Analyzing Prosodic Features and Student Uncertainty using Visualization

It has been hypothesized that to maximize learning, intelligent tutoring systems should detect and respond to both cognitive student states, and affective and metacognitive states such as uncertainty. In intelligent tutoring research so far, student state detection is primarily based on information available from a single student-system exchange unit, or turn. However, the features used in the detection of such states may have a temporal component, spanning multiple turns, and may change throughout the tutoring process. To test this hypothesis, an interactive tool was implemented for the visual analysis of prosodic features across a corpus of student turns previously annotated for uncertainty. The tool consists of two complementary visualization modules. The first module allows researchers to visually mine the feature data for patterns per individual student dialogue, and form hypotheses about feature dependencies. The second module allows researchers to quickly test these hypotheses on groups of students through statistical visual analysis of feature dependencies. Results show that significant differences exist among feature patterns across different student groups. Further analysis suggests that feature patterns may vary with student