• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Towards Using Eye-Tracking and Consumer-Grade Electroencephalogram Devices To Detect Usability Issues in Mobile Applications

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_19410_sip1_m.pdf
    Size:
    10.48Mb
    Format:
    PDF
    Download
    Author
    Zhang, Limin
    Issue Date
    2022
    Keywords
    cognitive workload
    cognitive workload measures
    EEG
    eye movement
    HCI
    usability
    Advisor
    Cui, Hong
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Despite the importance of cognitive workload in examining the usability of smartphoneapplications and the popularity of smartphone usage globally, cognitive workload as one attribute of usability tends to be overlooked in Human Computer Interaction (HCI) studies. Moreover, limited studies that have examined the cognitive workload aspect often measured some summative workloads using subjective measures (e.g., questionnaires). A significant limitation of subjective measures is that they can only assess the overall, subject-perceived cognitive workload after the procedures/tasks have been completed. Such measurements do not reflect the real-time workload fluctuation during the procedures. They, therefore, are not useful for pinpointing poor designs in user interfaces that are associated with cognitive workload surges in the user’s brain during a task. This dissertation used mixed methods to empirically study (1) the reliability of an eye-tracking device (i.e., Tobii Pro Nano) and a low-cost electroencephalogram (EEG) device (i.e., MUSE 2) for detecting real-time cognitive workload changes during N-back tasks, and (2) the potential to use the increased cognitive workload detected during tasks to pinpoint user interface areas containing potential usability issues in mobile applications. Results suggest that (1) the EEG measurements collected by MUSE 2 are not very useful as indicators of cognitive workload changes in our setting; (2) eye movement measurements collected by Tobii are useful for monitoring cognitive workload fluctuations and tracking down interface design issues in a smartphone setting; (3) more specifically, the maximum pupil diameter is the preeminent indicator of cognitive workload surges; and (4) cognitive workload surges may be caused by design issues. One usability issue has been detected and fixed this way in a mobile application designed by a National Science Foundation (NSF) sponsored project. In conclusion, the pupil diameter measure combined with other subjective ratings would provide a comprehensive user experience assessment of mobile applications. They can also be used to verify the successfulness of a user interface design solution in improving user experience.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Information
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.