Cognitive Architecture and Instructional Design: 20 Years Later

Human Cognitive Architecture Used in 1998

The cognitive architecture used in 1998 reflected our knowledge of human cognition at that time. The basic components of that architecture, working memory, long-term memory and the relations between them were well-known, although the intricate, critical relations between working and long-term memory seemed novel to at least some readers who doubted that working memory functioned differently depending on whether the source of the information was the external environment or long-term memory. In addition, the instructional implications derived from that cognitive architecture were largely unknown.

The capacity and duration limits of working memory have been known at least since Miller (1956) and Peterson and Peterson (1959), although the fact that these limits effectively applied only to novel, not familiar information, seemed more implicit rather than explicit in most treatments. The limitations of working memory when dealing with novel information were absent in most instructional recommendations. These recommendations, especially with regard to the use of instructional problem solving, proceeded as though the characteristics of working memory were an irrelevant consideration. Indeed, most instructional recommendations made no mention of working memory.

Long-term memory was, of course, equally well-known in the literature. Nevertheless, it played almost no role in instructional recommendations perhaps because it may have been associated with rote learning. While rote learning obviously required the storage of information in long-term memory, there seemed to be an assumption that long-term memory played a minimal or no role in learning with understanding. Our emphasis on the central role of long-term memory when learning with understanding and in general skill formation was an unusual aspect of cognitive load theory. That emphasis derived from the critical work of De Groot (1965) whose original work on chess expertise was published in 1946. His finding that chess skill could be entirely explained by remembered chessboard configurations and the best moves for each configuration placed long-term memory indelibly as the central factor in problem-solving skill. This presence had heretofore been largely absent. Again, despite the ramifications of this finding for instructional design, few if any instructional recommendations placed any emphasis on the role of long-term memory.

While working and long-term memory were well-known, the important relations between them were much less emphasised. Working memory was limited in capacity and duration when dealing with novel information but these limitations effectively disappeared when working memory dealt with information transferred from long-term memory. A ready availability of large amounts of organised information from long-term memory results in working memory effectively having no known limits when dealing with such information. Ericsson and Kintsch (1995) in their theory of long-term working memory, reflected this point. Expertise, reliant on information held in long-term memory, transforms our ability to process information in working memory and transforms us, reflecting the transformational consequences of education on individuals and societies. It follows that the major function of instruction is to allow learners to accumulate critical information in long-term memory. Because it is novel, that information must be presented in a manner that takes into account the limitations of working memory when dealing with novel information. These processes provided the cognitive architecture that led to the instructional implications of cognitive load theory in 1998.

Categories of Cognitive Load

In 1998, we discussed three categories of cognitive load: intrinsic, extraneous and germane. Intrinsic cognitive load referred to the complexity of the information being processed and was related to the concept of element interactivity. Because of the characteristics of human cognitive architecture described above, determining the complexity of information processed by humans is difficult. Most measures of informational complexity refer purely to the characteristics of the information. Such measures are inadequate when referring to information being processed by humans because of the relations between working and long-term memory discussed above. Information that has been organised and stored in long-term memory has very different characteristics for humans than the same information prior to it being stored. For readers of this paper, the English word ‘characteristics’ and its Roman letters are processed easily and unconsciously as a single element retrieved from long-term memory. For someone learning to read English, the written word must be processed in working memory as multiple, interacting elements because the written word has not yet been stored as a single element in long-term memory. Complexity or element interactivity depends on a combination of both the nature of the information and the knowledge of the person processing the information. For someone learning to read English, interpreting the multiple squiggles that constitute the word ‘characteristics’ may constitute a very high element interactivity task that overwhelms working memory. For an expert, the same squiggles may constitute only a single element that imposes a minimal cognitive load due to minimal element interactivity. Accordingly, intrinsic cognitive load is determined by both the complexity of the information and the knowledge of the person processing that information. Given these characteristics of the human cognitive system, measures that ignore knowledge when determining complexity are largely useless. Based on this analysis, intrinsic cognitive load only can be changed by changing what needs to be learned or changing the expertise of the learner.

Extraneous cognitive load is not determined by the intrinsic complexity of the information but rather, how the information is presented and what the learner is required to do by the instructional procedure. Unlike intrinsic cognitive load, it can be changed by changing instructional procedures. In 1998, it was assumed that element interactivity only was relevant to intrinsic cognitive load. Subsequently, it became apparent that it equally determines extraneous cognitive load (Sweller 2010). Effective instructional procedures reduce element interactivity while ineffective ones increase element interactivity. The vast bulk of the instructional effects reported below are due to variations in extraneous cognitive load.

