In cognitive load theory, element interactivity has been used as the basic, defining mechanism of intrinsic cognitive load for many years. In this article, it is suggested that element interactivity ...underlies extraneous cognitive load as well. By defining extraneous cognitive load in terms of element interactivity, a distinct relation between intrinsic and extraneous cognitive load can be established based on whether element interactivity is essential to the task at hand or whether it is a function of instructional procedures. Furthermore, germane cognitive load can be defined in terms of intrinsic cognitive load, thus also associating germane cognitive load with element interactivity. An analysis of the consequences of explaining the various cognitive load effects in terms of element interactivity is carried out.
Cognitive-load researchers attempt to engineer the instructional control of cognitive load by designing methods that substitute productive for unproductive cognitive load. This article highlights ...proven and new methods to achieve this instructional control by focusing on the cognitive architecture used by cognitive-load theory and aspects of the learning task, the learner, and the learning environment.
We discuss four ways in which emotion may relate to cognitive load during learning. One perspective describes emotions as extraneous cognitive load, competing for the limited resources of working ...memory by requiring the processing of task-extra or task-irrelevant information. Another perspective shows that encoding, storage, and retrieval of information are affected by emotion even before awareness of the material, and that emotion may directly affect memory by broadening or narrowing cognitive resources, and by mechanisms such as mood-dependent and mood-congruent processing. A third perspective describes how emotion may affect intrinsic cognitive load, such as when emotion regulation is part of the learning outcomes. We also discuss a dual-channel assumption for emotions. A final perspective is that emotion affects motivation, and, in turn, mental effort investment. These four ways of considering emotion as part of CLT are best understood when taking an interval view of cognitive load.
Cognitive load theory has been a major influence for the field of educational psychology. One of the main guidelines of the theory is that extraneous cognitive load should be reduced to leave ...sufficient cognitive resources for the actual learning to take place. In recent years, research regarding various design factors, in particular from the field of digital and online learning, have challenged this assumption. Interactive learning media, immersion, disfluency, realism, and redundant elements constitute five major challenges, since these design factors have been shown to induce task-irrelevant cognitive load, i.e., extraneous load, while still promoting motivation and learning. However, currently there is no unified approach to integrate such effects into cognitive load theory. By including aspects of constructive alignment, an approach aimed at fostering deep forms of learning in order to achieve specific learning outcomes, we devise a strategy to balance cognitive load in digital learning. Most importantly, we suggest considering both the positive and negative effects on cognitive load that certain design factors of digital learning can cause. In addition, a number of research results highlight that some types of positive effects of digital learning can only be detected using a suitable assessment method. This strategy of aligning cognitive load with desired learning outcomes will be useful for formulating theory-guided and empirically testable hypotheses, but can be particularly helpful for practitioners to embrace emerging technologies while minimizing potential extraneous drawbacks.
Cognitive Load Theory (CLT) has started to find more applications in medical education research. Unfortunately, misconceptions such as lower cognitive load always being beneficial to learning and the ...continued use of dated concepts and methods can result in improper applications of CLT principles in medical education design and research. This review outlines how CLT has evolved and presents a synthesis of current-day CLT principles in a holistic model for medical education design. This model distinguishes three dimensions:
task fidelity
: from literature (lowest) through simulated patients to real patients (highest);
task complexity
: the number of information elements; and
instructional support
: from worked examples (highest) through completion tasks to autonomous task performance (lowest). These three dimensions together constitute three steps to proficient learning: (I) start with high support on low-fidelity low-complexity tasks and gradually fade that support as learners become more proficient; (II) repeat I for low-fidelity but higher-complexity tasks; and (III) repeat I and II in that order at subsequent levels of fidelity. The numbers of fidelity levels and complexity levels within fidelity levels needed depend on the aims of the course, curriculum or individual learning trajectory. This paper concludes with suggestions for future research based on this model.
Previous studies found that working memory maintenance contributes to long-term memory formation, and some evidence suggests that this effect could be larger when individuals are informed of the ...final long-term memory test. However, no study so far has explored whether and how working memory maintenance adapts when long-term retention is intentional. In this study, we conducted two experiments using verbal complex span tasks followed by delayed-recall tests. In both experiments, we evaluated working memory maintenance by varying the cognitive load of the concurrent task and with memory strategics reports. We manipulated intentions to remember at long term by warning participants of the final delayed recall or not (Experiment 1) or by monetarily rewarding immediate or delayed-recall performance (Experiment 2). We found no evidence that intentions changed the working memory maintenance mechanisms and strategies used, yet the cognitive load (Experiment 1) and rewards (Experiment 2) effects on delayed recalls were increased with a higher intention to remember at long term. We discuss possible interpretations for these results and suggest that the effect of intentions may not be due to a change in the kind of maintenance mechanisms used. As our results cannot be explained solely by encoding or maintenance processes, we instead propose that intentions produce a combined change in encoding and maintenance. However, the exact nature of this modulation will need further investigation. We conclude that understanding how intentions modulate the effect of working memory on long-term memory could shed new light on their relationship.
This article discusses the findings from a collection of six studies linked together by the common theme of cognitive load theory and published in Computers in Human Behavior. A number of familiar ...cognitive load conditions and effects are investigated in computer-based environments, namely worked examples, split-attention, and the expertise reversal effect; but in many cases cognitive load considerations are combined with other learning strategies such as pre-training, thinking aloud, self-explanations, embodied cognition, and presentation pausing. A number of key findings are identified including the use of contemporary physiological instruments to measure cognitive load. There is also a focus on real-world tasks and medical education, as well as the self-management of cognitive load.
Research on self-regulated learning and on cognitive load has been two of the most prominent and influential research lines in educational research during the last decades. However, both lines ...developed quite independently from one another. This paper aims to bridge both concepts in order to better understand self-regulation as well as cognitive affordances of complex and dynamic learning processes. In fact, most learning environments require learners to self-regulate their learning process. They have to set their goals and plan, use strategies and monitor their learning progress and if necessary they have to regulate, i.e. to adapt their learning behavior. However, in all these phases of self-regulation, learners need to invest cognitive and metacognitive resources in addition to dealing with the original learning task. The affordances of self-regulation thus will cause cognitive load. This paper analyzes the affordances in the different phases of self-regulated learning in terms of intrinsic, extraneous and germane load. As a conclusion, the interplay between different affordances of self-regulation and learners’ resources and aptitudes is described in an integrated model of self-regulation and cognitive load.
•Until now both concepts developed quite independently.•The interplay between self-regulation and cognitive load is mutual.•All phases of self-regulation cause intrinsic, extraneous and germane load.•The interplay between self-regulation and load highly depends on learner characteristics.•For building bridges improved measures of self-regulation and load are needed.
A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that ...required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.
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