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A human–machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)

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  • Ha, Jun Su
  • Seong, Poong Hyun

Abstract

A human–machine interface (HMI) evaluation method, which is named “difficulty evaluation method in information searching (DEMIS)†, is proposed and demonstrated with an experimental study. The DEMIS is based on a human performance model and two measures of attentional-resource effectiveness in monitoring and detection tasks in nuclear power plants (NPPs). Operator competence and HMI design are modeled to be most significant factors to human performance. One of the two effectiveness measures is fixation-to-importance ratio (FIR) which represents attentional resource (eye fixations) spent on an information source compared to importance of the information source. The other measure is selective attention effectiveness (SAE) which incorporates FIRs for all information sources. The underlying principle of the measures is that the information source should be selectively attended to according to its informational importance. In this study, poor performance in information searching tasks is modeled to be coupled with difficulties caused by poor mental models of operators or/and poor HMI design. Human performance in information searching tasks is evaluated by analyzing the FIR and the SAE. Operator mental models are evaluated by a questionnaire-based method. Then difficulties caused by a poor HMI design are evaluated by a focused interview based on the FIR evaluation and then root causes leading to poor performance are identified in a systematic way.

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  • Ha, Jun Su & Seong, Poong Hyun, 2009. "A human–machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1557-1567.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:10:p:1557-1567
    DOI: 10.1016/j.ress.2009.02.025
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    References listed on IDEAS

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    1. Jun Su Ha & Poong Hyun Seong, 2009. "HUPESS: Human Performance Evaluation Support System," Springer Series in Reliability Engineering, in: Poong Hyun Seong (ed.), Reliability and Risk Issues in Large Scale Safety-critical Digital Control Systems, chapter 9, pages 197-229, Springer.
    2. Kim, Man Cheol & Seong, Poong Hyun, 2006. "A computational model for knowledge-driven monitoring of nuclear power plant operators based on information theory," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 283-291.
    3. Kim, Man Cheol & Seong, Poong Hyun, 2006. "An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 270-282.
    4. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
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    Cited by:

    1. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2015. "An abnormal situation modeling method to assist operators in safety-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 33-47.
    2. Zhang, Xiaoge & Mahadevan, Sankaran & Lau, Nathan & Weinger, Matthew B., 2020. "Multi-source information fusion to assess control room operator performance," Reliability Engineering and System Safety, Elsevier, vol. 194(C).

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