Using Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approach
| dc.contributor.advisor | Beal, Carole R. | en_US |
| dc.contributor.author | Cirett Galan, Federico M. | |
| dc.creator | Cirett Galan, Federico M. | en_US |
| dc.date.accessioned | 2012-09-07T21:44:51Z | |
| dc.date.available | 2012-09-07T21:44:51Z | |
| dc.date.issued | 2012 | |
| dc.identifier.uri | http://hdl.handle.net/10150/241971 | |
| dc.description.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. | |
| dc.language.iso | en | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.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. | en_US |
| dc.subject | Intelligent Tutoring Systems | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | physiology | en_US |
| dc.subject | Computer Science | en_US |
| dc.subject | behavior | en_US |
| dc.subject | Electroencephalography | en_US |
| dc.title | Using Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approach | en_US |
| dc.type | text | en_US |
| dc.type | Electronic Dissertation | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.contributor.committeemember | Cohen, Paul | en_US |
| dc.contributor.committeemember | Barnard, Kobus | en_US |
| dc.contributor.committeemember | Morrison, Clayton | en_US |
| dc.contributor.committeemember | Beal, Carole R. | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.name | Ph.D. | en_US |
| refterms.dateFOA | 2018-06-24T12:57:37Z | |
| html.description.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. |
