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Cognitive Load Measurement Methods for Usability Testing: A Critical Analysis and Framework for Interface Evaluation.

Ali Darejeh1, Nadine Marcus1, Gelareh Mohammadi1

  • 1School of Computer Science, Faculty of Engineering, UNSW, Sydney, Australia.

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Summary
This summary is machine-generated.

This review analyzes cognitive load measurement methods for software usability testing. It proposes a framework to help select the best techniques for different interfaces, improving user-centered design.

Keywords:
EEGNASA-TLXcognitive load measurementcognitive load theorydual-task paradigmeye trackinghuman–computer interactionperformance metricsusability testinguser interface evaluation

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Software Engineering

Background:

  • Cognitive Load Theory (CLT) is crucial for understanding user mental effort in software usability.
  • While cognitive load measurement is growing in usability testing, a comprehensive analysis across diverse software interfaces is lacking.

Purpose of the Study:

  • To systematically review cognitive load measurement methods used in software usability testing.
  • To provide a framework for selecting appropriate methods based on interface type and evaluation goals.

Main Methods:

  • Systematic analysis of 87 experimental studies (2001-2025) from major scientific databases.
  • Inclusion criteria focused on studies applying cognitive load measurement to diverse software interface usability testing.

Main Results:

  • Cognitive load methods are categorized as subjective (e.g., NASA-TLX) or objective (e.g., EEG, eye-tracking).
  • Performance measures (19%), NASA-TLX (12%), and eye fixations (11%) were most frequent.
  • Commonly evaluated platforms included websites, VR systems, and productivity tools.

Conclusions:

  • The review synthesizes the strengths and limitations of various cognitive load measurement methods.
  • A novel framework is proposed to guide the selection of techniques for targeted, user-centered usability evaluations.
  • This framework enhances evaluation accuracy and informs better software design by operationalizing CLT.