Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies

Roger Azevedo

Nicholas Mudrick

Robert Sawyer

James Lester

Abstract: Affect and metacognition play a central role in learning. We examine the relationships between students’ affective state dynamics, metacognitive judgments, and performance during learning with MetaTutorIVH, an advanced learning technology for human biology education. Student emotions were tracked using facial expression recognition embedded within MetaTutorIVH and transitions between emotions theorized to be important to learning (e.g., confusion, frustration, and joy) are analyzed with respect to likelihood of occurrence. Transitions from confusion to frustration were observed at a significantly high likelihood, although no differences in performance were observed in the presence of these affective states and transitions. Results suggest that the occurrence of emotions have a significant impact on students’ retrospective confidence judgments, which they made after submitting their answers to multiple-choice questions. Specifically, the presence of confusion and joy during learning had a positive impact on student confidence in their performance while the presence of frustration and transition from confusion to frustration had a negative impact on confidence, even after accounting for individual differences in multiple-choice confidence.

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