Analyzing learner affect in a scenario-based intelligent tutoring system

Abstract

Scenario-based tutoring systems influence affective states due to two distinct mechanisms during learning: (1) reactions to performance feedback and (2) responses to the scenario context or events. To explore the role of affect and engagement, a scenario-based ITS was instrumented to support unobtrusive facial affect detection. Results from a sample of university students showed relatively few traditional academic affective states such as confusion or frustration, even at decision points and after poor performance (e.g., incorrect responses). This may show evidence of “over-flow,” with a high level of engagement and interest but insufficient confusion/disequilibrium for optimal learning.