Affective learning companions : strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance

摘要

This thesis has developed an affective agent research platform that advances the architecture of relational agents and intelligent tutoring systems. The system realizes non-invasive multimodal real-time sensing of elements of user's affective state and couples this ability with an agent capable of supporting learners by engaging in real-time responsive expressivity. The agent mirrors several non-verbal behaviors believed to influence persuasion, liking, and social rapport, and responds to frustration with empathetic or task-support dialogue. Pilot studies involved 60 participants, ages 10-14 years-old, and led to an experiment involving 76 participants, ages 11-13 years-old, engaging in the Towers of Hanoi activity. The system (data collection, architecture, character interaction, and activity presentation) was iteratively tested and refined, and two "mirroring" conditions were developed: "sensor driven non-verbal interactions" and "pre-recorded non-verbal interactions". The development and training of the classifier algorithms showed the ability to predict frustration/help seeking behavior with 79% accuracy across a pilot group of 24 participants.