TY - GEN
T1 - Emotion sensors go to school
AU - Arroyo, Ivon
AU - Cooper, David G.
AU - Burleson, Winslow
AU - Woolf, Beverly Park
AU - Muldner, Kasia
AU - Christopherson, Robert
PY - 2009
Y1 - 2009
N2 - This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experiments the tutor dynamically collected data streams of physiological activity and students' self-reports of emotions. Evidence indicates that state-based fluctuating student emotions are related to larger, longer-term affective variables such as self-concept in mathematics. Students produced self-reports of emotions and models were created to automatically infer these emotions from physiological data from the sensors. Summaries of student physiological activity, in particular data streams from facial detection software, helped to predict more than 60% of the variance of students emotional states, which is much better than predicting emotions from other contextual variables from the tutor, when these sensors are absent. This research also provides evidence that by modifying the "context" of the tutoring system we may well be able to optimize students' emotion reports and in turn improve math attitudes.
AB - This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experiments the tutor dynamically collected data streams of physiological activity and students' self-reports of emotions. Evidence indicates that state-based fluctuating student emotions are related to larger, longer-term affective variables such as self-concept in mathematics. Students produced self-reports of emotions and models were created to automatically infer these emotions from physiological data from the sensors. Summaries of student physiological activity, in particular data streams from facial detection software, helped to predict more than 60% of the variance of students emotional states, which is much better than predicting emotions from other contextual variables from the tutor, when these sensors are absent. This research also provides evidence that by modifying the "context" of the tutoring system we may well be able to optimize students' emotion reports and in turn improve math attitudes.
KW - Infer cognition
KW - Infer student affect
KW - Intelligent tutor
KW - Physiological activity
KW - Sensors
KW - Student emotion
UR - http://www.scopus.com/inward/record.url?scp=73149108640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73149108640&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-028-5-17
DO - 10.3233/978-1-60750-028-5-17
M3 - Conference contribution
AN - SCOPUS:73149108640
SN - 9781607500285
T3 - Frontiers in Artificial Intelligence and Applications
SP - 17
EP - 24
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -