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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Beal, C., Lee, H.: Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction. In: AIED Workshop on Motivation and Affect in Educational Software (2005)Google Scholar
  2. 2.
    Burleson, W., Picard, R.: Affective agents: Sustaining motivation to learn through failure and a state of stuck. In: Workshop of Social and Emotional Intelligence in Learning Environments, with the 7th Intl. Conf. on Intelligent Tutoring Systems (2004)Google Scholar
  3. 3.
    Conati, C., Mclaren, H.: Data-driven refinement of a probabilistic model of user affect. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 40–49. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Journal of Cognitive Systems Research 5(4), 269–306 (2004)CrossRefGoogle Scholar
  5. 5.
    Lazarus, R.: Emotion and Adaptation. Oxford University Press, New York (1991)Google Scholar
  6. 6.
    McQuiggan, S., Lee, S., Lester, J.: Predicting user physiological response for interactive environments: an inductive approach. In: Proc. of the 2nd Conf. on Artificial Intelligence and Interactive Digital Entertainment, pp. 60–65. AAAI Press, Stanford, California, USA (2006)Google Scholar
  7. 7.
    Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions Pattern Analysis and Machine Intelligence 23(10), 1185–1191 (2001)CrossRefGoogle Scholar
  8. 8.
    Prendinger, H., Ishizuka, M.: The empathic companion: A character-based interface that addresses users’ affective states. Applied Artificial Intelligence 19, 267–285 (2005)CrossRefGoogle Scholar
  9. 9.
    de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: an empirical study. In: Proc. of the 6th Intl. Conf. on Intelligent Tutoring Systems., pp. 933–943. Springer, New York (2002)Google Scholar
  10. 10.
    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufman, San Francisco (2005)zbMATHGoogle Scholar

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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