EEG Based Analysis of Cognitive Load Enhance Instructional Analysis

Autor/inn/en	Dan, Alex; Reiner, Miriam
Titel	EEG Based Analysis of Cognitive Load Enhance Instructional Analysis
Quelle	In: Journal of Educational Data Mining, 9 (2017) 2, S.31-44 (14 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Brain Hemisphere Functions; Diagnostic Tests; Cognitive Ability; Psychophysiology; Instructional Design; Short Term Memory; Neurosciences; Teaching Methods; Measurement; Statistical Analysis; Learning Processes; Learning Theories + Suchen Sie Ihr Suchwort? Diagnostic test; Diagnostischer Test; Denkfähigkeit; Psychophysiologie; Lesson concept; Lessonplan; Unterrichtsentwurf; Kurzzeitgedächtnis; Neuroscience; Neurowissenschaften; Neurowissenschaft; Teaching method; Lehrmethode; Unterrichtsmethode; Messverfahren; Statistische Analyse; Learning process; Lernprozess; Learning theory; Lerntheorie
Abstract	One of the recommended approaches in instructional design methods is to optimize the value of working memory capacity and avoid cognitive overload. Educational neuroscience offers novel processes and methodologies to analyze cognitive load based on physiological measures. Observing psychophysiological changes when they occur in response to the course of a learning session allows adjustments in the learning session based on the individual learner's capabilities. The availability of non-invasive electroencephalogram (EEG)-based devices and advanced near-real-time analysis techniques have improved our understanding of the underlying mechanisms and have impacted the way we design instructional methods and adapt them to the current learner's cognitive load and valence states. We will review Cognitive Load Theory, how cognitive load may be measured, and how analysis of EEG data can be applied to enhance learning through real-time measurements of the learner's cognitive load. We show a learning experiment in an attempt to provide a proof of concept of learning and real-time measures of EEG as indicators of mental states. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01