EEG estimates of engagement and cognitive workload predict math problem solving outcomes

Federico Cirett Galán*, Carole R. Beal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

48 Scopus citations

Abstract

The study goal 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 (N = 16) 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. EEG data were 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.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization - 20th International Conference, UMAP 2012, Proceedings
Pages51-62
Number of pages12
DOIs
StatePublished - 13 Jul 2012
Externally publishedYes
Event20th International Conference on User Modeling, Adaptation and Personalization, UMAP 2012 - Montreal, QC, Canada
Duration: 16 Jul 201220 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7379 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on User Modeling, Adaptation and Personalization, UMAP 2012
Country/TerritoryCanada
CityMontreal, QC
Period16/07/1220/07/12

Keywords

  • behavior
  • Electroencephalography
  • Intelligent Tutoring Systems
  • Machine Learning
  • Math
  • physiology

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