REVIEWARTICLE

Interdisciplinary Perspectives Inspiring a NewGeneration of Cognitive Load Research

Paul Ayres & Fred Paas

Published online: 11 November 2008# Springer Science + Business Media, LLC 2008

Abstract This special issue consists of six theoretical papers and an introduction. Eachpaper describes a current advance to the applications and focus of cognitive load theory(CLT). Four of the papers use an interdisciplinary approach outside of educationalpsychology by combining CLT with elements of evolutionary biology, mirror neuronresearch, cognitive brain science, and the philosophy of science. The remaining two papersuse an intradisciplinary approach within educational psychology by applying CLT to self-regulation and heuristic learning. This paper introduces CLT, overviews each contribution,and summarizes the main themes.

Keywords Cognitive load theory . Interdisciplinary

This special issue contains a set of six theoretical papers that report on significant newdirections taken by cognitive load theory (CLT). The genesis of the collection came fromcontributions to the First International Conference on Cognitive Load Theory in Sydney,Australia, in 2007. The most important inspiration from the conference was the conclusionthat challenges of contemporary education require new forms of collaboration andcommunication across disciplines. Interdisciplinary perspectives seem to be necessary toenable us to make truly original and useful contributions to cognitive load theory andeducational practice. Interdisciplinary research builds on theories and previous researchfrom more than one discipline and uses methods for data collection and analysis from morethan one research tradition. This move towards interdisciplinarity has materialized in thepapers of this special issue, suggesting that the cutting edge of cognitive load research liesacross the boundaries of disciplines.

Educ Psychol Rev (2009) 21:1–9DOI 10.1007/s10648-008-9090-7

P. AyresSchool of Education, University of New South Wales, Sydney, Australia

F. Paas (*)Centre for Learning Sciences and Technologies (CELSTEC), Open University of the Netherlands,P.O. Box 2960, 6401 DL Heerlen, The Netherlandse-mail: fred.paas@ou.nl

The papers are organized according to two themes. The first theme representsinterdisciplinary research and includes four papers. One of the exciting new approachestaken by CLT researchers is to go outside the discipline of educational psychology to notonly broaden the theoretical base but also to find answers to some of the unresolved issues.These four papers represent this new wave of research by utilizing evolutionary biology,mirror neuron research, cognitive brain research, and the philosophy of science. Thesecond theme includes two papers, entitled intradisciplinary research because itdemonstrates how researchers have used other learning theories from within educationalpsychology to optimize CLT-based learning environments, as well as explore differentlearning domains. The fifth and six papers focus on self-regulated learning and heuristiclearning, respectively. This editorial introduction commences by giving a short introductionto CLT and then introduces each paper in turn. It concludes by summarizing the mainfindings of the collection.

Cognitive Load Theory

The central notion of cognitive load theory (Paas et al. 2003, 2004; Sweller 1988, 2004;Sweller et al. 1998) is that instructional approaches should take account of the structuresthat constitute human cognitive architecture. The theory focuses on complex cognitivetasks, in which instructional control of cognitive load is critically important to meaningfullearning. To realize this control, CLT uses current knowledge about the human cognitivearchitecture to generate instructional techniques. This architecture consists of an effectivelyunlimited long-term memory (LTM), which interacts with a working memory (WM) that isvery limited in both capacity and duration; for new, yet to be learned information, theprocessing capacity is limited to only 4 ± 1 element, and if not rehearsed, the information islost within 30s (Cowan 2001). LTM contains cognitive schemas that are used to store andorganize knowledge by incorporating multiple elements of information into a singleelement with a specific function. Skilled performance develops through the building ofincreasing numbers of ever more complex schemas by combining elements consisting oflower level schemas into higher level schemas. If the learning process has occurred over along period of time, the schema may incorporate a huge amount of information.

