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Attentional Characteristics of Anomaly Detection in Conceptual Modeling

In: Information Systems and Neuroscience

Author

Listed:
  • Karl-David Boutin

    (HEC Montréal)

  • Pierre-Majorique Léger

    (HEC Montréal)

  • Christopher J. Davis

    (University of South Florida)

  • Alan R. Hevner

    (University of South Florida)

  • Élise Labonté-LeMoyne

    (HEC Montréal)

Abstract

We use eye tracking to better understand the attentional characteristics specific to successful error detection in conceptual models. This phase of our multi-step research project describes the visual comportments associated with successful semantic and syntactic error identification and diagnosis. We test our predictions, based on prior studies on visual attention in an error detection task, or studies comparing experts and non-experts in diverse tasks, in a controlled experiment where participants are tasked with detecting and diagnosing errors in 75 BPMN® models. The results suggest that successful error diagnostics are linked with shorter total view time and shorter fixation duration, with a significant difference between semantic and syntactic errors. By identifying the visual attention differences and tendencies associated with successful detection tasks and the investigation of semantic and syntactic errors, we highlight the non-polarity of the ‘scale’ of expertise and allow clear recommendations for curriculum development and training methods.

Suggested Citation

  • Karl-David Boutin & Pierre-Majorique Léger & Christopher J. Davis & Alan R. Hevner & Élise Labonté-LeMoyne, 2019. "Attentional Characteristics of Anomaly Detection in Conceptual Modeling," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph (ed.), Information Systems and Neuroscience, pages 57-63, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-01087-4_7
    DOI: 10.1007/978-3-030-01087-4_7
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