Predicting task execution time on handheld devices using the keystroke-level model

  • Authors:
  • Lu Luo;Bonnie E. John

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • CHI '05 Extended Abstracts on Human Factors in Computing Systems
  • Year:
  • 2005

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Abstract

The Keystroke-Level Model (KLM) has been shown to predict skilled use of desktop systems, but has not been validated on a handheld device that uses a stylus instead of a keyboard. This paper investigates the accuracy of KLM predictions for user interface tasks running on a Palm OS based handheld device. The models were produced using a recently developed tool for KLM construction, CogTool, and were compared to data obtained from a user study of 10 participants. Our results have shown that the KLM can accurately predict task execution time on handheld user interfaces with less than 8% prediction error.