Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network

• Published in: 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) pp. 156 - 163
• Main Authors: Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 14-10-2019
• Series: ACM Other Conferences
• Subjects: Computational modeling; Computing methodologies; Computing methodologies > Machine learning; Computing methodologies > Machine learning > Learning paradigms; Computing methodologies > Machine learning > Learning paradigms > Supervised learning; Computing methodologies > Machine learning > Machine learning approaches; Computing methodologies > Machine learning > Machine learning approaches > Neural networks; Data models; Data structures; Educational data mining; Graph neural network; Information systems; Information systems > Information systems applications; Knowledge engineering; Knowledge tracing; Learning sciences; Learning systems; Neural networks; Predictive models; Educational data mining; Knowledge tracing; Learning sciences; Graph neural network
• ISBN: 1450369340; 9781450369343
• DOI: 10.1145/3350546.3352513

Recent advancements in computer-assisted learning systems have caused an increase in the research of knowledge tracing, wherein student performance on coursework exercises is predicted over time. From the viewpoint of data structure, the coursework can be potentially structured as a graph. Incorporating this graph-structured nature into the knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of the graph neural network (GNN), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information.