SQKT: A Student Attention-Based and Question-Aware Model for Knowledge Tracing

Abstract

The goal of Knowledge Tracing (KT) is to trace student’s knowledge states in relation to different knowledge concepts and make prediction of student’s performance on new exercises. With the growing number of online learning platforms, personalized learning is more and more urgently required. As a result, KT has been widely explored for recent decades. Traditional machine learning based methods and Deep Neural Network based methods have been constantly introduced for improving prediction accuracy of KT models and have achieved some positive results. However, there are still some challenges for KT research, such as information representation of high-dimentional question data, consideration of personalized learning ability, and so on. In this paper we propose a novel Student attention-based and Question-aware model for KT (SQKT), which can address the challenges by estimating student attention on different type of questions through history exercise trajectory. Firstly, we devise a weighted graph and propose a weighted deepwalk method to get the question embedding which is combined with the correlated skills as question representation. Secondly, we propose a novel student attention mechanism, which is dedicated for the updating of student’s knowledge state. Finally, comprehensive experiments are conducted on 4 real world datasets, the results demonstrate that our SQKT model outperforms the state-of-the-art KT models on all datasets.