TY - GEN
T1 - Affect recognition in learning scenarios
T2 - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
AU - Gonzalez-Sanchez, Javier
AU - Chavez-Echeagaray, Maria Elena
AU - Lin, Lijia
AU - Baydogan, Mustafa
AU - Christopherson, Robert
AU - Gibson, David
AU - Atkinson, Robert
AU - Burleson, Winslow
PY - 2013/10/15
Y1 - 2013/10/15
N2 - The ability of a learning system to infer a student's affects has become highly relevant to be able to adjust its pedagogical strategies. Several methods have been used to infer affects. One of the most recognized for its reliability is face-based affect recognition. Another emerging one involves the use of brain-computer interfaces. In this paper we compare those strategies and explore if, to a great extent, it is possible to infer the values of one source from the other source.
AB - The ability of a learning system to infer a student's affects has become highly relevant to be able to adjust its pedagogical strategies. Several methods have been used to infer affects. One of the most recognized for its reliability is face-based affect recognition. Another emerging one involves the use of brain-computer interfaces. In this paper we compare those strategies and explore if, to a great extent, it is possible to infer the values of one source from the other source.
KW - affect recognition
KW - brain computer interfaces
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=84885197670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885197670&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2013.26
DO - 10.1109/ICALT.2013.26
M3 - Conference contribution
AN - SCOPUS:84885197670
SN - 9780769550091
T3 - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
SP - 70
EP - 71
BT - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Y2 - 15 July 2013 through 18 July 2013
ER -