CLT distinguishes three different types of cognitive load: intrinsic, extraneous, andgermane. Intrinsic is caused by the complexity of the content of the materials to be learnt,extraneous by the format of the instructional materials provided, and germane load is themental effort invested directly in learning (see Sweller et al. 1998). The overall aim of CLTis to reduce extraneous load and increase germane load, while dealing with high intrinsicload. However, as expertise develops, instructional designs need to change. For moreknowledgeable learners, the limitations of WM are not the same as novices, becausepreviously learned information stored in LTM can be activated, effectively increasing thecapacity of WM for domain-related information. Consequently, the demands made byintrinsic and extraneous load differ according to the knowledge base of the learner.Similarly, the challenge to promote germane load varies accordingly. CLT has successfullymet these challenges by identifying a number of effects that inhibit or facilitate learning forboth novices and experts and successfully incorporating these phenomena into a range ofdifferent learning environments (see Van Merriënboer and Ayres 2005). The papers in thisspecial edition build on this previous research, but by applying an interdisciplinaryapproach suggest new ways that CLT can continue to advance the field of learning andinstruction.

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The Individual Papers in the Special Issue

Interdisciplinary research

Evolutionary biology In the last few years, Sweller has reconceptualized CLT to includebiological principles (see Sweller 2004; Sweller and Sweller 2006). In particular, parallelshave been drawn between human cognitive architecture and evolution by natural selection.Sweller proposes the following five principles as analogy for human cognition: (a)information store principle, (b) borrowing and reorganizing principle, (c) randomness asgenesis principle, (d) narrow limits of change principle, and (e) environmental organizingand linking principle. More detail on these principles can be found in previous papers bySweller (see Sweller 2004; Sweller and Sweller 2006). Notably, the fundamental elementsof CLT such as schema theory and the interactions between working memory and long-termmemory are maintained (see Sweller et al. 1998); however, Sweller has expanded the theoryto include a biological base.

In making these changes, Sweller has been influenced by the work of Geary and hisidentification of two biological categories of knowledge. Geary (2005, 2007) differentiatesbetween primary and secondary knowledge. Primary knowledge is that which has beenacquired over a vast amount of time and relates to some of the earliest human behaviors,such as learning to speak. In contrast, secondary knowledge is much more recent andincludes the formal development of mathematics or writing. Of particular interest to CLT isthat primary learning happens almost effortlessly, because humans have evolved to acquirethis knowledge. On the other hand, secondary knowledge has not had the sameevolutionary advantage and requires much effortful learning. Sweller (2008) argues thatprimary knowledge can be learnt without conscious effort, while secondary knowledgerequires much more explicit instruction to be acquired. The WM implication of both typesof knowledge is clear: The acquisition of secondary knowledge is much more demandingon WM than primary knowledge. As a consequence, Sweller and colleagues (see Wong etal. 2008) argue that CLT applies almost exclusively to secondary knowledge only and notprimary knowledge.

In this special issue, Sweller uses his five newly defined principles of CLT to explainhow creativity (in the form of novelty) is itself created. Drawing parallels with naturalselection and how it creates biological novelty, Sweller uses the randomness as genesisprinciple to argue that when faced with a novel problem-solving task humans can only use arandom generate and test strategy to find a solution. If the problem is truly novel to theproblem solver, then there is no prior-knowledge guidance to suggest a move in favor ofanother move; hence, a move must be generated at random. Sweller goes to considerablelengths to emphasize the impact of prior knowledge on this process. The knowledge base ofindividuals dictate exactly how many and of what quality moves can be randomlygenerated. If no relevant knowledge exists about the domain, then creativity is impossible.However, a large knowledge base allows different solutions to be generated and testedleading to creativity.

Sweller also suggests an explanation for how brainstorming works. By generating anumber of random ideas from an existing knowledge base, groups can find a solution to anunknown problem by the random generate and test principle. Interestingly, Sweller makes alink between the goal-free effect (Owen and Sweller 1985) and brainstorming, because in agoal-free environment, learners are required to generate a number of moves, whichultimately can be tested. From the perspective of CLT, brainstorming and goal-freestrategies both can avoid increased levels of cognitive load by removing the possibility of

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working directly back from the goal in a means-ends analysis approach—a heuristic knownto increase extraneous cognitive load when problem solving

By linking CLT to creativity, Sweller (2008) has applied the theory in a new direction.He argues that an important concept like creativity has no proper empirical research base tosupport it, similarly brainstorming, that is, it works but we do not know why. From thisperspective, CLT may be able to provide deeper insights into the underlying cognitiveprocesses of brainstorming, as well as other educational techniques, which are still not fullyunderstood. From such knowledge, more effective learning environments can be designed.

