We examine correlations between student learning and student acoustic-prosodic profiles, which prior research has shown to be predictive of emotional states. We compare these correlations in two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. Our results suggest that rather than relying on emotion prediction models developed via the more labor-intensive method of manually labeling emotions, adaptive strategies for our spoken dialogue tutoring system can be developed based on observed acoustic-prosodic profiles that we hypothesize to be reflective of emotion.