A Cognitive Approach to Instructional Design for Multimedia Learning

Informing Science Journal
Volume 8, 2005
A Cognitive Approach to Instructional
Design for Multimedia Learning
Stephen D. Sorden
Northern Arizona University
Flagstaff, AZ, USA
steve.sorden@nau.edu
Abstract
Aimed at both newcomers to online learning as well as experienced multimedia developers, this
paper addresses the issue of how to avoid unproductive multimedia instructional practices and
employ more effective cognitive strategies. Baddeley’s model of working memory and Paivio’s
dual coding theory suggest that humans process information through dual channels, one auditory
and the other visual. This, combined with Sweller’s Theory of Cognitive Load and Anderson’s
ACT-R cognitive architecture, provides a convincing argument for how humans learn, which
leads to the question of how multimedia instruction can be designed to maximize learning. Cogni-
tive theory and frameworks like Mayer’s Cognitive Theory of Multimedia Learning provide em-
pirical guidelines that may help us to design multimedia instruction more effectively. Mayer ar-
gues that the best way to present multimedia instruction is through visual graphics and informal
voice narration, which takes advantage of both verbal and visual working memories without over-
loading one or the other.
Keywords: working memory, multimedia, cognitive load, act-r, production system, dual coding.
Introduction
Cognitive theory is borne from the relatively new interdisciplinary field of cognitive science.
Cognitive science studies the nature of the mind by drawing from research in a number of areas
including psychology, neuroscience, artificial intelligence, computer science, linguistics, philoso-
phy, and biology. The term cognitive refers to perceiving and knowing, and cognitive scientists
seek to understand mental processes such as perceiving, thinking, remembering, understanding
language, and learning (Stillings, Weisler, Chase, Feinstein, Garfield, & Rissland, 1995). As
such, cognitive science can provide powerful insight into human nature, and, more importantly,
the potential of humans to develop increasingly powerful information technologies.
This paper addresses the problem that much of what we are currently seeing in multimedia in-
struction may actually hinder the
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Cognitive Theory & Multimedia Instruction
include the Theory of Working Memory, Dual Encoding Theory, Cognitive Load Theory, ACT-R
Production System Theory, and the Cognitive Theory of Multimedia Learning. Since most in-
structors have either already been tasked with creating multimedia instruction, or soon will be,
this paper is aimed as much at the general practitioner of multimedia instruction as it is the ex-
perienced e-learning developer.
Popular forms of multimedia instruction, such as online learning and the more inclusive com-
puter-based training (CBT), have created many new possibilities for education. They provide new
ways of delivering content, and they often promote learner-centered environments that can moti-
vate students and add variety to learning. In this environment, instructional units are often ac-
companied by a liberal use of multimedia that is intended to add excitement to the lesson and
hold the learner’s attention. However, visual and auditory components that are intended to stimu-
late rather than educate do not always make for sound instructional design in multimedia delivery
and can quickly become counter-productive to learning.
The human mind is limited in the amount of information that it can process (Miller, 1956). Be-
cause computer-based training can quickly overwhelm these limited capacities (Sweller, 1988,
1994), it becomes important for the instructional designer to understand the principles of cogni-
tive science and how they apply to effective instructional design for online learning. Concepts,
such as working memory, cognitive load, production system theories of knowledge and learning,
self-explaining behaviors, and transfer, all become important considerations for the instructional
designer who must learn to use technology effectively and intelligently, rather than simply be-
cause it is available and seems flashy or exciting.
This is especially relevant as education begins to turn to gaming as the latest innovative technol-
ogy that some educators claim will revolutionize learning. Proponents of gaming in education,
however, should remember that similar predictions were made for mimeograph machines, over-
head projectors, movies, radios, television, and the computer, only to produce disappointing re-
sults after considerable expenditures of money (Cuban, 1996, 2001). One concern should be that
using video games as an educational medium may actually decrease learning in comparison to
simply presenting the information in a straightforward manner using text and pictures.
Until recently, much of what we have seen in multimedia instructional design appears to be based
more on intuition than empirically-based research. For example, it might seem that an online ac-
tivity that uses flashy multimedia and game-like strategies to hold a learner’s attention is good.