Germane cognitive load was defined as the cognitive load required to learn, which refers to the working memory resources that are devoted to dealing with intrinsic cognitive load rather than extraneous cognitive load. The more resources that must be devoted to dealing with extraneous cognitive load the less will be available for dealing with intrinsic cognitive load and so less will be learned. In that sense, intrinsic and germane cognitive load are closely intertwined.

This characterisation of germane cognitive load is a departure from the 1998 paper. In that paper we assumed that germane cognitive load contributed to total cognitive load by substituting for extraneous load. Currently, we assume that rather than contributing to the total load, germane cognitive load redistributes working memory resources from extraneous activities to activities directly relevant to learning by dealing with information intrinsic to the learning task. The need for this alteration arose from the issue that if germane cognitive load simply replaced extraneous load when extraneous load was reduced, then there should be no change in total load following a reduction in extraneous load. Numerous empirical studies indicated a reduction in load following a reduction in extraneous load. The current formulation eliminates this problem by assuming that germane cognitive load has a redistributive function from extraneous to intrinsic aspects of the task rather than imposing a load in its own right.

Instructional Effects Reported in 1998

Human Cognitive Architecture Seen Through the Prism of Evolutionary Psychology

While our 1998 version of human cognitive architecture, with its emphasis on the intricate relations between working memory and long-term memory, provided a critical aspect of how we learn, think and solve problems, the continual advances in our knowledge of human cognition indicated a need to expand that earlier conceptualisation. Much of that expansion revolved around evolutionary psychology which provided an impetus for that work.

Based on Geary’s distinction between biologically primary and secondary knowledge (Geary 2008, 2012; Geary and Berch 2016), evolutionary educational psychology allows us to categorise information in instructionally meaningful ways that now are central to cognitive load theory (Sweller 2016a). Biologically primary knowledge is knowledge that we have evolved to acquire over countless generations. That category of knowledge tends to be critically important to humans providing, as examples, knowledge that allows us to listen and speak, recognise faces, engage in basic social functions, solve unfamiliar problems, transfer previously acquired knowledge to novel situations, make plans for future events that may or may not happen, or regulate our thought processes to correspond to our current environment. Humans must learn to engage in these very complex cognitive activities but because of their importance, we have evolved to acquire the necessary skills effortlessly and automatically. Consequently, they cannot be taught to most people.

Biologically primary knowledge is modular with little relation between the cognitive processes associated with one skill and another (Geary 2008, 2012). Each skill is likely to have evolved in different evolutionary epochs requiring very different cognitive processes. Our ability to regulate our thought processes to correspond to our current environment is likely to have evolved before we became modern humans as did our tendency to use gestures to communicate, while our ability to organise our lips, tongue, breath and voice to speak is likely to have evolved far more recently.

A very large number of biologically primary skills are generic-cognitive in nature such as general problem-solving skills or even our ability to construct knowledge (Sweller 2015, 2016b; Tricot and Sweller 2014). A generic-cognitive skill is a basic cognitive skill that we have evolved to acquire instinctively because it is indispensable to a very wide range of cognitive functions. Generic-cognitive skills tend to be more concerned with how we learn, think and solve problems rather than the specific subject matter itself. Over the last few decades, many educationalists, correctly realising the importance of such skills, have advocated that they be taught. Such campaigns tend to fail, not because the skills are unimportant but because they are of such importance to humans that we have evolved to acquire them automatically without instruction. The enormous emphasis on teaching general problem-skills last century provides an example. Evidence for the effectiveness of teaching generic-cognitive skills requires randomised, controlled trials using far transfer tests. Far transfer is required because the rationale of generic-cognitive skills is that they will enhance performance over a wide range of areas and, of course, it is essential to ensure that any performance improvement is not due to domain-specific knowledge.

It should be noted that while the acquisition of biologically primary skills tends to occur automatically and unconsciously without explicit teaching, it does not follow that use of the skills occurs unconsciously in every context. For example, we learn to speak our native language unconsciously without explicit effort but may require considerable effort to find appropriate words with appropriate meanings in any given situation. We need to learn how to use biologically primary knowledge in specific domains, which leads to biologically secondary knowledge.

Biologically secondary knowledge is knowledge we need because our culture has determined that it is important. Examples of biologically secondary information can be found in almost all topics taught in education and training contexts. Educational institutions were invented because of our need for people to acquire knowledge of biologically secondary information.

