COMMENTARY

Enhancing Instructional Efficiency of InteractiveE-learning Environments: A Cognitive Load Perspective

Slava Kalyuga

Published online: 24 August 2007# Springer Science + Business Media, LLC 2007

Abstract This concluding paper summarizes the main points and recommendations of theprevious papers in this Special issue within a conceptual framework of cognitive loadtheory. Design of efficient interactive learning environments should take into account mainfeatures and limitations of our cognitive architecture. The paper provides a brief overviewof this architecture and sources of cognitive load, considers their instructional implicationsfor interactive e-learning environments, and analyzes methods for managing cognitive loadand enhancing instructional efficiency of such environments.

Keywords Interactive learning . Cognitive load .Working memory

Interactive learning environments respond dynamically to learners actions and areassociated with active, learner-engaged processing of learning materials. Such environ-ments are expected to promote deep cognitive processes and result in active construction ofnew knowledge. It is rather obvious that learner physical activity within interactive settingsmay not necessarily translate into required cognitive processes. Instead, it may imposeadditional processing demands on learner cognitive resources and thus hinder learning.Mixed findings in research on effectiveness of interactivity and learner control support thisconcern and require interactivity research to be directed on analyses of associated cognitiveprocesses and structures. The cognitive aspects of learning in interactive e-learningenvironments are the main focus of this special issue.

In this concluding commenting paper, I try to summarize the main points of view,suggestions and recommendations of the papers in this issue within a cognitive loadframework. Cognitive load aspects of interactivity in learning are well-recognized issues inthe field, and they have been considered with more or less details in most of the presentedpapers. I believe this framework can also provide a suitable conceptualization for moregeneral analysis of the conditions and methods for enhancing instructional efficiency ofinteractive learning environments. It is also important that the cognitive load approach

Educ Psychol Rev (2007) 19:387–399DOI 10.1007/s10648-007-9051-6

S. Kalyuga (*)University of New South Wales, Sydney, Australiae-mail: s.kalyuga@unsw.edu.au

considers learning and instruction in terms of efficiency rather than mere effectiveness, thatis, in terms of cognitive cost (cognitive resources spent, mental effort invested, or cognitiveload imposed) of achieving instructional effects. With a sufficiently high level ofmotivation, students can eventually learn from any, even primitive or poorly designed,learning environments. The whole point of investing considerable human and financialresources into the design and development of sophisticated high-tech interactive e-learningenvironments is to achieve returns in terms of efficiency: learning faster and without mentalstress.

The paper starts with a brief overview of main characteristics of human cognitivearchitecture and sources of cognitive load, and then proceeds to considering theirinstructional implications for interactive learning environments. Although traditionalclassroom instruction may also fall under the definition of interactive learning, in thispaper similar to most of reviews in this issue, I consider electronic, computer-based, onlineenvironments or, in short, interactive e-learning environments.

Cognitive Architecture Underlying Human Learning

Human cognitive architecture is characterized by several major features that are essentialfor understanding cognitive processes in learning and performance. These features areassociated with general principles that may apply to all natural information processingsystems (Sweller 2003, 2004). An essential characteristic of our cognitive architecture is theavailability of a large store of organized information with practically unlimited capacity andduration. The concept of long-term memory (LTM) is traditionally associated with thestorage of organized knowledge base in the form of hierarchical schematic knowledgestructures.

Our cognitive system also includes a functional mechanism that significantly restricts therange of immediate changes to this massive information store in LTM. The concept ofworking memory (WM) as a locus of conscious information processing and short-termmaintenance signifies this feature in most cognitive models. Different models consider WMeither as an independent component of our cognitive system or as an activated part of LTM.However, the essential common characteristics of WM are its severe limitations in capacityand duration when dealing with novel information (e.g., Baddeley 1986; Cowan 2001;Miller 1956). It could be easily overloaded if more than a few chunks of new informationare processed simultaneously. Cognitive load experienced by learners is caused by theseprocessing limitations, and cognitive load theory (Sweller 2003, 2004; van Merriënboer andSweller 2005) considers them as a major factor influencing learning and performance.

Processing limitations of WM impose severe restrictions on changes to LTM knowledgebase that should necessarily be incremental and slow. Even though we borrow mostcomponents of our vast knowledge base from other sources rather than discover or buildthem from the scratch, we always actively reconstruct this knowledge within our WM. Theborrowed elements of information are reorganized and integrated with available priorknowledge structures from LTM. In situations where information cannot be borrowed fromother sources, we use our default problem-solving mechanisms based on search for possiblesolution steps followed by tests of their effectiveness (e.g., see Newell and Simon 1972, forgeneral problem solving methods). Since these processes occur within limited WM, theyimpose a heavy cognitive load and leave minimal, if any, cognitive resources formeaningful construction of organized knowledge structures in LTM, thus providinginadequate conditions for learning.