The learner is, after all, engaged and his or her attention is fully focused on the activity at hand.
Because it is possible and it seems to emulate a tutoring session, why not throw in a talking figure
that appears on screen and guides the student through the learning process with jokes and lively
gestures? If there is some educational purpose tied to all of the activity on the screen, then, at the
very least, some implicit learning must be happening, which, one might argue, is better than no
learning at all. But cognitive scientific research and instructional science literature is starting to
call some of these assumptions into question (Clark & Mayer, 2002). It is very probable that
much of what is occurring under the label of CBT and e-learning is wasted time or less-than-
optimal instruction. Research suggests that there is a place for CBT and online learning, but it
also cautions us to structure it in a way that efficiently maximizes learning. What is most impor-
tant is not whether the instruction takes place in a classroom or on a computer screen, but whether
empirically-tested strategies for multimedia instruction are employed that facilitate knowledge
construction by the learner.
We will look at some of Richard Mayer’s recommended guidelines for more effective multimedia
instruction, but first let’s consider some of the key assumptions that form the basis of cognitive
theory in relation to human memory and how we learn, beginning with working memory and its
limitations.
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Working Memory
Working memory is a concept that grew out of the older model of short term memory (Atkinson
& Shiffrin, 1968), which was seen more as a structure for temporarily storing information before
it passed to long-term memory. By the late 60’s and early 70’s, researchers began to question
some of the assumptions of short-term memory, however, and a few started to look for more sat-
isfactory explanations. Baddeley and Hitch (1974) eventually proposed a more robust model of
short-term memory, which they called working memory. Their model for working memory was a
system with subcomponents that not only held temporary information, but processed it so that
several pieces of verbal or visual information could be stored and integrated.
Under this model, Baddeley (1986, 1999) proposed that there was a component in working mem-
ory that controlled subcomponents or slave systems. This core system, dubbed the central execu-
tive, was responsible for controlling the overall system and engaging in problem solving tasks
and focusing attention. Baddeley theorized that the central executive could transfer storage tasks
to two slave systems in working memory, so that the central executive would continue to have
capacity for performing more demanding information processing tasks.
These two slave systems eventually became known as the visuo-spatial sketch pad and the pho-
nological loop. The visuo-spatial sketch pad is assumed to maintain and manipulate visual im-
ages. The phonological loop stores and rehearses verbal information, and it has been suggested
that it also has an important evolutionary function in that it facilitates the acquisition of language
by maintaining a new word in working memory until it can be learned (Baddeley, Gathercole, &
Papagno, 1998). More recently, Baddeley (2002) has proposed that it may be necessary to add a
third subsystem to his model, known as an episodic buffer, which has acquired some of the tasks
that were originally attributed to the central executive (now seen as a purely attentional system),
specifically functioning as a storage structure which acts as a limited capacity interface to inte-
grate multiple sources of information from other slave systems.
If we accept the concept of working memory in instructional design, the next question we should
probably ask is, are there limits to how much information can be processed by working memory,
and if so, how can we manage this bottleneck? Cognitive Load Theory states that there is indeed a
limit to the amount of information that can be processed at one time, which creates important
considerations for multimedia instructional design.
Cognitive Load Theory
Cognitive Load Theory (Chandler & Sweller, 1991; Sweller, 1988, 1994), or CLT, states that
working memory is limited in its capacity to selectively attend to and process incoming sensory
data. CLT is concerned with the way in which a learner’s cognitive resources are focused and
used during learning and problem solving, suggesting that for instruction to be effective, care
must be taken to design instruction in a way as to not overload the mind’s capacity for processing
information. The implication for multimedia instruction is that if we only have a very limited
amount of information processing capacity in working memory at any single moment, then in-
structional designers should not be seduced into filling up this limited capacity with unimportant
but flashy “bells and whistles” in a multimedia instructional unit.
An example of what this means for multimedia instructional design is that the layout should be
visually appealing and intuitive, but that activities should remain focused on the concepts to be
learned, rather than trying too much to entertain. This is especially true if the entertainment is
time consuming to construct and is complicated for the learner to master. Working memory can
be overloaded by the entertainment or activity before the learner ever gets to the concept or skill
to be learned.

