Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load

Abstract

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.

Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

“…While some studies revealed null results of realism on transfer and comprehension tasks, other studies on processes resulted both in positive and negative effects. An explanation for these contradicting effects may be a mismatch between the cognitive load induced by realism, the cognitive processes it triggers, and the assessment method (Skulmowski & Xu, 2021). While a realistic rendering may help learners in their construction of a mental representation of the surface of objects, it may entail a cognitive cost that reduces learners cognitive capacity for understanding the abstract processes depicted in these visualizations.…”

“…While a realistic rendering may help learners in their construction of a mental representation of the surface of objects, it may entail a cognitive cost that reduces learners cognitive capacity for understanding the abstract processes depicted in these visualizations. If the ensuing test focuses on the process rather than surface details, this cognitive misalignment results in a disadvantage of realistic visualizations (see Skulmowski & Xu, 2021). Therefore, future research that considers the fine-grained aspects of realism and testing methods needs to be conducted.…”

“…At the stage of schema creation, learner characteristics such as their spatial ability affect how well relevant information is extracted and converted to a stable representation in long-term memory. In addition, using the CLT terminology, we can assume that different learning goals can transform realistic details from being a form of extraneous load towards contributing towards ICL (and vice versa) as recently discussed by Skulmowski and Xu (2021). The level of realism in terms of the global shape (usually generated using geometry) needs to enable the generation of a stable mental representation for retention tasks while not "locking" learners into a specific visual format in case they need to transfer this information.…”

When color coding backfires: A guidance reversal effect when learning with realistic visualizations

“…This strategy has recently been adapted to the specific challenges involved in digital learning. The approach of cognitive load alignment posits that cognitive load cannot be avoided in most instances of digital learning (Skulmowski & Xu, 2021 ). For perceptually rich media in particular, this strategy indicates that learning settings must be designed with an appropriate mode of testing in mind.…”

“…For perceptually rich media in particular, this strategy indicates that learning settings must be designed with an appropriate mode of testing in mind. Only if a suitable testing method is chosen, the additional cognitive load that may be required by forms of digital learning can be justified (Skulmowski & Xu, 2021 ). For instance, when using realistic visualizations, this approach suggests that the cognitive load caused by realistic details can only be acceptable if the learning objective includes knowledge of such visual details (Skulmowski & Xu, 2021 ).…”