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cognitive load theory
Cognitive load theory (CLT) is one of the most influential theories in instructional
design, a highly effective guide for the design of multimedia and other learning materials.
This edited volume brings together the most prolific researchers from around the world
who study various aspects of cognitive load to discuss its current theoretical as well
as practical issues. The book is divided into three parts: The first part describes the
theoretical foundations and assumptions of CLT, the second discusses the empirical
findings about the application of CLT to the design of learning environments, and the
third part concludes the book with discussions and suggestions for new directions for
future research. It aims to become the standard handbook in CLT for researchers and
graduate students in psychology, education, and educational technology.
Jan L. Plass is Associate Professor of Educational Communication and Technology in
the Steinhardt School of Culture, Education, and Human Development at New York
University (NYU), where he co-directs the Games for Learning Institute. He is also
the founding director of the Consortium for Research and Evaluation of Advanced
Technologies in Education (CREATE). His research is at the intersection of cognitive
science, learning sciences, and design, and seeks to enhance the educational effectiveness
of visual environments. Dr. Plass’s current focus is on the cognitive and emotional
aspects of information design and the interaction design of simulations and educational
games for science education and second language acquisition. He has received funding
for his research from the U.S. Department of Education’s Institute of Education Sciences,
the National Science Foundation, the National Institutes of Health, and, most recently,
Microsoft Research and the Motorola Foundation.
Roxana Moreno is Educational Psychology Professor at the University of New Mexico.
Her research interests are in applying cognitive–affective theories of learning to derive
principles of instructional design for a diversity of learners. Her investigations involve
undergraduate students as well as K–12 students who are culturally and linguistically
diverse. Dr. Moreno’s most recent projects include an engineering education grant
aimed at applying empirically based technology tools to foster problem solving and
cognitive flexibility in pre-college students and the “Bridging the Gap Between Theory
and Practice in Teacher Education: Guided Interactive Virtual Environments for CaseBased Learning” grant, for which she received the prestigious Presidential Early Career
Award in Science and Engineering. Other awards and honors include the American
Psychological Association Richard E. Snow Award, being a Fulbright Senior Specialist in
the areas of education and instructional media design, and an appointment as a veteran
social scientist for the Department of Education.
Roland Brünken is Full Professor in Education and Dean for Student Affairs of the Faculty of Empirical Human Sciences at Saarland University, Germany. He is also Speaker of
the special interest group Educational Psychology of the German Psychological Association (DGPs). His main research interests are concerned with using new technology for
education, direct measurement of cognitive load by behavioral measures, and applying
cognitive psychology to the instructional design of multimedia learning environments.
Cognitive Load Theory
Edited by
Jan L. Plass
New York University
Roxana Moreno
University of New Mexico
Roland Brünken
Saarland University, Germany
CAMBRIDGE UNIVERSITY PRESS
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Cambridge University Press
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Published in the United States of America by Cambridge University Press, New York
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© Cambridge University Press 2010
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may take place without the written permission of Cambridge University Press.
First published in print format 2010
ISBN-13
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ISBN-13
978-0-521-86023-9
Hardback
ISBN-13
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Paperback
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of urls for external or third-party internet websites referred to in this publication,
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contents
Contributors
page vii
Introduction
1
part one. theory
1 Cognitive Load Theory: Historical Development and Relation
to Other Theories
Roxana Moreno and Babette Park
9
2 Cognitive Load Theory: Recent Theoretical Advances
John Sweller
29
3 Schema Acquisition and Sources of Cognitive Load
Slava Kalyuga
48
4 Individual Differences and Cognitive Load Theory
Jan L. Plass, Slava Kalyuga, and Detlev Leutner
65
part two. empirical evidence
5 Learning from Worked-Out Examples and Problem Solving
Alexander Renkl and Robert K. Atkinson
6 Instructional Control of Cognitive Load in the Design of
Complex Learning Environments
Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
7 Techniques That Reduce Extraneous Cognitive Load
and Manage Intrinsic Cognitive Load during
Multimedia Learning
Richard E. Mayer and Roxana Moreno
v
91
109
131
vi
Contents
8 Techniques That Increase Generative Processing in
Multimedia Learning: Open Questions for Cognitive
Load Research
Roxana Moreno and Richard E. Mayer
153
part three. discussion
9 Measuring Cognitive Load
Roland Brünken, Tina Seufert, and Fred Paas
10 From Neo-Behaviorism to Neuroscience: Perspectives on the
Origins and Future Contributions of Cognitive Load Research
Richard E. Clark and Vincent P. Clark
11 Cognitive Load in Learning with Multiple Representations
Holger Horz and Wolfgang Schnotz
12 Current Issues and Open Questions in Cognitive
Load Research
Roland Brünken, Jan L. Plass, and Roxana Moreno
Index
181
203
229
253
273
contributors
robert k. atkinson
Arizona State University
roxana moreno
University of New Mexico
roland brünken
Saarland University
fred paas
Erasmus University Rotterdam
richard e. clark
University of Southern California
babette park
Saarland University
vincent p. clark
University of New Mexico
jan l. plass
New York University
holger horz
University of Applied Sciences,
Northwestern Switzerland
alexander renkl
University of Freiburg
slava kalyuga
University of New South Wales
wolfgang schnotz
University of Koblenz-Landau
liesbeth kester
Open University of the Netherlands
tina seufert
Ulm University
detlev leutner
University of Duisburg-Essen
john sweller
University of New South Wales
richard e. mayer
University of California, Santa
Barbara
jeroen j. g. van merriënboer
University of Maastricht
vii
cognitive load theory
introduction
What is Cognitive Load Theory (CLT)? The objective of CLT is to predict
learning outcomes by taking into consideration the capabilities and limitations of the human cognitive architecture. The theory can be applied to a
broad range of learning environments because it links the design characteristics of learning materials to principles of human information processing.
CLT is guided by the idea that the design of effective learning scenarios has
to be based on our knowledge about how the human mind works. Starting
from this premise, different processes of knowledge acquisition and understanding are described in terms of their demands on the human cognitive
system, which is seen as an active, limited-capacity information processing
system. Taking into account the demands on cognitive resources induced
by the complexity of the information to be learned, the way in which the
instruction is presented to the learner, and the learner’s prior experience
and knowledge, CLT aims to predict what makes learning successful and
how learning can be effectively supported by teaching and instruction.
Because of its applicability for a broad range of instructional materials,
including Web-based and multimedia instruction, CLT is a frequently discussed concept in educational psychology and applied learning sciences. A
growing body of empirical research has become available in recent years that
describes the relationships among human cognitive architecture, the design
of educational materials, and successful learning. Moreover, the research
conducted in past years has led to a more detailed description of the theoretical components of CLT, including processes of schema acquisition,
capacity limitations, and different causes for load, namely, intrinsic load
(generated by the difficulty of the materials), extraneous load (generated by
the design of the instruction and materials), and germane load (the amount
of invested mental effort).
1
2
Introduction
Considering the theoretical and empirical developments that have been
made in this area, as well as the importance of the implications of CLT
for the design of learning environments, especially for those using Webbased or multimedia formats for the delivery of instruction, there is a need
to present the current knowledge about CLT in a handbook for research,
education, and application. This edited volume brings together the most
prolific researchers from around the world who study various aspects of
cognitive load to discuss current theoretical as well as practical issues of CLT.
The book is divided into three parts: The first part describes the theoretical foundation and assumptions of CLT, the second part examines the
empirical findings about the application of CLT to the design of learning
environments, and the third part concludes the book with a discussion and
directions for future research.
The chapters in the first part of this book discuss the theoretical underpinnings of CLT. In Chapter 1, Moreno and Park place CLT into the broader
context of the learning sciences by providing a historical review of the
assumptions underlying CLT and by relating the theory to other relevant
theories in psychology and education. In Chapter 2, Sweller presents five
assumptions underlying CLT using an analogy between evolution by natural selection and human cognitive architecture. Specifically, the chapter
describes Sweller’s most recent information store, borrowing, randomness
as genesis, narrow limits of change, and environment organizing and linking
CLT assumptions (Sweller, 2004). In addition, Chapter 2 describes the three
categories of cognitive load and the additive load hypothesis, according to
which intrinsic, extraneous, and germane cognitive load add to produce a
total cognitive load level during learning. In Chapter 3, Kalyuga describes
more fully the process of schema acquisition according to CLT and presents
three instructional principles in its support: the direct initial instruction
principle, the expertise principle, and the small step-size of knowledge
change principle. In Chapter 4, Plass, Kalyuga, and Leutner expand on the
first three chapters by offering a typology of individual differences that may
have an effect on learners’ working memory capacity. To this end, they
distinguish between differences in information gathering, information processing, and regulation of processing and explain how such differences may
affect cognitive load during learning. Taken together, the first four chapters
of this book synthesize the history of CLT, describe the main principles
underlying the current CLT, highlight the relation of CLT to individual
learner differences, and relate CLT to other theoretical models.
As Sweller argues in Chapter 2, not only is the type of load imposed by
the difficulty of the material (intrinsic load) and the instructional design
Introduction
3
(germane or extraneous loads) critical to CLT, but the learner’s prior knowledge is as well. Information or instructional activities that are crucial to
novices may interfere with further learning by more expert learners, giving rise to the expertise reversal effect (Kalyuga, Chapter 3, this volume).
Instructional methods that promote schema acquisition in novices (leading
to increased germane cognitive load) may contribute to extraneous cognitive load for more expert learners. Moreover, as Plass, Kalyuga, and Leutner
argue in Chapter 4, cognitive load is most likely to arise when spatial ability
is low or when students do not have good metacognitive skills. In sum, the
relationship between the three types of load and learners’ characteristics is
far from simple.
The second part of this book synthesizes the findings of recent empirical
studies conducted by the leading researchers in the cognitive load field and
translates the insights gained from this work into guidelines for the design
of learning environments. In Chapter 5, Renkl and Atkinson summarize
research in which CLT is used to design learning environments that promote problem solving with worked-out examples. In Chapter 6, Kester, Paas,
and van Merriënboer summarize research in which CLT is used to design
learning environments that promote complex cognitive processes such as air
traffic control systems. Finally, Chapters 7 and 8 summarize the research program of Mayer and Moreno, who have developed a set of empirically based
principles to reduce intrinsic and extraneous cognitive load (Chapter 7)
and increase generative processing (Chapter 8) in multimedia learning.
CLT began as an instructional theory that, based on assumptions regarding the characteristics of the human cognitive architecture, was used to
generate a series of cognitive load effects in randomised, controlled experiments. Some examples are the modality effect, according to which multiple
sources of information that are unintelligible in isolation result in less learning when they are presented in single-modality as opposed to dual-modality
format (Low & Sweller, 2005; Mayer & Moreno, Chapter 7, this volume);
the redundancy effect, according to which the presence of information that
does not contribute to schema acquisition or automation interferes with
learning (Mayer & Moreno, Chapter 7, this volume; Sweller, 2005); and
the worked example effect, according to which studying worked examples
promotes problem solving compared with solving the equivalent problems (Renkl, 2005; Renkl & Atkinson, Chapter 5, this volume). The fact
that each one of these cognitive load effects was replicated across a variety of learning environments and domains led cognitive load researchers
to derive corresponding evidence-based instructional principles. CLT can,
therefore, provide instructional designers with guidelines for the design
4
Introduction
of multimedia learning environments that include verbal representations
of information (e.g., text, narrated words) and pictorial representations of
information (e.g., animation, simulation, video, photos), as well as for the
design of Web-based learning environments. The research reviewed in the
second part of this volume focuses on empirical work that applied CLT to
multimedia and online learning environments.
According to CLT’s additivity hypothesis, learning is compromised when
the sum of intrinsic, extraneous, and germane loads exceeds available working memory capacity and any cognitive load effect is caused by various
interactions among these sources of cognitive load. For example, many cognitive load effects occur because a reduction in extraneous cognitive load
permits an increase in germane cognitive load, which in turn enhances
learning. This is presumably the underlying cause of the redundancy effect
reviewed by Mayer and Moreno in Chapter 7. However, these effects only
occur when intrinsic cognitive load is high. If intrinsic cognitive load is low,
sufficient working memory resources are likely to be available to overcome a
poor instructional design that imposes an unnecessary extraneous cognitive
load. Kester, Paas, and van Merriënboer (Chapter 6, this volume) explore
this hypothesis by examining cognitive load effects in complex instructional
environments. Ideally, good instructional design should reduce extraneous
cognitive load and use the liberated cognitive resources to increase germane
cognitive load and learning. Renkl and Atkinson (Chapter 5, this volume)
explore this hypothesis by examining cognitive load effects in worked-out
example instruction (aimed at reducing extraneous cognitive load) that
includes different cognitive activities to engage students in deeper learning
(aimed at increasing germane cognitive load). As Moreno and Park describe
in Chapter 1, the empirical findings produced by cognitive load researchers
over the past twenty years motivated a series of revisions of CLT since its
inception.
The third part of the book includes chapters that discuss the current state
of CLT as well as open questions for future developments. In Chapter 9,
Brünken, Seufert, and Paas discuss the general problem of measuring cognitive load, summarize the different types of measures that are commonly
used, and discuss current issues in cognitive load measurement, such as
the problem of global versus differential measurement of the three types
of cognitive load and the relationship between cognitive load and learners’
prior knowledge. A critical evaluation of CLT from the perspective of the
broader field of educational psychology and cognitive psychology is provided by Clark and Clark in Chapter 10. In Chapter 11, Horz and Schnotz
compare CLT with other theoretical models used in the field of instructional
Introduction
5
design, namely Mayer’s (2005) cognitive theory of multimedia learning and
Schnotz’s (2005) integrated model of text and picture comprehension. The
last chapter of this volume presents some current open questions in cognitive load research (Brünken, Plass, & Moreno, Chapter 12).
This edited volume could not have been completed without the help
of numerous collaborators. We thank the contributing authors for their
patience in completing this book, which changed its form more than once
based on the chapters we received. We would also like to thank Simina
Calin, our editor at Cambridge University Press, who has patiently guided
us through the process of completing this volume, as well as her assistant
Jeanie Lee. Our work has been generously supported by a number of funding agencies, including the National Science Foundation,1 the Institute of
Education Sciences,2 the National Institutes of Health,3 and the German
Research Foundation (DFG),4 as well as by Microsoft Research5 and the
Motorola Foundation.6 Any opinions, findings, conclusions, or recommendations expressed in this book are those of the authors and do not necessarily
reflect the views of the funding agencies.
We especially thank our partners and loved ones, who made this work
possible through their enduring emotional support.
Jan L. Plass, Roxana Moreno, and Roland Brünken
New York, Albuquerque, and Saarbrücken
August 2009
references
Low, R., & Sweller, J. (2005). The modality principle in multimedia learning. In
R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 147–158).
New York: Cambridge University Press.
Mayer, R. E. (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.),
Cambridge handbook of multimedia learning (pp. 31–48). New York: Cambridge
University Press.
1
Support from the National Science Foundation: Grant Nos. 0238385 and 0648568 to
Roxana Moreno, and Grant No. HSD-0332898 to Jan L. Plass.
2
Support from the Institute of Education Sciences, U.S. Department of Education: Grant
Nos. R305K050140, R305A090203 and R305B080007 to Jan L. Plass.
3
Support from the National Institutes of Health: Grant No. 1R01LM009538–01A1 from the
National Library of Medicine to Jan L. Plass.
4
Support from the Deutsche Forschungsgemeinschaft (German Research Foundation):
Grant No. BR2082/6–1 to Roland Brünken.
5
Grant to Jan L. Plass and Ken Perlin.
6
Innovation Generation Grant to Jan L. Plass.
6
Introduction
Renkl, A. (2005). The worked-out examples principle in multimedia learning. In
R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 229–245).
New York: Cambridge University Press.
Schnotz, W. (2005). An integrated model of text and picture comprehension. In
R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 49–69). New
York: Cambridge University Press.
Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science,
32, 9–31.
Sweller, J. (2005). The redundancy principle in multimedia learning. In R. E. Mayer
(Ed.), Cambridge handbook of multimedia learning (pp. 159–167). New York: Cambridge University Press.
part one
THEORY
1
Cognitive Load Theory: Historical Development
and Relation to Other Theories
roxana moreno and babette park
The goal of this introductory chapter is to provide a historical review of
the assumptions underlying Cognitive Load Theory (CLT) and to place
the theory into the broader context of the learning sciences. The chapter
focuses on the theoretical developments that guided the research on cognitive load and learning for the past twenty years and is organized in the
following way. First, we examine the nature of the cognitive load construct
and compare it to similar psychological constructs. Second, we present a
historical review of the development of CLT’s assumptions in the following
four stages: (a) extraneous cognitive load in problem solving, (b) intrinsic
cognitive load and the first additivity hypothesis, (c) germane cognitive load
and the second additivity hypothesis, and (d) the evolutionary interpretation of CLT. Finally, we conclude the chapter by examining the constructs
and assumptions of CLT in relation to other theories in psychology and
education.
the cognitive load construct
CLT is a psychological theory because it attempts to explain psychological
or behavioral phenomena resulting from instruction. Psychological theories are concerned with the possible relationships among psychological
constructs or between a psychological construct and an observable phenomenon of practical consequence. A psychological construct is an attribute
or skill that happens in the human brain. In CLT, the main constructs of
interest are cognitive load, hence the name of the theory, and learning. CLT
was developed to explain the effects of instructional design on these two
constructs.
The idea of cognitive load, however, was not new at the time the theory
was developed. A similar psychological construct called “mental load” was
9
10
Roxana Moreno and Babette Park
already defined in the human factors psychology domain by Moray (1979)
as the difference between task demands and the person’s ability to master
these demands. The mental load construct is essential to the human factors science, which is concerned with understanding how human-specific
physical, cognitive, and social properties may interact with technological
systems, the human natural environment, and human organizations. The
relation of mental load or workload and performance has been investigated in many fields, including cognitive ergonomics, usability, human
computer/human machine interaction, and user experience engineering
(Hancock & Desmond, 2001; Hancock & Meshkati, 1988; Huey & Wickens,
1993; Wickens & Hollands, 2000). Likewise, the construct of task difficulty
was used to refer to the mental load experienced during performance with
the practical goal of developing measures of job difficulty for several professional specialties (Madden, 1962; Mead, 1970). The influence of this work
in the development of CLT is clear. For instance, the development of the
first subjective cognitive load scale (Paas & van Merriënboer, 1994) was
inspired by a previously developed scale to assess perceived item difficulty
in cognitive tests (Bratfisch, Borg, & Dornic, 1972).
After conducting a careful review of the human factors literature, MacDonald (2003) concluded that mental workload is more than just the
amount of work that has to be done to accomplish a task. Other psychological factors, such as demand expectations, the actual effort expended
during performance, and the perceived adequacy of performance, need to
be taken into consideration when predicting mental load. For example,
even if the amount of work that needs to be done to accomplish a task is
high, different workload levels will result from individual differences in the
willingness to spend effort on such a task. This willingness will depend on,
among other factors, the learner’s self-schemas and how relevant the task is
perceived to be in terms of helping the learner achieve meaningful, personal
goals (Eccles, Wigfield, & Schiefele, 1998; Thrash & Elliott, 2001).
The cognitive load construct is similar to the workload construct in that
it takes into consideration the demands that a certain task imposes on an
individual. However, it does not take into consideration the psychological
effects that individuals’ beliefs, expectations, and goals have on their load
perceptions. This has been argued to be one of the limitations of CLT (Bannert, 2002; Moreno, 2006). Early psychological theories have recognized
the multidimensional nature of the mental load construct by defining it
as the psychological experience that results from the interaction of subjective individual characteristics and objective task characteristics (Campbell,
1988; Wood, 1986). In the words of Kantowitz (1987), mental load is “a
Cognitive Load Theory
11
subjective experience caused by . . . motivation, ability, expectations, training, timing, stress, fatigue, and circumstances in addition to the number,
type and difficulty of tasks performed, effort expended, and success in
meeting requirements” (p. 97).
CLT has mostly focused on how the objective characteristics of the task
affect cognitive load and, in turn, learning. The only individual characteristic that is explicitly included in its theoretical framework is students’ prior
knowledge (Kalyuga, Chandler, & Sweller, 1998). Other individual characteristics that are highly predictive of learning, such as cognitive abilities and
styles, self-regulation, motivation, and affect, are not considered within the
CLT framework (Moreno, 2005). Nevertheless, several studies have examined additional individual differences that are relevant to cognitive load and
learning (see Chapter 4, this volume).
the stages of clt development
Stage I: Extraneous Cognitive Load in Problem Solving
Traditional CLT focused on the relation between the type of cognitive processes elicited by different problem-solving methods and schema acquisition. Although not fully developed as a theory, the first articles using
the term cognitive load date to the late 1980s (Sweller, 1988, 1989). In this
work, the founder of CLT, John Sweller, focused on the cognitive demands
of the means–ends analysis method used in conventional problem-solving
practice, a method in which learners independently solve a large number of
problems to develop expertise. Using a production system approach, Sweller
argued that means–ends analysis imposes a higher cognitive load on students’ limited cognitive processing capacity than using a non-specific goal
strategy to solve problems. The theoretical conclusion was that the cognitive
effort spent in means–ends analysis leads to problem solution (the goal of
the immediate task) but does not leave sufficient cognitive resources for
schema acquisition (the goal of instruction). Therefore, the first hypothesis
raised by CLT established a relationship between the instructional methods used to promote problem solving and the cognitive load induced by
such methods. More specifically, “cognitive processing load is an important
factor reducing learning during means-ends analysis” (Sweller, 1988, p. 263).
Later empirical studies cite the 1988 and 1989 articles as the main reference
to CLT and further elaborate on its initial ideas. For instance, Sweller,
Chandler, Tierney, and Cooper (1990) state that CLT “is concerned with how
cognitive resources are distributed during learning and problem solving.
12
Roxana Moreno and Babette Park
Many learning and problem-solving activities impose a heavy, extraneous
cognitive load that interferes with the primary goal of the task at hand”
(p. 176). This article and several others show that the cognitive load construct
in this first stage of the theory was mainly concerned with the unnecessary
cognitive demands imposed by instructional design (van Merriënboer &
Ayres, 2005). Because this source of load can be eliminated by appropriately
redesigning the instructional materials, it was called extraneous cognitive
load.
In addition to stating the goal of CLT, this stage is characterized by
having set the following main assumptions (Cooper & Sweller, 1987; Sweller
& Cooper, 1985; Sweller et al., 1990):
(a) Schema acquisition is the building block of skilled performance.
(b) Schema acquisition requires attention directed to problem states and
their associated solution moves.
(c) Learning is enhanced when learners attend to schema acquisition.
(d) Other cognitive activities must remain limited to avoid imposing a
heavy cognitive load that interferes with learning.
As seen from the previous list, the early stage of CLT was concerned with
the question of how to design instruction to promote problem solving in
well-defined domains and was inspired by the research on expert problemsolving and schema theory (Bartlett, 1932; Chase & Simon, 1973; Chi, Glaser,
& Rees, 1982; De Groot, 1966; Simon & Simon, 1978). According to schema
theory, people represent knowledge as networks of connected facts and
concepts that provide a structure for making sense of new information
(Anderson & Bower, 1983; Rumelhart & Ortony, 1976). Experts in a domain
have well-structured schemata that are automatically activated during problem solving, which allows them to categorize problems according to their
structural characteristics (Egan & Schwartz, 1979; Simon & Gilmartin, 1973).
This is why CLT states that instruction should avoid using methods that are
unrelated to the development of problem-solving schemas such as means–
ends analysis.
Although studying worked-out problems is a less demanding method
to develop problem-solving skills than the means–ends analysis method
(see Chapter 5, this volume), it is also susceptible to inducing extraneous
cognitive load when the worked examples are poorly designed. Therefore,
during the initial stage of CLT development, researchers also began to
examine the effects that manipulations of the design of worked examples
had on students’ learning, such as the five cognitive-load effects listed in
Table 1.1 (Sweller, van Merriënboer, & Paas, 1998).
13
Cognitive Load Theory
table 1.1. Traditional cognitive-load effects focusing on the reduction of extraneous
cognitive load
Effect and references
Description
Goal-free effect (Owen & Sweller, 1985;
Sweller, Mawer, & Ward, 1983; Tarmizi
& Sweller, 1988)
Goal-free problems reduce extraneous
cognitive load compared with
means–ends analysis by focusing
students’ attention on problem states
and available operators.
Replacing means–ends analysis with the
study of worked examples reduces
extraneous cognitive load by focusing
students’ attention on problem states
and solution steps.
Replacing multiple sources of mutually
referring information with a single,
integrated source of information
reduces extraneous cognitive load by
avoiding the need to mentally integrate
the information sources.
Completing partially completed problems
rather than solving entire problems
reduces extraneous cognitive load by
reducing the size of the problem space,
which helps focus attention on
problem states and solution steps.
Replacing multiple sources of
information that can be understood
in isolation with one source of
information reduces extraneous
cognitive load by eliminating the
processing of redundant information.
Worked-example effect (Cooper &
Sweller, 1987; Sweller & Cooper, 1985)
Split-attention effect (Chandler &
Sweller, 1991, 1992; Sweller & Chandler,
1994; Sweller et al., 1990)
Completion effect (Paas, 1992; van
Merriënboer & De Croock, 1992)
Redundancy effect (Chandler & Sweller,
1991; Sweller & Chandler, 1994)
For instance, presenting non-integrated mutually referring pieces of
information (e.g., graphics, symbols) in worked-out problems was predicted to produce extraneous cognitive load by forcing the learner to mentally integrate the information, a process that is unrelated to the development of problem-solving schemas. Therefore, CLT predicts that presenting
integrated rather than non-integrated problem information sources should
promote learning by eliminating extraneous load (Chandler & Sweller,
1991). Several studies have shown, indeed, that integrated mutually referring sources of information promote better learning (Chandler & Sweller,
1991, 1996; Kalyuga, Chandler, & Sweller, 1999; Tarmizi & Sweller, 1988).
14
Roxana Moreno and Babette Park
An interesting development that started in the early 1990s was the use of a
seven-point self-reported rating of participants’ perceived difficulty to test
the theory’s assumptions (Paas & van Merriënboer, 1993).
It is important to note that the assumptions articulated at this early stage
were heavily influenced by the theoretical and empirical advances of cognitive psychology, which eventually launched the development of cognitive
approaches to learning (Ausubel, 1960; Bruner, Goodnow, & Austin, 1956;
Chomsky, 1957). For instance, the theory assumes that the human cognitive
architecture is characterized by a very limited short-term memory (Kahneman, 1973; Miller, 1956) and a very large long-term memory, which are two
of the basic assumptions in early information-processing models (Atkinson
& Shiffrin, 1968).
In addition, the theory assumes that the function of learning is to store
automated schemas in long-term memory so that working memory load
can be reduced during problem solving (Sweller, 1994). This assumption
is based on the distinction between automatic and controlled processing
proposed by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977)
more than a quarter-century ago. Considerable research had identified two
qualitatively distinct ways to process information. Automatic processing is
fast, effortless, not limited by working memory capacity, and developed
with extensive practice; in contrast, controlled processing is relatively slow,
mentally demanding, and highly dependent on working memory capacity
(Fisk & Schneider, 1983; Logan, 1979, 1980; Posner & Snyder, 1975).
In sum, schema development reduces the constraints of a limited capacity working memory in two ways. First, a highly complex schema can be
manipulated as one element rather than as multiple interacting elements
when brought into working memory. Second, well-developed schemata are
processed automatically, minimizing the demands of cognitive resources
to tackle the task at hand. Consequently, the thrust of CLT during its first
development stage was to assist instructional designers to structure information appropriately so that extraneous cognitive load was reduced and
novice learners could spend their limited cognitive resources in schema
development.
Two issues related to these assumptions, however, were still underspecified during the first stage of CLT. First, the theory did not explain which
cognitive processes lead to schema acquisition. Although the literature on
the role of extensive practice was cited as the basis for developing automated
schemas in problem solving (Kotovsky, Hayes, & Simon, 1985; Schneider &
Schiffrin, 1977; Shiffrin & Schneider, 1977), only examples of potential
schema acquisition activities were provided by the early CLT developments.
Cognitive Load Theory
15
Free Capacity
Total
Working
Memory
Capacity
Schema
Acquisition
& Automation
Activities
Other Mental
Activities
Extraneous Load = Total Cognitive Load
figure 1.1. A visual representation of the assumptions underlying the first stage of
cognitive load theory development.
A precise definition of the mental activities that promote schema acquisition is not only necessary to make predictions about learning from different
instructional designs, it is crucial to accurately predict cognitive load and
learning (the main goal of CLT) because the definition of extraneous processing is, by exclusion, “any activity not directed to schema acquisition and
automation” (Sweller & Chandler, 1994, p. 192). Second, there was a question as to whether engaging in schema acquisition and automation activities
would have any effects on the learner’s cognitive load. If so, how might this
type of load be distinguished from the extraneous load that was posited to
produce negative learning effects? As will be seen in the next sections, this is
still a topic of intense debate and one that led to the current triarchic theory
of cognitive load (see Chapter 7, this volume). Figure 1.1 summarizes the
assumptions of CLT during its first development stage.
Stage II: Intrinsic Cognitive Load and the First Additivity Hypothesis
The second stage of CLT is characterized by the introduction of an additional source of cognitive load, namely, intrinsic cognitive load. CLT moved
from focusing solely on the extraneous cognitive load that may originate
by the way in which instructional materials and methods are designed to
including the load that “is imposed by the basic characteristics of information” (Sweller, 1994, p. 6). More specifically, some materials are difficult to
learn or some problems are difficult to solve because they require processing several elements that simultaneously interact with each other. Intrinsic
load can, therefore, be estimated “by counting the number of elements that
16
Roxana Moreno and Babette Park
must be considered simultaneously in order to learn a particular procedure”
(Sweller & Chandler, 1994, p. 190).
The very idea of intrinsic load stemmed from the research of Halford,
Maybery, and Bain (1986) and Maybery, Bain, and Halford (1986), as cited
by Sweller and colleagues (Sweller, 1993; Sweller & Chandler, 1994). In these
investigations, the researchers found that difficulty in the processing of
transitive inference in children’s reasoning (e.g., a is taller than b; b is taller
than c; which is the largest?) was heavily influenced by the need to consider
all the elements of the premises simultaneously.
According to CLT, intrinsic cognitive load depends on two factors: the
number of elements that must be simultaneously processed in working
memory on any learning task and the prior knowledge of the learner. The
load resulting from element interactivity varies among and within different subject areas. For instance, solving algebra problems involves dealing
with higher element interactivity than learning the vocabulary of a second
language, and creating grammatically correct sentences in a second language involves higher element interactivity than learning the vocabulary
itself (Sweller, 1993). In addition, prior knowledge has an effect on intrinsic
load in that a large number of interacting elements for a novice may be a
single element for an expert who has integrated the interacting elements in
one schema.
In addition to introducing a second source of cognitive load, the first
version of what we have called the “additivity hypothesis” was developed
during this stage (see Chapter 12, this volume):
When people are faced with new material, the cognitive load imposed by
that material will consist of the intrinsic cognitive load due to element
interactivity and extraneous cognitive load determined by the instructional design used. If that total cognitive load is excessive, learning and
problem solving will be inhibited. (Sweller, 1993, p. 7)
The original additivity hypothesis motivated the development of the following two additional CLT assumptions. The first one is that extraneous
load is the only source of load that can be reduced by good instructional
design. In contrast, instructors have no control over intrinsic load (Sweller,
1994), a claim that continues to be held by many cognitive load researchers
(Paas, Renkl, & Sweller, 2003). The practical implication of this new assumption is that the only way to manage high intrinsic load is to help students
develop cognitive schemata that incorporate the interacting elements.
The second new assumption embedded in the original additivity hypothesis is that the extent to which extraneous load should be reduced depends
17
Cognitive Load Theory
Free Capacity
Total
Working
Memory
Capacity
Extraneous Load
(Reducible by
instructional
design)
Intrinsic Load
(Irreducible by
instructional
design)
Total
Cognitive
Load
figure 1.2. A visual representation of the assumptions underlying the second stage
of cognitive load theory development.
on the existing level of intrinsic load: if the level of intrinsic load is low,
then a high extraneous load may not impede learning because students are
able to handle low interactivity material; if intrinsic load is high, adding a
high extraneous load will result in a total load that might exceed cognitive
resources (Sweller, 1994; Sweller & Chandler, 1994). This assumption refocused CLT as a theory that is mainly concerned with the learning of complex
tasks, where students are typically overwhelmed by the amount of elements
and interactions that need to be processed simultaneously (Paas, Renkl, &
Sweller, 2004). Despite the fact that the theory has offered methods to measure element interactivity (Sweller & Chandler, 1994), what is not clear is the
criterion for determining a priori when a task is sufficiently complex to be
likely to produce cognitive-load effects on learning. Figure 1.2 summarizes
the assumptions of CLT at its second stage of development.
Stage III: Germane Cognitive Load and the Second Additivity Hypothesis
More recently, CLT has undergone two major revisions. The first one was
the introduction of the third source of cognitive load, germane load. The
distinctive characteristic of germane cognitive load is that, unlike the other
two, it has a positive relationship with learning because it is the result of
devoting cognitive resources to schema acquisition and automation rather
than to other mental activities. The idea of germane load originated from
the need to specify the cognitive-load effects of the schema acquisition
and automation activities that were proposed to be beneficial to learning according to the original CLT. In addition, many scholars questioned
the theory’s focus on the need to reduce extraneous load and the idea
18
Roxana Moreno and Babette Park
Free Capacity
Germane Load
(Increasable by
instructional
design)
Total
Working
Memory
Capacity
Extraneous Load
(Reducible by
instructional
design)
Total
Cognitive
Load
Intrinsic Load
(Irreducible by
instructional
design??)
figure 1.3. A visual representation of the assumptions underlying the most recent
cognitive load theory development.
that the purpose of instruction is to keep mental effort at a minimum during the learning process. According to the revised theory, “as long as the load
is manageable, it is not the level of load that matters but its source” (Paas
et al., 2004, p. 3). Freeing available cognitive capacity by reducing extraneous load will not necessarily result in increased learning unless the freed
resources are directed to activities that are relevant for schema acquisition.
The second development during this stage was the revision of the additivity hypothesis, which now integrates the three load sources as follows:
Intrinsic, extraneous, and germane cognitive loads are additive in that,
together, the total load cannot exceed the working memory resources
available if learning is to occur. The relations between the three forms of
cognitive load are asymmetric. Intrinsic cognitive load provides a base
load that is irreducible other than by constructing additional schemas
and automating previously acquired schemas. (Paas et al., 2003, p. 2)
Figure 1.3 summarizes the assumptions of CLT in its current form. The
revised theory has inspired a large amount of empirical work that has been
useful in pointing out the theoretical and methodological issues that still
need investigation. We discuss a few of the major open questions next.
First, a careful review of the most recent stage of CLT development
reveals that the theory is still unclear about the irreducible nature of intrinsic
Cognitive Load Theory
19
cognitive load. Several theoretical articles hold the initial assumption that
intrinsic load cannot be altered by the instructional designer (Paas et al.,
2003; Paas, Tuovinen, Tabbers, & van Gerven, 2003). Others suggest that
intrinsic load may be altered by reducing element interactivity, although “by
artificially reducing intrinsic cognitive load, understanding is also reduced”
(van Merriënboer & Sweller, 2005, p. 157), suggesting that a reduction
in the intrinsic difficulty of the materials to be learned hurts learning.
This conclusion is puzzling in that, according to the additivity hypothesis, reductions in this load type should free cognitive resources that can
be used in other, productive mental activities. The disagreement on this
assumption is represented by the question marks next to the intrinsic cognitive load construct in Figure 1.3 and has motivated several studies that
attempted to reduce intrinsic load using instructional methods such as pretraining, sequencing, and chunking (Gerjets, Scheiter, & Catrambone, 2004,
2006; Lee, Plass, & Homer, 2006; Pollock, Chandler, & Sweller, 2002; van
Merriënboer, Kirschner, & Kester, 2003).
However, an issue that emerges when reviewing this research is that it is
difficult to reconcile the findings because they do not consistently support
either prediction. Moreover, even in the cases in which a positive learning effect is found (for all or a subgroup of students), it is possible to
offer an alternative explanation using the same theory. More specifically,
the original instructional design of the materials (without the intervention
to reduce intrinsic load) may have been poorly designed. The success of
the intervention can then be explained as the result of having reduced the
extraneous source of load imposed by the original instructional design.
Unfortunately, the lack of good measures of intrinsic, extraneous, and germane load has prevented this research from advancing CLT because it is not
possible to understand the causes of the diverse findings (see Chapter 9, this
volume).
In the same vein, the introduction of the third load type in CLT has
inspired many studies in which manipulations of germane sources of load
are examined (Renkl, Atkinson, & Grobe, 2004; Gerjets et al., 2004; Berthold
& Renkl, 2009). For instance, some studies have investigated the learning effects of combining methods aimed at reducing extraneous load and
increasing germane load, thus redirecting students’ cognitive resources from
irrelevant to relevant schema acquisition activities (Seufert & Brünken,
2006; Seufert, Jänen, & Brünken, 2007). In these studies, the investigators
tested the CLT hypothesis that methods that reduce extraneous load will free
cognitive resources, which then can be used to engage in schema acquisition
activities.
20
Roxana Moreno and Babette Park
Stage IV: The Evolutionary Interpretation of CLT
For the past twenty years, cognitive load research has focused on investigating the effects of instructional design on learning and cognitive load using
the theoretical developments summarized in the previous sections. Very
recently, however, Sweller (2003, 2004) has suggested that the principles
underlying evolution by natural selection are the same as those underlying the human cognitive architecture, therefore offering an evolutionary
perspective about CLT. More specifically, five biological principles, namely,
the information store, borrowing, randomness as genesis, narrow limits of
change, and environmental organizing and linking principles, are proposed
as an essential connection between the human cognitive system and human
biology. We do not describe this new interpretation of CLT here because
John Sweller provides a detailed description of the five principles and their
instructional implications in the next chapter of this book.
where does clt fit in the big picture?
In this section, we examine the constructs and assumptions of CLT in
relation to other theories in psychology and education. The relation of
CLT to the theoretical models of multimedia learning offered by Mayer
(2001) and Schnotz (2001) are given special consideration in a separate
chapter (see Chapter 11, this volume); therefore, they are not discussed in this
chapter.
An important first point that needs to be made is that CLT was never
claimed to be a learning theory. Instead, it was articulated to explain the
relation between the human cognitive architecture, instructional design,
and learning. Nevertheless, CLT should be parsimonious with contemporary thought in the learning sciences. In this regard, it should be noted that
when CLT was first advanced, it was heavily inspired by the contributions
that cognitive psychology had to offer at that point in time. Although several developments have refined the theory throughout its lifespan, none
of them have challenged the basic assumptions drawn from the computational models of the mind that inspired the theory (Simon & Gilmartin,
1973).
Cognitively based approaches to learning, however, are only one of the
four main frameworks under which learning theories fall. The other three
are behaviorism, which focuses only on the objectively observable aspects
of learning (Guthrie, 1959); sociocognitive theories of learning, which focus
on the type of learning that occurs even when there is no direct interaction
with the environment (Bandura, 2000); and constructivist learning theories,
Cognitive Load Theory
21
which view learning as a process in which the learner actively constructs or
builds new ideas or concepts. How does CLT relate to these approaches?
Similar to other cognitively based theories, CLT looks beyond behavior
by concentrating on “unobservable” phenomena, specifically, the cognitive load experienced by individuals in different instructional conditions.
Unlike sociocognitive theories of learning, CLT has limited its scope to
enactive learning scenarios, in which students learn by experiencing the
consequences of their own actions. Moreover, a sociocognitive area that
continues to receive increasing attention from educational psychologists
and educators alike is self-regulation (Boekaerts, Pintrich, & Zeidner, 2000;
Winne, 2005; Zimmerman, 2002).
Self-regulated learners are able to set more specific learning goals, use
more learning strategies, better monitor their learning, and more systematically evaluate their progress toward learning goals than their counterparts
(Boekaerts, 2006). Although these learner characteristics are very likely to
have an effect on how successfully students deal with high cognitive load
situations, CLT does not currently provide insight into how the internal and
external management of cognitive load may interact (Bannert, 2000).
Finally, the relation between CLT and constructivism merits special consideration. Constructivist theories have been very influential in guiding
educational practices and curriculum, and have become the basis for the
standards of teaching developed by national education groups, such as the
National Council of Teachers of Mathematics (2000) and the American
Association for the Advancement of Science (1993). Constructivist learning
theories focus not only on how people construct knowledge within themselves (i.e., individual constructivism) but also on how they co-construct
knowledge with others (i.e., social constructivism). Furthermore, social
constructivist perspectives extend sociocognitive theories by considering a
wider range of social influences, such as those stemming from individuals’
culture, history, and direct interaction with others.
CLT does not include assumptions about the relationship between cognitive load, instruction, and co-constructing knowledge with others. The
psychological theory of distributed cognition, however, suggests that cognitive processes may be distributed across the members of a social group
(Hutchins, 1995), and Vygotsky’s (1978) social constructivism asserts that the
interactions with “more knowledgeable others” support the development
of individuals’ schemas and thinking processes. Therefore, differences in
effort and learning are likely to arise when students work alone compared
with learning with more capable others or in groups (Moreno, 2009).
Nevertheless, CLT has provided explanations for the ineffective use of
some of the most advocated constructivist strategies of the past three or
22
Roxana Moreno and Babette Park
four decades, such as discovery learning, inquiry, and problem-based learning. Some forms of discovery ask students to try to find a solution to a
problem or an explanation for a phenomenon with minimum guidance
(Kato, Honda, & Kamii, 2006). According to CLT, when learners are novices
in a domain, the cognitive load associated with unguided discovery is too
high to promote learning because novices lack well-developed schemas to
guide their knowledge construction process (Kirshner, Sweller, & Clark,
2006; Tuovinen & Sweller, 1999). This idea has received empirical support.
Poorer learning outcomes related to unguided discovery appear rather general (Mayer, 2004; Moreno & Mayer, 2007; Taconis, Ferguson-Hessler, &
Broekkamp, 2001).
However, the research shows that when problem-based learning and
inquiry methods are designed to support high-order thinking, they can be
highly effective. As explained by Hmelo-Silver, Duncan, and Chinn (2007),
an important difference between the discovery method and the other two
constructivist methods is that the latter typically include a large range of
scaffolds to guide the process of constructing knowledge individually or
in groups (see Chapter 6, this volume). Interestingly, cognitive load theorists disagree among each other when called to evaluate the effectiveness
of problem-based learning and inquiry methods using the CLT framework. Some argue that inquiry and problem-based learning are instructional approaches that allow for flexible adaptation of guidance; therefore,
they are compatible with the way in which the human cognitive structures
are organized and can be effectively used to promote learning (Schmidt,
Loyens, van Gog, & Paas, 2007).
Others cite the classic literature on the worked-example effect and argue
that presenting the solution of a problem to novice students should always
lead to better learning than requiring them to search for a solution, which
will necessarily lead to a heavy working memory load (Sweller, Kirschner, &
Clark, 2007). Because constructivist methods are aimed at more actively
engaging learners in the learning process, it seems that they are likely to be
good candidates for promoting germane load and learning (see Chapter 8,
this volume). Similar to other open questions identified in this chapter, the
question of whether and how instruction should be designed to support
students’ knowledge construction is likely to remain open until carefully
controlled experimental studies, including appropriate controls and measures of the three cognitive load types, are conducted. The third part of
this volume is dedicated to discussing in detail some potential venues that
basic research (Chapter 12, this volume) and neuroscience (Chapter 10, this
volume) have to offer to advance CLT in future years.
Cognitive Load Theory
23
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2
Cognitive Load Theory: Recent Theoretical Advances
john sweller
Cognitive Load Theory (CLT) began as an instructional theory based on our
knowledge of human cognitive architecture. It proved successful in generating a series of cognitive load effects derived from the results of randomised,
controlled experiments (Clark, Nguyen, & Sweller, 2006). This chapter summarises the theory, including its general instructional implications. Many
of the theory’s specific instructional implications, which provide its prime
function and purpose, are discussed in other chapters in this volume and
therefore will not be discussed in detail in this chapter (see Table 2.1 for a
summary).
human cognition
The processes of human cognition constitute a natural information-processing system that mimics the system that gave rise to human cognitive
architecture: evolution by natural selection. Both human cognition and
biological evolution create novel information, store it for subsequent use,
and are capable of disseminating that information indefinitely over space
and time. By considering human cognition within an evolutionary framework, our understanding of the structures and functions of our cognitive
architecture are being transformed. In turn, that cognitive architecture has
profound instructional consequences. CLT is an amalgam of human cognitive architecture and the instructional consequences that flow from that
architecture.
From an evolutionary perspective, there are two categories of human
knowledge: biologically primary and biologically secondary knowledge
(Geary, 2007, 2008). Biologically primary knowledge is knowledge we have
evolved to acquire over many generations. Examples are general problemsolving techniques, recognising faces, engaging in social relations, and
29
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John Sweller
table 2.1. Cognitive load effects
Cognitive load
effect
Worked-Example
Completion
Split-Attention
Modality
Redundancy
Expertise reversal
Guidance fading
Goal-Free
Element interactivity
Isolated/interacting
elements
Variable examples
Imagination
Description
Studying worked examples results in better
performance on subsequent tests of problem
solving than solving the equivalent problems
(Renkl, 2005).
Requiring learners to complete partially solved
problems can be just as effective as worked
examples (Paas & van Merriënboer, 1994).
Multiple sources of information that are unintelligible
in isolation result in less learning when they are
presented in split-attention as opposed to
integrated format (Ayres & Sweller, 2005).
Multiple sources of information that are unintelligible
in isolation result in less learning when they are
presented in single-modality as opposed to
dual-modality format (Low & Sweller, 2005).
The presence of sources of information that do not
contribute to schema acquisition or automation
interfere with learning (Sweller, 2005).
With increasing expertise, instructional procedures
that are effective with novices can lose their
effectiveness, whereas ineffective techniques can
become effective (Kalyuga, 2005).
With increasing expertise, learners should be presented
worked examples followed by completion problems
and then full problems rather than worked examples
alone (Renkl, 2005).
Problems presented in goal-free form enhance learning
compared with conventional problems (Paas, Camp,
& Rikers, 2001).
Cognitive load effects are only obtainable using
high rather than low element interactivity
material (Sweller, 1994).
Learning is enhanced if very high element
interactivity material is first presented as isolated
elements followed by interacting elements
versions rather than as interacting elements
form initially (Pollock, Chandler & Sweller,
2002).
Examples with variable surface features enhance
learning compared with examples with similar
features (Paas & van Merriënboer, 1994).
Imagining procedures or concepts enhance
learning compared with studying materials
(Leahy & Sweller, 2004).
Primary cognitive
load source
Extraneous
Extraneous
Extraneous
Extraneous
Extraneous
Extraneous
Extraneous
Extraneous
Intrinsic
Intrinsic
Germane
Germane
Cognitive Load Theory: Recent Theoretical Advances
31
listening to and speaking our native language. Primary knowledge is modular in that we have independent, cognitive modules that allow us to acquire
the relevant knowledge unconsciously, effortlessly, and without external
motivation simply by membership in a human society. Learning to speak
our native language provides a clear example. We are not normally explicitly
taught how to organise our lips, tongue, voice, and breath when learning to
speak. We have evolved to learn these immensely complex procedures just
by listening to others speaking.
In contrast, biologically secondary knowledge is culturally dependent.
We have evolved to acquire such knowledge in a general sense rather than
having evolved to acquire particular knowledge modules such as speaking. Biologically secondary knowledge is acquired consciously and usually
requires mental effort. In modern times, we invented educational institutions to impart biologically secondary knowledge precisely because, unlike
biologically primary knowledge, it tends not be learned simply by immersion in a functioning society. Virtually everything taught in educational
institutions consists of biologically secondary knowledge. For example,
unlike listening and speaking, few people are likely to learn to read and
write without being explicitly taught to read and write. Simple immersion
in a reading and writing society is unlikely to be sufficient.
CLT and, indeed, instructional design in general, applies to biologically
secondary knowledge (Sweller, 2007, 2008). It does not apply to biologically
primary knowledge. Thus, CLT is relevant to those aspects of knowledge
dealt with in educational institutions (secondary knowledge) rather than
the possibly far larger body of primary knowledge that we have specifically
evolved to acquire.
When dealing with biologically secondary knowledge, human cognition
can be characterised by five basic principles that govern its functions and
processes. These principles apply equally to the processes that govern biological evolution (Sweller, 2003, 2004; Sweller & Sweller, 2006) and as such
constitute a natural information processing system. They will be discussed
in more detail subsequently but can be summarised as follows. The information store principle states that human cognition includes a large store of
information that governs the bulk of its activity. Long-term memory provides this function. The borrowing and reorganising principle states that
almost all of the information held in long-term memory has been borrowed
from other long-term memory stores. Information obtained by imitation,
listening, or reading exemplifies this process.
The randomness as genesis principle indicates that random generation
followed by tests of effectiveness provide the initial source for the generation
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John Sweller
of all information held in long-term memory. When faced with a problem
for which solution knowledge is not available or only partly available, the
random generation of moves followed by tests of the effectiveness of those
moves is an example. The narrow limits of change principle indicates that
all effective changes to long-term memory occur slowly and incrementally. The capacity limitations of working memory when dealing with novel
information exemplifies this principle. The environment organising and
linking principle states that unlimited amounts of organised information
from long-term memory can be used by working memory to determine
interactions with the external world. These principles constitute a natural information processing system and derive from evolutionary theory.
Although the derivation will not be discussed in this chapter (see Sweller,
2003, 2004; Sweller & Sweller, 2006), the link with biological evolution
establishes an essential connection between the human cognitive system
and human biology. A detailed description of the five principles follows.
Long-Term Memory and the Information Store Principle
Functioning in a complex environment requires a complex store of information to govern activity in that environment. The primary driver of activity
of human cognition is its large store of information held in long-term
memory. The realisation that long-term memory is not simply a repository
of isolated, near-random facts but, rather, the central structure of human
cognition, developed slowly. Its origins can probably be traced to the early
work on expertise in the game of chess. When De Groot (1965) followed
by Chase and Simon (1973) found that the only difference between chess
masters and less able players was in memory of chess-board configurations
taken from real games, it established the central importance of long-term
memory to cognition. Chess has long been seen, appropriately, as a game
that required the most sophisticated of human cognitive processes, a game
of problem solving and thought. The discovery that the major difference
between people who differed in ability was in terms of what they held in
long-term memory changed our view of cognition. (It should be noted that
what is held in long-term memory includes problem-solving strategies.)
Long-term memory was not just used by humans to reminisce about the
past but, rather, was a central component of problem solving and thought.
If long-term memory is essential to problem solving, we might expect
results similar to those obtained using the game of chess to also be obtained
in educationally more relevant areas. Increasing levels of expertise should
be associated with increasing ability to reproduce relevant problem states
Cognitive Load Theory: Recent Theoretical Advances
33
and, indeed, the same expert–novice differences obtained in chess have been
established in a variety of educationally relevant areas (e.g. Egan & Schwartz,
1979; Jeffries, Turner, Polson, & Atwood, 1981).
The amount of information required by the human cognitive system is
huge. Although we have no metric for measuring the amount of information
held in long-term memory, it might be noted that Simon and Gilmartin
(1973) estimated that chess grand masters have learned to recognise many
tens of thousands of the board configurations that are required for their
level of competence. It is reasonable to assume that similar numbers of
knowledge elements are required for skilled performance in areas more
relevant to everyday life, including areas covered in educational contexts.
If so, long-term memory holds massive amounts of information to permit
adequate levels of performance in the various areas in which an individual
is competent.
Although a large amount of information is held in long-term memory,
where does the information come from? The next two sections discuss this
issue.
Schema Theory and the Borrowing and Reorganising Principle
Based on the information store principle, human cognition includes a large
store of information that governs most activity. What is the immediate
source of that information? The borrowing and reorganising principle
explains how most of the information found in any individual’s longterm memory is acquired. Almost all information in long-term memory
is obtained by imitating other people’s actions or hearing or reading what
others have said. In effect, our knowledge base is borrowed almost entirely
from the long-term memory of other people. Nevertheless, the information
borrowed is almost invariably altered and constructed. We do not remember
exactly what we have heard or seen but, rather, construct a representation
based on knowledge already held in long-term memory. Schema theory
reflects that constructive process. A schema permits multiple elements of
information to be treated as a single element according to the manner in
which it will be used. Thus, a problem-solving schema permits us to classify
problems according to their solution mode. A chess master has schemas that
allow the classifying of chess-board configurations according to the moves
required.
The modern origins of schema theory can be found in Piaget (1928) and
Bartlett (1932), although the theory was largely ignored for several decades
during the Behaviourist era. The relevance of schemas to problem solving
34
John Sweller
was emphasised by Larkin, McDermott, Simon, and Simon (1980) and Chi,
Glaser, and Rees (1982), who provided theory and data indicating that the
possession of domain-specific schemas differentiated novices from experts
in a particular area. The extent to which one is skilful in an area depends
on the number and sophistication of one’s schemas stored in long-term
memory.
Schema construction, by indicating the form in which information is
stored in long-term memory, provides us with a learning mechanism; therefore, learning includes the construction of schemas. Learning also includes
the automation of schemas. Automation has occurred when knowledge is
processed unconsciously (Schneider & Shiffrin, 1977; Shiffrin & Schneider,
1977) rather than consciously in working memory. Problem solving using
automated knowledge is much easier and more rapid than when basic processes must be consciously considered (Kotovsky, Hayes, & Simon, 1985).