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According to Sweller, content knowledge is organized into schemas found in long-term memory,
which can be loosely equated to Miller’s (1956) concept of a chunk, and these schemas control
how new information is handled as it enters working memory. Schemas organize simpler ele-
ments and can then act as elements in higher order schemas. In other words, as learning occurs,
increasingly sophisticated schemas are developed and learned procedures are transferred from
controlled to automatic processing. Automation frees capacity in working memory for other
functions.
In multimedia instructional design, this suggests that a task analysis should be done to break
down the skills and information that are needed to learn or perform the educational objective. The
multimedia lesson should try to ensure that the learner has sufficiently automated key core knowl-
edge or tasks, before trying to tackle an overall task that may be beyond the learner’s current abil-
ity range, causing unnecessary frustration and possibly even that the learner drops out of the ac-
tivity. Readers may recognize features of Vygotsky’s Zone of Proximal Development and Piaget’s
concept of scaffolding here.
This process of developing increasingly complicated schemas that build on each other is also
similar to the explanation given by Chi, Glaser, and Rees (1982) for the transition from novice to
expert in a domain as illustrated by De Groot’s (1966) study of chess grand masters compared to
less able players. One explanation is that the grand masters achieve expert status not necessarily
because they processed information any faster than the novices, but because they memorize entire
patterns or configurations of chess pieces on the board and employ appropriate strategies based
on an overall pattern, rather than the positions of individual pieces. Similar to these memorized
chess patterns, schemas increase the amount of information that can be held in working memory
by chunking individual elements of information into more complicated elements.
CLT suggests that instructional techniques that require students to engage in activities that aren’t
directed at schema acquisition and automation can quickly exceed the limited capacity of working
memory and hinder learning objectives. In simple terms, this means that you shouldn’t create un-
necessary activities in connection with a lesson that require excessive attention or concentration
that may overload working memory and prevent one from acquiring the essential information that
is to be learned. This is an important rule in any form of instruction, but it is an essential rule in
multimedia instruction because of the ease with which distractions can be incorporated.
According to Sweller, Van Merrienboer, and Paas (1998), there are three types of cognitive load:
intrinsic, extraneous, and germane. The first, intrinsic cognitive load, occurs during the interac-
tion between the nature of the material being learned and the expertise of the learner. The second
type, extraneous cognitive load, is caused by factors that aren’t central to the material to be
learned, such as presentation methods or activities that split attention between multiple sources of
information, and these should be minimized as much as possible. The third type of cognitive load,
germane cognitive load, enhances learning and results in task resources being devoted to schema
acquisition and automation. Intrinsic cognitive load cannot be manipulated, but extraneous and
germane cognitive load can.
CLT states that an instructional presentation that minimizes extraneous cognitive load can facili-
tate the degree to which learning occurs. Chandler and Sweller (1991) demonstrated that one
method for reducing extraneous cognitive load is to eliminate redundant text. Mousavi, Low, and
Sweller (1995) and Sweller et al. (1998) argued that cognitive load is reduced by the use of dual-
mode (visual-auditory) instructional techniques and that the limited capacity of working memory
is increased if information is processed using both the visual and auditory channels, based on
Baddeley’s model of working memory. Intrinsic, extraneous, and germane cognitive loads form
an equation in which the sum total of the three cannot exceed working memory resources if learn-
ing is to occur (Paas, Renkl, & Sweller, 2003). Following this assumption, Sweller et al. (1998)
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proposed several instructional design techniques based on Cognitive Load Theory. These instruc-
tional principles are identified as the goal-free effect, worked example effect, completion problem
effect, split-attention effect, modality effects, redundancy effect, and the variability effect.
Goal-Free Effect
The goal free-effect suggests that problems should not be given with an end-goal, because it
causes the learner to have to maintain several conditions in working memory while they engage
in problem solving. A goal-free problem reduces extraneous cognitive load and aids in schema
construction. One example is that a conventional geometry problem will require the learner to
find a value for a particular angle, while goal-free problems ask students to find the values of as
many angles as they can.