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However, even in conditions of severe WM limitations, our cognitive system issomehow capable of dealing with very complex environments, organizing varioussituations, appropriately directing our attention, and timely coordinating different cognitiveactivities. Our high-level cognitive processes, including learning and construction of newknowledge, are governed by an executive mechanism that engages and coordinates differentprocesses and directs learner attention to appropriate sources of information. Suggesting theexistence of a specialized separate executive subsystem within our cognitive architecture isnot productive, because it requires explaining how this subsystem is governed in turn (aninfinite regress problem). A more productive way to explain the operation of a flexible andpowerful executive mechanism is to assume that organized knowledge structures in LTMperform this role in high-level cognitive processes (Sweller 2003).

Each type of familiar situations is associated with a set of cognitive schemas in our LTMthat provide an executive function. When activated in a specific case, these schemas areintegrated into a situation model that is continuously updated in accordance with incominginformation. Thus, the executive mechanism is not a fixed component of our cognitivearchitecture, but rather a functional entity constructed for every specific situation or task toperform the role of managing cognitive processes (Kalyuga and Sweller 2005). The theoryof long-term working memory (Ericsson and Kintsch 1995) provides a possible mechanismof executive functioning of LTM knowledge base. Knowledge structures associated withactive elements of information in WM create a virtual construct of long-term workingmemory (LTWM) that has effectively unlimited capacity and prolonged duration. Whensuch a knowledge-based executive is not available in a specific novel situation, directexternal instruction can perform the required guiding role by telling us exactly how tohandle the situation or solve a task. If neither appropriate LTM knowledge base, no externalinstructions are available to provide executive guidance in a novel situation, then we usegeneral search strategies by default. Even though such strategies may be effective inreaching the goal, they are cognitively inefficient and cause high levels of working memoryload (Sweller 1988).

Balancing Knowledge-based and Instructional Guidance in Learning

In selecting among three possible candidates for the executive guiding role in a specificsituation (internal LTM knowledge structures, external instructional guidance, or randomsearch), our cognitive system seems to follow a ‘cognitive economy’ principle byminimizing cognitive resources involved in completing the task. For example, usingavailable LTM knowledge structures in executive role could be more ‘economical’ thanrelying on search processes or even following an external guidance. If we can access LTMknowledge that is suitable for a task, we usually use it first. Better entrenched andpreviously more frequently activated knowledge structures could be more preferable thanrecently learned new knowledge, even though such well-entrenched knowledge mayoccasionally provide wrong guidance. For example, scientific misconceptions and folkbeliefs are deep-rooted and simple structures that may require less WM resources and,therefore, be more preferable in the executive guiding role than more comprehensive andsophisticated scientific knowledge (thus partially explaining the durability and perpetuationof many scientific misconceptions or religious dogmas).

Because of this powerful role of LTM knowledge structures in cognition and learning,cognitive processes of more knowledgeable and experienced learners differ considerablyfrom those of novice learners. Well-organized knowledge structures allow us to effectively

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reduce WM load by encapsulating many elements of information into larger chunks thatcould be treated as single elements in WM. Similar cognitive-load-reduction effects couldalso be achieved by practicing procedures and rules until they can operate under automaticrather than controlled processing (Anderson 1993; Kotovsky et al. 1985). While noviceshave to deal with many elements of information in a mostly random way (thus overloadingWM) or rely on an external guidance, experts use available well-learned LTM knowledgestructures in their area of expertise.

As learner expertise in a task domain increases, an optimal balance between knowledge-based and external guidance shifts towards the available knowledge. For novice learners,external guidance may be the only source of executive function while, on the opposite sideof a continuum, experts could have all required knowledge structures available. Atintermediate levels, an optimal executive function should be knowledge-based whendealing with familiar elements of information and externally-based when dealing with newelements of information. Ideally, these two sources should complement each other with aminimal gap that would be within the ability of learners to manage the missing guidance ontheir own or, using a well-known concept of Vygotsky (1978), within the learners’ zone ofproximal development. If a situation becomes too difficult to handle within a learner’savailable knowledge base, an external instructional guidance should be provided. When theappropriate balance is not maintained, the learning could be inhibited.