The automation of lower level schemas is frequently essential for the construction of higher level schemas. For example, without the automatic processing of the letters of the alphabet, because of the automation of schemas
associated with recognising those letters, it would be difficult to combine
those letters into words and sentences to permit reading.
A well-known study of Bartlett (1932), in providing a graphic example
of the process of schema acquisition, also indicates how the borrowing and
reorganising principle functions. One person read several paragraphs of a
Native American legend and then wrote down from memory as much as
possible of the passage. That remembered passage was then given to another
person who again wrote down as much as possible from memory, with the
process repeated with ten people. There were many alterations to the passage
as it passed from person to person, and those alterations provided a window
into memory. The alterations were not random, with Bartlett identifying
two major categories. First, there was a levelling or flattening of aspects of the
passage that were novel to the participants, resulting in a reduced emphasis
or disappearance of these aspects entirely. Second, there was a sharpening of
those aspects that accorded with knowledge in long-term memory, resulting
in those aspects being emphasised. Thus, participants did not remember
the passage as it was presented but rather, remembered a construction that
consisted of a combination of the passage and previous information held
in long-term memory. What we hold in memory consists of schematised
constructions – schemas. While being constructed, the information held
by schemas is essentially borrowed, via the borrowing and reorganising
principle, from schemas held by others.
Cognitive Load Theory: Recent Theoretical Advances
35
The various cognitive load effects shown in Table 2.1 provide strong evidence for the borrowing and reorganising principle. Each of the CLT effects
listed in the table is concerned with techniques for presenting information to
learners rather than having them generate information. The narrow limits
of change principle, discussed later in the chapter, indicates why generating
information is ineffective; therefore, since its inception, CLT has been concerned with techniques for presenting information to learners rather than
having learners attempt to generate information. All cognitive load effects
are intended to indicate how to provide auditory and visual information in
a manner that best facilitates learning. In other words, CLT is concerned
with how information held in the long-term memory of instructors can be
borrowed for use by learners via schema acquisition.
Based on the borrowing and reorganising principle, learned information
can be maintained by transmitting it among people indefinitely. But the
transmission is rarely exact. It normally includes a constructive element
that, over time, can result in substantial changes to the store. There is an
inevitable random component to this process, and whether any changes
are retained or jettisoned depends on their adaptive value, with beneficial
changes retained and non-beneficial changes jettisoned. Adaptive value also
is critical to the next principle discussed.
Problem Solving and the Randomness as Genesis Principle
Despite its constructive elements, the borrowing and reorganising principle
is basically a device for combining and communicating information. It
does not generate new information, which begs the question, how is the
information that is transmitted via the borrowing and reorganising principle
created in the first instance? A likely answer seems to be random generation
followed by tests of effectiveness.
Consider a person dealing with a novel set of circumstances, for example,
solving a problem. Most of the activity of that person will be based on
knowledge held in long-term memory acquired through the borrowing and
reorganising principle. Nevertheless, on many occasions, a problem solver
will be faced with two or more possible moves and have no knowledge
indicating which move should be made. Under these circumstances, random
generation of novel problem solving moves followed by tests of effectiveness
are needed. Random generation could be expected to lead to many deadends that will only be discovered after the event and, of course, when faced
with a difficult novel problem, most problem solvers will, indeed, reach
36
John Sweller
many dead-ends. A difficult problem may result in many more dead-ends
than appropriate moves.
The relation between knowledge-based move generation and randomly
generated moves can be described in terms of a central executive (Sweller,
2003). A central executive must know what the consequences of a move are
prior to it occurring and then arrange for it to occur. An executive system
such as this is possible for familiar moves generated by knowledge in longterm memory but impossible for novel moves. The moves made to solve a
problem can originate from long-term memory but only to the extent that
information is available in long-term memory indicating potential solution
moves. Knowledge held in long-term memory can indicate what type of
problem we are faced with and what types of moves are appropriate for
that problem. That knowledge is acquired from previous experience – it is
learned. Such knowledge provides the characteristics expected of a central
executive and, in that sense, it is a learned central executive.
If information concerning potential solution moves is not available in
long-term memory, the problem solver can select a move randomly and test
the effectiveness of that move. Failing knowledge held in long-term memory,
there may be no logical alternative. The potential effectiveness of a move
cannot be determined prior to its selection, so random selection is needed.
Prior knowledge of either the correct move or knowledge of the potential
usefulness of a range of moves that permits a hierarchy of moves to be
established eliminates the need for random selection. Failing that knowledge
held in long-term memory, random selection can be used as a substitute. It
should be noted that for the reasons outlined here, computational models
of human problem solving require random generation of moves as a last
resort when knowledge is unavailable (e.g., see Sweller, 1988). Of course, if
knowledge is available to us, we are highly likely to use it.
On this argument, human cognition does include an executive but it is a
learned executive held in the information store. That information acts as an
executive indicating what should be done, when it should be done, and how
it should be done. To the extent that this executive is unavailable because
of a lack of relevant information held by an information store, random
generation followed by tests of effectiveness are necessary procedures to
alter the store. Because the procedures are required and because there is
no substitute for them unless another store can be found from which the
relevant information can be communicated, random generation followed
by effectiveness testing provides the genesis of all information held by longterm memory. Indeed, it can be argued that it is the source of all human
creativity (Sweller, 2009).
Cognitive Load Theory: Recent Theoretical Advances
37
Novice Working Memory and the Narrow Limits of Change Principle
A major consequence that flows from the randomness as genesis principle
is that all random alterations to the information store must be incremental
and slow. A large, rapid, random change to long-term memory is unlikely
to be adaptive in the sense that it is likely to destroy critical aspects of the
store. Small, incremental changes will leave most of the store intact and
functioning and are therefore unlikely to be fatal. The larger the change, the
larger the probability that previous structures that have been established
as effective have been compromised. Furthermore, small changes can be
reasonably tested for effectiveness. Assume a small working memory that
must deal with three elements that must be combined in some manner.
Assume further that the various combinations are tested using the logic of
permutations. There are 4! = 4 × 3 × 2 × 1 = 24 possible permutations.
It is within the bounds of possibility to test 24 permutations. In contrast,
assume a somewhat larger working memory that can handle ten elements.
This working memory must test 10! = 10 × 9 × 8 × 7 × 6 × 5 × 4 ×
3 × 2 × 1 = 3,628,800 permutations, which in most situations is unlikely
to be possible. Thus, working memory is the structure that ensures that
alterations to long-term memory are limited, with working memory unable
to handle large amounts of novel information. We are unable to hold more
than about seven items of novel information in working memory (Miller,
1956) and can probably process no more than about four items (Cowen,
2001).
The narrow limits of change principle provides a central plank of CLT.
Competence derives from a large store of information held in long-term
memory and largely borrowed from the long-term memories of others. The
narrow limits of change principle suggests that that information must be
carefully structured to ensure working memory is not overloaded and that
schemas are effectively constructed and transferred to long-term memory.
The CLT effects listed in Table 2.1 are intended to meet this objective. The
narrow limits of change principle indicates why CLT, with its emphasis on
a limited working memory, argued that encouraging learners to generate
information that could readily be presented to them was likely to be an
ineffective instructional technique. A working memory that can process no
more than two to four elements of information is unlikely to be capable
of rapidly generating the knowledge required. That knowledge is available
via the borrowing and reorganising principle. Each of the effects listed in
Table 2.1 is based on the assumption that instruction should emphasise the
borrowing and reorganising of knowledge from knowledgeable instructors
38
John Sweller
and should be structured in a manner that reduces unnecessary cognitive
load. According to CLT, the more that is borrowed and the less that learners
need to generate themselves, the more effective the instruction is likely to
be (Kirschner, Sweller, & Clark, 2006).
It must be emphasised that the limitations imposed by the narrow limits
of change principle only apply to novices who, by definition, are dealing with
information with which they are unfamiliar. As indicated next, information
that has been organised by schemas in long-term memory does not suffer
these limitations.
Expert Working Memory and the Environment Organising
and Linking Principle
In contrast to working memory limitations when dealing with novel information, there are no known limits to the amount of information that working memory can process if it has been organised and tested for effectiveness,
that is, if it comes from long-term memory. The environment organising
and linking principle explains how we are able to transfer massive amounts
of organised information from long-term to working memory to effect
the complex actions required of the human cognition. To account for the
huge amount of organised information from long-term memory that can
be handled by working memory compared with the small amount of novel
information that can be handled, Ericsson and Kintsch (1995) suggested
a new construct, long-term working memory. Unlike short-term working
memory, long-term working memory allows the rapid processing of large
amounts of information providing that information has previously been
organised in long-term memory.
The environment organising and linking principle provides the ultimate justification for human cognition: the ability to function in a complex
external environment. Experts can transfer large amounts of organised,
schematic information from long-term to working memory to perform
appropriately in their environment. CLT assumes that novice–expert differences (see next chapter) primarily result from differences in schematic
information held in long-term memory that can be transferred as a single entity to working memory to generate actions appropriate to an environment. Furthermore, the environment organising and linking principle
requires the preceding principles. According to the environment organising
and linking principle, we need a large information store and learning mechanisms to build that store. The learning mechanisms are the borrowing and
reorganising and randomness as genesis principles. In turn, the narrow
limits of change principle permits the learning principles to function
Cognitive Load Theory: Recent Theoretical Advances
39
without destroying the information store. Once information is stored in
the information store, the environment organising and linking principle
allows that information to be used to guide appropriate action.
instructional implications
These five principles provide a base for human cognitive architecture. Together, they result in a self-perpetuating, integrated, information processing
system. As indicated earlier, it is a natural information processing system
that is also used during biological evolution (Sweller, 2003, 2004; Sweller &
Sweller, 2006), thus effecting a necessary connection between cognition and
the biological world. The elimination of any one of the five principles will
eliminate the functionality of the system. Its ability to generate action and
accommodate to changing circumstances to continue to generate action
requires all five principles.
The centrality of the five principles to human cognitive architecture mirrors their centrality to CLT and to instructional design. Each principle has
instructional design consequences. From the information store principle,
we know that the major function of instruction is to alter the information held in long-term memory. Based on the information store principle,
learning can be defined as a change in long-term memory. According to
this definition, we have no grounds for assuming the effectiveness of proposed instructional techniques that cannot specify the changes in long-term
memory that follow from use of the techniques (Kirschner et al., 2006). In
contrast, the accumulation of knowledge in long-term memory is central
to CLT.
The borrowing and reorganising principle suggests that the bulk of
human knowledge is learned from others rather than discovered by problem solving or a similar process. We acquire some of the knowledge held in
the long-term memory of other people by copying what they do, listening
to what they say, or reading what they have written. We must engage in
the difficult task of constructing knowledge in our own long-term memories by these processes of copying, listening, or reading (Kirschner et al.,
2006; Mayer, 2004). Accordingly, CLT emphasizes ways of communicating knowledge in educational contexts through observing, listening, and
reading, including reading diagrams.
Discovery occurs over generations, with its guiding assumption being
the randomness as genesis principle. The random components of true discovery ensure that the process may be difficult or impossible to teach. In
contrast, teaching can assist in the accumulation of knowledge held in longterm memory. What instructional designs are likely to assist in the goal
40
John Sweller
of changing the information held in long-term memory? Those designs
that take into account the narrow limits of change principle. In human
cognition, that assumption is expressed through the limitations of working
memory when dealing with novel information. In contrast, there are not
only no limits to the amount of organised information that can be held
in long-term memory, there are no known limits to the amount of organised information from long-term memory that can be used by working
memory. This alteration in the characteristics of working memory from a
limited capacity, limited duration structure when dealing with novel information to an unlimited capacity, unlimited duration structure when dealing
with familiar, organised information from long-term memory is expressed
through the environment organising and linking principle and is central to
CLT. The primary purpose of CLT has been to indicate how to present novel
information structured according to the narrow limits of change principle to reduce unnecessary working memory load and facilitate change in
long-term memory. In turn, changes in long-term memory permit complex
actions through the environment organising and linking principle. There
are three categories of cognitive load that affect working memory and these
will be discussed next.
categories of cognitive load
Whereas the function of instruction is to increase schematic knowledge in
long-term memory, novel information must first be processed by working memory, and when dealing with novel information, working memory
is limited in capacity (e.g., Miller, 1956) and duration (e.g. Peterson and
Peterson, 1959). All instructional material imposes a working memory or
cognitive load, and that cognitive load can be divided into two independent
categories – intrinsic and extraneous – with a third category, germane cognitive load, dependent on intrinsic cognitive load. Intrinsic and extraneous
cognitive load are additive, and together, they determine the total cognitive
load. If that cognitive load exceeds working memory capacity, information
processing, including learning, will be compromised. In other words, if
total working memory load is excessive, the probability of useful changes
to long-term memory is reduced. Each of the three categories of cognitive
load will be discussed separately before a discussion of how they interact.
Intrinsic Cognitive Load
Some material is intrinsically difficult to understand and learn regardless of
how it is taught. The critical factor is element interactivity, which refers to
Cognitive Load Theory: Recent Theoretical Advances
41
the number of elements that must be simultaneously processed in working
memory to understand and learn material under instruction. For example,
assume a student is learning the symbols for chemical elements. Each element can be learned independently of every other element. The task may be
difficult because there are many elements that must be learned, but it does
not impose a heavy working memory load. Because the working memory
load is light, the issue of “understanding” does not arise. We may have failed
to learn or forgotten the symbol for a particular element, but we are not
likely to use the term “understanding” in this context.
In contrast, assume that the learner is learning how to balance a chemical
equation or deal with an algebraic equation. The number of relevant elements may be far less than the number dealt with when learning chemical
element symbols, but element interactivity is high. When learning how to
solve the problem (a + b)/c = d, solve for a, one cannot just attend to one
of the elements in the equation or one of the solution steps while ignoring
the others if the solution is to be understood. All of the learning elements
interact and unless all are considered simultaneously in working memory,
the problem and its solution will not be understood. Element interactivity
is high, so working memory load and intrinsic cognitive load are high.
In some senses, element interactivity is fixed because it is an intrinsic
property of all material that must be learned and cannot be altered. Nevertheless, this statement needs to be modified by two points. First, regardless
of element interactivity, it is always possible to learn material one element
at a time. In the case of high element interactivity material, that means
the interacting elements are treated as though they do not interact. Learning can proceed in this manner but understanding cannot. Until all of the
elements are processed in working memory, understanding will not occur.
For very complex material, the learning of interacting elements as isolated
elements may be unavoidable, leading to the isolated/interacting elements
effect (Pollock et al., 2002; van Merriënboer & Sweller, 2005). By presenting learners with high element interactivity material as isolated elements
and only requiring them to learn the relevant interactions later, learning
is enhanced compared with requiring learners to learn the interacting elements immediately when instruction commences.
Learning itself provides the second way in which the effects of high
element interactivity can be reduced. Element interactivity cannot be determined merely by analysing the nature of the material that needs to be
learned. Depending on the schemas that have been acquired, material that
is complex for one individual may be very simple for another. If a set of
interacting elements have become incorporated into a schema, only that
schema needs to be processed in working memory, not the interacting
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John Sweller
elements. Accordingly, working memory load is low. For readers of this
chapter, the word “accordingly” is treated as a single element because we all
have schemas for this written word. For someone who is just beginning to
learn to read written English, the individual letters may need to be processed
simultaneously, and that is a high element interactivity task that may exceed
the capacity of working memory. Once the word is learned, individual words
can be treated as a single element rather than as multiple elements. In this
way, a high intrinsic cognitive load due to element interactivity is altered by
learning.
As can be seen, this alteration in the intrinsic cognitive load due to
learning is a consequence of the alteration in the characteristics of working memory depending on whether information is organised or random.
According to the environment organising and linking principle, there are
no limits to the amount of organised information that can be used for
action. This organised information is held in long-term memory, and large
amounts of such information can be transferred to working memory. In
contrast, according to the randomness as genesis and the narrow limits of
change principles, there are severe limits to the extent to which the store
can be changed to permit new actions. Accordingly, only a limited amount
of novel information can be organised in a manner that alters long-term
memory.
Extraneous Cognitive Load
This category of cognitive load also depends on element interactivity but
unlike intrinsic cognitive load, the interacting elements are fully under
instructional control, and CLT was devised primarily to provide principles
for the reduction of extraneous cognitive load. Whether an instructional
procedure imposes an extraneous cognitive load can be assessed by determining whether it is in accord with the cognitive principles outlined earlier.
If an instructional procedure does not facilitate change to the information
store (long-term memory), if the procedure attempts to alter that store by
use of the randomness as genesis principle instead of the borrowing and
reorganising principle or functions on the assumption that randomness as
genesis can or should be taught, or if the instructional procedure ignores the
narrow limits of change principle by either ignoring the limitations of working memory or proceeding on the assumption that working memory has no
limitations, that instructional procedure is likely to be ineffective because it
unnecessarily introduces interacting elements that should be eliminated. As
an example, consider a person attempting to learn via a discovery learning
Cognitive Load Theory: Recent Theoretical Advances
43
technique. Rather than being told a scientific rule, the person is given minimal information and required to work out the rule from that information.
The act of discovering a rule is highly likely to make heavy demands on
working memory, thus violating the narrow limits of change principle. It
depends minimally on the communication of knowledge, thus violating the
borrowing principle. To the extent that knowledge is unavailable, discovery relies heavily on random generation followed by tests of effectiveness,
which is an extremely slow, ineffective way of accumulating information
because discovery introduces a large range of interacting elements unrelated to learning. As a consequence, there is minimal emphasis on building
knowledge in the information store – long-term memory – which should
be the primary goal of instruction. All discovery and problem-solving based
teaching techniques follow this pattern, and as a consequence, all violate
every one of the five cognitive principles outlined earlier.
Based on the previously described theory, we might expect there to be no
systematically organised body of research demonstrating the effectiveness
of discovery-based teaching techniques and, indeed, after almost a halfcentury of protagonism, the lack of evidence for these techniques is glaring
(Kirschner et al., 2006). Evidence needs to consist of randomised, controlled
studies altering one variable at a time. In contrast, there is extensive evidence
for the advantages of providing learners with information rather than having
them discover it themselves. The worked-example effect, according to which
learners provided with worked examples learn more than learners provided
with the equivalent problems to solve, flows directly from the cognitive
architecture described earlier. The effect indicates in the clearest possible
terms the advantages of providing learners with information rather than
having them discover it for themselves.
There are many other CLT effects. All depend on one or more of the five
cognitive principles outlined previously. Many are discussed in the chapters
of this volume. All are summarised in Table 2.1. More detailed summaries
may also be found in Sweller (2003, 2004).
Germane Cognitive Load
Reducing extraneous cognitive load would have little function if the working memory resources so freed were not used for productive learning.
Instruction should be designed to ensure that the bulk of working memory
resources is germane to the goal of schema acquisition and automation.
In other words, working memory resources should be devoted to dealing
with intrinsic cognitive load rather than extraneous cognitive load because
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John Sweller
schema acquisition is directed to the interacting elements associated with
intrinsic cognitive load. Instructional designs that increase the use of working memory resources devoted to intrinsic cognitive load have the effect of
increasing germane cognitive load, which should be increased to the limits
of working memory capacity. Beyond that point, increases in germane cognitive load become counterproductive and can be categorised as extraneous
cognitive load.
Much of the work on extraneous cognitive load assumed that as that
category of cognitive load was reduced, germane cognitive load would automatically increase because learners would devote a similar effort to learning
regardless of the effectiveness of the instruction. For that reason, there are
relatively few germane cognitive load effects. There are some, nevertheless, and the example variability and imagination effects are summarised in
Table 2.1.
Interactions among Sources of Cognitive Load
As indicated earlier, intrinsic and extraneous cognitive load are additive,
and if they exceed available working memory capacity, learning (indeed,
all information processing) will be compromised and is likely to cease.
All of the cognitive load effects listed in Table 2.1 are caused by various
interactions among these sources of cognitive load. Most of the effects
occur because a reduction in extraneous cognitive load permits an increase
in working memory resources devoted to intrinsic cognitive load, increasing
germane cognitive load and enhancing learning. These effects only occur if
intrinsic cognitive load is high. If intrinsic cognitive load is low, alterations
in extraneous cognitive load may not matter because sufficient working
memory resources are likely to be available to overcome a poor instructional
design that imposes a heavy extraneous cognitive load, giving rise to the
element interactivity effect (Table 2.1).
Not only is the type of material critical to CLT, so is learner knowledge.
Information or learner activities that are important to novices may interfere
with further learning by more expert learners, giving rise to the expertise
reversal effect (Table 2.1). In other words, as expertise increases, procedures
that were important for novices, and therefore part of germane cognitive
load, contribute to extraneous cognitive load for more expert learners.
Although complex, considerable information is now available concerning
these various interactions between sources of cognitive load that give rise
to the various cognitive load effects. Nevertheless, interactions between
Cognitive Load Theory: Recent Theoretical Advances
45
various categories of cognitive load constitute a major, current research
area, and much still remains to be done.
conclusions
There is likely to be widespread agreement that instructional design requires
knowledge of cognition. If we do not understand the mechanisms of learning
and problem solving, our chances of designing effective instruction are
likely to be minimal. The success of CLT as an instructional theory is heavily
dependent on its view of human cognition. The theory does more than
merely pay lip service to the organization and function of the structures that
constitute human cognitive architecture. Cognitive architecture is central to
the theory. Unless we have a conception of the bases of human intelligence
and thought, effective instructional procedures are likely to elude us. The
suggested principles that constitute human cognition provide one such
possible base. That cognitive base, in turn, can inform us of the types of
instructional procedures that are likely to be effective. The success of CLT
in generating the instructional effects shown in Table 2.1 provides some
evidence for the validity of the underlying assumptions of the theory.
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3
Schema Acquisition and Sources of Cognitive Load
slava kalyuga
introduction
The previous chapter outlined the general features of human cognitive architecture relevant to learning. According to this architecture, our schematic
knowledge base in long-term memory represents the major critical factor
influencing the way we learn new information. In the absence of a relevant
knowledge base for a specific situation or task, we apply random search processes to select appropriate actions. Any modifications to our knowledge
base for dealing with novel situations occur only under certain restrictive
conditions on the amount of such change. Based on these general characteristics of learning within a cognitive load framework, it is possible to
formulate general instructional principles that support processes of schema
acquisition and enable understanding and learning. This chapter suggests
a number of such Cognitive Load Theory (CLT)–generated principles for
efficient instruction aimed at acquisition of an organized knowledge base: a
direct initial instruction principle, an expertise principle, and a small stepsize of change principle. To substantiate these principles, it is necessary first
to describe in more detail the concept of schematic knowledge structures and
analyze sources of cognitive load that are irrelevant to learning processes.
learning as schema acquisition
Schemas represent knowledge as stable patterns of relationships between
elements describing some classes of structures that are abstracted from specific instances and used to categorize such instances. Multiple schemas can
be linked together and organized into hierarchical structures. Such organized knowledge structures are a major mechanism for extracting meaning
from information, acquiring and storing knowledge in long-term memory,
48
Schema Acquisition and Sources of Cognitive Load
49
circumventing limitations of working memory, increasing the strength of
memory, guiding retrieval and recall of information, and providing connections to prior knowledge.
A generalized example of a schema is the concept of a chunk of information that has traditionally served as a unit of measurement for memory
capacity in studies of short-term memory (Miller, 1956) and expert–novice
differences. For example, de Groot’s (1966) classical finding that chess masters could recall many more pieces from briefly exposed real chess positions
than novices was explained by masters having larger chunks. Chase and
Simon (1973) found that experts placed chess pieces on the board in groups
that represented meaningful configurations. Similarly, experts in electronics could reconstruct large circuit diagrams from memory recalling them in
chunks of meaningfully related components (Egan & Schwartz, 1979).
Schematic knowledge structures can be empirically evaluated by using
grouping and categorizing tasks (e.g., by asking learners to cluster problems
based on their similarity or to categorize problems after hearing only part of
the text); using problems with ambiguous material in the statements (e.g.,
by replacing some content words with nonsense words); or using the “text
editing” technique (classifying problems in terms of whether the text of each
problem provides sufficient, missing, or irrelevant information for solution;
Low & Over, 1992). Cognitive science methods that are traditionally used in
laboratory studies for diagnosing individual knowledge structures are based
on interviews, think-aloud procedures, and different forms of retrospective
reports.
Some recent studies in rapid online methods of cognitive diagnosis of
organized knowledge structures are based on the assumption that if schemas in long-term memory alter the characteristics of working memory,
then tracing the immediate content of working memory during task performance may provide a measure of levels of acquisition of corresponding
schematic knowledge structures. The idea has been practically implemented
as the first-step method: learners are presented with a task for a limited
time and asked to indicate rapidly their first step towards solution of the
task. For learners with different levels of expertise, their first steps could
involve different cognitive activities: an expert may immediately provide
the final answer; whereas a novice may start attempting some random solution search. Different first-step responses would indicate different levels
of acquisition of corresponding knowledge structures (Kalyuga & Sweller,
2004, 2005; Kalyuga, 2006).
Studies of expert–novice differences have demonstrated that experts’
performance is determined not by superior problem-solving strategies or
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better working memories but rather, a better knowledge base that includes a
large interconnected set of domain-specific schematic knowledge structures,
well-developed cognitive skills (automated knowledge), and metacognitive
self-regulatory skills that allow experts to control their performance, assess
their work, predict its results, and, generally, use the available knowledge
base (Glaser, 1990). When knowledge becomes automated, conscious cognitive processing requires very limited cognitive resources, and freed capacity
can be concentrated on higher, more creative levels of cognition that enable
the transfer of learning (Cooper & Sweller, 1987; Kotovsky, Hayes, & Simon,
1985; Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977).
Organized, schematic knowledge structures in long-term memory allow
the chunking of many elements of information into a single, higher-level
element (Chi, Glaser, & Rees, 1982; Larkin, McDermott, Simon, & Simon,
1980). By treating many elements of information as a single element, longterm memory schematic knowledge structures may reduce informationprocessing demands on working memory. For example, according to the
concept of long-term working memory (Ericsson & Kintsch, 1995), longterm memory knowledge structures activated by elements in working memory may form a long-term working memory structure that is capable of
holding huge amounts of information for extended periods of time in an
interference proof way.
Novice learners possess only very limited lower-level knowledge associated with surface aspects of a domain, whereas experts are capable of activating higher-level schematic structures that contain information critical to
problem solutions (Chi & Glaser, 1985). These schematic knowledge structures in long-term memory effectively provide necessary executive guidance
during high-level cognitive processing. Without such guidance and in the
absence of external instructions, learners have to resort to random search or
weak problem-solving methods aimed at a gradual reduction of differences
between current and goal problem states. Such methods are cognitively inefficient, time consuming, and may impose a heavy working memory load
interfering with the construction of new schemas (Sweller, 1988, 2003).
As learners become more experienced in the domain, their problem perception and levels of specificity of their schemas change. These schemas
become more general and indexed by the underlying principles (Chi,
Feltovich, & Glaser, 1981). Using available schematic knowledge bases in
long-term memory, expert learners categorize different problem states and
select the most appropriate solution moves, perform with greater accuracy, and lower cognitive loads. Organization of their knowledge into large
groups of chunks decreases the demands on working memory and allows
Schema Acquisition and Sources of Cognitive Load
51
expert learners to rapidly activate appropriate procedures as soon as they
retrieve a problem schema.
New information is always encoded in terms of existing domain-specific
schemas. As learners acquire more knowledge and experience in the domain,
their schemas evolve and become more refined. New schemas may also be
created by modeling them on best-suited existing schemas from different
task domains, followed by further gradual refinement (learning by analogy). In any case, learning always involves integrating new information
with existing knowledge structures. However, learners may often activate
different schemas from those intended by the instruction. Students’ existing
schemas in particular domains are often quite different from those of experts
or teachers. For example, some initial intuitive schemas acquired from
everyday experiences (sometimes called “alternative frameworks,” “misconceptions,” “preconceptions,” or “phenomenological primitives”) might
make much of new conceptual scientific information incomprehensible for
novice learners (diSessa, 1993; Howard, 1987; Slotta, Chi, & Juram, 1995).
Such preexisting schemas often resist change, and everything that cannot be
understood within the available schematic frameworks is ignored or learned
by rote.
Cognitive conflicts between mismatching instruction-based cognitive
models and learners’ simplified internal knowledge structures may increase
processing demands on limited working memory. However, for more
advanced learners who have acquired well-organized schemas in the domain, simplified and detailed instruction-based conceptual models may
also conflict with the students’ more sophisticated knowledge structures
and inhibit learning (Mayer, 1989). Effective instructional methods should
always be tuned up to the students’ existing schematic knowledge base to
minimize processing loads on working memory.
Thus, from a cognitive load perspective, the major goal of learning
is the acquisition and automation of schematic knowledge structures in
long-term memory. Working memory limitations are of minimal concern
to learners whose knowledge in a domain is well organized in long-term
memory. Another way to reduce working memory-processing limitations
is to practice the skills provided by schemas until they can operate under
automatic rather than controlled processing, which is also achieved by
acquiring higher levels of expertise in a specific task domain. Therefore,
effective instructional procedures and techniques should be aimed at acquiring an organized schematic knowledge base and reducing any diversion of
cognitive resources to tasks and activities that are not directly associated
with this goal. Because a schematic knowledge base in long-term memory
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represents the foundation of our intellectual abilities and skilled performance, the acquisition of the organized knowledge base should be regarded
as a key general instructional objective that positions the design of instruction in a way that optimizes cognitive load during learning.
sources of cognitive load in instruction
Cognitive load during learning is determined by the demands on working
memory by cognitive activities designed to achieve specific learning goals.
A major source of cognitive load is provided by activities that are necessary for learning essential connections between elements of information
and building new knowledge structures in working memory. This type of
load is referred to as intrinsic cognitive load (see Chapter 2, this volume)
and is associated with the internal intellectual complexity of the instructional material. The magnitude of the intrinsic cognitive load experienced
by a learner is determined by the degree of interactivity between individual
learning elements relative to the level of learner expertise in the domain.
A learning element is a highest-level chunk of information for a particular
learner and specific task domain. The content of various chunks is determined by the schemas learners hold in their long-term memory base. With
the development of expertise, the size of a person’s chunks increases: many
interacting elements for a novice become encapsulated into a single element
for an expert.
When learning elements need to be processed simultaneously to comprehend the instruction (even if the number of elements is relatively small),
the material is high in element interactivity and can impose a high intrinsic
cognitive load. For example, learning the operation of an electronic circuit
is much more difficult than learning symbolic meanings of individual elements of this circuit. All elements in the circuit might be well known to the
learner, assuming that he or she has pre-acquired lower-level schemas for
those components (otherwise, the number of elements will be expanded
considerably). Combined in the circuit, they become interconnected and
need to be considered simultaneously as a whole to understand the operation of the circuit. Once the interactions of the components of the circuit
have been learned, corresponding lower-order schemas become the elements of a higher-order schema that can further act as a single element.
If the learner encounters this configuration of electronic components in a
new circuit, cognitive processing would require a minimal cognitive effort.
When a learner actually attends to the learning elements and attempts
to establish connections between them in working memory, he or she
Schema Acquisition and Sources of Cognitive Load
53
experiences intrinsic cognitive load. Without active processing of essential
connections between learning elements, the element interactivity (relative
to the assumed level of learner expertise) would remain a characteristic of
instructional material that indicates only a potential (not actual) level of
intrinsic cognitive load for a specific person. Because this load is essential for
comprehending the material and constructing new higher-level schematic
structures, it is vital to provide all the necessary resources to accommodate
the intrinsic cognitive load without exceeding the limits of working memory
capacity.
The concept of germane load as cognitive load that contributes to schema
construction was introduced into CLT to account for learning-relevant
demands on working memory (Paas & van Merriënboer, 1994; Sweller, van
Merriënboer, & Paas, 1998). Although, according to this general definition,
intrinsic cognitive load should be regarded as germane load (the distinction between them is not clearly delineated yet), germane cognitive load
has been traditionally associated with various additional cognitive activities
intentionally designed to foster schema acquisition and automation. For
example, cognitive load imposed by explicit self-explanations during learning from worked examples (Chi, Bassok, Lewis, Reimann, & Glaser, 1989)
or by activities of imagining procedures described in instruction (Cooper,
Tindall-Ford, Chandler, & Sweller, 2001) represent typically cited examples
of germane load. According to this view, the sources of germane cognitive load are auxiliary cognitive activities designed to enhance learning
outcomes or increase levels of learner motivation. Such activities would
obviously increase total cognitive load; however, they directly contribute to
learning. Thus, cognitive load that directly contributes to schema acquisition includes both intrinsic and germane load. In contrast to this relevant
(“good,” productive, or constructive) load, extraneous (“bad,” unproductive, or non-constructive) load is associated with a diversion of cognitive
resources to activities irrelevant to learning that are caused by poor instructional design.
Extraneous cognitive load is imposed by the design of instruction that
can take various forms (written instructions, practical demonstrations, etc.),
use various modes (e.g., verbal and/or pictorial) and modalities (e.g., visual
and/or auditory), require different activities from learners (solving problems, studying worked examples, exploring task domains, etc.), and involve
different sequences and step-sizes of learning tasks (e.g., different arrangements of part-tasks and whole tasks). Extraneous load is associated with
cognitive activities that a learner is involved in because of the way the
learning tasks are organized and presented rather than because the load
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Slava Kalyuga
is essential for achieving instructional goals (Sweller, Chandler, Tierney, &
Cooper, 1990; Sweller & Chandler, 1994). For example, when some interrelated elements of instruction (textual, graphical, audio, etc.) are separated
over distance or time, their integration might require intense search processes and recall of some elements until other elements are attended and
processed. Such processes need additional resources and might significantly
increase demands on working memory.
If a diagrammatic representation of an electronic circuit is accompanied by separate textual instructional explanations, understanding these
instructions requires integration of the text and diagram. This involves
holding segments of text in working memory until corresponding components of the circuit’s diagram are located, attended, and processed, or
keeping active some images of the diagram until corresponding fragments
of the text are found, read, and processed. This search-and-match process is
likely to increase extraneous cognitive load. Similarly, problem solving using
search processes usually involves a large number of interacting statements
in working memory (e.g., interconnected sub-goals and steps to solution).
Such problem solving might require significant cognitive resources that
become unavailable for learning. These cognitive demands are extraneous
to the learning goal and should be considered as an extraneous cognitive
load.
In general, extraneous cognitive load could be imposed by one or more
of the following sources:
1. An insufficient learner knowledge base that is not compensated by
provided instructional guidance, thus forcing learners to search for
solution steps using random procedures (instead of directly learning
solution procedures from instruction).
2. An overlap of an available knowledge base with provided instructional guidance, both aimed at the same cognitive activities, thus
requiring learners to establish co-references between representations
of the same information (instead of using cognitive resources on
constructing new representations).
3. An excessive step-size of change of knowledge base required by the
instructional sequence of learning tasks that introduces too many new
elements of information into working memory to be incorporated
into long-term memory structures.
4. Separated (in space and/or time) related instructional representations that require learners to perform extensive search-and-match
processes.
Schema Acquisition and Sources of Cognitive Load
55
According to CLT, for instruction to be efficient, intrinsic and extraneous
cognitive loads together should not exceed limited working memory capacity. When learning specific instructional materials does not require high
levels of intrinsic cognitive load (e.g., because it is low in element interactivity relative to the current level of learner expertise), the extraneous cognitive
load imposed by poor instructional design may be of little concern because
total cognitive load may not exceed working memory capacity. In contrast,
when instructional material is characterized by a high degree of element
interactivity relative to the learner level of expertise, it might require a heavy
intrinsic cognitive load to comprehend the instruction. In such a situation,
an additional extraneous cognitive load caused by an inappropriate design
can leave insufficient cognitive resources for learning because total cognitive load may exceed a learner’s working memory capacity. The available
cognitive resources may be inadequate for sustaining the required level of
intrinsic load and any additional (germane) cognitive activities designed to
enhance meaningful learning. In this situation, elimination or reduction of
extraneous cognitive load by improving instructional design may be critical
for learning. Therefore, a cognitively effective instructional design should
provide the necessary resources for sustaining intrinsic cognitive load and
reduce extraneous load as much as possible.
In some situations, because of an inadequate selection of learning goals,
poor sequencing or excessive step-sizes of learning tasks, or other instructional design omissions, required intrinsic load may exceed limits of working memory capacity for a given level of learner expertise. This excessive intrinsic load would cause the design-generated disruption of learning
processes and effectively become a form of extraneous load. Similar transformations could also take place with any auxiliary germane cognitive activities that require cognitive resources that exceed available working memory limits. For example, when novice learners are required to explicitly
respond to self-explanation prompts when studying complex material with
a high degree of element interactivity, this form of learning is unlikely to be
productive.
The following section provides general guidelines for minimizing extraneous cognitive load by focusing on the instructional goal of building an
organized knowledge base in learners’ long-term memory. These guidelines
suggest providing direct access to required knowledge structures, eliminating unwarranted random search processes and avoiding diversion of
cognitive resources on other irrelevant cognitive activities, and securing
manageable incremental changes in the knowledge base within the narrow
limits of working memory capacity.
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schema acquisition and cognitive load
The Direct Initial Instruction Principle
As learners become more knowledgeable in a domain, their cognitive activities change. Whereas for novice learners, construction of new schemas
represents the dominant cognitive activity, experts tend to use available
long-term memory knowledge structures to handle situations and tasks
within their domain of expertise. These long-term memory knowledge
structures coordinate and control cognitive activities, thus performing an
executive function in complex cognitive tasks. The theory of long-term
working memory (Ericsson & Kintsch, 1995), which establishes connections
between components of working memory and associated experts’ schemas,
effectively describes a mechanism of executive functioning of schemas held
in long-term memory. Therefore, the content of long-term working memory during complex cognitive activities is a good indicator of the levels of
learner expertise in a domain.
The executive role of long-term memory knowledge structures consists
of determining what information learners attend to and what cognitive
activities they engage in. On each specific stage of cognition, such decisions
could be based on either available knowledge structures or random factors.
Other alternatives inevitably lead to postulating a fixed central executive,
which always leads to an infinite regress problem where a higher-level executive is required to control a lower-level one (Sweller, 2003). Schematic
knowledge structures in long-term memory eliminate the problem of an
infinite regress. They are a major source of knowledge able to carry out an
executive function during high-level cognitive processes. Such a knowledgebased central executive is constructed for every specific task by retrieving
a set of appropriate schematic knowledge structures from long-term memory and combining them to manage incoming information and cognitive
activities for the task.
When dealing with a novel situation or task, there might be no longterm memory knowledge available to guide learners’ cognitive activities.
In this case, to make sense of this situation, learners may need to process
unfamiliar information in working memory element by element by using
unorganized, random-search approaches that are cognitively inefficient.
For example, substantial empirical evidence has indicated that extensive
practice in conventional problem solving is not an effective way of acquiring problem solving schemas (Sweller & Cooper, 1985). Search strategies
Schema Acquisition and Sources of Cognitive Load
57
used during problem solving focus attention on specific features of the
problem situation to reduce the difference between current and goal problem states. Maintaining sub-goals and considering alternative solution pathways might result in working memory overload; yet, such activities are
unrelated to schema acquisition and the learning of important aspects of
the problem structure. Goal-free problems and worked examples are more
effective means of acquiring schemas than conventional problem solving
(Ayres & Sweller, 1990; Sweller, 1988).
Direct instructional explanations and guidance in fully worked-out
examples essentially provide a substitute for the schema-based executive
at the initial stages of learning by showing the learner exactly how to handle a situation or task. In contrast, problem-solving or discovery learning
techniques provide the least effective executive function for novice learners.
An inadequate knowledge base or, alternatively, insufficient instructional
guidance to serve as an executive function in a given situation, are major
sources of extraneous cognitive load. This load is imposed primarily by
the random search processes that novice learners need to engage in rather
than accessing the required knowledge directly. The direct initial instruction
principle ensures that an appropriate level of executive function is provided
to novice learners to eliminate or reduce effects of this source of extraneous
cognitive load.
The Expertise Principle
The relative weight of long-term memory schemas and instructional explanations in providing executive functions for a task depends on the levels
of learner expertise. For novices, instruction is the only available source of
guidance, whereas for experts, all required knowledge might be available
in long-term memory. At intermediate stages, learners should be able to
retrieve available schemas for dealing with previously learned elements of
information and should be provided full instructional guidance for dealing with unlearned components. If, for some elements of information, no
guidance is supplied by either providers of executive functions (a longterm memory knowledge base or direct instruction), learners need to use
search strategies that may cause a high extraneous cognitive load. However, an overlap of a learner’s knowledge base and instructional guidance
both serving as an executive function for the same units of information,
could also impose an extraneous cognitive load. In this situation, learners
require additional cognitive resources to establish co-references between
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representations of the same information instead of using these resources
in constructing new representations. The expertise principle ensures that
executive function is tailored to the levels of learner expertise at each stage of
instruction, thus eliminating or reducing the effect of this source of extraneous cognitive load (Kalyuga, 2005, 2007; Kalyuga, Ayres, Chandler, & Sweller,
2003).
The expertise principle assumes continuous optimization of cognitive
load by presenting required instructional guidance at the appropriate time
and the removal of unnecessary redundant instructional support and activities as learners progress from novice to more advance levels of proficiency
in a domain. For novice learners, direct instruction in the form of fully
worked-out examples could be a cognitively optimal format because worked
examples effectively assist in structuring information in working memory,
thus providing a substitute for missing schemas (Renkl & Atkinson, 2003;
Chapter 5, this volume). At intermediate levels of expertise, worked examples could support construction of higher-level schemas not yet available,
and problem solving could be used for practicing retrieval and application
of previously acquired lower-level schemas. Completion problems or fading worked examples may effectively combine these two different forms
of cognitive support (Renkl, Atkinson, & Große, 2004; van Merriënboer,
Kirschner, & Kester, 2003). For expert learners, most cognitive activities are
based on activating previously acquired schematic knowledge structures to
organize relatively new information in working memory. Problem solving
and guided exploration could be effective instructional methods, whereas
studying worked examples is likely to be a redundant and inefficient cognitive activity at this stage.
Determining the optimal level of instructional guidance relative to levels
of learner expertise in a domain is a difficult task: sufficient instructional
details should be provided for students to understand the material, and
redundant details that may overload working memory should be eliminated. For more advanced learners, an instructional format with redundant
material eliminated may be superior to the format that includes the redundant material. For example, in computer-based environments, advanced
learners may avoid unnecessary explanations by turning off the auditory
or on-screen verbal explanations. Adaptive dynamic online instructional
systems may present the same information in different ways to different
learners or to the same learner at different stages of instruction. However,
to monitor actual changes in learner levels of expertise and suggest optimal instructional formats, appropriate, rapid online diagnostic methods
are needed.
Schema Acquisition and Sources of Cognitive Load
59
The Small Step-Size of Knowledge Change Principle
The process of learning as a change in the long-term memory store is
based on conscious processing of information within working memory.
Previously acquired knowledge structures are the most important factor
that influences learning new materials. To comprehend instruction, students
need to instantiate appropriate familiar schemas that would allow them
to assimilate new information to prior knowledge. In the absence of a
relevant knowledge base, a novice learner has to deal with isolated pieces of
information without an organizing structure. Available lower-level schemas
could be used to partially interpret these isolated pieces of information.
However, if an immediate instructional goal is too distant from the current
level of the learner’s knowledge, and the learning task introduces too many
new elements, constructing new organizing higher-order schemas would
be problematic. In this situation, the excessive intrinsic load caused by
too many new interacting elements of information may effectively become
extraneous load with resultant negative learning effects. In other words,
providing too much information too quickly, even if the information is
essential, is an example of extraneous cognitive load.
To reduce the required resources and keep this load within the learner’s
cognitive capacity, the chain of instructional sub-goals and corresponding
sequence of learning tasks could be defined in smaller step-sizes with manageable load within each step. The number of new interacting elements in
working memory could also be reduced by sequencing instructional subgoals properly. For example, some learning elements could be developed
to a high degree of automaticity first to free working memory capacity for
the following changes in knowledge structures. In other cases, simplified
knowledge structures could be presented first followed by details and practice with components. Pollock, Chandler, and Sweller (2002) demonstrated
an isolated/interacting elements technique that artificially suspended interactions between elements and presented complex instructional material as
isolated elements of information at initial stages of instruction. In this way,
some partial rudimentary schemas are acquired first by novice learners,
allowing the reduction of the resources required for subsequent learning of
the original, highly interactive material.
Thus, an excessive step-size of knowledge base change represents a potential source of extraneous cognitive load. Too many new elements of information that cannot be encapsulated into a smaller number of chunks based
on available long-term memory schemas could overwhelm limited working memory capacity and cause cognitive overload. Limiting step-sizes of
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incremental knowledge change could eliminate this source of extraneous
load. Construction of new schematic knowledge structures based on learners’ prior experiences and available knowledge could be more efficient if
it progresses gradually and is based on small revision steps. Any changes
to the available knowledge base should involve a limited number of unfamiliar novel elements. The small step-size of knowledge change principle
ensures that the knowledge base in long-term memory is altered in small
increments without drastic and rapid changes that would exceed learner
cognitive capacity, thus eliminating or reducing the effect of this source of
extraneous cognitive load.
According to the small step-size of knowledge change principle, it is
important to gradually build new knowledge on top of students’ existing
schemas or directly teach them appropriate schematic frameworks by relating them to something already known. Instructional analogies could be
useful for establishing such links with existing knowledge. For example,
Clement and Steinberg (2002) used a set of analogies that were based on
learners’ actual experiences to design a sequence of learning tasks in which
conceptual changes in learners’ knowledge of electrical circuits evolved
in small increments. Each such change was based on concrete, mentally
runnable explanatory schematic models of electrical circuits. Proceeding
directly from students’ simplistic ideas about electricity to the operation
of electrical circuits could easily exceed novice learners’ cognitive capacity limits and produce extraneous cognitive loads. Instead, the suggested
multistep instructional sequence was based on small, one-at-a time, and
manageable conceptual changes. Each step involved observations of a relatively new feature of the circuit that could not be explained by the available explanatory schema, and a small modification of this schema was
initiated using analogies from learners’ prior everyday experiences (e.g.,
concepts of a container, pressure in a tire, or resistance to mechanical
movements).
An example of the appropriate management of instructional sequences is the four-component instructional design model (4C/ID) of van
Merriënboer (1997). This model provides the methodology for analysis
of the complex cognitive skills and knowledge structures required for performing these skills and development of appropriate sequences of wholetask learning situations that support gradual acquisition of these skills in a
cognitively sustained manner. The procedure takes into account the limited
processing capacity of working memory by gradually increasing the level
of difficulty of whole tasks (van Merriënboer et al., 2003). According to
this methodology, learning tasks for complex environments are organized
Schema Acquisition and Sources of Cognitive Load
61
in a simple-to-complex sequence of task classes, with gradually diminishing levels of support within each class (process of “scaffolding”). Sufficient
supportive information is provided for general aspects of the learning tasks,
and just-in-time (algorithmic) information is provided for invariant aspects
of the learning tasks. Also, part-task practice is offered to train constituent
skills that need to be performed at a very high level of automaticity (van
Merriënboer, Clark, & de Croock, 2002).
conclusion
Schemas as the units of knowledge representation allow us to treat elements
of information in terms of larger higher-level chunks, thus reducing capacity
demands on working memory and allowing efficient use of basic information processing features of our cognitive architecture. Cognitive mechanisms of schema acquisition and transfer from consciously controlled to
automatic processing are the major learning mechanisms and foundations
of our intellectual abilities and skilled performance. Many instructional
materials and techniques may be ineffective because they ignore limitations
of the human cognitive processing system and impose a heavy cognitive
load. CLT assumes that a proper allocation of cognitive resources is critical
to learning.
To enhance schema acquisition, instructional designs should minimize
learners’ involvement in cognitive activities that overburden their limited
working memory capacity and cause excessive extraneous cognitive load.
In general, we need to reduce the diversion of learners’ cognitive resources
on activities and tasks that are not directly associated with schema acquisition. Extraneous cognitive load could result from an insufficient learner
knowledge base or instructional guidance, an overlapping knowledge base
and instructional guidance, excessive step-size of changes in the knowledge
base, or interrelated instructional representations that are separated in space
and/or time.
To reduce or eliminate the negative effects of these sources of extraneous cognitive load, a set of general guidelines (principles) for designing
instruction within the cognitive load framework has been suggested. These
guidelines prescribe providing direct (worked example-based) instruction
to novice learners, adapting instruction to changing levels of learner expertise, and exercising gradual knowledge base change. (Specific principles of
reducing extraneous cognitive load generated by interrelated complementary representations separated in space and/or time are described in Chapter 9.) These instructional principles are directed not only to achieving
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desired instructional effects, but accomplishing them efficiently and with
optimal expenditure of cognitive resources and instructional time.
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4
Individual Differences and Cognitive Load Theory
jan l. plass, slava kalyuga, and detlev leutner
The previous chapters discussed sources of cognitive load that are a result
of the difficulty of the materials, the design of instruction, and the amount
of mental effort invested by learners to process the new information. As
outlined in these chapters, the major cause of cognitive load effects is the
limited capacity of working memory. In this chapter, we discuss how individual differences relate to the level of cognitive load that a particular learner
experiences.
Individual differences in learner characteristics take many different
forms, ranging from preferences for learning from different presentation
formats (e.g., verbal, pictorial) or modalities (auditory, visual, haptic) and
preferences for learning under different environmental conditions (e.g.,
lighting, noise level, or physical position) to cognitive styles (e.g., field
dependency/independency), cognitive abilities (e.g., verbal, spatial ability),
and intelligence (Carroll, 1993; Jonassen & Grabowski, 1993). The influence
of individual differences on learning has been studied for several decades
as aptitude-treatment interactions (ATIs; Cronbach & Snow, 1977; Leutner,
1992; Lohman, 1986; Mayer, Stiehl, & Greeno, 1975; Plass, Chun, Mayer, &
Leutner, 1998; Shute, 1992; Snow, 1989, 1994; Snow & Lohman, 1984, 1989).
Aptitude-treatment interactions occur when different instructional treatment conditions result in differential learning outcomes depending on student aptitudes, in other words, when the effect of a given treatment is
moderated by a given aptitude. Different aptitudes may influence learning in specific instructional environments, and the impact of a particular
aptitude on a particular condition may only be observed for a particular
type of learning outcome. For example, Plass et al. (1998) found that learners with visualizer versus verbalizer learning preferences used multimedia
links in a reading environment for second-language acquisition differently,
resulting in different learning outcomes for text comprehension but not for
65
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
table 4.1. Categories of individual differences in learning
Information gathering
Learning styles
Learning preferences
Personality types
Information processing
Regulation of processing
Cognitive controls
Cognitive abilities
Prior knowledge
Motivation
Metacognition/self-regulation
vocabulary acquisition. The goal of this chapter is to focus on learner characteristics that are likely to affect the amount of available working memory
and that, therefore, are expected to influence cognitive load during learning. It should be noted, however, that few studies exist that measured both
individual differences and cognitive load.
Which types of individual differences might be expected to have a significant effect on cognitive load that is of practical relevance? For the purpose
of this chapter, we use a typology of individual differences that distinguishes
between differences in information gathering, information processing, and
regulation of processing (Table 4.1). Individual differences related to information gathering include learning styles, learning preferences, and personality types. This type of individual differences is characterized as value-neutral,
that is, as indicators of typical performance that are not linked to outcomes
in a directional sense (Jonassen & Grabowski, 1993). For example, the visual
versus verbal learning style indicates a person’s preference to learn with
visual (i.e., pictorial) versus verbal learning material, but does not generally
predict better performance for learners with one learning style versus the
other.
The ATI hypothesis states that to be instructionally effective, learning
environments need to match learners’ individual differences. Although confirmed under specific circumstances (Homer, Plass, & Blake, 2006; Plass,
Chun, Mayer, & Leutner, 1998), to date, this hypothesis has not been empirically supported as a general principle for designing learning environments
(Pashler, MacDaniel, Rohrer, & Bjork, 2009). One reason for this lack of
empirical evidence may be the fact that few valid instruments exist to reliably
measure individual differences variables, specifically in the field of learning
styles and learning preferences (Leutner & Plass, 1998; Moreno & Plass, 2006).
Individual differences in information processing include cognitive controls and cognitive abilities, including intelligence and prior knowledge,
which are viewed as value directional, that is, as indicators of maximal performance and as predictors for learning success (Jonassen & Grabowski,
1993). Due to our focus on individual differences that can potentially affect
Individual Differences and Cognitive Load Theory
67
cognitive load during learning, we will examine the role of prior knowledge (Kalyuga, 2005) and spatial abilities (Mayer & Sims, 1994; Plass et al.,
2003) because research has established a strong relationship between these
constructs and working memory (Shah & Miyake, 1996).
Differences in regulation of processing include learners’ motivation and
metacognition/self-regulation. Self-regulation was found, at least in a number of studies, to be a strong predictor for learning (Graesser, McNamara, &
VanLehn, 2005; Leopold, den Elzen-Rump, & Leutner, 2007; Pintrich &
de Groot, 1990; White & Fredriksen, 2005; Zimmerman & Schunk, 2001)
that significantly affects the level of cognitive load experienced by learners
(Winne, 2001).
In this chapter, we first provide a more detailed discussion of the expertise reversal effect, that is, the interaction of learners’ level of expertise and
instructional design on learning outcome. Second, we extend the discussion
to individual differences on spatial abilities and self-regulation. Third, we
describe an adaptive approach that can be used to optimize instructional
design in response to these individual differences. Finally, we outline questions and methodologies for future research on the relationship of individual
learner characteristics and cognitive load.
prior knowledge (expertise reversal effect)
The previous chapter described a set of general instructional principles that
support processes of schema acquisition and enable learning. One of these
principles, the expertise principle, reflects the primary role of learner’s organized knowledge structures (schemas) in the learning processes. Research
has identified prior knowledge as one of the most important individual difference factors influencing cognitive load during learning (Kalyuga, 2005;
Mayer, 2001).
