IDT 873: 4C / ID Model and the Cognitive Load of Authentic Tasks

IDT 873 Abstract: Cognitive Task Analysis Jennifer Maddrell van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the Load Off a Learner's Mind: Instructional Design for Complex Learning. Educational Psychologist, 38(1), 513. Overview Citing decades of prior cognitive load theory and research, van Merrienboer, Kirschner, and Kester (2003) offer a theoretical framework and instruction design model for complex learning. Noting a recent emphasis on authentic learning tasks (such as project and problembased learning approaches) to support complex learning, they consider the implications on cognitive load and offer a model designed to manage both intrinsic and extraneous cognitive load. Theory While the theories underlying the use of authentic learning tasks may vary, a common assumption is that authentic tasks help learners to integrate the knowledge and skills necessary for complex task performance (van Merrienboer et al., 2003). However, given the novice learner’s weak problem-solving methods, they face high extraneous cognitive load when confronted with authentic tasks. In addition, the complexity inherent in the authentic task presents high intrinsic cognitive load. Therefore, based on cognitive load theory, engaging in highly complex authentic learning tasks may strain the novice learner’s limited working memory and subject the learner to excessive cognitive load. Proposal van Merrienboer et al. focus their attention on both the nature and the delivery timing of the presented information. They suggest that supportive information (knowledge necessary for problem solving and reasoning) is best presented before the learner engages in the learning task. Such supportive task specific information is inherently complex and needed in order to know how to approach the learning task. Presenting the supportive information first helps learners construct schemas to be used as they begin task performance. In contrast, van Merrienboer et al. suggest that procedural information (the how to instructions for rule application) is best presented when needed during task performance. They argue that such just-in-time presentation of procedural information reduces the potential for splitattention effects that may occur when the learner attempts to integrate procedural information learned previously with actions he or she is taking now. Heuristics From these suggested practices, van Merrienboer et al. offer an instructional design model (the 4C / ID model) for complex learning that focuses on four components: 1) learning tasks, 2) supportive information, 3) procedural information, and 4) part-task practice. The heuristics for designers within the 4C / ID model is to sequence from simple versions of the whole task beginning with a high level of support and ending with a complex version without support. In addition, as discussed above, supportive information is to be presented in advance of performance while procedural information required to perform the task is to be presented as the task is being performed. Finally, to encourage automaticity, additional repetitive practice should be incorporated for parts of the task. Critique The focus of the article is not an examination of the effects of authentic learning tasks on learning, but rather the implications of incorporating such tasks on the learner’s cognitive load. As such, the article offers a bridge across theory, research, and practice. A key strength of the article is the authors’ focus on the reality of limited working memory and the high cognitive load IDT 873 Abstract: Cognitive Task Analysis Jennifer Maddrell imposed by authentic learning tasks. The 4C / ID model offers designers a way of incorporating authentic tasks while at the same time better managing cognitive load. However, as a theoretical article, it does not offer results from a study of the model in practice. Do the heuristics within the 4C / ID model help to manage cognitive load? Further, do authentic learning tasks designed within the framework of the 4C / ID model effectively and efficiently support learning? These questions are left to future research.