Cognitive Architecture and Instructional Design: 20 Years Later

The self-explanation effect was demonstrated independently of cognitive load theory (Chi et al. 1989) but can be explained by the theory. It stems from the worked example effect and in the context of cognitive load theory was first described by Renkl et al. (1998). As indicated above, learners will not always be inclined to carefully study worked examples and may only briefly scan them before trying to solve conventional problems. In this case, worked examples will not yield positive effects on learning. Variability of practice might help learners to process the examples more deeply, because variation stimulates them to compare and contrast the different examples. But this will only work when more than one example is presented and when the total load, which increases due to the germane processing, remains within the capacity limits of working memory. Alternatively, when only one example is available, one might provide the learner with self-explanation prompts that elicit sophisticated self-explanations from the learners. Several studies showed that worked examples combined with self-explanation prompts can be superior to worked examples without self-explanation prompts, provided that total cognitive load does not exceed available capacity.

Imagination Effect

It has been known for some time that when learners are asked to mentally rehearse a motor task, learning is improved (Sackett 1934). Furthermore, the extent to which improvement occurs depends on the extent to which the motor task has cognitive components (Ginns 2005b). These findings provided the initial source of the imagination effect that occurs when learners asked to imagine or mentally rehearse a concept or process learn more than learners asked to study equivalent instructional material. For example, learners may be asked to study the problem-solving moves of a worked example as opposed to turning away and imagining those moves. Many such experiments have been conducted within a cognitive load theory framework. In order for an imagination effect to occur, learners must be able to imagine the relevant concepts or procedures. They must be able to process the information in working memory. Novices in a given area may be unable to adequately process high element interactivity information because of working memory limits when dealing with novel information. For such learners, imagination may be difficult or impossible and so studying the information results in enhanced learning compared to imagining the same information. With increased knowledge, working memory limits expand and so imagining the material becomes increasingly feasible because it can be more readily processed in working memory. Once information can be adequately imagined, imagination instructions are superior to study instructions (Cooper et al. 2001). Until that point, study instructions are superior to imagination instructions.

Isolated Elements Effect

Some very high element interactivity information vastly exceeds working memory limits when dealing with novel information and so cannot be processed in working memory. Since such complex information can be learned, the question of which processes are used was raised (Pollock et al. 2002). Pollock et al. hypothesised that perhaps individual elements were learned first without learning the interactions between them. Once the individual elements are stored in long-term memory, it may be feasible to subsequently integrate the elements by learning the interactions between them. If so, a sequence of only presenting the individual elements to learners followed by all of the information including both the individual elements and their interactions would be superior to presenting all of the information twice. Results supported this hypothesis. Learners presented all of the information twice were unable to process it properly on either occasion due to an excessive working memory load. In contrast, learners only presented the isolated elements could easily process them and store them in long-term memory. When subsequently presented the fully integrated information, they only needed to learn how to integrate the individual elements and so more readily assimilated the entire, high element interactivity information. A similar effect is provided by simple-to-complex sequencing, where learners first practice simple, low element interactivity versions of a task and only later increasingly more complex versions of this task (van Merriënboer et al. 2003; van Merriënboer and Sweller 2005, 2010).

Collective Working Memory Effect

The collective working memory effect was first described by Kirschner et al. (2009; see also 2011), who argued that collaborative learners can be considered as a single information processing system consisting of multiple, limited working memories which can create a larger, more effective, collective working space. In collaborative learning, it is not necessary that all group members possess all necessary knowledge, or process all available information alone and at the same time, when faced with a gap in their knowledge they can fill that gap from knowledge provided by other members of the group (borrowing). As long as there is communication and coordination between the group members, the information elements within the task and the associated cognitive load caused by the intrinsic nature of the task can be divided across a larger reservoir of cognitive capacity. However, communication and coordination require group members to invest an additional cognitive effort (i.e. transaction costs), an effort that individuals do not have to exert.

Kirschner et al. (2011) showed that the efficiency of group versus individual learning from tasks imposing a high or low cognitive load was affected by the trade-off between the benefits of dividing information processing among group members and the transaction costs. More specifically, they found an interaction effect, indicating that learning from tasks imposing a high cognitive load led to more efficient collaborative learning, and learning from tasks imposing a low cognitive load, resulted in more efficient individual learning. For learning tasks imposing a high load, individual learners did not have sufficient processing capacity to successfully process the information. For collaborative learners, the benefits of distributing the cognitive load among each other proved to be higher than the transaction costs. Consequently, learners were able to devote the freed cognitive capacity to activities that fostered learning. For learning tasks imposing a low cognitive load, learners working either individually or collaboratively had sufficient cognitive capacity to process all information by themselves. Hence, inter-individual communication and coordination of information were unnecessary and resulted in transaction costs that were higher than the benefits of distributing the cognitive load across group members during the collaborative learning process. Consequently, when cognitive load was low, qualitative differences in constructed knowledge materialised in higher learning efficiency for those who learned individually than for those who learned collaboratively.

Human Movement Effect

Whereas the transient information effect has been used to explain why students generally learn less from dynamic than from static visualisations, the human movement effect holds that it is better to use animation rather than statics to teach cognitive tasks involving human movement. The effect originated from unexpected findings of CLT research, which indicated superior learning outcomes of transient visualisation formats over non-transient formats (e.g. Ayres et al. 2009; Wong et al. 2009). What these studies had in common was that they used animations and statics to teach human motor skills (e.g. paper folding, knot tying). Paas and Sweller (2012) used Geary’s (2008) concept of biologically primary knowledge to explain that humans have evolved the ability to learn from observing others engage in action and copy it effortlessly. Therefore, asking learners to observe an animation in order to learn a motor skill may not place an excessive burden on working memory resources. This idea was confirmed by a meta-analysis of Höffler and Leutner (2007), who showed that superior learning (the largest effect size) was found when the animations were highly realistic and procedural-motor knowledge was involved. Van Gog et al. (2009) have suggested that the human movement effect reflects the finding of neuroscience research that the same cortical circuits that are involved in executing an action oneself also automatically respond to observing someone else executing the same action (i.e. mirror neuron system; Rizzolatti and Craighero 2004).