Deep Knowledge Tracing with Side Information

TL;DR

This paper introduces a novel deep learning framework, DTKS, that leverages side relations between questions to enhance the accuracy of student knowledge state monitoring in intelligent tutoring systems.

Contribution

The paper proposes a new framework that incorporates side relations into deep knowledge tracing models, demonstrating improved performance over existing methods.

Findings

Side information significantly improves knowledge tracing accuracy

DTKS outperforms baseline models on real education data

Side relations enrich understanding of student knowledge states

Abstract

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing.