition relevant
to the writing task. More recently, (Salomon, 1993) this idea has evolved even
further, to encompass the notion of distributing cognition among students and
machines (and, of course, other students) to "offload" cognitive
processing from one individual, to make it easier to do (Bell & Winn, 2000).
This research has had two main influences on educational
technology. The first, derived from work in imagery of the kind reported by Kosslyn and Shepard, provided an attractive theoretical
basis for the development of instructional systems that incorporate large
amounts of visual material (Winn, 1980, 1982). The promotion and study of
visual literacy (Dondis, 1973; Sless,
1981) is one manifestation of this activity. A number of studies have shown that
the use of visual instructional materials can be beneficial for some students
studying some kinds of content. For example, Dwyer (1972, 1978) has conducted
an extensive research program on the differential benefits of different kinds
of visual materials, and has generally reported that realistic pictures are
good for identification tasks, line drawings for teaching structure and
function, and so on. Explanations for these different effects rest on the
assumption that different ways of encoding material facilitate some cognitive
processes rather than others -- that some materials are more effectively
manipulated in the mind's eye for given tasks than others.
The second influence of this research on educational technology
has been in the study of the interaction between technology and cognitive
systems. Salomon's research which we just described is of course an example of this. The
work of Papert and his colleagues at MIT's Media Lab.
is another important example. Papert (1983) began by
proposing that young children can learn the "powerful ideas" that
underlie reasoning and problem-solving by working (perhaps "playing"
is the more appropriate term) in a microworld over
which they have control. The archetype of such a microworld
is the well-known LOGO environment in which the student solves problems by
instructing a "turtle" to perform certain tasks. Learning occurs when
the children develop problem-definition and debugging skills as they write
programs for the turtle to follow. Working with LOGO, children develop fluency
in problem solving as well as specific skills, like problem decomposition and
the ability to modularize problem solutions. Like Salomon's (1988) subjects , the children who work with LOGO (and in other
technology-based environments [Harel & Papert, 1991]) internalize a lot of the computer's ways of
using information and develop skills in symbol manipulation that they use to
solve problems.
There is, of course, a great deal of research into
problem-solving through symbol manipulation that is not concerned particularly
with technology. The work of Simon and his colleagues is central to this
research. (See Klahr & Kotovsky's [1989] edited volume that pays tribute to his
work.) It is based largely on the notion that human reasoning operates
by applying rules to encoded information that manipulate the information in
such a way as to reveal solutions to problems. The information is encoded as a "production
system" which operates by testing whether the conditions of rules are true
or not, and following specific actions if they are. A simple example: "If
the sum of an addition of a column of digits is greater than ten, then write
down the right-hand integer and carry one to add to the next column". The "if
... then ..." structure
is a simple production system in which a mental action is carried
out (add one to the next column) if a condition is true (the number is greater
than 10).
An excellent illustration is to be found in Larkin and Simon's (1987) account of
the superiority of diagrams over text for solving certain classes of problems.
Here, they develop a production system model of pulley systems to explain how
the number of pulleys attached to a block, and the way in which they are
connected, affects the amount of weight that can be raised by a given force.
The model is quite complex. It is based on the idea that people need to search
through the information presented to them in order to identify the conditions
of a rule (e.g. If a rope passes over two pulleys between its point of
attachment and a load, its mechanical advantage is doubled.) and then compute
the results of applying the production rule in those given circumstances. The
two steps, searching for the conditions of the production rule and computing
the consequences of its application, draw upon cognitive resources (memory and
processing) to different degrees. Larkin and Simon's argument is that diagrams
require less effort to search for the conditions and to perform the
computation, which is why they are so often more successful than text for
problem-solving. Winn, Li & Schill (1991) provided
an empirical validation of Larkin and Simon's account. Many other examples of
symbol manipulation through production systems exist. In the area of
Mathematics education, the interested reader will wish to look at projects
reported by Resnick (1976) and Greeno
(1980) in which instruction makes it easier for students to encode and
manipulate mathematical concepts an relations. Applications of
For the educational technologist, the question arises of how to
make symbol manipulation easier so that problems may be solved more rapidly and
accurately. Larkin & Simon show that one way to do this is to show
conceptual relationships by layout and links in a graphic. A related body of
research concerns the relations between illustrations and text. (See summaries in Willows & Houghton, 1987; Houghton &
Willows, 1987; Mandl & Levin, 1989; Schnotz & Kulhavy, 1994).
