Running head: EMOTE-ALOUD DURING LEARNING Emote-Aloud during Learning with AutoTutor: Applying the Facial Action Coding System to Cognitive-Affective States during Learning

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 verbalized 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 analyzed using association rule mining techniques. The two expert raters received an overall Kappa that ranged between .76 to .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 nonintrusive affect-sensitive capabilities.