Minimally guided (e.g., discovery) learning environments could be suboptimal fornovice learners with an insufficient prior knowledge base. On the other hand, when well-guided instructional messages are provided to more experienced learners, an overlapbetween available LTM knowledge structures and external guidance that deal with the sameunits of information could make the learners to cross-reference and reconcile theoverlapping internal and external guidance structures thus consuming additional WMresources. For example, in some situations, experts may need to take apart or dismantletheir well-learned and automated LTM structures. Therefore, presenting experiencedlearners with redundant guidance may inhibit their learning relative to using a minimalguidance-free instruction. This is an example of the expertise reversal effect that occurswhen instructional methods that are optimal for novices may hinder learning for moreexperienced users, and vice versa (see Kalyuga 2005, 2006a for recent overviews). Animmediate implication of this effect is that instructional methods need to be tailored tolevels of learner expertise in a task domain. Adaptive interactive learning environmentscould provide an effective means for such tailoring.

In general, the design of interactive learning environments should support the acquisitionand use of learners’ organized knowledge structures by reducing unnecessary irrelevantforms of WM load that may prevent the allocation of sufficient cognitive resources tolearning processes, and by enhancing forms of processing load that are essential forlearning. The following section describes the main sources of cognitive load that need to bedealt with in interactive learning environments.

Sources of Cognitive Load in Learning Environments

In order to learn, we need to attend to and process elements of new information, establishkey connections between them, integrate them with available knowledge base, and buildnew or modified knowledge structures. These processes occur in WM and inevitablyimpose a cognitive load. However this load is essential for learning. One part of thisessential cognitive load (which is usually called an intrinsic cognitive load) is caused by the

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complexity of a task or learning material. This complexity could be measured by a numberof interacting elements of information that need to be considered simultaneously in order tobe understood, and by the degree of interactivity between these elements relative to thelevel of task-specific expertise of a learner. Another part of the essential cognitive load(called a germane cognitive load) is caused by learning activities that are intentionallydesigned to enhance acquisition of appropriate schematic knowledge structures, forexample, asking learners to self-explain principles and reasons behind the providedsolution procedures, or to compare problem solution procedures in structurally similar butcontextually variable situations. Because these types of cognitive load are essential forcomprehending material and constructing new higher-level knowledge, it is important toprovide all the necessary resources to accommodate this cognitive load without exceedingWM capacity.

In contrast to the essential load, an extraneous cognitive load is associated with adiversion of cognitive resources on activities that are irrelevant to learning and caused bypoor instructional design. A simple example is artificially separating in space and/or time(e.g., placing on different hyperlinked pages) related elements of information that need tobe processed simultaneously to understand an instructional message. In this case, mentalintegration of these elements may require additional search-and-match and holding of someunits of information in WM while other units are located, attended and processed. Anotherexample is searching for suitable solution steps for an unfamiliar problem when noknowledge of a solution procedure is available. Such search may also require keeping andprocessing a large number of elements (problem statements, sub-goals, solution moves) inWM, thus consuming additional resources that are not directly relevant to knowledgeconstruction and learning.

The following general situations represent common sources of extraneous cognitiveload:

& related elements of information or representations that need to be processedsimultaneously are separated in space and/or time;

& too many new elements of information are introduced into WM and/or areintroduced too fast to be successfully incorporated into LTM structures;

& learners do not have appropriate prior knowledge to deal with the situation, andinstruction does not provide sufficient external guidance thus forcing learners touse random search procedures;

& learners have sufficient prior knowledge that overlaps with provided externalguidance thus requiring learners to mentally coordinate different representations ofthe same information.

For efficient learning to occur, total cognitive load should not exceed limited WMcapacity. When a learner deals with a complex task that is characterized by a high degree ofelement interactivity relative to the learner level of expertise (a situation with high intrinsiccognitive load), an additional extraneous cognitive load caused by an inappropriate designcould leave insufficient resources for learning. In this situation, the required essentialcognitive resources could be provided only if non-essential, irrelevant forms of cognitiveload are eliminated or reduced. The general methods for reducing extraneous cognitive loadin learning environments include eliminating spatial and temporal split of related sources ofinformation, managing step-size and rate of introducing new elements of information,providing a direct access to the required external guidance, and avoiding diversion ofcognitive resources on redundant cognitive processes.