Worked Example Effect
The worked example effect states that providing learners with worked-out examples of problems
to study can be just as or even more effective in building schemas and performance transfer than
having them work out similar problems themselves. This means that if a multimedia instructional
unit was appealing enough to hold the learner’s attention and cause the learner to really study the
process of a worked-out problem in detail, then it could likely be just as much or more effective
than having them work the problem out themselves, at least initially. One strategy that encourages
learners to process a worked example at a meaningful, deeper level is self-explaining, which we
will discuss shortly.
Completion Problem Effect
The key to learning from worked examples, however, is that the examples must be carefully stud-
ied, which many learners do not do. Completion problems provide a goal state and a partial solu-
tion, and then require the learners to complete the partial solution. This type of problem combines
the strong points of worked examples and conventional problems, because the learner must care-
fully study the partially-worked example and then apply what they have learned to actively solv-
ing the problem.
Split-Attention Effect
Split-attention occurs when learners are presented with multiple sources of information that have
to be integrated before they can be understood. This principle simply states that instruction should
not be designed that causes the learner to have to divide attention between two tasks, such as
searching for information to solve a problem or reading a manual while trying to practice a soft-
ware application on a computer. In the computer example, it is better to have learners read the
manual first and then sit down at the computer to practice what they have read.
Modality Effects
This draws from theories such as Baddeley’s (1986) theory of visual and auditory working mem-
ory subcomponents. It asserts that effective working memory capacity can be increased by using
auditory and visual working memory together rather than using one or the other alone. The in-
formation that is directed at each channel, however, should be such that it can’t be understood in
isolation, but needs to be integrated with information in the other channel in order to be fully un-
derstood. This of course, is one of the strong points of multimedia instruction, where it is easy to
present information visually while also providing related or supporting information through nar-
ration, for example.

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Redundancy Effect
The redundancy effect occurs when information that can be fully understood in isolation, as either
visual or auditory information, is presented to both channels as essentially the same information.
Integrating redundant information in both working memories can actually increase cognitive load.
What is actually happening when this occurs is a form of split-attention. This strategy can vary,
though, depending on the experience of the learner. It is suggested that a diagram with text may
be beneficial for novice learners because they need the text to make sense of the diagram, while a
similar instructional strategy may become redundant for a more experienced learner and the dia-
gram alone would be more effective. Computer manuals that have minimal text and ample dia-
grams are another example of a good way to do this. The general message of the redundancy ef-
fect is that less is often more when it comes to learning so that cognitive capacity is overtaxed.
Variability Effect
This technique recommends variability of practice because it encourages the learner to develop
schemas that aid in transfer of training to similar situations. The more variability in instruction,
the more the learner will develop multiple schemas that allow them to recognize common com-
ponents under different conditions and apply what they have learned to solve problems in other
areas.
In addition to understanding working memory and cognitive load for designing multimedia in-
struction, it is helpful to be familiar with production system theory, which seeks to provide a
model and explanation for how information is transferred from working memory to long term
memory and then retrieved at a later time when it is needed. Some of the production system con-
cepts that we will consider include declarative and procedural knowledge, self-explaining behav-
iors, and transfer of learning.
A Production System Theory
of Knowledge and Learning
Production system theory is important for this discussion in that it further expands the under-
standing of human working memory and how it interacts with long-term memory to identify goals
needed to solve a problem or construct new knowledge. A production system is a model that is
based on a set of condition-action pairs (if-then statements) known as production rules that
form the basis of cognitive skills.
For a production to become active or “fire”, it will test incoming information against a pre-
determined condition. The stronger the production and the more closely the incoming data meet
the condition, the easier it is to trigger the production, which causes a chain reaction, also known
as spread activation, which results in a cognitive action of some sort. According to production
system theory, learning and automation is actually the process of strengthening these production
paths.
One production system that is increasingly being used as a guide for the development of com-
puter-based training (or more specifically, intelligent tutoring systems) is ACT-R, which was
originally developed by John Anderson for simulating human cognition and understanding how
people organize knowledge and produce intelligent behavior.