The mirror neuron system The article by van Gog et al. (2008) uses the biological theory ofboth Geary (2007) and Sweller (Sweller and Sweller 2006) to argue that humans haveevolved to learn by imitation and observation. Observation can take the form of studyingexpert models, for example, worked examples, or simply viewing a video. Furthermore,they propose that humans are able to understand and imitate actions because of the mirrorneuron system (see Rizzolatti and Craighero 2004). Mirror neurons evolved to enablehumans to effortlessly imitate certain types of actions that are consistent with biologicallyprimary knowledge.

In what might lead to a significant breakthrough, van Gog et al. use the mirror neuronsystem to explain why some forms of animated instructions can be more effective thatequivalent static diagrams. It has been somewhat puzzling to researchers that animationshave been not been found to be that advantageous compared with statics (see Mayer et al.2005; Tversky et al. 2002). Although researchers have started to unravel this puzzle byidentifying some conditions where animations are clearly effective (see Höffler and Leutner2007; Tversky et al. 2002), the underlying cognitive processes underpinning theseconditions have not been fully understood. Ayres and Paas (2007a, b) provided a plausibleexplanation as to why animations can sometimes fail, in that many contain transitoryinformation. If needed information disappears from the computer screen or video recordingbefore the learner can adequately process or combine it with new information, then WMresources are over stretched accordingly. This form of extraneous load will inhibit learning.However, van Gog et al. develop this argument further by suggesting that if the animationcontains information about human movement, then transitory information is less an issue ashumans have evolved, through the mirror neuron system to deal with it effortlessly withless stress on WM resources. Such an explanation is consistent with the meta-analysis ofHöffler and Leutner (2007), who found that the strongest effect in favor of animations waswith the acquisition of procedural motor knowledge.

Cognitive brain research The article by Kirschner et al. (2008) uses cognitive brainresearch as a source of inspiration for CLT to formulate hypotheses on the differentialeffects of task complexity in collaborative learning environments. Their review of theliterature on learning effectiveness of individual versus collaborative learning revealed thatthere is no clear and unequivocal picture of how, when, and why the effectiveness andefficiency of these two approaches to learning differ. They identify several methodologicalproblems as causes for the mixed findings and propose possible solutions to thoseproblems. The key solution combines CLT and the results of cognitive brain research.

Cognitive brain research on interhemispheric interaction (e.g., Maertens and Pollmann2005) has shown that the capacity of the brain can be increased by dividing the processingof complex tasks between the two hemispheres of the brain, instead of using onehemisphere. Processing within one hemisphere becomes less efficient than processingbetween the two hemispheres as task complexity increases. They conclude that whereas

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dividing processing across the hemispheres is useful when cognitive load is high, under lowload conditions, a single hemisphere can adequately handle the processing requirements.Combining those results with CLT, Kirschner et al. hypothesize that with complex tasks,groups have an advantage above individual learners—as is the case in the research oninformation processing between two hemispheres or within one hemisphere—becausecollaboration allows for distributing cognitive effort among group members.

Philosophy of science The article by Gerjets et al. (2008) uses a philosophical approach totest the legitimacy of whether CLT can be considered a true theory or not. Examining CLTfrom the methodological perspectives of Popper and Sneed, the conclusion is reached thatCLT fails the Popperian test but can be accommodated under the structuralist position ofSneed. Problematical from the Popper view concerns the assumption that cognitive loadconsists of three different cognitive loads (intrinsic, extraneous, and germane). Becausethese three constructs have yet to be empirical validated, CLT cannot be considered a truetheory according to Popper. In contrast, The Sneed position does not require that thefundamental assumptions need be validated experimentally but are considered nontestableaxioms. Therefore, it is less important to test the existence of these basic axioms but moreimportant to test the verifiable hypotheses and applications that the overall network ofassumptions generates.

