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Though effectively modeling student knowledge would have high\neducational impact, the task has many inherent challenges. In this paper we\nexplore the utility of using Recurrent Neural Networks (RNNs) to model student\nlearning. The RNN family of models have important advantages over previous\nmethods in that they do not require the explicit encoding of human domain\nknowledge, and can capture more complex representations of student knowledge.\nUsing neural networks results in substantial improvements in prediction\nperformance on a range of knowledge tracing datasets. Moreover the learned\nmodel can be used for intelligent curriculum design and allows straightforward\ninterpretation and discovery of structure in student tasks. 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