Central to this research is the idea that pictures and words can work together
to help students understand information more effectively and efficiently. There
is now considerable evidence that people encode information in one of two
memory systems, a verbal system and an imaginal
system. This "Dual coding" (Paivio, 1983;
Clark & Paivio, 1991), or "Conjoint
retention" (Kulhavy, Lee & Caterino, 1985) has two major advantages. The first is
redundancy. Information that is hard to recall from one source is still
available in the other. Second is the uniqueness of each coding system. As
Levin, Anglin & Carney (1987) have ably
demonstrated, different types of illustration are particularly good at
performing unique functions. Realistic pictures are good for identification,
cutaways and line drawings for showing the structure or operation of things.
Text is more appropriate for discursive and more abstract presentations.
Specific guidelines for instructional design have been drawn
from this research, many presented in the summaries mentioned in the previous
paragraph. Other useful sources are chapters by Mayer and by Winn in Fleming &
Levie's (1993) volume on message design. The
theoretical basis for these principles is by and large the facilitation of
symbol manipulation in the mind's eye that comes from certain types of
presentation.
However, as we saw at the beginning of this chapter, the basic
assumption that we think by manipulating symbols that represent objects and
events in the real world has been called into question (Clancey,
1993) There are a number of grounds for this
criticism. The most compelling is that we do not carry around in our heads
representations that are accurate "maps" of the world. Schemata,
mental models, symbol systems, search and computation are all metaphors that
give a superficial appearance of validity because they predict behavior.
However, the essential processes that underlie the metaphors are more amenable
to genetic and biological than to psychological analysis. We are, after all,
living systems that have evolved like other living systems. And our minds are
embodied in our brains, which are organs just like any other. We shall leave
the implications of this line of argument to those writing other
chapters in this Handbook. For now, we shall turn to a relatively
uncontroversial and well-rooted corollary, that people construct knowledge for
themselves rather than receiving it from someone else.
Knowledge construction
through conceptual change.
One result of the mental manipulation of symbols is that new
concepts can be created. Our combining and recombining of
mentally-represented phenomena leads to the creation of new schemata that may
or may not correspond to things in the real world. When this activity is
accompanied by constant interaction with the environment in order to verify new
hypotheses about the world, we can say that we are accommodating our knowledge
to new experiences in the "classic" interactions described by Neisser (1976) and Piaget (1968), mentioned earlier. When
we construct new knowledge without direct reference to the outside world, then
we are perhaps at our most creative, conjuring from memories thoughts and
expressions of it that are entirely novel. When we looked at schema theory, we
described Neisser's (1976) "perceptual cycle",
which describes how what we know directs how we seek information, how we seek
information determines what information we get and how the information we
receive affects what we know. This description of knowledge acquisition
provides a good account of how top-down processes, driven by knowledge we
already have, interact with bottom-up processes, driven by information in the
environment, to enable us to assimilate new knowledge and accommodate what we
already know to make it compatible.
What arises from this description, which we did not make
explicit earlier, is that the perceptual cycle and thus the entire knowledge
acquisition process is centered on the person not the environment. Some (Duffy
& Jonassen, 1992; Cunningham, 1992a; and chapter
7 in this volume) extend this notion to mean that the schemata a person
constructs do not correspond in any absolute or objective way to the environment.
A person's understanding is therefore built from that person's adaptations to
the environment entirely in terms of the experience and understanding that the
person has already constructed. There is no process whereby representations of
the world are directly "mapped" onto schemata. We do not carry
representational images of the world in our mind's eye. Semiotic theory, which
has recently made an appearance on the Educational stage (Cunningham, 1992b;
Driscoll, 1990; Driscoll & Lebow, 1992) goes one step further, claiming that we do not
apprehend the world directly at all. Rather, we experience it through the signs
we construct to represent it. Nonetheless, if students are given responsibility
for constructing their own signs and knowledge of the world, semiotic theory
can guide the development and implementation of learning activities as Winn,
Hoffman & Osberg (1995) have demonstrated.
