Application of NeuroIS Tools to Understand Cognitive Behaviors of Student Learners in Biochemistry

Adriane Randolph (), Solome Mekbib (), Jenifer Calvert (), Kimberly Cortes () and Cassidy Terrell ()
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Adriane Randolph: Kennesaw State University
Solome Mekbib: Kennesaw State University
Jenifer Calvert: Kennesaw State University
Kimberly Cortes: Kennesaw State University
Cassidy Terrell: University of Minnesota Rochester

A chapter in Information Systems and Neuroscience, 2020, pp 239-243 from Springer

Abstract: 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.

Keywords: Cognitive load; EEG; Eye tracking; Student learners; Individual characteristics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-28144-1_26

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DOI: 10.1007/978-3-030-28144-1_26

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