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Your search keyword '"Song, Xiangyu"' showing total 8 results
Start Over Author "Song, Xiangyu" Topic graph neural network
8 results on '"Song, Xiangyu"'

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
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3. Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing.

Author

Song, Xiangyu, Li, Jianxin, Lei, Qi, Zhao, Wei, Chen, Yunliang, and Mian, Ajmal

Subjects
*DEEP learning, *RECURRENT neural networks, *INTELLIGENT tutoring systems
Abstract

The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students' learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: (1) they pay too much attention to details of the nodes rather than to high-level semantic information; (2) they struggle to effectively establish spatial associations and complex structures of the nodes; and (3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi -Graph C ontrastive L earning based K nowledge T racing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an "exercise-to-exercise" (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Graph-Enforced Neural Network for Attributed Graph Clustering

Author

Sheng, Zeang, Zhang, Wentao, Ouyang, Wen, Tao, Yangyu, Yang, Zhi, Cui, Bin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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5. Learning Temporal Graph Representation via Memory-Aware Autoencoder

Author

Chen, Jingyu, He, Chengxin, Qu, Yuening, Yu, Zhenyang, Zhang, Yuanhao, Duan, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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6. The Way to Success: A Multi-level Attentive Embedding Framework for Proposal Teamwork Analysis in Voting-Oriented System

Author

Zha, Rui, Zhou, Ding, Zhang, Le, Xu, Tong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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7. Joint Training Graph Neural Network for the Bidding Project Title Short Text Classification

Author

Li, Shengnan, Wu, Xiaoming, Liu, Xiangzhi, Xue, Xuqiang, Yu, Yang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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8. Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning

Author

Zhang, Yuanhao, He, Chengxin, Li, Longhai, Zhang, Bingzhe, Duan, Lei, Zuo, Jie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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