These ideas have led to two relatively recent developments in
cognitive theories of learning. The first is the emergence of research on how
students' conceptions change as they interact with natural or artificial
environments. The second is the emergence of new ways of conceptualizing the
act of interacting itself.
Students' conceptions about something change when their
interaction with an environment moves through a certain sequence of events. Windschitl & André (1998),
extending earlier research by Posner et al. (1982) in science education,
identified a number of these. First, something occurs that cannot be explained
by conceptions the student currently has. It is a surprise. It pulls the
student up short. It raises to conscious awareness processes that have been
running in the background. Winograd & Flores
(1986) say that knowledge is now "ready to hand". Reyes
& Zarama (1998) talk about "declaring a
It is clear that conceptual change, thus conceived, takes place
most effectively in a problem-based learning environment that requires students
to explore the environment by constructing hypotheses, testing them, and
reasoning about what they observe. Superficially, this account of learning
closely resembles theories of schema change that we looked at earlier. However,
there are important differences. First, the student is clearly much more in
charge of the learning activity. This is consistent with teaching and learning
strategies that reflect the constructivist point of view. Second, any teaching
that goes on is in reaction to what the student says or does rather than a
proactive attempt to get the student to think in a certain way. Finally, the
kind of learning environment in which conceptual change is easiest to attain is
a highly interactive and responsive one, often that is quite complicated, and
one that more often than not requires the support of technology.
The view of learning proposed in by theories of conceptual
change still assumes that, though interacting, the student and the environment
are separate. Earlier, we encountered Rosch's (1999)
view of the one-ness of internal and external representations. The unity of the
student and the environment has also influenced the way we consider mental
processes. This requires us to examine more carefully what we mean when say a
student interacts with the environment.
The key to this examination lies in two concepts, the "embodiment"
and "embeddedness" of cognition. Embodiment
(Varela et al., 1991) refers to the fact that we use our bodies to help us
think. Pacing off distances and counting on our fingers are examples. More
telling are using gestures to help us communicate ideas (Roth, 2002), or moving
our bodies through virtual spaces so that they become data points on
three-dimensional graphs (Gabert, 2001). Cognition is
as much a physical activity as it is a cerebral one. Embeddedness
(
All of this leads to a view of learning as adaptation to an
environment.
Summary.
Information-processing models of cognition have had a great deal
of influence on research and practice of Educational Technology. Instructional
designers' day-to-day frames of reference for thinking about cognition, such as
working memory and long-term memory, come directly from information-processing
theory. The emphasis on rehearsal in many instructional strategies arises from
the small capacity of working memory. Attempts to overcome for this problem
have led designers to develop all manner of strategies to induce chunking.
Information-processing theories of cognition continue to serve our field well. Research into cognitive processes involved in symbol manipulation
have been influential in the development of intelligent tutoring systems
(Wenger, 1987) as well as in information-processing accounts of learning and
instruction. The result has been that the conceptual bases for some (though not
all) instructional theory and instructional design models have embodied a
production-system approach to instruction and instructional design (see Landa, 1983; Scandura, 1983;
Merrill, 1992). To the extent that symbol-manipulation accounts of cognition
are being challenged, these approaches to instruction and instructional design
are also challenged by association.
If cognition is understood to involve the construction of
knowledge by students, it is therefore essential that they be given the freedom
to do so. This means that, within Spiro et al.'s (1992) constraints of "advanced
knowledge acquisition in ill-structured domains", instruction is less
concerned with content, and sometimes only marginally so. Instead, educational
technologists need to become more concerned with how students interact with the
environments within which technology places them and with how objects and
phenomena in those environments appear and behave. This requires educational
technologists to read carefully in the area of human factors (for example,
Ellis, 1993; Barfield & Furness, 1995) where a great deal of research exists
on the cognitive consequences human-machine interaction. It requires less
emphasis on instructional design's traditional attention to task and content
analysis. It requires alternative ways of thinking about (Winn, 1993b) and
doing (Cunningham, 1992a) evaluation. In short, it is only through the
cognitive activity that interaction with content engenders, not the content
itself, that people can learn anything at all. Extending the notion of
interaction to include embodiment, embeddedness and
adaptation, requires
Accounts of learning through the construction of knowledge by
students have been generally well-accepted since the mid 'seventies and have
served as the basis for a number of the assumptions educational technologists
have made about how to teach. Attempts to set instructional design firmly on
cognitive foundations (DiVesta & Rieber, 1987; Bonner, 1988; Tennyson & Rasch, 1988) reflect this orientation. We examine these in
the next section.
