Detecting learner frustration: Towards mainstream use cases

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. © 2010 Springer-Verlag.