What constitutes good teaching, and what factors do learners consider when evaluating teachers? Prior developmental work suggests that even young children accurately recognize and evaluate under-informativeness. Building on prior work, we propose a Bayesian model of teacher evaluation that infers the teacher's quality from how carefully he selected demonstrations given what he knew. We test the predictions of our model across 15 conditions in which participants saw a teacher who demonstrated all or a subset of functions of a novel device and rated his helpfulness. Our results suggest that human adults seamlessly integrate information about the number of functions taught, their values, as well as what the teacher knew, to make nuanced judgments about the quality of teaching; the quantitative pattern is well predicted by our model.