@inproceedings{5b84f6fcd10348ee970268928cf37592,
title = "EEG estimates of engagement and cognitive workload predict math problem solving outcomes",
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. {\textcopyright} 2012 Springer-Verlag.",
author = "{Cirett Gal{\'a}n}, Federico and Beal, {Carole R.}",
year = "2012",
month = jul,
day = "13",
doi = "10.1007/978-3-642-31454-4_5",
language = "American English",
isbn = "9783642314537",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "51--62",
booktitle = "EEG estimates of engagement and cognitive workload predict math problem solving outcomes",
note = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Conference date: 01-01-2014",
}