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Measuring Cognitive Load : = A Comparison of Self-report and Physiological Methods.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Measuring Cognitive Load :/
Reminder of title:	A Comparison of Self-report and Physiological Methods.
Author:	Joseph, Stacey.
Description:	1 online resource (98 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 74-08(E), Section: A.
Subject:	Educational technology. -
Online resource:	click for full text (PQDT)
ISBN:	9781303059117

Measuring Cognitive Load : = A Comparison of Self-report and Physiological Methods.
Joseph, Stacey.

Measuring Cognitive Load :A Comparison of Self-report and Physiological Methods. - 1 online resource (98 pages)

Source: Dissertation Abstracts International, Volume: 74-08(E), Section: A.

Thesis (Ph.D.)--Arizona State University, 2013.

Includes bibliographical references

This study explored three methods to measure cognitive load in a learning environment using four logic puzzles that systematically varied in level of intrinsic cognitive load. Participants' perceived intrinsic load was simultaneously measured with a self-report measure---a traditional subjective measure---and two objective, physiological measures based on eye-tracking and EEG technology. In addition to gathering self-report, eye-tracking data, and EEG data, this study also captured data on individual difference variables and puzzle performance. Specifically, this study addressed the following research questions: 1. Are self-report ratings of cognitive load sensitive to tasks that increase in level of intrinsic load? 2. Are physiological measures sensitive to tasks that increase in level of intrinsic load? 3. To what extent do objective physiological measures and individual difference variables predict self-report ratings of intrinsic cognitive load? 4. Do the number of errors and the amount of time spent on each puzzle increase as the puzzle difficulty increases? Participants were 56 undergraduate students. Results from analyses with inferential statistics and data-mining techniques indicated features from the physiological data were sensitive to the puzzle tasks that varied in level of intrinsic load. The self-report measures performed similarly when the difference in intrinsic load of the puzzles was the most varied. Implications for these results and future directions for this line of research are discussed.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303059117Subjects--Topical Terms:

556755
Educational technology.
Index Terms--Genre/Form:

554714
Electronic books.

Measuring Cognitive Load : = A Comparison of Self-report and Physiological Methods.
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500 $a Source: Dissertation Abstracts International, Volume: 74-08(E), Section: A.
500 $a Adviser: Robert K. Atkinson.
502 $a Thesis (Ph.D.)--Arizona State University, 2013.
504 $a Includes bibliographical references
520 $a This study explored three methods to measure cognitive load in a learning environment using four logic puzzles that systematically varied in level of intrinsic cognitive load. Participants' perceived intrinsic load was simultaneously measured with a self-report measure---a traditional subjective measure---and two objective, physiological measures based on eye-tracking and EEG technology. In addition to gathering self-report, eye-tracking data, and EEG data, this study also captured data on individual difference variables and puzzle performance. Specifically, this study addressed the following research questions: 1. Are self-report ratings of cognitive load sensitive to tasks that increase in level of intrinsic load? 2. Are physiological measures sensitive to tasks that increase in level of intrinsic load? 3. To what extent do objective physiological measures and individual difference variables predict self-report ratings of intrinsic cognitive load? 4. Do the number of errors and the amount of time spent on each puzzle increase as the puzzle difficulty increases? Participants were 56 undergraduate students. Results from analyses with inferential statistics and data-mining techniques indicated features from the physiological data were sensitive to the puzzle tasks that varied in level of intrinsic load. The self-report measures performed similarly when the difference in intrinsic load of the puzzles was the most varied. Implications for these results and future directions for this line of research are discussed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Educational technology. $3 556755
650 4 $a Cognitive psychology. $3 556029
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a Arizona State University. $b Educational Technology. $3 1185802
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