Predictive human performance modeling made easy

  • Authors:
  • Bonnie E. John;Konstantine Prevas;Dario D. Salvucci;Ken Koedinger

  • Affiliations:
  • Carnegie Mellon Univ., Pittsburgh, PA;Carnegie Mellon Univ., Pittsburgh, PA;Drexel University, Philadelphia, PA;Carnegie Mellon Univ., Pittsburgh, PA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2004

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Abstract

Although engineering models of user behavior have enjoyed a rich history in HCI, they have yet to have a widespread impact due to the complexities of the modeling process. In this paper we describe a development system in which designers generate predictive cognitive models of user behavior simply by demonstrating tasks on HTML mock-ups of new interfaces. Keystroke-Level Models are produced automatically using new rules for placing mental operators, then implemented in the ACT-R cognitive architecture. They interact with the mock-up through integrated perceptual and motor modules, generating behavior that is automatically quantified and easily examined. Using a query-entry user interface as an example [19], we demonstrate that this new system enables more rapid development of predictive models, with more accurate results, than previously published models of these tasks.