TY - GEN
T1 - Defining the behavior of an affective learning companion in the affective meta-tutor project
AU - Girard, Sylvie
AU - Chavez-Echeagaray, Maria Elena
AU - Gonzalez-Sanchez, Javier
AU - Hidalgo-Pontet, Yoalli
AU - Zhang, Lishan
AU - Burleson, Winslow
AU - Vanlehn, Kurt
PY - 2013
Y1 - 2013
N2 - Research in affective computing and educational technology has shown the potential of affective interventions to increase student's self-concept and motivation while learning. Our project aims to investigate whether the use of affective interventions in a meta-cognitive tutor can help students achieve deeper modeling of dynamic systems by being persistent in their use of meta-cognitive strategies during and after tutoring. This article is an experience report on how we designed and implemented the affective intervention. (The meta-tutor is described in a separate paper.) We briefly describe the theories of affect underlying the design and how the agent's affective behavior is defined and implemented. Finally, the evaluation of a detector-driven categorization of student behavior, that guides the agent's affective interventions, against a categorization performed by human coders, is presented.
AB - Research in affective computing and educational technology has shown the potential of affective interventions to increase student's self-concept and motivation while learning. Our project aims to investigate whether the use of affective interventions in a meta-cognitive tutor can help students achieve deeper modeling of dynamic systems by being persistent in their use of meta-cognitive strategies during and after tutoring. This article is an experience report on how we designed and implemented the affective intervention. (The meta-tutor is described in a separate paper.) We briefly describe the theories of affect underlying the design and how the agent's affective behavior is defined and implemented. Finally, the evaluation of a detector-driven categorization of student behavior, that guides the agent's affective interventions, against a categorization performed by human coders, is presented.
KW - Affective computing
KW - Affective learning companion
KW - Intelligent tutoring system
KW - Meta-cognition
KW - Robust learning
UR - https://www.scopus.com/pages/publications/84879995980
U2 - 10.1007/978-3-642-39112-5_3
DO - 10.1007/978-3-642-39112-5_3
M3 - Conference contribution
AN - SCOPUS:84879995980
SN - 9783642391118
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 21
EP - 30
BT - Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
PB - Springer Verlag
T2 - 16th International Conference on Artificial Intelligence in Education, AIED 2013
Y2 - 9 July 2013 through 13 July 2013
ER -