Evaluating the consequences of affective feedback in intelligent tutoring systems

Abstract

The link between affect and student learning has been the subject of increasing attention in recent years. Because of the possible impacts of affective state on learning, it is a goal of many intelligent tutoring systems to attempt to control student emotional states through affective interventions. While much work has gone into improving the quality of these interventions, we are only beginning to understand the complexities of the relationships between affect, learning, and feedback. This paper investigates the consequences associated with providing affective feedback. It represents a first step toward the long-term objective of designing intelligent tutoring systems that can utilize this information for analysis of the risks and benefits of affective intervention. It reports on the results of two studies that were conducted with students interacting with affect-informed virtual agents. The studies reveal that emotion-specific risk/reward information is associated with particular affective states and suggests that future systems might leverage this information to make determinations about affective interventions.