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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

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
Country/TerritoryAustria
CityVienna
Period6/4/196/6/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

Keywords

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

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