According to the Cognitive Load Theory (CLT), the magnitude of mental
load in learning depends on the schemas that have been previously acquired
by the learner. As explained in previous chapters, a learning element is
a function of the level of learner expertise. What constitutes a learning
element and which elements interact with each other depends on a learner’s
schemas: a set of many interacting elements for one person may be a single
element for another, more expert learner. Therefore, although experts in a
particular domain do not possess larger working memory capacities, they
experience a decreased working memory load because they have larger
organized knowledge structures (or chunks of information) stored in longterm memory.
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
In some learning scenarios, however, expertise may actually trigger additional cognitive load because experts have to process information that, given
their high level of expertise in the given domain, is unnecessary for them
to assure successful learning. The expertise reversal effect (see Kalyuga,
Ayres, Chandler, & Sweller, 2003; Kalyuga, 2005, 2007) occurs when an
instructional method that is effective for novices becomes ineffective for
more knowledgeable learners (see also Lohman, 1986). Such a decline in
the relative effectiveness of instruction with changes in learners’ levels of
expertise can be explained within a cognitive load framework: when more
experienced learners need to reconcile their available schemas with the conceptual models presented during instruction, working memory processing
and storage load is likely to increase and cause an unnecessary extraneous
cognitive load. Therefore, instruction that eliminates unnecessary material
for a particular learner should be superior to instruction that includes such
material.
Detailed instructional explanations, often essential for novices to understand the learning materials, may, with increasing levels of knowledge,
become unnecessary. If detailed explanations are provided for these more
experienced learners, processing this information may increase cognitive
load and interfere with learning instead of assisting it. For example, many
diagrams require additional explanations to be comprehended by novice
learners. However, if a more advanced learner has sufficient knowledge to
understand a diagram by itself, any additional verbal explanations of this
diagram could be unnecessary. Yet, it would be difficult to ignore text that
is physically integrated into the diagram or to ignore a narrated explanation accompanying the diagram. Processing the explanation, relating it to
the diagram, and, most importantly, relating it to the knowledge structures
the learner has already stored in long-term memory, may result in significantly higher cognitive load than learning from the format that presents the
diagram by itself (Kalyuga, 2005, 2006a; Sweller, 2005).
Techniques that integrate textual explanations into diagrams, replace
visual text with auditory narration, and use worked examples to increase
instructional guidance, were found to be effective means of reducing working memory overload for less knowledgeable learners (see Kalyuga et al.,
2003, for an overview). However, with the development of learners’ knowledge in a domain, these techniques often result in negative rather than
positive or neutral effects. Subjective measures of cognitive load supported the hypothesis that processing components of instruction that were
unnecessary for more knowledgeable learners increased working memory
load.
Individual Differences and Cognitive Load Theory
69
In most of the original studies on the split-attention effect in learning
from diagrams and text, participants were novices that did not have a sufficient schematic knowledge base (Chandler & Sweller, 1991; Sweller, Chandler, Tierney, & Cooper, 1990; Tarmizi & Sweller, 1988; Ward & Sweller, 1990).
However, even at those early stages of cognitive load research, it was noticed
that differences between learners in domain-specific knowledge influenced
the observed effects on learning outcome. For example, Mayer and Gallini
(1990) demonstrated that physically integrated parts-and-steps explanative
illustrations were more effective in promoting scientific understanding (how
brakes, pumps, and generators work) for low-prior-knowledge learners than
for high-prior-knowledge learners. Although the results of this study did
not show a complete reversal of cognitive load effects, it showed that the
split-attention effect took place only for learners with low levels of expertise.
Similarly, Mayer and Sims (1994) found that only novice students benefited
from temporal coordination of verbal explanations with visual representations. There were no differences for high-experience learners who had
already developed a sufficient long-term memory knowledge base.
Longitudinal research has demonstrated that the level of learner expertise was a critical factor that influenced the occurrence of the split-attention
and redundancy effects (Kalyuga, Chandler, & Sweller, 1998). Direct integration of textual explanations into diagrams was beneficial for learners
with very limited experience in the domain. Such materials were easier to
process and resulted in a higher level of performance. However, in subsequent stages, as the learners’ levels of expertise in the domain gradually
increased, the pattern of results changed. The effectiveness of the integrated diagram-and-text condition decreased whereas the effectiveness of
the diagram-alone condition increased. More experienced learners, who
studied relatively new and more complex materials in the domain, reported
relatively higher levels of mental load, which suggests that the text interfered with learning. The diagram-alone materials were easier to process
and resulted in higher levels of performance on the subsequent tests.
Similar patterns of results were obtained in other studies using different
instructional formats and methods, such as dual-modality versus singlemodality presentations of text and graphics, worked-example instruction
versus problem-solving practice, and worked-example instruction versus
exploratory learning (Kalyuga, Chandler, & Sweller, 2000, 2001; Kalyuga,
Chandler, Tuovinen, & Sweller, 2001; Tuovinen & Sweller, 1999). Additions
to the original instructional text, designed to increase text coherence, were
found to only benefit low-knowledge readers; high-knowledge readers benefited from using the original text only (McNamara, Kintsch, Songer, &
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
Kintsch, 1996). Other researchers tested cognitive-load predictions regarding individual differences in learning from multiple representations. Elementary students learned how to add and subtract integers with an interactive multimedia game that included visual and symbolic representations
of the procedure, with or without verbal guidance. Verbal guidance helped
to minimize cognitive load only for students with low prior knowledge, low
computer experience, and a less reflective cognitive style (Moreno, 2002,
2005).
The previous studies concerned instructional techniques for reducing
extraneous cognitive load. As mentioned in the previous chapter, when
intrinsic or germane load exceeds the limits of working memory capacity
for a given level of learner expertise, it could effectively become a form of
extraneous load inhibiting learning processes. The expertise reversal effect
was also observed with instructional methods used to manage intrinsic cognitive load. For example, to reduce the intrinsic load of some complex materials, an isolated-interactive elements instructional technique, suggested by
Pollock, Chandler, and Sweller (2002), recommends first presenting separate units of information without showing the relations between them and
then presenting the original material showing all the interactions. However,
this instructional method did not offer any benefits to learners who already
possessed basic schemas in the domain. Reduction of intrinsic load is effective only for low-knowledge learners, but not for high-knowledge learners
for whom the material does not have a high degree of element interactivity.
A similar expertise reversal with isolated-interactive elements effect has
been demonstrated by Ayres (2005) in the domain of simple algebra transformations such as 5(3x − 4) – 2(4x − 7). A part-task, isolated-element
strategy, in which the constituent elements were isolated from each other
(required only a single calculation to be made), benefited learning only for
students with low prior knowledge. In contrast, students with a higher level
of prior knowledge learned more from whole tasks in which all elements
were fully integrated (required four calculations to be completed per problem). A mixed strategy, in which students progressed from part-tasks to
whole tasks, proved to be ineffective for both levels of prior knowledge.
In a study on visual complexity in learning from chemistry simulations,
Lee, Plass, and Homer (2006) manipulated intrinsic cognitive load of the
visual display by either presenting a simulation with three variables (temperature, pressure, and volume of an ideal gas) on one screen or separating
the simulation into two parts ([1] temperature and volume, and [2] pressure and volume of an ideal gas). Extraneous load was manipulated by
optimizing the screen design using established cognitive load principles.
Individual Differences and Cognitive Load Theory
71
They found an expertise reversal effect for comprehension and transfer,
which manifested itself in that the measures of reducing extraneous load
were effective for both low- and high-prior-knowledge learners in the low
intrinsic load conditions. In the high intrinsic load conditions, however,
these load-reducing measures supported low-prior-knowledge learners but
hindered high-prior-knowledge learners.
There have also been some preliminary indications of expertise-treatment interaction effects with instructional techniques designed to enhance
germane cognitive load in learning. For example, Renkl (2005) demonstrated that an instructional technique that required learners to find and
fix intentionally introduced errors in worked examples to increase germane
cognitive load was beneficial for high-prior-knowledge learners but not for
low-prior-knowledge learners.
Thus, some of the instructional design principles and techniques intended for expert learners are, as a result of the described findings, contrary to those recommended for novice learners. For example, it would
be beneficial for expert learners to eliminate components of multimedia
presentations that are unnecessary for them, even if the resulting format
might only use a single presentation form of information (e.g., only visual
diagram). Problem- and discovery-based learning environments with limited guidance could be effective for advanced learners, but would typically
not be recommended for novices. Similar reversal effects are expected to
be found with other cognitive load reduction methods as learners become
more advanced in a domain. An expertise reversal may be expected in situations in which well-guided instructional presentations intended for novice
learners are used with more advanced learners and, therefore, require an
unnecessary additional expenditure of cognitive resources.
If the efficiency of instructional designs depends on levels of learner
prior knowledge in a domain, with learners gaining optimal benefits from
different formats at different levels of expertise, a major instructional implication of the expertise reversal effect is that instructional techniques and
procedures need to change as learners acquire more knowledge in a domain
to minimize redundant activities at each level of expertise. According to
the instructional design principles of fading (Renkl & Atkinson, 2003) and
scaffolding (van Merriënboer, Kirschner, & Kester, 2003), which are based
on CLT, novice learners should be provided with considerable instructional support that could be gradually reduced as levels of learner expertise
increase. Completion tasks (van Merriënboer, 1990), faded worked examples (Atkinson, Derry, Renkl, & Wortham, 2000; Renkl, Atkinson, Maier, &
Staley, 2002), or just varying the number and degree of details of guidelines
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
or hints provided to students as they solve problems or explore learning
environments could gradually change the levels of instructional support at
intermediate and higher levels of learner expertise.
spatial abilities
Spatial abilities include three basic factors related to the processes of generating, retaining, and manipulating visual images: spatial relations, the
ability to mentally rotate visual images; spatial orientation, the ability to
imagine how visual images might look from a different perspective; and
visualization, the ability to manipulate visual patterns and identify mental
images (Carroll, 1993; Lohman, 1979). According to current working memory models, spatial abilities rely heavily on working memory resources,
especially on its visio-spatial sketchpad (VSSP) and executive control components (Baddeley, 1986; Miyake & Shah, 1999). Individual differences in
spatial abilities are attributed to differences in spatial working memory,
which is distinct from verbal working memory (Hegarty, Shah, & Miyake,
2000; Shah & Miyake, 1996). Research has also found that different spatial
ability factors involve the VSSP and the executive control components of
working memory to different degrees. For example, tests of spatial visualization appear to demand more involvement of executive control than
tests of spatial relations, whereas the VSSP is important for all factors
to maintain the visio-spatial information in memory (Hegarty & Waller,
2005).
Spatial Ability and Extraneous Load
Most of the studies that related individual differences and cognitive load
have investigated the effect of spatial abilities under different extraneous load
conditions. For example, the temporal contiguity effect, which describes
learning advantages for materials with concurrent presentation of narration
and animation over the successive presentation of narration and animation,
was strong for students with high but not for those with low spatial abilities (Mayer & Sims, 1994). Coordinated presentation of visual and verbal
explanations enhanced learning for high-spatial-ability learners and also
compensated for learners’ low level of prior knowledge (Mayer & Gallini,
1990; Mayer, Steinhoff, Bower, & Mars, 1995).
There is also evidence that levels of spatial abilities relate to extraneous
load in a virtual learning environment. Exploring a virtual environment’s
Individual Differences and Cognitive Load Theory
73
interface requires working memory resources; therefore, high-spatial-ability
students were better at exploring the interface than low-spatial-ability students. As a result, learners’ spatial abilities were highly correlated with levels
of learning (Waller, 2000). Similar findings were obtained in research on
audial navigation in voice-prompt systems. Untrained users were provided
with four different navigation conditions: hierarchical, flexible, guided (all
voice-controlled), and hierarchical (keypad-controlled; Goldstein, Bretan,
Sallnäs, & Björk, 1999). The authors suggest that because of their design, the
hierarchical and flexible structures offer more flexibility but require more
cognitive engagement, whereas the guided condition reduced cognitive load
but provided fewer options. Although no differences in the number of completed tasks, total completion time, or subjective attitudes were found across
these conditions, participants who scored high on tests of spatial abilities
completed their tasks more efficiently in the flexible structures than those
users who obtained lower scores. Users with low spatial ability completed
tasks more efficiently in the guided structures of navigation, suggesting that
for these users, the guidance condition with lower cognitive load was more
effective for the initial learning task, compared with high-spatial-ability
users, for whom the conditions with higher cognitive load (and more flexibility) were more effective (Goldstein et al., 1999).
Spatial abilities were found to differently affect various types of learning
outcomes. The research by Mayer and Sims (1994), for example, found an
effect of spatial abilities on transfer tasks but not on tests of retention. In
research on second-language acquisition, learners read a German text with
or without the following types of annotations: textual only, consisting of
English translations of the selected German words; visual only, consisting
of still images or video clips of the selected German words; or both text and
visual annotations. These annotations were designed to aid learners’ selection of relevant information for understanding the meaning of individual
vocabulary items. Learning outcomes were measured using a vocabulary and
a comprehension post-test. In the vocabulary post-test, high-spatial-ability
learners performed better than low-spatial-ability learners when only visual
annotations were available. Low-spatial-ability learners, on the other hand,
performed better than high-spatial-ability learners when no annotations
were available and when both visual and verbal annotations were available. This significant interaction effect on spatial abilities and treatment
conditions for the vocabulary test was not found in the comprehension
test (Plass et al., 2003). These results suggest that only high-spatial-ability
learners were able to focus on the main task of comprehending the text,
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
whereas low-spatial-ability learners spent more of their cognitive resources
on the low-level processing and decoding of vocabulary words and less on
the comprehension of the text. Learners’ spatial abilities, and the resulting different hypothesized levels of extraneous cognitive load, may have
influenced learning strategies in processing the reading text, which were
differently reflected in the two outcome measures.
Spatial Ability and Intrinsic Load
A small number of studies included research questions that could be interpreted as providing insight into the relationship between spatial abilities
and intrinsic cognitive load, even though none of them measured load
directly. For example, Gyselinck, Cornoldi, Dubois, De Beni, and Ehrlich
(2002) found that the beneficial effects of presenting illustrations with text
disappeared when a concurrent tapping task was used to suppress visiospatial working memory. However, this pattern of results was present only in
high- but not low-spatial-ability subjects. Similarly, pictorial scaffolding in a
geology multimedia simulation was more beneficial for high-spatial-ability
students than for low-spatial-ability students both on problem solving and
transfer tests (Mayer, Mautone, & Prothero, 2002). In addition, high-spatialability students took significantly less time than low-spatial-ability students
to process the learning materials. Authentic geology problems required high
levels of spatial thinking, in which the pictorial-based scaffolding was particularly relevant and had a strong positive effect on high-spatial-ability
students. In contrast, purely verbal scaffolding did not have a similar effect
(Mayer et al., 2002).
The instructional implications of these studies is that high spatial ability is
typically related to better performance when instruction induces high levels
of cognitive load, such as when it presents complex visio-spatial materials.
Whereas learners with lower spatial ability may not be able to process such
high-load materials deeply, learners with higher spatial ability have the
cognitive capacity to benefit from them. However, the majority of studies
examining spatial ability effects did not explicitly measure cognitive load.
With the exception of those studies in which cognitive load was manipulated
by design and predicted a priori (e.g., Goldstein et al., 1999), the levels of
cognitive load were inferred post hoc, for example, from the analysis of
study time and learning achievements. More systematic research is needed
to address the relationships among spatial abilities, cognitive load, and
learning outcomes and to directly measure cognitive load as well as cognitive
abilities.
Individual Differences and Cognitive Load Theory
75
self-regulation skills
The concept of self-regulation describes the self-directed process of monitoring and regulating one’s learning. Self-regulation is a cyclical cognitive
activity that involves forethought, performance or volitional control, and
reflection (Zimmerman, 1998).
Evidence for the relationship between students’ self-regulation and their
performance on academic tasks was found, for example, using the Motivated Strategies for Learning Questionnaire, where higher levels of reported
self-regulation were associated with higher levels of academic performance
(Pintrich & de Groot, 1990). Research has also shown that self-regulation
strategies can be taught, and that such training can result in better learning
outcomes when learning with instructional materials (see, e.g., Azevedo &
Cromley, 2004, for hypermedia learning and Leopold et al., 2007, and
Leutner, Leopold, & den Elzen-Rump, 2007, for learning from instructional texts). In this section, we will discuss research that can be interpreted
as relating self-regulation to intrinsic load and to extraneous load, as well
as research on the cognitive load impact of self-regulation scaffolds.
Self-Regulation and Intrinsic Cognitive Load
There is evidence that supports the notion that self-regulation is strongly
related to overall cognitive load and that high cognitive load can result in failure of effective self-regulation of performance in some learners (Baumeister,
Heatherton, & Tice, 1994; Vohs & Heatherton, 2000). An important determinant of learners’ self-regulation is their level of prior knowledge, which,
in turn, is a determinant of intrinsic cognitive load. Experts show more
metacognitive awareness and have developed better self-regulation strategies than novices (Eteläpelto, 1993; Schoenfeld, 1987; Shaft, 1995). Learners
with different levels of prior knowledge regulate their own learning by
employing different learning strategies (Hmelo, Nagarajan, & Day, 2000).
Variations in learners’ knowledge structures seem also to be related to
differences in individual learning strategies: the higher the prior domainspecific knowledge, the deeper the learning strategy that may be preferred
by the learner (Beishuizen & Stoutjesdijk, 1999). Research on expert–novice
differences in performance has shown that learners’ self-regulation skills
significantly influence working memory processes and the efficiency of
managing cognitive resources (Moreno, 2002; Moreno & Durán, 2004). A
study of children’s self-regulatory speech in mathematics activities, both
individually in the classroom and in pairs in a laboratory setting, showed
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
that, in the individual classroom work, high-achieving students had a statistically significantly larger frequency of regulatory speech than middle- and
low-achieving students. In the laboratory setting, where children worked
in pairs, these group differences disappeared, and the frequency of selfregulatory statements increased by a factor of up to five. Unlike the case
of the classroom setting, where students seem to have experienced high
load because of the difficulty of the assigned problems, the tasks given
to students in the laboratory setting were matched to their level of prior
knowledge (Biemiller, Shany, Inglis, & Meichenbaum, 1998). These results
suggest that higher intrinsic load may lead to lower self-regulation activity
compared with lower intrinsic load conditions.
Self-Regulation and Extraneous Cognitive Load
Self-regulation activities themselves can also be viewed as generating extraneous cognitive load, because the monitoring, control, and reflection activities involved in self-regulation require the investment of additional mental
effort. Self-regulation demands, therefore, may result – at least for unskilled
self-regulated learners – in decreased performance (Cooper & Sweller, 1987;
De Bruin, Schmidt, & Rikers, 2005; Kanfer & Ackerman, 1989) and failure to
engage in subsequent self-regulation (Muraven, Tice, & Baumeister, 1998).
Some of the reasons for these findings were highlighted in a study by Kanfer
and Ackerman (1989) that involved learning in a complex skills acquisition task (air traffic control task). Participants in one group were initially
instructed to do their best to complete the task, and they received specific
goals only after several trials. A second group received specific goals from
the beginning. Results showed that the group who initially did not receive
specific goals reported higher self-regulatory activity and outperformed the
group that worked with specific goals from the beginning, suggesting that
this method may have reduced extraneous cognitive load. Other studies
found that positive learning outcomes depended on the types of goals given
to the learners. In research on the acquisition of writing skills, Zimmerman
and Kitsantas (1999) demonstrated the benefits of setting goals that facilitate self-monitoring and self-regulation: learners who were given process
goals or outcome goals did not acquire writing skills as well as those who
first received process goals and then shifted to outcome goals. This shifting
of goals provided learners with a method to set hierarchical goals to guide
their learning, a method that is suggested to lead to more independent and
self-motivated learning (Bandura, 1997). Specific criteria for effective goals
were identified in a study on goal setting and metacognition. This research
Individual Differences and Cognitive Load Theory
77
showed that study goals that allowed learners to derive adequate monitoring standards (e.g., specific behavioral objectives for each study session)
were more effective in facilitating learning than goals that did not provide
these standards (e.g., general learning goals or specific time-related goals;
Morgan, 1985).
Self-Regulation Scaffolds and Cognitive Load
Research on the use of scaffolds to facilitate self-regulation in hypermedia
learning showed that the assistance of human tutors, externally facilitating
the processes of regulating students’ learning, was more effective than providing students with no scaffolding or with lists of sub-goals to guide their
learning (Azevedo, Cromley, & Seibert, 2004). It also showed that metacognitive scaffolds to support low self-regulated learners can be designed in
a way that does not have a negative impact on high self-regulated learners (Griffin, 2002). In Griffin’s study, scaffolds were included in an online
writing course that allowed learners to reflect on the specific elements of
the course that would be of value to them, allowed them to set specific
learning goals for each task, and asked self-regulation questions related to
the achievement of the learning goals. Results indicated that these scaffolds
had little effect on low-level tasks but helped low self-regulators perform
better in high-level tasks, suggesting that these scaffolds were more successful under high- than under low-load conditions. All learners receiving
these scaffolds spent more time on the task than those learners who did not
receive them (Griffin, 2002).
Other researchers have examined the influence of learners’ reported
use of self-regulated learning strategies on learning performance in learnercontrolled and program-controlled computer-based learning environments
(Eom & Reiser, 2000). The Self-Regulatory Skills Measurement Questionnaire was used to measure metacognitive, cognitive, self-management, and
motivational strategies prior to the study. High and low self-regulators
were randomly assigned to one of the two instructional conditions: learnercontrolled and program-controlled. In the learner-controlled group, students were allowed to control the order of instructional events, whereas
in the program-controlled group, the instructional sequence was predetermined. The results indicate that the performance differences between
learners with high and low self-regulation skills were greater in the learnercontrolled than in the program-controlled condition. High self-regulators
showed no significant differences in performance between conditions; however, low self-regulators scored higher in the program-controlled condition
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
than in the learner-controlled condition. Low self-regulation skills might
have contributed to the cognitive load experienced by learners in the learnercontrolled condition similar to the increased cognitive load experienced by
low-knowledge learners in low-guidance environments (such as exploratory
or discovery-based learning). In a similar study, Yang (1993) obtained a
marginally significant interaction effect between levels of self-regulation
and types of instructional control. High self-regulators achieved higher
post-test scores in the learner-controlled condition than in the programcontrolled condition, whereas low self-regulators achieved higher scores in
the program-controlled condition than in the learner-controlled one.
In summary, despite the general finding that learners with higher selfregulation perform better than learners with low self-regulation, the relationship between cognitive load and self-regulation is complex and depends
on several different factors that relate to both the learner and the design of
the materials. Learners with higher prior knowledge usually apply deeper
and more effective self-regulation strategies that use the available working
memory resources more efficiently than learners with low prior knowledge. There is evidence that under high cognitive load conditions, learners
use less appropriate strategies for self-regulation than under low cognitive
load conditions. The cognitive processes involved in self-regulation can add
to the experienced cognitive load as a function of the effectiveness of an
individual’s learning strategies. However, when goals and scaffolds are well
designed, this extraneous cognitive load can be reduced, and learning can
be facilitated.
However, many of our conclusions about self-regulation and cognitive load are interpretations that can only be inferred because none of the
research on self-regulation we reviewed included a direct measure of cognitive load. Further research should therefore more systematically explore the
relationships among self-regulation, cognitive load, and learning outcomes
by including appropriate measures of each construct.
optimizing cognitive load in adaptive
learning environments
Determining the most appropriate instructional design for each individual learner is a difficult task. The decision should provide sufficient verbal
and/or visual information and guidance to allow each learner to comprehend the material, yet avoid unnecessary verbal or visual information that may create extraneous cognitive overload and hinder learning. A
major instructional implication of the statistical interactions found between
Individual Differences and Cognitive Load Theory
79
learner individual characteristics and learning is that instructional designs
should be tailored to learners’ levels of knowledge, skills, and abilities
(Leutner, 1992, 2004).
To achieve the required levels of flexibility, dynamic online instructional
systems might include different interactive learning modes that allow different learners to access the same information represented in different formats
(Plass et al., 1998). The same instructional material may also be presented
in different ways to the same individual at different stages of learning as
her or his level of experience in the domain increases. For example, only
selected elements of the text, graphics, and links could be displayed on the
screen, and auditory explanations could be turned on or off when required
by an individual learner. In such learner-adapted instructional systems, the
tailoring of instructions to an individual learner can be guided by continuously assessing the person’s learning performance based on either a
sophisticated computational student model, such as in intelligent tutoring
systems (Anderson, Corbett, Fincham, Hoffman, & Pelletier, 1992), or using
appropriate dynamic diagnostic assessment tools (Leutner, 2004). The first
approach is limited to rather narrow instructional domains that need to
be analyzed and described in depth on the level of elementary production
rules and requires high levels of expertise in computational modeling. The
second approach is more straightforward and based on repeated cycles of
“test-adjust” steps. However, even this second approach requires more diagnostically powerful and rapid assessment instruments than those used in
traditional educational assessment. A third approach, though pedagogically
not as powerful and often not suitable for inexperienced learners, is to allow
learners to make their own choices that adapt the environment to their
needs.
Developing suitable embedded diagnostic tools is, therefore, a major
prerequisite for adapting instruction to individual learner characteristics
and optimizing cognitive load. Even experienced tutors often lack sufficient
diagnostic skills for adapting their level of instructional guidance to the
individual needs of their students (Chi et al., 2004). As a result, instead
of adapting learning tasks to student characteristics, the same uniformly
prescribed “subject matter logic” is often followed (Putnam, 1987). Online
learning environments usually constrain computer-mediated communication, thus making an accurate diagnosis of individual student characteristics
even more difficult (Nückles, Wittwer, & Renkl, 2005). At the same time,
these technologies offer new potentials for building adaptive learning environments based on embedded assessments of individual learners (Chang,
Plass, & Homer, 2008; Leutner & Plass, 1998).
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Jan L. Plass, Slava Kalyuga, and Detlev Leutner
For example, the empirical evidence for the expertise reversal effect
described earlier indicates that instructional designs that are optimal for less
knowledgeable learners might not be optimal for more advanced learners.
To adapt online instructional methods to levels of learner expertise, accurate and rapid online measures of expertise are required. Because learners
need to be assessed in real time during an online instructional session, traditional knowledge testing procedures may not be suitable for this purpose.
Using a rapid schema-based approach to assess levels of learner expertise
(Kalyuga, 2006b; Kalyuga & Sweller, 2004), recent cognitive load research
demonstrated the feasibility of embedding assessment methods into online
learning environments to optimize cognitive load. Two rapid diagnostic
methods were investigated within this approach: the first-step method and
the rapid verification method.
With the first-step method, learners are presented with a task for a limited
time and required to indicate rapidly their first step towards solution of the
task. Depending on a person’s specific level of expertise in a domain, the
first step could represent different cognitive processes. A more experienced
learner may rapidly indicate a very advanced stage of the solution (or even
the final answer), skipping all the intermediate solution steps, because of
high levels of acquisition and automation of corresponding processes. A
relatively novice learner may be able to indicate only a very immediate
small change in the problem state (or start applying some random search
processes). Therefore, different first-step responses would reflect different
levels of expertise in a specific task area.
With the rapid verification diagnostic method, learners are presented
with a series of possible (correct and incorrect) steps reflecting various
stages of the solution procedure for a task and are required to rapidly
verify the suggested steps (e.g., by immediately clicking on-screen buttons
or pressing specific keys on the computer keyboard). To successfully verify
more advanced steps of a solution procedure, a learner should be able to
rapidly construct and integrate more intermediate steps mentally, which is
an indicator of a more advanced level of expertise. Validation studies of
both methods indicated high levels of correlations between performance on
these tasks and traditional measures of knowledge that required complete
solutions of corresponding tasks. Test times were also reduced significantly
compared with traditional test times (by up to five times in some studies).
Both rapid assessment methods were combined with a measure of mental
load into an integrated indicator of the efficiency of performance that was
used as an online measure of expertise in adaptive learning environments
(Kalyuga, 2006a; Kalyuga & Sweller, 2005).
Individual Differences and Cognitive Load Theory
81
conclusion
In this chapter we argued that individual differences in learners could affect
cognitive load if they influenced working memory. We then focused on
the relationship of cognitive load and prior knowledge, spatial abilities,
and self-regulation. However, a strong limitation of our discussion is that,
with the exception of prior knowledge, the relationship among the specific
individual differences, cognitive load, and learning outcomes has not yet
been studied with sufficient detail, and many of our conclusions had to be
inferred from indirect measures of cognitive load.
Among the problems of many studies on individual differences is the
way these differences are measured. The use of self-report instruments for
measuring learner preferences or self-regulation seems to be a far less valid
way to assess such differences than, for example, the direct observation
of expressions of these differences either using log files of user behavior
(Leutner & Plass, 1998) or using protocol analysis of user comments
(Kalyuga, Plass, Homer, Milne, & Jordan, 2007).
It should also be noted that much of the present research is based
on undergraduate populations at highly selective universities, where prior
knowledge, cognitive abilities, and metacognitive skills are typically high.
Therefore, the participants in this research are not necessarily representative
of the general population. Research needs to provide deeper insights into
the effect of individual differences on working memory during learning and
include reliable and valid measures for both learners’ individual differences
and cognitive load during learning. Methods for measuring cognitive load
are discussed in the following chapter.
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part two
EMPIRICAL EVIDENCE
5
Learning from Worked-Out Examples
and Problem Solving
alexander renkl and robert k. atkinson
One of the classic instructional effects associated with the Cognitive Load
Theory (CLT) is the worked-example effect in cognitive skill acquisition (see
Chapters 2 and 3, this volume; Paas & van Gog, 2006). Worked-out examples
consist of a problem formulation, solution steps, and the final solution itself.
They are commonplace in the instructional material pertaining to wellstructured domains such as mathematics or physics (see Figure 5.1 for an
exemplary worked-out example). When CLT researchers discuss “learning
from worked-out examples,” they typically mean that after the introduction
of one or more domain principles (e.g., mathematical theorem, physics law),
learners should be presented with several examples rather than a single
example, as it is commonly the case. Despite this emphasis on learning from
examples, researchers working in this area acknowledge the importance of
requiring learners to solve problems later on in cognitive skill acquisition
so that they can reach proficiency in the domain they are studying.
In this chapter, we elaborate the theoretical assumptions and empirical
findings involving the studying of worked-out examples and learning by
problem solving in different phases of cognitive skill acquisition. Rather
than summarizing the extensive literature on example-based learning and
its implications for instructional design (for overviews, see Atkinson, Derry,
Renkl, & Wortham, 2000, and Renkl, 2005), we instead focus on addressing
the issues of: (a) when it is best to study worked-out solutions, (b) when
it is best to solve problems, and (c) how the transition between these two
learning methods should be structured.
In the following sections, we discuss the effectiveness of learning from
worked-out examples from a CLT perspective. Next, we describe the theoretical considerations that are relevant to the questions of when to study
examples and when to move on to problem solving. Finally, we summarize
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figure 5.1. A worked-out example from the domain of probability.
related experiments, propose instructional guidelines, and conclude by
proposing avenues for future research.
worked-out examples: a cognitive load perspective
on their effectiveness
Sweller and Cooper (1985; Cooper & Sweller, 1987) performed several seminal studies that documented the worked-example effect. Across a series
of experiments, they established that learning from multiple worked-out
examples is more efficient (i.e., requires less learning time) and leads to
better subsequent problem-solving performance on structurally identical
problems (near transfer: same solution procedure) than studying a single
example followed by problem solving. A caveat concerning these results was
Learning from Worked-Out Examples and Problem Solving
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that these positive effects did not extend to far transfer performance on
dissimilar problems requiring the learners to modify the example’s solution
procedure to solve the novel problems.
The traditional CLT explanation of the worked-example effect is relatively straightforward (see Chapter 3, this volume). In the early phases of
skill acquisition, learners attempt to solve problems by using general search
strategies, such as means–ends analyses. As a result, they focus their attention
on specific features of the problem to reduce the difference between current
and goal problem states rather than on schema-relevant principles. Moreover, the task of reducing the difference between problem states requires
learners to maintain sub-goals and consider different solution options,
which can result in cognitive overload. Not surprisingly, the activities connected with general search strategies do not productively contribute to
the construction of problem-solving schema that would enable learners to
detect relevant structural features in later problems to be solved and, on
this basis, to select an appropriate solution procedure. In sum, “premature”
problem solving in early phases of skill acquisition imposes a substantial amount of unnecessary (extraneous) load that does not contribute to
learning.
Since the publication of these seminal studies, research on learning from
worked-out examples and CLT in general has flourished. Two important
extensions of the worked-example effect are particularly noteworthy. First,
the cognitive capacity freed up by presenting examples instead of problems
to be solved is not necessarily used by all learners in a productive way (Chi,
Bassok, Lewis, Reimann, & Glaser, 1989; Pirolli & Recker, 1994). In fact, most
learners can be characterized as passive or superficial learners (Renkl, 1997).
Thus, prompting or training the learners to use their free cognitive capacity
for germane (productive) load activities is important to fully exploit the
potentials of example-based learning. More specifically, learners should be
prompted or trained to self-explain the presented solution steps so that they
can understand their underlying rationale. Learners’ active self-explanations
of worked-out examples lead not only to enhanced near transfer but also
to better far transfer (dissimilar problems; e.g., Atkinson, Renkl, & Merrill,
2003; Renkl, Stark, Gruber, & Mandl, 1998).
Second, the worked-example effect disappears when the learners progress
through the phases of cognitive skill acquisition. For instance, if learners
have high prior skill levels, then problem solving fosters learning more than
studying worked-out examples. In addition, even in the course of a single
learner’s cognitive skill acquisition, there should be a concerted effort to
move him or her from studying worked-out examples to problem solving
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because there is evidence that at some point, knowledge acquisition from
studying worked-out examples becomes a redundant activity that contributes little or nothing to further learning.
This so-called reversal of the worked-example effect is an instance of the
general expertise reversal effect, as described by Kalyuga and colleagues (e.g.,
Kalyuga, Ayres, Chandler, & Sweller, 2003; Kalyuga, Chandler, Tuovinen, &
Sweller, 2001; Chapter 4, this volume). This implies that the instructional
effects introduced by CLT eventually disappear and then reverse themselves
over the course of cognitive skill acquisition. In other words, studying
examples is productive in the initial stages of cognitive skill acquisition but
actually becomes detrimental to learning during the later phases. As learners
develop a sufficient knowledge base, they are better served by engaging in
independent problem solving. In line with this effect, the 4C/ID theory (e.g.,
van Merriënboer, Clark, & de Croock, 2002; van Merriënboer & Kester,
2005; Chapter 6, this volume) postulates that learning is best fostered by
high instructional support in the beginning (e.g., by worked-out examples)
that is gradually faded out (e.g., completion problems, incomplete workedout examples; see Figure 5.2) until the learners can solve problems on
their own.
learning from examples and problems in the course
of skill acquisition: theoretical assumptions
Stages of Cognitive Skill Acquisition
In the acquisition of cognitive skills, three phases are typically distinguished
in psychological theories. For example, VanLehn (1996) distinguishes among
early, intermediate, and late phases of skill acquisition. During the early
phase, learners attempt to gain a basic understanding of the domain and
its principles without necessarily striving to apply the acquired knowledge.
During the intermediate phase, learners turn their attention to learning
how to solve problems. Ideally, learners reflect on how abstract principles
are used to solve concrete problems (e.g., by self-explanation activities).
One potential outcome of this intermediate phase is that flaws or misunderstandings in the learners’ knowledge base are corrected. Finally, the learners
enter the late stage, in which speed and accuracy are increased by practice.
During this phase, actual problem solving rather than reflective activities,
such as self-explanations, is crucial.
With respect to the intermediate stage, which is the focus of this chapter,
it is important to note that the construction of a sound knowledge base is not
Learning from Worked-Out Examples and Problem Solving
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figure 5.2. An incomplete worked-out example (completion problem).
an automatic by-product of studying examples or solving problems. Rather,
learners must actively self-explain the solutions of the worked-out example,
that is, they must reason out the rationale of the example’s solutions (Chi
et al., 1989; Neuman & Schwarz, 1998; Renkl, 1997; VanLehn, 1996). Renkl
and colleagues have recently conceptualized this instructional approach as
learning by self-explaining examples (e.g., Schworm & Renkl, 2007).
Another important caveat about cognitive skill acquisition is that the
three stages do not have precise boundaries, particularly in the case of complex cognitive skills that encompass multiple sub-components. Under these
circumstances, a learner may be entering the late stage in the acquisition
of one of the skill’s sub-components while simultaneously operating in the
early or intermediate phase of acquiring the skill’s other sub-components.
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Thus, a learner may be simultaneously in different stages with respect to different sub-components of a skill. Consequently, different learning activities
might be germane or optimal with respect to different knowledge components. For example, when learners study the type of examples shown
in Figures 5.1 and 5.2 to gain understanding in the domain of probability
calculation, they may gain some understanding of the multiplication rule
and its application (i.e., the principle underlying the first step in each of
the examples shown in Figures 5.1 and 5.2). They may, however, still lack
an understanding of the addition rule (i.e., the principle underlying the
second step in each of the examples shown in Figures 5.1 and 5.2). In this
case, learners will likely benefit from solving solution steps related to the
multiplication rule on their own but will still need to study a worked-out
solution step related to the addition rule.
As previously mentioned, the fact that studying worked-out steps is more
favorable in the beginning stages of cognitive skill acquisition and problem
solving is superior in the later stages can be explained by the general expertise
reversal effect within the framework of CLT. However, we see two drawbacks
with this conceptualization. First, the term expertise is misleading because
the learners who are typically participants in learning experiments do not
truly gain domain expertise during the brief course of instruction (Moreno,
2006). To become a domain expert, it takes typically several years (cf. the
10-years-of-practice rule by Ericsson and colleagues; Ericsson & Charness,
1994; Ericsson, Krampe, & Tesch-Römer, 1993). Therefore, we prefer to
describe this phenomenon as the knowledge-gain reversal effect. Second, the
reversed example effect is typically ascribed to situations in which studying
examples is a redundant activity that induces unnecessary (extraneous)
load. However, there is no detailed account of the actual learning processes
involved with this phenomenon, particularly what can account for the
favorable learning outcomes from solving problems in later stages of skill
acquisition. Against this background, we propose a more elaborated model
of the empirically observable knowledge-gain reversal effect involved with
learning from examples and problems to be solved (see Renkl, 2005).
The Knowledge-Gain Reversal Effect
The knowledge-gain reversal effect can be explained by the following
assumptions. When learners lack an understanding of how domain principles can be instantiated for solving a certain type of problem-solving step,
they typically employ shallow or general search strategies during problem
solving to determine the numerical answer. For example, they can employ
Learning from Worked-Out Examples and Problem Solving
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table 5.1. A model of the knowledge-gain reversal effect with a focus on
instructional implications
Understanding of how
a domain principle is
applied in problem
solving
Typical actions during . . .
. . . studying examples
. . . problem solving
Lacking understanding
Reading a worked solution
step; self-explaining can
be elicited by prompts
productive activity
leading eventually to
“Given understanding”
Not principle-based,
shallow, or general search
strategies in problemsolving attempts
unproductive activity
Given understanding
Reading a worked solution
step and/or self-explaining
lead to redundant
elaborations
unproductive activity
Principle-based
problem-solving attempts;
in the case of an impasse:
repair of knowledge gaps;
in the case of success:
formation of production
rules
productive activity
a general search strategy such as means–ends analysis, as emphasized in
the CLT account of the worked-example effect (see Chapter 3, this volume). However, they can also use shallow strategies, such as a key word
strategy (i.e., selecting a procedure by a key word in the cover story of a
problem; Clement & Bernhard, 2005) or a copy-and-adapt strategy (i.e.,
copying the solution procedure from a presumably similar problem and
adapting the numbers in the procedure; VanLehn, 1998). Because of their
lack of principle understanding, they cannot rely on domain strategies in
their problem-solving efforts that refer to the principles to-be-learned (cf.
VanLehn et al., 2005). However, employing general or shallow strategies for
problem solving does not deepen domain understanding and can therefore be classified as activities inducing extraneous load (see the upper right
quadrant of Table 5.1).
Let us image, for example, that learners are asked to solve a probability
problem, such as the one shown in Figure 5.2, but they do not understand the multiplication rule – a domain-specific rule required to solve
the problem – and its application. Because the learners are not familiar
with the multiplication rule, they are unable to recognize that this principle must be applied in the first solution step. In this instance, they might
simply resort to revisiting a worked-out example that they had seen earlier
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figure 5.3. A worked-out example with a self-explanation prompt.
(e.g., the example in Figure 5.1), copying the first solution step and applying
the copied solution step to the current problem by updating the numerical
values. Even though this approach might lead learners to solve the problem
step correctly, they gain little or no new understanding of the multiplication rule and its application. In light of this possibility, we argue that it is
more favorable in the beginning of skill acquisition to provide worked-out
steps that require the learner to self-explain. Encouraging learners to engage
in the self-explanation process can help them to gain an understanding of
how the domain principles are instantiated. To overcome potentially passive
learning behaviors and to assure self-explanation activities, self-explanation
prompts can be used (see the upper left quadrant of Table 5.1). Figure 5.3
Learning from Worked-Out Examples and Problem Solving
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table 5.2. Schema for a backward fading sequence from a fully worked-out example
to a problem to be solved (for a solution procedure with three steps)
Sequence of examples/problems
Fully worked-out example
Incomplete example
Incomplete example
Problem to be solved
Solution step 1
Solution step 2
Solution step 3
Worked
Worked
Worked
Faded
Worked
Worked
Faded
Faded
Worked
Faded
Faded
Faded
shows a worked-out example with a self-explanation prompt that encourages the learner to think about the principle underlying the first solution
step.
It is important to note, however, that once a learner has gained an
understanding of the domain principles and their instantiations, engaging
in additional self-explanation activities may not support further learning
(see the lower left quadrant of Table 5.1). When learners understand the
relation between a type of problem-solving step and a domain principle, it is
sensible to encourage them to solve a solution step requiring the application
of this principle. Even in cases in which the learner’s understanding is less
than perfect, for example, because of a narrowly defined understanding of
when to apply the principle, a faded step could be provided (as shown in
Figure 5.2’s third step). In the case of a temporary impasse, the knowledge
deficit can be repaired by further reflection (self-explanation). In the case of
successful problem solving, a specific production rule (Anderson & Lebiere,
1998) can be formed (see the lower right quadrant of Table 5.1).
Based on this knowledge-gain reversal model, we offer the following
instructional implications (see also Renkl & Atkinson, 2003, 2007). First,
provide worked-out steps together with self-explanation prompts. When
the learner indicates understanding (e.g., by successful self-explanations),
fade the step and require the learner to solve it. More specifically, a fading
method should be employed in which the worked-out steps are gradually
faded from worked-out examples to problems. With a fading procedure, a
completely worked example is presented initially. When the learner gains
understanding, a single step can be faded, and the learner has to solve the
corresponding step in the next example. After trying to solve the faded step
in this incomplete example, the learner receives feedback in the form of
the step’s correct solution. Then, in the following examples, the number of
blanks is increased step by step until the whole problem needs to be solved.
Table 5.2 shows a fading sequence for an example/problem type with three
solution steps. With such a fading procedure, a smooth transition from
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Alexander Renkl and Robert K. Atkinson
studying examples, to working on incomplete examples, to problem solving
is implemented.
faded examples: empirical findings
Establishing the Fading Effect
We conducted a first test of the proposed fading procedure in a small-scale
field study (Renkl, Atkinson, Maier, & Staley, 2002; Experiment 1). We tested
whether a fading procedure was more effective than learning by exampleproblem pairs, as they are used in many studies on learning from examples
(example-problem pairs consist of a fully worked-out example followed by
an isomorphic problem to be solved). We compared the learning outcomes
of two ninth-grade classrooms. In each classroom, a physics lesson about
electricity was conducted, in which example-problem pairs or fading examples were employed, respectively. In the fading condition, the first task was
to study a completely worked-out example. In the second task, students
had to solve the last solution step of a problem, which was omitted. In
the third task, students had to solve the last two solution steps of a problem, which were omitted. Finally, all three steps were left out for students
to solve. This fading method is called backward fading of solution steps.
In a post-test presented two days after the lessons, the fading classroom
outperformed the example-problem pairs classroom significantly in near
transfer performance, but not (significantly) on far transfer. Based on this
encouraging result, we conducted two more controlled laboratory experiments to examine the efficacy of a fading procedure relative to learning by
example-problem pairs.
In a first laboratory experiment, psychology students worked on examples and problems from probability calculation (Renkl et al., 2002; Experiment 2). They were randomly assigned to either the fading or to the
example-problem condition. In this study, we employed a forward fading
procedure (omitting the first solution step first, then the second, etc.). We
found that the fading procedure clearly fostered near transfer performance.
This was not, however, true for far transfer performance. The effect on near
transfer was mediated by the lower number of errors committed during
the learning phase.
We obtained converging results in the two experiments previously mentioned, even though the laboratory study and the field study differed
with respect to the type of learners (school students vs. university students), the learning domain (physics/electricity vs. mathematics/probability
Learning from Worked-Out Examples and Problem Solving
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calculation), the learning setting (school lesson vs. computer-based learning in the laboratory), and the kind of fading out worked-out solution
steps (“backward” vs. “forward”). We interpreted the stability of this finding despite these very different context conditions as an indicator that the
effectiveness of our fading procedure was reliable and stable.
The Effects of Different Fading Procedures
An open question arose from the fact that we employed two ways of fading
out worked-out solution steps, a backward and forward procedure, across
the two experiments. Because the context conditions in our two studies
varied substantially, we could not compare the relative effectiveness of these
two procedures. Such a comparison was necessary to answer the questions
of whether the specific type of fading procedure significantly influences
learning outcomes or whether it is of minor importance. Thus, Renkl et al.
(2002; Experiment 3) implemented the condition of example-problem pairs
(control) as well as the two fading procedures used in the previous experiments: forward fading and backward fading. The participants (students
enrolled in educational psychology courses) were randomly assigned to one
of the conditions. The positive effect of fading on near transfer was replicated. This effect was again mediated by reduced problem-solving errors
during learning. In contrast to our previous studies, we found also a positive
effect on far transfer. The statistically significant effect on far transfer was,
however, primarily due to the backward fading condition. In addition, this
type of fading procedure was more favorable compared with forward fading
because it was more efficient. The learners in the backward condition spent
less time on the examples without hindering their transfer performance
(cf. Renkl et al., 2002; see also Renkl & Atkinson, 2003).
In a subsequent study, Renkl, Atkinson, and Große (2004) examined
the learning processes associated with the fading procedure to explain the
observed differences between backward and forward fading performance.
Perhaps the previously found difference between these fading methods
might have been due to the specific order in which the domain principles
were to be applied in the examples and problems that were presented for
learning. In their first experiment, Renkl et al. (2004; Experiment 1) documented that the position of the faded steps did not influence learning
outcomes. Instead, individuals learned most about those principles that
were faded; whether a backward or forward rationale was employed did not
lead to differences in learning outcomes. This finding suggested that specific
self-explanation activities are triggered by faded steps.
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Alexander Renkl and Robert K. Atkinson
In their second experiment, Renkl et al. (2004; Experiment 2) investigated
this hypothesis more directly by collecting and analyzing think-aloud protocols generated by the learners during their interaction with backward faded
examples or with example-problem pairs. First, in a comparison between
the fading condition and the example-problem condition, we replicated a
positive fading effect on near transfer and on far transfer performance. Second, we found that fading was associated with fewer unproductive learning
events, which were defined as impasses that were not followed by selfexplanations.
Combining Fading with Self-Explanation Prompting
In an effort to optimize our fading procedure, we introduced some selfexplanation prompting at the worked-out steps in a subsequent laboratory
experiment (Atkinson et al., 2003). As already mentioned, many learners do
not spontaneously leverage the fact that worked-out steps afford learners
sufficient cognitive capacity to generate productive self-explanations (germane load activities). The learners’ sub-optimal self-explanation activities
may explain the somewhat limited effects of our fading procedure on far
transfer in the previous experiments. We assumed that prompting students
to self-explain the worked-out steps (not the to-be-completed steps; see also
Moreno & Mayer, 2005) would render our fading procedure more effective,
especially with respect to far transfer. More specifically, we again used probability examples and problems and asked the learners to determine at each
worked-out step which probability rule was applied (see Figure 5.3). We
compared the performance of four conditions: backward fading with and
without self-explanation prompts and example-problem pairs with and
without self-explanation prompts (Atkinson et al., 2003; Experiment 1). We
found substantial effects of fading as well as of prompting on near transfer
and on far transfer. Both effects were additive. We replicated the prompting
effect in a subsequent experiment (Atkinson et al., 2003; Experiment 2).
Thus, we have shown that employing instructional means to effectively use
free cognitive capacity is particularly effective at fostering near transfer and
far transfer performance.
Are Faded Examples Superior to Well-Supported Learning
by Problem-Solving?
Although the previously described studies suggested that learning by selfexplaining the worked-out steps in faded examples is an effective learning
Learning from Worked-Out Examples and Problem Solving
103
method, some caveats remained. On a more general level, Koedinger and
Aleven (2007) have recently argued that the effectiveness of example-based
learning has been shown in relation to problem solving that included no
instructional support except for corrective feedback (e.g., Sweller & Cooper,
1985). These authors argue that the worked-example effect may disappear
when well-supported learning by problem solving is considered. Although
Koedinger and Aleven (2007) do not reason within a CLT framework, their
conjecture is compatible with CLT. This theory suggests that a high level
of support is important for beginning learners, and providing worked-out
examples is one way to provide such support (see Chapter 3, this volume).
Hence, an empirical test of the effects of example-based learning in relation
to supported problem solving is actually necessary to evaluate the generality
of the worked-example effect.
To date, the fading procedure’s effectiveness has been demonstrated relative to example-problem pairs. It would be informative to see whether learning from self-explaining faded examples is also superior to well-supported
problem solving, as provided by teachers in the classroom or by an (intelligent) tutoring system.
To address this issue, we included a faded example sequence in Cognitive
Tutors (Schwonke, Renkl, Krieg, Wittwer, Aleven, & Salden, 2009), an intelligent tutoring system that has been proven to be very effective in supporting
students’ learning in a variety of domains, such as mathematics and genetics
(e.g., Anderson, Corbett, Koedinger, & Pelletier, 1995; Koedinger & Corbett,
2006). On the basis of a real-time assessment of the student’s learning,
Cognitive Tutors provides individualized support for guided learning by
problem solving. Specifically, the tutor selects appropriate problems, gives
just-in-time feedback, and presents hints. Aleven and Koedinger (2002)
included self-explanation prompts in Cognitive Tutors that required students to provide an explanation for each of their solution steps, by making
an explicit reference to the underlying principle. This instructional method
made Cognitive Tutors more effective (Aleven & Koedinger, 2002). Although
Cognitive Tutors already included self-explanation prompts and many other
supportive features, we were interested in examining if students’ conceptual
understanding could be further improved by gradually fading worked-out
examples. In addition, the empirical results on the worked-example effect
also led us to the expectation that the learners would need less learning
time (see Sweller & Cooper, 1985) when using an example-enriched tutor
compared with the standard version.
In the first experiment, there were no significant differences in the effectiveness of the standard and the example-enriched tutor versions (Schwonke
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et al., 2009; Experiment 1). However, the example-enriched version was
more efficient (i.e., students needed less learning time). A problem that was
informally observed was that students had many problems in appropriately
using the example-enriched tutor. As a result, in Experiment 2 (Schwonke
et al., 2009), we provided students with additional instructions on how
to use the tutor. In this case, the students acquired a deeper conceptual
understanding when they worked with the example-enriched tutor and, as
we predicted, they needed less learning time than with the standard tutor.
These findings show ways in which the instructional models of tutored
problem solving and example-based learning can be fruitfully combined.
Adapting Fading to the Individual Learner
Although there is substantial evidence to support the fading approach, we
think that it can be improved. We have argued that when acquiring a complex cognitive skill, learners may be in an earlier stage with respect to one
sub-component (i.e., when a principle still needs to be understood) and they
may be in a later stage with respect to another sub-component (i.e., a principle is already understood). From an instructional perspective, it would be
optimal to encourage a learner to study examples with self-explanations for
the former sub-component while engaging them in problem solving for the
latter one. However, our present fading procedure is not adaptive to an individual learner’s level of understanding of different sub-components. As it is
presently structured, the problem-solving demands are gradually increased
assuming a prototypical learner rather than taking into consideration the
significant amount of variability among learners that we know exists (Plass
et al., this volume).
Kalyuga and Sweller (2004; Experiment 4) experimentally tested a realtime adaptive fading procedure when participants were learning to solve
algebraic equations. To diagnose the learners’ knowledge with respect to
certain steps, these authors used the rapid-assessment technique (see Chapter 3, this volume). In this technique, learners receive a (partially solved)
task and are asked to indicate rapidly their next solution step. Students’
answers range from using a trial-and-error strategy to providing directly the
final answer, indicating the availability of certain schemata in the domain.
Kalyuga and Sweller (2004) steered the provision of worked-out or faded
steps right from the beginning on the basis of the individual learner’s performance on rapid-assessment tests. The learners in the adaptive fading
condition significantly outperformed their yoked counterparts with respect
to knowledge gains.
Learning from Worked-Out Examples and Problem Solving
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Salden, Aleven, Renkl, and Schwonke (2008) implemented an adaptive fading procedure using the Cognitive Tutors technology. The Cognitive
Tutors assessment was based on the student’s self-explanation performance.
