A lightweight, human-in-the-loop evaluation scheme for machine translation (MT) systems is proposed. It extrinsically evaluates MT systems using human subjects' scores on second language ability test problems that are machine-translated to the subjects' native language. A large-scale experiment involving 320 subjects revealed that the context-unawareness of the current MT systems severely damages human performance when solving the test problems, while one of the evaluated MT systems performed as good as a human translation produced in a context-unaware condition. An analysis of the experimental results showed that the extrinsic evaluation captured a different dimension of translation quality than that captured by manual and automatic intrinsic evaluation.