The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Affective computing
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Affective Learning — A Manifesto
BT Technology Journal
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Automatic prediction of frustration
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
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Fundamentals of physiological computing
Interacting with Computers
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
A decision theoretic model for stress recognition and user assistance
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
The painful face - Pain expression recognition using active appearance models
Image and Vision Computing
International Journal of Human-Computer Studies
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper we seek to model the users' experience within an interactive learning environment. More precisely, we are interested in assessing three extreme trends in the interaction experience, namely flow (a perfect immersion within the task), stuck (a difficulty to maintain focused attention) and off-task (a drop out from the task). We propose a hierarchical probabilistic framework using a dynamic Bayesian network to simultaneously assess the probability of experiencing each trend, as well as the emotional responses occurring subsequently. The framework combines three-modality diagnostic variables that sense the learner's experience including physiology, behavior and performance, predictive variables that represent the current context and the learner's profile, and a dynamic structure that tracks the temporal evolution of the learner's experience. We describe the experimental study conducted to validate our approach. A protocol was established to elicit the three target trends as 44 participants interacted with three learning environments involving different cognitive tasks. Physiological activities (electroencephalography, skin conductance and blood volume pulse), patterns of the interaction, and performance during the task were recorded. We demonstrate that the proposed framework outperforms conventional non-dynamic modeling approaches such as static Bayesian networks, as well as three non-hierarchical formalisms including naive Bayes classifiers, decision trees and support vector machines.