ACT-R makes several assumptions about how knowledge is represented. The first is that knowl-
edge is stored in two long-term memory structures known as procedural memory and declara-
tive memory. The second is that a chunk represents the basic unit of knowledge in declarative
memory, and the third is that productions (production rules) form the basic unit of knowledge in
procedural memory (Anderson, 1993). One of the most important concepts in ACT-R is this dis-
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tinction between declarative and procedural knowledge and how the two work together to form
human cognition, and that memory and behavior is often a result of some combination or interac-
tion between the two (Anderson & Gluck, 2001). Let’s take a closer look at each of them and how
they contribute to the acquisition or construction of knowledge.
Declarative Knowledge
Declarative knowledge is factual knowledge that can be reported or described, and its most basic
unit is a chunk, which can be hierarchical (chunks within increasingly complicated chunks). De-
clarative knowledge is spread-activated, and each node has an associated strength, which be-
comes stronger with use. If these cognitive units are activated frequently, they can eventually be-
come proceduralized and pass to procedural knowledge. The complexity of the chunks that de-
clarative knowledge is able to manage is affected by existing productions in procedural knowl-
edge. This appears to be in line with the discussion of expert/novice knowledge and research sug-
gesting that experts simply have more sophisticated chunks (or schemas) available to them (Chi,
Glaser, & Farr, 1988).
In regard to declarative knowledge and the building of schemas, it is important to note that mul-
timedia instruction should not strive to teach with the least amount of cognitive load possible, but
at a level that is appropriately tailored to the prior-knowledge of the learner. Research suggests
that students with prior knowledge of a subject tend to process the information at a shallower
level if the material presented is not challenging, while students with no prior knowledge of the
subject do better when cognitive load is kept low (Grace-Martin, 2001). This again raises the
point that a multimedia lesson should try to determine the skill level or knowledge of the learner,
and then adjust the complexity (cognitive load) to an appropriate level in the learner’s Zone of
Proximal Development. This can be achieved through a complicated and expensive form of ad-
justing each step according to the learner’s right or wrong answers, or a simple pretest at the be-
ginning of the tutorial, which then suggests certain units.
Procedural Knowledge
Procedural knowledge is dynamic and involves rules, or productions, that guide how thinking
occurs. Productions are activated through pattern matching and the stronger productions have
their conditions met more quickly. For a production to fire, its condition must be matched against
information or recognized goals that are in working memory. Although Baddeley (2002) might
disagree, Anderson seems to believe that working memory isn't necessarily a separate structure,
but rather activated long-term declarative knowledge and temporary structures from encoding
processes and productions (Anderson 1983). This cognitive input then follows different paths
multiple times as feedback adjusts the weights of connections until the output approximates the
expected results. As knowledge is strengthened it is applied at an increasing rate, which eventu-
ally results in more capacity being left over (cognitive load is reduced) to acquire new knowledge
while the production is being used (Anderson, Boyle, Corbett, & Lewis, 1990). One important
conclusion to this is that regardless of one’s view about the nature of working memory, Sweller’s
(1988, 1994) Cognitive Load Theory is relevant.
Acquisition of Knowledge under the ACT-R Model
Declarative knowledge can be acquired quickly from direct encoding of the environment, while
procedural knowledge takes longer and must be compiled from declarative knowledge through
practice (Anderson, 1993). After a certain amount of practice, the path or production becomes
stable and procedural learning has occurred. The conditions under which we learn procedures,
therefore, are determined by existing declarative knowledge. Once this happens, it becomes

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harder to “rewire” a path, which is why it seems difficult to unlearn something and learn new
conflicting information or skills.
Elaboration refers to the process of thinking about and encoding new concepts and prior knowl-
edge together so that the two become more deeply connected. Anderson (1976) states that rich
elaboration is critical, because it produces multiple redundant paths for recall in procedural
knowledge. Elaboration differs from increasing production rule strength in that strength involves
the encoding of a specific memory record, while elaboration creates additional records that can
help retrieve the original record (Anderson, 2000).

ACT-R states that knowledge can be acquired either in a passive, receptive mode or in an active,
constructive mode. Anderson & Schunn (2000) argue that constructive learning offers no benefits
regarding memory and retrieval of knowledge over passive learning other than constructive learn-
ing may, at times, provide a redundancy of encoding, but even this generative effect is elusive
and not always obtained (Burns, 1992; Hirshman & Bjork, 1988; Slamecka & Graf, 1978;
Slamecka & Kkatsaiti, 1987).