V. COGNITIVE THEORY AND EDUCATIONAL
TECHNOLOGY
Educational technology has for some time been influenced by
developments in cognitive psychology. Up until now, we have focused mainly on
research that has fallen outside the traditional bounds of our field. We have
referred to sources in philosophy, psychology, computer science, and more
recently biology and cognitive neuroscience. In this section, we review the
work of those who bear the label "Educational Technologist" who have
been primarily responsible for bringing cognitive theory to our field. We are,
again, of necessity selective, focusing on the applied side of our field,
instructional design. We begin with some observations about what scholars
consider design to be. We then examine the assumptions that underlay behavioral
theory and practice at the time when instructional design became established as
a discipline. We then argue that research in our field has helped the theory
that designers use to make decisions about how to instruct keep up with
developments in cognitive theory. However, design procedures have not evolved
as they should have. We conclude with some implications about where design
should go.
Theory, Practice and Instructional Design
At the beginning of this chapter we noted that the discipline of
Educational Technology hit its stride during the heyday of behaviorism. This
historical fact was entirely fortuitous. Indeed, our field could have started
equally well under the influence of Gestalt or of cognitive theory. However,
the consequences of this coincidence have been profound and to some extent
troublesome for our field. To explain why, we need to examine the nature of the
relationship between theory and practice in our field. (Our argument is equally
applicable to any discipline.) The purpose of any applied field, such as
educational technology, is to improve practice. The way in which theory guides
that practice is through what Simon (1981) and Glaser (1976) call "design".
The purpose of design, seen this way, is to select the alternative from among
several courses of action that will lead to the best results. Since these
results may not be optimal, but the best one can expect given the state of our
knowledge at any particular time, design works through a process Simon (1981)
calls "satisficing".
The degree of success of our activity as instructional designers
relies on two things: first, the validity of our knowledge of effective
instruction in a given subject domain and, second, the reliability of our
procedures for applying that knowledge. Here is an example. We are given the
task of writing a computer program that teaches the formation of regular
English verbs in the past tense. To simplify matters, let us assume that we
know the subject matter perfectly. As subject-matter specialists, we know a
procedure for accomplishing the task -- add "ed" to the infinitive
and double the final consonant if it is immediately preceded by a vowel. Would
our instructional strategy therefore be to do nothing more than show a sentence
on the computer screen that says, "Add 'ed' to the infinitive and double
the final consonant if it is immediately preceded by a vowel"? Probably not (though such a strategy might be all that is needed
for students who already understand the meanings of "infinitive", "vowel",
and "consonant"). If we know something about instruction, we
will probably consider a number of other strategies as well. Maybe the students
would need to see examples of correct and incorrect verb forms. Maybe they
would need to practice forming the past tense of a number of verbs. Maybe they
would need to know how well they were doing. Maybe they would need a mechanism
that explained and corrected their errors. The act of designing our
instructional computer program in fact requires us to choose from among these
and other strategies the ones that are most likely to "satisfice"
the requirement of constructing the past tense of regular verbs.
Knowing subject matter and something about instruction are
therefore not enough. We need to know how to choose among alternative
instructional strategies. Reigleuth (1983) has pointed the
way. He observes that the instructional theory that guides instructional
designers' choices is made up of statements about relations among the
conditions, methods and outcomes of instruction. When we apply prescriptive
theory, knowing instructional conditions and outcomes leads to the selection of
an appropriate method. For example, an instructional prescription might consist
of the statement, "To teach how to form the past tense of regular English
verbs (outcome) to advanced students of English who are familiar with all
relevant grammatical terms and concepts (conditions), present them with a
written description of the procedure to follow (method)." All the designer
needs to do is learn a large number of these prescriptions and all is well.
There are a number of difficulties with this example, however.
