Cognitive Architecture and Instructional Design: 20 Years Later

Since its inception, cognitive load theory has undergone continuous theoretical development as new data have become available. Theoretical developments in turn have generated further data in a constant process. Currently, there is every indication of that spiral continuing and providing a sign-post to future developments. This section will discuss future directions that extend cognitive load theory with constructs taken from other theories, such as resource depletion; self-regulated learning; stress, emotions and uncertainty; and human movement.

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.