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Types of Interactive E-learning Environments

Before considering how the described sources and methods for managing cognitive loadapply to interactive e-learning environments, a common classification scheme for suchenvironments needs to be established. The main defining feature of interactive learningenvironments is their responsiveness to learners’ actions. Although the papers in this issueagree with this explicitly or implicitly, they differ to various degrees in the proposed typesor levels of interactivity. Besides, a closely related concept of learner control is alsoconsidered (e.g., Scheiter and Gerjets 2007). While most of higher level interactiveenvironments usually allow some form(s) of learner control, simple interactive environ-ments may not involve such control, for example, responsive system-controlled environ-ments that provide automatic feedback on all learners’ entries. Even relatively complexinteractive learning environments could be entirely system-controlled, for example,adaptive tutoring systems that automatically tailor instructional procedures and formats ofpresentation to learners’ behavior and responses. On the other hand, any learner-controlledenvironment is an interactive one, since it responds to learners actions at least in regard tothe controlled parameter(s). Therefore, the learner control represents an important feature ordimension of interactive learning environments.

Based on the presented reviews, in general it is possible to distinguish betweencontrolling characteristics of information delivery, representational forms, and content. Theinformation delivery control involves pacing (changing the rate of delivery) and sequencing(changing the order of delivery). The representation control allows learners to select formsof presentation (e.g., modality and dynamics of presentations, angles of viewing, visualcues). The control of content ranges from selecting the amount of information (e.g., simplevs extended feedback, hints, or help), segmenting information into proper digestible units(e.g., zooming in/out), to selecting units or elements of information to be learned (e.g.,navigating through the information space, selecting content from a menu, selecting answeroptions from the Internet search results).

Since the defining feature of interactive environments is their responsiveness, they couldbe classified based on different types of responses to learner activities. Two importantdimensions for describing such responses are their flexibility and dependence on learnerprevious activities. Accordingly, the environmental responses could be (1) fixed,predetermined, and independent of the history of learner previous behavior; (2) flexible,variable, and independent of the history of learner previous behavior; (3) adapted or tailoredto the history of learner previous behavior with a fixed set of options; and (4) dynamic anditeratively tailored to the history of learner previous behavior with a flexible set of options.

The lowest level of interactivity (a feedback level) is associated with providing a pre-defined feedback on specific learners’ actions (solution steps, questions, local searchqueries, answers, next step hints, etc.). The feedback could be immediate or delayed;simple, corrective (yes–no, correct–incorrect) or extended, explanatory (e.g., principle-based explanations, word references, glossaries, help); with or without a learner control (e.g.,an automatic feedback or feedback on demand). Different combinations of these featureswould determine different sub-levels of interactivity from simple automatic feedback toextended on-demand feedback (Atkinson and Renkl 2007).

The next level of interactivity (a manipulation level) involves real-time online change ortransformation of information in response to learners’ actions. In contrast to the fixed,ready-made responses at the previous level, the manipulation level provides flexible,variable responses, although not tailored to the learner previous behavior. Usually, this levelof interactivity allows different degrees of learner control (e.g., moving objects by using a

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click-and-drag technique, rotating an object, manipulating a simulation by entering specificvalues for input parameters, selecting answer options for web search queries etc.), althoughit could also be fully system-controlled, for example, worked-out simulations or dynamicvisualizations (animation, rotation, etc.) that demonstrate the process to the learner asresponses to her/his actions.

The following level of interactivity (an adaptation level) involves responses that aretailored to the learners’ previous behavior, even though they are drawn from a fixed set ofoptions. Adaptive interactive e-learning environments dynamically tailor the selection oflearning tasks, instructional procedures and formats based on the information about learneractions and online behavior. What is presented next is determined in real time by what thelearner is doing now and has done previously. Adaptive interactive environments could beeither system-controlled (automatic adaptation) or learner-controlled (e.g., advisory systemsthat suggest possible options for learners to select from).

Finally, the upper level of interactivity (a communication level) is represented bydynamic online learning environments that involve flexible, non-predetermined iteratively-adapted responses to learners live queries. This level may include features of all previouslevels, for example, dynamic feedback, manipulation, real-time personalized task selectionand information tailoring. Online prompting for and submitting self-explanations orpredictions for next procedural steps (Atkinson and Renkl 2007), alternating betweenobservations and practice while working in dyads (Wouters et al. 2007) represent someexamples of this level of interactivity. Technically, this level requires online synchronous orasynchronous communication channels between learners and between learners andinstructors.

