Part of human adaptability is our ability to learn from instruction by other humans. Such instruction is typically an informal mix of demonstration and telling, presented by a situational expert who is not a teaching expert. If machines are to cooperate effectively with humans across a broad range of situations, they must exhibit human-like learning flexibility, even in the face of poor or ambiguous instruction. A key challenge, however, is how to effectively measure and compare the adaptability of machine learners. To address this challenge, we have developed the instrument of a spectrum curriculum (Beal, Leung, and Laddaga 2010): a suite of lessons, incrementally varying along a dimension of interest and presented in order from hardest to easiest. The adaptivity of a student is then expected to be characterized by its performance curve across the suite of lessons, with more adaptive students expected to show a smooth increase of comprehension as the quality of teaching improves. We have developed seven spectrum curricula for simulated RoboCup, and preliminary tests of human-adapted versions of four of those curricula support this theory.