Cognitive Architecture and Instructional Design: 20 Years Later

The 1998 article Cognitive Architecture and Instructional Design discussed human cognitive architecture including an outline of cognitive load theory and its general principles, a description of seven cognitive load effects generated by the theory and issues associated with measuring cognitive load. Below, we will briefly revisit the human cognitive architecture and the original cognitive load effects described in the 1998 article; measurement issues will be discussed in the ‘Measuring Cognitive Load’ section.

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

In the 1998 article, seven cognitive load effects were reported (see the upper part of Table 1). All of these effects with the exception of the variability effect were due to a reduction in element interactivity associated with a decrease in extraneous cognitive load. The variability effect is due to alterations in intrinsic cognitive load. These effects were based on multiple, overlapping experiments carried out in several research centres around the globe and using a variety of materials and a variety of populations. Yet, many of these experiments did not attempt to directly measure comparative cognitive load; rather, cognitive load theory was used to generate instructional techniques and if these techniques produced the expected effects on learning outcomes they were assumed to strengthen the theory. Below, we will briefly describe each of the original effects but we will not attempt to discuss the research body that is available for each effect; for several effects, extensive review studies have been published elsewhere.

Goal-Free Effect

The goal-free effect is also called the reduced goal specificity effect or no goal effect. It is the oldest effect studied in the context of cognitive load theory (Sweller and Levine 1982). It started from the observation that conventional problems (e.g. A car is uniformly accelerated from rest for 1 min. Its final velocity is 2 km/min. How far has it travelled?) are typically solved by means-ends analysis, a process that is exceptionally expensive of working memory capacity because the learner must hold and process in working memory, the current problem state, the goal state, relations between them, problem-solving operators that could reduce differences and any sub-goals. When conventional problems are replaced by goal-free problems (e.g. A car is uniformly accelerated from rest for 1 min. Its final velocity is 2 km/min. Calculate the value of as many variables as you can), learners are no longer able to extract differences between a current problem state and a goal state simply because no goal state is provided. They will now consider each problem state encountered and find any problem-solving operator that can be applied; once an operator has been applied, a new problem state has been generated and the process can be repeated. Whereas means-ends analysis bears little relation to knowledge construction processes, goal-free problem solving greatly reduces cognitive load and provides precisely the combination of low load and focus on solutions that is required for knowledge construction.

Worked Example Effect

Like goal-free problems, worked examples aim to reduce the cognitive load caused by conventional problems and to facilitate knowledge construction. Worked examples provide a full problem solution that learners must carefully study. The worked example effect was first reported by Sweller and Cooper (1985) in the domain of algebra. In contrast to conventional problems, worked examples focus the learners’ attention on problem states and associated operators (i.e. solution steps), enabling them to induce generalised solutions. Thus, studying worked examples may facilitate knowledge construction and transfer performance more than actually solving the equivalent problems. Later research has distinguished between product-oriented worked-out examples that only provide a solution to some problem, process-oriented examples that show the process of finding the solution (e.g. van Gog et al. 2008) and modelling examples that show a human model who is generating the solution (e.g. Hoogerheide et al. 2014). Although there is very strong empirical evidence for the worked example effect, there are also important constraints: Worked examples are less effective for high-expertise learners and the design of good worked examples is difficult, for example, because they should not require the learner to mentally integrate different sources of information (see the split-attention effect below) or combine redundant information (see the redundancy effect below). A review of studies on learning from examples is provided by Renkl (2013).

Completion Problem Effect

A potential disadvantage of worked examples is that they do not force learners to carefully study them. Therefore, van Merriënboer and Krammer (1987) suggested the use of completion problems in the field of introductory computer programming. Such problems provide a given state, a goal state, and a partial solution that must be completed by the learners. In the field of computer programming, this means that the learners receive incomplete computer programs that need to be finished. Although fully worked examples do not explicitly induced learners to study them, learners must carefully study and understand the partial worked examples provided in completion problems because they otherwise will not be able to complete the solution correctly. Completion problems may also be seen as a bridge between worked examples and conventional problems: worked examples are completion problems with a complete solution and conventional problems are completion problems with a partial solution. When designing a course, these differing solution levels allow commencement with completion problems that provide almost complete solutions and gradually work to completion problems for which all or most of the solution must be generated by the learners. This strategy became known as the ‘completion strategy’ (van Merriënboer and Krammer 1990) and can be seen as a forerunner of the guidance-fading effect that will be described below.

