Emotions and Learning with AutoTutor

  • Authors:

  • Arthur Graesser;Patrick Chipman;Brandon King;Bethany McDaniel;Sidney D'Mello

  • Affiliations:

  • Institute for Intelligent Systems, The University of Memphis, 365 Innovation Drive, University of Memphis, Memphis, TN, 38152, USA;Institute for Intelligent Systems, The University of Memphis, 365 Innovation Drive, University of Memphis, Memphis, TN, 38152, USA;Institute for Intelligent Systems, The University of Memphis, 365 Innovation Drive, University of Memphis, Memphis, TN, 38152, USA;Institute for Intelligent Systems, The University of Memphis, 365 Innovation Drive, University of Memphis, Memphis, TN, 38152, USA;Institute for Intelligent Systems, The University of Memphis, 365 Innovation Drive, University of Memphis, Memphis, TN, 38152, USA

  • Venue:
  • Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
  • Year:
  • 2007

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Abstract

The relationship between emotions and learning was investigated by tracking the emotions that college students experienced while learning about computer literacy with AutoTutor. AutoTutor is an animated pedagogical agent that holds a conversation in natural language, with spoken contributions by the learner. Thirty students completed a multiple-choice pre-test, a 35-minute training session, and a multiple-choice post-test. The students reviewed the tutorial interaction and were stopped at strategically sampled points for emotion judgments. They judged what emotions they experienced on the basis of the dialogue history and their facial expressions. The emotions they judged were boredom, flow (engagement), frustration, confusion, delight, surprise, and neutral. A multiple regression analysis revealed that post-test scores were significantly predicted by pre-test scores and confusion, but not by any of the other emotions.