We have evolved to acquire secondary knowledge but it is acquired very differently to primary knowledge. Except insofar as it is related to primary knowledge, secondary knowledge is not modular but rather, is part of a single, unified system. Because it is a unified system, there are considerable similarities in acquiring biologically secondary knowledge irrespective of the domain under consideration. All secondary knowledge tends to require conscious effort on the part of the learner and explicit instruction on the part of an instructor. It is rarely acquired automatically. The distinction between the two processes can be exemplified by the distinction between learning to listen and learning to read. As indicated above, we learn to listen automatically, without tuition. Most people do not learn to read automatically. Despite reading and writing having been invented thousands of years ago, few people learned to read and write until the advent of modern education very recently.

In contrast to the generic-cognitive skills associated with most biologically primary knowledge, biologically secondary knowledge is heavily domain-specific (Sweller 2015, 2016b; Tricot and Sweller 2014). We have evolved to learn how to solve a variety of problems using generic-cognitive skills. We have not specifically evolved to learn how to read and write a particular word in English or Chinese, or that the best first move when solving a problem such as (a + b)/c = d, solve for a, is to multiply both sides by the denominator on the left side. These domain-specific, biologically secondary skills need to be explicitly taught (Kirschner et al. 2006; Sweller et al. 2007) and actively learned.

Most educationists intuitively understand that generic-cognitive skills are far more important to human functioning than domain-specific skills and this understanding has led to a substantial emphasis on generic-cognitive skills. Nevertheless, there is an increasing recognition that while domain-specific skills are eminently teachable, purely generic-cognitive skills are not teachable and attempts to teach them lead to dead-ends (Sala and Gobet 2017). The previous emphasis on generic-cognitive skills is due to a failure to realise the distinction between biologically primary and secondary knowledge (Tricot and Sweller 2014). An accent on teaching generic-cognitive skills was, of course, present in 1998 with the popularity of teaching generic problem-solving skills. The 1998 paper was partially a reaction to that accent. That phase has passed but other generic-cognitive skills have replaced that emphasis with, as indicated by Sala and Gobet, no greater success. Furthermore, despite about a century of effort, there is little evidence from far transfer studies, that generic-cognitive skills which transcend domain-specific areas can be taught. The ‘natural’, minimal guidance procedures used to acquire biologically primary, generic-cognitive skills are inappropriate for the acquisition of biologically secondary, domain-specific skills that tend not to be acquired easily, unconsciously and automatically.

The data summarised by Sala and Gobet (2017) provide a major justification for the post 1998 inclusion of evolutionary psychology into cognitive load theory. Of course, none of the above eliminates the possibility of new data becoming available indicating the possibility of teaching purely generic-cognitive skills leading to far transfer effects. Should a substantial body of such data become available, further modifications to cognitive load theory would be required.

We should not conclude from the above argument that biologically primary knowledge and generic-cognitive skills are irrelevant to instructional issues. While we doubt that attempts to teach biologically primary, generic-cognitive skills will be successful, they can be used to assist in teaching biologically secondary, domain-specific skills (Paas and Sweller 2012). For example, students may know how to randomly generate problem solution moves without being instructed in the procedures to do so, but may not be aware of the domain-specific conditions where the technique might be effective. Pointing out to students that a generic-cognitive skill should be used on a particular class of specific problems can be instructionally effective (Youssef-Shalala et al. 2014). Furthermore, it is important to note that teaching anything involves a combination of primary and secondary skills with the secondary skill being the only part that is learned. For example, medical doctors are taught how to effectively communicate in an emergency team using the SBAR method, always reporting on the situation, background, assessment and recommendations (Beckett and Kipnis 2009). Obviously, the SBAR method can be taught because doctors are able to speak with each other. But, although it makes no sense to teach doctors how to speak with each other in a generic sense because this is primary knowledge, teaching them the specific SBAR method is secondary knowledge and might have a very positive effect on team communication in emergency situations.

The cognitive architecture required to process biologically secondary information consists of biologically primary processes that provide a base for cognitive load theory. Together, the processes mimic the information processing procedures of biological evolution (Sweller and Sweller 2006) and can be described as constituting a natural information processing system. They can be described by five basic, biologically primary principles. These principles provide the cognitive architecture that underlies the instructional procedures of cognitive load theory.

4C/ID and Cognitive Load

Cognitive load theory provides evidence-informed principles that can be applied to the design of instructional messages or relatively short instructional units, such as lessons, written materials consisting of text and pictures, and educational multimedia (instructional animations, videos, simulations, games). It shares several of its principles with mental workload models, which focus on workplace performance rather than learning and instructional design (e.g. Wickens 2008), and with the cognitive theory of multimedia learning, which has an exclusive focus on the design of multimedia materials (CTML; Mayer 2014). A closely related model that is based on precisely the same cognitive architecture as cognitive load theory and that has been developed fully in parallel is four-component instructional design (4C/ID). The 4C/ID model provides an important extension to cognitive load theory because it focuses on the design of educational programs of longer duration (e.g. courses or whole curricula).

