Using Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approach

Persistent Link:
http://hdl.handle.net/10150/241971
Title:
Using Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approach
Author:
Cirett Galan, Federico M.
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The goal of this study was to evaluate whether Electroencephalography (EEG) estimates of attention and cognitive workload captured as students solved math problems could be used to predict success or failure at solving the problems. Students solved a series of SAT math problems while wearing an EEG headset that generated estimates of sustained attention and cognitive workload each second. Students also reported on their level of frustration and the perceived difficulty of each problem. Results from a Support Vector Machine (SVM) training indicated that problem outcomes could be correctly predicted from the combination of attention and workload signals at rates better than chance. The EEG data was also correlated with students' self-report of problem difficulty. Findings suggest that relatively non-intrusive EEG technologies could be used to improve the efficacy of tutoring systems.
Type:
text; Electronic Dissertation
Keywords:
Intelligent Tutoring Systems; Machine Learning; physiology; Computer Science; behavior; Electroencephalography
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Computer Science
Degree Grantor:
University of Arizona
Advisor:
Beal, Carole R.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleUsing Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approachen_US
dc.creatorCirett Galan, Federico M.en_US
dc.contributor.authorCirett Galan, Federico M.en_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe goal of this study was to evaluate whether Electroencephalography (EEG) estimates of attention and cognitive workload captured as students solved math problems could be used to predict success or failure at solving the problems. Students solved a series of SAT math problems while wearing an EEG headset that generated estimates of sustained attention and cognitive workload each second. Students also reported on their level of frustration and the perceived difficulty of each problem. Results from a Support Vector Machine (SVM) training indicated that problem outcomes could be correctly predicted from the combination of attention and workload signals at rates better than chance. The EEG data was also correlated with students' self-report of problem difficulty. Findings suggest that relatively non-intrusive EEG technologies could be used to improve the efficacy of tutoring systems.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectIntelligent Tutoring Systemsen_US
dc.subjectMachine Learningen_US
dc.subjectphysiologyen_US
dc.subjectComputer Scienceen_US
dc.subjectbehavioren_US
dc.subjectElectroencephalographyen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBeal, Carole R.en_US
dc.contributor.committeememberCohen, Paulen_US
dc.contributor.committeememberBarnard, Kobusen_US
dc.contributor.committeememberMorrison, Claytonen_US
dc.contributor.committeememberBeal, Carole R.en_US
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