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Neural imaging to track mental states while using an intelligent tutoring system
Neural imaging to track mental states while using an intelligent tutoring system
- Source :
- Proceedings of the National Academy of Sciences of the United States of America. 107(15)
- Publication Year :
- 2010
-
Abstract
- Hemodynamic measures of brain activity can be used to interpret a student's mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.
- Subjects :
- Cognitive model
Adult
Male
Adolescent
Brain activity and meditation
Computer science
170199 Psychology not elsewhere classified
Machine learning
computer.software_genre
Intelligent tutoring system
Pattern Recognition, Automated
User-Computer Interface
Software
Artificial Intelligence
medicine
Humans
Hidden Markov model
Problem Solving
Brain Mapping
Multidisciplinary
medicine.diagnostic_test
business.industry
Teaching
Biological Sciences
Linear discriminant analysis
Magnetic Resonance Imaging
FOS: Psychology
Female
Artificial intelligence
Functional magnetic resonance imaging
business
computer
Algorithms
Test data
Computer-Assisted Instruction
Subjects
Details
- ISSN :
- 10916490
- Volume :
- 107
- Issue :
- 15
- Database :
- OpenAIRE
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Accession number :
- edsair.doi.dedup.....319a168f6f9ea46757efaa011973e099