Gerjets et al. use the structuralist theory position of Sneed to suggest a research agendafor CLT. Firstly, they present an argument based on the research of Gerjets et al. (2008) thatCLT can be refined, by combining it with a specific theoretical model such as cognitive taskanalysis. Secondly, they suggest that although it is not important to verify the existence ofthe three loads from a structuralist point of view, CLT could benefit significantly fromindividual measures of them. The study of Cierniak et al. (2008) is cited as an example ofsuch an approach than can add value to the theory. In this study, Cierniak et al. useddifferent scales to measure the three loads individually, finding that the split-attention effect(see Ayres and Sweller 2005) was influenced by both extraneous and germane load. Thecommon interpretation of the benefits of integrated materials over separated (split) materialsis that the integrated approach lowers extraneous load. However, the finding of Cierniak etal. suggests that germane load is also facilitated, demonstrating that multiple measures canoffer a clear advantage to researchers in untangling the cognitive processes activated inlearning environments.

Intradisciplinary research

Self-directed learning The article by Van Merriënboer and Sluijsmans (2008) combinesCLT with the four-component instructional design model (4C/ID, see Van Merriënboer andKirschner 2007) to suggest ways to design learning environments for self-directed learning.The authors comment that the 4C/ID has much in common with CLT, particularly in howconsiderations of cognitive load underlie both, particularly in the assumption that cognitiveload consists of intrinsic, extraneous, and germane loads. However, Van Merriënboer andSluijsmans make the important distinction that 4C/ID has historically focused more ondesigning educational programs whereas CLT has predominantly dealt with designinginstructional materials. The point is also made that both approaches have contributedtowards complex learning environments; however, 4C/ID has been more focused on real-life situations and rich learning tasks, consistent with its lifelong learning emphasis. Incontrast, much of the experimental work of CLT, in common with many psychology-basedstudies, has featured well-controlled laboratory-based short and/or more artificial tasks.

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In this article, Van Merriënboer and Sluijsmans start with the assumption that richlearning tasks, essential for learning in complex environments, inevitably create a highcognitive load. To overcome this impediment to learning, as well as promote directedlearning, a system based on the 4C/ID is mapped out. To foster self-regulation, wholetasks are used which require the learner to complete the task to acquire domain-specificskills, but equally, the learner is required to assess their own performance and choosefuture learning tasks. Because each of these three skills (task performance, self-assessment, task selection) create both extraneous and intrinsic cognitive load, the modelcarefully uses scaffolding techniques such as fading guidance (see Kester et al. 2007) tomanage the load.

After outlining methods to reduce intrinsic and extraneous, strategies to promotegermane load within the contexts of self-regulated learning are also proposed. Acknowl-edging the importance of task variability in the facilitation of germane load in previousresearch into CLT and 4C/ID, Van Merriënboer and Sluijsmans apply similar principles toself-assessment and task selection skills. Regarding self-assessment, a high premium isplaced on different types of assessments and how self-explanations prompts are used tomake comparisons between these assessments. In the case of developing task selectionskills, tasks are selected according to peer assessments of performance in conjunction witha set of principles based on performance standards. Leading to self-explanations of why thetasks were chosen.

Heuristic learning The paper by Renkl et al. (2008) use worked examples to extend thedesign principles of CLT into the learning domain of heuristics. The authors comment thatmuch of the research into worked examples (see Sweller and Cooper 1985; Atkinson et al.2000) has been primarily focused on algorithmic type learning in the fields of mathematicsand science; however, there is a more recent trend examining the learning of heuristics, insuch domains as concept mapping, cooperation, and augmentation. Examining the types ofexamples used, Renkl et al. define three categories according to their different types ofcontent, as single, double, or triple content examples. Focusing on double-contentexamples, the paper uses composition to show how a domain can have two content levels.If the goal is to learn how to acquire composition skills, then one content level (the mainfocus) is concerned with the structure of composition itself, while the second level isconcerned with the subject of the composition (e.g., an composition on a science topic).Composition is the learning domain and the science topic is the exemplifying domain.Unlike most worked examples domain which have a single content level (e.g., learning howto solve linear equations), double-content domains tend to be more nonalgorithmic innature.

