Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Affective Learning — A Manifesto
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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
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ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
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ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
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ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Modeling Task-Based vs. Affect-based Feedback Behavior in Pedagogical Agents: An Inductive Approach
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Affective Gendered Learning Companions
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
On modeling the affective effect on learning
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Challenges for inclusive affective detection in educational scenarios
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: design methods, tools, and interaction techniques for eInclusion - Volume Part I
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The impact of affect on learning has been the subject of increasing attention. Because of the differential effects of students' affective states on learning outcomes, there is a growing recognition of the important role that intelligent tutoring systems can play in providing affective feedback to students. Although we are only beginning to understand the complex interactions between affect, feedback, and learning, it is evident that affective interventions can both positively and negatively influence learning experiences. To investigate how student personality traits can be used to predict responses to affective feedback, this paper presents an analysis of a large student affect corpus collected from three separate studies. Student personality profiles augmented with goal orientation and empathetic tendency information were analyzed with respect to affect state transitions. The results indicate that student personality profiles can serve as a powerful tool for informing affective feedback models.