First, instructional prescriptions rarely, if at all, consist of statements at
the level of specificity as the previous one about English verbs. Any theory
gains power by its generality. This means that instructional theory contains
statements that have a more general applicability, such as "to teach a
procedure to a student with a high level of entering knowledge, describe the
procedure". Knowing only a prescription at this level of generality, the
designer of the verb program needs to determine whether the outcome of
instruction is indeed a procedure -- it could be a concept, or a rule, or
require problem-solving -- and whether or not the students have a high level of
knowledge when they start the program.
A second difficulty arises if the designer is not a subject
matter specialist, which is often the case. In our example, this means that the
designer has to find out that "forming the past tense of English verbs"
requires adding "ed" and doubling the consonant. Finally, the
prescription itself might not be valid. Any instructional prescription that is
derived empirically, from an experiment or from observation and experience, is
always a generalization from a limited set of cases. It could be that the
present case is an exception to the general rule. The designer needs to
establish whether or not this is so.
These three difficulties point to the requirement that
instructional designers know how to perform analyses that lead to the level of
specificity required by the instructional task. We all know what these are.
Task analysis permits the instructional designer to identify exactly what the
student must achieve in order to attain the instructional outcome. Learner
analysis allows the designer to determine the most critical of the conditions
under which instruction is to take place. And the classification of tasks,
described by task analysis, as facts, concepts, rules, procedures,
problem-solving and so on links the designer's particular case to more general
prescriptive theory. Finally, if the particular case the designer is working on
is an exception to the general prescription, the designer will have to
experiment with a variety of potentially effective strategies in order to find
the best one, in effect inventing a new instructional prescription along the way.
Even from this simple example, it is clear that, in order to be able to select
the best instructional strategies, the instructional designer needs to know
both instructional theory and how to do task and learner analysis, to classify
learning outcomes into some theoretically-sound taxonomy and to reason about
instruction in the absence of prescriptive principles. Our field, then, like
any applied field, provides to its practitioners both theory and procedures
through which to apply the theory. These procedures are predominantly, though
not exclusively, analytical.
Embedded in any theory are sets of assumptions that are amenable
to empirical verification. If the assumptions are shown to be false, then the
theory must be modified or abandoned as a paradigm shift takes place (Kuhn,
1970). The effects of these basic
assumptions are clearest in the physical sciences. For example, the assumption
in modern Physics that it is impossible for the speed of objects to exceed that
of light is so basic that, if it were to be disproved, the entire edifice of
Physics would come tumbling down. What is equally
important is that the procedures for applying theory rest on the same set of
assumptions. The design of everything from cyclotrons to radio telescopes
relies on the inviolability of the "light barrier".
It would seem reasonable, therefore, that both the theory and
procedures of instruction should rest on the same set of assumptions and,
further, that should the assumptions of instructional theory be shown to be
invalid, the procedures of instructional design should be revised to accommodate the paradigm
shift. In the next section, we show that this was the case when instructional
design established itself within our field within the behavioral paradigm.
However, we do not believe that this is the case today.
The Legacy of Behaviorism
The most fundamental principle of behavioral theory is that
there is a predictable and reliable link between a stimulus and the response it
produces in a student. Behavioral instructional theory therefore consists of
prescriptions for what stimuli to employ if a particular response is intended.
The instructional designer can be reasonably certain that with the right sets of instructional
stimuli all manner of learning outcomes can be attained. Indeed, behavioral
theories of instruction can be quite intricate (Gropper,
1983) and can account for the acquisition of quite complex behaviors. This
means that a basic assumption of behavioral theories of instruction is that
human behavior is predictable. The designer assumes that if an instructional
strategy, made up of stimuli, has had a certain effect in the past, it will
probably do so again.
The assumption that behavior is predictable also underlies the
procedures that instructional designers originally developed to implement
behavioral theories of instruction (Andrews & Goodson, 1981; Gagné,
Briggs & Wager 1988; Gagné & Dick, 1983). If behavior is
predictable, then all the designer needs to do is to identify the subskills the student must master that, in aggregate,
permit the intended behavior to be learned, and select the stimulus and
strategy for its presentation that builds each subskill.