Complex interactive e-learning environments usually include several (or even all) of theabove levels of interactivity and learner control. For example, multi-user virtual environ-ments (Nelson and Ketelhut 2007) represent very rich inquiry-based collaborativeinteractive e-learning environments that involve feedback, manipulation, and communica-tion levels of interactivity. In such environments, students can also exercise different levelsof control up to the full control of the content. Cognitive tutors (Koedinger and Aleven2007) represent another example of sophisticated interactive e-learning environments thatinvolve different types of feedback and hints on each problem solution step and adaptiveprocedures for task selection based on student problem-solving performance. Feedback andhints could be either requested on-demand or system-generated, and sequenced with agradual increase in the level of provided details.

Managing Cognitive Load in Interactive E-learning Environments

Different types of interactivity provide means for managing various sources of cognitiveload. The feedback interactivity is instrumental for balancing the executive function inlearning between knowledge-based and instruction-provided guidance. This guidance-balancing problem is described as the assistance dilemma by Koedinger and Aleven (2007)or as guided activity principle by Moreno and Mayer (2007). According to the feedbackprinciple (Moreno and Mayer 2007), novice learners learn better with explanatory ratherthan corrective (simple) feedback alone because the explanatory feedback is the onecapable of providing external instructional guidance instead of missing internal mentalstructures. A similar general conclusion about usefulness of extended feedback provided inresponse to learner problem-solving errors is supported by studies with cognitive tutors(Koedinger and Aleven 2007).

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With lower levels of learner control (e.g., no control or delivery control—pacing andsequencing), it is only possible to provide external guidance retroactively in response tocertain learner actions or on demand. To balance the guidance more flexibly by providingor withdrawing information with changing levels of learner experience in a task domain,higher levels of learner control—representation and content control—are required. Forexample, allowing learners to limit the representation format to pictorial-only mode wouldeliminate redundant verbal explanations that could otherwise overload limited WMprocessing capacity for more experienced learners. However, there is an evidence indicatingthat relying on learner-controlled decisions on when they could benefit from the externalguidance on-demand (feedback, hints, etc.) may not necessarily be more effective that usinga system-controlled provision of guidance (Koedinger and Aleven 2007), especially fornovice learners. For example, system-controlled worked-out animated procedures (amanipulation level of interactivity) may provide effective external guidance for learnerswith lower levels of prior knowledge.

Adaptation interactivity is an important means of balancing executive guidance as wellas the rate and/or amount of information provided to learners. By adapting instructionalprocedures and formats to levels of learner expertise in a task domain, an optimal level ofinstructional guidance could be provided at each level. For example, fully worked-outsolution procedures or direct guidelines could be provided to novice learners at the initialobservation phase of a skill acquisition process to facilitate construction of organizedknowledge (Wouters et al. 2007). In adaptive environments, as learners acquire moreexperience in the domain, detailed explanations need to be gradually omitted and a relativeshare of problem-solving practice or exploration increased (Atkinson and Renkl 2007;Renkl and Atkinson 2003). For instance, worked examples could be gradually faded andreplaced with completion tasks that ask learners to complete remaining steps of theprocedure. Finally, as learners reach higher levels of experience in the domain, problem-solving practice or exploratory learning environments could be used to learn relatively newprocedures in this task domain. When used with low-knowledge learners, such environ-ments need to be supplemented with appropriate scaffolding to provide sufficient guidance.For example, an individualized guidance system embedded into a highly interactiveexploratory multi-user virtual environment demonstrated significant learning benefits overunguided exploration (Nelson and Ketelhut 2007). Consistent exposure of learners toguidance messages produced higher gains in scientific inquiry skills and domain-specificknowledge.

Generally, a specific format in which guidance is provided to students may not beimportant (e.g., extended feedback messages could serve the same guiding role asembedded worked examples). What is important though is that the guidance should beprovided when learners lack sufficient task-specific knowledge base to serve in theexecutive role to prevent reverting to unproductive search activities. Therefore, worked-outexamples embedded into interactive learning environments that already provide extendedfeedback and hints, may not improve learning because these redundant examples couldduplicate feedback and hint messages. Interactive problem-solving environments thatprovide rich hints at the student request are effectively converted into annotated workedexamples (Koedinger and Aleven 2007). It is possible that in some situations, embeddingworked examples into such environments could even inhibit learning because of wastingadditional cognitive resources on coordinating different sources of duplicated guidance (aredundancy effect).