When the self-explanation performance indicated that a student understood a principle, the solution for the steps involving that principle was
faded in the next instance; thus, the learners had to solve these steps on
their own. To test such an adaptation procedure, one laboratory and one
classroom experiment were conducted. Both studies compared a standard
Cognitive Tutors with two example-enhanced Cognitive Tutors, in which
the fading of worked-out examples occurred either fixed or adaptively.
Results indicate that the adaptive fading of worked-out examples leads
to higher transfer performance on delayed post-tests than the other two
methods.
implications for instructional design
Based on the aforementioned findings, we offer the following instructional
design principles that guide the use of faded worked-out examples.
(1) Use a sequence of isomorphic examples. After introducing one or more
domain principles, worked-out examples should be provided. Providing learners with more than one example before problem solving
is the “heart” of CLT’s concept of example-based learning.
(2) Elicit self-explanations. The provision of worked-out examples or steps
reduces the cognitive load imposed on the learners. However, many
or even most learners do not naturally take advantage of the free
cognitive capacity to engage in germane load activities such as selfexplaining. To address this deficit, self-explanation prompts should
be used at the worked-out steps.
(3) Fade worked-out steps. After an initial completely worked-out example, incomplete examples with faded steps should be presented. As
the learners gain knowledge, studying examples becomes a redundant
activity. Thus, the number of blanks should be successively increased
until a problem is left that has to be solved completely by the learners. In this fashion, a smooth transition from studying examples to
problem solving is implemented.
(4) Individualize fading. Although a non-adaptive fading procedure is
effective, the effects can be further enhanced by tailoring the fading procedure to the individual trajectory of cognitive skill acquisition.
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avenues for future research
In sum, there is substantial evidence that cognitive skill acquisition can be
fostered by employing the approach of learning by self-explaining faded
examples. It is also important to note that not only procedural skills are
fostered but also conceptual understanding and the learners’ ability to
transfer knowledge to problems that require a modified solution procedure. Despite these achievements, there are still a number of open issues.
One drawback of the worked-out examples investigated so far is that they
typically involve well-structured domains, mostly with algorithmic solution procedures. For example, in mathematics, physics, or programming,
a manageable set of solution steps can be provided that directly lead to a
final answer. For activities such as cooperative learning, designing effective
learning materials, scientific argumentation, finding a mathematical proof,
and many other skills, solutions steps are more difficult to describe. With
respect to the four previously mentioned types of skills, recent studies have
shown that it is possible to extend the worked-out examples approach to illstructured, non-algorithmic domains (e.g., Hilbert, Renkl, Kessler, & Reiss,
2008: mathematical proof; Rummel, Spada, & Hauser, 2006: cooperating
in a productive way; Schworm & Renkl, 2006: designing effective learning materials; Schworm & Renkl, 2007: scientific argumentation). Hence,
these studies have begun to extend the applicability of the CLT concept of
example-based learning to a wider range of learning domains. Nevertheless,
the problem of how to structure the transition from studying examples to
problem solving and how to create a fading procedure in non-algorithmic
domains still needs to be addressed.
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6
Instructional Control of Cognitive Load in the Design
of Complex Learning Environments
liesbeth kester, fred paas, and
jeroen j. g. van merriënboer
Recent instructional design theories (e.g., the case method, project-based
education, problem-based learning, and competence-based education) tend
to focus on authentic learning tasks that are based on real-life experiences
as the driving force for complex learning (Merrill, 2002; van Merriënboer
& Kirschner, 2001). According to these theories, authentic learning tasks
have many solutions, are ecologically valid, cannot be mastered in a single
session, and pose a very high load on the learner’s cognitive system. Consequently, complex learning has little to do with learning separate skills in
isolation, but foremost it deals with learning to coordinate the separate skills
that constitute real-life task performance. Thus, in complex learning, the
whole is clearly more than the sum of its parts, because it also includes the
ability to coordinate the parts. In addition, in complex learning, effective
performance relies on the integration of skills, knowledge, and attitudes,
where, for instance, complex knowledge structures are underlying problemsolving and reasoning skills and particular attitudes are critical to interpersonal skills or to performing safety procedures. Moreover, complex learning
requires differentiation by recognizing qualitative differences among the task
characteristics that influence the constituent skills that have to be applied.
Figure 6.1 shows an example of a simulated, authentic learning task for
novice electricians in vocational education, namely, troubleshooting electrical circuits.
Some constituent skills are performed in a variable way across problem
situations (e.g., troubleshooting skills, such as orienting or diagnosing).
Experts can effectively perform such non-recurrent skills because they have
highly complex cognitive schemata available that help them to reason about
the domain and to guide their problem-solving behavior. Other constituent
skills may be performed in a highly consistent way across problem situations (e.g., building or operating an electrical circuit). Experts can effectively
109
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
figure 6.1. This example shows a malfunctioning electrical circuit (a). It contains
two faults that appear when switch 1 is closed, that is, no current is flowing because
the voltmeter and the ammeter are incorrectly connected (b). After this is fixed, two
lamps explode because the voltage of the battery is too high for the lamps (c). The
learner has to repair this circuit, and to do that, he or she has to coordinate his or her
troubleshooting skills (i.e., orient, diagnose, and plan action) and circuit operating
skills (i.e., execute the plan), integrate his or her knowlegde about electrical circuits
and skills to correctly perform the troubleshooting task, and recognize the features
of the electrical circuit that are relevant to reach a solution and those that are not.
If these skills are properly executed, this will result in a well-functioning electrical
circuit (d).
perform such recurrent skills because their cognitive schemata contain rules
that directly associate particular characteristics of the problem situation to
particular actions. The classification between non-recurrent and recurrent
aspects of complex performance is particularly important because the associated learning processes are fundamentally different from each other. For
non-recurrent skills, the main learning processes are related to schema construction and include induction or mindful abstraction from concrete experiences and elaboration of new information. For recurrent skills, the main
Instructional Control of Cognitive Load
111
learning processes are related to schema automation and include restricted
encoding or proceduralization of new information in to-be-automated rules
and compilation, and strengthening of those rules.
This chapter is about the cognitive implications of focusing on authentic
or complex tasks in education for the use of instructional methods. Because
high cognitive load is a key characteristic of complex tasks, effective learning can only commence if the specific instructions within a complex task
are properly aligned with cognitive architecture (Paas, Tuovinen, Tabbers, &
Van Gerven, 2003). The notion that the human cognitive architecture should
be a major consideration when choosing or designing instructional methods for meaningful learning of complex cognitive tasks is central to the
Cognitive Load Theory (CLT; Paas, Renkl, & Sweller, 2003; Sweller, 1988;
Sweller, van Merriënboer, & Paas, 1998; van Merriënboer & Sweller, 2005).
CLT assumes that if individuals are to learn effectively in a learning environment, the architecture of their cognitive system, the learning environment,
and interactions between both must be understood, accommodated, and
aligned.
According to CLT, well-chosen or well-designed instructional methods
should decrease the load that is not necessary for learning (i.e., extraneous
load, typically resulting from badly designed instruction; see Figure 6.2) and
optimize the load that directly contributes to learning (i.e., germane load),
within the limits of total available capacity to prevent cognitive overload.
However, this chapter is about complex tasks, which implicates that even
after the removal of all sources of extraneous cognitive load, the intrinsic
load resulting from dealing with the element interactivity in the tasks is still
too high to allow for efficient learning. CLT, therefore, recommends that
instructional designers or teachers use germane-load-inducing methods
only in combination with relatively simple tasks, in which the simultaneous processing of all interactive information elements leaves some spare
cognitive capacity. In this chapter, however, we oppose this approach by
arguing that germane-load-inducing methods can be used with complex
tasks. To accomplish this, intrinsic load and germane load must be balanced
by limiting the element interactivity of learning tasks while using germaneload-inducing methods. First, we discuss research findings indicating that
germane-load-inducing instructional methods used for practicing simple
tasks are not effective for practicing complex tasks, at the cost of transfer of
learning. Second, we explain how the intrinsic load of complex tasks can be
managed to allow the germane load to increase. Third, the implications of
this CLT-oriented perspective on learning for instructional design theories
are discussed on the basis of three instructional design models for complex
112
figure 6.2. This figure contrasts a learning task with a high extraneous load because it requires a visual search between text and
circuit (i.e., split attention; [a]) and a learning task with a lower extraneous load because it does not require this search (b).
113
figure 6.2 (continued)
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
learning. The chapter ends with a discussion of main conclusions and future
research issues.
task complexity and cognitive load
Research indicates that many instructional methods that work well for
simple tasks do not work well for complex tasks and vice versa (for overviews,
see Bainbridge, 1997; Wulf & Shea, 2002). In this section, we first discuss the
differential effects of germane-load-inducing methods on learning simple
and complex tasks, indicating that the positive effects of these methods
decrease with task complexity. Second, we argue that for transfer of training
to commence, it is essential to teach complex tasks with germane-loadinducing methods.
Germane-Cognitive-Load-Inducing Instructional Methods
and Task Complexity
A first important germane-load-inducing method affecting learning is practice variability and, in particular, the way that different versions of a learning
task are scheduled over practice trials. A common distinction is between low
and high contextual interference. In a practice schedule with low contextual
interference (i.e., blocked practice), one version of a task is repeatedly practiced before another version of the task is introduced. Under high contextual
interference (i.e., random practice), all versions of the task are mixed and
practiced in a random order. Contextual interference can be induced by
varying the surface features of a task (e.g., context, representation; Quilici
& Mayer, 1996) or the structural features of a task (e.g., underlying procedures). Varying the type of battery used in an electrical circuit, for example,
would be varying a surface feature because it would not affect the laws of
physics that apply to the circuit, whereas varying the type of circuit (i.e.,
series or parallel) would be varying a structural feature because it would
influence the laws of physics that apply to the circuit. For simple tasks,
a robust finding is that high contextual interference results in less effective performance during practice (e.g., more time and/or more trials are
necessary to reach a pre-specified level of performance) but higher performance during retention tests (for a review, see Magill & Hall, 1990). Possible
explanations for the beneficial effects of high contextual interference are
that the different versions of a task reside together in working memory and
can be compared and contrasted with each other to yield more elaborate
representations in memory (Shea & Zimny, 1983) and that high contextual
Instructional Control of Cognitive Load
115
interference conditions result in repeated forgetting of the action plan,
resulting in reconstructive activities that eventually yield more accessible
representations in memory (Lee & Magill, 1985). What the different explanations have in common is their assumption that random practice of different versions of a task induces germane learning processes that require more
effort than does blocked practice but yield cognitive representations that
increase later transfer test performance.
The findings for contextual interference are less clear for complex tasks,
which may be partly due to the fact that learners have difficulty distinguishing surface and structural features of such tasks (Ross & Kilbane, 1997). For
complex tasks in sports, beneficial effects of high contextual interference are
not found at all or are only found for high-expertise learners but not for lowexpertise learners (Hebert, Landin, & Solmon, 1996). Using drawing tasks,
Albaret and Thon (1999) explicitly manipulated task complexity (number of
line segments to draw) and studied the effects of contextual interference. As
expected, they found that the positive effects of random practice decreased
with task complexity and that for the most complex task, blocked practice
was even superior to random practice. These results convey the impression
that complex tasks leave no processing capacity for the germane cognitive
processes that help learners construct better cognitive representations.
A second germane-load-inducing method relevant to the design of practice is providing limited guidance and delayed feedback. For simple tasks,
reducing the amount of guidance is typically beneficial to learning. For
instance, physical guidance in learning motor skills (e.g., using a mechanical stop to indicate a target position, moving the performer’s limb) is more
effective when it is used for a limited number of trials than when it is used for
a high proportion of trials, and guidance that focuses a learner’s attention
only on the external goal of a movement is more effective than guidance that
focuses attention also on the specifics of the movement itself (Schmidt, 1991).
Paas, Camp, and Rikers (2001) showed that providing limited guidance by
loosely indicating the goal (i.e., the end point of the maze) is more effective
in maze learning tasks than giving a precise description of the goal. Results
indicate that for simple tasks, extensive guidance often has strong positive
effects on performance during practice, but when it is withdrawn during
tests, learners who practiced with less or no guidance perform better than
learners who practiced with extensive guidance. Similarly, giving feedback
on some of the practice tasks or on varying aspects of performance results
in more effective learning than giving feedback on all tasks or all aspects
of performance. Moreover, slightly delayed feedback is more effective than
concurrent or immediate feedback (Balzer, Doherty, & O’Connor, 1989).
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
The findings for the effects of guidance and feedback on complex tasks,
however, show another picture. For complex movements in sports, extensive
physical assistance proved to be superior to limited physical assistance (Wulf,
Shea, & Whitacre, 1998). For striking tasks, Guadagnoli, Dornier, and Tandy
(1996) convincingly demonstrated that relatively long feedback summaries
(i.e., delayed feedback) were most effective for teaching simple tasks to
low-expertise and high-expertise learners and teaching complex tasks to
high-expertise learners, but single-task feedback (i.e., immediate feedback)
was most effective for teaching complex tasks to low-expertise learners (i.e.,
a situation with high intrinsic cognitive load). These results suggest that
neither limited guidance and feedback nor alternation for the aspects of the
task that receive feedback has positive effects on learning complex tasks.
In contrast, it seems that the intrinsic load imposed by the complex tasks
leaves no processing capacity, allowing learners to develop their own internal
monitoring and feedback mechanisms or cognitive representations of how
different task aspects interact with each other early in the learning process.
The Transfer Paradox
The research on instructional design for simple and complex cognitive tasks
shows that complex tasks leave no processing capacity for the germane cognitive processes that help learners construct better cognitive representations.
In general, the results indicate that the positive effects of germane-loadinducing methods (i.e., random practice, limited guidance, and delayed
feedback) decrease as a function of task complexity. Therefore, it seems that
instruction of complex cognitive tasks should not be based on the use of
germane-load-inducing methods but on highly structured methods (i.e.,
blocked practice, step-by-step guidance, and immediate feedback) that primarily facilitate performance by taking over part of the cognitive processing
from the learner. We do not support this conclusion, however. Highly structured methods may indeed have a positive effect on the acquisition curve
and performance on retention tests, but not on problem solving and transfer
of learning. Instead, we believe that if one aims at transfer of learning and
the ability to show performances that go beyond given learning objectives,
it is necessary to use germane-load-inducing methods. This phenomenon,
in which the methods that work best for reaching specific objectives are
not the methods that work best for reaching transfer of learning, has been
described as the ‘transfer paradox’ (van Merriënboer, De Croock, & Jelsma,
1997; see also Eaton & Cottrell, 1999). This phenomenon has important
implications for the selection of instructional methods for complex tasks.
Instructional Control of Cognitive Load
117
The germane-load-inducing methods that explicitly aim at transfer
of learning should take two complementary dimensions of transfer into
account. These dimensions are rooted in Selz’s Gestalt approach to transfer
(cited in Mandler & Mandler, 1964) and Thorndike and Woodworth’s (1901)
‘identical elements’ approach to transfer. They are closely related to the high
road and the low road to transfer (Salomon & Perkins, 1989), innovation
and efficiency in transfer (Schwartz, Bransford, & Sears, 2005), and schemabased and rule-based transfer (van Merriënboer, 1997). The first approach
stresses that transfer may be partly explained by general or abstract knowledge that may be interpreted in the transfer situation (i.e., other use of
the same general knowledge); the second approach stresses that transfer
may be partly explained by the application of knowledge elements that are
shared between the practice and the transfer situation (i.e., the same use of
the same specific knowledge). The germane-load-inducing methods balance
both complementary dimensions and facilitate the interpretive aspects of
knowing for those aspects of a complex task that are different from problem
to problem situation (e.g., troubleshooting an electrical circuit) as well as
facilitate the applicative aspects of knowing for those aspects of a complex
task that are highly similar from situation to situation (e.g., building or
operating an electrical circuit; van Merriënboer, 1997).
Whereas both transfer dimensions need to be carefully balanced, and
adaptive experts score high on both dimensions (Gentner et al., 1997), it is
important to note that instructional methods that explicitly aim for one or
the other can also conflict with each other. The main problem is that starting
with highly structured methods that give priority to the applicative aspects
of knowing (e.g., building routines) seriously hampers the later development of interpretive aspects of knowing (e.g., building general schemas).
These methods constrain the problem spaces within which learners work
and then make it more difficult for them to generate creative solutions or
‘think outside the box’. An example is provided in a study by Schwartz,
Martin, and Pfaffman (2005), in which children learned to manipulate
pieces to help solve fraction problems. One group learned with pie pieces of
different sizes, with a focus on routine building because the pieces are easily
seen as fractions of a whole; the other group learned with tile pieces of equal
sizes, with a focus on interpretation because the pieces should be interpreted as parts of a whole rather than just units. For subsequent problem
solving with new materials (beans, bars, etc.), it was found that the interpretation group was better able to use the novel materials, showed better
progress, and eventually became more efficient than the routine-building
group.
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Concluding, highly structured methods, such as blocked practice, stepby-step guidance, and immediate feedback, may help to efficiently reach
pre-specified objectives but yield low transfer of learning. In addition, they
may block the later development of the second, interpretive dimension of
transfer. Therefore, not these germane-load reducing methods, but their
counterparts – random practice, limited guidance, and delayed feedback –
should be used to teach complex tasks. However, to avoid cognitive overload additional measures have to be taken. We argue that the intrinsic load
of complex tasks and the germane load of instructional methods should
be balanced during task performance. For a long time, intrinsic load was
considered unalterable by instruction, but recently, the effects of different approaches to reduce intrinsic load on learning have been investigated
(Ayres, 2006), and some techniques have been described that seem successful in reducing this load (Gerjets, Scheiter, & Catrambone, 2004; Pollock,
Chandler, & Sweller, 2002).
managing intrinsic load and inducing germane load
According to CLT, the complexity of a task is largely determined by its degree
of element interactivity. High-element interactivity requires the learner to
process several elements and their relationships simultaneously in working
memory in order to learn the task. Low-element interactivity allows the
learner to serially process only a few elements at a time. In the next section,
we explain how intrinsic load can be managed so that germane load can be
induced.
Managing Intrinsic Load
Instructional approaches to gradually increase the intrinsic load in a training are based on the sequencing of learning tasks from low-element to
high-element interactivity. Basically, this sequencing can be operationalized
in part-whole or whole-part approaches (see Figure 6.3). In a part-whole
approach, the number of elements and interactions between elements may
be initially reduced by simplifying the tasks, after which more and more
elements and interactions are added. In a whole-part approach, the number
of elements and interactions between elements may be immediately presented in their full complexity, but the learner has to take more and more
interacting elements into account when performing the tasks.
With regard to part-whole approaches, many studies indicate that
learners benefit from learning tasks that are sequenced from simple, with
Instructional Control of Cognitive Load
119
figure 6.3. Two approaches to ordering complex tasks: the part-whole approach,
which increases the number of interacting elements, and the whole-part approach,
which emphasizes more and more interacting elements.
relatively few interacting elements, to complex, with all interacting elements
that are necessary for complete understanding. For instance, Mayer and
Moreno (2003) discuss studies that show better transfer test performance
when students first had to study which components make up a system and
only then how the system works. Kester, Kirschner, and van Merriënboer
(2004a, 2004b, 2006) studied the effects of presenting information necessary to solve a complex task. They found that not presenting all information at once leads to better transfer test performance. Pollock et al. (2002)
and Clarke, Ayres, and Sweller (2005) considered mathematical learning
tasks and found that, especially for low-expertise learners and high-element
interactivity materials, first presenting isolated elements and only then the
interacting elements yields higher transfer test performance than presenting all elements simultaneously from the start. Finally, Ayres (2006) also
used mathematical learning tasks and found that especially low-expertise
learners benefit from the initial reduction in element interactivity, whereas
high-expertise learners benefit from high-element interactivity materials
used right from the start.
Whole-part approaches present high-element interactivity materials in
their full complexity right from the beginning, but use learning tasks that
focus the learner’s attention on particular subsets of interacting elements.
One way to emphasize varying interacting elements of a learning task is to
constrain learners’ performance, either through forcing them to behave as
an expert would do by requiring them to successfully complete a particular problem-solving phase before entering a next phase (Dufresne, Gerace,
Thibodeau-Hardiman, & Mestre, 1992) or through the use of particular task
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
formats, such as worked examples and completion tasks. Worked examples
focus the learners’ attention on elements that represent correct solution
steps only, so that they do not have to worry about potential solution
steps that are not relevant for the task at hand. Completion tasks present a
partial solution that must be completed by the learner. Like worked examples, they constrain the learner’s performance because not all potential
solution steps need to be taken into consideration. Many studies indicate
that low-expertise learners learn more from studying worked examples or
from completing partial solutions than from independently performing the
equivalent conventional tasks (for an overview, see Atkinson, Derry, Renkl,
& Wortham, 2000). Furthermore, Kalyuga, Chandler, Tuovinen, and Sweller
(2001) found that this effect reverses for high-expertise learners. Thus, to
accommodate the learner’s increase in expertise during practice, task formats with low-element interactivity (worked examples, completion tasks)
should be gradually replaced by conventional tasks with high-element interactivity. To ensure a smooth transition, one may start with worked examples, continue with completion tasks, and end with conventional tasks in an
instructional strategy known as the ‘completion strategy’ (van Merriënboer,
1990; see also Renkl & Atkinson, 2003).
Inducing Germane Load
Next to a low-to-high-element interactivity sequencing strategy that lowers
intrinsic load and thus frees up cognitive capacity, learning should be promoted by simultaneously implementing germane-load-inducing methods
(for an example, see Figure 6.4). As discussed earlier, random practice, limited guidance, and delayed feedback are promising germane-load-inducing
methods. Paas and van Merriënboer (1994) investigated random practice in
combination with worked examples and found that learners who received a
training sequence of random worked examples invested less time and mental
effort in practice and attained a better transfer performance than learners
who received a sequence of blocked worked examples. van Merriënboer,
Schuurman, De Croock, and Paas (2002) obtained similar results showing
that a training combining the completion strategy with random practice
yielded higher transfer test performance than a training combining it with
blocked practice.
With regard to limited guidance and delayed feedback as methods to
induce germane cognitive load, a study by Renkl (2002) indicated that using
guidance in the form of a minimalist description of the probabilistic rule that
was used in a worked example provided had beneficial effects on learning.
121
figure 6.4. The starting point in this example of the two-stage approach to complex learning (i.e., troubleshooting an electrical circuit)
is that all sources of extraneous load are removed (see Figure 6.2b); next, intrinsic load is managed by lowering the element interactivity
of the learning task (a) so that germane-load-inducing methods can be introduced (b).
122
figure 6.4 (continued).
Instructional Control of Cognitive Load
123
In addition, Renkl and Atkinson (2003) studied the use of self-explanation
prompts in combination with the completion strategy in the domain of
statistics (probability). During studying the worked examples, they guided
the learners by asking them which probability rule was applied in each solution step. They found a strong effect on transfer test performance for learners
who received the self-explanation prompts compared with learners who did
not receive these prompts. Robins and Mayer (1993) presented sets of worked
examples in a training ordered by type and accompanied by feedback that
described the problem types. They found that learners who received sets of
worked examples together with delayed feedback had superior transfer test
performance. These studies suggest that once the task complexity is reduced
by lowering the element interactivity as a function of learner expertise, that
is, by using a low-to-high-element interactivity sequence or performance
constraints, implementing germane-load-inducing methods has beneficial
effects on transfer test performance.
As shown in this chapter, instructional methods that attempt to balance
intrinsic and germane cognitive load during complex learning have clear
implications for instructional design and, in particular, the organisation of
learning tasks in educational programs that are based on projects, real-life
problems or cases, and other complex tasks. We first describe three example
instructional design models that specifically aim at complex learning. We
will indicate how these models are consistent with the presented methods
that aim at balancing the intrinsic and germane cognitive load. First, we
describe elaboration theory. This theory stresses the notion that working
from simple to complex is a sine qua non for complex learning. Second,
we examine goal-based scenarios that focus on the importance of realworld application and transfer of learning. Finally, the four-component
instructional design is discussed as an example of a theory that attempts to
implement all basic principles of complex learning.
implications for instructional design
The basic principle of Reigeluth’s Elaboration Theory (Reigeluth, 1987, 1999;
Reigeluth, Merrill, Wilson, & Spiller, 1980; Reigeluth & Stein, 1983; Van
Patten, Chao, & Reigeluth, 1986) is that instruction should be organized
from the simplest representation of the learning task (i.e., the ‘epitome’,
which contains the most fundamental and representative ideas at a concrete
level), for example, a simple electrical circuit connected in series or parallel,
to increasingly more complex and elaborated representations, for instance,
a complex electrical circuit connected in series and parallel. Originally, the
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
theory focused on the sequencing of instructional content in conceptual
and theoretical domains. The broadest, most inclusive concepts are taught
first, including the supporting content (i.e., relevant knowledge, skills, and
attitudes) related to them, and subsequently, the ever narrower, detailed
concepts are taught together with related supporting content. Later, the
theory also focused on sequencing interrelated sets of principles. Such a
sequence first teaches the broadest, most inclusive, and most general principles along with the supporting content, and then proceeds to teach ever
narrower, less inclusive, more detailed, and more precise principles and
supporting content.
Elaboration theory clearly reflects the presented principles of complex
learning. The elaborative approach to sequencing works from simple to
complex wholes, which closely resembles a whole-part approach to a lowto-high-element interactivity sequencing strategy. The combination of organizing content (conceptual, theoretical) and supporting content aims at the
integration of knowledge, skills, and attitudes, which characterizes complex
learning. The concept of ‘learning episodes’ is used to denote instructional
units that allow for review and synthesis without breaking up the idea of
a meaningful whole and can be used to incorporate limited guidance and
delayed feedback.
Goal-based scenarios (Schank, 1993/1994; Schank, Fano, Bell, & Jona,
1993/1994) are the backbone of learning in Schank’s learning-by-doing
paradigm (Schank, Berman, & MacPherson, 1999). These goal-based scenarios represent ‘a learning-by-doing simulation in which students pursue
a goal by practicing target skills and using relevant content knowledge to
help them achieve their goal’ (Schank et al., 1999, p. 165). Like the learning
episodes in elaboration theory, goal-based scenarios provide an opportunity
to integrate knowledge, skills, and attitudes in meaningful wholes, which
characterizes complex learning. Unlike the elaboration theory, however,
goal-based scenarios pay far less attention to the sequencing of instruction.
In contrast, there is a stronger focus on the performance of real-life tasks
in authentic contexts to facilitate transfer of learning. This fits the Gestalt
approach to transfer, which maintains that more general goals (i.e., integrated objectives) should drive the learning process, because highly specific
learning objectives invite learners to apply strategies that do not allow for
transfer of learning (see also Machin, 2002, for the role of goals in reaching
transfer of learning).
van Merriënboer’s four-component instructional design (4C/ID) model,
(van Merriënboer, 1997; van Merriënboer, Clark, & De Croock, 2002;
Instructional Control of Cognitive Load
125
van Merriënboer et al., 2003) maintains that learning environments for
complex tasks can always be described in four components:
1. Learning tasks, which are preferably based on real-life tasks and fulfill
the role of a backbone for the training program.
2. Supportive information, which is made available to learners because
it helps them to perform the problem-solving and reasoning aspects
of learning tasks. It mainly concerns information on how the domain
is organized and how problems in the domain can be systematically
approached by the learner.
3. Procedural information, which is presented to learners because it helps
them to perform the routine aspects of learning tasks. It mainly concerns procedural steps that precisely specify under which conditions
particular actions must be taken by the learner.
4. Part-task practice, which may provide learners with additional practice
for routine aspects of the complex task that need to be developed to
a very high level of automaticity.
Three basic prescriptions of the 4C/ID model correspond with the main
principles discussed in the previous sections. First, the model suggests that
learning tasks should be ordered into so-called task classes, in which earlier
task classes have lower element interactivity than later task classes (i.e., a
whole-part approach). Even the first task class contains whole and meaningful tasks (i.e., the most essential interacting elements) so that the learners
may quickly develop a holistic vision of the whole task that is then gradually
embellished in subsequent task classes. Second, when learners start to work
on tasks in a new, more complex task class, it is essential to initially focus
their attention on those elements that are most important for learning.
This may be reached by first constraining and then increasingly relaxing
their performance or by starting with worked examples, continuing with
completion tasks, and ending with conventional tasks. Third, and probably
most important, the combination of ordering learning tasks in simple-tocomplex task classes, with scaffolding learners within a task class, enables
the use of instructional methods that evoke a germane cognitive load. Thus,
learning tasks should always, right from the beginning of the training program, show random practice, give limited guidance to learners, and provide
them with delayed feedback on varying aspects of performance.
The three other components of the 4C/ID model explicitly take the
two transfer dimensions into account. Supportive information relates to
the Gestalt approach that transfer is explained by general or abstract
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Liesbeth Kester, Fred Paas, and Jeroen J. G. van Merriënboer
information that may be interpreted by a task performer to solve a new
problem situation. Procedural information and part-task practice mainly
relate to the identical elements approach that transfer may be explained by
the application of knowledge elements that are shared between the practice
and the transfer situation.
discussion
In this chapter, we argued that the increasing focus of instructional design
theories on the use of complex ‘real-life’ tasks has important implications
for the use of instructional methods. Even after removal of all sources of
extraneous load, these tasks are often so cognitively demanding that it is
impossible to use transfer-enhancing instructional methods right from the
start of a training program. We used cognitive load theory to explain how to
balance the intrinsic load imposed by a complex task and the germane load
caused by instructional methods that aim for transfer. First, intrinsic load
can be decreased early in learning by manipulating the element interactivity
of the learning tasks. Then, learning tasks can be immediately combined
with methods that induce germane cognitive load, such as random practice,
limited guidance, and delayed feedback. We showed that these instructional
methods can easily be implemented in contemporary instructional design
models for complex learning, such as the elaboration theory (Reigeluth,
1987, 1999), Schank’s learning-by-doing paradigm (Schank et al., 1999), and
the 4C/ID model (van Merriënboer, 1997).
Our analysis points out three important directions for future research.
First, the assumed interaction between intrinsic-load-reducing methods
and germane-load-inducing methods has only been empirically confirmed
for a limited number of concrete instructional methods. More research is
needed to show that the interaction holds across a wide variety of methods. Second, more research is needed with highly complex real-life tasks
performed in ecologically valid settings. Particular instructional methods
such as variability might then have unexpected effects, for instance, because
it is difficult for learners to distinguish between the surface and structural
features of such tasks. Finally, progress must be made with regard to the
measurement of cognitive load. Instruments that allow researchers to disentangle changes in cognitive load into changes in intrinsic load on the one
hand and germane load on the other hand would be especially helpful to
the in-depth analysis of research findings.
An important point to consider in the design of training of complex
tasks is that the element interactivity or intrinsic load of a task depends
Instructional Control of Cognitive Load
127
on the expertise of the learner: the higher the expertise, the lower the
intrinsic load. In other words, if an individual task performer develops
more expertise in a task, the functional complexity of the task decreases.
In a flexible and adaptive educational program, it should be possible to
take differences between individual students into account when suitable
learning tasks are selected. Some students have skills acquired elsewhere
that should be taken into account, and some students are better able to
acquire new skills and therefore need less practice than other students. In
the 4C/ID framework, this means that for each individual student, it should
be possible at any given point to select the best task class to work on, as
well as the amount of performance constraints applied to the selected task.
Consequently, a high-ability student may quickly proceed from task class
to task class and mainly work on tasks with little performance constraints,
whereas a low-ability student may need many more tasks to complete the
program, progress slowly from task class to task class, and work mainly on
tasks with sizeable performance constraints.
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7
Techniques That Reduce Extraneous Cognitive Load
and Manage Intrinsic Cognitive Load during
Multimedia Learning
richard e. mayer and roxana moreno
what is multimedia learning?
Suppose you open an online multimedia encyclopedia and click on the
entry for “pumps.” Then, the computer presents a narrated animation
describing how a pump works. Alternatively, suppose you are playing an
educational science game on your computer in which you fly to a new planet
and must design a plant that would survive there. An on-screen character
guides you and explains how the characteristics of the roots, stem, and
leaves relate to various environmental conditions. Both of these examples –
multimedia lessons and agent-based simulation games – are forms of
computer-based multimedia learning environments. They are multimedia
learning environments because they involve words (e.g., printed or spoken
words) and pictures (e.g., animation, video, illustrations, or photos). They
are computer-based learning environments because they are presented via
computer. Our goal in this chapter is to explore research-based principles for
improving the instructional design of computer-based multimedia learning.
We begin with the premise that research on multimedia learning should
be theory based, educationally relevant, and scientifically rigorous. By calling for theory-based research, we mean that research on multimedia learning should be grounded in a cognitive theory of multimedia learning.
In this chapter, we build on the cognitive theory of multimedia learning
(Mayer, 2001, 2005a, 2005b; Mayer & Moreno, 2003), which is adapted from
Cognitive Load Theory (CLT) (Paas, Renkl, & Sweller, 2003; Sweller, 1999,
2005).1 By calling for educationally relevant research, we mean that research
on multimedia learning should be concerned with authentic learning
1
In this chapter, we generally concur with the editors’ definitions of key terms, such as
dual channel, extraneous load, germane load, intrinsic load, limited capacity, multimedia
learning, and online learning, although our wording may differ slightly.
131
132
Richard E. Mayer and Roxana Moreno
MULTIMEDIA
PRESENTATION
Words
Pictures
SENSORY
MEMORY
Ears
Eyes
selecting
words
selecting
images
LONG-TERM
MEMORY
WORKING MEMORY
Sounds
Images
organizing
words
organizing
images
Verbal
Model
integrating
Prior
Prior
Knowledge
Pictorial
Model
figure 7.1. Cognitive theory of multimedia learning.
situations and materials. In this chapter, we focus on guidelines for the
design of short narrated animations, typically found in multimedia encyclopedias or as part of larger lessons, and on educational simulation games
mainly dealing with topics in science and mathematics. By calling for scientifically rigorous research, we mean that research on multimedia learning
should use appropriate research methods. In this chapter, our goal is to
determine which features affect learning, so we focus on meta-analyses of
well-controlled experiments.
how do people learn with multimedia instruction?
How do people learn with multimedia presentations and simulations? We
begin with three main principles based on cognitive science research:
dual channels – humans possess separate channels for processing visual/
pictorial material and auditory/verbal material (Baddeley, 1999; Paivio,
1986),
limited capacity – humans are limited in the amount of material they
can process in each channel at one time (Baddeley, 1999; Chandler &
Sweller, 1991), and
active processing – meaningful learning depends on active cognitive processing during learning, including selecting relevant information for
further processing, organizing selected material into a coherent mental representation, and integrating incoming material with existing
knowledge (Mayer, 2001; Wittrock, 1989)
In short, the challenge of meaningful learning is that people must actively
process the incoming material in information processing channels that are
highly limited.
Figure 7.1 summarizes the process of multimedia learning. As shown
on the left side, words and pictures are presented in a multimedia lesson,
such as a narrated animation. The auditory material impinges on the ears,
Techniques That Reduce Extraneous Cognitive Load
133
whereas the visual material impinges on the eyes. If the learner attends to
the incoming information, some of the words are transferred to working
memory (indicated by the “selecting words” arrow) and some of the visual
material is transferred to working memory (indicated by the “selecting
images” arrow). In working memory, the learner mentally organizes the
selected words into a verbal model (indicated by the “organizing words”
arrow) and mentally organizes the selected images into a pictorial model
(indicated by the “organizing images” arrow). Finally, the learner mentally
integrates the verbal and pictorial material with each other and with relevant
prior knowledge (indicated by the “integrating” arrow). The management of
these processes is coordinated with existing knowledge, the learner’s goals,
and the learner’s metacognitive strategies.
The cognitive theory of multimedia learning presented in Figure 7.1 is
based on CLT, which is a broader theory of human cognition. Although the
cognitive theory of multimedia learning is a more specialized theory (i.e.,
dealing with learning from words and pictures), it is consistent with the
major features of CLT, particularly with the focus on designing instruction
that does not overload the learner’s cognitive system.
Specifically, we draw on a central tenet common to CLT and the cognitive
theory of multimedia learning, which can be called the triarchic theory of
cognitive load. The triarchic theory of cognitive load specifies three kinds of
cognitive processing demands during learning:
Extraneous cognitive load (corresponding to extraneous processing in the
cognitive theory of multimedia learning [CTML]) is cognitive processing that does not contribute to learning. It is caused by presenting
the material in a poorly designed layout or including non-essential
material in a lesson.
Intrinsic cognitive load (corresponding to essential processing in CTML) is
cognitive processing imposed by the inherent difficulty of the material.
It is caused by having to hold a large number of elements in working
memory at the same time. According to CTML, essential processing
involves the learner’s initial comprehension of the material through
engaging in the cognitive process of attending to the relevant material.
Germane cognitive load (corresponding to generative processing in the
CTML) is cognitive processing that contributes to learning. It is caused
by challenging or motivating the learner to exert effort toward understanding the material. According to the CTML, generative processing
involves the learner’s deep understanding of the material through
engaging in the cognitive processes of organizing and integrating.
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Richard E. Mayer and Roxana Moreno
table 7.1. Three goals of instructional design
Cognitive theory of
multimedia learning
Cognitive load theory
Reduce extraneous
cognitive processing
Reduce extraneous
cognitive load
Manage essential
cognitive processing
Manage intrinsic cognitive
load
Foster generative
cognitive processing
Foster germane cognitive
load
Description of cognitive
processing
Cognitive processing that does
not support learning the
essential material
Cognitive processing aimed at
mentally representing the
essential material
Cognitive processing aimed at
mentally organizing the
representation and integrating
it with existing knowledge
Given the limits on each learner’s overall amount of cognitive capacity for
processing information at any one time, problems occur when the total
amount of extraneous, essential, and generative processing exceeds the
learner’s cognitive capacity.
three goals for the design of multimedia
learning environments
Table 7.1 summarizes three goals for the design of multimedia learning
environments – reduce extraneous cognitive processing, manage essential
cognitive processing, and foster generative processing. The first column
describes the goals using the terminology of the cognitive theory of multimedia learning and the second column describes the goals using the corresponding terminology of CLT. The third column provides a brief definition
of each of the three goals.
Reduce Extraneous Cognitive Processing
First, multimedia lessons should minimize the amount of extraneous processing – processing that does not support learning – required of the learner.
The most common obstacle to multimedia learning occurs when the presented material contains extraneous material or is poorly laid out. In this
situation, the learner is primed to engage in extraneous cognitive processing, that is, cognitive processing that is not directly relevant to learning the
essential material. Given that the amount of cognitive capacity is limited,
Techniques That Reduce Extraneous Cognitive Load
135
when learners engage in large amounts of extraneous processing they may
have insufficient remaining capacity for essential and generative processing that are needed for meaningful learning. We refer to this situation as
extraneous overload – extraneous processing exhausting the available cognitive capacity. Techniques for reducing extraneous cognitive processing are
explored in the next section of this chapter.
Manage Essential Processing
Second, multimedia learning should help the learner to manage essential
processing so that it does not overload the learner’s cognitive system. If
one is successful in removing extraneous material and inconsiderate layout
from a multimedia lesson, then the learner does not have to waste precious
cognitive capacity on extraneous processing. Instead, the learner can use
cognitive capacity for essential processing – that is, mentally representing
the essential material – and generative processing – that is, mentally organizing the essential material and integrating it with prior knowledge. However, in some situations, the demands of essential processing may exceed
the learner’s cognitive capacity, resulting in essential overload (Mayer &
Moreno, 2003; Mayer, 2005b).
Essential overload can occur when the essential material is complex,
unfamiliar, or presented at a fast pace. First, material is complex when it
contains many components that interact with one another. For example,
in an explanation of lightning, there are more than a dozen key elements
that interact in multiple ways, such as updrafts, downdrafts, negatively
charged particles, positively charged particles, warm air, cool air, and so on.
Sweller (1999) defines complexity in terms of element interactivity – that
is, the number of interacting components and the nature of their interactions. Second, material is unfamiliar when the learner lacks relevant prior
knowledge. Existing knowledge can be used to chunk the incoming material into larger meaningful units, effectively reducing cognitive load. For
example, if a learner knows that hot air rises and negative and positive
charges attract, this will help chunk an explanation of lightning formation.
Sweller (1999) used the term schemas to refer to relevant prior knowledge
in long-term memory. Third, material is fast-paced when the presentation
rate is faster than the amount of time the learner requires for representing
the material. For example, in a narrated animation on lightning formation, the learner may not be able to fully represent one step in the process before the next step is presented. Techniques for managing essential
processing are described in a subsequent section of this chapter.
136
Richard E. Mayer and Roxana Moreno
table 7.2. Evidence concerning the coherence principle in
computer-based multimedia learning
Source
Moreno & Mayer (2000, Expt. 1)
Moreno et al. (2000, Expt. 2)
Mayer, Heiser, & Lonn (2001, Expt. 3)
Mayer & Jackson (2005, Expt. 2)
Median
Content
Effect size
Lightning
Brakes
Lightning
Ocean waves
1.49
0.51
0.70
0.69
0.70
Foster Generative Processing
Finally, suppose a multimedia lesson is presented in a way that eliminates
extraneous processing and manages essential processing so that the learner
has capacity available to engage in generative processing. How can we promote generative processing without exceeding the available cognitive capacity? This issue is discussed in Chapter 8.
research-based principles for reducing extraneous
cognitive load
One major instructional design problem occurs when multimedia instruction is insensitive to the information processing limitations of the learner.
Cognitive overload can occur when too much extraneous material is presented or the material is displayed in confusing ways, or both. In this situation, the learner may use precious cognitive capacity for extraneous processing – cognitive processing that does not enhance learning of the essential
material – which may leave insufficient remaining cognitive capacity for
essential processing and generative processing. In this section, we explore
five research-based principles for overcoming the insensitivity problem:
coherence, redundancy, signaling, spatial contiguity, and temporal contiguity principles. Tables 7.2 to 7.6 show evidence supporting each one of
these principles on measures of students’ problem-solving transfer. Different letters following experiment numbers indicate separate experimental
comparisons.
Coherence Principle
Consider the following situation. You open a multimedia encyclopedia and
click on the entry for “lightning.” On the screen, you see a 140-second
Techniques That Reduce Extraneous Cognitive Load
137
animation depicting the steps in lightning formation, and through the
speakers you hear corresponding narration describing the steps in lightning
formation. In an effort to spice up the lesson, we could insert several 10second video clips showing sensational lightning storms, and we could
include concurrent narration describing interesting facts about the dangers
of lightning. However, when Mayer, Heiser, and Lonn (2001, Experiment 3)
inserted interesting video clips, students performed worse on transfer tests
than when no such video clips were inserted in the lesson.
In a similar effort to spice up the lesson, we could insert background
music and/or appropriate environmental sounds, such as blowing wind
or cracking ice. However, Moreno and Mayer (2000, Experiments 1 and 2)
found across two different science lessons that students performed better on
retention and transfer tests when the lessons did not have extraneous sounds
and that students’ learning was hurt the most when both music and environmental sounds were combined. More recently, Mayer and Jackson (2005,
Experiment 2) found that students learned better from a computer-based
lesson explaining the formation of ocean waves if formulas and numerical
computations were excluded.
In four of four experimental tests, students performed better from a
narrated animation that was concise rather than elaborated, as shown in
Table 7.2. We refer to this finding as the coherence principle – delete extraneous material from multimedia instruction. The median effect size2 of the
coherence principle is 0.70. Similar results were reported with paper-based
lessons in which students performed better on transfer tests when interesting
but extraneous facts and illustrations were excluded rather than included
(Harp & Mayer, 1997, Experiment 1; Harp & Mayer, 1998, Experiments 1,
2, 3, & 4; Mayer, Bove, Bryman, Mars, & Tapangco, 1996, Experiments 1,
2, & 3).
How does the coherence principle work? Cognitive processing capacity
is limited and must be allocated to extraneous, essential, and generative
processing. When extraneous material is excluded or inconsiderate layouts
are corrected, the learner engages in less extraneous processing. This leaves
more capacity for essential and generative processing (as indicated by the
organizing and integrating arrows in Figure 7.1) and thus is more likely to
lead to meaningful learning outcomes.
2
Effect sizes are based on Cohen’s (1988) d, in which effect sizes below 0.2 are considered
negligible, effect sizes between 0.2 and 0.5 are considered small, effect sizes between
0.5 and 0.8 are considered medium, and effect sizes above 0.8 are considered large.
138
Richard E. Mayer and Roxana Moreno
table 7.3. Evidence concerning the redundancy principle in computer-based
multimedia learning
Source
Kalyuga, Chandler, & Sweller (1999, Expt. 1)
Kalyuga, Chandler, & Sweller (2000, Expt. 1)
Craig, Gholson, & Driscoll (2002, Expt. 2)
Mayer, Heiser, & Lonn (2001, Expt. 1)
Mayer, Heiser, & Lonn (2001, Expt. 2)
Moreno & Mayer (2002a, Expt. 2)
Moreno & Mayer (2002b, Expt. 2a)
Moreno & Mayer (2002b, Expt. 2b)
Median
Content
Effect size
Electrical engineering
Electrical engineering
Lightning
Lightning
Lightning
Lightning
Environmental science game
Environmental science game
1.38
0.86
0.67
0.88
1.21
0.72
0.19
0.25
0.79
Redundancy Principle
In another attempt to improve the narrated animation explaining lightning
formation, we could add captions to the bottom of the screen that mirror the
narration. For example, each caption could consist of the same sentence(s)
that the narrator is saying. You might think that adding captions (i.e., onscreen text that is identical to the narration) would allow people to choose
their preferred mode for receiving words – either in printed or spoken form.
In this way, auditory learners could listen to the narration and visual learners
could read the on-screen text.
However, when redundant on-screen text was added to a narrated animation on lightning, learners performed worse on transfer tests (Craig,
Gholson, & Driscoll, 2002, Experiment 2; Mayer et al., 2001, Experiments 1 &
2; Moreno & Mayer, 2002a, Experiment 2). Similarly, students performed
worse on transfer tests when redundant on-screen text was added to narrated animations explaining plant growth in an environmental science game
(Moreno & Mayer, 2002b, Experiments 2a & 2b). In a set of similar studies,
Kalyuga, Chandler, and Sweller (1999, Experiment 1; 2000, Experiment 1)
presented a series of electrical diagrams on a computer screen along with
an audio message containing spoken words (non-redundant group), but
for some students, they added printed words to the screen that were identical to the ongoing audio message (redundant group). Consistent with the
foregoing studies, students in the non-redundant group performed better
on subsequent transfer tests than did students in the redundant group.
Overall, in eight of eight experimental tests, students learned better
when redundant on-screen text was excluded. The studies are summarized
in Table 7.3 and yield a median effect size of 0.79. We refer to this finding as
Techniques That Reduce Extraneous Cognitive Load
139
the redundancy principle – exclude redundant on-screen text from narrated
animations. Similar results were obtained by Mousavi, Low, and Sweller
(1995, Experiments 1 & 2) in a paper-based lesson on mathematics. There
may be situations in which redundant on-screen text makes pedagogic sense,
such as when the students are non-native speakers or are hearing impaired
or when the words are technical terms or hard to pronounce.
How does the redundancy principle work? Redundant on-screen text
creates extraneous processing, because learners may attempt to reconcile
the two incoming verbal streams and may have to scan the animation to
find elements corresponding to words at the bottom of the screen. When the
redundant on-screen text is removed, the learner engages in less extraneous
processing, freeing up cognitive capacity to be used for essential and generative processing (as indicated by the organizing and integrating arrows in
Figure 7.1).
Signaling Principle
Sometimes it may not be feasible to delete extraneous material from a
computer-based lesson, so a useful alternative is to provide cues that direct
the learner’s attention to the essential material in the lesson. For example,
Mautone and Mayer (2001, Experiments 3a & 3b) asked people to view
a narrated animation on how an airplane achieves lift. For some people
(signaled group), the narration included signals, such as a sentence outlining
the main sections, phrases that served as headings for each section, and
intonation emphasis on linking words, such as “because of this.” For others
(non-signaled group), no signals were provided. On a subsequent transfer
test, the signaled group outperformed the non-signaled group. Likewise, a
study with pre-service teachers showed that signaling different sources of
learner diversity within virtual classroom cases promoted their ability to
adapt instruction to the special needs of the learners depicted in the cases
(Moreno & Abercrombie, in press).
This work provides preliminary evidence for the signaling principle –
incorporate signals in the narration, such as outlines, headings, and pointer
words. In two of two experimental tests, signaling improved transfer performance, yielding a median effect size of 0.70. Table 7.4 summarizes these
findings, but more empirical research is needed. Similar results were
obtained with a paper-based lesson on lightning formation (Harp & Mayer,
1998, Experiment 3a).
How does the signaling principle work? When extraneous material is
included in a lesson, learners engage in extraneous processing to the extent
140
Richard E. Mayer and Roxana Moreno
table 7.4. Evidence concerning the signaling principle in
computer-based multimedia learning
Source
Mautone & Mayer (2001, Expt. 3a)
Mautone & Mayer (2001, Expt. 3b)
Moreno & Abercrombie (in press)
Median
Content
Effect size
Airplane
Airplane
Teaching principles
0.60
0.70
0.81
0.70
that they process that extraneous material. Signals such as outlines, headings, and highlights can help direct learners’ attention toward the essential
material (as indicated by the “selecting” arrows in Figure 7.1), thus decreasing extraneous processing. This leaves more capacity for essential and generative processing, and thus is more likely to lead to meaningful learning
outcomes.
Temporal Contiguity Principle
Sometimes extraneous processing is caused by inconsiderate layout of the
instructional materials. For example, suppose you clicked on the entry for
“brakes” in an electronic car manual, and on the screen appeared a speaker
icon and a movie icon. When you click on the speaker icon, you hear an
explanation of how a car’s braking system works; when you click on the
movie icon, you see an animation of how a car’s braking system works
without sound.
What’s wrong with this scenario? This scenario is likely to lead to extraneous processing because the learner must use a lot of cognitive capacity
to hold the entire narration in working memory until the animation is
presented (or vice versa). If meaningful learning depends on holding corresponding words and pictures in working memory at the same time, then
successive presentation of narration and animation can easily overload the
learner’s cognitive system.
For example, Mayer and Anderson (1991, Experiments 1 & 2a; 1992, Experiment 1) and Mayer and Sims (1994, Experiment 1) presented students with
a short narration and animation explaining how a tire pump works. Some
students received a narrated animation in which corresponding segments of
the narration and animation were presented simultaneously (simultaneous
group), whereas other students received the entire narration either before or
after the entire animation (successive group). On a subsequent transfer test,
the simultaneous group outperformed the successive group, even though
141
Techniques That Reduce Extraneous Cognitive Load
table 7.5. Evidence concerning the temporal contiguity principle in
computer-based multimedia learning
Source
Mayer & Anderson (1991, Expt. 1)
Mayer & Anderson (1991, Expt 2a)
Mayer & Anderson (1992, Expt. 1)
Mayer & Anderson (1992, Expt. 2)
Mayer & Sims (1994, Expt. 1)
Mayer & Sims (1994, Expt. 2)
Mayer, Moreno, Boire, & Vagge (1999, Expt. 1)
Mayer, Moreno, Boire, & Vagge (1999, Expt. 2)
Median
Content
Effect size
Tire pump
Tire pump
Tire pump
Brakes
Tire pump
Lungs
Lightning
Brakes
0.92
1.14
1.66
1.39
0.91
1.22
2.22
1.40
1.31
both groups received identical material. The same pattern of results was
obtained with animation and narration lessons on brakes (Mayer & Anderson, 1992, Experiment 2; Mayer, Moreno, Boire, & Vagge, 1999, Experiment
2), lungs (Mayer & Sims, 1994, Experiment 2), and lightning (Mayer et al.,
1999, Experiment 1).
Overall, in eight of eight experimental tests, students performed better
on transfer tests after receiving simultaneous rather than successive presentations of animation and narration. The studies are summarized in Table 7.5,
and yield a median effect size of 1.31. We refer to this pattern as the temporal contiguity principle – present corresponding segments of animation and
narration concurrently. In a recent review of 13 experimental comparisons,
Ginns (2006) reported a mean weighted effect size of d = 0.78, favoring the
temporal contiguity principle.
How does the temporal contiguity principle work? Extraneous cognitive load is created when the learner must use cognitive capacity to
hold the entire narration in working memory until the animation is presented or vice versa (as indicated in the “WORKING MEMORY” section of
Figure 7.1). By presenting corresponding segments of the animation and
narration at the same time, the learner is able to mentally integrate the
verbal and pictorial material within each segment, thereby eliminating the
need to hold material in working memory over long periods.
Spatial Contiguity Principle
Another example of extraneous processing caused by inconsiderate layout
occurs when corresponding words and pictures are not near one another
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table 7.6. Evidence for the spatial contiguity principle in
computer-based multimedia learning
Source
Moreno & Mayer (1999, Expt. 1)
Content
Effect size
Lightning
0.82
on the screen. For example, suppose you viewed an animation on lightning formation in which the sentences describing each step were presented
at the bottom of the screen (separated group) or next to the action they
described in the animation (integrated group). You might suppose that
students should perform equally well in both scenarios because exactly the
same information is presented in each. However, in the separated presentation, students must engage in extraneous processing – namely, scanning the
screen to see what the caption at the bottom of the screen is referring to. In
contrast, extraneous processing is minimized in the integrated presentation
because the learner is directed where to look on the screen.