Mayer (2004) supports the theory of constructivist learning while questioning what he calls the
constructivist teaching fallacy, which insists that active learning can only be brought about by
active teaching methods such as discovery learning. In other words, the student can be passively
sitting in a chair, watching a presentation, but still be very engaged mentally and actively con-
structing new knowledge. While entertainment and physical activity may be helpful as a change
of routine, they do not automatically equate to constructivist learning.
Educators should also be aware that encouraging knowledge construction without structure
through activities, such as discovery learning, can have unintended consequences and can lead to
the encoding of inaccurate knowledge and incorrect assumptions, while simply presenting mate-
rial in a meaningful way can be much more efficient and just as effective. All things being equal,
it may be best to present the material in a way that allows the learner to construct new knowledge
through connecting it with prior knowledge.
There are behaviors that learners can engage in to help them acquire deeper understanding (mean-
ingful learning) through the mental construction of what has been presented. One critical behavior
is the practice of self-explanation.
Self-Explanation
Anderson & Schunn (2000) believe that procedural skills are acquired by making references to
past problems and then practicing. ACT-R, therefore, is a theory of learning by doing and a the-
ory of learning by example. (Recall the worked example effect in Cognitive Learning Theory.)
But simply providing the examples is not enough. A learner must thoroughly understand the ex-
amples, and one of the best ways to achieve this is through the activity of self-explaining (Aleven
& Koedinger, 2002; Anderson & Schunn, 2000).
Chi, Bassok, Lewis, Reimann & Glaser (1989) and Mayer, Dow, and Mayer (2003) demonstrated
that students learn better when they apply self-explaining as a metacognitive strategy. Self-
explaining is defined by Chi (2000) as the activity of explaining to oneself in the attempt to make
sense of new information, usually in the context of learning from an expository text.
Chi is careful to point out that self-explaining is different from talking to or explaining something
to others. The focus in self-explaining is simply to understand or make sense of something, while
the purpose of talking or explaining to others is to convey information to them. Talking or ex-
plaining to others adds the requirement to the learner of monitoring the listener's comprehension,
which might prevent the learner from acquiring the knowledge if cognitive load becomes a prob-
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lem. It is reasonable to assume that the cognitive capacity that is taxed through talking may hin-
der the learner from engaging in critical self-explaining behaviors.
An example of how this might be done in multimedia learning would be to intersperse a lesson or
activity with breaks where the learner is encouraged to pause for a moment and engage in an ac-
tivity that cause them to reflect on what has been covered or to re-explain the concept to them-
selves.
Of course, learning or construction of knowledge is generally only useful if it can be transferred
from the situation in which we learn it to another situation where the information or skill needs to
be applied.
Transfer
Transfer of knowledge is one of the main goals of learning and instruction. In practice, however,
transfer is not as clear-cut as it may first seem. Singley & Anderson (1989) demonstrated that
transfer between domains is not usually an all-or-none scenario, but varies depending on how
much the two domains use the same knowledge. Task analysis of the knowledge structures is
critical for understanding how the knowledge that a learner has acquired in one domain may ap-
ply to the other domain.
The importance of this from an educational technology viewpoint is that according to ACT-R,
encoding specificity becomes critical in the development of tutoring applications and online
learning environments if the educational goal is to prepare for performance on a test or task in the
short term. The principle of encoding specificity suggests that for recall to occur, the environment
in which something is learned should approximate the environment in which it is to be applied.
It should not be assumed that transfer of a narrow set of cognitive skills will occur if computer-
assisted learning does not closely approximate the actual situation in which the learner is ex-
pected to apply the newly-acquired knowledge. When it is said that a person understands a do-
main in depth, what is really being said is that the person possesses a rich network of readily
available declarative chunks and production rules that can be used flexibly to solve problems in
many different contexts (Anderson & Schunn, 2000). If transfer is to occur across broad domains,
then it should be expected that extensive practice is needed so that a rich network of highly avail-
ably chunks and productions are developed, which can be used to solve problems flexibly in
many contexts.
