Abstract
By integrating real-time brain input into personalized learning environments, it would be possible to capture a learner's changing cognitive state and adapt the learning experience appropriately. Working toward this goal, we aim to develop a robust system that can classify a user's cognitive state during a learning activity, using brain data collected with functional near-infrared spectroscopy, an emerging non-invasive neuroimaging tool. This paper describes preliminary steps we have taken toward this objective as well as the underlying vision and research goals. This work has implications for online education as well as the growing fields of brain-computer interfaces and physiological computing.
Original language | English (US) |
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Title of host publication | CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems |
Publisher | Association for Computing Machinery |
Pages | 1698-1705 |
Number of pages | 8 |
Volume | 07-12-May-2016 |
ISBN (Electronic) | 9781450340823 |
DOIs | |
State | Published - May 7 2016 |
Event | 34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 - San Jose, United States Duration: May 7 2016 → May 12 2016 |
Other
Other | 34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 |
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Country | United States |
City | San Jose |
Period | 5/7/16 → 5/12/16 |
Keywords
- Brain-computer interfaces
- Educational data mining
- Functional near-infrared spectroscopy
- Machine learning
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design
- Software