Detecting learner frustration: towards mainstream use cases

Authors:
Judi McCuaig;Mike Pearlstein;Andrew Judd
Affiliations:
Department of Computing and Information Science, University of Guelph;Department of Computing and Information Science, University of Guelph;Department of Computing and Information Science, University of Guelph
Venue:
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Year:
2010

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Abstract

When our computers act in unexpected (and unhelpful) ways, we become frustrated with them Were the computers human assistants, they would react by doing something to mitigate our frustration and increase their helpfulness However, computers typically do not know we are frustrated This paper presents research showing that user frustration can be detected with good accuracy (84%) using only two types of input data (head tilt and pupil dilation) We also show that reasonable accuracy (73%) can be achieved using only information about head tilt We then propose how such technology could be employed to reduce learner frustration in adaptive tutoring applications.