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Improving interface quality: an investigation of human-computer interaction task learning

D A Mitta1, S J Packebush

  • 1Georgia Tech Research Institute, ELSYS/CAD, Atlanta 30332-0840, USA.

Ergonomics
|July 1, 1995
PubMed
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This study validates learning rate as a key metric for interface quality. Learning rate analysis effectively identifies user interface difficulties, offering a more efficient evaluation than traditional methods.

Area of Science:

  • Human-Computer Interaction
  • Usability Engineering
  • Cognitive Science

Background:

  • Interface usability and quality are critical for user experience.
  • Traditional metrics like task completion time and error frequency have limitations.
  • User learning is a crucial, yet often underutilized, factor in interface evaluation.

Purpose of the Study:

  • To validate learning rate as a reliable measure of interface quality.
  • To compare the effectiveness of learning rate against traditional interface quality metrics.
  • To identify specific tasks that present learning difficulties for users.

Main Methods:

  • A stochastic model was employed to represent the user learning process.
  • Learning rate was quantified and correlated with task completion time and error frequency.

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  • An empirical study was conducted with 32 participants learning 16 tasks in SuperCard.

    • Main Results:

    • Correlation analyses supported the usefulness of learning rate as an indicator of interface quality.
    • Learning rate analysis identified four tasks with learning difficulties, exceeding traditional methods.
    • Learning rate data encompassed information from traditional metrics and revealed additional problem areas.

    Conclusions:

    • Learning rate is a valuable and efficient metric for assessing interface quality.
    • Incorporating learning rate analysis enhances the identification of usability issues.
    • This approach reduces the need for time-intensive traditional evaluation methods like video analysis.