1. JKT: A joint graph convolutional network based Deep Knowledge Tracing.
- Author
-
Song, Xiangyu, Li, Jianxin, Tang, Yifu, Zhao, Taige, Chen, Yunliang, and Guan, Ziyu
- Subjects
- *
EXERCISE , *INTELLIGENT tutoring systems , *MASTERY learning , *INFORMATION modeling - Abstract
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 J oint graph convolutional network based deep K nowledge T racing (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 [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF