Most spoken dialogue systems are still lacking in their ability to accurately model the complex process that is human turn-taking. This research analyzes a human-human tutoring corpus in order to identify prosodic turn-taking cues, with the hopes that they can be used by intelligent tutoring systems to predict student turn boundaries. Results show that while there was variation between subjects, three features were significant turn-yielding cues overall. In addition, a positive relationship between the number of cues present and the probability of a turn yield was demonstrated.