Moreno and Mayer (1999, Experiment 1) found that students performed
better on a transfer test when on-screen text was placed next to the corresponding element in the animation than when it was placed at the bottom
of the screen. We refer to this finding as the spatial contiguity principle –
place on-screen text near corresponding elements in the screen. This preliminary finding is based on one experimental comparison, with an effect
size of 0.82, as shown in Table 7.6. Similar results, however, were obtained
with paper-based lessons on how brakes work (Mayer, 1989, Experiment 1),
lightning (Mayer, Steinhoff, Bower, & Mars, 1995, Experiments 1, 2, & 3),
electrical engineering (Chandler & Sweller, 1991, Experiment 1; TindallFord, Chandler, & Sweller, 1997, Experiment 1), and mathematical problem solving (Sweller, Chandler, Tierney, & Cooper, 1990, Experiment 1).
In a recent review of 37 experimental comparisons concerning spatial
contiguity, Ginns (2006) reported a mean weighted effect size of d = 0.72,
favoring the spatial contiguity principle.
How does the spatial contiguity principle work? When learners receive
separated presentations, they must scan the screen to find which part of
the graphic corresponds to the words. This is a form of extraneous processing that can be eliminated when the words are placed next to the part
of the graphic they describe, thereby enabling more capacity to be used for
essential and generative processing (as indicated by the “organizing” and
“integrating” arrows in Figure 7.1).
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Techniques That Reduce Extraneous Cognitive Load
table 7.7. Evidence concerning segmenting principle in computer-based
multimedia learning
Source
Mayer & Chandler (2001, Expt. 2)
Mayer, Dow, & Mayer (2003, Expt. 2a)
Mayer, Dow, & Mayer(2003, Expt. 2b)
Moreno (2007, Expt. 1)
Moreno (2007, Expt. 2)
Median
Content
Effect size
Lightning
Electric motor
Electric motor
Teaching principles
Teaching principles
1.13
0.82
0.98
0.39
0.61
0.82
research-based principles for managing intrinsic
cognitive load
We can eliminate the need for extraneous processing by removing extraneous material and laying out the presentation in a considerate way. This
allows the learner to use all of his or her cognitive capacity to engage in
essential processing – that is, mentally representing the essential material –
and generative processing – that is, mentally organizing and integrating the
essential material with existing knowledge. However, even when extraneous
processing is eliminated or reduced, the demands of essential processing may
be so great that little or no cognitive capacity remains for deeper learning.
For example, heavy intrinsic cognitive load occurs when the essential material is complex, fast paced, or unfamiliar to the learner. In this section, we
explore three principles for overcoming the difficulty problem by managing
intrinsic cognitive load: segmenting, pretraining, and modality principles.
Tables 7.7 to 7.9 show evidence supporting each one of these principles on
measures of students’ problem-solving transfer. Different letters following
experiment numbers indicate separate experimental comparisons.
Segmenting Principle
Suppose we ask a novice to study a concise narrated animation on how
an electric motor works. The electric motor has many interacting parts
and its operation is based on a complex chain of electrical and magnetic
events. If learners have little prior knowledge to help in organizing the
incoming information, they are likely to experience cognitive overload in
their attempts to mentally represent the material. How can we overcome
this threat of intrinsic overload? One technique is to break the explanation
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Richard E. Mayer and Roxana Moreno
into bite-sized chunks whose presentation is under the learner’s control – a
technique that we call segmenting.
For example, Mayer, Dow, and Mayer (2003, Experiments 2a & 2b)
presented some students (continuous group) with a continuous narrated
animation explaining how an electric motor works. Other students (segmented group) could view meaningful segments of the narrated animation
by clicking on a part of the electric motor and then choosing from a list of
questions. All students saw the same narrated animation, but the segmented
group saw it in bite-sized chunks with pacing under their control. On a
transfer test, the segmented group outperformed the continuous group. In
a related study, Mayer and Chandler (2001, Experiment 2) presented some
students (continuous group) with a narrated animation on lightning formation. For other students (segmented group), after each of 16 segments a
“CONTINUE” button appeared in the lower right corner of the screen. The
next segment began as soon as the student clicked on the button. Although
both groups received the identical narrated animation in the identical order,
the segmented group could pause to digest one segment before moving on
to the next, thus managing intrinsic cognitive load. As predicted, the segmented group outperformed the continuous group on a transfer test. Using
the same method, a recent study (Moreno, 2007) found that college students
are better able to transfer a set of teaching principles to novel teaching scenarios when learning with exemplar videos (Experiment 1) or animations
(Experiment 2) that are segmented rather than non-segmented.
Overall, in five of five experimental tests, segmenting had a positive
effect on transfer performance. The studies are summarized in Table 7.7,
and yielded a median effect size of 0.82. These findings support the segmenting principle – break narrated animations or narrated videos into learnercontrolled segments. Lee, Plass, and Homer (2006) obtained complementary results when they reduced the complexity of a science simulation on
the ideal gas law by separating it from one screen into two screens.
How does the segmenting principle work? It is not possible to make the
to-be-learned system simpler than it actually is, but it is possible to help the
learner understand it by breaking the presentation into bite-sized chunks.
In this way, the learner can mentally represent one portion of the system
before moving on to the next (as indicated in the “WORKING MEMORY”
section of Figure 7.1). Evidence for the reduction in cognitive load resulting
from segmenting videos or animations was found in Moreno’s (2007) study,
where students’ cognitive load ratings were significantly lower when learning from segmented rather than non-segmented dynamic visual displays
(d = 0.64 and 1.00 for Experiments 1 and 2, respectively).
Techniques That Reduce Extraneous Cognitive Load
145
Pretraining Principle
Another approach to managing intrinsic cognitive load is to help learners
acquire prerequisite knowledge that will help them process the narrated animation. For example, when students receive a narrated animation explaining
how a car’s braking system works, they must build component models of
each part (e.g., the piston in the master cylinder can be forward or back)
and a causal model of the system (e.g., a change in one part affects a change
in another part, and so on). If a learner is unfamiliar with cars, then the task
of identifying each part may be so demanding that little capacity remains
for building a causal model. The solution to this problem is to provide
pretraining in the main components of the to-be-learned system, including
the name, location, and behavior of the component.
For example, Mayer, Mathias, and Wetzell (2002, Experiments 1 & 2)
presented students with a narrated animation explaining how a car’s braking
system works. Before the narrated animation, some students received a brief
pretraining in which they could click on any part in an illustration of the
braking system and then see the name of the part and a description of the
states the part could be in (e.g., “the piston in the master cylinder can be
forward or back”). The pretrained group performed better on a transfer test
than did the group without pretraining. In a related study (Mayer et al., 2002,
Experiment 3), students received a narrated animation describing how a tire
pump works either after pretraining that emphasized the name, location,
and behavior of each part (such as the inlet valve and the outlet valve)
or no pretraining. On a subsequent transfer test, students in the pretrained
group outperformed those who had not received pretraining. Similar results
were reported in a computer-based lesson in electrical engineering (Pollock,
Chandler, & Sweller, 2002, Experiments 1 & 3) and a computer-based geology
simulation game (Mayer, Mautone, & Prothero, 2002, Experiments 2 & 3).
Overall, in seven of seven experimental tests, pretraining in the names,
location, and behavior of key components resulted in improvements in
transfer test performance. These findings are summarized in Table 7.8
and yielded a median effect size of 0.92. The pretraining principle calls for
providing learners with pretraining on the names, locations, and behavior
of key components before presenting a narrated animation that is difficult,
fast paced, or unfamiliar.
How does the pretraining principle work? Knowledge in working memory can be used by the learner to help chunk the incoming material, effectively decreasing cognitive load. This is the advantage that experienced
learners have in processing multimedia lessons. Pretraining is aimed at
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Richard E. Mayer and Roxana Moreno
table 7.8. Evidence concerning the pretraining principle in computer-based
multimedia learning
Source
Pollack et al. (2002, Expt. 1)
Pollack et al. (2002, Expt. 3)
Mayer, Mathias, & Wetzell (2002, Expt. 1)
Mayer, Mathias, & Wetzell (2002, Expt. 2)
Mayer, Mathias, & Wetzell (2002, Expt. 3)
Mayer, Mautone, & Prothero (2002, Expt. 2)
Mayer, Mautone, & Prothero (2002, Expt. 3)
Median
Content
Effect size
Electrical engineering
Electrical engineering
Brakes
Brakes
Tire pump
Geology simulation game
Geology simulation game
1.22
1.15
0.79
0.92
1.00
0.57
0.85
0.92
providing relevant knowledge in long-term memory (indicated as “prior
knowledge” in Figure 7.1), so learners need to allocate less processing to new
incoming material.
Modality Principle
The most heavily researched principle of multimedia design is the modality
principle – present animation and narration rather than animation and
on-screen text. The modality principle may be particularly important in
situations where difficult and unfamiliar material is presented at a fast pace.
In this situation, presenting animation and on-screen text can create split
attention (Ayres & Sweller, 2005), in which learners must divide their visual
processing between the animation and the on-screen text. In this case, the
learner can be overwhelmed by the demands of essential processing, or what
Sweller (1999) calls intrinsic cognitive load.3 A solution to this problem is
3
It should be noted that in The Cambridge Handbook of Multimedia Learning, Mayer (2005a)
lists the modality principle as an example of managing intrinsic cognitive processing,
whereas Low and Sweller (2005) describe the modality principle in terms of extraneous
cognitive load. The rationale for viewing the modality principle as an example of reducing
extraneous cognitive processing is that presenting concurrent graphics and on-screen text
constitutes poor instructional design. This poor design can be corrected by converting
the printed text into spoken text. When redesigning an instructional message results in
improved learning, the reason is that extraneous processing has been reduced, as is the
case, for example, with the spatial contiguity principle (or split-attention principle) or
the coherence principle. In short, changing from printed to spoken text is just a case of
changing from poor layout to good layout, so the underlying cognitive process involved
is extraneous processing.
The rationale for viewing the modality principle as an example of managing intrinsic
processing is that the learner’s visual channel is overloaded with essential material. When
a learner receives a concise animation with printed text placed next to corresponding
Techniques That Reduce Extraneous Cognitive Load
147
to present the words as concurrent narration rather than as concurrent
on-screen text, thus off-loading the processing of the words from the visual
channel to the verbal channel.
For example, in four experiments, students performed better on a transfer test after studying a narrated animation on lighting formation than
after studying the same animation along with the same words presented as
captions on the screen (Craig et al., 2002, Experiment 2; Mayer & Moreno,
1998, Experiment 1; Moreno & Mayer, 1999, Experiments 1 & 2). Moreno and
Mayer (1999, Experiment 2) also found that students learned more deeply
about brakes from animation and narration than from animation and onscreen text, even though exactly the same animation and the same words
were presented. Similar results were reported with computer-based lessons
on electrical engineering (Kalyuga et al., 1999, Experiment 1; Kalyuga et al.,
2000, Experiment 1) and mathematical problem solving (Jeung, Chandler, &
Sweller, 1997, Experiments 1, 2, & 3).
Mayer et al. (2003, Experiment 1) reported that students learned better
in an interactive simulation of how an electric motor works when they
received explanations in the form of animation and narration rather than
animation and on-screen text. O’Neil et al. (2000, Experiment 1) also found
that students learned better in a virtual reality simulation of an aircraft’s
fuel system when they received explanations in the form of animation and
narration rather than animation and on-screen text. The same pattern of
results was found in the context of a computer-based simulation game
intended to teach environmental science in which explanations of plant
growth were presented as animation and narration or animation and onscreen text (Moreno & Mayer, 2002b, Experiments 1a & 1b; Moreno et al.,
2001, Experiments 4a, 4b, 5a, & 5b).
Across 21 of 21 experimental comparisons, students performed better on
transfer tests after receiving animation and narration rather than animation
and on-screen text. These results are summarized in Table 7.9 and yielded a
median effect size of 0.97. The modality principle – present words in spoken
form – may help manage essential processing by distributing the cognitive processing across both information-processing channels. The modality
principle is most relevant when the material is complex, unfamiliar, or
fast-paced.
aspects of the pictures, the material is well designed and concise, but the message simply
overloads the learner’s visual channel. To better manage the processing of this essential
information, the text can be off-loaded from the visual channel to the verbal channel by
converting it from printed to spoken form. In short, changing from printed to spoken form
is an example of managing essential processing – similar to segmenting and pretraining.
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Richard E. Mayer and Roxana Moreno
table 7.9. Evidence concerning modality principle in computer-based multimedia
learning
Source
Jeung et al. (1997, Expt. 1)
Jeung et al. (1997, Expt. 2)
Jeung et al. (1997, Expt. 3)
Mayer & Moreno (1998, Expt. 1)
Mayer & Moreno (1998, Expt. 2)
Kalyuga et al. (1999, Expt. 1)
Moreno & Mayer (1999, Expt. 1)
Moreno & Mayer (1999, Expt. 2)
Kalyuga et al. (2000, Expt. 1)
O’Neil et al. (2000, Expt. 1)
Moreno, Mayer, Spires, & Lester
(2001, Expt. 4a)
Moreno et al. (2001, Expt. 4b)
Moreno et al. (2001, Expt. 5a)
Moreno et al. (2001, Expt. 5b)
Craig et al. (2002, Expt. 2)
Moreno & Mayer (2002b, Expt. 1a)
Moreno & Mayer (2002b, Expt. 1b)
Moreno & Mayer (2002b, Expt. 1c)
Moreno & Mayer (2002b, Expt. 2a)
Moreno & Mayer (2002b, Expt. 2b)
Mayer, Dow, & Mayer (2003, Expt. 1)
Median
Content
Effect size
Math problems
Math problems
Math problems
Lightning
Brakes
Electrical engineering
Lightning
Lightning
Electrical engineering
Aircraft simulation
Environmental science game
0.87
0.33
1.01
1.49
0.78
0.85
1.02
1.09
0.79
1.00
0.60
Environmental science game
Environmental science game
Environmental science game
Lightning
Environmental science game
Environmental science game
Environmental science game
Environmental science game
Environmental science game
Electric motor
1.58
1.41
1.71
0.97
0.93
0.62
2.79
0.74
2.24
0.79
0.97
In a recent review of 43 experimental comparisons, Ginns (2005) reported
a mean weighted effect size of d = 0.72, favoring the modality principle. The
modality effect is also consistent with classic research on modality reported
by Penney (1989). Brünken, Plass, and Leutner (2004) have developed a
dual-task methodology for measuring cognitive load caused by modality.
How does the modality principle work? The learner is able to off-load
some of the cognitive processing from the visual channel – which is overloaded – to the verbal channel – which is not overloaded. In Figure 7.1,
the arrow from “words” to “eyes” is changed to an arrow from “words” to
“ears,” thereby allowing the learner to use the “selecting words” and the
“selecting images” arrows rather than just the “selecting images” arrow.
where do we go from here?
The research summarized in this chapter has both practical and theoretical
implications. On the practical side, we have been able to suggest eight
Techniques That Reduce Extraneous Cognitive Load
149
research-based guidelines for the design of computer-based multimedia
instruction. The research is limited to the extent that much of the research
was conducted in short-term, laboratory contexts with college students.
Future research is needed that examines whether the principles apply in
more authentic learning environments. Another limitation is the focus on
selected topics in science and mathematics, so future research should address
a broader array of the curriculum.
On the theoretical side, the principles are consistent with the cognitive
theory of multimedia learning and CLT, from which it is derived. This
research is consistent with the premise that cognitive load issues are at the
center of instructional design. In particular, the major challenge of instructional designers is to reduce extraneous cognitive load and manage essential
cognitive load. This would free cognitive capacity for deep processing –
which we call fostering generative processing – as described in the next
chapter. Further work is needed to elaborate features of the theory such as:
(1) What is the role of prior knowledge in guiding each of the five cognitive
processes shown in Figure 7.1? (2) How can we measure the cognitive load
experienced by the learner in each channel? (3) How can we measure the
level of complexity of the presented material? (4) How can we calibrate
the amount of extraneous and essential processing required in a computerbased presentation? and (5) What is the nature of the mental representation
created by integrating verbal and pictorial material?
Overall, research on multimedia learning has made significant progress
in the past 15 years. Future research is needed to meet the practical demands
for offering guidance to multimedia instructional designers and the theoretical demands for crafting a cognitive theory of how people learn from
words and pictures.
author note
This chapter is based on chapters 11 and 12 in The Cambridge Handbook
of Multimedia Learning (Mayer, 2005a, 2005b). Preparation of this chapter was supported by Grant No. N000140810018 from the Office of Naval
Research, entitled “Research-Based Principles for Instructional Games and
Simulations.”
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8
Techniques That Increase Generative Processing
in Multimedia Learning: Open Questions
for Cognitive Load Research
roxana moreno and richard e. mayer
In Chapter 7, we defined multimedia learning, described how people learn
from verbal and pictorial information according to the Cognitive Theory of
Multimedia Learning (CTML; Mayer, 2005), and examined the relationship
between CTML and cognitive load theory (CLT; Sweller, 1999). Specifically,
we offered a triarchic theory of cognitive load according to which there are
three kinds of cognitive processing demands during learning: extraneous,
essential, and generative. We defined extraneous processing as the cognitive
processes that are not necessary for making sense of the new information,
essential processing as the cognitive processes that are required to mentally select the new information that is represented in working memory,
and generative processing as the processes of mentally organizing the new
information into a coherent structure and integrating the new knowledge
representations with prior knowledge.
As explained in the previous chapter, the different nature of the three
cognitive demands suggests three goals for the design of multimedia learning
environments, namely, to reduce extraneous cognitive processing, to help
students manage essential cognitive processing, and to foster generative
processing. In the present chapter, we focus on the third of these goals by
reviewing techniques that are aimed at increasing generative processing in
multimedia learning. As in Chapter 7, the methods reviewed in the present
chapter have been distilled from the research program of the authors, which
is aimed at better understanding how aspects of media design correspond
to the cognitive processes that affect knowledge acquisition.
According to the active processing principle of CTML, even when the
learning environment is carefully designed to exclude unnecessary processing and to reduce the complexity of the materials to-be-learned, students
may fail to learn unless instruction includes methods aimed at engaging the
learner in investing mental effort in the construction of knowledge. In CLT
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terms, generative processing increases students’ germane cognitive load, the
load that results from cognitive activities that are relevant to the processes
of schema acquisition and automation. According to CLT, germane cognitive load is the only type of load that should be increased during learning
because “it contributes to, rather than interferes with learning” (Sweller,
van Merriënboer, & Paas, 1998, p. 264). Germane cognitive load is the result
of exerting effort toward understanding the material. Therefore, germane
cognitive load should be the result of actively engaging in the cognitive
processes of organizing and integrating relevant instructional information.
Because CLT posits that intrinsic, extraneous, and germane loads are additive, generative processing should also result in increased overall cognitive
load. Do students who engage in the active organization and integration
of instructional materials experience higher cognitive load levels? In this
chapter we summarize the findings of our work on generative processing
methods and suggest directions for future cognitive load research that can
help answer this question.
In the past, we emphasized the distinction between behavioral activity and cognitive activity (Moreno & Mayer, 2005, 2007). This distinction becomes relevant to our discussion of generative processing methods.
According to CTML, deep learning depends on cognitive activity, such as
selecting relevant information from a lesson, mentally organizing it into
a coherent structure, and integrating the new knowledge with existing
knowledge. In contrast, behavioral activity is not necessary or sufficient
to achieve deep learning in the CTML model. As we discuss in the forthcoming sections, student behavior may lead to deep learning only when the
activity is designed to prime the learner to be cognitively active. In other
words, hands-on activity needs to be combined with minds-on activity.
Thus, an important goal of our research program has been to examine how
interactive multimedia environments (which ask students to become behaviorally active during learning) should be designed to promote appropriate
cognitive processing (for a review of interactive multimedia, see Moreno
& Mayer, 2007). In this chapter, we review research on five methods for
fostering generative processing in non-interactive and interactive learning
environments.
how can we promote generative processing
in multimedia learning?
Suppose a multimedia lesson is presented in a way that eliminates extraneous
processing and manages essential processing so that the learner has capacity
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155
available to engage in generative processing. How can we promote generative processing without exceeding the learner’s available cognitive capacity?
In this section, we explore five research-based design principles that can be
used to increase generative processing: multimedia, personalization, guided
activity, feedback, and reflection. Tables 8.1 to 8.5 show evidence from our
research program supporting each one of these principles on measures of
students’ problem-solving transfer. Different letters following experiment
numbers indicate separate experimental comparisons. As will be seen in
the following sections, for each principle, we cite additional support from
individual studies conducted by researchers outside of our workgroup, discuss our theoretical interpretation of the principle and limitations of the
research, and offer suggestions for future cognitive load research.
Multimedia Principle
Consider the following scenario. A teacher who is interested in teaching
her students about the process of lightning formation provides them with
a one-page description of the causal chain of events that leads to the phenomenon. After studying the text, several students raise their hands with
questions such as “Where are the clouds with respect to the freezing level?
How do the negative and positive charges interact within the cloud? Does
the step leader make it all the way to the ground?” In this situation, students’
questions are suggesting that they are having a difficult time organizing the
elements described in the text, which, in turn, will reduce the likelihood of
meaningfully integrating the new information with their prior knowledge.
To promote the cognitive processes of organization and integration, the
teacher may choose to show them a computer animation depicting each
one of the steps in lightning formation and how the elements involved
in the phenomenon (i.e., clouds, temperature, electric charges, and step
leader) interact with one another. In 9 of 9 experimental studies, Mayer and
colleagues found that college students who learned science by receiving text
and illustrations or narration and animations performed better on transfer
tests than did learners who received text alone or narration alone, respectively (Mayer, 2001). This finding was replicated in a set of experiments in
which students who learned science with a combination of picture frames
and text outperformed those who received text alone or the picture frames
alone on tests of transfer (Moreno & Valdez, 2005). Furthermore, Moreno
and colleagues found a similar pattern of results in teacher education and art
education. In three of four experimental studies, pre-service teachers who
learned with a multimedia program that included a video or animation
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table 8.1. Evidence concerning the multimedia principle in computer-based
multimedia learning
Source
Mayer & Anderson (1991, Experiment 2a)
Mayer & Anderson (1992, Experiment 1)
Mayer & Anderson (1992, Experiment 2)
Mayer et al. (1996, Experiment 2)
Mayer & Gallini (1990, Experiment 1)
Mayer & Gallini (1990, Experiment 2)
Mayer & Gallini (1990, Experiment 3)
Mayer (1989b, Experiment 1)
Mayer (1989b, Experiment 2)
Moreno & Valdez (2005, Experiment 1a)
Moreno & Valdez (2005, Experiment 1b)
Moreno & Valdez (2007, Experiment 1)
Moreno & Valdez (2007, Experiment 2)
Moreno & Ortegano-Layne (2008, Experiment 1)
Moreno & Morales (2008)
Median
Content
Effect size
Pumps
Pumps
Brakes
Lightning
Brakes
Pumps
Generators
Brakes
Brakes
Lightning
Lightning
Learning principles
Learning principles
Learning principles
Painting techniques
2.43
1.90
1.67
1.39
1.19
1.00
1.35
1.50
1.71
0.73
1.27
1.01
1.15
1.95
1.51
1.39
illustrating how an expert teacher applied principles of learning to her
teaching practice performed better on transfer tests than did those who
learned with an identical program that included a narrative corresponding
to the video or animation (Moreno & Ortegano-Layne, 2008; Moreno &
Valdez, 2007). Additionally, in a recent study in which middle-school students were asked to learn about a set of masterpiece paintings by studying
a reproduction of the artwork (visual group), a description of the artwork
(verbal group), or both (dual group), the dual group outperformed the verbal and visual groups on a transfer task (Moreno & Morales, 2008). Several
studies outside of our laboratory replicated these findings by showing that
the combination of pictorial and verbal information can increase reading
comprehension (Duquette & Painchaud, 1996; Kost, Foss, & Lenzini, 1999)
and the learning of a second language (Al-Seghayer, 2001; Chun & Plass,
1996).
We refer to the previously discussed pattern of findings as the multimedia
principle – instruction that includes verbal and pictorial representations
of knowledge are more likely to lead to meaningful learning than those
that present verbal information alone (Fletcher & Tobias, 2005; Mayer,
2001). Table 8.1 shows the mean effect sizes resulting from comparing the
transfer scores for students who received words and pictures (multimedia
Techniques That Increase Generative Processing
157
group) with students who received words only (verbal group) for each
of the 14 experimental comparisons conducted in our laboratory. In each
comparison, the multimedia group performed better than the verbal group
on the transfer test. As can be seen in the table, the effect sizes are medium
to large, with a median of 1.39, which is a large effect (Cohen, 1988).
How does the multimedia principle work? The multimedia principle is
based on dual coding theory, or the idea that different coding systems
(such as those for words and pictures) reinforce each other (Paivio, 1986).
According to CTML’s dual channel assumption, humans possess separate
channels for processing pictorial material and verbal material (Mayer &
Moreno, 2003). When learners are presented with verbal and pictorial representations of the system to-be-learned, they become more cognitively
active because they need to organize each representation and build connections between them. The act of building connections between verbal and
pictorial representations is an important step in conceptual understanding
(Schnotz, 2005; Seufert, Jänen, & Brünken, 2007). Therefore, students who
receive well-constructed multimedia instruction should perform better on
transfer tests, which are designed to measure understanding, than students
who receive verbal explanations alone. It is important to note that the
experimental studies that included a measure of students’ interest showed
consistent significant differences favoring the multimedia groups (Moreno
& Ortegano-Layne, 2008; Moreno & Valdez, 2007). This finding supports
a Cognitive-Affective Theory of Learning with Media (CATLM; Moreno,
2005, 2006, 2009) by suggesting that the improved transfer for multimedia
groups may rely not only on additive coding but also, at least in part, on
increased student interest. The CATLM was proposed to integrate affective and metacognitive factors into the more traditional, cognitively driven
CTML model.
Limitations and suggestions for future cognitive load research. Despite its
robustness, the multimedia principle needs to be reconsidered under the
light of the individual differences reviewed in Chapter 4 of this volume.
For example, Mayer and Gallini (1990) found support for the multimedia
principle for low-prior-knowledge students but high-prior-knowledge students showed much less difference in performance between multimedia and
text-only presentations. This finding suggests that applying the multimedia
principle is especially important for fostering learning when learners have
little or no prior knowledge in a domain (i.e., novice learners). According
to CLT, high-prior-knowledge students already have appropriate schematic
knowledge structures from long-term memory that they can retrieve to
guide their understanding of the verbal information contained in the
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presentation. Consequently, the added visual code may not be necessary
to promote deeper understanding of the system to-be-learned. On the
other hand, because low-prior-knowledge students do not have appropriate
schemas to guide the processing of new information, instruction that
includes additional visual coding can help structure information in working
memory, thus providing a substitute for missing schemas. A caveat, however, is that not all novices will benefit equally from multimedia instruction.
Specifically, Mayer and Sims (1994) found that students with high spatial
ability benefit significantly more from the simultaneous presentation of animations and narrations than their counterparts. This finding suggests that
students with low spatial ability may need additional guidance to support
the processing of dynamic visual materials.
It is important to note that the reviewed research was not designed to test
CLT’s assumptions. Because the amount and/or type of cognitive load experienced by students in each experimental condition were not measured, the
implications for CLT are limited and only indirectly derived from students’
learning outcomes. Therefore, future cognitive load research on the multimedia principle should investigate the relationship between students’ prior
knowledge, spatial abilities, and cognitive load. In addition, future research
should test our hypothesis that the benefits of multimedia instruction are
indeed the result of investing more effort during the encoding of the two
sources of information. Although this hypothesis is consistent with CLT
and supported by our evidence that students report higher interest during
multimedia learning, an alternative hypothesis is that the transfer benefits
or multimedia learning are the result of the stronger retrieval produced by
dual rather than single representations of the learned system (Paivio, 1986).
Personalization Principle
Suppose you are a student playing an educational science game called
Design-a-Plant (Lester, Stone, & Stelling, 1999), in which you fly to a new
planet and must design a plant that would survive there. An on-screen
pedagogical agent interacts with you by giving you hints, feedback, and
explanations about how the characteristics of the roots, stem, and leaves
relate to various environmental conditions. In a non-personalized version
of the game, the agent speaks in a formal, monologue style, addressing you
as an observer. In a personalized version of the game, the agent speaks in an
informal, conversational style, addressing you as if you were both sharing
the learning experience. For example, in the non-personalized version of
the game the agent might say, “The goal of this program is to design a plant
that will survive, maybe even flourish, in an environment of heavy rain. The
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table 8.2. Evidence concerning the personalization principle in computer-based
multimedia learning
Source
Moreno & Mayer (2000, Experiment 1)
Moreno & Mayer (2000, Experiment 2)
Moreno & Mayer (2000, Experiment 3)
Moreno & Mayer (2000, Experiment 4)
Moreno & Mayer (2000, Experiment 5)
Moreno & Mayer (2004, Experiment 1a)
Moreno & Mayer (2004, Experiment 1b)
Mayer et al. (2004, Experiment 1)
Mayer et al. (2004, Experiment 2)
Mayer et al. (2004, Experiment 3)
Median
Content
Effect size
Lightning (narration)
Lightning (text)
Botany (narration)
Botany (text)
Botany (narration)
Botany (narration)
Botany (narration,
virtual reality)
Lungs (narration)
Lungs (narration)
Lungs (narration)
1.05
1.61
1.92
1.49
1.11
1.58
1.93
0.52
1.00
0.79
1.30
leaves need to be flexible so they won’t be damaged by the heavy rain.” In
the personalized version of the game, the agent might say instead, “Your
goal here is to design a plant that will survive, maybe even flourish, in this
environment of heavy rain. Your leaves need to be flexible so they are not
damaged by the heavy rain.” In ten of ten experimental studies, learners who
received personalized messages (as narrated or written text) performed better on transfer tests than did learners who received non-personalized messages. We refer to this finding as the personalization principle – instruction
that includes personalized messages is more likely to lead to more meaningful learning than those that use non-personalized messages (Moreno &
Mayer, 2000, 2004). The positive effects of personalization, however, have
been found to extend to reading comprehension (Reeder, McCormick, &
Esselman, 1987) and the comprehension of mathematical word problems
(Anand & Ross, 1987; d’Ailly, Simpson, & MacKinnon, 1997; Davis-Dorsey,
Ross, & Morrison, 1991).
Table 8.2 shows the mean effect sizes resulting from comparing the transfer scores of students who learned in personalized and non-personalized
learning conditions for each of the ten experimental studies conducted in
our laboratory. In each comparison, the personalized group performed better than the non-personalized group on the transfer test. As can be seen in
the table, the effect sizes are medium to large and consistent, with a median
of 1.30, which is considered a large effect.
How does the personalization principle work? Similar to the case of
other self-referential effects found in experimental psychology (Symons &
Johnson, 1997), the personalization principle is thought to be based on the
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idea that personalization promotes more active processing of the new information by having students relate the material to themselves, thus creating
deeper memories of the learning experience. An additional interpretation is
that when students are induced to believe that they are participants rather
than observers of the learning environment, they become more engaged in
making sense of the learning materials. This last interpretation is consistent
with the CATLM (Moreno, 2005; Moreno & Mayer, 2007), which proposes
that motivation and affect determine how much of the available cognitive
resources will be assigned to the learning task. Thus, personalized messages
may help learning by influencing students to spend more effort on the task
(Pintrich & Schunk, 2002).
Limitations and suggestions for future cognitive load research. The personalization principle has been supported for written and auditory messages,
for non-interactive and interactive multimedia learning environments, and
for desktop and virtual reality displays. However, similar to the multimedia principle, research on the personalization principle very seldom
included measures of cognitive load. In one of the more recent experiments
(Moreno & Mayer, 2004), students in personalized groups reported significantly lower levels of perceived cognitive load than did students in the
non-personalized groups (effect size, d = 0.67, a medium-to-large effect).
However, the mechanism underlying students’ cognitive load perceptions
needs further investigation. It is not clear why self-referencing might more
actively engage students in the learning experience and simultaneously promote lower cognitive load perceptions. Does personalization promote the
elaboration of the instructional materials as suggested by past self-referential
studies (Symons & Johnson, 1997) or could it be that “personalized messages
are more consistent with our schemas for communicating in normal conversations, therefore requiring less cognitive effort to process” (Moreno &
Mayer, 1999, p. 725)? A number of studies on narrative comprehension suggest that narrative discourse is easier to comprehend and remember than
other, more formal discourse genres (Graesser, Golding, & Long, 1998).
Future cognitive load research on the personalization principle should
include differentiated measures of intrinsic, extraneous, and germane cognitive load to advance our understanding of this phenomenon.
Guided Activity Principle
An alternative to presenting a multimedia explanation to teach how a complex system works may consist of asking students to engage in mixedinitiative problem solving with a pedagogical agent (Lester et al., 1999).
Techniques That Increase Generative Processing
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Guided activity occurs when learners are able to interact in multimedia
environments and receive guidance about their actions during learning.
Therefore, at the heart of the guided activity principle are the following two
ideas: interactivity and feedback. In recent work, we distinguished among
five common types of interactivity: manipulating, dialoguing, controlling,
searching, and navigating (Moreno & Mayer, 2007). The guided activity
principle reviewed in this section focuses mainly on manipulating and dialoguing types of interactivity. In interactivity by manipulating, learners
experiment with instructional materials, such as when they set parameters
before a simulation runs to test a hypothesis. In interactivity by dialoguing, learners ask questions and receive answers or give answers and receive
feedback.
In our research program, we tested the guided activity principle with two
instructional programs. Using the Design-A-Plant learning environment, we
found in three of three experimental studies that learners who were allowed
to make choices about the characteristics that plants needed to survive in
different weather conditions, outperformed learners who received direct
instruction (Moreno, Mayer, Spires, & Lester, 2001). In the first two experiments, middle-school students (Experiment 1) and college students (Experiment 2) who learned with direct instruction could see the same set of roots,
stems, and leaves, and received the same instructional words as in the guided
activity condition, but were not able to design the plants before listening to
the explanations of the program. The third experiment was identical to the
second experiment with the exception that college students in both conditions received instructional messages from an on-screen pedagogical agent.
A later study showed that elementary-school children who learned with
the verbal guidance of an on-screen agent as they independently practiced
the addition and subtraction of integers outperformed those who learned
without guidance on a transfer measure (Moreno & Durán, 2004).
Many studies outside of our own workgroup provide evidence for the
benefits of presenting students with guided activity. For instance, early
research has documented that students learn rules and principles significantly better from guided-discovery rather than pure-discovery methods
(Shulman & Keisler, 1966) and that kindergarten children are able to successfully solve conservation tasks when given adult guidance (Gelman, 1969).
Students who attempt to learn LOGO programming language with extensive hands-on experience and no guidance are no better than those who
receive no programming experience on learning post-tests (Pea & Kurland, 1984), and pure-discovery hinders programming performance compared with learning with guided-discovery methods (Fay & Mayer, 1994;
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table 8.3. Evidence concerning the guided activity principle in
computer-based multimedia learning
Source
Content
Effect size
Moreno et al. (2001, Experiment 1)
Moreno et al. (2001, Experiment 2)
Moreno et al. (2001, Experiment 3)
Moreno & Durán (2004)
Median
Botany
Botany
Botany
Math
0.95
1.20
0.70
0.50
0.83
Lee & Thompson, 1997). Sweller and colleagues demonstrated that students
learn how to solve problems significantly better when they are presented
with a worked-out problem followed by a practice problem rather than
by the traditional method of solving problems with no guidance (Mwangi
& Sweller, 1998). More recent studies have found that learning is improved
when students are guided to map different sources of information (Seufert &
Brünken, 2006) or guided to actively integrate multiple sources of information (Bodemer, Plötzner, Bruchmüller, & Häcker, 2005).
Table 8.3 shows the mean effect sizes resulting from comparing the
transfer scores of students who learned in guided activity and no guided
activity (control group) conditions for each of the experimental studies
conducted in our laboratory. In each comparison, the guided activity group
performed better than the control group on the transfer test. We refer
to this finding as the guided activity principle – instruction that allows
students to interact by dialoguing and manipulating the learning materials
is more likely to lead to meaningful learning than instruction that does not
allow for dialoguing (i.e., pure discovery) or for manipulating the learning
materials (i.e., direct instruction). As can be seen in the table, the effect
sizes are medium to large, with a median of 0.83, which is considered
large.
How does the guided activity principle work? The theoretical rationale
that we offer for the guided activity principle is that prompting students
to actively engage in the selection, organization, and integration of new
information, encourages essential and generative processing. Guided activity leads to deeper understanding than having students passively process
identical instructional materials (Mayer & Moreno, 2003). Yet, meaningful
learning may not occur if despite the ability to interact by manipulating the
instructional materials, there are no opportunities to engage in dialoguing
interactivity with a pedagogical agent, such as the case of pure-discovery
learning (Mayer, 2004). Although it may be argued that pure discovery is
Techniques That Increase Generative Processing
163
a way to facilitate active learning by allowing students to explore, manipulate, and test hypotheses (Bruner, 1961; Piaget, 1954; Wittrock, 1966), when
novice students are prevented from receiving feedback from a pedagogical agent, they often become lost and frustrated, and their confusion may
reinforce or compound existing misconceptions (Brown & Campione, 1994;
Garnett, Garnett, & Hackling, 1995). In short, guided activity increases the
likelihood that learners who lack proper schemas will select and organize
the new information successfully (Mayer, 2004). CLT explains the positive
effects of guidance as the result of the more efficient use of students’ limited cognitive resources (Sweller, 1999). Specifically, the unguided search
for meaning demands a substantial portion of students’ cognitive capacity,
thus, leaving relatively little capacity available to engage in the development of new schemas (Sweller, van Merriënboer, & Paas, 1998). “Cognitive
load theory suggests that search imposes an extraneous cognitive load that
interferes with learning” (Tuovinen & Sweller, 1999, p. 335).
Limitations and suggestions for future cognitive load research. Interestingly,
across the first three experiments reported in Table 8.3, participants who
learned with guided activity did not differ from those who learned with
direct instruction on self-reported measures of cognitive load. Therefore,
a puzzling result of our research is that students’ cognitive load reports
fail to support our hypothesis that providing generative learning activities
will result in higher levels of perceived cognitive load as a result of the
increased germane load that these activities induce. Moreover, these data
do not support CLT’s assumption that unguided search imposes extraneous
cognitive load either. A possible interpretation of this finding is that the
self-reported measures used in our study, although typical in cognitive
load research, are not sensitive enough to capture differences in cognitive
load. However, we should not discard the possibility that guided activity
may reduce extraneous cognitive load and increase germane cognitive load
simultaneously. According to this alternative hypothesis, extraneous and
germane cognitive load effects may cancel each other out and lead to similar
levels of total cognitive load in guided and unguided instruction. Future
research should examine more carefully the relationships among guidance,
activity, and measures of the three types of cognitive load.
Feedback Principle
As pointed out in the previous section, meaningful learning may not occur
if students are not given appropriate feedback about their understanding.
For example, in a meta-analysis of the effects of feedback in computer-based
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Roxana Moreno and Richard E. Mayer
table 8.4. Evidence concerning the feedback principle in
computer-based multimedia learning
Source
Content
Effect size
Moreno & Mayer (1999)
Moreno (2004, Experiment 1)
Moreno (2004, Experiment 2)
Moreno & Mayer (2005)
Median
Math
Botany
Botany
Botany
0.47
1.16
1.58
1.31
1.24
instruction, Azevedo and Bernard (1995) conclude that feedback messages,
to be effective, should stimulate the cognitive processes necessary to gain
deep understanding. In our research program, we investigated the role of
feedback in discovery-based multimedia learning across four experiments.
First, we asked middle-school children to solve a set of sixty-four addition
and subtraction practice problems over four training sessions with two feedback methods: corrective feedback (CF), consisting of information about
the correctness of their response, or CF plus explanatory feedback (EF),
consisting of a verbal explanation relating the arithmetic procedure to a
visual metaphor for the procedure to-be-learned (Lakoff & Nunez, 1997).
Students who received CF and EF showed greater gains on solving difficult
problems than those who learned with CF alone (Moreno & Mayer, 1999).
In the next three experiments, we used the Design-A-Plant learning
environment. Experiments 2 and 3 showed that students who learned about
botany with CF and EF produced higher transfer scores and perceived the
program as being less difficult than students who learned with CF alone
(Moreno, 2004). Finally, in the fourth study (Moreno & Mayer, 2005), students who learned with EF produced higher transfer scores and showed a
greater reduction of their misconceptions over time (effect size, d = 1.88)
than those who learned with CF alone. Many other studies, in both classrooms and technology-based environments, support the idea that offering
any form of explanatory feedback in combination with corrective feedback
is preferable to offering corrective feedback alone (Butler & Winne, 1995;
Hattie & Timperley, 2007; Kulhavy & Wager, 1993; Mory, 2004; Pashler,
Cepeda, Wixted, & Roher, 2005).
Table 8.4 shows the mean effect sizes resulting from comparing the transfer scores of students who learned in EF and CF learning conditions for each
of four experimental studies conducted in our laboratory. As can be seen in
the table, the effect sizes are medium to large, with a median of 1.24, which
is a large effect. We refer to this finding as the feedback principle – novice
Techniques That Increase Generative Processing
165
students learn better when presented with explanatory feedback during
learning.
How does the feedback principle work? According to CTML, the effectiveness of multimedia learning will depend on the relationship between
the amount of feedback given by the system and student’s prior knowledge (Mayer, 2004). EF encourages essential and generative processing by
guiding students’ selection and organization of new information when no
mental model is available (Schauble, 1990). Although the studies reviewed
in this section did not include affective measures, we cannot discard the
potential motivational effects of presenting students with EF. Feedback that
provides students with information about how their performance can be
improved is found to lead to greater intrinsic motivation, task engagement,
and persistence than performance feedback (Pressley et al., 2003). Therefore, according to the CATLM, the feedback principle may rely, at least in
part, on the facilitative effect that increased motivation has on learning
(Moreno, 2009). This hypothesis, however, should be empirically tested in
future research.
Limitations and suggestions for future cognitive load research. The contribution of the feedback principle to CLT is limited in that most studies
did not include direct measures of students’ cognitive load during learning.
The only two studies that presented students with a self-report measure of
cognitive load suggest that EF helps students by reducing the difficulty that
arises from learning with no internal or external guiding schemas (i.e., EF
groups reported lower cognitive load than CF groups). Nevertheless, we
chose to include the feedback principle among methods aimed at increasing germane cognitive load because according to both CTML and CLT,
principle-based explanations promote learning by engaging the learner in
cognitive processes that are necessary for schema construction. To test the
two alternative CLT hypotheses – EF reduces extraneous load versus EF
increases germane load – would require having valid and reliable measures
for each load type. Moreover, because of the potential mediation effect that
motivation may have on learning, future research should include not only
measures of learning but other self-reported and behavioral measures of
students’ motivation (Moreno, 2009).
In addition, a profitable venue for future research is to extend this work
by examining the role of students’ individual differences when learning
with different feedback types. For instance, students who have high prior
knowledge are likely to experience an expertise reversal effect (see Chapter 3,
this volume), and perform better when receiving less rather than more elaborated feedback during problem solving. A cognitive load hypothesis to test
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Roxana Moreno and Richard E. Mayer
in the future is that experts will experience higher extraneous load when
receiving EF and higher germane load when receiving CF alone. Likewise,
self-regulated students may use their metacognitive skills to compensate
for their lack of knowledge and require less feedback from the system than
their counterparts. According to the CATLM, when students are aware of
the strengths and limitations of their knowledge, strategies, and motivation, they are better able to regulate their own learning by planning and
monitoring the cognitive processes needed for understanding (Moreno,
2005, 2006, 2009). In sum, future research should examine the relationships
among feedback, individual differences that may have an impact on cognitive processing (see Chapter 4, this volume), and the three cognitive load
types.
Reflection Principle
Reflection can be implemented in many different ways in multimedia learning environments. For example, a popular reflection method to promote
reading comprehension is elaborative interrogation, which consists of asking
students to answer “why” questions about information they have just read
(Moreno et al., 2001; Seifert, 1993). Another reflection method that can be
used for math and science learning is called self-explanations, which consists of asking students to explain their answers to problems during learning
(Chi, de Leeuw, Chiu, & La Vancher, 1994). Overall, reflection methods are
based on the idea that even when multimedia learning environments are
designed with the guided activity and feedback principles in mind, deep
learning will depend on the degree to which students invest their cognitive
resources to reflect on their actions and feedback.
To examine the role of reflection in multimedia learning, we conducted
the following two experiments using the Design-A-Plant program. First, we
used a two-factor design in which students learned with or without EF for
their choices (feedback factor) and with or without elaborative interrogation
(reflection factor). In the elaborative interrogation condition, students were
asked to provide a rationale for the choices they made as they attempted
to discover botany principles. For example, a student in this condition
may have been asked “Why did you choose a plant with deep roots for this
environment?” There was no reflection effect on transfer and no interaction
between reflection and feedback (Moreno & Mayer, 2005, Experiment 1).
Although we expected a main reflection effect similar to the one found
in the reading comprehension literature, we hypothesized that the effects
of elaborative interrogation were diminished in the botany game because
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167
students were already primed to actively engage in cognitive activity when
asked to decide on a particular plant design. To test this hypothesis, we
conducted a follow-up study in which students were asked to manipulate
or not manipulate the instructional materials (guided activity factor) and
to reflect or not reflect on the principles underlying their plant designs
(reflection factor). As expected, the findings showed a significant interaction
between the two factors (Moreno & Mayer, 2005). For groups who were
not allowed to manipulate the materials, students who were prompted
to reflect on worked-out examples presented by a pedagogical agent had
higher transfer scores than those who were not prompted to reflect on
the examples. Similar to the first experiment, there were no significant
differences between reflective and non-reflective treatments on transfer for
groups who were allowed to manipulate the materials (Moreno & Mayer,
2005, Experiment 2). A third experimental study revealed that, for reflection
to be effective, students must be asked to reflect on correct models of the new
information (Moreno & Mayer, 2005, Experiment 3). Specifically, we used a
self-explanation method to test the hypothesis that the beneficial effects of
reflection are contingent on the quality of the elaboration made by students.
The results showed that students who were asked to reflect on correct
solutions (i.e., worked-out examples) performed better on transfer tests
than did those who were asked to reflect on solutions that presented errors.
The positive effects of promoting student reflection have been replicated
using a variety of methods. For example, student teachers who were verbally
prompted to make connections between learned principles and classroom
animations took less time to study the animations, produced higher scores
on a transfer test, and showed higher motivation to learn than those who
were not prompted (Moreno, 2009); students who were prompted to explain
the reasons for choosing each navigation step in a hypermedia system outperformed those who learned without reflection prompting on transfer tests
(Bannert, 2006); and students who produced self-explanations of workedout examples (see Chapter 5, this volume) outperformed those who did not
on near and far transfer measures (Atkinson, Renkl, & Merrill, 2003; Renkl,
Stark, Gruber, & Mandl, 1998; Schworm & Renkl, 2007).
How does the reflection principle work? Reflection is at the heart of
CTML’s generative processing assumption. Encouraging students to provide principle-based explanations for their thinking promotes the organization and integration of new information with students’ prior knowledge.
Moreover, we interpret the findings to support CATLM’s metacognitive
mediation assumption by showing that, when learning environments are
not interactive, it might be necessary to prompt students to become more
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table 8.5. Evidence for the reflection principle in computer-based multimedia learning
Source
Moreno & Mayer (2005; Experiment 2)
Moreno & Mayer (2005; Experiment 3)
Moreno & Valdez (2005; Experiment 3)
Moreno, Reisslein, & Ozogul (2009)
Median
Content
Effect size
Botany
Botany
Botany
Electrical engineering
0.98
0.80
0.71
0.74
0.77
mentally active (Azevedo, 2005; Moreno, 2009). However, an additional
study warns us about the potential mindless processing that some interactive learning environments may unintentionally promote (Moreno &
Valdez, 2005). Specifically, asking students to organize the steps corresponding to a causal chain of events in an interactive multimedia program was not
sufficient to improve their understanding about the topic compared with
having students study the organized chain of events. Only when the program was modified to prompt students to evaluate their responses before
submitting them for feedback did it promote students’ transfer (Moreno &
Valdez, 2005, Experiment 3). A similar phenomenon occurred in a recent
study in engineering education (Moreno, Reisslein, & Ozogul, 2009). A fading method (see Chapter 5, this volume) failed to promote transfer until
students were asked to reflect on their solutions by comparing them with
those of a worked-out example.
In sum, it is most important to carefully examine whether the design of
students’ interaction promotes superficial or deep processing of the instructional materials (Bangert-Drowns, Kulik, Kulik, & Morgan, 1991). Table 8.5
shows the mean effect sizes resulting from comparing the transfer scores
of students who learned with and without reflection methods. In four of
four comparisons, the reflection group performed better than the control
group on the transfer test, yielding a median effect size of 0.77. We refer to
this finding as the reflection principle – instruction that prompts students
to reflect on correct solutions is more likely to lead to meaningful learning
than instruction that does not present such opportunities, especially when
learning environments are not interactive.
Limitations and suggestions for future cognitive load research. Similar to the
previously discussed principles, the contribution of the reflection principle
to CLT is limited because most reported studies were not designed to test
CLT assumptions directly. Nevertheless, two of the four studies included a
self-report measure of cognitive load (Moreno & Valdez, 2005, Experiment.
3; Moreno et al., 2009). In both studies, however, students in reflective and
Techniques That Increase Generative Processing
169
non-reflective treatments did not differ on their perceived cognitive load
during learning. Thus, the reflection principle presents another set of open
questions that are ripe for future cognitive load research. For instance, still
open is the question of whether methods aimed at promoting germane
processing, such as those that prompt students to reflect or evaluate their
own learning, will result in greater germane cognitive load than those that
do not. An additional area for extending this research is that of examining
the role of students’ individual differences in self-regulation ability. An
interesting hypothesis to be tested is that the reflection principle will not
hold for self-regulated learners because such students already possess the
ability to control all aspects of their learning, from planning and monitoring
to performance evaluation (Bruning, Schraw, Norby, & Ronning, 2004).
Therefore, according to CLT, it is likely that reflection prompts will be
redundant to self-regulated learners and therefore will hinder rather than
promote learning by increasing extraneous cognitive load. In sum, future
research should examine the relationships among reflection methods, selfregulation, and the three cognitive load types.
conclusion
Theoretical and Practical Implications
The research summarized in this chapter has theoretical and practical implications similar to those discussed in Chapter 7 in this volume. On the
practical side, we have been able to suggest five additional research-based
guidelines for the design of computer-based multimedia instruction. The
goal of the present chapter was to summarize our research on methods that
have the potential to promote generative processing when learning from
verbal and pictorial information and to propose some future directions for
cognitive load research.
On the theoretical side, the principles that we offer are consistent with
CTML (Mayer, 2005) and with the more recent CATLM, which integrates
assumptions about the role of affect, motivation, and metacognition in
learning with media (Moreno, 2009). In particular, our empirical work
supports the active processing principle defined in Chapter 7 – meaningful
learning depends on active cognitive processing during learning including
selecting relevant information for further processing, organizing selected
material into a coherent mental representation, and integrating incoming
material with existing knowledge (Mayer, 2001; Wittrock, 1989). We suggest
that generative processing can be promoted: (1) by asking students to build
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referential connections between verbal and pictorial representations (multimedia principle); (2) by inducing the feeling that students are participants
rather than observers of a learning environment (personalization principle); (3) by scaffolding the exploration of interactive games (guided activity
principle); (4) by providing principle-based feedback to students’ responses
(feedback principle); and (5) by prompting students to explain and evaluate
their understanding (reflection principle).
On the other hand, as can be seen from the sections that offer suggestions
for future cognitive load research, it is difficult to derive strong theoretical
implications for CLT from our work because most of the reviewed studies
did not measure students’ cognitive load. Although a few studies included
a reliable self-reported measure of overall mental effort (Paas, Tuovinen,
Tabbers, & Van Gerven, 2003), this measure does not discriminate between
the different cognitive load sources. Furthermore, in some studies, students
who learned with active processing methods reported similar or even lower
ratings on the effort scales than those who learned more passively, a finding
that is counter to CLT’s assumption that germane, extraneous, and intrinsic
loads are additive.
Future Research Directions in Germane Cognitive Load
and Generative Processing
A challenge for cognitive load research is to establish what type of cognitive
load is being affected when students engage in generative processing. Therefore, one productive direction for future research would entail replicating
some of the reviewed experimental studies using valid and reliable measures
for the three load types. These measures should help advance the theory
of instructional design by testing the interpretations offered in this chapter
against those offered by alternative theoretical models.
Nevertheless, the generative methods reviewed in this chapter are limited and future research could also extend the present work to other methods aimed at promoting the active selection, organization, and integration
of new information with students’ prior knowledge. Promising environments that allow investigating issues of germane cognitive load further are
inquiry- and problem-based learning scenarios because they offer students
the opportunity to select and manipulate instructional materials, explore
multiple representations, test hypotheses, self-assess learning, and reflect on
the outcome of knowledge construction. Because these environments are
highly complex, the potential sources of extraneous and germane cognitive
load can be manipulated to shed light on their interaction and learning
Techniques That Increase Generative Processing
171
effects. For example, an experimental study in an inquiry-based environment may consist of the following cognitive load conditions: low extraneous/low germane, high extraneous/low germane, low extraneous/high germane, and high extraneous/high germane, with extraneous cognitive load
being induced by spatial or temporal discontiguity and germane cognitive
load being induced by generative processing methods.
In addition, because CLT predicts different learning and cognitive load
outcomes for learners of different expertise and ability levels, it would be
important to test each examined method using a variety of learners. For
instance, according to CLT, learners’ level of prior knowledge is extremely
diagnostic in categorizing instructional materials and/or methods as imposing intrinsic, extraneous, or germane loads, and to predict learning outcomes accordingly (Sweller, van Merriënboer, & Paas, 1998).
