High versus low contextual interference in simulation-based training of troubleshooting skills: effects on transfer performance and invested mental effort

Croock, M.B.M. de; Merriënboer, Jeroen J. G. van; Paas, Fred

doi:10.1016/s0747-5632(98)00005-3

Cited by 89 publications

(51 citation statements)

References 13 publications

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“…This technique has mainly been studied in motor tasks (Brady, 1998;Cross, Schmitt, & Grafton, 2007;Guadagnoli & Lee, 2004;Lee & Magill, 1983;Magill & Hall, 1990;Shea & Morgan, 1979;Simon, 2007). However similar findings have been obtained with, for example, procedural tasks (Carlson, 1989;Carlson, Sullivan, & Schneider, 1989;Carlson & Yaure, 1990), cognitive operational tasks, such as interacting with automatic teller machines (Jamieson & Rogers, 2000), language learning (Jacoby, 1978), foreign vocabulary learning (Schneider, Healy, & Bourne, 1998, learning logical rules (Schneider, Healy, Ericsson, & Bourne, 1995), learning problem-solving from worked examples (Paas & Van Merriënboer, 1994), and troubleshooting tasks (De Croock, Van Merriënboer, & Paas, 1998).…”

Section: Introductionsupporting

confidence: 60%

The effects of practice schedule on learning a complex judgment task

Helsdingen

Gog

Merriënboer

2011

Learning and Instruction

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The effects of practice schedule on learning a complex judgment task were investigated. In Experiment 1, participants' judgment accuracy on a retention test was higher after a random practice schedule than after a blocked schedule or operational schedule. Experiment 2 demonstrated that judgment on a transfer test was also better after a random practice schedule than after a blocked schedule. Both experiments failed to show any effects of practice schedule on performance during learning. These findings show that benefits of random practice for retention and transfer apply to learning a complex judgment task, and may be achieved without performance degradation during practice.

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Section: Introductionsupporting

confidence: 60%

The effects of practice schedule on learning a complex judgment task

Helsdingen

Gog

Merriënboer

2011

Learning and Instruction

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“…These findings have been confirmed in the domain of troubleshooting (de Croock, van Merriënboer, & Paas, 1998;van Merriënboer et al, 2002). …”

Section: How To Optimize Learning 28mentioning

confidence: 53%

How to Optimize Learning From Animated Models: A Review of Guidelines Based on Cognitive Load

Wouters

Paas

Merriënboer³

2008

Review of Educational Research

187

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Animated models explicate the procedure to reach a problem solution as well as the rationale behind this procedure. For abstract cognitive processes, animations might be beneficial especially when explanations are provided by a supportive pedagogical agent. We argue that animated models can be an effective instructional method provided that they are designed in such a way that cognitive capacity is optimally employed. This review proposes three sets of design guidelines based on cognitive load research. The first set aims at managing the complexity of subject matter. The second set focuses on preventing activities -due to poor design-that obstruct learning. The last set of guidelines incites learners to engage in active and relevant processing of subject matter. Finally, an integrative framework is presented for designing effective animated models. How to Optimize Learning 3How to Optimize Learning from Animated Models? A Review of Guidelines based on Cognitive LoadThe current focus on lifelong learning and flexibility in task performance increasingly emphasizes the mastering of complex cognitive skills (Jonassen, 1999). Instructional methods, such as modeling and vicarious learning, in which learners observe how experts perform problem-solving tasks and simultaneously explain the reasoning underlying their actions, fit this focus on complex learning. At the same time, rapid developments in computer and software technology in the last decades have enabled the use of animations to illustrate abstract cognitive processes or concepts (Casey, 1996;Chee, 1995;Collins, 1991) and programmable pedagogical agents to support learners.We refer to the combined use of animations and pedagogical agents in modeling as animated models. These animated models illustrate the solving of problems such as scientific problems (e.g., solving a problem about gravity), mathematical problems (e.g., probability calculation problems), or search problems (finding information on the Internet). The pedagogical agent functions as a social model and guides the learner through the animation, for example, by moving around the screen and guiding the learner's attention to specific parts of the animation, by addressing the learner in a personalized style and/or by showing which errors typically may occur and how they may be avoided by the learner. For example, in solving a problem in the domain of probability calculation, it is important to know whether it is a 'drawing with or without replacement'. For novices this concept may be rather abstract and difficult to understand. An animation can visualize the concept by showing what is happening in, for instance, a situation in which the probability has to be calculated that a How to Optimize Learning 4 person guesses the correct pin code of a cash card in one trial. As depicted in Figure 1a (Paas, Renkl, Sweller, 2003Sweller, 1988Sweller, , 1999Sweller, , 2004Sweller, van Merriënboer, & Paas, 1998;van Merriënboer & Sweller, 2005). In this paper we argue that animated models can be an effective ...

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“…The fragility of Sweller et al's conclusion is further illustrated by work in which the effect mentioned by Sweller et al did not occur. In a study in which learners had to acquire troubleshooting skills in a distillery system, participants in a condition in which practice problems were offered under a high variability schedule showed better performance on transfer problems than a low variability group, but, as measured with a subjective rating scale, did not show higher cognitive load during learning (de Croock et al 1998). …”

Section: Germane Cognitive Loadmentioning

confidence: 99%

Cognitive load theory, educational research, and instructional design: some food for thought

2009

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Cognitive load is a theoretical notion with an increasingly central role in the educational research literature. The basic idea of cognitive load theory is that cognitive capacity in working memory is limited, so that if a learning task requires too much capacity, learning will be hampered. The recommended remedy is to design instructional systems that optimize the use of working memory capacity and avoid cognitive overload. Cognitive load theory has advanced educational research considerably and has been used to explain a large set of experimental findings. This article sets out to explore the open questions and the boundaries of cognitive load theory by identifying a number of problematic conceptual, methodological and application-related issues. It concludes by presenting a research agenda for future studies of cognitive load.

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High versus low contextual interference in simulation-based training of troubleshooting skills: effects on transfer performance and invested mental effort

Cited by 89 publications

References 13 publications

The effects of practice schedule on learning a complex judgment task

The effects of practice schedule on learning a complex judgment task

How to Optimize Learning From Animated Models: A Review of Guidelines Based on Cognitive Load

Cognitive load theory, educational research, and instructional design: some food for thought

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