The first description of 4C/ID appeared in 1992 (van Merriënboer et al. 1992) and the book Training Complex Cognitive Skills, which provided the first complete description of the 4C/ID model, appeared in the same period as the 1998 cognitive load article (van Merriënboer 1997). The 4C/ID model exclusively deals with complex learning, which is characterised by high element interactivity in a learning process that is often aimed at the development of complex skills or professional competencies. A first basic assumption of 4C/ID is that complex skills include ‘recurrent’ constituent skills, which are consistent over tasks and situations and can be developed into routines, as well as ‘non-recurrent’ constituent skills, which rely on problem solving, reasoning and decision-making (van Merriënboer 2013). A second basic assumption is that courses or programs aimed at the development of complex skills can always be built from four components: (1) learning tasks, (2) supportive information, (3) procedural information and (4) part-task practice (see Fig. 1).

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Learning tasks (indicated by the big circles in Fig. 1) are preferably based on real-life tasks and by performing these tasks learners acquire both non-recurrent and recurrent constituent skills and learn to coordinate them. In order to manage intrinsic cognitive load, learning tasks are first organised according to levels of increasing complexity (indicated by dotted boxes around series of learning tasks in Fig. 1); thus, learners start to work on simple learning tasks but the more expertise they acquire the more complex the tasks they work on (i.e. a spiral curriculum). Second, in order to manage extraneous cognitive load, learner support and guidance gradually decrease at each level of complexity (indicated by the diminishing filling of the circles at each level of complexity in Fig. 1); thus, learners first receive a lot of support and guidance but support/guidance gradually decreases until learners can perform the learning tasks at a particular level of complexity without support/guidance—only then, they continue to work on more complex learning tasks for which they initially receive a lot of support/guidance again, after which the whole process repeats itself. The 4C/ID model describes several approaches to fading-guidance but the completion strategy, from studying worked examples via completion tasks to conventional tasks, is a particularly important one. Third, in order to stimulate germane processing, all learning tasks in a course or program show high variability of practice (indicated by the triangles at different positions in the learning tasks), stimulating learners to compare and contrast tasks with each other.

Supportive information (indicated by the L-shapes in Fig. 1) helps learners learn to perform the non-recurrent aspects of learning tasks (i.e. problem-solving, reasoning, decision-making). It explains how the domain is organised (often called ‘the theory’) and how tasks in the domain can be systematically approached; it is connected to levels of complexity because for performing more complex tasks, learners need more, or more elaborated, supportive information. It provides a bridge between what learners already know and what they need to know to successfully carry out the learning tasks. Both the work on the learning tasks and the study of supportive information aim at knowledge construction (through, in order, inductive learning and elaboration). Because supportive information typically has high element interactivity, it is preferable not to present it to learners while they are working on the learning tasks. Simultaneously performing a task and studying the supportive information would almost certainly cause cognitive overload. Instead, supportive information is best presented before learners start working on a learning task, or, at least apart from working on a learning task. In this way, learners can construct knowledge structures in long-term memory that can subsequently be activated in working memory and be further restructured and tuned during task performance. Retrieving the already constructed cognitive structures is expected to be less cognitively demanding than activating the externally presented complex information in working memory during task performance.

Procedural information (indicated by the black beam with upward pointing arrows in Fig. 1) and part-task practice (indicated by the series of small circles in Fig. 1) help learners learn to perform the recurrent aspects of learning tasks—they aim at knowledge automation. Procedural information consists of ‘how-to instructions’ and corrective feedback and typically has much lower element interactivity than supportive information. From a cognitive load perspective, it is best presented just-in-time, precisely when learners need it during their work on the learning tasks, because the formation of cognitive rules (one subprocess of automation) requires that relevant information is active in working memory during task performance so that it can be embedded in those rules. That is, for example, the case when teachers give step-by-step instructions to learners during practice, acting as an ‘assistant looking over the learners’ shoulder’. Finally, part-task practice of selected recurrent task aspects may further strengthen cognitive rules (another subprocess of automation). In general, an over-reliance on part-task practice is not helpful for complex learning but fully automating basic or critical recurrent constituent skills (e.g. the multiplication tables in primary education, operating medical instruments in a health professions program) may decrease the cognitive load associated with performing the whole learning tasks and so free up processing resources for performing and learning non-recurrent task aspects. The 4C/ID model has been developed in parallel with cognitive load theory and its latest version is described in the book Ten Steps to Complex Learning (van Merriënboer and Kirschner 2018a; for a short description of the model, see van Merriënboer and Kirschner 2018b).

