A dynamic model of stress, and sustained attention
Human Factors
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Neuroergonomics: The Brain at Work
Neuroergonomics: The Brain at Work
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
The relative impact of student affect on performance models in a spoken dialogue tutoring system
User Modeling and User-Adapted Interaction
UM '07 Proceedings of the 11th international conference on User Modeling
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Integrating innovative neuro-educational technologies (I-Net) into K-12 science classrooms
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Online workload recognition from EEG data during cognitive tests and human-machine interaction
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Mental workload, engagement and emotions: an exploratory study for intelligent tutoring systems
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
EEG estimates of engagement and cognitive workload predict math problem solving outcomes
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Cardiovascular physiology predicts learning effects in a serious game activity
Computers & Education
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Endowing systems with abilities to assess a user's mental state in an operational environment could be useful to improve communication and interaction methods. In this work we seek to model user mental workload using spectral features extracted from electroencephalography (EEG) data. In particular, data were gathered from 17 participants who performed different cognitive tasks. We also explore the application of our model in a non laboratory context by analyzing the behavior of our model in an educational context. Our findings have implications for intelligent tutoring systems seeking to continuously assess and adapt to a learner's state.