Cognitive Architecture and Instructional Design: 20 Years Later

Natural information processing systems such as human cognition require a large store of information in order to function in our complex natural world. Long-term memory provides that structure in human cognition. We do not need to teach people how to store or organise information in long-term memory indicating its biologically primary function. Long-term memory was explicitly articulated in the 1998 paper.

The Borrowing and Reorganising Principle

The vast bulk of information stored in long-term memory comes from other people. Humans are intensely social with powerfully evolved procedures for obtaining information from others and for providing information to others. Because it is a biologically primary skill, we automatically assume that we will provide and receive information from others during our lives. This principle was to some extent assumed in the 1998 paper, although it was not explicitly stated. Cognitive load theory with its emphasis on explicit instruction places prominence on this principle.

The Randomness as Genesis Principle

While most of the information stored in long-term memory is obtained from others, if no one is available from whom to borrow the information, it will need to be generated. Novel information is generated using a random generate and test procedure during problem solving. The procedure only is used when information is unavailable from one’s own or someone else’s long-term memory. When problem solvers do not have information indicating which moves should be made at a given point, they have no choice other than to randomly generate a move and test it for effectiveness with effective moves retained and ineffective ones jettisoned. Again, this procedure does not require instruction because it is biologically primary.

The Narrow Limits of Change Principle

When dealing with human cognition, this principle refers to the severe limitations of working memory when processing novel information. This principle has always been central to cognitive load theory and was clearly articulated as such in 1998. A basic assumption of the principle has been that for any given individual, general working memory capacity is fixed. With recent evidence that working memory depletion occurs after cognitive effort and recovers after rest (Chen et al. 2018), that assumption must be modified to allow such capacity variations. (This issue is discussed further in the ‘Future Directions’ section.)

The Environmental Organising and Linking Principle

While working memory is limited when processing novel information, there are no known limits when familiar, organised information from long-term memory is processed. Once information is stored in long-term memory, environmental cues can be used to generate actions appropriate to that environment. In this manner, the previous principles can be used to construct knowledge in long-term memory that can be used to govern action that is appropriate to the environment. The impetus to use previously organised and stored information in this fashion is biologically primary and does not require tuition. This principle was heavily emphasised in the 1998 version of cognitive load theory.

This cognitive architecture, with its emphasis on the importance of explicit instruction when dealing with the biologically secondary, domain-specific content that is characteristic of most educational programs, provides a base for cognitive load theory and an explanation for its success in generating novel instructional procedures. Instruction should be explicit because we have evolved to learn directly from other people via the borrowing and reorganising principle. In line with the narrow limits of change principle, it needs to be organised in a manner that reduces working memory load because working memory load primarily occurs when processing novel, domain-specific information. Obtaining information from others using the borrowing and reorganising principle reduces working memory load compared to generating information ourselves using the randomness as genesis principle. Once information has been obtained and stored in long-term memory via the information store principle, the limitations of working memory disappear and the information can be transferred back to working memory using the environmental organising and linking principle to generate appropriate action. By devising instruction to accord with this cognitive architecture, we can expect learning to be enhanced. Of course, the ultimate test is empirical and is provided by the cognitive load effects.

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).

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).