In accordance with the expertise reversal effect, the level of learner prior knowledge isan important mediating factor that has consistently been found to influence the effectiveness

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of interactivity in dynamic e-learning environments (Lowe 2004; Schnotz and Rasch 2005;Wouters et al. 2007). Computational knowledge-tracing methods (Koedinger and Aleven2007) or rapid diagnostic assessment techniques (Kalyuga 2006a) could be used for real-time evaluation of levels of learner task-specific expertise required for building adaptiveonline environments. Available evidence indicate that adaptive systems using eitherevaluation method produce better learning results than equivalent non-adaptive learningenvironments (see Koedinger and Aleven 2007, for a review of effectiveness of masterylearning with model-tracing intelligent adaptive tutors; Kalyuga 2006b; Kalyuga andSweller 2005, for adaptive rapid assessment-based tutors).

For example, with the first-step rapid diagnostic assessment method, learners arepresented with a task for a limited time and asked to indicate their first step towardssolution. The first step would involve different responses for users with different levels ofexpertise: while an expert may immediately provide the final answer, a novice may onlybegin a search process. Thus, the method would determine what structures the learner canrapidly retrieve from LTM and bring into WM to apply to a task or situation she or he isfacing. With the rapid verification method, after studying a task for a limited time, users arepresented with a series of possible (both correct and incorrect) solution steps reflectingvarious stages of the solution procedure, and are asked to rapidly verify the suggested steps(for example, by pressing corresponding keys on the computer keyboard) (see Kalyuga2006a, for an overview of the methods). Since higher levels of expertise in a domain arealso characterized by reduced mental effort in task performance, adaptive learningenvironments could be more efficient if rapid tests are combined with measures of mentaleffort. Such combined measures (efficiency indicators) were used by Salden et al. (2004)for the dynamic selection of learning tasks in air traffic control training, and by Kalyuga(2006b) and Kalyuga and Sweller (2005) in adaptive tutors in algebra and kinematics.

Interactive e-learning environments with a communication level of interactivity mayincrease essential cognitive load by prompting learners for self-explanations and predictions(Atkinson and Renkl 2007). The first technique requires learners to explain explicitlysuggested solution steps or actions based on learned principles of the domain, while thesecond method asks learners to predict the next procedural step before demonstrating ordescribing this step. These techniques could also be implemented at lower levels ofinteractivity by placing appropriate prompts after explaining each procedural step (Wouterset al. 2007) or asking learners to reflect upon the essential material and their cognitiveactivities (Moreno and Mayer 2007). However, only the communication level ofinteractivity may allow effective verification and evaluation of student responses and (ifnecessary) correction of their cognitive structures by providing appropriate feedback,additional questions or prompts.

Some recent technological innovations in communication systems (see Clark et al. 2007)could be used to manage cognitive load in these interactive learning environments. Forexample, collaboration supporting and promoting facilities can help to structure the taskand set up a shared text construction space. Indexed knowledge bases from embeddedglossaries to online digital libraries can provide students with source material andbackground information for constructing their arguments and evaluating ideas. Asynchro-nous communication environments allow learners to manage rate and amount ofinformation processed at one time. Rich online visual representations of the learning taskmay reduce extraneous and enhance essential load by providing common contextualanchors and support for argumentation. Dynamic visualizations may allow students torepresent their arguments in cognitively efficient graphical forms such as dynamic conceptmaps and diagrams, thus reducing required WM resources. Socio-cognitive structuring

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tools assist in providing sequencing, role scripting, dynamic tracking of participants’positions, and representing them in a graphical form. These techniques may reducerandomness in computer-mediated collaborative environments and provide cognitivesupport for participants, thus reducing extraneous load and enhancing essential processing.As new technologies for tracking student interactions in real time are developed, moresupport functionality will be integrated into communication-based interactive learningenvironments to provide higher levels of interactivity and customized scaffolding tolearners (Clark et al. 2007).

Reducing Extraneous Load Caused by Interactivity

Similar to other learning environments, the design of interactive e-learning environmentsmay impose excessive extraneous cognitive load that disrupts learning (Moreno and Mayer2007). The same types of interactivity that allow managing essential load may also produceextraneous load. For example, poorly designed feedback messages may produce split-attention when they appear away from the original task or on top of the learner entries (e.g.,in a separate window that covers the task statement or the solution entry field). Feedbackmessages may also provide an excessive amount of information and/or excessive rate of itsintroduction. As mentioned previously, redundant feedback information could also producean unbalanced executive guidance for learners with higher levels of prior knowledge.