The obvious application here for multimedia instruction, and where multimedia instruction should
excel, is that for transfer to occur, the multimedia learning environment should approximate the
situation in which the skill or concept is to be applied as closely as possible, and then have the
learner practice as many potential variations as much and as often as possible.
A Cognitive Theory of Multimedia Learning
This now leads us to the topic of how cognitive science can guide us to create more effective
computer-based training and multimedia instruction, which Mayer (2001) simply defines as the
presentation of material using words and pictures. This definition includes printed materials and
emphasizes what Mayer describes as a learner-centered approach rather than the technology-
centered approach normally associated with the concept of multimedia. Mayer calls for instruc-
tion with multimedia methods that are based on empirical evidence. His Cognitive Theory of
Multimedia Learning (Mayer & Moreno, 2002) states that multimedia narration and graphical
images produce verbal and visual mental representations, which integrate with prior knowledge to
construct new knowledge. According to Mayer and Moreno (1998) and Mayer (2003), the Cogni-
tive Theory of Multimedia Learning is based on several assumptions. First, working memory in-
cludes auditory and visual channels, which are equivocal to the phonological loop and the visuo-

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spatial sketch pad in Baddeley’s (1986) theory of working memory. Second, each subsystem of
working memory has a limited capacity, consistent with Cognitive Load Theory (Sweller, 1988,
1994). Third, humans are knowledge-constructing processors who produce meaningful learning
when they attend to relevant incoming information, organize the information in coherent repre-
sentational structures, and then integrate it with other existing knowledge (Mayer, 1996, 1999).
Fourth, connections can be made only if corresponding visual and verbal representations are in
working memory at the same time, which is similar to Paivio’s (1986; Clark and Paivio, 1991)
Dual Coding Theory.
Meaningful Learning
Mayer and Moreno (1998, 2003) describe meaningful learning as deep understanding of the mate-
rial, which includes attending to salient aspects of the presented material, retaining relevant in-
formation in both visual working memory and auditory working memory, organizing it into a co-
herent mental structure, and integrating it with relevant prior knowledge. Mayer (2001) asserts
that multimedia learning combining animation with narration generally improves performance on
retention tests better than when information is presented as either text or narration alone. More
importantly, meaningful learning is demonstrated when the learner can apply what is presented in
new situations, and students perform better on problem-solving transfer tests when they learn
with words and pictures.
Mayer, Fennell, Farmer, and Campbell (2004) cite evidence that two important ways to promote
meaningful learning in e-learning are to design activities that reduce cognitive load, which frees
working memory capacity for deep cognitive processing during learning, and to increase the
learner’s interest, which encourages the learner to use this freed capacity for deep processing dur-
ing learning. Once again, interest can be stimulated simply by presenting the material in a visu-
ally appealing way, accompanied by lively and personable wording or narration. Mayer (2003)
lists five cognitive processes that contribute to meaningful learning from multimedia: selecting
words, selecting images, organizing words, organizing images, and integrating.
The Science of E-Learning
Mayer (2003) defines a science of e-learning as including three elements: evidence, theory, and
applications. According to Mayer, the element of evidence means that there is a base of repli-
cated findings from rigorous and appropriate research studies. The element of theory requires
that there must be a research-based theory of how people learn in electronic learning environ-
ments, which yields testable predictions. Applications are theory-based principles for how to de-
sign electronic learning environments, which themselves can be tested in research studies. As part
of his evidence-seeking efforts for the science of e-learning, Mayer (2001, 2003) presents nine
major effects which developed out of dozens of studies. These replicated effects are: modality
effect, contiguity effect, multimedia effect, personalization effect, coherence effect, redundancy
effect, pre-training effect, signaling effect, and the pacing effect. An explanation of each of these
nine effects, referred to here as principles (Moreno & Mayer, 2000), follows:
Modality Principle
The modality principle states that better transfer occurs when multimedia combines anima-
tion/pictures and narration as opposed to animation/pictures and on-screen text, i.e. students learn
better in multimedia messages when words are presented as spoken language rather than printed
text. This relates directly to the Theory of Dual Coding which suggests that we have two types of
working memory, one verbal and one visual, and that we learn best when both channels are used
together, rather than overloading one or the other.
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