Affect-Targeted Interviews for Understanding Student Frustration

摘要

Frustration is a natural part of learning in AIED systems but remains relatively poorly understood. In particular, it remains unclear how students' perceptions about the learning activity drive their experience of frustration and their subsequent choices during learning. In this paper, we adopt a mixed-methods approach, using automated detectors of affect to signal classroom researchers to interview a specific student at a specific time. We hand-code the interviews using grounded theory, then distill particularly common associations between interview codes and affective patterns. We find common patterns involving student perceptions of difficulty, system helpfulness, and strategic behavior, and study them in greater depth. We find, for instance, that the experience of difficulty produces shifts from engaged concentration to frustration that lead students to adopt a variety of problem-solving strategies. We conclude with thoughts on both how this can influence the future design of AIED systems, and the broader potential uses of data mining-driven interviews in AIED research and development.