From surface realism to training considerations: a proposal for changing the focus in the design of training systems

Abstract

Traditionally, it was believed that realistic, high-fidelity simulation could result in effective training-systems. By showing the potentials of low-fidelity simulations, numerous research projects cast doubt on the traditional belief in high-fidelity simulation. Because the construct of fidelity has guided the training-system design for decades, a growing disbelief in fidelity has created a void in design, leaving designers without resources in their decision-making. This article first presents a historical review that shows how fidelity has been used in research and design, and how it was later challenged by researchers. Then, in filling the void of fidelity, attempts at providing design guidelines, such as trainee- and task-analysis, are reviewed and their strengths and limitations are discussed. Toward the end, the lessons from the review are used to call for the creation of design methods. An example of such a method is discussed that uses the videorecording of expert performance as a resource in design.

Keywords:

• Simulation fidelity
• design method
• training
• expert performance
• task-analysis

Acknowledgements

For providing helpful comments during the preparation of this article, I am grateful to Frank E. Ritter, Shekoofeh Hedayati, Mathieu Brener, Shota Matsubayashi, Martin Yeh, James Niehaus, Mallory Dixon and Matthew Norris.

Disclosure statement

Frank E. Ritter, the co-principal investigator of the project, is required by the Penn State Conflict of Interest Program to include this paragraph [sic]: ‘I have financial interest with Charles River Analytics Inc., a company in which I provide consulting services and could potentially benefit from the results of this research. The interest has been reviewed and is being managed by The Pennsylvania State University in accordance with its individual Conflict of Interest policy, for the purpose of maintaining the objectivity of research at The Pennsylvania State University’.

Additional information

Notes on contributors

Pooyan Doozandeh

Pooyan Doozandeh is a PhD student in the College of Information Sciences and Technology at The Pennsylvania State University. His educational background is in Psychology and Computer Engineering, and he has the experience of research in various areas of applied psychology. His current research is focused on topics in applied psychology and human factors, with a focus on task-analysis, job-analysis, visual methods, and training.