In other words, task analysis, strategy selection, try-out and revision also
rest on the assumption that behavior is predictable. The procedural counterpart
of behavioral instructional theory is therefore analytical and empirical, that
is reductionist. If behavior is predictable, then the
designer can select the most effective instructional stimuli simply by
following the procedures described in an instructional design model.
Instructional failure is ascribed to the lack of sufficient information which
can be corrected by doing more analysis and formative testing.
Cognitive Theory and the
Predictability of Behavior.
The main theme of this chapter has been cognitive theory. We
have argued that cognitive theory provides a much more complete account of
human learning and behavior because it considers factors that mediate between
the stimulus and the response such as mental processes and the internal
representations that they create. We have documented the ascendancy of
cognitive theory and its replacement of behavioral theory as the dominant
paradigm in educational psychology and technology. However, the change from
behavioral to cognitive theories of learning and instruction has not necessarily
been accompanied by a parallel change in the procedures of instructional design
through which the theory is implemented.
You might well ask why a change in theory should be accompanied
by a change in procedures for its application. The reason is that cognitive
theory has essentially invalidated the basic assumption of behavioral theory,
that behavior is predictable. Since the same assumption underlies the
analytical, empirical and reductionist technology of
instructional design, the validity of instructional design procedures is
inevitably called into question.
Cognitive theory's challenges to the predictability of behavior
are numerous and have been described in detail elsewhere (Winn, 1987, 1990,
1993b). The main points may be summarized as follows:
1. Instructional theory is incomplete. This point is trivial at first
glance. However, it reminds us that there is not a prescription for every
possible combination of instructional conditions, methods and outcomes. In
fact, instructional designers frequently have to select strategies without
guidance from instructional theory. This means that there are often times when
there are no prescriptions with which to predict student behavior.
2. Mediating cognitive variables differ in their nature and
effect from individual to individual. There is a good chance that everyone's
response to the same stimulus will be different because everyone's experiences,
in relation to which the stimulus will be processed, are different. The role of
individual differences in learning and their relevance to the selection of
instructional strategies has been a prominent theme in cognitive theory for
more than two decades (Cronbach & Snow, 1977;
Snow, 1992). Individual differences make it extremely difficult to predict
learning outcomes for two reasons. First, to choose effective strategies for students,
it would be necessary to know far more about the student than is easily
discovered. The designer would need to know the student's aptitude for learning
the given knowledge or skills, the student's prior knowledge, motivation,
beliefs about the likelihood of success, learning style, level
of anxiety and stage of intellectual development. Such a prospect would prove
daunting even to the most committed determinist! Second, for prescriptive
theory, it would be necessary to construct an instructional prescription for
every possible permutation of, say, high, low and average levels on every
factor that determines an individual difference. This obviously would render
instructional theory too complex to be useful for the designer. In both the
case of the individual student and of theory, the interactions among many
factors make it impossible in practice to predict what the outcomes of
instruction will be. One way around this problem has been to let students
decide strategies for themselves. Learner control (Merrill, 1988; Tennyson
& Park, 1987) is a feature of many effective computer-based instructional
programs. However, this does not attenuate the damage to the assumption of predictability. If
learners choose their course through a program, it is not possible to predict
the outcome.
3. Some students know how they learn best and will not
necessarily use the strategy the designer selected for them. Metacognition is another important theme in cognitive
theory. It is generally considered to consist of two complementary processes
(Brown, Campione & Day, 1981). The first is
students' ability to monitor their own progress as they learn. The second is to
change strategies if they realize they are not doing well. If students do not
use the strategies that instructional theory suggests are optimal for them,
then it becomes impossible to predict what their behavior will be.
Instructional designers are now proposing that we develop ways to take
instructional metacognition into account as we do
instructional design (Lowyck & Elen, 1994).
4. People do not think rationally as instructional designers
would like them to. Many years ago, Collins (1978) observed that people reason "plausibly".