Instructional Effects Described After 1998

This section will describe the most important cognitive load effects that have been studied and reported between 1998 and 2018. Eight new effects are listed in the bottom part of Table 1. We will, however, begin this section by discussing the element interactivity effect, an effect already known before 1998 but not presented as a cognitive load effect in the 1998 article. The reason for not previously listing the element interactivity effect is that it is not a ‘simple’ effect but a so-called compound effect, which is an effect that alters the characteristics of other cognitive load effects. In the 1998 article, only simple effects were reported. Compound effects frequently indicate the limits of other cognitive load effects. Four of the eight new effects (excluding the element interactivity effect) discussed below are also classified as compound effects and are discussed first. This may be seen as an indication of a maturing theory, because such a theory not only includes simple effects but also higher-order effects that limit the reach of simpler effects.

Measuring Cognitive Load

Since the publication of the 1998 article, there has been an ongoing research effort to examine issues related to the measurement of cognitive load. Therefore, one would expect that in the past 20 years substantial progress has been made regarding these issues. Here, we evaluate this progress and present a concise overview of three major developments regarding the measurement of cognitive load. This evaluation is based on previous reviews of cognitive load measurement, such as the ones presented in the theoretical articles of Paas et al. (2003, 2008); and van Gog and Paas (2008). In addition, it takes account of a recent reconceptualisation of germane cognitive load as referring to the actual working memory resources devoted to dealing with intrinsic cognitive load (Sweller et al. 2011). As a result of this reconceptualisation, only intrinsic and extraneous cognitive load are distinguished as basic categories of cognitive load.

The first important development is related to the further specification of the subjective measurement technique that was originally introduced by Paas (1992) to provide an overall measure of cognitive load. Although this measure has been extensively and successfully used showing good psychometric properties, some researchers remain sceptical about its capacity to measure cognitive load, even when comparisons with physiological measurement techniques have shown that the subjective rating scale is just as valid and reliable and easier to use as objective techniques (e.g. Szulewski et al. 2018). The main advantages of the subjective technique over physiological techniques are its sensitivity and its simplicity. In contrast to physiological measurement techniques, the subjective rating scale is sensitive to small differences in invested mental effort and task difficulty. Whereas the simplicity of the subjective rating scale is considered its major strength, because it can be easily used in research and practice, it is also considered by many as its major weakness. Due to its simplicity, it provides an overall measure of cognitive load (i.e. intrinsic plus extraneous load) and therefore cannot easily be used to differentiate between the different types of cognitive load. This has resulted in a search for techniques that can be used to differentiate between the different types of cognitive load as well as a search for ‘objective’ measurement techniques that can be used as an online measure of cognitive load. Before these two research developments are discussed in more detail, we first will discuss the ongoing further specification of the subjective rating scale technique.

Paas et al. (2003) and van Gog and Paas (2008) have noted that the subjective rating scale is used in many different ways than originally suggested by Paas and van Merriënboer (1994a, 1994b). Among other differences, researchers have used different verbal labels (i.e. ‘task difficulty’ instead of ‘invested mental effort’), used fewer categories (e.g. 5 or 7 instead of 9) and used only one measurement after all learning or test tasks, instead of an average of multiple measurements after each learning and test task. Whereas the first two examples would require more research into the psychometric properties of the adapted rating scales, the latter example has been further investigated by Gog et al. (2012) and Schmeck et al. (2015). They compared the original way of measuring load using an average score based on multiple measurement after each learning and test task to an adapted approach using only one measurement after the learning phase and one after the test phase. The question was whether this would lead to a different estimate of the magnitude of cognitive load. Results showed that the one measure after the instruction or test phase was always higher than the average of multiple measurements during the learning or test phase. Although the exact reason for the difference is not clear, the higher single score after the learning or test phase could be suggestive for depletion of working memory resources (Chen et al. 2018).

There are still a lot of questions that need to be answered in future research with regard to the optimal use of the subjective ratings scale. What is the effect of time on task on perception of invested mental effort or task difficulty? What are the effects of age and gender on the ratings? Do we need to present participants with a baseline before they start to rate? More research is needed to answer these and other questions and further specify the conditions for effective use of the rating scale.

