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JKT: A joint graph convolutional network based Deep Knowledge Tracing

journal contribution
posted on 2021-11-01, 00:00 authored by Xiangyu Song, Jianxin LiJianxin Li, Yifu TangYifu Tang, Taige ZhaoTaige Zhao, Y Chen, Z Guan
Knowledge Tracing (KT) aims to trace the student's state of evolutionary mastery for a particular knowledge or concept based on the student's historical learning interactions with the corresponding exercises. Taking the “exercise-to-concept” relationships as input, several existing methods have been developed to trace and model students’ mastery states. However, these studies face two major shortcomings in KT: 1) they only consider “exercise-to-concept” relationships; 2) the multi-hot embeddings lack interpretability. In order to address the above issues, we propose a Joint graph convolutional network based deep Knowledge Tracing (JKT) framework to model the multi-dimensional relationships of “exercise-to-exercise”, and “concept-to-concept” into graph and fuse them with “exercise-to-concept” relationships. In JKT, it is not only possible to establish connections between exercises under cross-concepts, but also to help capture high-level semantic information and increase the model's interpretability. In addition, sufficient experiments conducted on four real-world datasets have demonstrated that JKT performs better than the other baseline models. We further illustrate a case study to demonstrate its interpretability for learning analysis

History

Journal

Information Sciences

Volume

580

Pagination

510 - 523

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0020-0255

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

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