However, the learning benefits of methods aimed at increasing active
learning are not only dependent on students’ prior knowledge. In this
regard, it is important to note that the active processing principle underlying the CATLM rests on the following three assumptions (Moreno, 2005,
2006, 2009). First, it assumes that the learner has enough capacity available to engage in generative processing. Second, it assumes that the learner
has the necessary skills to be successful in the required mental activity.
Third, it assumes that the learner is willing to spend his/her available cognitive resources and relevant skills on generative processing. These assumptions, however, may not hold for all learners. In fact, recent research shows
that adding cognitive activities aimed at increasing germane load does not
necessarily increase student learning (Moreno, 2006). To mention a few
examples of this phenomena: some learners do not spontaneously engage
in germane cognitive activities such as example elaboration or comparison (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Gerjets, Scheiter, &
Catrambone, 2004), learners vary in the degree of elaboration requested
by the learning environment (Renkl, Atkinson, Maier, & Staley, 2002), and
learners may show an “illusion of understanding” by reporting lower ratings on effort scales but no increased learning when engaged in more
active processing methods (Renkl, 2002). Whether these findings are the
result of insufficient cognitive resources, skills, motivation, or metacognition is an open question in cognitive load research. In sum, to advance
our understanding about who learns from generative processing methods and how, new CLT developments should specify the mediating effects
of students’ individual differences, especially those related to their abilities, motivation, and self-regulation on the three load types (Moreno,
in press).
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Finally, we should note that similar to the research reported in the previous chapter, most of the reported studies in this chapter were conducted
in short-term, laboratory contexts using low-prior-knowledge college students as participants. Future research should examine whether the reviewed
principles apply to more authentic learning environments and to a variety
of learners and content domains.
author note
This material is based upon work supported by the U.S.A. National Science Foundation under Grant No. 0238385. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the author and do not necessarily reflect the views of the funding agency.
Corresponding author’s address and email: Educational Psychology Program, Simpson Hall 123, University of New Mexico, Albuquerque, NM
87131, moreno@unm.edu.
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part three
DISCUSSION
9
Measuring Cognitive Load
roland brünken, tina seufert, and fred paas
the problem of cognitive load measurement:
what are good cognitive load indicators?
The previous chapters have outlined the basic theoretical assumptions for
cognitive load theory (Chapter 2), described how cognitive load affects
the process of schema acquisition (Chapter 3), and discussed the role that
learners’ individual differences play in the process of knowledge construction (Chapter 4). The central problem identified by Cognitive Load Theory
(CLT) is that learning is impaired when the total amount of processing
requirements exceeds the limited capacity of human working memory.
In addition to the fundamental assumption that learning is a function
of available cognitive resources, CLT makes some additional assumptions
with respect to the relation among cognitive resources, demands, and learning. The first of these additional assumptions is that instructional design
and/or methods may induce either a useful (germane) or a wasteful (extraneous) consumption of cognitive capacity. The second assumption is that
the source of cognitive load can also vary depending on the complexity
of the task to-be-solved (intrinsic cognitive load defined by element interactivity). There is a large body of empirical research supporting the assumptions of CLT by analyzing the relation between the factors influencing
cognitive load and learning outcomes. For example, several empirically
well-established instructional design principles (Mayer, 2005) were identified in that line of research, which are discussed in other chapters of this book
(see Chapters 7, 8). However, can CLT’s assumptions be verified directly? Are
there valid and reliable methods to test the complex interactions between
the learner’s cognitive resources, demands, and learning? In this chapter,
we describe and discuss the various measures of cognitive load currently
available.
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Roland Brünken, Tina Seufert, and Fred Paas
Cognitive load research has produced several methods and approaches
within the last fifteen years – yet, we have neither a single standardized
method for cognitive load measurement nor a common measurement
paradigm. In the following section, we organize these currently used approaches based on the source of information about the resource consumption (subjective, objective, or combined; Brünken, Plass, & Leutner, 2003).
measurement of cognitive load
The basic question of cognitive load measurement is whether there are valid,
reliable, and practical methods to measure cognitive load. Generally, there
are two different approaches to assess cognitive load: (1) learners can be
asked to rate their perceived cognitive load subjectively, and (2) objective
measures of cognitive load, such as physiological measures, can be used. For
both approaches, several measures exist and are discussed in the following
section.
Subjective Measures of Cognitive Load
Probably the most common way of measurement in CLT is the use of subjective self-reported rating scales for the assessment of perceived mental effort,
often combined with a measure of subjectively perceived task difficulty. The
benefits and problems of both approaches are discussed next.
Subjective Rating of Perceived Mental Effort
In this type of cognitive load measure, learners are asked to rate their
perceived cognitive load with items such as “I invested . . . mental effort” on
a semantically differential scale varying from “very, very low” to “very, very
high” (Paas & van Merriënboer, 1993, 1994; Paas, Tuovinen, Tabbers, & Van
Gerven, 2003). The scale method is based on the assumption that learners
can make a reliable and valid estimation of the amount of load they were
confronted with in a specific situation.
Several studies using subjective rating scales demonstrate the utility of
this method for CLT research. Learners in general report different amounts
of perceived load depending on different instructional designs of learning
materials (for an overview, see Paas et al., 2003). Several rating scales are
used in cognitive load research (Whelan, 2005), most of them using 7- to 9point Likert scales. Moreover, the subjective load rating usually is combined
with subjective ratings of variables indirectly related to cognitive load, such
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as difficulty (see the next section) or fatigue, to form a multidimensional
assessment tool. However, the different assessments usually are highly correlated so that a unidimensional scale is also able to assess cognitive load in
valid and reliable ways (Paas & van Merriënboer, 1994). Perhaps the most
obvious benefit of assessing cognitive load with subjective rating scales is
its simplicity. However, they also have some serious limitations. First, they
usually deliver a one-point post hoc assessment of cognitive load imposed
by a learning task or working situation. Usually, learners are asked to assess
the cognitive load they experienced after they have finished the learning
task. Thus, the resulting assessment is a global scaling across different parts
of the learning situation and different tasks. It remains unclear, however,
which specific aspects of a learning situation caused the level of cognitive
load reported. On the other hand, this does not seem to be a major limitation for using subjective rating scales because they can be applied repeatedly
during the learning situation (e.g., using a pop-up window in a multimedia
scenario) to get a time series measurement that could be easily synchronized
with the learning task or material presented. Yet, to date, there are only a
few empirical studies using this approach (e.g., Tabbers, Martens, & van
Merriënboer, 2004).
A second, more serious problem of the subjective rating scales is related
to their content validity, namely, to the question of which type of cognitive
load the scale measures and how this assessment is related to learning.
Although learners are assumed to be able to be introspective about their
own cognitive processes and quantify their perceived mental load during
learning (Paas et al., 2003), these measures are unable to provide useful
information regarding which processes have caused the perceived amount
of mental load. Furthermore, it is not possible to conclude which of the
three types of load originated the reported mental effort level. Was it caused
by extraneous load (i.e., poor instructional design), germane load (i.e.,
learners’ problem-solving activities), a combination of both, or perhaps
simply by the intrinsic load of the material? Currently, only one study from
Ayres (2006) analyzed an approach to subjectively rate the intrinsic cognitive
load by measuring task difficulty. In his research design, the global rating of
difficulty can be clearly ascribed to intrinsic cognitive load because germane
load was controlled by using a task that required students to use their existing
knowledge rather than to learn new knowledge, and extraneous load was
controlled by excluding instructional materials. Nevertheless, the question
of how different types of load can also be measured in domains where
new information has to be learned is crucial because (as defined in the
prior sections of this book) different types of load are related to learning
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in different ways. Hence, the question of how mental load and learning are
related needs to be analyzed in more detail in future research.
Subjective Rating of Perceived Task Difficulty
As mentioned earlier, the cognitive load rating is often combined with a
rating of perceived task difficulty (Paas et al., 2003). If the same content is
rated as more or less difficult by the learner, depending on the form of its
presentation, then this would serve as an additional indicator for the extraneous load concept of CLT. However, task complexity ratings can also be an
indicator for the intrinsic load of the material. One could argue that task
complexity depends on the amount of interrelated elements (i.e., the element interactivity; see Chapter 2) in a presented concept. Moreover, element
interactivity is not only a function of the presented learning materials but
also a function of the interaction between learning material and the learner’s
prior domain-specific knowledge. Based on these assumptions, we should
expect different ratings for task complexity depending on the learner’s level
of expertise. Indeed, in our current research, we found evidence for two
cognitive load factors (i.e., intrinsic and extraneous) within students’ cognitive load ratings. In a 2-factor experimental design (Seufert, Jänen, &
Brünken, 2007) with 142 high school students, we compared the learning
results from two presentation modes for two prior-knowledge levels. With
respect to the subjective rating of task complexity, an analysis of variance
revealed main effects for both factors but no interaction. The results clearly
showed two different sources for the task complexity assessment, which,
in our view, reflect two different aspects of cognitive load: the intrinsic
part (demonstrated by the generally lower scores of the higher knowledge
group) and the extraneous part (demonstrated by the differences in the two
presentation modes independent of the expertise factor). Overall, the rating
of task difficulty comes along with the same benefits and problems as the
rating of mental effort, which suggests the need to supplement the subjective measures with additional objective measures discussed in the following
sections.
Objective Measures of Cognitive Load
Besides the subjective measurement approaches, several objective indicators for cognitive load have been addressed in cognitive load research.
They can be distinguished by their relation to the learning process in outcome variables (learning outcomes), input variables (task difficulty), and
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process-related behavioral variables (e.g., physiological parameters or timeon-task).
Learning Outcomes as a Measure of Cognitive Load
At first sight, the most obvious objective indicator of the level of cognitive load is the learning outcome itself. CLT predicts differences in learning
outcomes based on the different amounts of cognitive load induced by a specific situation. Hence, if we find the proposed differences within controlled
experimental learning situations, we assume these differences are caused by
the differences in cognitive load (e.g., Chapters 7 and 8). Although this was
the most common way of argumentation in the early years of cognitive load
research, most researchers have by now acknowledged the serious methodological problems of this approach. Assuming that we make no mistakes in
our experimental design and procedure, we can explain differences in the
dependent variables (learning) by the experimental variation of the independent variable (e.g., the instructional design of the material), but we cannot be sure that the different experimental variations really caused different
amounts of cognitive load and did not, at least additionally, cause differences in other variables relevant to learning (i.e., arousal or motivation).
Therefore, from a viewpoint of theory validation, we need a manipulation
check to ensure that the differences in learning outcomes are indeed caused
by differences in cognitive load, as CLT would suggest. Although learning outcomes are the central variables for assessing the fundamental effect
of instruction, thus making them indispensable measures in cognitive load
research (as in any research on learning and instruction), by themselves they
are not valid measures for cognitive load measurement. However, in combination with other cognitive load measures, they can deliver convergent
validity for the underlying theoretical assumptions.
Task Complexity as a Measure of Cognitive Load
Another indicator for cognitive load, and at least implicitly addressed in
recent research, is task complexity (which can be defined by the number
of interacting elements needed to solve the task) or task difficulty (which
usually is defined as the mean probability of task solution). Task complexity
affects cognitive processing; the more complex the task, the more cognitive
resources needed. In most research on cognitive load, learning outcomes are
measured by different tasks, varying in complexity. Usually, these tasks are
integrated in scales, for example, labeled “retention,” “problem solving,”
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and “transfer” (Mayer, 2001). The usual pattern found with respect to these
variables is that cognitive load effect sizes are stronger as task complexity
increases (for a review of modality effects and the moderating effect of task
difficulty, see Ginns, 2005). According to CLT, the higher the complexity
of the learning task is, the higher the intrinsic load imposed on the learner
will be. In the previously mentioned study by Ayres (2006), the complexity,
hence, the intrinsic cognitive load, was measured by subjective ratings.
However, the exact relation between task complexity and intrinsic load
remains unclear. Currently, cognitive load research does not assess intrinsic
cognitive load objectively (e.g., by cognitive task analysis). Nevertheless,
it seems logical to conclude that if the learning goal is to understand the
functionality of a complex technical system, intrinsic load must be higher
than in cases where the learning goal is to memorize the names of the system
elements. Therefore, it might be more useful to define intrinsic cognitive
load not in terms of element interactivity, but in terms of the amount of
information that has to be extracted from the information source with
respect to a specific learning goal.
Behavioral Data as Indicators of Cognitive Load during
the Learning Process
In contrast to learning outcomes as an outcome-related measure of cognitive
load or task difficulty as an input measure, the learners’ behavior is more
directly related to the learning process. Thus, several behavioral parameters can serve as an indicator of cognitive load, such as neuro-physiological parameters, invested time-on-task, information retrieval patterns,
and resource consumption measured by a dual-task approach.
Neuro-physiological measures for cognitive load. Cognitive neuroscience
research provides direct, basic measures of resource consumption. The most
common technique in this fast-growing field of research is functional magnetic resonance imaging (fMRI), a technique that depicts metabolic activity,
for example, in the human brain. It provides qualitative information about
the location of activation and can be supplemented with other imaging
techniques in which the quantity of activation can be depicted. Based on
a research review, Whelan (2007) argues that fMRI studies make it possible to measure the different types of cognitive load specifically. However,
because such techniques require a highly sophisticated technological apparatus, they can only be used in very specialized laboratory contexts and are
not yet appropriate for typical learning situations. Thus, other physiological
parameters can serve as indicators for cognitive load. General psychology
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approaches suggest a number of potential candidates for this type of measure, such as heart rate, galvanic skin reaction, and eye-tracking behavior.
There is a limited body of research that focuses on the issue of cognitive
load measurement using behavioral data (for a review, see Paas et al., 2003).
The study by Paas and van Merriënboer (1994) provides the first example
of a physiological method to measure cognitive load within the cognitive
load framework. The spectral-analysis technique of heart-rate variability is
based on the assumption that changes in cognitive functioning are reflected
in physiological functioning. The spectral analysis of heart-rate variability offers a measure of the intensity of mental effort. Spectral analysis is a
method for investigating whether a signal contains periodic components.
Aasman, Mulder, and Mulder (1987) and Mulder (1992), among others, have
validated this technique with several cognitive tasks (e.g., multidimensional
classification and sentence comprehension). Although these studies suggest
that the 10-Hz component of heart-rate variability exhibits a reliable reflection of mental effort associated with different tasks (Tattersall & Hockey,
1995), the heart-rate variability was not found to be sensitive to the subtle
fluctuations in cognitive load that are typically investigated in cognitive
load research within or between instructional conditions. In another study,
Van Gerven, Paas, van Merriënboer, and Schmidt (2004) measured taskevoked pupillary responses as a function of different levels of cognitive load
in both young and old participants. They found that mean pupil dilation,
as measured by a remote eye-tracking device, is a highly sensitive indicator of tracking fluctuating levels of cognitive load, especially for young
adults (for an overview of task-evoked pupillary responses, see Beatty &
Lucero-Wagoner, 2000).
Although most of the physiological techniques discussed previously are
highly reliable, they raise questions of construct validity. For example,
although the number and duration of eye movements and fixations on
different parts of visually presented information can be observed with high
precision by using modern eye-tracking cameras, if differences are found,
for example, in the duration of eye fixation, there could be several causes
for such a change, such as information complexity, interest, or readability.
As we discussed earlier with respect to the subjective ratings of cognitive
load, global measures always have the problem of multi-causality: They can
be caused by various factors and, at least in experimental settings, it sometimes remains unclear which of the potential causes have been addressed
with the experimental variation. Therefore, behavioral data analysis requires
extremely careful experimental designs to assure that the measure is not only
reliable but also valid.
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With respect to CLT, one could ask, “For which cognitive load effects
is eye-tracking observation a useful tool?” Although it might be extremely
useful for observing behavior related to the integration of different visual
information sources (as addressed in the split-attention effect or the colorcoding effect in Chapters 4 and 7), it might be less useful to obtain cognitive
load differences caused by different information modes (such as the multimedia effect) or different information modalities (such as the modality
effect). However, used within sensitive experimental settings, the observation of behavioral data can offer interesting and highly relevant results that
are closely related to the assumptions of CLT. For example, Folker, Ritter,
and Sichelschmidt (2005) compared two variants of a multimedia presentation of learning materials in the domain of cell biology, one with and
one without color coding. From a cognitive load perspective, color coding
should reduce the imposed extraneous load of the material by giving hints
about how to integrate different representations and therefore facilitating
coherence formation. Using eye-tracking observation, Folker et al. found
different fixation patterns for the two learning conditions, with fewer fixations and fewer changes between the textual and pictorial information in
the color-coded condition, which suggests that color coding indeed reduces
integration costs. This (relative) reduction of cognitive costs, for example,
by fewer fixation changes, can be seen as a direct indicator for different
cognitive load caused by the learning materials, which leads to different
amounts of knowledge acquisition.
Time-on-task as a measure of cognitive load. All cognitive processes take
time. The amount of time needed to reach a solution is affected by several
factors, including the complexity of the task, the learners’ prior knowledge,
the time needed to search information, and so forth. Nevertheless, time-ontask is directly related to cognitive processing and in a good experimental
setting, it is possible to control for most of these factors by measuring them
or by randomly assigning participants to different conditions. In this way,
it is possible to come closer to the basic processing variables causing differences in time-on-task. Recent research by Tabbers et al. (2004) illustrates
this issue. In a series of experiments, they found that the modality effect in
multimedia learning (Mayer & Moreno, 1998) vanishes when learners are
given the opportunity to control the pace of the presentation: Students who
learn with text and pictures spend more time-on-task to reach the performance level of students who learn with narration and pictures. These results
are highly interesting for basic cognitive load research because they demonstrate that differences in learning outcomes caused by different instructional
designs can be compensated for by additional time-on-task. This finding
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highlights the relation of instructional design and cognitive processing as it
is assumed by CLT: Because the verbal material (which is intrinsically equally
demanding) is presented in different modalities, differences in learning time
are necessary to come to the same learning results. This is strong evidence
for what CLT calls extraneous load: the additional consumption of cognitive resources caused by the instructional design without a corresponding
learning benefit. However, the theoretical relation between time-on-task
and cognitive load remains indirect. It is the timeframe in which cognitive
processes take place, and the size of the timeframe that affects the efficiency
of cognitive processing. Moreover, little is known about exactly how timeon-task and cognitive load are related. Usually, we would expect a linear
relationship (the more time needed, the more the load imposed by the
material), but it might also be plausible to argue that very little time-ontask indicates high cognitive load because the load might be so high that
the learner stops investing effort on the learning situation.
Information retrieval behavior as a measure of cognitive load. The way in
which learners search and select information can also serve as an indicator
of cognitive load within the learning process. For example, the analysis of
navigation pathways in hypertext environments could serve as an indicator
for schema construction, because it highlights aspects of individual information search, which is related to the learners’ prior knowledge (Möller &
Müller-Kalthoff, 2000). Imagine a large, well-structured hypertext learning environment containing information about a specific domain, such as a
complex medical topic. A novice learner, unfamiliar with the domain, might
start reviewing hypertext pages containing basic information. A domain
expert, however, might directly navigate to pages containing specific information, for example, about alternative medications or diagnostic strategies.
This will result in two different navigation patterns that are typical for the
respective level of prior knowledge. Therefore, with regard to a specific task,
the navigation pathway – the pages visited as well as the pages skipped –
allows for insights into the structure of the underlying domain-specific
knowledge of the learner. Other examples could be the amount of helpseeking or the use of optional explanatory annotations (Wallen, Plass, &
Brünken, 2005), which could indicate differences in the amount of help
needed and therefore could be argued to indirectly measure cognitive load.
But, again, the relation between these measures and cognitive load is not
straightforward. Differences in navigation behavior can result from various
causes, and careful experimental designs are needed to control for possible influencing factors not related to cognitive load. Despite the fact that
all these measures are easy to administer, are objective, and produce fairly
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reliable scores, especially when using technology-based learning environments, we found few examples for using these cognitive load measurements
in the literature (see also Paas et al., 2003).
Dual-task measures. Another type of cognitive load measurement based
on the analysis of objective behavioral data recently used by Brünken et al.
(2003) is based on the dual-task paradigm (see also Chandler & Sweller,
1996; Van Gerven, Paas, van Merriënboer, & Schmidt, 2006). This approach
is based on a simple hypothesis derived from the “limited capacity assumption” of CLT (see Chapters 2 and 3, this volume). If the total cognitive
capacity for a certain learner at a certain time is limited in its amount,
it has to be distributed to all cognitive processes that have to be carried
out at that point in time. If a learner has to process two different tasks
at the same time, which in addition, consume his complete capacity, then
the performance on the one task can be used as a measure of the capacity
consumption of the other task. The assumption of complete resource consumption by the two tasks is crucial for dual-task research in general. The
secondary task can only be sensitive and reliable if it is exhaustive (Wickens,
1984).
Imagine two groups of students learning the same information from
two different learning environments in a dual-task scenario. If the capacity
requirements of the two different learning environments vary because of the
instructional design, then the performance on a simultaneously processed
secondary task should vary accordingly. In a series of studies (Brünken,
Plass, & Leutner, 2004; Brünken, Steinbacher, Plass, & Leutner, 2002), we
demonstrated the utility of using this secondary task method as a direct
estimation of cognitive load. Other studies confirmed these results (e.g.,
Renkl, Gruber, Weber, Lerche, & Schweitzer, 2003). Moreover, the results of
all of these studies were in line with the predictions of CLT.
However, the dual-task method also has some serious limitations. First,
it is limited to a comparative measurement of the cognitive load induced
by different instructional designs. It fails to provide an absolute estimation
of resource consumption. However, it should be noted that this is not only
a problem of dual-task measurement. We will discuss this as an important
issue of cognitive load measurement in the next section. Second, because of
the complexity of the experimental setting of dual-task measures, especially
when using reaction times as a secondary task measure, their use is usually
limited to laboratory research. The third and perhaps most problematic
issue is that, because of the nature of shared cognitive resources in this
approach, the secondary task may interfere with or hinder the primary
task (which is usually learning). A final point to be addressed in dual-task
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research is the modality specificity of the secondary task. Our research shows
that the consumption of cognitive resources is modality specific, that is, the
sensitivity of the secondary task measure depends on the specific modality
chosen for the primary and secondary tasks in the learning environment
(Brünken et al., 2002, 2004). For example, an auditory secondary task might
be insensitive for assessing extraneous cognitive load caused by a splitattention effect because this effect is primarily related to visual processing.
However, if well designed, the dual-task method is a powerful approach for
cognitive load measurement and applicable to the evaluation of learning
scenarios (e.g., in multimedia learning) and the usability of software in
general.
Combined (Efficiency) Measures of Cognitive Load
A well-established approach to model the relation between mental effort
and performance has been introduced by Paas and van Merriënboer (1993,
see also Paas et al., 2003). Imagine two groups of students learning about
the same content material but with different instructional designs: The first
group reports a lower amount of invested mental effort, but the second
group outperforms the first one with respect to knowledge acquisition.
Which instructional variant should be favored? Such a question cannot
be answered easily because the answer may depend on the concrete learning task as well as on the available alternatives. For example, for a task of
monitoring critical technical systems, in which the prevention of errors is
crucial, the imposed cognitive load might be less important because users
have to be able to prevent errors even under heavy load conditions. For
other tasks, such as introducing new knowledge to novice learners, it might
be more beneficial to avoid cognitive load that is too high. To resolve this
ambiguous relation, Paas and van Merriënboer (1993) proposed an approach
that combines the two dimensions of mental effort and performance. The
method consists of first transforming both variables to a comparable scale
by normalizing the respective scores and then calculating the relationship
between mental effort and performance using a simple equation: the difference between the standardized scores of performance and mental effort
divided by the square root of two. This calculation yields an efficiency score,
which indicates high instructional efficiency when the perceived cognitive
load of a specific instructional design is lower than should be expected by
the performance results or low instructional efficiency when the perceived
level of cognitive load is higher than should be expected by the performance
results.
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Recently, Tuovinen and Paas (2004) introduced an extension of the
original two-dimensional instructional efficiency measure. This new threedimensional approach can either combine two measures of effort (i.e.,
learning effort and test effort) and test performance, or one effort measure
and one time measure, and test performance. Each of these approaches,
with their associated insights and analyses, may be useful for instructional
researchers as diagnostic instruments to identify different aspects of efficient
or inefficient instructional conditions.
The efficiency approach is appealing because it is easy to calculate (see
Paas et al., 2003; van Gog & Paas, 2008) and independent from the specific
way of measuring load or performance. Moreover, it can easily be adapted to
situations in which one of the dimensions is more important than the other
by including weight terms in the formula. However, the approach is limited
to the comparison of different instructional variants of the same material
and provides only a measure of the relative efficiency of the investigated variants. Each time a new variant has to be tested, the efficiency has to be recalculated, and the relationships among the variants might change. Moreover,
one could question (Paas et al., 2003) whether efficiency is only affected by
cognitive load and performance or if other learning-related variables, such
as time-on-task or motivation, should also be included in the calculation.
Although the issue of conceptualizing efficiency is highly important, it does
not challenge the principle of efficiency calculation. Conceptually, all these
variables could (a valid and reliable measurement presumed) be included
in an efficiency formula. This was recently recognized by Paas, Tuovinen,
van Merriënboer, and Darabi (2005), who introduced a similar procedure to
combine mental effort and performance to compute the differential effects
of instructional conditions on learner motivation. From a theoretical perspective, the discussion about instructional efficiency highlights a more
general aspect of cognitive load measurement: its relation to the theoretical
concepts of CLT and the impact of various internal and external factors
on cognitive load. We address this point in the next part of the chapter.
Table 9.1 summarizes the classification of cognitive load measures we just
reviewed.
To summarize, most recent empirical studies on CLT incorporate cognitive load measures, indicating that next to learning performance measures,
they are the second method used to assess the effectiveness of instruction. However, although most of the studies include subjective rating
scales for practical reasons, other, more objective methods are of increasing interest, especially when they can be incorporated into computer-based
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table 9.1. Classification of cognitive load measures
Type of cognitive
load measure
Measure
Subjective
Subjective rating
scales
Objective
Learning outcomes
Time-on-task
Navigation
behavior, helpseeking behavior
Task complexity
Behavioral data
(heart rate, pupil
dilation)
Secondary task
analysis
Eye-tracking
analysis
Combined
Efficiency measures
Main research question
Learner’s subjective
assessment of task
demands
Relation between
instructional design and
knowledge acquisition
Learner’s investment in the
learning process
Learner’s information need
Relation among affordances,
instructional design, and
knowledge acquisition
Global or specific
physiological reactions of
the organism involved in
a learning process
Mental load induced by the
(primary) learning task
Basic behavioral aspects of
information processing
and their relation to
learning outcomes
Optimizing instructional
design decisions by
calculating the relation of
invested effort and
learning outcome
Research examples
Paas & van
Merriënboer, 1993
Mayer, 2005; Mayer &
Moreno, 1998
Tabbers et al., 2004
Möller & MüllerKalthoff, 2000
Seufert et al., 2007
Van Gerven et al.,
2004
Brünken et al., 2002
Folker et al., 2005
Paas et al., 2003
environments for learning, such as multimedia or simulation-based learning. In addition, efficiency calculations are of growing interest, especially
for guiding instructional design decisions.
discussion: open questions and future directions
In the previous sections, we showed that cognitive load can be measured
by various methods more or less easily by using subjective ratings as well
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as by conducting objective observations of learners’ behavior. In our view,
it is not useful to ask which type of measurement is the best one because
they vary in their objectives as well as in their practicality. They all have
benefits and disadvantages, and the decision regarding which one to use
highly depends on the specific research questions raised.
Open Questions in Cognitive Load Measurement
Going back to the initial theoretical concepts of CLT, we will try to reconceptualize cognitive load from a measurement point of view. In this view,
cognitive load is related to all processes performed in working memory
independent of their relation to the task. Cognitive load simply means that
something non-automatic happens in the mind, which causes the consumption of mental resources. One might argue that this definition is trivial, but
with respect to the limited nature of mental resources, it is crucial whether
the activity performed in the mind is actually related to a specific goal or
not. Following are some of the current challenges to the measurement of
cognitive load during learning.
Cognitive Load Is Ambiguous with Respect to Its Learning Outcomes
CLT proposes that cognitive load can foster learning as well as hinder it.
An aspect directly related to learning is the task orientation of the actual
cognitive activity. So in our view, cognitive load is related to learning by
differentiating between task-relevant (germane) and task-irrelevant (extraneous) consumption of cognitive resources. However, both types of load
consume the same resources, which causes serious measurement problems,
because it is not possible to predict the impact of cognitive load on learning
without distinguishing between the two types of load.
Cognitive Load Depends on the Learner’s Characteristics
Coming to a more detailed concept of cognitive load, which could guide us
to more sensitive measures, we are confronted with a second characteristic
of cognitive load that causes serious measurement problems – its relativity.
Cognitive load is not only ambiguous with respect to learning outcomes but
it is also relative in several ways. First, and maybe most obvious, cognitive
load depends on the characteristics of the task. The more complex a task,
the more cognitive capacity is necessary to solve it. This aspect is integrated
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in CLT by the concept of intrinsic load. Usually, intrinsic load is conceptualized by element interactivity, which is an indicator of task complexity
based on the number of conceptual elements that have to be held in the
mind simultaneously to solve a specific task (e.g., to understand a complex
concept). However, this definition is questionable because it assumes that
intrinsic load only depends on task characteristics. Analyzing this concept
in more detail, we will find, for example, that the differences in learning
success might also depend on the learner’s prior domain-specific knowledge. But what exactly is prior knowledge? In terms of CLT, we can define
prior knowledge as the amount of task-relevant elements already available
in the learner’s mind. So the intrinsic load of a specific learning situation not
only depends on the number of elements needed for understanding that are
presented by the material, but also on the number of externally presented
elements needed for understanding and not already available in the learner’s
mind. The fact that intrinsic load depends on the learner’s prior knowledge
suggests that cognitive load is also dependent on learner aptitudes (Seufert,
Jänen, & Brünken, 2007).
Maybe the most compelling evidence for this idea is the expertise reversal
effect (Kalyuga, Ayres, Chandler, & Sweller, 2003). Similarly, other learner
aptitudes may be seen as influential to cognitive load, such as students’
memory span or speed of information processing, as well as specific cognitive abilities, such as verbal or spatial abilities. Several empirical studies
support this idea (e.g., Mayer & Sims, 1994; Plass, Chun, Mayer, & Leutner, 1996; Wallen et al., 2005). Paas, Camp, and Rikers (2001; see also Paas,
Van Gerven, & Tabbers, 2005) showed that the cognitive load and learning
effects resulting from different instructional designs are also highly dependent on learner age. According to cognitive aging research, people experience declines in working memory capacity, processing speed, inhibition,
and integration as they age. Consequently, working memory processing
becomes less efficient for older adults. Studying the differential effects of
goal specificity on maze learning between younger (20 years old) and older
(65 years old) adults, Paas and colleagues (2001) showed that instruction
designed according to CLT principles compensates for these age-related cognitive declines. More specifically, the researchers found that the absence of a
specific goal disproportionately enhanced the elderly participants’ learning
and transfer performance, almost up to the level of the younger adults. It
is becoming clearer from these results that learner aptitudes and individual differences should be taken into consideration not only when trying to
define the concept of intrinsic load, but also when trying to measure CL.
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Cognitive Load Is Not Constant during Learning
In addition, our engagement in learning tasks is not always constant but
varies depending on other affective factors, such as motivation and selfconcept. To date, neither theoretical nor empirical cognitive load developments take these factors into account. This may be one of the main
shortcomings of the present state of the art in CLT, which is only based
on the analysis of cognitive factors of learning. Nevertheless, in terms of
cognitive load measurement, we can assume that cognitive engagement is
the total amount of cognitive resources that can be allocated to a specific
task in a specific situation. Last but not least, cognitive load also varies, both
with respect to the imposed load by the learning situation and the resources
available (for a conceptual discussion of this point, see Paas et al., 2003). The
transient nature of cognitive load should be taken into account for measurement purposes.
It should be clear from this review that whichever type of measurement
is used, cognitive load cannot be seen as a constant factor that only depends
on objective attributes of the learning material or design. Cognitive load
measurement is relative, and the efficiency of a learning situation has to be
seen in light of relevant personal and environmental variables. However,
measurement of cognitive load can be more or less sensitive to several of
the discussed aspects. For instance, the impact of load distributed over time
can be investigated by a repeated real-time measurement rather than by a
one-time, ex post assessment. The role of learner aptitudes can be studied in
experimental aptitude-treatment-interaction analyses (Cronbach & Snow,
1977) as well as by correlation analyses in field experiments. Moreover, we
do not only need more and better experimental studies on the relation of
cognitive load and learner characteristics, but we also have to adapt existing
measures to capture these potential interactions. A critical issue that is
currently under discussion is the relation of cognitive load and the perceived
amount of invested mental effort, which either can be a result of differences
in knowledge, abilities, motivation (see Paas et al., 2005), or time-on-task.
Future Directions in Cognitive Load Measurement
Research in the area of cognitive load measurement methods is currently
concerned with two additional issues, highly relevant to both the theory
and measurement of cognitive load: (1) the differential measurement of
the three sources of cognitive load, and (2) the additivity assumption of
cognitive load.
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The Differential Measurement of Intrinsic, Extraneous,
and Germane Loads
CLT proposes three different sources of cognitive load – intrinsic, extraneous, and germane – differing in their origin and their relation to performance. However, at the moment, there is no measure available to empirically distinguish between these three load types. Coming to a more precise
description of the relation between these loads and learning performance
may be currently the most important challenge for measurement research.
There is surely more than one definition of this relation in use by cognitive load researchers, depending on the chosen measurement approach,
but there are some general requirements that should be addressed by each
method. For example, different types of load cannot be measured simultaneously on a one-dimensional scale or by a one-dimensional value. This
requires either a multidimensional measurement approach or a sequential
or isolated measurement of the different load types. A multidimensional
approach could most easily be employed by using multiple subjective rating
scales, each of them related to one type of load. For instance, “How easy
or difficult was it for you to work with the learning environment?” could
be used as a question specifically related to extraneous cognitive load, or
“How easy or difficult do you consider the specific learning content ‘x’ at
this moment?” as an item related to intrinsic load, or “How easy or difficult
was it to understand the specific concept ‘y’?” as a question to assess the
germane load.
Although the dimensional structure of such a questionnaire can be evaluated using factor analysis, a more fundamental question is whether learners
are able to distinguish the contribution of each cognitive load type during
learning. For example, can learners assess the amount of germane load in
valid and reliable ways? The experiences with self-assessment in the domain
of learning style research are not very encouraging (Leutner & Plass, 1998).
Although it seems to be possible to use questionnaires to assess the amount
of learning strategy use (Wild & Schiefele, 1994), it seems to be much more
problematic to assess the quality of strategy use. This problem of lacking metacognitive reflection is usually reflected in low or zero correlations
between self-reported strategy use and performance (Leutner & Plass, 1998).
However, the use of learning strategies can be seen in terms of CLT as cognitive activities, which cause germane load and which may lead to similar
problems with the assessment of germane activities.
A possible way to ease introspection might be to refer closely to concrete
activities directly related to the information to be learned instead of referring
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to the use of global strategies. For example, the question “Did you underline
the basic concept ‘x’ in the presented material?” can be used instead of the
question “How frequently did you use underlining?” Similarly, to assess the
intrinsic load with respect to the individual’s expertise level, it seems more
useful to ask students to assess the perceived difficulty of a concrete concept
than the perceived difficulty of the overall learning experience. This would
demand a repeated measurement of cognitive load during learning.
The continuous observation of cognitive demands in a learning situation
could, at least for research objectives, be more easily accomplished using
appropriate objective measurement techniques, such as dual-task analysis or
eye movement tracking. However, this method requires the development of
better, less invasive measures as well as the application of adequate statistical
methods, such as time series analysis, which have not been used in cognitive
load research to date.
The Additivity Hypothesis
A second issue not addressed in current research on cognitive load measurement is related to a fundamental but empirically unproven assumption
of CLT: the additivity hypothesis. According to this hypothesis, the different
sources of load (intrinsic, extraneous, and germane) are additive elements
of the total cognitive load imposed by a specific learning situation. This
assumption is crucial because it implies that a reduction/increase in one
type of load automatically produces an identical reduction/increase in total
amount of cognitive load. For example, reductions of intrinsic cognitive
load should free up cognitive resources that can be used to engage in other
cognitive activities, such as germane learning processes (Pollock, Chandler,
& Sweller, 2002; see also Chapter 2). However, recent research shows that this
is not always the case (Seufert & Brünken, 2006). Specifically, a reduction
of extraneous load does not always lead to increased learning. Moreover,
the additivity assumption seems questionable from a theoretical point of
view. First, a large body of research is concerned with the phenomenon
called “illusion of understanding,” in which learners stop knowledge acquisition before they fully understand a concept because they overestimate
their level of understanding (Glenberg, Wilkinson, & Epstein, 1982; Renkl &
Atkinson, 2003). What triggers this illusion? A possible explanation is that
instructional design with low extraneous load may be perceived as “an
easy task” and therefore may result in cognitive disengagement, an effect
also shown in Salomon’s early research on media effects (Salomon, 1984).
However, a complex task with high extraneous load could be perceived as
Measuring Cognitive Load
199
challenging, leading the learner to invest more mental effort in learning.
Second, the assumption that intrinsic and extraneous loads are independent
seems problematic. Is a specific instructional design for learners of different expertise levels equally “extraneously loading”? The expertise reversal
effect suggests that this is not the case (Kalyuga et al., 2003). Whereas
some instructional material seems to be beneficial for novice learners, the
same material is redundant for experts, resulting in decreased knowledge
acquisition. All these questions concerning the additivity hypothesis require
groundbreaking experimental research on the relation of the different load
types that in turn need differential measures of cognitive load.
To summarize, cognitive load measurement is still in its infancy. Most
currently available cognitive load measures are less than perfect measures
of students’ overall cognitive load. However, even these imprecise measures contribute to CLT by providing empirical evidence for the global
relation of instructional design, cognitive processes, and performance. The
development of better measures will therefore be beneficial for CLT in general, as well as for its application to the instructional design of learning
materials. Nevertheless, three basic aspects of cognitive load have to be
taken into account, both to optimize measurement techniques as well as
to clarify the basic concepts of CLT: (1) the relative nature of cognitive
load, (2) the dependency among the three cognitive load types, and (3) the
dimensionality of cognitive load measurement. As we shed more light on
these crucial issues, we should be able to bridge the gap between CLT and
research.
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10
From Neo-Behaviorism to Neuroscience: Perspectives
on the Origins and Future Contributions
of Cognitive Load Research
richard e. clark and vincent p. clark
historical perspectives on cognitive load research
and theory
European and American psychology may have developed in a way that
prevented or delayed the development of Cognitive Load Theory (CLT)
until George Miller’s (1956) classic paper on working memory capacity
appeared a half century ago. At the beginning of the twentieth century and
fifty years before Miller’s paper kick-started the field of cognitive science,
Charles Hubbard Judd (1908) lost an important argument with Edward
Thorndike (1903) about the role of mental effort in the transfer of learning. The loss helped to sidetrack psychology into emphasizing behaviorism
over cognitive processing. Judd, an American who was Wilhelm Wundt’s
student in Leipzig at the end of the nineteenth century, hypothesized that
internal cognitive processes and external instructional strategies supported
the mental work necessary to transfer knowledge between different problem
contexts and settings. Judd had learned from Wundt to emphasize a version
of scientific psychology that favored the study of consciousness, problem
solving, thinking, and sensations. Judd’s (1908) famous bow and arrow
experiment demonstrated that effortful cognitive processes could support
the generalization of a principle about the diffractive properties of water and
so allow people to adjust their aim with the bow to hit an underwater target
The authors want to acknowledge their debt to a number of colleagues who reviewed previous
drafts of this chapter and gave advice, including Mary Helen Immordino-Yang, Robert Rueda,
and the three editors of this volume. Any errors that remain are our responsibility.
The first author wants to acknowledge that his contribution to this chapter has been partially sponsored by the U.S. Army Research, Development, and Engineering Command
(RDECOM). Statements and opinions expressed do not necessarily reflect the position
or the policy of the U.S. Government, and no official endorsement should be inferred.
203
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Richard E. Clark and Vincent P. Clark
that appeared to be somewhere else. Thorndike (1903) focused his research
on animal maze learning and proposed an “identical elements” transfer
theory, arguing that it was positive reinforcement that led to learning and
transfer – and not cognitive processing. Because Thorndike was a student
of the powerful William James, who supported his work, Judd’s theory and
evidence were largely ignored.
William James’s support for Thorndike’s view of transfer marked a turning point in psychology. James’s earlier work had emphasized the role of
mental effort in cognition when, for example, he described attention as
“the taking possession by the mind, in clear and vivid form, of one out
of what seem several simultaneously possible objects or trains of thought.
Focalization and concentration of consciousness are of its essence. It implies
withdrawal from some things in order to deal effectively with others” (James,
1890, pp. 403–404). In an 1898 lecture that mirrors some of the arguments
made recently about the possible evolutionary selection advantage offered
by limitations on working memory by John Sweller (Chapter 2), James gave
a series of lectures at Johns Hopkins University in which he claimed that
consciousness had an evolutionary function or it would not have been naturally selected in humans. A few years later, James (1904) reversed himself and
expressed strong misgivings in an article titled “Does ‘Consciousness’ Exist?”
Judd (1910) later protested and argued for a selection bias for consciousness,
but at the same time, Thorndike (1903) and others were more successfully
arguing that learning was “not insightful” but instead was incremental.
Thorndike’s claim essentially denied any important role for consciousness
or working memory in learning or problem solving.
A number of historians have proposed that the transition in psychology
during James and Thorndike’s era was due in large measure to an increasing
interest by the American public in the development of the physical and
biological sciences and a distrust of the introspective approach in philosophy and imprecise psychological research methods. This may have been the
reason that American psychologists such as James, Thorndike, and others
at that time were attracted to the learning research of 1904 Nobel Prize
winner Ivan Pavlov and supported the use of animal experiments and the
careful control of observable and measurable events favored in medical
research. This exclusive focus on animal learning and connectionism was
not reflected in European psychology, where researchers continued to be
concerned with experimental work as well as introspection, Gestalt studies of consciousness, physiology, experimentation, and case study methods. The more flexible approach taken by European researchers may be
the reason many of the prime movers in CLT have been trained in the
From Neo-Behaviorism to Neuroscience
205
European psychological tradition. The irony is that behaviorism resulted
in important advances in measurement, the specification of instructional
method variables, and precise experimental methods while it discouraged
hypotheses based on cognitive processing during learning and transfer. It
also became increasingly obvious that behaviorism focused primarily on
motivation to learn through reinforcement and emphasized very simple
forms of learning. That recognition eventually made it possible for neobehaviorists to hypothesize internal cognitive processes to explain complex
learning.
One of the very early attempts to deal with complaints that behaviorism
only focused on simple learning tasks was the neo-behaviorist research on
complexity by Canadian psychologist Daniel Berline (1960). In the 1960s,
information processing theory was developing, and Berline offered a model
for representing cognitive stimulus and response bonds to describe the cognitive processing required for handling uncertainty and novelty. He proposed a method of measuring individual uncertainty about any stimulus
and hypotheses that guided research on the relationship of problem uncertainty and learning. His internationalism and his neo-behaviorist theories
made early attempts at cognitive science more acceptable to behaviorists in
North America. During this time, cognitive science was developing slowly,
forced to swim upstream against powerful behaviorists who resisted change.
In addition to Miller’s (1952) classic “Magical Number Seven” article, Ulric
Neisser’s (1967) book Cognitive Psychology also had a major impact on the
development of CLT. Neisser proposed a computer processing metaphor
for cognition and urged psychologists to study the function of working
memory in daily activities. Although many cognitive psychologists now
avoid the restrictive computer metaphor for cognition, educational psychology benefitted from the analogy during a formative stage. A decade
after Neisser’s book was published, the article by Schneider and Shiffrin
(1977) on controlled and automated processing had a huge impact on our
view of complex learning, memory, and problem solving. With these events
in the background, a decade later, John Sweller’s (1988) article in Cognitive
Science laid the groundwork for CLT.
An important lesson to be learned from the history of psychology is
that education and psychology must permit more diversity in theoretical
and methodological approaches. With a more interdisciplinary approach,
we might have started to develop CLT a half-century earlier and so would
have been considerably more advanced at this point. Yet, it may also be the
case that one of the benefits of the historical delay caused by the dominance
of behavioral theories was the development of a clear focus on pragmatic
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instructional research. Behaviorists such as B. F. Skinner encouraged psychologists to conduct careful instructional research in schools. CLT researchers have retained the behavioral focus on instruction and as a result,
CLT has made significant contributions to instructional design.
clt contributions and challenges
to instructional design
An emphasis on the application of research findings to instruction requires
that we understand the conditions necessary for selecting and implementing
the most efficient and effective instructional design for different learning
tasks, learners, and delivery media. This decision has worked to the benefit
of instructional design in at least two ways. First, we are no longer inclined
to make quick inferences about how to support learning by reasoning from a
descriptive theory of learning or from empirical studies unsupported by theoretical insights. Learning can accurately be described as a process in which
people construct new knowledge by drawing on their prior experience and
blending it with new information about a task (Mayer, 2004). We also have
clear evidence that asking students to construct what they must learn without guidance is consistently less effective and efficient than worked examples
that demonstrate how to perform a task or solve a problem (Mayer, 2004;
Kirschner, Sweller, & Clark, 2006). CLT accurately predicts that when students are required to construct or discover how to solve problems or perform
complex tasks, the cognitive effort most often overloads working memory
and inhibits learning for students who have novice to intermediate levels of
germane prior knowledge. Most of the chapters in this book and the research
on the use of CLT for instructional design that preceded this book are clearly
focused on helping those who design, develop, and present all types of
instruction to learners at every age and level of expertise. Recently published
examples are Richard Mayer’s (2001, 2005) edited handbooks on multimedia
design, his book with Ruth Colvin Clark (Clark & Mayer, 2007) on designing e-learning instruction, and the systematic instructional design strategy
for teaching complex knowledge published by Jeroen van Merriënboer and
Paul Kirschner (2007). These developments can be viewed as attempts to use
CLT to identify the many ways that common instructional practices cause
overload and suggest concrete and systematic ways to avoid them. Because
many of the researchers who are committed to CLT development are also
interested in instructional design, some of the most important educational
contributions serve to define and clarify the role of instructional methods.
From Neo-Behaviorism to Neuroscience
207
CLT and Instructional Methods
Another important advantage of the behaviorism that preceded the development of CLT may be CLT researchers’ adaptation of the goal to provide
specific, evidence-based operational definitions of “instructional methods”
and welcome explanations of how different methods serve to maximize germane cognitive load and so lead to more learning. Most instructional design
systems suggest that those who are developing instruction should “select
appropriate instructional methods” without providing adequate guidance
about the definition, design, or selection of effective methods.
When a young cognitive science was developing in the early 1970s, Lee
Shulman famously complained that an obsessive emphasis on aptitude in
learning theories had led to the situation in which instructional methods
“are likely to remain an empty phrase as long as we measure aptitudes with
micrometers and instructional methods with divining rods” (Shulman,
1970, p. 374). Cronbach and Snow (1977) reviewed all instructional research
conducted for approximately four decades and recommended that we invest
much more emphasis on understanding instructional methods.
Until CLT, our failure to focus adequate attention on the specification
and presumed cognitive function of instructional methods continued to be
one of the most embarrassing failures of instructional psychology. Instructional experiments typically employ treatments described as lectures, discussion, collaborative groups, graphic organizers, case studies, computer programs, and video and text materials. None of these descriptions (and often
their accompanying operational definitions in research reports) are focused
on the “active ingredients” in the instruction that may or may not have led
to measured differences in learning outcomes (Clark & Estes, 1999; Clark,
2001). CLT’s emphasis on elements of instructional methods that are germane and so contribute to learning and those that are extraneous and so
distract and inhibit learning is a huge contribution to instructional psychology. Examples of methods suggested by CLT to support novice learners
include formatting instructional content in focused, integrated pictorial
and narrative presentations of topics (Chapters 3 and 7, this volume) and
providing demonstrations of how to perform tasks or solve problems in
“worked examples” (Chapter 5). CLT research provides strong indications
that these methods maximize the processing time in working memory for
task information that must be elaborated and stored in long-term memory
while they minimize the extraneous cognitive effort required to support
learning. CLT advocates also suggest that these methods provide effective
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support for the limited executive learning functions available to learners
with less prior knowledge (Chapter 2). The explanation for the benefits
of these CLT instructional methods helps to explain the half-century of
research that demonstrates the failure of discovery, problem-based, inquiry,
and constructivist learning (Kirschner et al., 2006).
Challenges to CLT-Inspired Instructional Design
CLT has developed rapidly but like any theory, there are many unanswered
questions and a number of areas in which current theoretical explanations
and measures are inadequate. In the next section of this chapter, we review
two urgent issues and examine the possible contributions we could expect
from reconsidering the importance of biological, physiological, and neuroscience research. Two important problems that must be addressed before
we can advance much further with CLT are that we have not yet found
an unobtrusive and reliable way to measure cognitive load and we need to
determine whether any specific source of cognitive load is productive for
individual learners during instruction.
Measuring cognitive load during learning. Gross measures of mental workload, such as self-report and secondary tasks (Megaw, 2005), have been challenged (Gimino, 2000). Self-report measures appear to be confounded with
personal judgments about the difficulty of a task rather than the amount of
mental effort invested. Secondary measures capture the time required for
individual learners to react to a random interruption during a task. These
latency measures divert learners’ attention from tasks and introduce a variety of messy confounds (see a review by Iqbal, Adamczyk, Zheng, & Bailey,
2005). Brünken, Seufert, and Paas (Chapter 9) discuss different solutions
and conclude, “cognitive load measurement is still in its infancy” (p. 199).
Past attempts to provide a definition of cognitive load in an educational
context have focused either on the number of steps and/or interactions
between steps required to perform a task – most often called “intrinsic”
load (Sweller, 2006) – or on the mental workload experienced by individuals who are learning. One often-repeated example of the difference between
low and high levels of intrinsic load is the difference between learning
vocabulary in a foreign language and the presumably higher load required
to learn to speak a foreign language (Sweller, 2006). Yet, there have been
arguments that the construct of intrinsic load may be an unnecessary and
distracting return to the behaviorist emphasis on the environment and the
directly observable (Clark & Elen, 2006). Is load in the environment or is
it a function of the amount of mental work necessary for any individual
From Neo-Behaviorism to Neuroscience
209
learner to accomplish a task depending on individual differences in prior
expertise – or some combination of the two factors?
Most definitions of cognitive load emphasize the non-automated cognitive operations that must be assembled by any given individual to complete
the task (Clark & Elen, 2006; Clark, Howard, & Early, 2006; Lohman, 1989;
Salomon, 1983; Snow, 1996). We could expect huge individual differences in
cognitive load for any task depending on the amount of automated prior
knowledge any one individual brings to the task. Brünken, Seufert, and Paas
(Chapter 9, this volume) suggest that a learner’s prior knowledge influences
load and also that we do not have adequate measures of automated prior
learning. We propose that more effort be invested in exploring physiological
measures of mental workload to identify the amount of automated knowledge learners bring to instruction and to reliably quantify the mental effort
they must invest to achieve a unit of learning.
Prior knowledge and germane cognitive load. From a cognitive perspective, the working load experienced during any task is determined in part
(and perhaps entirely) by an individual’s prior experience with the task
(Chapter 2, this volume). The germane cognitive load necessary to succeed
at a task is inversely related to the level of automation of necessary prior
knowledge (Clark & Elen, 2006). Other things being equal, when we have
less of the prior knowledge required when learning a new task, we must use
more mental effort to construct new cognitive operations that support task
performance. The more automated the prior knowledge, the less cognitive
effort required to apply it during learning. For example, the amount of
germane cognitive load required for children to learn division is lower if
they have more automated addition and subtraction skills. Children who
have learned addition and subtraction routines recently and have had less
time to practice and automate would need to invest more mental effort at
multiplication than those who have practiced longer (Clark & Elen, 2006).
Conscious cognitive processing that serves to assemble and/or implement
a productive approach to learning a task is the source of relevant cognitive
load. Providing a worked example of a successful approach to a new task
during instruction for students with highly automated prior knowledge
reduces the necessary, relevant load to its lowest possible level (Kirschner,
Sweller, & Clark, 2004).
Prior task experience fosters the development of implicit (automated,
largely unconscious, procedural) task-relevant cognitive processes (e.g.,
Woltz, 2003) that are presumed to operate without consuming working
memory space and so reduce the demand on working memory. Lohman
(1989) described the problem of estimating the amount of cognitive load
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from the prior experience measures of individuals on any task when he
cautioned: “What is novel for one person may not be novel for another
person or even for the same person at a different time . . . [thus] inferences
about how subjects solve items that require higher level processing must be
probabilistic, since the novelty of each [item] varies for each person” (words
in brackets added, p. 348). Brünken, Seufert, and Paas (Chapter 9, this volume) discuss this problem and acknowledge that we have not yet found
precise measures of cognitive load for individual learners. Kalyuga and
Sweller (2005) have suggested that one way to measure implicit knowledge
might be to provide students with a problem and some of the initial steps
necessary to solve the problem, and then ask them to describe what must be
done next. The difficulty with this approach is the evidence that people who
have highly automated knowledge about a task can perform the task but
cannot accurately or completely describe the steps they follow (see a review
by Feldon & Clark, 2006). Variable levels of prior knowledge automation
may account for some of the error reported in Kalyuga and Sweller’s (2005)
experiments. In general, the lack of a reliable, efficient measure of automated germane prior knowledge is a serious problem for CLT. A primary
goal of CLT is to describe specific instructional methods that will maximize
relevant and minimize irrelevant cognitive load for each learner at all stages
of learning. Thus, when we have determined the total amount of cognitive
load experienced by any individual in learning or problem-solving tasks, the
next challenge is to break that total down into the proportion of germane
(relevant) and extraneous (irrelevant) load being experienced. Yet, identifying the type and origin of mental workload is problematic because for any
individual, the amount of load experienced during learning is influenced
by the amount of prior knowledge he or she possesses and how automated
that knowledge has become with use.
