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Micro and Macro Predictions: Using SGOMS to Predict Phone App Game Playing and Emergency Operations Centre Responses

  • Robert West
  • Lawrence Ward
  • Kate Dudzik
  • Nathan Nagy
  • Fraydon Karimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)

Abstract

In this study, we examine the ability of SGOMS models to predict human behaviour on two different scales, in micro cognitive task performance and in high level problem solving roles to better understand strategy use and training. To do this, two experiments were designed to isolate the role of knowledge structures in task performance. The first experiment involves modelling an application-based game, played on mobile phones. Results were compared to two models: the SGOMS model that matched the knowledge structures the players had learned during training, and a model optimized for speed, resulting in the fastest game play possible using ACT-R. In the second experiment we examined SGOMS predictions in a high level problem space of an Emergency Operations Center (EOC) simulation, with many interruptions and communication demands, comparing professional EOC managers and undergraduate performance. By comparing results between tasks, HCI design can be augmented using predictive modeling to inform the design to produce efficient and effective training programs.

Keywords

HCI Training SGOMS ACT-R App 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Robert West
    • 1
  • Lawrence Ward
    • 2
  • Kate Dudzik
    • 1
  • Nathan Nagy
    • 1
  • Fraydon Karimi
    • 1
  1. 1.Institute of Cognitive ScienceCarleton UniversityOttawaCanada
  2. 2.Department of PsychologyUniversity of British ColombiaVancouverCanada

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