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A critical analysis of cognitive load measurement methods for evaluating the usability of different types of interfaces: guidelines and framework for Human-Computer Interaction

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arxiv 2402.11820 v1 pith:BSZG7Y3B submitted 2024-02-19 cs.HC

Ali Darejeh , Nadine Marcusa , Gelareh Mohammadi , John Sweller This is my paper · ORCID
classification cs.HC
keywords usabilitycognitiveloadmeasurementmethodsuserinterfacesreview
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Usability testing is an essential part of product design, particularly for user interfaces. To enhance the reliability of usability evaluations, employing cognitive load measurement methods can be highly effective in assessing the mental effort required to complete tasks during user testing. This review aims to provide an overview of the most suitable cognitive load measurement methods for evaluating various types of user interfaces, serving as a valuable resource for guiding usability assessments. To bridge the existing gap in the literature, a systematic review was conducted, analyzing 76 articles with experimental study designs that met the eligibility criteria. The review encompasses different methods of measuring cognitive load applicable to assessing the usability of diverse user interfaces, including computer software, information systems, video games, web and mobile applications, robotics, and virtual reality applications. The results highlight the most widely utilized cognitive load measurement methods in software usability, their respective usage percentages, and their application in evaluating the usability of each user interface type. Additionally, the advantages and disadvantages of each method are discussed. Furthermore, the review proposes a framework to assist usability testers in selecting an appropriate cognitive load measurement method for conducting accurate usability evaluations.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification

    cs.CR 2026-02 unverdicted novelty 6.0 of 10

    Cognitive Load Correlation from keystroke timings distinguishes genuine human composition from mechanical transcription with estimated 85-95% accuracy in a non-intrusive framework.

  2. Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making

    cs.HC 2025-01 reject novelty 5.0 of 10

    In a controlled comparison, AI confidence levels and text explanations improved human-AI decision accuracy, while reflective questions and human feedback increased effort and reduced trust.

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