Text-Based Affect Detection in Intelligent Tutors

Abstract

Affect-sensitive Intelligent Tutoring Systems are an exciting new educational technology that aspire to heighten motivation and enhance learning gains in interventions that are dynamically adaptive to learners’ affective and cognitive states. Although state of the art affect detection systems rely on behavioral and physiological measures for affect detection, we show that a textual analysis of the tutorial discourse provides important cues into learners’ affective states. This chapter surveys the existing literature on text-based affect sensing and focuses on how learners’ affective states (boredom, flow/engagement, confusion, and frustration) can be automatically predicted by variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. The authors discuss the generalizability of findings to other domains and tutoring systems, the possibility of constructing real-time cohesion-based affect detectors, and implications for text-based affect detection for the next generation affect-sensitive learning environments.