Autism Spectrum Disorders (ASD) are characterized by deficits in social communication skills, such as response to joint attention (RJA). Robotic systems have been designed and applied to help children with ASD improve their RJA skills. One of the most important goals of robot-assisted intervention is helping children generalize social interaction skills to interact with other people. Thus predicting children's human-human interaction (HHI) performance based on their human-robot interaction (HRI) process is an important task. However, to the best of our knowledge, little research exists exploring this topic. The Early Social-Communication Scales (ESCS) test is a measurement of nonverbal social skills, including RJA, for young children. We conducted two longitudinal user studies with a robot-mediated RJA system in young children with ASD, followed by HHI sessions consisting of ESCS administration. In this paper, we present findings regarding how to predict participants' RJA performance in HHI based on their head pose patterns in HRI, under a semi-supervised machine learning framework. As a three-class classification problem, we achieved a micro-averaged accuracy of 73.5%, which indicates the potential effectiveness of the proposed method.