Distinguishing between germane and extraneous cognitive load. A second
urgent problem, related to the measurement of gross load and also addressed
by Brünken, Seufert, and Paas (Chapter 9, this volume) is that we have also
not yet found a way to reliably determine whether mental work is being
invested in productive or unproductive mental activity. CLT is based on the
distinction between “extraneous” or irrelevant load (mental effort invested
in activities that do not support learning goals) and “germane” or relevant
load (mental effort that supports learning and problem solving). And yet,
these two key constructs are only inferred post hoc from differences between
learning scores that result from different treatments that are presumed to
provide more of one than the other type of load. In a later section, we
suggest that eye movement and gaze direction technology might be used
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as an indicator of what is being processed cognitively and therefore an
indicator of extraneous load.
It is likely that we will solve the measurement of gross mental workload
before we are able to deal with the more difficult problem of distinguishing
between different types of load being experienced by a single individual.
The next section discusses the construct definition and measurement problems that exist with CLT and possible ways to handle those problems with
neuroscience research methods.
possible neuroscience contributions to measuring
mental effort in clt
Recent neuroscience research has made significant advances toward a better understanding of brain function during learning and problem solving
(Szucs & Goswami, 2007). During learning, all information is coded in
the brain in the form of synaptic activity that underlies the symbolic representations hypothesized by cognitive psychologists. The combination of
neuroscience and cognitive science permits the development of a common,
integrated framework consisting of connections between higher-level cognitive representations (such as the hypothesized constructs and relationships
in CLT) and lower-level data concerning neuronal and biological functions
in the brain and sensory systems (Szucs & Goswami, 2007). The ultimate
goal of this integration is to add to our ability to predict and explain how
our brain function and biology give rise to our mental functioning during
learning and problem solving. This integration would bring us full circle
and perhaps redress some of the historical mistakes we made at the turn
of the last century. Although neuroscience may not yet have much to offer
instructional designers or teachers, researchers might benefit from its focus
on precise measurements of brain and sensory processes. One exciting possibility can be found in neuroscience research on mental workload and pupil
dilation.
Pupil Dilation and Vascular Constriction as Measures
of Mental Workload
Promising neuroscience measures of cognitive load may be available in two
established physiological measurement technologies called pupillometrics
(Megaw, 2005) and peripheral vasoconstriction (Iani, Gopher, & Lavie,
2004; Marshall, 2007). In the case of pupillometrics, devices have been developed to measure the amount of pupil dilation along with the direction and
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duration of a learner’s gaze. Vasoconstriction measurement requires the
wearing of a device on one finger that measures variations in blood flow to
the finger.
Pupil dilation and mental effort. Considerable evidence supports the claim
that pupil dilation is highly correlated with mental effort during learning
and problem solving (Beatty, 1982; Beatty & Wagoner, 1978; Iqbal et al.,
2005; Iqbal, Zheng, & Bailey, 2005; Kahneman & Beatty, 1966; Recarte &
Nunes, 2003). Kahneman and Beatty (1966) compared a variety of encoding, processing, and retrieval tasks, and found that pupil diameter increased
proportionally with the mental workload required. In a digit storage and
recall task, pupil width increased proportionally with the number of digits
encoded and decreased as they were reported. In a separate experiment, digit
encoding was compared with digit transformation for the same series of digits. Pupil width was larger when the numbers were added before encoding.
They also found that pupil width decreased with task repetition over the
course of the study, as task difficulty decreased. This work was extended
by Beatty and Wagoner (1978), who examined pupil diameter for a series
of letter comparison tasks that increased in complexity, from physical comparisons to comparisons by name, then by category. Again, pupil diameter
increased with increasing task complexity. Beatty (1982) reviewed all experimental data on pupil dilation and effort, and concluded that the relationship
survives alternative explanations.
One controversial aspect of these findings is that the neural circuitry
thought to control pupil diameter, located in a variety of deep sub-cortical
regions and in the brainstem, is not closely associated with the circuitry involved in working memory, located primarily in the dorsolateral prefrontal
cortex. A considerable amount of evidence seems to support the claim that
increasing cognitive load affects pupil diameter indirectly through changes
in affect-based arousal, perhaps caused by the need to perform mental
work (Kahneman & Beatty, 1966; Iqbal et al., 2004; Recarte & Nunes, 2003).
And yet, it must be noted that because pupil dilation is apparently mediated by affect-based arousal, we need to learn more about the nature of
the relationship between arousal, working memory, and mental effort. If,
as some neuroscientists have suggested (Iqbal et al., 2004), mental work
is always accompanied by arousal, then pupil dilation might serve as a
highly reliable measure of workload. If we find significant individual differences in arousal with prior task knowledge held constant, we would
be less inclined to settle on pupil dilation as a measure of mental effort.
Early studies of this issue seem to indicate that emotionality may not influence dilation as much as mental effort. A dissertation by Simpson (2006)
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provided subjects with both abstract and concrete words that were either
very pleasant or very unpleasant and found that, as expected, pupil dilation was greater for abstract words. However, dilation was not different
for pleasant and unpleasant words. This question requires more research
on individual and group differences in pupil dilation, but the uncertainty it
raises does not eliminate the utility of pupil dilation as a measure of cognitive
load.
Individual and group differences in pupil dilation. Studies have examined
individual and group differences in pupil dilation with tasks held constant.
For example, Van Gerven, Paas, van Merriënboer, and Schmidt (2004) found
differences between the pupil dilation of younger and older subjects in a
study that examined six levels of memory load based on the classic Sternberg
memory task. They concluded that dilation might not always be a good
measure of mental effort for older (senior) learners. In an age judgment task
in which photographs of faces were either gazing directly at the observer or to
the side, Gillian, Hood, Troscianko, and Macrae (2006) reported that pupil
dilation was greater and more sustained in female than in male participants
when analyzing directly gazing faces of both genders. The authors concluded
that their female subjects invested more effort in processing socially relevant
(direct-gaze) than socially irrelevant (deviated-gaze) faces regardless of the
gender of the face. Heitz, Schrock, Payne, and Engle (2003) described two
experiments in which groups of subjects with greater or lesser working
memory spans engaged in a memory task. They reported that both groups
demonstrated equal pupil dilation during tasks requiring similar mental
effort, even though those with greater working memory span achieved
higher scores. These data suggest that mental effort may not be a good
explanation for differences in working memory but that dilation may be a
good indicator of mental work.
Individual differences in the automaticity of task prior knowledge are
an important issue in all studies of cognitive load (Clark & Elen, 2006).
The more practice we experience with a task or critical components of a
task, the less mental effort we require to learn related tasks or to assemble
component tasks into a more complex set of skills. Cognitive load would
presumably be less for a more experienced learner with more automated
prior knowledge than one who has less prior knowledge. If we ask students
to take pretests consisting of a sample of the types of tasks to be learned
and/or tasks requiring the necessary prior knowledge for new learning,
the amount of automated prior knowledge should be indicated by the
correlation between the amount of pupil dilation during different pretest
items and outcome measures, such as item solution speed and accuracy.
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Individuals who dilate more and are slower and less accurate will most
likely have less automated levels of prior knowledge or less access to relevant
knowledge and therefore be required to invest more effort to succeed. The
less prior knowledge and the less automated that knowledge is, the more it
is necessary to provide instruction that eliminates all extraneous load and
provide only the essential steps in a worked example of how to perform
the task to be learned. Carswell (2005) used pupil dilation to assess mental
workload when surgical residents were practicing with novel laparoscopic
surgical technology. He was looking for novel ways to not only improve
instruction but also to test alternative technologies for surgery. He tested
surgeons with different prior experience levels with traditional technology
and with laparoscopic technologies in order to reason about the relative
contribution of prior knowledge and variations in the technology to mental
workload. Recarte and Nunes (2003) described a study using pupillometry
in which the responses of different individuals to similar task conditions
could be interpreted as different levels of prior automation. Finally, van
Gog, Paas, and van Merriënboer (2008) studied the eye movements of
people with different levels of expertise at electrical troubleshooting tasks.
They reported that experts spent more time than novices looking at faultrelated components of devices but did not measure pupil dilation. They
also found an expertise reversal effect (Kalyuga, Ayres, Chandler, & Sweller,
2003) in which conceptual knowledge about troubleshooting interfered
with the learning of experts, perhaps because it served as extraneous load
for experts but was germane for novices. It would be interesting to replicate
this study and others by van Gog and colleagues (e.g., Nievelstein, van Gog,
Boshuizen, & Prins, 2008) to collect data on the relative amount of mental
effort invested by experts and novices during problem solving. In general,
the combination of dilation, eye movement, and duration as measures of
mental effort should be combined with subjects that differ in expertise and
tasks that differ in complexity.
Pupil dilation as a method to assess extraneous load during learning. Most
important to CLT researchers is developing reliable ways to measure the
amount and origin of extraneous (irrelevant) load during learning. The
instructional goal is to anticipate and eliminate all sources of extraneous
load so that working memory processing is as efficient as possible. Recarte
and Nunes (2003) designed a creative way to combine pupil dilation and eye
movement technology to test the amount of extraneous (irrelevant) mental
load experienced by drivers to attend to a “hands-free” telephone conversation while driving and compared it with the load experienced attending to
the same conversation “live” with a person riding with them in a car. They
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employed visual cues such as unexpected emergency road signals during the
conversations to see if drivers noticed fewer of these important cues while
engaging in conversations. It is interesting but not surprising to note that
the amount of cognitive load was identical during both hands-free telephone
and live conversations as measured by eye movement tracking and pupil
dilation. It was also determined by eye movement tracking and behavioral
observation that the extraneous load imposed by the conversations resulted
in a 30% reduction in the drivers’ noticing of emergency cues during both
the hands-free and live conversations. In a very different study of extraneous load, Verney, Granholm, and Marshall (2004) used pupil dilation to
examine the differences between college student performances on a backward masking task that required them to overcome distractions in order to
solve target detection problems. Their analysis indicates that students with
lower SAT scores invested more wasteful effort focusing on the distractions
in the task, which were accounted for by socio-economic differences and
prior target detection accuracy. Marshall (2007) describes three problemsolving studies in which pupil dilation reliably distinguished between rest
and work, between germane or extraneous effort, and between rested and
fatigued states.
Devices for measuring pupil dilation. A number of devices are currently
available that will measure and analyze the pupil dilation for individuals
during learning from computer displays or other fixed display technologies
(Recarte & Nunes, 2003). Iqbal et al. (2004) concluded that “pupil size is
the most promising single measure of mental workload because it does not
disrupt a user’s ongoing activities, provides real-time information about
the user’s mental workload and is less obtrusive than other physiological
measures such as heart rate or EEG [electroencephalogram]” (p. 1477). In
order to measure gaze and pupil dilation, it is often necessary to place a
research subject’s head in a vice-like frame (similar to those used during
eye examinations) to prevent head movement. Recently, however, relatively
light and unobtrusive equipment is beginning to be developed, such as a
camera mounted on a light headband worn by subjects described recently
by Marshall (2007). It is highly likely that pupil dilation measured by the
headband technology is much less intrusive than the interruptions caused
by head fixation devices or secondary (latency) measures.
Pupillometry may improve our measurement of the amount of cognitive
load, and combining dilation with the direction and duration of gaze may
also help to solve the problem of the relevancy of the load being experienced.
Another less studied technology that also seems to offer the possibility of
unobtrusive measurement of mental load is vascular constriction.
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Vascular constriction and mental effort. Iani et al. (2004) reported that
a measure of the constriction of blood vessels in the fingers is a measure
of sympathetic nervous system activation and might serve as a reliable
measure of mental effort. They conducted two experiments in which they
varied task difficulty and the level of engagement of their subjects in the
task, and reported that increased vascular constriction (reduced blood flow
to the fingers) was highly correlated with performing tasks (constriction
was greater when working than when resting) and was greater with more
difficult than with less difficult tasks. They also reported a strong correlation
between vasoconstriction and pupil dilation. Iani, Gopher, Grunwald, and
Lavie (2007) examined the vascular constriction of pilot performance in a
computer-based flight simulator in which the difficulty of the task could
be manipulated. They found that constriction was greater with difficult
than with easier tasks. In general, vasoconstriction seems to provide an
alternative way to measure gross cognitive load, yet it does not seem to offer
a way to determine the source of the load being measured. At this point,
the most promising way to measure both mental load and the source of the
load seems to be the use of technology that captures pupil dilation along
with gaze direction and duration.
imaging methods for monitoring changes
in cognitive load
Whereas pupil dilation provides intriguing evidence regarding the changes
in neuro-cognitive activity that underlie cognitive load, more direct measures of brain function are available. A large number of brain imaging studies
have examined working memory. Working memory provides a temporary
store that supports cognitive processing, and the capacity of working memory is commonly thought to be closely associated with cognitive load. The
higher the cognitive load required to perform a task, the greater the demand
on working memory. The imaging methods discussed in the following sections can be used to examine the neural activity that supports working
memory and therefore can indicate how changes in cognitive load affect
brain function.
Three basic processes of working memory have been identified: a brain
network for the maintenance of auditory and verbal information, a separate network for the maintenance of visual and spatial information, and a
central executive network for attentional control and manipulation of items
in working memory (Baddeley, 1986), although evidence for the central
executive is controversial (see the discussion by Sweller, 2004). Working
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memory includes three processing activities that occur in sequence: encoding, maintenance, and retrieval. Each of these processes involves a different
pattern of brain activity, and each can be affected differently by changes in
load. Each can be distinguished by differences in time, for example encoding must occur before retrieval. Isolation of the brain processes supporting
each of these stages based on timing can be accomplished using event-related
potentials (ERPs), which record the small fluctuations in voltage at the scalp
surface generated by neural activity in the brain. This can be used to infer
the timing of events, but it offers poor spatial resolution and therefore inadequate information about where the processing is occurring in the brain.
By contrast, imaging methods that rely on hemodynamic measures, such
as functional magnetic resonance imaging (fMRI) and positron emission
tomography typically measure changes in blood flow and/or oxygenation
that are related to changes in brain function. These hemodynamic methods
offer superior spatial resolution compared with ERPs, which is necessary to
unambiguously identify the anatomical location of brain networks supporting the different processes of working memory. However, because changes
in blood flow are relatively slow, these methods are usually unable to identify rapid changes in brain activity. Event-related fMRI is a method that
can be used to achieve a balance between spatial and temporal resolution
(Clark, 2002; Clark, Maisog, & Haxby, 1998) by focusing on the characterization of small changes in signal over short periods of time. These methods
can distinguish changes in neural activity occurring on the order of a few
hundred milliseconds apart, depending on how the data are acquired and
analyzed.
ERP Studies of Working Memory
As described earlier, working memory has a fundamental role in supporting
cognitive load. ERPs can be used to examine the neural activity that supports
working memory, and therefore how changes in cognitive load affect brain
function. Many neuroscience studies have employed delayed response tasks
to study working memory. Delayed response tasks require subjects to maintain information in working memory for a period of time before a response
is made. This might be a word, an object, a location in space, or some other
sensory feature or groups of features that must be held in memory. Often,
such tasks involve one or more items that must be held in memory, to be
compared with additional items presented later in time before a response
can be made. Delayed response tasks often evoke a characteristic sustained
negative electrical potential over the scalp termed the contingent negative
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variation (CNV). CNVs are evoked during the maintenance of information
stored in working memory (Tecce, 1972). It is likely that the CNV results
from increased synaptic activity associated with maintaining information
in the working memory store. Working memory tasks have been found to
evoke activity in a variety of brain regions. Gevins, Smith, and Le (1996)
used high-resolution evoked potentials during verbal and spatial working
memory tasks. In this study, verbal or spatial attributes were compared
between each test stimulus and a preceding stimulus. All stimuli evoked
the CNV and a number of other components, which varied in amplitude,
depending on the specific requirements of the task. They concluded that
working memory is a function of distributed neural systems with both taskspecific and task-independent components and that these and other ERP
components can be used to study working memory processes.
However, subsequent studies have shown that the interpretation of ERP
components to study working memory can be more complex than is typically assumed. Kok (2001) found that the amplitude of positive components
evoked from 300 to 500 msec post-stimulus reflected the activation of elements in an event-categorization brain network that is controlled by the
joint operation of attention and working memory. This limits the use of
these components as a measure of processing capacity or cognitive load
because variations in both attention and working memory can influence
their production. Luck, Woodman, and Vogel (2000) supported this view
by suggesting that many studies confound attention and working memory.
They proposed that attention may operate to adjust brain networks supporting working memory and other cognitive processes when brain systems
are overloaded and therefore operates to adjust the brain’s ability to process the extra information under conditions of higher cognitive load and
thus optimize performance. Finally, Wager and Smith (2003) suggested that
selective attention to features of a stimulus to be stored in working memory leads to separate patterns of activation from working memory storage.
Selective attention is the process whereby specific objects or classes of stimuli are selected for further processing based on certain defining stimulus
characteristics. Depending on the nature of these characteristics (e.g., color,
shape, or spatial location), a different pattern of brain response is seen that
is unique to those characteristics. Thus, the dynamic properties of these
interrelated neural and cognitive systems make it difficult to use these measures to quantify specific features, such as cognitive load. Even with these
limitations, carefully designed studies that take these and other issues into
consideration can reveal much about how the brain deals with variations in
cognitive load.
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fMRI Studies of Cognitive Load
Most fMRI studies of cognitive load effects examine the identity of brain
regions that support different aspects of working memory. These studies
typically use parametric designs. These designs reveal the neural correlates of
working memory load by identifying those regions in which activity changes
as the level of cognitive load is changed across repeated measurements.
This method assumes that additional cognitive load will increase the brain
responses in a proportional way, otherwise known as the pure insertion
hypothesis (Raichle, 1994). Using these methods, a number of published
studies have characterized brain networks that support working memory
and how these networks change with changes in cognitive load. N-back tasks
are one such design that involves the presentation of stimuli in a series, in
which subjects are asked to compare the current stimulus with stimuli
presented one or more items earlier in a series. For a delay of one stimulus,
the N-back task is similar to the delayed response task. However, for more
than one stimulus delay, N-back tasks differ from delayed response tasks
in the use of intervening stimuli presented between the two stimuli being
compared. With an increasing delay between the first and second item to
be compared, the number of intervening items that must be maintained in
working memory to perform the task increases, and this increases cognitive
load in turn. These tasks also differ in that two comparisons are made for
most stimuli in an N-back task, first with the stimulus presented N stimuli
before it and then with the stimulus presented N stimuli after. Callicott et al.
(1999) used fMRI to identify characteristics of working memory capacity
using a parametric N-back working memory task. In this study, as the
number of items was increased, task performance decreased. As cognitive
load was increased, some brain regions indicated changes in activity that
followed an inverted U shape. Large regions of dorsolateral prefrontal cortex,
along with smaller regions of premotor cortex, superior parietal cortex, and
thalamus, revealed changes in activity. The authors concluded that this
pattern was consistent with a capacity-constrained response. At lower levels
of load, less activity was required to support the working memory processes.
At middle levels, more activity was required to maintain the same level of
performance. At very high levels of load, the performance of the network
breaks down, resulting in both reduced activity and reduced performance.
These results reflect the findings in cognitive instructional psychology (e.g.,
Clark, 1999; Clark & Elen, 2006; Gimino, 2000; Salomon, 1983) where prior
knowledge predicts mental effort under conditions in which tasks become
increasingly difficult.
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These results demonstrated that a portion of the brain networks supporting working memory is sensitive to variations in cognitive load, whereas
other portions do not appear to be as sensitive. Jaeggi et al. (2003) employed
an N-back task with four levels of difficulty using auditory and visual
material, and did not find the same inverted U-shape relationship. The participants’ tasks were performed separately or simultaneously as dual tasks.
When performed separately, activation in the prefrontal cortex increased
continuously as a function of memory load. An increase of prefrontal activation was also observed in the dual tasks, even though cognitive load
was excessive in the case of the most difficult condition, as indicated by
reduced behavioral performance. These results suggest that excessive processing demands in dual tasks are not necessarily accompanied by a reduction in brain activity. More recently, O’Hare, Lu, Houston, Bookheimer, and
Sowell (2008) examined the development of these brain networks using a
Sternberg working memory task with three load levels. The Sternberg task
involves asking subjects to encode a set of stimuli (e.g., “1,” “3,” and “9”)
and later presenting a series of stimuli and asking them to indicate which of
these stimuli match the encoded set and which are new. The larger the size
of the encoded stimulus set, the greater the cognitive load. The activated
brain networks were found to depend on the participants’ age, which ranged
from 7 to 28 years. Adolescents and adults showed cognitive-load effects in
frontal, parietal, and cerebellar regions, whereas younger children showed
similar effects only in left ventral prefrontal cortex. These results demonstrate that increasing load produces different brain network responses from
childhood through adulthood. As a result, we may find developmental differences between the ways that young children and adults handle cognitive
load during learning.
Some of the differences observed across studies may result from variations in learning tasks. Using fMRI, working memory is often associated
with increased activity in the prefrontal cortex, typically in Brodmann areas
6, 9, 44, and 46 (Cabeza & Nyberg, 2000). In area 6, located in the frontal
cortex, activations are commonly found across tasks, including verbal, spatial, and problem-solving tasks, and thus may be related to general working
memory operations that are not associated with other sensory or cognitive
features of the task. By contrast, the exact pattern of activation in other brain
areas is related to the specific nature of the task used. Increased activity in
area 44, which lies next to area 6 in the lateral frontal cortex, is found for
verbal and numeric tasks compared with visuospatial tasks, which may be
related to phonological processing. Activations in areas 9 and 46, located on
the frontal pole, are stronger for tasks that require manipulation of working
memory contents compared with tasks that require only maintenance of
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items in working memory (Owen, 1997; Petrides, 1994, 1995). Ventrolateral
frontal regions (including areas 45 and 47) are involved in the selection and
comparison of information held in working memory, whereas medial and
anterior frontal regions (areas 9 and 46) are involved in the manipulation
of multiple pieces of information. Some studies have shown that working
memory for object information engages ventral prefrontal regions, whereas
working memory for spatial locations engages dorsal prefrontal regions
(Courtney, Ungerleider, Keil, & Haxby, 1996, 1997). However, other studies suggest that working memory for objects engages left frontal regions,
whereas working memory for spatial information engages right frontal regions (Belger et al., 1998; Smith, Jonides, & Koeppe, 1996; Smith et al., 1995).
Taken together, these studies suggest that the organization of frontal brain
networks that support working memory still holds a number of secrets in
terms of the cognitive basis around which they are organized.
Working memory studies also show activations in brain regions outside
of the frontal cortex, including the parietal areas. In the case of verbal tasks,
these activations tend to be larger on the left, which supports Baddeley’s
phonological loop model, which maintains that information is stored and
rehearsed in series (Awh et al., 1996; Paulesu, Frith, & Frackowiak, 1993).
Working memory tasks are also associated with altered activity in anterior
cingulate, occipital, and cerebellar cortices. However, these tend to be more
sensitive to stimulus characteristics and task demands, rather than cognitive
load, suggesting that they perform operations that support working memory
indirectly through their interaction with these other regions. One exception
to this is the finding of Druzgal and D’Esposito (2001), who showed that
activity in ventral extrastriate visual areas increased directly with load of
an N-back working memory task using facial stimuli. They concluded that
both prefrontal and extrastriate areas worked together to meet the demands
of increased cognitive load.
Advanced methods of brain imaging offer many insights into the neural
mechanisms that support working memory and the effects of changes in
cognitive load on these mechanisms. Some progress has already been made
in understanding the brain basis of processes related to cognitive load. Our
ultimate goal is to achieve a unified theory that bridges the gap between
cognitive psychology and neuroscience. We are beginning to see parallels
across these two fields, as described earlier, but there is still much work to
do. As brain imaging methods improve, and as cognitive psychologists are
more willing to understand brain imaging technologies and to use this sort
of information in forming hypotheses, a better understanding of cognitive
load than could be achieved by either discipline alone can ultimately be
achieved.
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summary and conclusion
We have come full circle since Judd, an early cognitive psychologist, lost an
argument to Thorndike, an early advocate of neurological and biological
psychology. That lost argument serves as a cautionary metaphor for the bias
that prevented American psychologists from focusing on cognitive questions for fifty years. It may also have produced a reaction whereby cognitive
psychology is now experiencing a reverse bias against biological and neurological insights about learning and problem solving. The point of this review
is to emphasize that the solution to some of the thorny problems facing CLT
requires that we step away from our century-long dispute and become open
to the insights offered by past and future advances in both cognitive psychology and neuroscience. It seems reasonable to expect that neuroscience
might aid the search for ways to reliably quantify cognitive load and to identify the sources of germane and extraneous load. We might also increase
our understanding of how individual and group differences in prior knowledge, culture, and working memory span might influence brain function,
resulting in quantifiable differences in activity recorded with brain imaging
methods such as ERPs and fMRI, and how this affects our understanding
of differences between various learning tasks and instructional methods.
We recommend a renewed commitment to exploring the use of pupil
dilation accompanied by gaze direction and intensity studies to develop a
more reliable and valid estimate of individual cognitive load and to help
identify sources of germane and extraneous load. Pupil dilation could also
be used to investigate how differences in the amount of prior expertise
in a knowledge domain influence the type and amount of cognitive load
experienced by learners. We expect that the more specific prior knowledge
learners possess about the class of tasks being learned, the less load they
will experience compared with learners who have less prior knowledge. It
may also be possible that germane load for novices might become irrelevant
load for experts and that this might be the source of the expertise reversal
effect described by Kalyuga et al. (2003). We also suggest that fMRI methods
of brain imaging offer many possible hypotheses based on evidence from
neural mechanisms that support working memory and on the effects of
changes in cognitive load on these mechanisms for different types of tasks
and learners.
Neuroscience studies draw most often on Baddeley’s (1986) model of
working memory and search for evidence for three separate networks that
maintain visual and spatial information, verbal information, and the control
of attention and manipulation of items being held. In addition, neuroscience
From Neo-Behaviorism to Neuroscience
223
looks for evidence for three processing activities that occur in sequence in
each of the three networks during learning and task performance – encoding,
maintenance, and retrieval. Although a number of technologies are used
to identify and validate these processes, the most complete and accurate is
event-related fMRI. In general, the brain regions associated with most of
these processes have been identified, but complex ambiguities and arguments persist.
To this point, fMRI studies have provided additional evidence for the
processes that occur in working memory and the brain structures that
appear to support those processes. It also appears that working memory load
consists of both task-specific and task-independent components. In addition, it appears that some experiments may confound working memory
and attention processes. Claims have been made, for example, that when
cognitive load increases, attention processes may be automatically evoked
and serve to reduce load by forcing attention to more germane attributes of
tasks (Wager & Smith, 2003). It is also possible that increases in load may
evoke processes that focus attention on extraneous events (Clark, 1999). In
addition, fMRI studies have provided evidence for the inverted-U hypothesis
about the relationship between cognitive load and mental effort similar to
the one suggested by Salomon (1983). When load is low, effort is also low,
but as cognitive load increases, fMRI indicators of load also increase until it
reaches a very high level in which the brain networks supporting working
memory seem to fail, with accompanying decreases in mental effort and test
performance. It also appears that some tasks may not produce the inverted
U. At least one well-designed study (Jaeggi et al., 2003) identified dualcoding memory tasks in which increasing load (judged by both fMRI data
and subject performance) did not yield decreasing effort.
In general, there appear to be a number of important interactions among
variations in task types, working memory processes, and cognitive load.
Some areas of the brain seem to be active during all working memory processing, and some areas seem to specialize in different types of processing.
For example, separate areas have been associated with verbal and numeric
tasks, whereas others seem to be active during visuospatial tasks. In addition, tasks that require manipulation of the contents of working memory
(thought to be associated with executive functions) activate different areas
than tasks that require maintenance of both visuospatial and verbal-numeric
information in working memory. Other studies have found evidence to
suggest different regions support spatial location and object information.
fMRI studies have also provided strong evidence for age-related developmental differences in the operation of working memory. As load increases
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Richard E. Clark and Vincent P. Clark
in younger children, working memory activities appear in the left ventral
prefrontal cortex, but in adolescents and adults, the same tasks produce
cognitive-load activity in the frontal, parietal, and cerebellar regions. The
reason for these differences and their consequence for instruction and/or
learning are unknown.
As neuroscience methods improve in spatial and temporal resolution and
as new methods are developed, more precise information will be obtained.
However, we know now that the cognitive sub-processes involved in performance of challenging learning and problem-solving tasks and the brain
networks that support them interact in complex ways. In a single study, it is
easy to confound the effects of changes in cognitive load on working memory with changes in attention as well as in perceptual and response processes,
affect, and arousal, which all occur together in related ways. Therefore, it
is vital that these methods are used carefully and alternative hypotheses be
considered as we progress. Ultimately, though, we expect that these methods
will lead to a better understanding of the neural and cognitive mechanisms
that underlie cognitive load.
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11
Cognitive Load in Learning with
Multiple Representations
holger horz and wolfgang schnotz
Technological innovations have led to important changes in teaching and
learning during the last decade. Advances in computer technology have
enabled the development of flexible learning arrangements, Web-based
collaboration, and a broad range of multimedia learning environments.
Multimedia allows the combination of different presentation formats, such
as pictures, animations, text, or music in flexible ways via different sensory modalities. Media designers often assume that multimedia allows for a
better adaptation of instruction to the learners’ needs and preferences. Multimedia is also expected to motivate learners, thus increasing their invested
cognitive effort, which in turn should result in better learning.
A major topic in the field of learning and instruction is how multimedia
instruction interacts with the human cognitive architecture. Mayer (2001,
2005) as well as Schnotz (2001, 2005; Schnotz & Bannert, 2003) have developed theoretical models of multimedia learning that address this issue. Both
models aim to explain what goes on in the mind of the learner when he
or she learns from spoken or written texts with static or animated pictures.
Both models share the assumption that the type and amount of presented
information has to be adapted to the limitations of the cognitive system,
especially those of working memory. The same assumption is at the core of
Cognitive Load Theory (CLT) developed by Sweller and colleagues, which
has become increasingly influential in instructional psychology during the
last decade (Chandler & Sweller, 1991; Paas, Renkl, & Sweller, 2004; Paas &
van Gog, 2006; Sweller, 1994, 2005; Sweller, van Merriënboer, & Paas, 1998).
The theoretical models of multimedia learning developed by Mayer and
Schnotz as well as the CLT developed by Sweller have been used to derive
principles and guidelines for the design of multimedia learning environments. In this chapter, we investigate how CLT relates to the theoretical
models of multimedia learning. First, we briefly describe and compare
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figure 11.1. Cognitive Theory of Multimedia Learning (Mayer, 2005, p. 37).
both models of multimedia learning. Second, we discuss guidelines and
techniques of improving multimedia learning environments from a cognitive load perspective. Third, we draw conclusions with respect to further
research regarding the design of multimedia learning environments.
models of multimedia learning
Cognitive Theory of Multimedia Learning
Mayer’s Cognitive Theory of Multimedia Learning (CTML) has become
probably the most influential approach to knowledge acquisition from
multimedia during the last decade (Mayer, 1997, 2001, 2005). A graphical representation of the theory is shown in Figure 11.1. Mayer refers to the
dual-coding theory of Paivio (1986) and assumes that the human cognitive system includes a verbal and pictorial (image) subsystem. Accordingly,
individuals use different representational formats to internally encode and
store knowledge. Based on the working memory model of Baddeley (1992),
Mayer assumes that two sensory subsystems exist in working memory: an
auditory system and a visual system. The first basic assumption of CTML
merges these two concepts. Humans are assumed to process information
in working memory through two channels: an auditory-verbal channel
and a visual-pictorial channel. The second basic assumption of CTML,
which reflects the work of Baddeley (1992) as well as Chandler and Sweller
(1991), is that these two channels have limited capacity to convey and
Cognitive Load in Learning with Multiple Representations
231
process information. The third basic assumption of CTML is that humans
are active sense-makers: they engage in active cognitive processing to construct coherent knowledge structures from the available external information and their prior knowledge.
According to Mayer, active learning from multimedia instructional messages includes a set of five coordinated processes:
(1) selecting relevant words,
(2) selecting relevant images,
(3) organising the selected words into a verbal mental model,
(4) organising the selected images into a pictorial mental model, and
(5) integrating the verbal model and the pictorial model with prior
knowledge into a coherent mental representation.
Verbal selection processes lead to a propositional text base, and verbal
organisation processes result in a text-based mental model. Similarly, pictorial selection processes lead to an image base, and pictorial organisation
processes result in a picture-based mental model. The verbal organisation
processes take place in the verbal part of working memory, whereas the pictorial organisation processes occur in the pictorial part of working memory
(Baddeley, 1992; Chandler & Sweller, 1991). The text-based model and the
picture-based model are then integrated in a one-to-one mapping process.
During this mapping, elements of the text-based model are mapped onto
elements of the picture-based model and vice versa. Similarly, relations
within the text-based model are mapped onto relations within the picturebased model and vice versa. Integration requires that elements and relations
of the text-based model and corresponding elements and relations of the
picture-based model are simultaneously activated in working memory.
As mentioned earlier, Mayer’s cognitive theory of multimedia learning
has become highly influential in the field, and it has received support by
numerous empirical studies. Nevertheless, there are some open questions,
which suggest further theoretical reflection and empirical research. It might
be beneficial for both sides if the theory would get into closer contact
with text processing research, which has had considerable success during
the last three decades (e.g., Gernsbacher, 1990; Graesser, Millis, & Zwaan,
1997; Kintsch, 1998; van Dijk & Kintsch, 1983). For example, Mayer’s ‘verbal
model’ (i.e., a mental structure created by organising selected words) might
be considered by text processing researchers as a propositional macrostructure, which serves as a basis for the construction of a mental model
or situation model. The pictorial model (i.e., a mental structure created by
organising pictorial information) seems to correspond to an analog mental
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representation, which is usually considered as the essential characteristic of
a mental model (Johnson-Laird, 1983).
The assumption of two channels in CTML – a visual-pictorial channel
and an auditory-verbal channel – blends sensory modality and representation format. The association between the visual modality and the pictorial
representation format on the one hand and the association between the
auditory modality and the verbal representation format on the other hand
are possibly not as close as the model suggests. We assume that verbal
information is not necessarily associated with the auditory modality (e.g.,
as demonstrated by the use of sign languages) and that pictorial information is not necessarily associated with the visual modality. For example, the
sound of an event (say, the starting up of an airplane or the call of a bird)
can be considered as a sound image (Schnotz, 2005). This implies that there
are not only visual pictures, but also auditory pictures in multimedia environments. In other words: visual patterns perceived by the eye can convey
verbal as well as pictorial information (i.e., as written text and visual pictures, respectively), and the same is true for auditory patterns perceived by
the ear, which can convey verbal and pictorial information (i.e., as spoken
text and sound images, respectively), too.
A further possible point of discussion refers to the assumed parallelism
between text processing and picture processing in CTML. According to
our point of view, texts and pictures use different sign systems resulting in
fundamentally different forms of representations (Baddeley, 1992; Kosslyn,
1994; Paivio, 1986), which we refer to as descriptive and depictive representations, respectively. This difference between representation formats is at the
core of the integrated model of text and picture comprehension (Schnotz,
2005; Schnotz & Bannert, 2003), which we describe in the following section.
integrated model of text and picture comprehension
Spoken or written texts, mathematical equations, and logical expressions,
for example, are descriptive representations. A descriptive representation
consists of symbols describing an object. Symbols have an arbitrary structure (e.g., words in natural language), and they are related to the content
they represent by means of a convention (cf. Peirce, 1906). Descriptive
representations contain signs for relations, such as verbs and propositions in natural language. Pictures, sculptures, or physical models, however, are depictive representations. A depictive representation consists of
iconic signs. Although depictive representations allow extracting relational
information, they do not contain symbols for these relations. Instead,
Cognitive Load in Learning with Multiple Representations
233
they possess specific inherent structural features that allow reading off relational information, and they are associated with the content they represent
through these common structural characteristics. Descriptive and depictive
representations have different advantages in learning: whereas descriptions
are more powerful in representing different kinds of subject matter, depictions are better suited to draw inferences (cf. Johnson-Laird, 1983; Schnotz,
1993).
Based on the assumption that descriptions and depictions are fundamentally different forms of representations, Schnotz and Bannert (2003) have
developed an integrated model of text and picture comprehension (ITPC),
which was further elaborated by Schnotz (2005). The model integrates the
concepts of multiple memory systems (Atkinson & Shiffrin, 1971), working
memory (Baddeley, 1986, 2000), and dual coding (Paivio, 1986). It also integrates the idea of multiple mental representations in text comprehension
and in picture comprehension (Kosslyn, 1994; van Dijk & Kintsch, 1983),
as well as structural components of the CTML of Mayer (1997, 2001). The
model refers not only to multimedia learning, but also to single-medium
learning, because written text, spoken text, visual pictures, and auditory pictures (i.e., sound images) can be understood in isolation or in combination.
Figure 11.2 shows a graphical outline of the model.
The ITPC model is based on the following assumptions: text and picture comprehension are active processes of coherence formation. During
comprehension of texts or pictures, individuals engage in building coherent knowledge structures from the available external verbal and pictorial
information and from their prior knowledge. Text and picture comprehension take place in a cognitive architecture, which includes modalityspecific sensory registers as information input systems with high capacity
but extremely short storage time, a working memory with a severely limited
capacity and short storage time, and a long-term memory with both very
high capacity and high storage time. Verbal information (i.e., information
from written texts or spoken texts) and pictorial information (i.e., information from visual pictures and from auditory pictures) is transmitted to
working memory through the visual channel or the auditory channel. Both
channels have limited capacity to process and store information. Further
information processing in working memory takes place in two different
representational channels – the descriptive channel and the depictive channel – whereas information from written or spoken text is processed in the
descriptive channel. Information from visual pictures or from auditory pictures (sounds) is processed in the depictive channel. Both channels have
limited capacity to process and store information.
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figure 11.2. Integrative Model of Text and Picture Comprehension (Schnotz, 2005).
Accordingly, a perceptual level and a cognitive level of processing can be
distinguished within the model. The perceptual level includes the information transfer between the sensory registers and working memory. This level
is characterized by the functioning of the sensory channels. The cognitive
level includes the information processing within working memory as well
as the exchange of information between long-term and working memory.
This level is characterized by the functioning of the descriptive channel and
the depictive channel.
For a written text to be understood, visually presented verbal information enters the visual register through the eye. The information is then
forwarded through the visual channel to visual working memory. The
resulting information pattern in visual working memory corresponds to
the text surface representation in reading comprehension. The verbal information is forwarded from visual working memory through the verbal channel to propositional working memory, where it triggers the formation of
Cognitive Load in Learning with Multiple Representations
235
propositions, which in turn triggers the construction or elaboration of a
mental model.
For a spoken text to be understood, auditorily presented verbal information enters the auditory register through the ear. Then the information
is forwarded through the auditory channel to auditory working memory.
The information pattern in auditory working memory corresponds to the
text surface representation in listening comprehension. The information is
then forwarded from auditory working memory through the verbal channel
to propositional working memory, where it leads to a propositional representation and finally triggers the construction or elaboration of a mental
model.
It should be noted that information in visual working memory can
be verbal or pictorial and that information in auditory working memory
can be verbal or pictorial as well. In some cases, it is even possible that
the same sign convey both verbal and pictorial information. Imagine, for
example, that the word ‘Pisa’ on a tourist poster is written in such a way
that the letter ‘I’ is replaced by a picture of the Pisa tower. In this case, the
picture of the tower has a double function, because it can be interpreted
in two ways: On the one hand, it can be interpreted as a picture, and on
the other hand, it can be interpreted as a verbal symbol (in this case, a
letter). Because both verbal and pictorial information can enter auditory
working memory and because both verbal and pictorial information can
also enter visual working memory, a device is needed in both cases that
directs verbal information to the verbal channel and pictorial information
to the pictorial channel. Accordingly, the ITPC model assumes a verbal filter
and a pictorial filter for auditory working memory as well as a verbal filter
and a pictorial filter for visual working memory (symbolized in Figure 11.2
by triangles). The verbal filters and pictorial filters, which have insofar the
function of routers, select the corresponding information from the visual
or the auditory working memory and forward it to the corresponding
representational channel (Schnotz, 2005).
Thus, in text comprehension, the reader or listener constructs a mental
representation of the text surface structure, generates a propositional representation of the semantic content (i.e., a text base), and finally constructs
from the text base a mental model of the subject matter described in the text
(Schnotz, 1994; van Dijk & Kintsch, 1983; Weaver, Mannes, & Fletcher, 1995).
These construction processes are based on an interaction of bottom-up and
top-down activation of cognitive schemata, which have both a selective and
an organising function. Task-relevant information is selected through topdown activation, and the selected information is organised into a coherent
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mental representation of the text surface structure. Processes of conceptual
organisation starting from the text surface representation (through bottomup activation) result in a coherent propositional representation, which in
turn triggers the construction of a mental model. Mental model construction implies a transition from a descriptive to a depictive representation.
Propositional representations and mental models are assumed to interact
continuously via processes of model construction and model inspection
guided by cognitive schemata. The mental model is constructed through
Gestalt-directed construction rules that lead to a mental model of a typical
instance of the content or the situation described in the text. After a mental
model has been constructed, schema-directed processes of model inspection
can be applied to extract new information from the model. This information
is encoded in a propositional format and, thus, elaborates the propositional
representation.
For a visual picture to be understood, visually presented pictorial information enters the visual register through the eye. Then information is
forwarded through the visual channel to visual working memory, where it
results in a visual perceptual representation of the picture. A pictorial filter
selects pictorial information from visual working memory and forwards it
through the pictorial channel, where it leads to the construction or elaboration of a mental model. The mental model can be used to extract new
information and to encode this information in propositional working memory. In sum, visual picture comprehension requires first creating a visual
mental representation of the image through perceptual processing and then
constructing both a mental model and a propositional representation of the
learning content through semantic processing.
In perceptual processing of visual pictures, task-relevant information
is selected through top-down activation of cognitive schemata and then
visually organised through automated visual routines (Ullman, 1984). Perceptual processing includes identification and discrimination of graphic
entities, as well as the visual organisation of these entities according to the
Gestalt laws (Wertheimer, 1938; Winn, 1994). The resulting visual perception
is an internal depictive representation, created as a surface representation
of the external picture. It retains structural characteristics of the external
picture, and it is sensory specific because it is linked to the visual modality
(cf. Kosslyn, 1994; Shepard, 1984). To understand an external picture rather
than only to perceive it, semantic processing is required. The individual
has to construct a mental model of the depicted subject matter through
a schema-driven mapping process, in which graphical entities (i.e., visual
configurations such as the bars in a graph) are mapped onto mental entities
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(such as the oil price at time x) and in which spatial relations (such as
‘higher than’) are mapped onto semantic relations (such as ‘more expensive than’) as encoded in the mental model (cf. Falkenhainer, Forbus, &
Gentner, 1989/1990; Schnotz, 1993). In other words, the comprehension of
external pictures is considered as a process of analogical structure mapping
between a system of visuo-spatial relations and a system of semantic relations
(cf. Gentner, 1989). In understanding realistic external pictures, the individual uses schemata of everyday perception. In contrast, for the understanding of logical external pictures such as diagrams or graphs, which contain
abstract non-realistic representations (Horz & Schnotz, 2008), the individual requires specific cognitive schemata (so-called graphic schemata) to
extract information from the visuo-spatial configuration (e.g., Lowe, 1996;
Pinker, 1990).
For an auditory external picture (i.e., a sound) to be understood, auditory external pictorial information enters the auditory register through the
ear. Then information is forwarded through the auditory channel to auditory working memory, where it results in an internal auditory perceptual
representation of the sound. An auditory pictorial filter selects pictorial
information from auditory working memory and forwards it through the
pictorial channel, where it leads to the construction or elaboration of a
mental model. The mental model can be used to extract new information
and to encode this information in propositional working memory.
Altogether, the ITPC model assumes a continuous interaction between
the propositional representation and the mental model, both in text comprehension and picture comprehension. In text comprehension, the starting point of this interaction is a propositional representation, which is
used to construct a mental model. This model can then be used to extract
new information to further elaborate the propositional representation. In
picture comprehension, the starting point of the interaction is a mental
model, which is used to extract new information that is also added to the
propositional representation. Accordingly, there is no one-to-one relationship between external and internal representations. External descriptive
(i.e., text) and depictive (i.e., picture) representations both lead to internal
descriptive and depictive mental representations.
Meaningful learning from text and pictures requires a coordinated set
of cognitive processes, including selection of information, organisation of
information, activation of prior knowledge, and active coherence formation
by integration of information from different sources. In the comprehension
of written or spoken texts, learners select relevant verbal information from
words, sentences, and paragraphs as external sources of information. They
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organise this information, activate related prior knowledge as an internal
source of information, and construct a coherent propositional representation as well as a coherent mental model. In the comprehension of visual
pictures, learners select relevant pictorial information from drawings, maps,
or graphs as an external source of information. They organise this information, activate related prior knowledge as a further (internal) source of
information, and construct a coherent mental model complemented by a
propositional representation. In the comprehension of auditory pictures
(i.e., sound comprehension), the learner selects relevant acoustic information, organises this information, activates related prior knowledge as an
internal source of information, and constructs a coherent mental model
complemented by a propositional representation.
commonalities and differences
Commonalities
The integrated model of text and picture comprehension and the cognitive
theory of multimedia learning have various assumptions in common, but
they also differ in some respects. One commonality is that both models
assume a cognitive architecture with multiple memory stores, including a
working memory system of limited capacity. Another commonality is that
they also assume different channels for processing verbal and pictorial information, which corresponds to the basic hypothesis of dual coding theory.
Moreover, both models assume hierarchically ordered levels of processing
in working memory (cf. Cermak & Craik, 1979).
Lower and Higher Order Processing
In this section, we distinguish between lower order processing and higher
order processing. Roughly speaking, lower order processing refers to the
processing of verbal and pictorial surface structures, whereas higher order
processing refers to the processing of semantic structures, such as propositional representations or mental models.
Lower order processing implies all subsemantic and perceptual processes, which are necessary to transfer information from the auditory and
visual registers to working memory. Furthermore, lower order processing
includes the cognitive processes that create a text-surface representation
in auditory working memory or a visual pictorial representation in visual
working memory. All lower order processes are normally executed in a
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239
widely automated way and are only marginally influenced by intentional
processes.
Higher order processing includes thematically oriented semantic selection and processing of information, which results in a propositional representation or a mental model of the learning content. Higher order processing also includes the interaction between propositional representations
and mental models, which we refer to as model construction and model
inspection (Schnotz, 2005). Finally, higher order processing includes access
to thematically related information in long-term memory and its integration into propositional representations and mental models. Contrary to
lower order processing, higher order processing can be influenced to a large
extent by intentional processes.
Differences
The two models differ in the following respects. In the CTML model of
Mayer (2005), sensory modality and representational format are merged by
the assumption of an auditory-verbal channel and a visual-pictorial channel.
In the ITPC model of Schnotz (2005), on the contrary, verbal information
is not necessarily associated with the auditory modality, but can also be
conveyed by the visual modality. Similarly, pictorial information is not
necessarily associated with the visual modality, but can also be conveyed by
the auditory modality (e.g., sound images). Accordingly, the ITPC predicts
that specific combinations of instructional elements create extraneous load.
For example, when learners are expected to learn how the brakes of a car stop
the car, instruction designers could present a schematic animation of how
the brakes work. Let us assume that the sound of a car while slowing down
is added to the animation to provide a more elaborated perceptual input.
If, by mistake, the sound of a truck were added, the inappropriate sound
would be propagated through the auditory sensory channel and through the
depictive representational channel. It would create an inappropriate sound
image and activate inappropriate prior knowledge. This would hamper the
construction of a correct mental model and, thus, cause extraneous cognitive
load. The CTML, on the contrary, does not consider sound images as a part
of multimedia learning and does therefore not consider them as a potential
source of additional cognitive load.
The ITPC model distinguishes between multiple sensory channels (visual, auditory, touch, and others) within lower order processing and distinguishes two representational channels (verbal and pictorial) within higher
order processing. A distinction between a lower level of processing and a
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higher level of processing is also included in the CTML model, in which
sounds and images correspond to the lower (perceptual) level, whereas
verbal and pictorial models correspond to the higher (cognitive) level.
A further difference between the two theoretical approaches is that the
CTML model assumes the construction of a verbal mental model and a pictorial mental model, which then have to be integrated. In contrast, the ITPC
model proposes that only one mental model, which integrates information
from different sources, is constructed. However, the models are consistent
with one another in that both assume that pictorial and verbal materials are
integrated in working memory.
Because the ITPC assumes that picture comprehension includes processes of structure mapping, the structure of pictures plays a crucial role
for the construction of mental models. If a visualization with a taskinappropriate structure is presented, mental model construction will be
hampered because the structural conflict between the required mental
model and the external visualization imposes an extraneous cognitive load
on working memory (Schnotz & Bannert, 2003). On the contrary, because
the CTML does not assume structure mapping processes, it does not predict such structural interference (Mayer, 2005). Whereas CTML assumes
parallel processes of text and of picture comprehension, the ITPC model
considers the relation between text comprehension and picture comprehension as essentially asymmetric. More specifically, the ITPC model assumes
that graphics in a learning environment will influence the mental model
more directly than text, whereas text will influence the propositional representation more directly than graphics. Depending on the required task
performance after learning, germane load would be enhanced by a stronger
emphasis on the text if primarily a propositional representation is required,
whereas germane load would be enhanced by a stronger emphasis on the
graphics, if primarily a mental model is needed.
cognitive load in multimedia comprehension
Basic Assumptions of CLT
The two models of multimedia learning described previously assume that
comprehension and learning are highly dependent on the constraints of
the human cognitive system. These constraints refer to the sensory registers
(which are highly temporally limited) and to working memory (which is
limited in capacity as well as temporally limited; cf. Baddeley, 1986), whereas
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long-term memory is assumed to have a practically unlimited capacity
(cf. Atkinson & Shiffrin, 1968).
The assumption of a highly limited working memory and its implications for teaching and learning is also at the core of CLT (Sweller et al., 1998).
According to CLT, instructional design should be adapted to the constraints
of the human cognitive architecture. During learning, new information is
processed in working memory, which eventually also changes the content
of long-term memory. Because, according to CLT, any kind of conscious
cognitive processing puts a cognitive load on working memory, processes
of guided cognitive learning are always associated with a cognitive load.
Consequently, CLT stresses the importance of optimally adapting instruction to efficient usage of the limited working memory capacity in learning
situations (Chandler & Sweller, 1991; Paas et al., 2004; Sweller & Chandler,
1994; Sweller et al., 1998).
CLT distinguishes among three kinds of loads: intrinsic, extraneous, and
germane (see Chapter 2, this volume). The complexity of a learning task
causes intrinsic load. The degree of intrinsic load depends on the learner’s
prior knowledge, his or her intellectual abilities, and the complexity of the
learning material (Schnotz & Kürschner, 2007). Cognitive activities that
specifically aim at learning cause germane load, which reflects the cognitive
effort of schema abstraction and schema automation. Germane load should
be increased because schema automation reduces the cognitive load of
learning tasks (see Chapters 2 and 8, this volume). Extraneous load is defined
as an unnecessary load resulting from an inappropriate instructional format.
Hence, extraneous load should be reduced as much as possible, whereas
germane load should be increased as much as possible.
guidelines for manipulating cognitive load at different
levels of processing in multimedia learning
Various design guidelines based on CLT have been published to optimize
learning processes with multimedia or to prevent negative cognitive load
effects in multimedia learning (e.g., Mayer & Moreno, 2003; Moreno &
Mayer, 2007; see Chapters 7 and 8, this volume). In the following sections,
we focus on the kinds of cognitive load affected by these guidelines and to
what extent the corresponding instructional design principles affect higher
order or lower order processes. We investigate three groups of guidelines or
principles: (1) guidelines that refer to general characteristics of media-based
learning (multimedia learning vs. single-medium learning), (2) guidelines
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concerning specific instructional design characteristics of multimedia learning, and (3) guidelines for adapting learning environments to specific learner
characteristics.
Multimedia Learning versus Single-Medium Learning
The general characteristic of multimedia learning is the usage of multiple
representational formats, such as text and pictures. This general characteristic is also reflected in the well-known multimedia principle: students
learn better from text and pictures than from text alone (Mayer, 1997, 2001).
The multimedia principle does not make suggestions for how multimedia learning environments should be designed. It suggests that multimedia
learning environments should be designed and used rather than singlemedium environments. The multimedia principle implies the prediction
that learning with verbal explanations and corresponding visual representations will result in more successful learning than learning with only verbal
explanations or only visual representations.
Recent research within the field of CLT has demonstrated, however, that
the multimedia principle does not apply under all conditions. If a learner’s
prior knowledge is high, learning from a single medium can lead to better
learning results than multimedia learning (Kalyuga, Ayres, Chandler, &
Sweller, 2003; see Chapter 3, this volume). The multimedia principle also
does not apply if learners possess insufficient spatial abilities (Plass, Chun,
Mayer, & Leutner, 2003). However, if there are no such obstacles, the design
and use of a multimedia learning environment is advisable. The multimedia
principle is the first principle that should be considered by instructional
designers. It is a precondition for the application of further guidelines.
