Paper:
Deep Knowledge Tracing
Knowledge tracing-where a machine models the knowledge of a student as they interact with coursework-is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge. Using neural networks results in substantial improvements in prediction performance on a range of knowledge tracing datasets. Moreover the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks. These results suggest a promising new line of research for knowledge tracing and an exemplary application task for RNNs.

Review:
This article proposes to model student learning in the context of MOOC using RNN. The system is evaluated according to its ability to predict the exercices on which a given student will succeed or not. The authors present a LSTM RNN architecture to tackle this problem and demonstrate the performance improvement over BKT standard method.This article is an well documented application paper on knowledge tracing, where LSTM RNN seems to be a great advance. Dataset & baselines are relevant and the authors give their code in supplementary material: this effort should be remarked. However, it would have been necessary to add a small README to make the code usable easily.The organization of the paper should be cleaned: the definition of knowledge tracing in subsection 1.1 is alone and it should not be part of the introduction. On top of that, section 4 is strange: it should be split and integrated in the introduction and the related work. I do not understand the motivation is using AUC as evaluation metrics: why not relying on accuracy? This choice is not motivated and does not seem to be the reference in knowledge tracing (even if I am not an expert of the field and I saw that Fig 3 presents also accuracy).Sec 6.2 (and supplementary material) is interesting. Could you imagine a way to build a quantitative evaluation of this task?In conclusion, I found this article interesting and well written. The breakthrough in performance should justify a publication in NIPS.