Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Learning empathy: a data-driven framework for modeling empathetic companion agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
Diagnosing self-efficacy in intelligent tutoring systems: an empirical study
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A domain-independent framework for modeling emotion
Cognitive Systems Research
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Because many students experience frustration during learning, it is important to develop affective strategies to support students' coping with frustration in interactive learning environments. First, we must devise affect recognition models to detect student affect. Second, we need to determine when to intervene; these conditions are likely to be different for each student. To determine how much frustration a student can persist through, we should utilize models of student self-efficacy to predict a student's frustration threshold. Third, we should devise techniques for responding empathetically before the student reaches her threshold of frustration. We propose an approach to support students' coping with frustration in intelligent tutoring systems that utilizes induced models of affect, self-efficacy and empathetic behavior to effectively reason about precisely when and how to intervene in frustration-ridden learning situations.