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ACII
2007
Springer

Early Prediction of Student Frustration

13 years 9 months ago
Early Prediction of Student Frustration
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.
Scott W. McQuiggan, Sunyoung Lee, James C. Lester
Added 06 Jun 2010
Updated 06 Jun 2010
Type Conference
Year 2007
Where ACII
Authors Scott W. McQuiggan, Sunyoung Lee, James C. Lester
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