By this he meant that they make decisions and take actions on the basis of
incomplete information, of hunches and intuition. Hunt (1982) has gone so far
as to claim that plausible reasoning is necessary for the evolution of thinking
in our species. If we were creatures who made decisions only when all the
information needed for a logical choice was available, we would never make any
decisions at all and would not have developed the degree of intelligence that
we have! Schon's (1983, 1987) study of
decision-making in the professions comes to a conclusion that is simliar to Collins'. More recently, research in situated
learning (Brown, Collins & Duguid, 1989, Lave
& Wenger, 1991; Suchman, 1987) has demonstrated
that most everyday cognition is not "planful"
and is most likely to depend on what is afforded by the particular situation in
which it takes place. The situated nature of cognition has led Streibel (1991) to claim that standard cognitive theory can
never act as the foundational theory for instructional design. Be that as it
may, if people do not reason logically, and if the way they reason depends on
specific and usually unknowable contexts, their behavior is certainly
unpredictable.
These and other arguments (see Csiko,
1989) are successful in their challenge to the assumption that behavior is
predictable. The bulk of this chapter has described the factors that come
between a stimulus and a student's response that make the latter unpredictable.
Scholars working in our field have for the most part shifted to a cognitive
orientation when it comes to theory. However, they have not shifted to a new
position on the procedures of instructional design. Since these procedures are
based, like behavioral theory, on the assumption that behavior is predictable,
and since the assumption is no longer valid, the procedures whereby educational
technologists apply their theory to practical problems are without foundation.
Cognitive Theory and
Educational Technology.
The evidence that educational technologists have accepted
cognitive theory is prominent in the literature of our field (Gagné
& Glaser, 1987; Richey, 1986; Spencer, 1988; Winn, 1989a). Of particular
relevance to this discussion are those who have directly addressed the
implications of cognitive theory for instructional design (Bonner, 1988;
Twenty-five years ago, Resnick (1976)
described "cognitive task analysis" for Mathematics. Unlike
behavioral task analysis which produces task hierarchies or sequences (Gagné,
Briggs & Wager, 1988), cognitive analysis produces either descriptions of
knowledge schemata that students are expected to construct, or descriptions of
the steps information must go through as the student processes it, or both. Greeno's (1976, 1980) analysis of mathematical tasks
illustrates the knowledge-representation approach and corresponds in large part
to instructional designers' use of information mapping that we discussed in
section III. Resnick's (1976) analysis of the way
children perform subtraction exemplifies the information-processing approach. Cognitive
task analysis gives rise to cognitive objectives, counterparts to behavioral
objectives. In Greeno's (1976) case, these appear as
diagrammatic representations of schemata, not written statements of what
students are expected to be able to do, to what criterion and under what
conditions (Mager, 1962).
The cognitive approach to learner analysis aims to provide
descriptions of students' mental models (Bonner, 1988), not descriptions of
their levels of performance prior to instruction. Indeed, the whole idea of "student
model" that is so important in intelligent computer-based tutoring (Van Lehn, 1988), very often revolves around ways of capturing
the ways students represent information in memory and how that information
changes, not on their ability to perform tasks.
With an emphasis on knowledge schemata and the premise that
learning takes place as schemata change, cognitively-oriented instructional
strategies are selected on the basis of their likely ability to modify schemata
rather than to shape behavior. If schemata change, DiVesta
and Rieber (1987) claim, students can come truly to
understand what they are learning, not simply modify their behavior.
These examples show that educational technologists concerned
with the application of theory to instruction have carefully thought through
the implications of the shift to cognitive theory for instructional design. Yet
in almost all instances, no-one has questioned the procedures that we follow.
We do cognitive task analysis, describe students' schemata and mental models,
write cognitive objectives and prescribe cognitive instructional strategies.
But the fact that we do task and learner analysis, write objectives and
prescribe strategies has not changed. The performance of these procedures still
assumes that behavior is predictable, a cognitive approach to instructional
theory notwithstanding. Clearly something is amiss.
Can Instructional Design Remain an Independent Activity?
