Toward a Feature-Driven Understanding of Students' Emotions during Interactions with Agent-Based Learning Environments: A Selective Review

摘要

This selective review synthesizes and draws recommendations from the fields of affective computing, intelligent tutoring systems, and psychology to describe and discuss the emotions that learners report experiencing while interacting with agent-based learning environments (ABLEs). Theoretically driven explanations are provided that describe the relative effectiveness and ineffectiveness of different ABLE features to foster adaptive emotions (e.g., engagement, curiosity) vs. non-adaptive emotions (e.g., frustration, boredom) in six different environments. This review provides an analytical lens to evaluate and improve upon research with ABLEs by identifying specific system features and their relationship with learners' appraisals and emotions.