Emotion Sensors Go To School

  • Authors:

  • Ivon Arroyo;David G. Cooper;Winslow Burleson;Beverly Park Woolf;Kasia Muldner;Robert Christopherson

  • Affiliations:

  • Department of Computer Science, University of Massachusetts Amherst;Department of Computer Science, University of Massachusetts Amherst;School of Computer Science and Informatics / Arts, Media and Engineering Arizona State University;Department of Computer Science, University of Massachusetts Amherst;School of Computer Science and Informatics / Arts, Media and Engineering Arizona State University;School of Computer Science and Informatics / Arts, Media and Engineering Arizona State University

  • Venue:
  • Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
  • Year:
  • 2009

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Abstract

This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experiments the tutor dynamically collected data streams of physiological activity and students' self-reports of emotions. Evidence indicates that state-based fluctuating student emotions are related to larger, longer-term affective variables such as self-concept in mathematics. Students produced self-reports of emotions and models were created to automatically infer these emotions from physiological data from the sensors. Summaries of student physiological activity, in particular data streams from facial detection software, helped to predict more than 60% of the variance of students emotional states, which is much better than predicting emotions from other contextual variables from the tutor, when these sensors are absent. This research also provides evidence that by modifying the “context” of the tutoring system we may well be able to optimize students' emotion reports and in turn improve math attitudes.