Reducing Cognitive Load using RLOs with Instructional Strategies (original) (raw)
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Cognitive load theory (CLT) can provide guidelines to assist in the presentation of infor- mation in a manner that encourages learner activities that optimise intellectual performance. It is based on a cognitive architecture that consists of a limited working memory, with partly independent processing units for visual and audio information, which interacts with an unlimi- ted long-term memory. According to the theory, the limitations of working memory can be circumvented by coding multiple elements of information as one element in cognitive schem- ata, by automating rules, and by using more than one presentation modality. This special issue consists of six articles from four countries and three continents on the instructional implications of CLT. The articles cover presenting instructional techniques for increasing germane CL in studying worked examples (van Merrienboer, Schuurman, De Croock, & Paas), effects of example elaboration training on decreasing cognitive interference and o...
Educational Psychology Review, 2021
Researchers of cognitive load theory and the cognitive theory of multimedia learning have identified several strategies to optimize instructional materials. In this review article we focus on five of these strategies or solutions to problematic instructional designs in multimedia learning: (a) the multimedia principle (use visualizations and drawings to complement texts); (b) the split-attention effect or spatial contiguity principle (show texts contiguously or integrated with visualizations); (c) the redundancy effect, alike the coherence principle (remove nonessential learning information); (d) the signaling principle (cue or signal essential learning information); and (e) the transient information effect or segmenting principle (segment or control the pace of animations and videos). Usually, both cognitive theories have investigated solutions that instructors, teachers, and designers should pursue to optimize students' learning. Here, in a novel approach, we show that these strategies can also be used by learners who want to self-manage their cognitive load and learning process. We provide several examples of both instructor-and learnermanaged solutions aligned with these strategies. When assessing which agent, either the instructor or the learner, was most effective, we observed mixed results in the literature. However, the expertise reversal effect may help predict the direction of these effects: novice students may learn better under instructor-managed conditions, whereas more expert students may learn more under learner-managed conditions.
Guest editorial: Managing cognitive load in technology-based learning environments
Educational Technology and Society, 2015
Cognitive load theory is an instructional theory that uses our knowledge of human cognitive architecture, especially processing limitations of working memory, to enhance effectiveness of instructional design. This paper reviews main assumptions and principles of cognitive load theory and discusses their application to technology-based learning environments. The paper concludes with a brief introduction to the structure and content of this Special Issue.
Cognitive load theory, learning difficulty, and instructional design
Learning and instruction, 1994
This paper is concerned with some of the factors that determine the difficulty of material that needs to be learned. It is suggested that when considering intellectual activities, schema acquisition and automation are the primary mechanisms of learning. The consequences of cognitive load theory for the structuring of information in order to reduce difficulty by focusing cognitive activity on schema acquisition is briefly surmnarixed. It is pointed out that cognitive load theory deals with learning and problem solving difticulty that is artificial in that it can be manipulated by instructional design. Intrinsic cognitive load in contrast, is constant for a given area because it is a basic component of the material. Intrinsic cognitive load is characterized in terms of element interactivity. The elements of most schemas must be learned simultaneously because they interact and it is the interaction that is critical. If, as in some areas, interactions between many elements must be learned, then intrinsic cognitive load will be high. In contrast, in different areas, if elements can be learned successively rather than ~~tan~~ly because they do not interact, intrinsic cognitive load will be low. It is suggested that extraneous cognitive load that interferes with learning orily is a problem under conditions of high cognitive load caused by high element interactivity. Under conditions of low element interactivity, re-designing instruction to reduce extraneous cognitive load may have no appreciable consequences. In addition, the concept of element interactivity can be used to explain not only why some material is difficult to learn but also, why it can be difficult to understand. Understanding becomes relevant when high element interactivity material with a naturally high cognitive load must be learned.
Cognitive-Load Theory: Methods to Manage Working Memory Load in the Learning of Complex Tasks
Current Directions in Psychological Science, 2020
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.
Research on cognitive load theory: Application to e-learning
Educational Technology Research and Development, 2005
The purpose of this article is to review and critique each of the research studies published in this special issue. We will critique each article, derive one or more instructional design heuristics based on the findings for each study, and provide recommendations for extending particular lines of research. Three suggestions are provided concerning cognitive load theory and instructional design adaptations
Measuring cognitive load and cognition: metrics for technology-enhanced learning
This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory’s central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts.
Learners' Cognitive Load When Using Educational Technology
Gaming and Cognition
Taking advantage of the rapid evolution of educational technology, simulations and games have been embodied in a variety of teaching and learning procedures. To a large extent, their effectiveness, in common with the effectiveness of all instructional design relies on how material and activities are optimally organized. That organization should be determined by the nature of human cognitive architecture when dealing with complex, biologically secondary information. Cognitive load theory has been devised to deal with such knowledge. Therefore, embodied simulations and serious games should take evidence-based cognitive load principles into account in both design and implementation.