Split-Attention Effect

The split-attention effect stems from research on worked examples and was first reported by Tarmizi and Sweller (1988). For instance, a worked example in the domain of geometry might consist of a diagram and its associated solution statements. The diagram alone reveals nothing about the solution to the problem and the statements, in turn, are unintelligible for the learners until they have been integrated with the diagram. Learners must mentally integrate the two sources of information in order to understand the solution, a process that yields a high cognitive load and hampers learning. This split-attention effect can be prevented by physically integrating the diagram and the solution statements, making mental integration superfluous and reinstating the positive effects of worked examples. The split-attention effect not only relates to the spatial organisation of information sources but also to their temporal organisation. Mayer and Anderson (1992) found that animation and associated narration need to be temporally coordinated in order to decrease cognitive load and facilitate learning. A recent review of the split-attention effect is provided by Ayres and Sweller (2014).

Redundancy Effect

While the split-attention effect grew out of the worked example effect, the redundancy effect, in turn, grew out of the split-attention effect. Split attention occurs when learners are confronted with two complementary sources of information, which cannot stand on their own but must be integrated before they can be understood. But what happens when the two sources of information are self-contained and can be understood without reference to each other? Chandler and Sweller (1991) used a diagram demonstrating the flow of blood in the heart, lungs and rest of the body together with statements that described this flow of blood in text. Thus, the diagram and the statements contained the same information and were fully redundant. It was found that only presenting the diagram was superior to presenting both sources of information together. This redundancy effect is due to the fact that effortful processing is required from the learners to eventually discover that the information from the two sources is identical. This finding is important and counter-intuitive based on the assumption that providing the same information twice can do no harm or is even beneficial. Inspection of the literature shows that the redundancy effect has been discovered, forgotten and rediscovered over many decades: As early as 1937, Miller reported that young children learning to read nouns made more progress if the words were presented alone rather than in conjunction with similar pictures.

Modality Effect

All cognitive load effects discussed in the 1998 article assumed that working memory capacity is fixed for a given individual in the sense that the number of elements that could be dealt with was unalterable, with the modality effect as an exception. The modality effect is based on the assumption that working memory can be subdivided into partially independent processors, one dealing with verbal materials based on an auditory working memory and one dealing with diagrammatic/pictorial information based on a visual working memory (e.g. Baddeley 1992). Consequently, effective working memory capacity can be increased by using both visual and auditory working memory rather than either processor alone. Mousavi et al. (1995) were the first to test the modality effect in geometry learning; they presented a diagram either with integrated written text (i.e. visual) or with auditory, spoken text and they hypothesised that the combination with spoken text would be most effective due to the modality effect. They indeed found the modality effect that has since been replicated in many other experiments. The modality effect has important implications for how to deal with split-attention effects: if split attention occurs, presenting the written information in an auditory mode may be equally or even more effective than physically integrating it in the diagram. Ginns (2005a) provided a meta-analysis of the modality effect that is still worth reading.

Variability Effect

Variability over problem situations is generally expected to encourage learners to construct more general knowledge, because it increases the probability that similar features can be identified and that relevant features can be distinguished from irrelevant ones. In other words, increases in variability increase intrinsic cognitive load allowing more to be learned provided there is sufficient working memory resources to handle the increase. Several studies showed that variability not only increased cognitive load during practice but also increased transfer of learning. Initially, the variability effect seemed to contradict all earlier reported cognitive load effects, because it combines an increase rather than a decrease of cognitive load with higher learning outcomes. In the domain of geometrical problem solving, Paas and van Merriënboer (1994a) were the first to describe the variability effect in the context of cognitive load theory: They hypothesised that high variability would have a positive effect on learning and transfer in situations in which cognitive load was low (i.e. learning from worked examples), because in such situations the total cognitive load would stay within limits, irrespective of the fact that variability increased intrinsic cognitive load. In contrast, they predicted that high variability would have a negative effect on learning and transfer in situations in which cognitive load was already high (i.e. learning by solving conventional problems), because the total cognitive load would then overburden learners’ working memory. Indeed, the expected interaction between problem format (worked examples, conventional problems) and variability (low, high) was found. Based on this and similar findings, a distinction was introduced between load that is caused by ‘extraneous’ processes not productive for learning and load that is caused by ‘germane’ processes and productive for learning. When instruction is designed, it should first decrease extraneous cognitive load, but as a new implication, instructional designs that are effective in decreasing extraneous cognitive load may become even more effective if they increase germane cognitive load, provided that total cognitive load stays within limits. As will be described in the ‘Developments in Cognitive Load Theory 1998–2018’ section, this opened up the way for measuring different types of cognitive load and identifying new effects that explicitly aimed at increasing germane processing.