Predicting Learning and Engagement in Tutorial Dialogue: A Personality-Based Model

Abstract

A variety of studies have established that users with different personality profiles exhibit different patterns of behavior when interacting with a system. Although patterns of behavior have been successfully used to predict cognitive and affective outcomes of an interaction, little work has been done to identify the variations in these patterns based on user personality profile. In this paper, we model sequences of facial expressions, postural shifts, hand-to-face gestures, system interaction events, and textual dialogue messages of a user interacting with a human tutor in a computer-mediated tutorial session. We use these models to predict the user's learning gain, frustration, and engagement at the end of the session. In particular, we examine the behavior of users based on their Extraversion trait score of a Big Five Factor personality survey. The analysis reveals a variety of personality-specific sequences of behavior that are significantly indicative of cognitive and affective outcomes. These results could impact user experience design of future interactive systems.