Guidelines Concerning Specific Instructional Design
Characteristics of Multimedia Learning
The second group of guidelines refers to specific instructional design characteristics of multimedia learning. These guidelines correspond to the principles of spatial and temporal contiguity, two redundancy principles, the
coherence principle, and the modality principle. Moreover, the techniques
of segmenting and aligning belong to this category. These principles and
techniques focus on single aspects of multimedia learning systems.
Spatial and temporal contiguity principles. The two contiguity principles claim that spatial or temporal distance between different semantically
related media (typically, written text and pictures) should be minimized.
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For the following reasons, effects of discontiguity are especially strong when
text is combined with animation. To be integrated into a mental model, verbal and pictorial information has to be simultaneously held available in
working memory. Because of the fleeting nature of animations and the
necessity to split one’s visual attention when reading text and observing an
animation, the mental surface structure representations of text and animation (resulting from lower level processing) will be relatively incomplete.
It is well known that viewing movies and reading subtitles simultaneously
is relatively difficult, particularly when a lot of text is included in subtitles
(Koolstra, 2002). The same problem exists in multimedia learning. Learners
cannot build up a coherent text surface representation or an image representation under the condition of high discontiguity combined with the fleeting
nature of animations, because this results in high costs of information search
processes. On the level of higher order processing, low contiguity may be
harmful too, because the construction of a mental model depends on the
availability of appropriate surface structure representations. As a result,
higher order cognitive processing is restricted by the effects of discontiguity on lower order (surface level) processing. Although these higher order
processes may be hampered as a result of low contiguity, the cause of these
problems does not lie within the higher order cognitive processing. In fact,
the problems result from an insufficient surface representation, which derive
from impeded lower order processing. Problems caused by discontiguity can
be attributed primarily to lower processing level in multimedia learning.
Hence, guidelines derived from spatial and temporal contiguity principles
aim at the lower level of information processing in working memory.
Redundancy principles. In research on multimedia learning, the term
‘redundancy’ has been used differently in different context, which has finally
resulted in the formulation of two different redundancy principles. Moreno
and Mayer (2002) use the term ‘redundancy’ in a relatively specific way,
namely, in the context of redundant narrated and written texts combined
with pictures. We will call the corresponding principle the specific redundancy principle. Sweller (2005) uses the term ‘redundancy’ in a more general
way, as he refers to any verbal or pictorial source of information as redundant if this source presents nothing new for learners because they already
possess the corresponding information. We will call the corresponding
principle the general redundancy principle (cf. Schnotz, 2005). If any of
these kinds of redundancy exist among different sources of information
in multimedia learning, cognitive load will increase without improvement
of learning. Hence, the additional load caused by redundancy should be
classified as extraneous load (Sweller, 2005). The question arises, ‘At which
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level of information processing does this additional extraneous load occur
in multimedia learning?’
Regarding the specific redundancy principle, we assume that when oral
and written text is presented concurrently, learners try to build up simultaneously a text surface representation of the auditory as well as a text surface
representation of the written text. After a visual representation of a written
text segment is constructed, the information of the written text also has
to be processed in auditory working memory. A cognitive overload of the
auditory working memory arises due to the parallel building of a visual and
auditory text representation, because learners tend to process both sources
of information (rather than ignoring one of them), even if the two texts are
identical. Working memory is impaired because of an overload at the level
of lower order processing of text-surface representations from two concurrent sources, whereas higher order processing is affected only indirectly.
There might also be a problem of asynchrony between listening and reading
because the speed of the narrative text is fixed, whereas reading speed can
vary according to the individual requirements of information processing
(Bornkessel, Fiebach, Friederici, & Schlesewsky, 2004). It should be noted
that learners are obviously unable to ignore a redundant written text in
order to avoid cognitive overload. Whether the single learning sequences in
the experiment of Moreno and Mayer (2002) were relatively short so that
learners had no chance to find out that the redundant written text could be
ignored seems to deserve futher investigation. If text length was important
for the specific redundancy effect, one could speculate that in case of longer
(redundant) spoken and written texts combined with pictures, learners
would be more likely to ignore the written text, which subsequently would
result in a considerably reduced cognitive load.
The general redundancy principle assumes that if a single medium is
self-explanatory and sufficient to understand the subject matter, redundancy between different information sources in a learning environment
will hamper learning performance (Sweller, 1994, 2005). In other words, if
one learning medium is sufficiently intelligible for the learner and a second medium provides the same (i.e., redundant) information, the latter
will impose an extraneous load on the learner’s working memory. In the
following sections, we focus on redundant combinations of written text and
pictures. Such combinations require always some split of attention, because
the learner’s eyes cannot read the text and observe the picture simultaneously. Besides this cognitive load, which can be reduced as much as possible
through the principle of spatial contiguity, reading the text or observing
the picture per se does not cause a cognitive overload within lower order
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cognitive processing, when neither the text nor the picture is too complex
at the surface level. In fact, the negative effect of two properly designed but
redundant media is a result of superfluous processing in terms of integrating information, which is only redundant and therefore does not further
contribute to mental model construction. It is still an open question as to
whether only total redundancy leads to negative consequences or whether
partial redundancy between different media will influence the learning process in a negative way as well. It seems plausible to assume that a partial
redundancy between different media in a learning environment is required
for an integration of both information sources (Moreno & Mayer, 2002).
In the case of partially redundant media, some redundancy (i.e., semantic overlap) is required to create cross-referential connections between the
different sources of information. Moreover, additional information, only
presented in one medium, supports the further elaboration of the mental
model.
Coherence principle. The coherence principle implies that learners perform better when extraneous material (such as interesting but irrelevant
words, pictures, sounds, or music) is excluded rather than included (Moreno
& Mayer, 2000; Harp & Mayer, 1997; Chandler & Sweller, 1991). A general
conceptual problem of the coherence principle is that it compares learning
from different sources, when one source entails important and unimportant
information, whereas the other source entails only important information.
Following the multimedia learning models, the different learning outcomes
are no surprise because different informational content was learned, and
one can therefore expect that different mental models were created. On the
one hand, the coherence principle aims at optimizing information processing at a higher level in working memory when it suggests that no confusing
information should be integrated into learners’ mental models. On the
other hand, an advantage of learning materials without irrelevant content
should be expected also with lower order processing, simply because less
information has to be processed. Overall, the coherence principle leads to
an optimization primarily at the level of higher order processing in working
memory, but it has positive effects at the level of lower order processing in
working memory as well.
Modality principle. The modality principle postulates that students learn
better from animation and narrated text than from animation and written
text. Following Mayer (2001) and Sweller (1999), using two channels expands
the effective working memory capacity and reduces the probability of an
overload that would be unavoidable if a single sensory channel rather than
two channels were used for two sources of information (Sweller, 1999). In
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the light of multimedia learning models, the effectiveness of the modality
principle is due to the lower level of information processing because the
visual channel has a lower load when it is processing only one source. If
text is presented in an auditory format, mental model construction will
be facilitated because the visual channel does not have to provide all the
information for constructing an image representation and a text-surface
representation simultaneously. The assumption of a higher overall working
memory capacity is based on the idea that capacities in working memory
are not flexibly allocated to the auditory or the visual channel, but are
rather partially dedicated to a specific channel. Therefore, learners are able
to process more information within a specific time interval by using both
processing channels. Besides the fact that a lower total cognitive load leads
to more free cognitive capacity of working memory, higher level processes
are not directly influenced by the modality principle.
Segmenting. The recommendation to segment complex materials in
smaller parts leads, on the one hand, to lower spatial or temporal contiguity,
which contradicts the contiguity principle. On the other hand, segmenting
has proved to be an important design option when complex transient learning materials contain too many relevant elements so that an overload would
otherwise occur in working memory. Such an overload might be caused by
two factors:
(1) Learners may be unable to generate an adequate mental model because
a continuous transient presentation of information, such as an animation or
an audio stream, is too fast or too manifold to be adequately processed with
limited capacity within a limited time. Learners can be cognitively overstrained by the need to integrate new elements in working memory while
the mental model construction based on the previously seen information
is still in progress. In this case, higher temporal distance between different
information elements (including some time for ‘cognitive wrap-up’; cf. Just
& Carpenter, 1980) may facilitate information processing. In addition, static
pictures (such as detailed anatomic pictures, for example) may also lead to
cognitive overload if learning time is limited. Segmenting the whole picture
would facilitate the visual processing of relevant content. In terms of multimedia learning models, segmenting is a technique that supports higher
order cognitive processing in working memory. First, presenting learning
content in smaller segments may better enable learners to construct an adequate mental model. Second, learners internalize the new information in
long-term memory by further cognitive processing. After processing one
segment, information from the following segment can be processed more
efficiently compared with complex (transient) learning materials that are
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not segmented. The costs of higher spatial and temporal incontiguity are
relatively unimportant if the segmentation does not separate content that is
essential for initial understanding. Therefore, a video and a corresponding
audio file, for example, should not be presented in different temporally
separated units so that they cannot be processed together.
(2) Additionally, segmenting may facilitate processes on a lower level of
information processing in working memory, but this effect is a relatively
indirect one. Mayer and Moreno (2003) posited segmenting as a possible
method to reduce cognitive overload if both channels are overloaded. This
cognitive overload arises from model building activities when an ongoing
stream of new dynamic information is presented to the learner. Hence, the
learner’s ability to build text surfaces and image representations in these
situations may be hampered because of the dynamic nature of instructional
materials when the mental model building processes at the higher level of
processing are still in progress. As a result, learners simply overlook critical
information and therefore do not incorporate it into the text surface and
pictorial surface representations.
Signaling. The recommendation to signal materials, that is, by inking
relevant sentences of a text, is different from all optimization guidelines. On
lower levels of information processing in working memory, the intention of
signaling is to solve problems of extraneous material. Learners’ perception
can be guided to the most essential elements on the learning environment
by marking them. In doing so, the attention of learners is drawn away from
extraneous material so that learners are more likely to allocate their cognitive
resources to the essential elements (see Chapter 7, this volume). On a higher
level of processing, signaling may support the processes of understanding
and integrating information. Altogether, instructional designers are able to
enhance all levels of information processing by signaling.
Guidelines for Adapting Learning Environments
to Specific Learner Characteristics
The third category of design guidelines is represented by principles of individualized learning (see Chapter 4, this volume), with a focus primarily on
specific learner characteristics. The design guidelines of this category specify
optimized instructional conditions for specific groups of learners according
to the research tradition regarding aptitude-treatment interaction (Snow,
1989).
Principle of individual differences. The principle of individual differences
aims at adapting elements of instructional design to different ability levels
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of learners. This principle postulates that instructional design may affect
learners differently, depending on their prior knowledge and their cognitive
spatial abilities. In other words, this principle moderates the effects of other
general design principles (e.g., the redundancy principle and contiguity
principle), as we demonstrate in the following paragraphs. Further, we
point out that prior knowledge and spatial abilities are effective at different
levels in multimedia learning.
(1) The effects of prior knowledge are related to the higher level of processing. As Mayer (2001) concludes from his research (e.g., Mayer & Sims,
1994), learners with higher prior knowledge are able to compensate for weaknesses of instructional design because they do not need as many cognitive
resources for mental model construction at the level of higher order cognitive processing as learners with lower prior knowledge. The mental model
is easier to construct for learners with higher prior knowledge because they
are able to use more elaborated schemas and because they need to integrate less new information than learners with lower prior knowledge do.
Additionally, learners with higher prior knowledge have more automated
schemas available, which can be applied in the process of learning and lead
to lower cognitive load compared with learners with lower prior knowledge.
(2) Contrary to prior knowledge, spatial ability seems to affect information processing at all levels of multimedia learning. Mayer and Sims
(1994) found that learners with high spatial ability performed better when
words and pictures were presented simultaneously rather than successively,
whereas learners with low spatial abilities showed no differences between
both kinds of presentation. This finding suggests that learners with high
spatial abilities are better able to integrate verbal and pictorial information
compared with learners with low spatial abilities.
conclusions
According to our analysis, the majority of principles to optimize instructional design and techniques to prevent cognitive overload focus on the
lower level of information processing by reducing extraneous load. Researchers such as Mayer, Sweller, Chandler, Moreno, van Merriënboer,
Plass, and many others have contributed highly influential research on this
topic. Although mindful learning is associated with higher level processes
of understanding and integrating information, most optimizing principles
and techniques that intend to prevent cognitive overload do not directly
influence this higher order cognitive processing. There are some techniques
that address the optimization at the higher level of information processing
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during learning (cf. part 3 of Mayer, 2005), but they address mostly specific
settings and contain fewer general instructional principles that would serve
as guidelines for multimedia design to enhance lower order information
processing. Perhaps for practical reasons, most research in this domain has
been conducted to investigate cognitive load at lower levels of cognitive processing: Surface level design characteristics, such as the distances between
words and pictures, continuous versus segmented video streams, or narrated versus written texts can be operationalized more easily than design
characteristics that refer to higher order semantic processing. We suppose
that there exists a still undetected potential for optimizing multimedia
learning at a higher level of cognitive processing by fostering metacognitive processes, as suggested by Roy and Chi (2005) or the use of graphical
and other instructional aids (Seufert & Brünken, 2006; Seufert, Jänen, &
Brünken, 2007). Related aspects of integrating information from different sources have also been described by Plötzner, Bodemer, and Feuerlein
(2001).
Concerning the relation between ITPC and CTML, instructional designers should decide which framework they will use to analyze instructional
materials or which framework should be the theoretical starting point when
constructing a learning environment. One aspect could be the granularity
of planning. CTML is adequate to explain major effects of multimedia
information processing, but it is less detailed with regard to graphics comprehension and does not consider auditory pictures. Future research in the
field of CLT could also be a source of evidence for the assumptions of the
CTML or ITPC models.
Altogether, it seems to us that the following conclusions can be drawn
with respect to optimization principles and techniques to prevent cognitive
overload in multimedia learning. First, research about multimedia instructional design has focused so far primarily on the importance of reducing
extraneous load to ease lower level information processing. Second, future
research in that field should focus more on techniques to optimize learning
processes at higher levels of information processing. Third, further research
should also take interactions between multimedia instructional design and
learner characteristics more systematically into account.
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12
Current Issues and Open Questions in Cognitive
Load Research
roland brünken, jan l. plass, and roxana moreno
The previous chapters have outlined the theoretical background, basic
assumptions, and some key applications of Cognitive Load Theory (CLT) in
its current state of development. The fundamental idea underlying CLT is
that instructional design decisions should be informed by the architecture
of the human cognitive system. CLT can therefore be described as a cognitive
theory of instructional design. CLT has been very influential in educational
research since the 1980s. It has inspired a growing number of research studies aimed at deriving empirically based guidelines for instructional design.
Moreover, at its present stage of development, CLT is arguably one of the
most influential instructional design theories.
However, the extant research on cognitive load raises questions about
the assumptions underlying CLT, some of which have not been consistently
supported by the empirical data, suggesting the need to update the theory by incorporating recent empirical findings on cognition and learning
(Schnotz & Kirschner, 2007). The first goal of this chapter is to summarize
the theoretical developments of CLT and highlight some of its strengths and
limitations.
An additional contribution of CLT research includes efforts to develop
practical, valid, and reliable measures of its main construct: cognitive load.
However, as suggested in Chapter 9, the existing body of cognitive load
research fails to exhibit methodological consistency regarding cognitive
load measurement and lacks appropriate methods to measure other relevant
constructs, such as the different load types proposed by the theory (DeLeeuw
& Mayer, 2008). The second goal of this chapter is therefore to summarize
the methodological developments of CLT research and highlight some of
their strengths and limitations.
Finally, most of CLT research has focused on identifying instructional
design principles for problem-solving tasks in well-defined domains, using
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laboratory studies that consist of very brief interventions. An open question
is whether CLT can be used to guide the design of more authentic learning
environments in ill-defined domains and over longer periods of time. The
third goal of this chapter is therefore to suggest new venues for research on
CLT that would broaden its empirical base.
In sum, the goal of this chapter is to synthesize the strengths and weaknesses of current theoretical and methodological developments of CLT and
to suggest new directions for future research. At present, the lack of theoretical clarity on key cognitive load concepts and the absence of valid measures
that can reliably distinguish among the three types of cognitive load threaten
the explanatory and predictive power of the theory. Consequently, several
empirical studies aimed at testing CLT show inconclusive findings or findings that contradict the very assumptions underlying CLT. In this chapter,
we present examples of this issue and suggest some productive directions
for the advancement of our understanding of the relation between cognitive
load and learning.
conceptual development of cognitive load
CLT conceptualizes learning as information processing that is actively carried out in the human cognitive system and that results in lasting mental
representations. A direct implication of CLT is that instructional methods
need to be based on the structures and affordances of the human cognitive
system to support the learning process.
Most of the original CLT assumptions about the human cognitive system were supported by basic research in cognitive science, neuropsychology,
and educational psychology. The two basic concepts on which CLT is built
are the limited capacity of working memory and the long-lasting structure of long-term memory (Cowan, 2001; Ericsson & Kintsch, 1995; Miller,
1956). According to CLT, learning is an active, resource-consuming process,
resulting in schema formation (Bartlett, 1932; Johnson-Laird, 1983).
Perhaps the most attractive aspect of CLT is its simplicity and intuitiveness. It seems logical that effort will only result in learning gains when
instruction is designed to avoid the overload of cognitive demands on the
learner. However, several issues arise when delving deeper into the theory.
In the next sections, we provide our evaluation about some of the assumptions underlying CLT, including the relation between cognitive load and
learning, the definition of the sources of cognitive load, and the additivity
hypothesis. In addition, we point out three essential areas that require significant development before CLT can become a viable theoretical framework
Current Issues and Open Questions in Cognitive Load Research
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to understand learning phenomena: the relation between cognitive load
and mental representation theories, the relation between cognitive load and
working memory theories, and the role of motivation, affect, and metacognition in cognitive load.
Current Issues regarding the Relation between Cognitive Load
and Learning
The most basic assumption, made in the original concept of CLT, is that there
is a negative relation between cognitive load and learning (Sweller, 1988):
the lower the load experienced during learning, the higher the learning outcome. The negative relation between cognitive load and learning has been
empirically supported by many cognitive load effect studies (see Chapter 7,
this volume). For example, the research on split attention and coherence
effects shows that when instruction is designed to force the learner to integrate visual and verbal materials or process extraneous information, learning is hindered. The negative relation between cognitive load and learning is
also supported by the research on worked-out examples, which shows that
asking students to study worked-out problems increases learning compared
with independently solving the same problems (see Chapter 5, this volume).
In sum, this line of research suggests that instruction should be designed to
make learning as easy or as effortless as possible.
However, several studies have challenged this conclusion (Schnotz &
Kirschner, 2007). For example, as described in Chapter 4 (this volume), the
inverse relation between load and learning depends on the prior knowledge
of the individual. Unlike novices, experts or students with higher levels
of knowledge in a domain may benefit from learning with non-integrated
rather than integrated information or from independent problem-solving
practice compared with worked-out example instruction. In addition, making instruction too easy may hinder learning by failing to provide the necessary challenge to motivate the learner (Pintrich & Schunk, 2002). More
than 50 years ago, Atkinson (1958) showed that the highest level of effort
invested in a task occurs when the task is of moderate difficulty. Tasks that
are either very easy or very hard elicit the lowest level of effort, which in
turn produces the lowest performance on a task.
The core instructional design issue, therefore, is not to reduce the amount
of cognitive load per se, but rather to find the appropriate level of cognitive
load for each learner. Therefore, a better description of the relationship
between cognitive load and learning is an inverse U-shape: low cognitive load hampers learning, learning increases with increased load until an
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optimal level of load has been reached, and then decreases as cognitive load
exceeds the learner’s cognitive capacity. The negative relationship between
cognitive load and learning only seems to apply to the particular case in
which the learner is operating at the limits of his or her capacity.
Current Issues regarding Definitions of the Three Cognitive Load Types
The lack of satisfaction with the oversimplistic assumption that all reductions in cognitive load result in increased learning motivated later revisions
of CLT. Specifically, CLT researchers distinguished intrinsic load from extraneous load (Sweller, 1988) as a way to capture the difference between the
cognitive demands that are intrinsic to the difficulty of the materials to
be learned and those that result from the unnecessary processing imposed
by poor design. A second theoretical development was the concept of germane load (Paas & van Merriënboer, 1994). It was suggested that this third
load type accounted for the finding that instruction that increases cognitive
load with methods that are aimed at fostering schema acquisition increases
learning. According to current CLT, being able to distinguish among these
three sources of cognitive load is key for understanding the relationship
between learning and instruction.
The definition of intrinsic load as load related to the complexity of the
information, extraneous load as the processing of information that is not
relevant to learning, and germane load as the mental effort invested in
relevant cognitive activity is both intuitive and clear at first glance. Yet, a
closer look at these assumptions reveals several inconsistencies. For example,
intrinsic and germane types of cognitive load may be interrelated: the higher
the intrinsic load inherent in the material, the more the germane load
required to process the material. The intrinsic load experienced by a given
learner would be a function of this learner’s actual engagement with the
material, not only the material’s complexity. Engagement, however, is a
concept that is not included in the current model of CLT.
Recent research has raised questions concerning the definition of intrinsic load. In the past, intrinsic load had been defined as an attribute of the
learning material based on the amount and interrelation of the concepts
that had to be learned (element interactivity) (Paas, Renkl, & Sweller, 2003).
According to this definition, intrinsic load cannot be manipulated by means
of instructional design because it is an attribute of the material to be learned
itself. However, recent research suggests there is a need to reconsider this
idea. For example, the segmenting principle (Mayer & Chandler, 2001) can
be interpreted as an instructional method that reduces intrinsic load by
Current Issues and Open Questions in Cognitive Load Research
257
chunking the material to be learned in smaller units (see Chapter 7, this volume). Still, this interpretation needs to be directly tested in future research
by including measures for the two types of load of interest.
Another conceptual problem with CLT’s definition of intrinsic load is
that it can be argued that the difficulty of the material to be learned also
depends on the learner’s prior knowledge. For a novice learner, the size of an
element to be learned might be much smaller than for a domain expert who
can chunk information more effectively and therefore can subsume more
information within one element. To date, there is no model integrating
the role of learner characteristics, such as individual differences in priorknowledge, working memory capacity, and domain-specific abilities that
can help predict the relative intrinsic difficulty of the materials for a specific
learner in a specific situation.
A further problem is that CLT’s definitions of extraneous and germane
loads are circular and therefore fail to explain and predict learning. For
example, the distinctive characteristic of extraneous load is that it arises
from the unnecessary cognitive processing that is imposed on the learner
because of suboptimal instructional design. But when is instructional design
suboptimal? It is only after observing the results of the studies that the
researchers offer conclusions about the type of load imposed by the design:
if it increases learning, the method is interpreted to be optimal and to
have decreased extraneous load, increased germane load, or produced a
combination of the two effects. In contrast, if the instructional method is
found to hinder learning, it is interpreted as having increased extraneous
load.
For example, most design principles of multimedia learning, such as
those presented in Chapter 7 (this volume), are based on empirical studies
comparing different design variants of the same information. Those designs
in which learners outperform others with respect to knowledge acquisition
are seen as imposing less extraneous cognitive load because of their better
learning outcomes, even though cognitive load is often not measured (e.g.,
Mayer, 2001). More research is needed that directly measures the types
of cognitive load imposed by the learning environment to shed light on
the distinctive characteristics of extraneous and germane load types (see
Chapter 9, this volume).
Similarly, CLT argues that germane load is the result of methods that
foster cognitive activities that are necessary for schema acquisition. Yet, the
theory is silent on the description of such activities. What types of processing are likely to promote schema acquisition and how? The lack of an
answer to this question in our view highlights a more basic problem of CLT.
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Usually, CLT argues that learning results in schema acquisition; however,
this is a largely unproven statement. CLT lacks a theoretical conceptualization of how information is processed in the cognitive system and how
knowledge is represented. This is the core difference between CLT and the
theoretical models of Mayer (2005) or Schnotz (see Chapter 11, this volume),
which make explicit assumptions about mental representational processes.
Nevertheless, the research on the relationships among cognitive load, cognitive processing, and mental representations is still in its infancy. The few
empirical studies available show that the relationships among the cognitive
process of organization, (germane) cognitive load, and learners’ learning
prerequisites are usually complex and not clearly explained in terms of simple main effects (Seufert & Brünken, 2006; Seufert, Jänen, & Brünken, 2007;
Seufert, Schütze, & Brünken, 2009).
A final problem is that whether a particular load is extraneous or germane will also depend on the specific educational goals of the instruction.
For example, manipulating a science simulation or using a chart to represent simulation results may generate germane load if the goal is to promote
the deeper understanding of science principles as measured by a problemsolving transfer test (Plass et al., 2009). However, the same methods may
induce extraneous load if the objective is to promote the recall of the principles to be learned (Plass, Homer, & Hayward, 2009; Wallen, Plass, &
Brünken, 2005). In the latter case, it may be optimal to teach the principles
explicitly rather than to ask students to infer the principles from their interactions with the simulations. In other words, the type of load induced by any
instructional method depends on the type of processing that is targeted by
the instruction. However, in many cases, learning goals are fluid and change
over time, and the goals of the learner may not be the same as the goals of
the instructional designer. In addition, although it may be possible to define
clear learning goals for brief interventions, complex learning environments
typically facilitate learning with outcomes that go beyond the comprehension of the content, such as the development of learning strategies,
metacognition, self-regulation, self-efficacy, and others.
The current issues surrounding CLT’s definitions of intrinsic, extraneous,
and germane cognitive load described earlier also raise concerns about the
definition of total cognitive load experienced by a learner, which we discuss
in the next section.
Current Issues regarding the Additivity Hypothesis
An additional assumption of CLT is the hypothesis that the total amount
of cognitive load experienced during a learning situation can be estimated
Current Issues and Open Questions in Cognitive Load Research
259
by adding the three types of cognitive load experienced by the learner
(Paas et al., 2003). According to CLT, efficient learning can only take place
when the total amount of cognitive load does not exceed the capacity of
the learner’s working memory resources. Otherwise, learning is impaired
due to cognitive overload. However, the specific distribution of resources
among the three types of cognitive load will affect how much is learned. For
instance, the lower the extraneous load imposed by a certain instructional
design is and the higher (up to a certain point) the germane load invested
by a learner is, the more efficient the learning will be.
The simplicity of the additivity hypothesis is attractive, yet, it is problematic on a variety of levels. One of the most important issues is that
this definition does not take into account the fact that recent models of
working memory postulate different subsystems that process verbal and
non-verbal information separately and independently from one another
(Baddeley, 1986, 2000). Effects such the modality effect and the resulting ideas of offloading of cognitive load from one subsystem of working
memory to another (Chapter 7, this volume) are not currently considered
in the additivity hypothesis (Rummer, Schweppe, Fürstenberg, Seufert, &
Brünken, 2010).
Therefore, the idea of simple linear additivity of the three load types,
as described in early cognitive load research (Paas et al., 2003), needs to
be revised to describe how the different sources of load contribute to the
overall cognitive load. Such a model would need to consider the working
memory model used as a foundation for an update of CLT and to define
additivity based on the different subsystems of this model. It would also
need to be based on the updated definitions of the different load types.
Schnotz (Schnotz & Kirschner, 2007; Chapter 11, this volume) has made
several related arguments about why intrinsic load may not be necessarily
fixed and how extraneous load could be further broken down in subcategories. However, the empirical base for such a revision of the additivity
hypothesis needs to be strengthened, for example by experimentally combining different extraneous and germane load effects in one experimental
setting and measuring the overall resulting cognitive load.
Mental Representations and Cognitive Load
With respect to the representational format of the knowledge stored in
long-term memory, CLT was originally based on schema theory. A schema
is a complex representational knowledge structure that includes declarative
and procedural knowledge related to a specific topic or process, such as
the functionality of a technical system or the steps of placing an order in a
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restaurant (Bartlett, 1932). However, in past years, CLT has also been discussed in the frame of other mental representation theories, such as Paivio’s
dual coding theory (Paivio, 1986), the cognitive theory of multimedia learning (Mayer, 2005), and the integrated model of text and picture comprehension (Schnotz & Kirschner, 2007; Chapter 11, this volume).
Although CLT seems compatible with several knowledge representation
models, it does not explicitly specify the relations between cognitive load
and specific forms of knowledge representation. However, the quality of
knowledge construction depends on the mental representations that have
been generated during the learning process. For example, a learner may have
constructed a propositional representation of the explanation for how a car
braking system works and would therefore be able to successfully describe
the elements involved in such a system on a later test. However, this does not
necessarily mean that the learner will be able to successfully solve a problem,
such as troubleshooting a malfunction in the car’s braking system. Several
studies have shown that the type of knowledge and skills acquired depend
on the type of mental representations fostered by instruction (Schnotz,
Boeckheler, & Grzondziel, 1999; Wallen et al., 2005).
If different knowledge representations foster different skills or levels of
understanding, one should also ask whether they produce different levels of
cognitive load during learning. For instance, presenting a visual model of a
scientific system in addition to a verbal explanation of how the system works
has been found to promote students’ problem-solving transfer skills (see
the multimedia effect, Chapter 7, this volume). However, CLT is currently
not able to explain whether the added pictures help learning by causing
a decrease in students’ overall cognitive load, an increase in students’ germane load, a decrease in students’ extraneous load, or by facilitating the
construction of a mental representation that is better suited for the problemsolving task. Future CLT developments should therefore specify the relation
between the construction of different mental representations and cognitive
load constructs. The current long-term memory assumptions underlying
CLT are unable to provide an answer to this issue.
Working Memory and Cognitive Load
According to CLT, learners experience cognitive load within their limited
working memories as the result of engaging in several cognitive activities,
such as extracting information from an external representation, integrating different external representations into one model, or integrating new
information into existing schemas. However, there is evidence that this
Current Issues and Open Questions in Cognitive Load Research
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assumption is overly simplistic. A good example is the split-attention effect
(see Chapter 7, this volume). According to CLT, this effect is the result of the
advantage of presenting multiple sources of information in an integrated
format rather than in a split-source format. Is this indeed a working memory
effect, or can it be explained as the result of perceptual and attentional processing mechanisms? Likewise, is the color-coding effect (Kalyuga, Chandler,
& Sweller, 1998) the result of reducing the amount of resources in working memory, or can it be explained by the process of selective attention?
Currently, CLT does not clearly distinguish among the basic perceptual,
attentional, and working memory processes that occur during learning.
Here, again, is a need to further develop the theoretical foundation of CLT
and its underlying assumptions of how the cognitive system works.
A related area in which CLT requires theoretical refinement concerns
the working memory model used by the theory. There is a large amount
of ongoing research in the field of working memory and a number of
different models describing how this cognitive system works (Miyake &
Shah, 1999). CLT seems to endorse Baddeley’s (1986, 2000) model of working
memory, which postulates that there are two specialized subsystems for the
processing of visual and verbal information (the visuo-spatial sketchpad and
the phonological loop, respectively) and a central executive that controls
and coordinates the processing of information. However, similar to the
case of mental representations, CLT has no explicit assumptions about the
architecture of working memory and its relation to cognitive load.
Most of the processes involved in learning activities, such as integration
and storing, are not exclusively carried out in the subsystems of the working
memory but also include the central executive. The connections of CLT to
this or another working memory model need to be developed in more detail,
clarifying, for example, the relation of the working memory subsystems to
CLT’s visual and verbal processing channels. The issues that we raised about
the additivity hypothesis may be partially the result of the lack of such a
connection. In Baddeley’s (1986, 2000) model, the two subsystems are not
specific to the modality of information (i.e., through which of our senses
the information is perceived), but rather to its mode of representation
(i.e., whether it uses visual or verbal representations). For example, verbal
information is always processed in the phonological loop, independent from
its modality (text or narration). This model, therefore, cannot be used as
the basis of the cognitive load explanation for the modality effect, which
assumes that written and spoken language are processed in separate channels
(Rummer, Fürstenberg, & Schweppe, 2008; Rummer, Schweppe, Scheiter, &
Gerjets, 2008; Rummer et al., 2010).
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Roland Brünken, Jan L. Plass, and Roxana Moreno
In summary, the relation between CLT and current working memory
theories needs clarification. Because the construct of working memory is
fundamental to the idea of cognitive load, future theoretical developments of
CLT should take a closer look at the basic foundations of working memory
as informed by experimental psychology and neuropsychology research.
The lack of theoretical clarity in this area shows a conflict between CLT
predictions and those that would result from taking into consideration the
empirical evidence of basic research on working memory (Rummer et al.,
2010; Seufert et al., 2009).
Motivation, Affect, Metacognition, and Cognitive Load
A final issue related to the conceptual development of CLT that we would like
to address is its restriction to the cognitive aspect of the learning process. The
same limitation inspired the revision of Mayer’s (2005) original cognitive
theory of multimedia learning (CTML), which has recently been reframed as
a cognitive-affective theory of learning with media (CATLM; Moreno, 2005,
2009, in press; Moreno & Mayer, 2007). In this model, affective, cognitive,
and metacognitive factors are integrated to explain learning from different
instructional methods and media.
Although it is well known that metacognitive, affective, and motivational
constructs are central to learning, they have not been the focus of cognitive
load research (Bannert, 2006; Paas, Tuovinen, van Merriënboer, & Darabi,
2005). Therefore, there is great potential to test specific hypotheses about
the relation among motivation, cognition, cognitive load, and learning
to advance CLT. Are cognitive load effects affected by students’ motivation? Can students’ use of learning strategies and metacognitive control
be described in terms of germane load, bridging two major research areas
of educational psychology? Although empirical evidence exists that could
inform this line of research, it has not yet been systematically investigated
within a CLT context.
In summary, although the strength of CLT is to have proposed the relations among the human cognitive architecture, learning, and instruction,
there are several questions regarding the conceptual development of CLT
that suggest that the theoretical base of CLT is in need of further development. CLT is a theory in its own right, with its own questions and empirical
base, yet its applied nature requires a more careful evaluation of the basic
research assumptions on which it relies. Future advances in the theoretical
development of CLT should incorporate recent findings from neuroscience,
neuropsychology, and brain research. CLT has the potential to become a
Current Issues and Open Questions in Cognitive Load Research
263
powerful framework for bridging basic and applied research on learning
and instruction. To reach its potential, however, CLT’s basic concepts need
to be made more precise, ill-defined concepts revised, and implicit assumptions made explicit.
cognitive load methodological developments
The relations among cognitive load, mental resources, and learning proposed by CLT raise important methodological questions about how to validate any prescriptive design effects in a clear and reliable manner. This
section summarizes the methodological developments in the cognitive
load field with their corresponding strengths and limitations. We focus on
four issues of the cognitive load methodology: the experimental research
paradigm, the question of load measurement, the assessment of learning
outcomes, and the relation to methodological approaches from other comparable research fields.
The Experimental Research Paradigm
As a psychological theory of learning and instruction, CLT is subject to verification by a strictly empirical, experimental approach. Almost all research in
CLT is conducted in highly controlled experimental laboratory studies with
highly structured materials in well-defined domains, such as mathematics,
science, or technical areas (Sweller, 1999). Although this type of research is
in line with the common paradigm in basic psychological research, it is not
without alternatives within applied psychological or educational inquiry.
For example, competing theoretical approaches to learning and instruction
coming from a constructivist point of view are using a wide variety of alternative research methods, which are often heavily criticized by cognitive load
researchers (Kirschner, Sweller, & Clark, 2006). However, it is necessary to
ask what the specific gains and losses of the chosen methodological approach
in cognitive load research are. Moreover, it is necessary to consider whether
a broader methodological approach could contribute to the development
of CLT.
The main concern regarding experimental research in applied psychology is associated with the question of generalization of empirical findings
to authentic learning scenarios. With respect to CLT, there are two primary
aspects that have to be taken into account: the limited number of subject
domains in which CLT has been tested, and the limited range of learning scenarios that have been under experimental investigation. Although
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many experiments have been carried out in well-defined highly structured
domains, such as mathematics, statistics, or technical areas, little is known
about the applicability of CLT to ill-structured, open domains, such as social
science, history, or philosophy. For example, in the domain of history, learning from visual external representations is of high importance; however, the
function of a visual representation, such as a historic picture, is likely to
vary from that of the visualization of a technical system, such as a car brake
system. Little is known about the processing of multiple external representations in history, and more research is needed to determine whether the
cognitive load effects found with technical visualizations can be generalized
to domains such as history.
The second question arises from the differences in treatment durations
between experimental and authentic learning settings. Within most of cognitive load research, laboratory learning scenarios include brief sequences of
instruction and immediate post-test assessments of knowledge acquisition.
However, this type of treatment is not representative of the way learning
takes place in real-life scenarios, such as in classrooms or apprenticeships,
which are characterized by longer periods of learning and delayed testing,
sometimes weeks after the learning phase. It is unclear whether cognitive
load effects are likely to persist in longer and more complex learning scenarios.
Issues of Cognitive Load Measurement
Chapter 9 (this volume) describes the current methods of cognitive load
measurement based on direct or indirect observations of learner behavior
related to the cognitive demands of a learning situation. The chapter argues
that most of these methods produce scores that are of acceptable reliability
and are correlated with the learning outcomes in the expected way. However, the question of validity needs further investigation. For example, with
respect to the dual task methodology, it is not clear if performance on the
secondary task is determined by lower-level processing, such as visual search
or processes of attention, or by higher-level processing, such as selecting or
organizing information in working memory. Likewise, it is not clear if selfreports of perceived mental effort are valid measures of cognitive load, as
they are likely to be affected by students’ motivation and affect. Because the
cognitive load construct is underspecified, as discussed earlier, it is not possible to assess the construct validity of these measures. Unless the definition
of cognitive load is clarified, the measurement of cognitive load cannot be
improved.
Current Issues and Open Questions in Cognitive Load Research
265
A second problem of cognitive load measurement, which in our view
has placed current cognitive load research at an impasse, is the lack of
methods that allow the differentiated measurement of intrinsic, extraneous,
and germane load types (Brünken, Plass, & Leutner, 2003, 2004; Moreno,
2006). To date, there are no widely accepted measures available that reliably
distinguish among these three dimensions. Most attempts to do so with selfreport measures show high correlations between the items that presumably
tap into separate load types, suggesting that the cognitive load construct is
one-dimensional (Paas, Tuovinen, Tabbers, & Van Gerven, 2003).
Moreover, as Paas demonstrated in earlier cognitive load research, using
multiple-item scales has no advantage compared with a simple one-item
scale for cognitive load measurement (Paas & van Merriënboer, 1993). Since
then, no substantial progress has been made on self-reported measures of
cognitive load constructs. Unfortunately, our understanding of cognitive
load is not likely to advance unless we start overcoming these measurement
limitations. Furthermore, is CLT’s impasse due to measurement limitations,
or do the measurement limitations signal a fundamental problem in the
theory’s assumptions (Moreno, 2006)? In our view, answering this question
is one of the main challenges in cognitive load research in the coming
years, and there is only a very small number of studies that attempt to
answer this question empirically (DeLeeuw & Mayer, 2008). The results
from DeLeeuw and Mayer seem to suggest that different measurement
approaches are sensitive to different aspects of cognitive load, but it remains
unclear whether these differences are caused by real conceptual differences
of the load constructs or whether they are artifacts of the shortcomings of
the measurement techniques.
Issues of Learning Measurement
CLT is aimed at helping us understand how instruction should be designed
to effectively support learning. But what exactly does “effective” instruction refer to? Does it mean making learning easier or effortless? Does it
mean making learning more interesting? Is it about learning faster or promoting deeper levels of understanding? Or does effective learning consist
of a combination of these factors? Many other factors could be added to
this list. However, the major questions behind this issue are of a methodological nature: What are appropriate variables to be included in cognitive
load research, and what can the learning outcome measures included in a
research study tell us about the cognitive load experienced by the learners?
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Roland Brünken, Jan L. Plass, and Roxana Moreno
Most research studies measure learning on different levels of processing, such as retention, comprehension, and transfer (Mayer, 2005). Several
studies show interesting differences in cognitive load effects with respect
to the types of learning outcome tests used: retention effects are usually
small or zero in cognitive load studies that investigate methods to reduce
extraneous CL. Far transfer effects are usually small or zero in cognitive
load worked-example studies using methods to increase germane cognitive
load (Seufert et al., 2007). This indicates that reducing extraneous load is
important for higher-level learning, whereas the procedures used to foster
germane processes are highly content-specific and seem to allow only for
limited transfer. However, systematic research with respect to the relation
of cognitive load effects and types of learning outcome tests is still in the
beginning stages. Some experiments show the dependency of cognitive load
effects on test difficulty as well as on the presentation format of test items
(see, e.g., the meta-analysis on dynamic visualizations by Höfler & Leutner,
2007). In our own research on the multimedia effect, we observed an interaction between the presentation modality of information (visual vs. verbal)
and the presentation modality of the test items (visual vs. verbal; Brünken,
Steinbacher, Schnotz, & Leutner, 2001). In more recent studies on dynamic
visualizations, we found a similar interaction between the presentation
format of information (static vs. dynamic) and the type of test items (process oriented vs. structure oriented; Münzer, Seufert, & Brünken, 2009).
Such results indicate that more research is needed in this area to understand which type of learning can be effectively supported by which type of
presentation.
However, several questions remain. Are the learning outcome measures
aligned with the instructional methods used in the studies? Most research
includes verbal measures of learning. If the learning environments typically
consist of multimedia presentations, with static and animated pictures and
video instruction components, is the modality of the learning assessments
psychologically neutral to cognitive load? Or, regarding the type of learning (intentional vs. incidental), has the student focused on aspects of the
presentation that were not deemed essential by the instructional designer?
Learners may use their cognitive resources to process irrelevant or less relevant materials. To know exactly whether and how the cognitive resources
were spent would require measuring not only learning of the target concepts
and skills but also any other incidental learning that might have occurred
during the lesson. Such questions have to be taken into account when CLT
makes predictions about instructional effectiveness.
Current Issues and Open Questions in Cognitive Load Research
267
Moreover, which additional variables should be considered and when
should they be assessed? An interesting approach has been introduced to
CLT by Paas and colleagues by measures of learning efficiency, using the
ratio of time on task and cognitive load as performance indicator (Paas &
van Merriënboer, 1993; van Gog & Paas, 2008). This approach considers that
instructional efficiency is not only determined by the amount of knowledge
acquired but also by factors describing the consumption of resources, such
as learning time or cognitive effort. However, in their present conceptualization, efficiency measures are based on the assumption that learning is
most efficient when high learning outcomes are combined with low resource
consumption, and it remains to be shown whether this approach will indeed
result in the design of learning environments with the highest impact.
Many of the recent cognitive load research studies used valid and reliable measures of cognitive load and learning outcomes. However, as we
argued earlier, to infer the cognitive load induced in any learning situation would also require accounting for variables that are likely to affect the
experienced cognitive load for any one learner, such as the level of prior
knowledge, dispositional and situational interest, anxiety, perceived support, and metacognitive and cognitive skills and styles. The need to measure
these relevant constructs raises some methodological as well as practical
questions. For example, should pre-tests be considered valid measures of
students’ “expertise,” or do the tests only reveal their readiness to build
new knowledge? Moreover, if individual differences are of such importance,
how can we handle this in concrete learning scenarios? Again, some modern
approaches, such as rapid online assessment of prior knowledge (Kalyuga
& Sweller, 2005), show promising new ways, but they also represent new
challenges in cognitive load measurement. For example, as argued earlier in
this chapter, the concept of intrinsic load is highly correlated with learner
expertise. Therefore, using dynamic online assessments of expertise, an
adaptive alignment of intrinsic load to the learner’s level of expertise, could
facilitate new ways of designing adaptive learning systems that overcome
the problems of existing approaches that use sophisticated user modeling,
but which have never worked satisfactorily (Leutner, 1992).
In summary, cognitive load research should address questions of the
methodological approaches used in conducting cognitive-load–related
studies. It also needs to focus on the validation of existing and the development of new cognitive load measures that are capable of discriminating the
three types of load, based on a clarification of the cognitive load construct
itself. Finally, cognitive load research needs to more systematically relate
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Roland Brünken, Jan L. Plass, and Roxana Moreno
measures of learning outcomes to specific instructional strategies, types of
knowledge representations, and learner characteristics.
conclusion
Without any doubt, CLT has become one of the most influential theoretical
frameworks of educational psychology in recent years, inspiring researchers
all over the world to conduct an enormous amount of high-quality experimental research on how to design instruction in an efficient learner-oriented
way. CLT has been widely used as the theoretical framework for several
instructional design areas, such as complex problem-solving environments,
worked-example instruction, and multimedia learning.
Which direction will CLT take in the next decade? In our view, the ongoing discussions in the cognitive load community suggest that new theoretical
developments are on their way. More specifically, the following three lines
of thought are currently being discussed by cognitive load researchers as
possible areas of future extension of the theory:
(1) the evolutionary foundation of CLT,
(2) the integration of motivational aspects of learning, and
(3) the integration of new basic research paradigms into cognitive load
measurement.
In past years, John Sweller, the founder of CLT, has discussed the relations between CLT and human cognitive architecture from an evolutionary
perspective (see Chapter 2, this volume), linking questions of instructional
design to restrictions of human information processing that are caused by
the demands of evolution. However, as fascinating as these ideas are, they
remain highly speculative with respect to their empirical base. Moreover,
they may be useful for an understanding of the basic constraints of CLT, but
their added value to answering the pressing questions of CLT is not clear.
CLT is based on cognitive principles of learning. However, learning is not
a purely cognitive phenomenon. It also depends on metacognitive, motivational, and emotional factors, such as task engagement, interest, and the
learners’ beliefs and emotions. Although the contribution of these factors
to learning has been well established by the learning sciences for quite a
long time, they are mostly ignored in cognitive load research.
Recent developments of CLT appear to remain focused on cognitive
aspects, yet attempt to incorporate more advanced methods of investigation.
Actual research presented at international conferences increasingly uses
advanced methods such as eye-tracking techniques for the observation of
Current Issues and Open Questions in Cognitive Load Research
269
learner behavior during information processing of multiple representations
or worked examples. For example, van Gog and Paas (2008) presented an
interesting approach that used experts’ eye movements as a model for novice
learners. Within that approach, eye tracking is not only used as a tool for
observation, but also as a means for instruction. Although the first results
are promising, more research is needed to test the sustainability of this
approach.
Nevertheless, eye tracking seems to be in vogue in educational research,
regardless of its methodological problems, such as whether the duration of
a fixation on a specific area of interest indicates information complexity,
interest, or simply low readability. This and other methodological questions
have to be answered before eye tracking will become a standard procedure
in cognitive load research. However, beyond actual research, a trend seems
to have become apparent: CLT is trying to link back to its roots in cognitive
psychology, which has made substantial progress in the last decade. Whereas
the last decade of CLT research was marked by a focus on working memory research, future developments should deal with questions of germane
load, information organization, and knowledge representation. This could
lead to a better understanding of germane processes and their support by
instructional means.
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index
active processing principle, 132, 153, 169,
231
assumptions, 171
activity, behavioral versus cognitive, 154
affect, 11, 262
borrowing and reorganising principle, 33–35
chunking, 135
cognitive-affective theory of learning with
media, 157, 160, 262
cognitive architecture, 14, 29, 111
cognitive load
construct, 9
interactions between sources, 44–45
measurement, 74, 80, 182, 187, 188, 190,
196, 208, 210, 212, 215, 219, 264
offloading, 259
relation to goals of instruction, 191, 207,
258
relation to learning, 55, 78, 79, 185, 189,
194, 213, 255
sources, 52, 197
cognitive load theory
additivity hypothesis, 16, 18, 198, 258–259
assumptions, 12, 14, 16, 18, 111, 254
complex learning scenarios, 41, 114, 118,
126, 186, 264
evolutionary interpretation. See
evolutionary framework
experimental research paradigm, 263
framework, 11
instructional methods, 207
limitations, 10, 14–15, 183, 210, 218, 264
relation to cognitive theory of
multimedia learning, 133–134
relation to learning theories, 20–21, 205,
206
cognitive overload, 136
cognitive skill acquisition, stages, 94–96
cognitive theory of multimedia learning,
188, 230–232, 260, 262
assumptions, 230–231
coherence principle, 136–137, 245, 255
color coding principle, 261
completion principle, 13, 120
constructivism, relation to CLT, 21–22,
42–43, 263
direct initial instruction principle, 56–57
direct instruction, 162
discovery learning, 162
distributed cognition, relation to CLT, 21
dual channel assumption, limitations of, 232
dual channel principle, 132, 230
dual coding theory, 260
dual task methodology, 264
element interactivity, 16, 30, 40–42, 52–53,
118, 123, 135, 184, 195, 256
engagement, 73, 196, 256
environment organising and linking
principle, 38–39
essential overload, 135
essential processing, 133, 135
evolutionary framework, 20
centrality of the five principles, 39
human cognition, 29
expertise reversal principle, 30, 44, 57–58, 68,
70, 71, 94, 120, 214
knowledge-gain reversal principle,
96–100
273
274
Index
extraneous cognitive load, 12, 18, 42–43,
53–54, 68, 70, 133, 197, 210, 241, 256
in multimedia learning, 134
measurement, 214, 265
methods of minimization, 55
questions concerning definition, 257
relation to self-regulation, 76
relation to spatial abilities, 72
sources, 54
extraneous processing, 133
fading, adaptive procedure, 104, 105
feedback principle, 164, 165
four-component instructional design
model, 60–61, 94, 124
generative processing, 133, 154, 155, 167, 169
germane cognitive load, 17, 18, 43–44, 53, 126,
133, 154, 197, 210, 241, 256
measurement, 265
methods of induction, 114, 115, 117,
120
questions concerning definition, 257
sources, 53
goal-free principle, 13, 30
guidance fading principle, 30
guided activity principle, 161, 162–163
dialoguing, 161, 162
manipulating, 161, 162
iconic representations, 232
imagination principle, 30
individual differences, 11, 157, 165, 195, 209,
212, 213, 257, 267
individual differences principle, 247–248
information store principle, 33
instructional systems, 79
integrated model of text and picture
comprehension, 233–238, 260
assumptions, 233
auditory picture (sound) comprehension,
237
cognitive architecture, 233
cognitive theory of multimedia learning,
compared to, 238, 239–240, 249
descriptive/depictive channels, 233
descriptive/depictive representations,
232–233
lower and higher order processing,
238–239
mental models, 235, 239, 240
perceptual and cognitive processing
levels, 234
propositional representations, 235, 239
spoken text comprehension, 235
structure mapping, 240
verbal and pictorial filters, 235
visual picture comprehension, 236–237
written text comprehension, 234–235
interactive learning environments, 154
intrinsic cognitive load, 15, 18, 40–42, 52–53,
75, 116, 126, 133, 183, 186, 197, 241, 256
alteration due to learning, 42
in multimedia learning, 143–148
irreducible nature, 18
management of, 118
measurement, 265
questions concerning definition, 256
relation to spatial abilities, 74
isolated/interacting elements principle, 30,
59, 119
knowledge
biologically primary, 29
biologically secondary, 31
learner characteristics. See individual
differences
learning
cognitive approaches, 12, 210
comprehension, 266
measurement, limitations of, 265–268
retention, 266
transfer, 116, 266
learning efficiency, 267
learning element, 67
limited capacity principle, 132, 230
long-term memory, 14, 32, 33, 36, 38,
48
long-term working memory, 38
meaningful learning, 132
means–ends analysis, 11
mental load, 10, 183, 191, 212, 214
mental representation, 258, 259–260
metacognition, 262
modality principle, 30, 146, 148, 188,
245–246, 259
motivation, 11, 165, 192, 255, 262
multimedia learning, 132–133
multimedia learning environments
goals for design of, 134
Index
multimedia principle, 156, 157, 242, 266
exceptions to, 242
narrow limits of change principle, 35, 37–38
pedagogical agents, 160, 163
personalization principle, 159–160
pretraining principle, 145
prior knowledge, 11, 36, 44, 60, 75, 135, 170,
195, 209, 213, 248, 257
rapid online assessment, 267
problem solving, 11, 210, 212
pupil dilation, 211, 212, 213, 215
measurement, 215
randomness as genesis principle, 35–36
redundancy principle, 13, 30, 138–139,
243
general, 243, 244–245
specific, 243, 244
reflection principle, 166, 167, 168
scaffolding
pictorial, 74
verbal, 74
schema acquisition, 11, 12, 14, 34, 48, 51, 56,
67, 110, 258
measurement of, 49
schema automation, 34, 51, 111
schema theory, 33, 48, 50, 259
segmenting principle, 144, 246–247, 256
self explanation, 93, 95, 102, 105, 123, 166
self regulation, 11, 75, 76, 77
relation to cognitive load, 75, 78
scaffolds, 77
sensory registers, 233
275
short-term memory, 14, 49
signaling principle, 139–140, 247
small step-size of knowledge change
principle, 59–61
socio-cognitive theories, relation to CLT, 21
spatial abilities, 72, 73, 248
spatial contiguity principle, 141–142, 242
split-attention principle, 13, 30, 69, 255, 261
symbolic representations, 232
temporal contiguity principle, 140–141, 242
transfer paradox, 116
triarchic theory of cognitive load, 133–134
variable examples principle, 30
visual/verbal channels. See dual channel
principle
worked-example principle, 13, 93–94
worked-out example, 12, 57, 91, 92–93, 95, 255
worked-out example fading, 99, 100, 105
different fading procedure, 101
evaluation, 103–104
working memory, 133
auditory, 235
capacity, 67, 68, 209, 257
capacity, relation to learning, 241
central executive, 36, 220, 261
limitations, 37
models, 72, 230, 259, 261
phonological loop, 261
propositional, 234
relation to cognitive load, 216, 217, 220,
260
visual, 234
visuo-spatial sketchpad, 261