Cognitive Architecture and Instructional Design: 20 Years Later

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.

Element Interactivity Effect

This effect was already known in 1998 but as a compound effect, it was not classed as a cognitive load effect (see Sweller 1994). It occurs when effects that can be obtained using high element interactivity information disappear or reverse using low element interactivity material. Element interactivity can be altered either by altering levels of expertise as occurs when demonstrating the expertise reversal effect (see description below) or by changing the material to incorporate either higher or lower levels of element interactivity. Examples of changed instructional advantages due to changing information that learners must process from high to low element interactivity may be found in Chen et al. (2015, 2016, 2017). They obtained a conventional worked example effect using high element interactivity mathematical material in which students had to learn to solve problems. In contrast, low element interactivity material in which students had to learn mathematical definitions yielded a reverse worked example effect. Students who were required to generate an appropriate response learned more than students who were shown the correct response.

Expertise Reversal Effect

The expertise reversal effect is, in essence, a variant of the more general element interactivity effect (Chen et al. 2017). The pre-1998 cognitive load effects can be obtained using novice learners processing high element interactivity information. With increases in expertise, element interactivity decreases due to the environmental organising and linking principle. Concepts and procedures that consisted of multiple elements can, with increases in expertise, be stored in long-term memory as a single element that is transferred to working memory for use in appropriate environments. Instructional procedures designed for novices dealing with multiple, interacting elements can be counterproductive as expertise increases and the interacting elements become embedded in knowledge structures held in long-term memory. As a consequence, with increasing expertise, the above effects first decrease in size, then disappear, and can eventually reverse (Kalyuga et al. 2003, 2012). For example, worked examples benefit novices. With increasing knowledge, practice at solving problems becomes increasingly important rather than having negative effects.

Guidance-Fading Effect

The guidance-fading effect is another compound effect that is closely related to the element interactivity and expertise reversal effects and for which the redundancy effect is central too. For novices, additional information or particular activities such as studying worked examples may be essential. With increases in expertise, these same activities may become redundant and impose an unnecessary cognitive load. Past a certain point, studying worked examples may be counterproductive and they should be faded out and replaced by problems. This general principle is particularly important for educational programs of longer duration, in which learners gradually acquire more expertise in the domain; it indicates, for instance, that instructional methods for first-year students need to be different from instructional methods for third-year students, simply because third-year students have much more knowledge of the domain. Whereas the element interactivity effect pertains to low element interactivity versus high element interactivity materials, and the expertise reversal effect pertains to low expertise learners and high expertise learners, the guidance-fading effects thus pertains to the beginning of a longer educational program versus the end this program. A forerunner of the guidance-fading effect is the completion strategy, where the educational program starts with providing worked examples, followed by completion problems for which the learners must complete increasingly larger parts of the solution and ending with conventional problems (van Merriënboer and Krammer 1990). Renkl (2012), Renkl and Atkinson 2003) and van Merriënboer and Kirschner (2018a) discuss other examples of the guidance-fading effect.

Transient Information Effect

Transient information is information that is presented to learners but disappears after a few seconds, for example, in spoken text or in instructional video or animation (Leahy and Sweller 2011). For non-transient information (e.g. a written text with pictures), all information is available to the learner at the same time and may be revisited when needed; for transient information, it may be necessary for the learner to actively retain information in working memory for later processing which increases extraneous cognitive load and so reduces learning. To overcome these negative effects, a number of compensatory strategies are available such as self-pacing or segmentation. The self-pacing effect was reported by Mayer and Chandler (2001), who found that it was beneficial to give learners control over the pace of an instructional animation, probably because it helps them deal with the transient nature of this information. The segmentation effect was reported by Spanjers et al. (2011), who found that segmented animations (i.e. segmented in parts with pauses in between) were more efficient than continuous animations for novice learners, but not for learners with higher levels of prior knowledge. As a final example, Leahy and Sweller (2011, 2016) reported an interaction effect of transient information on the modality effect: short pieces of audio-visual information were more effective than visual information only (i.e. traditional modality effect), but longer pieces of audio-visual information were less effective than visual information only because of the abundance of transient information in the longer, auditory piece.

Self-Management Effect

One of the most recent effects, the self-management effect, is based on the assumption that students can be taught to apply CLT principles themselves to manage their own cognitive load. Ideally, students should only have access to materials that have been designed with a consideration of cognitive load. However, in reality, the Internet enables information to be created and shared by anyone, which makes it more likely that students will be confronted with low-quality learning materials that have not been designed with any consideration of cognitive load. It can be hypothesised that students who are taught to apply CLT principles themselves to manage their own cognitive load (self-management of cognitive load) are better equipped to deal with these badly designed materials than students who are only exposed to an education system of consistent, well-structured learning materials based on CLT principles.

Until now, the self-management effect has only been studied with split-attention learning materials. Typically, studies investigating the self-management effect compare three experimental conditions and consist of two phases (see Roodenrys et al. 2012; Sithole et al. 2017). In the first phase, students in two experimental conditions study multimedia learning materials in a split-attention format. In the self-management condition, students are instructed how to self-manage their cognitive load, for example, by reorganising text and diagrams. In the third, physically integrated condition, students learn from the same materials in an instructor-managed physically integrated format. In the second phase, students in all three conditions are presented with the same split-attention learning materials in another domain. The most important finding demonstrating the self-management effect is reflected in superior performance of the students in the self-management condition on recall and transfer tests.