In this highly complex learning environment, consisting of both learning andexemplifying domains, as well as interactions with other auxiliary strategies, it is essentialto consider the different levels and types of cognitive load generated. Hence, Renkl et al.discuss how helpful additional strategies such as self-explanation prompts (see Renkl 2005)and instructional explanations (see Schworm and Renkl 2006) can be best utilized withworked examples to ensure that extraneous load is kept to a minimum and germane loadpromoted. Renkl et al. also discuss the role of expertise in creating the optimum learningconditions, not just from the perspective of the expertise reversal effect in avoidingredundancy (see Kalyuga et al. 2003) but also how expertise in the exemplifying domaininfluences skill acquisition in the learning domain. Accordingly, strategies are recom-mended to match different levels of expertise.

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Summary

The six papers in this collection have a strong common theme and that is CLT can beempowered by applying it to or combining it with other disciplines outside educationalpsychology or other theories within the discipline of educational psychology. Byextending CLT to include research from evolutionary biology (Sweller 2008), the mirrorneuron system (van Gog et al. 2008), and cognitive brain science (Kirschner et al. 2008),CLT can be applied to fields of research that it has not historically focused on, such ascreativity, human movement, and collaborative learning. By applying a philosophicalexamination (Gerjets et al. 2008), the fundamental assumptions of CLT can be scrutinizedin detail and a research agenda mapped out to maintain it as a robust theory. Similarly, byaligning it more closely with self-regulation (Van Merriënboer and Sluijsmans 2008) orheuristic learning (Renkl et al. 2008), CLT can be applied to more realistic and diverseenvironments.

At a more specific level, the authors in the special issue use CLT to provide someplausible answers to some of the more puzzling phenomena in the field of educationpsychology. Research into brainstorming, instructional animation, and collaboration hasoften revealed mixed results and/or lacked a consistent theoretical basis. Sweller (2008)argues that brainstorming is effective because learners are randomly generating and testingideas in an environment low in extraneous cognitive load. van Gog et al. (2008) argue thatlearners can learn from animation using human movement because they have beenbiologically programmed to observe and imitate human movement effortlessly. Similarly,“two heads are better than one” (collaboration) to learn because working memory capacityis relatively increased (Kirschner et al. 2008).

Two other key themes have also emerged. Firstly, considerations of task complexity arean essential requirement of creating effective learning environments. Van Merriënboer andSluijsmans (2008) use the 4C/ID model to outline a strategy to not only complete complexrealistic tasks but also to self-assess performance and develop task selection skills. Renkl etal. (2008) describe five theses for using worked examples with complex heuristic domains,while Kirschner et al. (2008) point out the importance of using complex tasks to best utilizecollaborative learning. Further, Gerjets et al. (2008) describe how specific elaborationprocesses can be used with worked examples to reduce high cognitive load situations.

Secondly, consistent with other recent commentaries (see Ayres and van Gog 2008), amuch greater focus is placed on the development of germane load. The papers by Kirschneret al. (2008), Renkl et al. (2008), and Van Merriënboer and Sluijsmans (2008) not onlyemphasize the importance of reducing extraneous load but also the importance of creatinggermane load—the process directly concerned with learning. Furthermore, as Gerjets et al.(2008) argue, by developing individual measures of cognitive load (including germane),researchers may get a better understanding of the different cognitive processes presentduring learning.

In conclusion, with this special issue, we hope to stimulate cognitive load researchers toexplore the boundaries of their own discipline and look across the boundaries of their owndisciplines. We are convinced that to conduct cutting edge research, cognitive loadresearchers should build on theories and previous research from more than one disciplineand use methods for data collection and analysis from more than one research tradition.Interdisciplinary perspectives, either achieved by the researchers themselves or incollaboration with researchers from other disciplines, might inspire a new generation ofcognitive load research.

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Acknowledgements The authors greatly acknowledge the contributions of Robert Atkinson, RichardCatrambone, Peter Gerjets, Harry O’Neil, Alexander Renkl, Remy Rikers, Katharina Scheiter, WolfgangSchnotz, Dominique Sluijsmans, Mike Spector, John Sweller, Tamara van Gog, and Pieter Wouters, whoacted as reviewers on this special issue.

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