The second development is related to extending the subjective technique by designing questionnaires that can differentiate between the different types of cognitive load. Whereas several researchers have tried to measure only changes in one specific type of cognitive load (e.g. Ayres 2006), others have investigated methods to measure the different types of cognitive load (e.g. Cierniak et al. 2009). One notable development was described by Leppink et al. (2013, 2014), who investigated the usefulness of a new psychometric instrument in which the different types of cognitive load were represented by multiple indicators. The authors concluded that the results of both studies provided support for the assumption that intrinsic and extraneous cognitive load can be differentiated using their 10-item psychometric instrument. Although it is clear that more empirical evidence is needed, for example, by using different questions and looking at different domains, the results so far are promising regarding the instrument’s capability of distinguishing between different types of cognitive load.

The third development is related to the ongoing efforts to find more objective measures of cognitive load. To this end, cognitive load researchers have been using secondary task techniques and physiological measures of cognitive load. Based on the assumption of a limited working memory capacity, secondary task techniques use performance on a secondary task as an indicator of cognitive load imposed by a primary task. It is assumed that low or high performance on the secondary task are indicative of high and low cognitive load imposed by the primary task. A recent example of a secondary task technique is the rhythm method developed by Park and Brünken (2015), which consists of a rhythmic foot-tapping secondary task. Korbach et al. (2017) showed that this technique was sensitive to hypothesised differences in cognitive load between a mental animation group, a seductive detail group and a control group. However, secondary task techniques have been criticised for their intrusiveness (i.e. imposing an extra cognitive load that may interfere with the primary task; Paas et al. 2003) and inability to differentiate between different types of cognitive load.

Physiological techniques are based on the assumption that changes in cognitive functioning are reflected by physiological variables. Whereas several researchers have proposed to use neuroimaging techniques, such as functional magnetic resonance imaging (fMRI; e.g. Whelan 2007), a technique that has actually been used in cognitive load research is electroencephalography (EEG; Antonenko and Niederhauser 2010; Antonenko et al. 2010). Using a hypertext-based learning environment, Antonenko and Niederhauser (2010) showed that several aspects of the EEG reflected hypothesised differences in cognitive load. A more frequently used technique for measuring cognitive load is based on eye-tracking variables, such as pupil dilation, blink rate, fixation time and saccades. For example, van Gerven et al. (2004) showed that pupil dilation is positively correlated with cognitive load in young adults, but not in old adults. Although, it has become increasingly easy to use physiological measures due to the development of mobile measuring devices, much more research is needed into these techniques and their potential to measure cognitive load.

Working Memory Resource Depletion

One new line of research is derived from recent work of Chen et al. (2018), who proposed a possible extension of CLT based on the working memory resource depletion hypothesis. This hypothesis holds that working memory resources become depleted after a period of sustained cognitive exertion resulting in a reduced capacity to commit further resources. Previous research has found both general and specific depletion effects. With regard to general depletion effects, Schmeichel (2007) showed that engaging in self-control tasks can lower performance on subsequent working memory tests. With regard to specific depletion effects, Healey et al. (2011) showed that depletion effects only occurred when there was a match between the to-be-ignored stimuli in the first task and the to-be-remembered stimuli in the working memory task.

Working memory resource depletion under some conditions may be linked to ego depletion, although it needs to be noted that many studies on ego depletion bear little relation to working memory. For example, it is unlikely that avoiding sweets while on a diet has the same working memory implications as learning mathematics. The vast differences in ego depletion tasks possibly contributes to doubts concerning the effects of ego depletion (see Etherton et al. 2018, and Dang 2018, for conflicting meta-analyses). Considered from a cognitive load theory perspective, most studies on ego depletion do not include learning and only some of the literature used tasks that are likely to have imposed a heavy working memory load. For those tasks, if extensive cognitive effort during learning can substantially deplete working memory resources, that factor may be important when designing instruction.

A possible extension of cognitive load theory with the instructional design implications of working memory resource depletion after extensive cognitive effort investment in learning tasks, provided the rationale for the study of Chen et al. (2018). They indicated that the resource depletion hypothesis could provide an explanation of the spacing effect that occurs when spaced presentation of information with spacing between presentation or practice episodes is superior to the same information processed for the same length of time in massed form without spacing between episodes. Chen et al. obtained the spacing effect using mathematics learning along with data indicating that massed presentations resulted in a reduced working memory capacity compared to spaced presentations. Massed practice may reduce working memory resources while spaced practice may allow resources to recover.

The spacing effect is arguably the oldest known psychology-generated instructional effect but there has never been agreement concerning its causes. If confirmed by subsequent work, Chen et al.’s (2018) findings may allow cognitive load theory to be used as a theoretical explanation of the effect.

