A paper accepted to AIED2013.
16th International Conference on Artificial Intelligence in Education
Memphis, TN, USA.
July 9-13, 2013
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.
Girard, S., Chavez-Echeagaray, M.E., Gonzalez-Sanchez, J., Hidalgo-Pontet, Y., Zhang, L., Burleson, W., and VanLehn, K. (2013). Defining the Behavior of an Affective Learning Companion in the Affective Meta-Tutor Project. Lecture Notes in Computer Science. Artificial Intelligence in Education. Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED). Memphis, TN, USA. July 2013. Springer-Verlag Berlin Heidelberg. Volume 7926 LNAI, pp 21-30. ISSN: 0302-9743.