Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction
Caring for Agents and Agents that Care: Building Empathic Relations with Synthetic Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
Early Prediction of Student Frustration
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
User Modeling and User-Adapted Interaction
Modeling learner affect with theoretically grounded dynamic bayesian networks
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
EMA: A process model of appraisal dynamics
Cognitive Systems Research
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Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are designed in ways that limit their ability to be deployed to a large audience of students by using expensive sensors or subjectdependent machine learning techniques. This paper presents work that investigates empirically derived Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, CRYSTAL ISLAND. These models are then tested on data from a second identical study involving 140 students to examine issues of generalizability of learned predictive models of student affect. The findings suggest that predictive models of affect that are learned from empirical data may have significant dependencies on the populations on which they are trained, even when the populations themselves are very similar.