Paper Info
Title
Relevance-Aware Q-matrix Calibration for Knowledge Tracing
Abstract
Knowledge tracing (KT) lies at the core of intelligent education, which aims to diagnose students' changing knowledge level over time based on their historical performance. Most of the existing KT models either ignore the significance of Q-matrix associated exercises with knowledge concepts (KCs) or fail to eliminate the subjective tendency of experts within the Q-matrix, thus it is insufficient for capturing complex interaction between students and exercises. In this paper, we propose a novel Relevance-Aware Q-matrix Calibration method for knowledge tracing (RAQC), which incorporates the calibrated Q-matrix into Long Short-Term Memory (LSTM) network to model the complex students' learning process, for getting both accurate and interpretable diagnosis results. Specifically, we first leverage the message passing mechanism in Graph Convolution Network (GCN) to fully exploit the high-order connectivity between exercises and KCs for obtaining a potential KC list. Then, we propose a Q-matrix calibration method by using relevance scores between exercises and KCs to mitigate the problem of subjective bias existed in human-labeled Q-matrix. After that, the embedding of each exercise aggregated the calibrated Q-matrix with the corresponding response log is fed into the LSTM to tracing students' knowledge states (KS). Extensive experimental results on two real-world datasets show the effectiveness of the proposed method.
Year
DOI
Venue
2021
10.1007/978-3-030-86365-4_9
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT III
Keywords
DocType
Volume
Knowledge tracing, Calibrated Q-matrix, High-order connectivity, Graph convolution network
Conference
12893
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Wentao Wang100.34
Huifang Ma229029.69
Yan Zhao300.34
Zhixin Li411124.43
Xiangchun He500.34