Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing  

作　　者：Xinhua Wang Shasha Zhao Lei Guo Lei Zhu Chaoran Cui Liancheng Xu 

机构地区：[1]School of Information Science and Engineering,Shandong Normal University,Jinan 250358,China [2]School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第11期2108-2123,共16页自动化学报（英文版）

基　　金：supported by the Natural Science Foundation of China (62372277);the Natural Science Foundation of Shandong Province (ZR2022MF257, ZR2022MF295);Humanities and Social Sciences Fund of the Ministry of Education (21YJC630157)。

摘　　要：With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning.To model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.

关 键 词：Contrastive learning counterfactual representation graph neural network knowledge tracing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]