SynthesisHuman factors2026
Cognitive Load Measurement Methods for Usability Testing: A Critical Analysis and Framework for Interface Evaluation.
Synthesis in Human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
ObjectiveThis systematic review evaluates the use of cognitive load measurement methods in usability testing across diverse software interfaces. It provides guidance for researchers and practitioners by proposing a framework for selecting appropriate cognitive load measurement techniques.BackgroundCognitive Load Theory offers insights into software usability by addressing users' mental effort during task performance. Although cognitive load measurement methods are increasingly used in usability testing, no comprehensive analysis has focused specifically on various software interfaces.MethodWe systematically analysed 87 experimental studies published between 2001 and 2025. Databases searched included IEEE, ACM, ScienceDirect, SpringerLink, and Scopus. Inclusion criteria focused on studies applying cognitive load measurement to usability testing for different types of software interfaces.ResultsCognitive load measurement methods were categorised as subjective (e.g., NASA-TLX and self-reports) or objective (e.g., EEG, eye-tracking, dual-task paradigms, and physiological sensors). The most frequently used methods were performance measures (19%), NASA-TLX (12%), and eye fixations (11%). Commonly evaluated platforms included websites, virtual reality systems, and productivity tools. Each method's applicability, strengths, and limitations were identified.ConclusionThe review synthesises the relative merits of cognitive load measurement methods in usability evaluations and introduces a framework to guide the selection of techniques based on interface type and evaluation goals.ApplicationThe proposed framework operationalises CLT to support targeted, user-centred usability testing. It facilitates the selection of effective cognitive load measurement strategies, enhancing evaluation accuracy and informing better software design.
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