  In Intelligent Tutoring System (ITS), tracing the student's knowledge state
during learning has been studied for several decades in order to provide more
supportive learning instructions. In this paper, we propose a novel model for
knowledge tracing that i) captures students' learning ability and dynamically
assigns students into distinct groups with similar ability at regular time
intervals, and ii) combines this information with a Recurrent Neural Network
architecture known as Deep Knowledge Tracing. Experimental results confirm that
the proposed model is significantly better at predicting student performance
than well known state-of-the-art techniques for student modelling.
