Emotion sensors go to school

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press BV
Number of pages8
ISBN (Print)9781607500285
StatePublished - 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


  • Infer cognition
  • Infer student affect
  • Intelligent tutor
  • Physiological activity
  • Sensors
  • Student emotion

ASJC Scopus subject areas

  • Artificial Intelligence


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