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Knowledge tracing, where a machine models the knowledge of a student as they interact with coursework, is an established and significantly unsolved problem in computer supported education. In this paper we explore the benefit of using recurrent neural networks to model student learning. This family of models have important advantages over current state of the art methods in that they do not require the explicit encoding of human domain knowledge, and have a far more flexible functional form which can capture substantially more complex student interactions. We show that these neural networks outperform the current state of the art in prediction on real student data, while allowing straightforward interpretation and discovery of structure in the curriculum. These results suggest a promising new line of research for knowledge tracing.