Application of neurois tools to understand cognitive behaviors of student learners in biochemistry

Adriane Randolph, Solome Mekbib, Jenifer Calvert, Kimberly Cortes, Cassidy Terrell

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    Cognitive load has received increased focus as an area that can be more richly explored using neuroIS tools. This research study presents the application of electroencephalography and eye tracking technologies to examine cognitive load of student learners in biochemistry. In addition to leveraging the Pope Engagement Index and eye tracking analysis techniques, we seek better understanding of the relationship that various individual characteristics have with the level of cognitive load experienced. While this study focuses on a particular STEM student population as they manipulate various learning models, it has implications for further studies in human-computer interaction and other learning environments.

    Original languageEnglish (US)
    Title of host publicationInformation Systems and Neuroscience - NeuroIS Retreat 2019
    EditorsFred D. Davis, René Riedl, René Riedl, Jan vom Brocke, Pierre-Majorique Léger, Adriane Randolph, Thomas Fischer
    PublisherSpringer
    Pages239-243
    Number of pages5
    ISBN (Print)9783030281434
    DOIs
    StatePublished - 2020
    EventInternational Conference on Information Systems and Neuroscience, NeuroIS Retreat 2019 - Vienna, Austria
    Duration: Jun 4 2019Jun 6 2019

    Publication series

    NameLecture Notes in Information Systems and Organisation
    Volume32
    ISSN (Print)2195-4968
    ISSN (Electronic)2195-4976

    Conference

    ConferenceInternational Conference on Information Systems and Neuroscience, NeuroIS Retreat 2019
    CountryAustria
    CityVienna
    Period6/4/196/6/19

    Bibliographical note

    Funding Information:
    This work was funded by the National Science Foundation under Grant Num-

    Funding Information:
    This work was funded by the National Science Foundation under Grant Number 1711425.

    Publisher Copyright:
    © Springer Nature Switzerland AG 2020.

    Keywords

    • Cognitive load
    • EEG
    • Eye tracking
    • Individual characteristics
    • Student learners

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