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Early Prediction of Student Frustration

  • Scott W. McQuiggan
  • Sunyoung Lee
  • James C. Lester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4738)

Abstract

Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately.

Keywords

Support Vector Machine Learning Environment Affective State Early Prediction Intelligent Tutoring System 
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

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

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