Manipulation interactivity may produce split-attention and excessive amount and/or rateof information introduction by engaging learners into manipulating too many elements. Forexample, manipulating a simulation by varying values of several entry parameters mayrequire learners to observe concurrent changes in different locations on the screen (spatialsplit-attention) or keep track of sequential events (temporal split-attention). Rotating ormoving an object may generate a temporal split-attention because of the need to maintainimages of its previous states in WM for comparing them mentally with the subsequentstates. Implementing facilities for tracking sequential changes in interactive dynamicvisualizations (e.g., in instructional animations) could provide a means of reducing thisextraneous load (Wouters et al. 2007).

Even though adaptive and communication-based interactive learning environments couldbe efficient means of balancing executive guidance by tailoring instruction to cognitivecharacteristics of individual learners, they could also generate split-attention and involveexcessive rate and/or amount of information that need to be processed. For example,communication environments, especially those involving synchronous communicationchannels, may require learners to process overwhelming amounts of information fromdifferent sources, coordinate these sources of information, and integrate the selectedelements of information with a situation model in WM and available prior knowledge.

Appropriate learner control techniques could be instrumental in reducing extraneouscognitive load in interactive e-learning environments. For example, means of deliverycontrol (e.g., pacing and sequencing information units) may properly balance the rate andamount of information processed in WM at one time (pacing principle, Moreno and Mayer2007) and reduce temporal split attention. The effects could be moderated by the levels oflearner expertise. For learners with lower levels of prior knowledge, pacing may not beeffective on its own and need to be supplemented with segmenting materials into smallersections (Mayer and Chandler 2001; Wouters et al. 2007).

Methods for representation control may help to balance the amount of information andreduce spatial split attention by selecting appropriate modes of presentations, and to direct

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learner attention by displaying appropriate visual cues. Content control methods could alsobalance the amount of information provided to individual learners according to their needsand available task-specific knowledge base. For example, more experienced learners couldthemselves segment the content and appropriately sequence the segments, thus facilitatingknowledge construction. Content control methods could often be effective only forrelatively more experienced learners who have sufficient prior knowledge of the taskdomain (Wouters et al. 2007). To reduce the influence of missing prior knowledge on theeffectiveness of interactive learner-controlled environments, Moreno and Mayer (2007)suggested including pre-training sessions that would activate or provide learners withrelevant prior knowledge (the pre-training principle).

When exploring interactively a complex hypermedia environment, a learner (even arelatively advanced one) could have difficulties in maintaining goals, be lost insearching for relevant sub-goals and solution moves. This search might consumeresources that would become unavailable for constructing relevant knowledgestructures. A large number of navigational choices may cause learner disorientationand distraction (Scheiter and Gerjets 2007) that could also increase extraneous cognitiveload. The level of learner prior experience in a domain is an important factor influencingthe effectiveness of higher levels of learner control. Unsupported hypermedia environ-ments seem to be more suitable for experienced learners with sufficient levels of priorknowledge that could guide these learners in their exploration of the environment(Scheiter and Gerjets 2007).

For relatively advanced learners, the efficient interactive nonlinear exploration incomplex ill-structured domains (e.g., literature or history) could be based on traversing theinformation space along several pre-defined intersecting dimensions and revisiting the samecontent material in a variety of different contexts (Spiro and Jehng 1990). With this method,learners are effectively provided overlapping instructional sub-goals that may preventirrelevant random search activities that could otherwise overload WM capacity. Interest-ingly, for advanced learners, such partially directed exploration in complex unstructuredinteractive environments might have cognitive load consequences similar to eliminatingspecific problem-solving goals for novice learners in simple well-structured domains (goal-free effect; Sweller and Levine 1982). Both techniques could reduce extraneous cognitiveload irrelevant to learning.

Conclusion

Studies of cognitive processes in learning have significantly improved our understanding ofthe structure, characteristics, and limitations of our cognitive system. Instructionalimplications of this knowledge for different types of interactive learning environmentsreviewed by contributors to this Special issue reveal a rather complex picture. Most of theapproaches and techniques used in interactive e-learning environments may generate bothessential and unnecessary cognitive processes, and contribute to both reduction and increasein cognitive load. A common notion across most papers in this issue is that interactivity andlearner control might help, but might also hinder learning. Specific conditions of theirapplication, in particular learner cognitive characteristics, should be taken into account ineach case rather than relying rigidly on some fixed principles. The aim is to transfer learnerbehavioral activity (or functional interactivity, according to Wouters et al. 2007) intocognitive interactivity that generates essential cognitive processing relevant to knowledgeconstruction without increasing non-essential extraneous processing load.

