Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning

Abstract

In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert raters using Ekman's Facial Action Coding System and analysed using association rule mining techniques. The two expert raters received an overall kappa that ranged between .76 and .84. The association rule mining analysis uncovered facial actions associated with confusion, frustration, and boredom. We discuss these rules and the prospects of enhancing AutoTutor with non-intrusive affect-sensitive capabilities.

Acknowledgements

This research conducted by the authors was supported by the National Science Foundation (REC 0106965, ITR 0325428, REC 0633918) the Tutoring Research Group (visit http://www.autotutor.org). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

We would also like to thank Laurentiu Cristofor for use of the ARMiner client server data mining application used for the association rule mining.