Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
This addresses the need for more accurate student modeling in educational technology, though it appears incremental as it builds on existing Deep Knowledge Tracing with added grouping.
The paper tackles the problem of predicting student performance in Intelligent Tutoring Systems by proposing a model that dynamically groups students by learning ability and integrates this with Deep Knowledge Tracing, achieving significantly better prediction results than state-of-the-art methods.
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.