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By using appropriate types of interactivity and levels of learner control, as well as somespecific design techniques suggested in this Special issue, interactive e-learning environ-ments could be improved to better match the nature of human cognition. For example,direct guidance could be provided to low-prior knowledge learners at the appropriate timeor on-demand using various forms of feedback and hints; unnecessary or redundantscaffolding could be timely removed as learners progress through the task domain inadaptive interactive environments; step-sizes and rates of presentation could be learner-controlled to ensure that the capacity of WM is not exceeded; split-attention situationscould be eliminated or reduced by appropriately controlling presentation formats; adaptivee-learning environments may allow efficient dynamic tailoring of content, presentationformats, and delivery features to changing cognitive characteristics of individual learners.Structural characteristics and processing limitations of human cognition represent a majorfactor defining the efficiency of learning in interactive environments and need to be takeninto account when designing and evaluating such environments.

References

Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.Atkinson, R. K., & Renkl, A. (2007). Interactive example-based learning environments: Using interactive

elements to encourage effective processing of worked examples. Educational Psychology Review, 19.DOI 10.1007/s10648-007-9055-2.

Baddeley, A. D. (1986). Working memory. New York: Oxford University Press.Clark, D. B., Sampson, V., Weinberger, A., & Erkens, G. (2007). Analytic frameworks for assessing dialogic

argumentation in online learning environments. Educational Psychology Review, 19. DOI 10.1007/s10648-007-9050-7.

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storagecapacity. Behavioral and Brain Sciences, 24, 87–114.

Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.Kalyuga, S. (2005). Prior knowledge principle. In R. Mayer (Ed.), Cambridge handbook of multimedia

learning (pp. 325–337). New York: Cambridge University Press.Kalyuga, S. (2006a). Instructing and testing advance learners: A cognitive load approach. NY: Nova Science.Kalyuga, S. (2006b). Assessment of learners’ organized knowledge structures in adaptive learning

environments. Applied Cognitive Psychology, 20, 333–342.Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of

adaptive e-learning. Educational Technology Research and Development, 53, 83–93.Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive

tutors. Educational Psychology Review, 19. DOI 10.1007/s10648-007-9049-0.Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problem hard? Evidence from Tower of

Hanoi. Cognitive Psychology, 17, 248–294.Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257–274.Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster

deeper understanding of multimedia messages? Journal of Educational Psychology, 93, 390–397.Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for

processing information. Psychological Review, 63, 81–97.Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology

Review, 19. DOI 10.1007/s10648-007-9047-2.Nelson, B. C., & Ketelhut, D. J. (2007). Scientific inquiry in educational multi-user virtual environments.

Educational Psychology Review, 19. DOI 10.1007/s10648-007-9048-1.Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in

cognitive skills acquisition: A cognitive load perspective. Educational Psychologist, 38, 15–22.Salden, R. J. C., Paas, F., Broers, N. J., & van Merriënboer, J. J. G. (2004). Mental effort and performance as

determinants for the dynamic selection of learning tasks in air traffic control training. InstructionalScience, 32, 153–172.

398 Educ Psychol Rev (2007) 19:387–399

Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational PsychologyReview, 19. DOI 10.1007/s10648-007-9046-3.

Schnotz, W., & Rasch, T. (2005). Enabling, facilitation, and inhibiting effects of animations in multimedialearning: Why reduction of cognitive load can have negative results on learning. Educational TechnologyResearch and Development, 53, 47–58.

Spiro, R. J., & Jehng, J. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinearand multidimentional traversal of complex subject matter. In D. Nix & R. Spiro (Eds.). Cognition,education, and multimedia: Exploring ideas in high technology. Hillsdale, NJ: Lawrence ErlbaumAssociates.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.Sweller, J. (2003). Evolution of human cognitive architecture. In B. Ross (Ed.), The psychology of learning

and motivation (vol. 43, pp. 215–266). San Diego, CA: Academic.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., & Levine, M. (1982). Effects of goal specificity on means-ends analysis and learning. Journal of

Experimental Psychology: Learning, Memory, and Cognition, 8, 463–474.Van Merriënboer, J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments

and future directions. Educational Psychology Review, 17, 147–177.Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.Wouters, P., Tabbers, H. K., & Paas, F. (2007). Interactivity in video-based models. Educational Psychology

Review, 19. DOI 10.1007/s10648-007-9045-4.

Educ Psychol Rev (2007) 19:387–399 399