The affective reasoner: a process model of emotions in a multi-agent system
The affective reasoner: a process model of emotions in a multi-agent system
Affective computing
Deictic and emotive communication in animated pedagogical agents
Embodied conversational agents
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
International Journal of Human-Computer Studies - Special issue: Subtle expressivity for characters and robots
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 Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Modeling and evaluating empathy in embodied companion agents
International Journal of Human-Computer Studies
Affective learning companions: strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance
The politeness effect: Pedagogical agents and learning outcomes
International Journal of Human-Computer Studies
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Lifelike pedagogical agents and affective computing: an exploratory synthesis
Artificial intelligence today
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
A domain-independent framework for modeling emotion
Cognitive Systems Research
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Affective reasoning holds significant potential for intelligent tutoring systems. Incorporating affective reasoning into pedagogical decision-making capabilities could enable learning environments to create customised experiences that are dynamically tailored to individual students' ever-changing levels of engagement, interest, motivation and self-efficacy. Because physiological responses are directly triggered by changes in affect, biofeedback data such as heart rate and galvanic skin response can be used to infer affective changes in conjunction with the situational context. This article explores an approach to inducing affect models for a learning environment. The inductive approach is examined for the task of modelling students' self-efficacy and empathy for companion agents. Together, these studies on affect in a narrative learning environment suggest that it is possible to build models of affective constructs from observations of the situational context and students' physiological response.