Inducing User Affect Recognition Models for Task-Oriented Environments

  • Sunyoung Lee
  • Scott W. McQuiggan
  • James C. Lester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Accurately recognizing users’ affective states could contribute to more productive and enjoyable interactions, particularly for task-oriented learning environments. In addition to using physiological data, affect recognition models can leverage knowledge of task structure and user goals to effectively reason about users’ affective states. In this paper we present an inductive approach to recognizing users’ affective states based on appraisal theory, a motivational-affect account of cognition in which individuals’ emotions are generated in response to their assessment of how their actions and events in the environment relate to their goals. Rather than manually creating the models, the models are learned from training sessions in which (1) physiological data, (2) information about users’ goals and actions, and (3) environmental information are recorded from traces produced by users performing a range of tasks in a virtual environment. An empirical evaluation with a task-oriented learning environment testbed suggests that an inductive approach can learn accurate models and that appraisal-based models exploiting knowledge of task structure and user goals can outperform purely physiologically-based models.


Affective State Emotional Intelligence Inductive Approach Intelligent Tutor System Task Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sunyoung Lee
    • 1
  • Scott W. McQuiggan
    • 1
  • James C. Lester
    • 1
  1. 1.Department of Computer Science, North Carolina State University, Raleigh, NC 27695 

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