By confirming the working memory resource depletion hypothesis for learning tasks using the spacing effect as a vehicle, Chen et al. (2018) argued that the assumption of cognitive load theory that the content of long-term memory provides the only major determinant of working memory characteristics may be untenable. An implicit assumption of cognitive load theory, based on the narrow limits of change principle, has been that working memory capacity is relatively constant for a given individual with the only major factor influencing capacity being the content of long-term memory. As indicated by the environmental organising and linking principle, the limitations of working memory when dealing with novel information can be eliminated if the same information has been stored in long-term memory. High element interactivity information, once organised and stored in long-term memory can be easily and rapidly transferred in large quantities to working memory imposing a minimal working memory load.

The results of the Chen et al. (2018) study suggest that working memory capacity can be variable depending not just on previous information stored via the information store, the borrowing and reorganising, and the randomness as genesis principles, but also on working memory resource depletion due to cognitive effort. Consequently, a fixed working memory assumption needs to be discarded in favour of a working memory depletion assumption following cognitive effort. It is believed that this change will have considerable consequences and result in a considerable extension of cognitive load theory.

Cognitive Load Theory and Self-Regulated Learning

A second new line of research relates cognitive load theory to self-regulated learning. Both cognitive load theory and models of self-regulated learning, which deal with learners’ monitoring and control of their learning processes, may be seen as particularly important perspectives for supporting lifelong learners in an information-rich, complex and fast-changing society (van Merriënboer and Sluijsmans 2009). Both theoretical frameworks already pay attention to learners’ regulation decisions, such as the allocation of cognitive resources (cf. the self-management effect, where learners apply cognitive load principles themselves in order to decrease cognitive load) and the selection of study activities. In the context of cognitive load theory, Paas et al. (2005) introduced ‘task involvement’ as a measure of resource allocation: it is high when learners show relatively high performance combined with high invested mental effort (‘no pain, no gain’); it is low when learners show relatively low performance in combination with low invested mental effort. In addition, task selection has been used in a series of experiments (e.g. Nugteren et al. 2018) as an indicator of regulation accuracy: when learners are asked to select their own learning tasks for study, measures of performance and invested mental effort can be used to draw conclusions on their quality of task selection (i.e. do learners select tasks that are either too difficult or too easy for them?). In the context of self-regulated learning, the allocation of study time is typically used as a measure of resource allocation and for the selection of study activities, judgements of learning and restudy decisions (‘which part of the text do you want to restudy in order to improve your understanding?’) in combination with performance measures serve as an indicator of regulation accuracy. Future research might profit from combining these different measures of resource allocation and regulation accuracy.

A general finding is that learners are not good in regulating their learning (Bjork et al. 2013). The question is then whether it can be taught. On the one hand, the self-regulation of learning processes will largely rely on primary knowledge and might thus be impossible to teach. On the other hand, all learning involves a combination of primary and secondary knowledge and the secondary knowledge component certainly is teachable. Higher-level regulation processes such as reducing the negative effects of split attention by applying cognitive load principles oneself (cf. self-management effect), selecting suitable learning tasks (cf. self-directed learning) and selecting relevant learning resources (cf. information literacy) may rely on a mix of primary and secondary knowledge and thus—at least partly—be teachable. De Bruin and van Merriënboer (2017) suggested the use of Koriat’s cue-utilisation framework (1997) as a bridge between cognitive load theory and models of self-regulated learning and as a basis for doing research on teaching self-regulation. The idea is that students use cues to inform them about their learning and future performance and, moreover, that they are inclined to use invalid cues. For example, students often use ‘ease of processing’ as a cue for understanding and future performance: if a text is easily read, students typically judge their understanding of the text and their performance on a future test as high. Yet, a much more valid cue would be the ability to generate keywords some time after reading the text. Similarly, a learner who is solving a conventional problem using means-ends-analysis may use the high cognitive load as an invalid cue for learning (‘this cost me a lot of effort so I must have learned a lot’). Yet, a much more valid cue for germane processing would be the ability to explain the generated solution to a peer student. Teachers might then give prompts to students that help them learn to use more valid cues for regulating their learning (e.g. ‘Can you generate keywords for this text about one hour after reading it?’; ‘Can you explain the solution that you just generated for this problem to your peer?’).

Notwithstanding these possibilities, as is typical of biologically primary knowledge, currently, there is virtually no evidence that self-regulation is teachable as a domain-general procedure that will improve performance on far transfer tasks (Sweller and Paas 2017). Such findings are essential for this area to remain viable but there is little evidence of such results from any area (Sala and Gobet 2017).

