We evaluate the Machine Coaching paradigm, a human-in-the-loop machine learning methodology, according to which a human coach and a machine engage in an iterative bidirectional exchange of explanations, towards improving the machine’s ability to reach conclusions and justify them in a way that is acceptable to the human coach. To support the systematic empirical investigation of the efficacy and efficiency of Machine Coaching, we adopt proxy (algorithmic) coaches in the stead of human ones.