We are at the point where our acceptance of the assumptions of
cognitive theory forces us to rethink the procedures we use to apply it through
instructional design. The key to what it is necessary to do lies
in a second assumption that follows from the assumption of the predictability
of behavior. That assumption is that the design of instruction is an activity
that can proceed independently of the implementation of instruction. If
behavior is predictable and if instructional theory contains valid
prescriptions, then it should be possible to perform
analysis, select strategies, try them out and revise them until a predetermined
standard is reached, and then deliver the instructional package to those who
will use it with the safe expectation that it will work as intended. If, as we
have demonstrated, that assumption is not tenable, we must also question the
independence of design from the implementation of instruction (Winn, 1990).There
are a number of indications that educational technologists are thinking along
these lines. All conform loosely with the idea that decision-making about
learning strategies must occur during instruction rather than ahead of time. In
their details, these points of view range from the philosophical argument that
thought and action cannot be separated and therefore the conceptualization and
doing of instruction must occur simultaneously (Nunan,
1983; Schon, 1987) to more practical considerations
of how to construct learning environments that are adaptive, in real time, to
student actions (Merrill, 1992). Another way of looking at this is to argue
that, if learning is indeed situated in a context (for arguments on this issue,
see McLellan, 1996), then instructional design must
be situated in that context too.
A key concept in this approach is the difference between
learning environments and instructional programs. Other chapters in this volume
address the matter of media research. Suffice it to say here that the most
significant development in our field that occurred between Clark's (1983)
argument that media do not make a difference to what and how students learn and
Kozma's (1991) revision of this argument was the
development of software that could create rich multimedia environments. Kozma (1994) makes the point that interactive and adaptive
environments can be used by students to help them think, an idea that has a lot
in common with Salomon's (1979) notion of media as "tools for thought".
The kind of instructional program that drew much of
The implementation of cognitive principles in the procedures of
educational technology requires a re-integration of the design and execution of
instruction. This is best achieved when
we develop stimulating learning environments whose function is not entirely
prescribed but which can adapt in real time to student needs and proclivities.
This does not necessarily require that the environments be "intelligent"
(although at one time that seemed to be an attractive proposition [Winn,
1987]). It requires, rather, that the system be responsive to the student's
intelligence in such a way that the best ways for the student to learn are
determined, as it were, "on the fly".
There are three ways in which educational technologists have
approached this issue. The first is by developing highly interactive
simulations of complex processes that require the student to used scaffolded strategies to solve problems. One of the best
examples of this is the "World watcher" project (Edelson,
2001; Edelson et al., 2002), in which students use
real scientific data about the weather to learn science. This project has the
added advantage of connecting students with practicing scientists in an
extended learning community. Other examples include Barab
et al's (2000) use of such environments, in this case constructed by the
students themselves, to learn astronomy and Hay et al.'s (2000) use of
atmospheric simulations to teach science.
A second way educational technologists have sought to re-integrate design and learning is
methodological. Brown (1992) describes "design experiments", in which
designers build tools that they test in real classrooms, gather data that
contributes both to the construction of theory and to the improvement of the
tools. This process proceeds iteratively, over a period of time, until the tool
is proven to be effective and our knowledge of why it is effective has been
acquired and assimilated to theory. The design experiment is now the
predominant research paradigm for educational technologists in many research
programs, contributing equally to theory and practice.
Finally, the linear instructional design process has evolved
into a non-linear one, based on the notion of systemic, rather than just
systematic decision-making (Tennyson, 1997). The objectives of instruction are
just as open to change as the strategies offered to students to help them learn
– revision might lead to a change in objectives as easily as it does to a
change in strategy. In a sense, instructional design is now seen to be as
sensitive to the environment in which it takes place as learning is, within the
new view of embodiment and embeddedness described
earlier.
Section Summary
In this section we have reviewed a number of important issues
concerning the importance of cognitive theory to what educational technologists
actually do, namely design instruction. This has led us to consider the
relations between theory and the procedures employed to apply it in practical ways.
We observed that when behaviorism was the dominant paradigm in our field both
the theory and the procedures for its application adhered to the same basic
assumption, namely that human behavior is predictable. We then noted that our
field was effective in subscribing to the tenets of cognitive theory, but that
the procedures for applying that theory remained unchanged and continued to
subscribe to the by now discredited assumption that behavior is predictable. We
concluded by suggesting that cognitive theory requires of our design procedures
that we create learning environments in which learning strategies are not
entirely predetermined, which requires that the environments be highly adaptive
to student actions. Recent technologies that permit the development of virtual
environments offer the best possibility for realizing this kind of learning
environment. Design experiments and the systems dynamics view of instructional
design offer ways of implementing the same ideas.
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