Affective Learning — A Manifesto
BT Technology Journal
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
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affect-aware tutors: recognising and responding to student affect
International Journal of Learning Technology
Multimethod assessment of affective experience and expression during deep learning
International Journal of Learning Technology
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
International Journal of Human-Computer Studies
User Modeling and User-Adapted Interaction
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Modeling confusion: facial expression, task, and discourse in task-oriented tutorial dialogue
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Characterizing the effectiveness of tutorial dialogue with hidden markov models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Toward a machine learning framework for understanding affective tutorial interaction
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Affect plays a vital role in learning. During tutoring, particular affective states may benefit or detract from student learning. A key cognitiveaffective state is confusion, which has been positively associated with effective learning. Although identifying episodes of confusion presents significant challenges, recent investigations have identified correlations between confusion and specific facial movements. This paper builds on those findings to create a predictive model of learner confusion during task-oriented human-human tutorial dialogue. The model leverages textual dialogue, task, and facial expression history to predict upcoming confusion within a hidden Markov modeling framework. Analysis of the model structure also reveals meaningful modes of interaction within the tutoring sessions. The results demonstrate that because of its predictive power and rich qualitative representation, the model holds promise for informing the design of affective-sensitive tutoring systems.