The benefits of caregivers implementing Pivotal Response Treatment (PRT) with children on the Autism spectrum is empirically supported in current Applied Behavior Analysis (ABA) research. Training caregivers in PRT practices involves providing instruction and feedback from trained professional clinicians. As part of the training and evaluation process, clinicians systematically score video probes of the caregivers implementing PRT in several categories, including if an instruction was given when the child was paying adequate attention to the caregiver. This paper examines how machine learning algorithms can be used to aid in classifying video probes. The primary focus of this research explored how attention can be automatically inferred through video processing. To accomplish this, a dataset was created using video probes from PRT sessions and used to train machine learning models. The ambiguity inherent in these videos provides a substantial set of challenges for training an intelligence feedback system.