Monitoring Affective Trajectories during Complex Learning

This study investigated the transitions between affective states (i.e., boredom, flow, confusion, frustration, delight, and surprise) during learning while college students were tutored in computer literacy by AutoTutor, an automated tutoring system with natural language dialogue. Videos of participants’ faces and the interaction histories were recorded and then played back for the participants to judge their own affective states. We developed a metric to measure the relative likelihood of transitioning from an affective state at time ti to a subsequent affective state at time ti+1. Several significant trajectories between affective states were identified. Instructional implications are discussed in the context of an expanded version of a cognitive disequilibrium model.