  The goal of knowledge tracing is to track the state of a student's knowledge
as it evolves over time. This plays a fundamental role in understanding the
learning process and is a key task in the development of an intelligent
tutoring system. In this paper we propose a novel approach to knowledge tracing
that combines techniques from matrix factorization with recent progress in
recurrent neural networks (RNNs) to effectively track the state of a student's
knowledge. The proposed \emph{DynEmb} framework enables the tracking of student
knowledge even without the concept/skill tag information that other knowledge
tracing models require while simultaneously achieving superior performance. We
provide experimental evaluations demonstrating that DynEmb achieves improved
performance compared to baselines and illustrating the robustness and
effectiveness of the proposed framework. We also evaluate our approach using
several real-world datasets showing that the proposed model outperforms the
previous state-of-the-art. These results suggest that combining embedding
models with sequential models such as RNNs is a promising new direction for
knowledge tracing.