Emotions, Stress and Uncertainty

A third research line is based on a new model of cognitive load that aims to identify the environment-related causal factors of cognitive load (Choi et al. 2014). The new model distinguishes three types of effects of the physical learning environment on cognitive load and learning: Cognitive effects (e.g. uncertainty), physiological effects (e.g. stress) and affective effects (e.g. emotions; but note that the different effects on learning may be closely intertwined; Evans and Stecker 2004). The basic assumption is that stress, emotions and uncertainty may restrict the capacity of working memory by competing with task-relevant processes; thus, they increase cognitive load, hamper learning and decrease transfer (Moran 2016). There has been a considerable amount of research on this phenomenon, but the basic premise of the great majority of this research is that learning is best supported by preventing states that might negatively affect learning (e.g. Plass and Kaplan 2016). This might be true in general education, but in vocational and professional education, emotions, stress and uncertainty are often an integral part of performing professional tasks. For example, nurses must learn to handle negative emotions when caring for patients who are in the last phase of their life; security officers must learn to deal with stress in high-risk violence situations, and medical doctors must learn to face uncertainty when fast decision-making is required on the basis of incomplete patient information. In such cases, it is unproductive to prevent emotions, stress and uncertainty during training; on the contrary, educational programs must be carefully designed in such a way that learners develop professional competencies enabling them to perform professional tasks up to the standards, including the ability to deal with emotions, stress and uncertainty and to maintain overall wellbeing.

If emotions, stress and uncertainty are seen as undesirable states for learning, one might say that they cause extraneous load that should be decreased by preventing these states. But if emotion, stress and uncertainty are seen as an integral element of the task that must be learned, they contribute to intrinsic cognitive load and must be dealt with in another way. Then, future research should contribute to identifying instructional interventions that help learners deal more effectively with stress, emotions and uncertainty. For example, imagination or mental practice prior to performing the task may be expected to lower intrinsic load during actual task performance, counterbalancing the high load resulting from stress, emotions or uncertainty and so improving learning and the ability to deal with stress, emotions or uncertainty for future tasks (Arora et al. 2011). Novice learners, however, will not yet be able to imagine the processes that are required for successful task performance in a vivid way (Ginns 2005b). For them, imagination will probably not work but, as an example, collaboration might possibly increase effective working memory capacity because of the collective working memory effect and so counteract the high load resulting from stress, emotions or uncertainty.

Human Movement

A fourth new research line builds on the human movement effect. This effect has been used to explain why animations are more effective than statics when cognitive tasks involving human movement are taught. Although research into the human movement effect has mainly focused on learning by observing movement, recent research suggests that similar effects on cognitive load and learning may be obtained as a result of making movements during learning. There is ample evidence that making movements, such as gestures and tracing, can affect available working memory resources and cognitive load. It has been shown that making gestures can be used for cognitive offloading of information during problem-solving, leading to a reduction in working memory load (Wagner-Cook et al. 2012; Goldin-Meadow et al. 2001; Ping and Goldin-Meadow 2010; Risko and Gilbert 2016). With regard to tracing, Hu et al. (2015) showed that students who traced angle relationships with their index finger when studying paper-based worked examples in geometry showed higher learning outcomes than students who only studied the examples. Similarly, in a study with a group of primary-school children who studied worked examples on an iPad either by tracing temperature graphs with their index finger or without such tracing, Agostinho et al. (2015) found higher transfer performance in the tracing group.

Together with the cognitive load theory view that biologically primary information, such as human movement, is at most marginally affected by working memory limitations (Paas and Sweller 2012), the theoretical framework of grounded or embodied cognition has been used to explain the effects of movements on cognitive load and learning, by asserting that cognitive processes, including information processing and learning, are inextricably linked with sensory and motor functions within the environment, including gestures and other human movements (Barsalou 1999). Research supporting the embodied cognition view shows that observing or making gestures leads to richer encoding and therefore richer cognitive representations. Interestingly, the involvement of the more basic motor system seems to reduce load on working memory during instruction (e.g. Goldin-Meadow et al. 2001), which means that this richer encoding is less cognitively demanding and which confirms the evolutionary account of cognitive load theory.

From the research, it is clear that motor information may constitute an additional modality that can also occupy WM’s limited resources. As it seems difficult to firmly reconcile the cognitive effects of human movement with the working memory model adopted by cognitive load theory, it can be argued that human movement may constitute an additional modality that should be considered within existing WM models. This argument is substantiated in the integrated distributed attention model of working memory proposed by Sepp et al. (this issue). It is believed that a single working memory system that can integrate multiple sources of information across modalities while adjusting the distribution of attentional focus during processing may assist in explaining and supporting findings in cognitive load theory and other areas of inquiry in the future.