Knowledge Tracing Based on Gated Heterogeneous Graph Convolutional Networks
Paper
Paper/Presentation Title | Knowledge Tracing Based on Gated Heterogeneous Graph Convolutional Networks |
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Presentation Type | Paper |
Authors | Zhang, Yang, Wang, Zhen, Yu, Ting, Lu, Mingming, Ren, Zujie and Zhang, Ji |
Journal or Proceedings Title | Proceedings of the 10th IEEE International Conference on Big Data (2022) |
Journal Citation | pp. 6847-6849 |
Number of Pages | 3 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BigData55660.2022.10020547 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10020547 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding |
Conference/Event | Proceedings of the 10th IEEE International Conference on Big Data (2022) |
Event Details | Proceedings of the 10th IEEE International Conference on Big Data (2022) Parent IEEE International Conference on Big Data Delivery In person Event Date 17 to end of 20 Dec 2022 Event Location Osaka, Japan |
Abstract | The advancement of science and technology provides the possibility of personalized intelligent education. Representation learning of students’ behavior data is challenging because whether time sequences and interactive behaviors or the correlation between knowledge points and students carrying important information. Some researchers propose knowledge tracing to provide ideas for solving this dilemma. However, existing knowledge tracing methods are divided into machine learning and deep learning. Machine learning-based methods require manual feature extraction and a large amount of prior knowledge. Although deep learning-based methods can automatically extract features, most methods either only use the time series information of the data, or use the association between knowledge points. All the methods ignore the association between knowledge points and students. To fill this gap, we propose a Gated Heterogeneous Graph Convolutional Network (GHGCN) model. We utilize the encoder-decoder framework to predict student performance using the representations of nodes, which is learned from heterogeneous convolutional networks and gate recurrent unit. To validate the effectiveness of the proposed GHGCN model, we conduct the experiments on three public datasets: Simulated Data, Assistments 2009, and Assistments 2015. The results indicate that our method can achieve better performance compared with state-of-the-art algorithms. |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Zhejiang Lab, China |
Central South University, China | |
University of Southern Queensland |
https://research.usq.edu.au/item/z5904/knowledge-tracing-based-on-gated-heterogeneous-graph-convolutional-networks
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