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Experienced mental workload, perception of usability, their interaction and impact on task performance

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  • Luca Longo

Abstract

Past research in HCI has generated a number of procedures for assessing the usability of interacting systems. In these procedures there is a tendency to omit characteristics of the users, aspects of the context and peculiarities of the tasks. Building a cohesive model that incorporates these features is not obvious. A construct greatly invoked in Human Factors is human Mental Workload. Its assessment is fundamental for predicting human performance. Despite the several uses of Usability and Mental Workload, not much has been done to explore their relationship. This empirical research focused on I) the investigation of such a relationship and II) the investigation of the impact of the two constructs on human performance. A user study was carried out with participants executing a set of information-seeking tasks over three popular web-sites. A deep correlation analysis of usability and mental workload, by task, by user and by classes of objective task performance was done (I). A number of Supervised Machine Learning techniques based upon different learning strategy were employed for building models aimed at predicting classes of task performance (II). Findings strongly suggests that usability and mental workload are two non overlapping constructs and they can be jointly employed to greatly improve the prediction of human performance.

Suggested Citation

  • Luca Longo, 2018. "Experienced mental workload, perception of usability, their interaction and impact on task performance," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-36, August.
    • Handle: RePEc:plo:pone00:0199661
    DOI: 10.1371/journal.pone.0199661
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    1. Heather L. O'Brien & Elaine G. Toms, 2008. "What is user engagement? A conceptual framework for defining user engagement with technology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(6), pages 938-955, April.
    2. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
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    1. Nannan Xi & Juan Chen & Filipe Gama & Marc Riar & Juho Hamari, 2023. "The challenges of entering the metaverse: An experiment on the effect of extended reality on workload," Information Systems Frontiers, Springer, vol. 25(2), pages 659-680, April.

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