Title page for ETD etd-05012003-130105

Type of Document Dissertation
Author Staderman, William P.
Author's Email Address wstade@vt.edu
URN etd-05012003-130105
Title Communicating expertise in system operation and fault diagnosis to non-experts
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Kleiner, Brian M. Committee Chair
Hudlicka, Eva Committee Member
North, Christopher L. Committee Member
Smith-Jackson, Tonya L. Committee Member
Williges, Robert C. Committee Member
  • mental models
  • device models
  • augmented reality
Date of Defense 2003-04-25
Availability unrestricted
The use of systems that span many knowledge domains is becoming more common as technology advances, requiring expert-performance in a domain from users who are usually not experts in that domain. This study examined a means of communicating expertise (in system operation and fault diagnosis) to non-experts and furthering the understanding of expert mental models. It has been suggested that conceptions of abstract models of system-functions distinguish expert performance from non-expert performance (Hanisch, Kramer, and Hulin, 1991). This study examined the effects on performance of augmenting a simple control panel device with a model of the functions of the device, interacting with the model, and augmenting the device with graphically superimposed procedural indicators (directions). The five augmented display conditions studied were: Device Only, Device + Model, Device + Procedural Indicators, Interactive Model, and Interactive Model + Procedural Indicators. The device and displays were presented on a PC workstation. Performance measures (speed and accuracy) and subjective measures (questionnaires, NASA TLX, and structured interviews) were collected. It was expected that participants who interact with the device + procedural indicators would exhibit the shortest performance time and least errors; however, those who interacted with the simplest display (device only) were fastest and exhibited the least errors. Results of this study are discussed in terms of building a mental model and identifying situations that require a mental model.
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