DyGKT: Dynamic Graph Learning for Knowledge Tracing
Author:
Affiliation:
1. SKLSDE Lab, Beihang University, Beijing, China
2. SKL-IOTSC, Department of Computer and Information Science, University of Macau, Macau, China
3. School of Transportation Science and Engineering, Beihang University, Beijing, China
Funder
the Start-up Research Grant of University of Macau
Natural Science Foundation of China
Science and Technology Development Fund
S\&T Program of Hebei
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671773
Reference40 articles.
1. Evolutionary dynamics of higher-order interactions in social networks
2. F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
3. Jiahao Chen et al. 2023. Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-Centric Cognitive Representations. In Proceedings of the 37th AAAI Conference on Artificial Intelligence.
4. Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
5. Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
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