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) |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | San Jose |
| Period | 5/7/16 → 5/12/16 |
Fingerprint
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
Cite this
Toward real-time brain sensing for learning assessment : Building a rich dataset. / Keating, Shelby; Walker, Erin; Motupali, Anil; Solovey, Erin.
CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems. Vol. 07-12-May-2016 Association for Computing Machinery, 2016. p. 1698-1705.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Toward real-time brain sensing for learning assessment
T2 - Building a rich dataset
AU - Keating, Shelby
AU - Walker, Erin
AU - Motupali, Anil
AU - Solovey, Erin
PY - 2016/5/7
Y1 - 2016/5/7
N2 - 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.
AB - 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.
KW - Brain-computer interfaces
KW - Educational data mining
KW - Functional near-infrared spectroscopy
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85014657752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014657752&partnerID=8YFLogxK
U2 - 10.1145/2851581.2892496
DO - 10.1145/2851581.2892496
M3 - Conference contribution
VL - 07-12-May-2016
SP - 1698
EP - 1705
BT - CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
ER -