Corpus ID: 221654987

GIKT: A Graph-based Interaction Model for Knowledge Tracing

@article{Yang2020GIKTAG,
  title={GIKT: A Graph-based Interaction Model for Knowledge Tracing},
  author={Yang Yang and Jian Shen and Yanru Qu and Yunfei Liu and Kerong Wang and Yaoming Zhu and Wei-nan Zhang and Y. Yu},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.05991}
}
  • Yang Yang, Jian Shen, +5 authors Y. Yu
  • Published 2020
  • Computer Science
  • ArXiv
  • With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems, and are always associated with much fewer skills. However, the previous literature fails to involve question information together with high-order question-skill correlations, which is mostly limited by data sparsity and multi-skill problems. From… CONTINUE READING

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    SHOWING 1-10 OF 34 REFERENCES
    Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network
    • 8
    • PDF
    Deep Hierarchical Knowledge Tracing
    • 6
    • PDF
    Prerequisite-Driven Deep Knowledge Tracing
    • 22
    • PDF
    Knowledge Tracing with Sequential Key-Value Memory Networks
    • 10
    • Highly Influential
    • PDF
    Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
    • 20
    • Highly Influential
    • PDF
    KGAT: Knowledge Graph Attention Network for Recommendation
    • 162
    • PDF
    Deep Knowledge Tracing
    • 333
    • Highly Influential
    • PDF
    Dynamic Key-Value Memory Networks for Knowledge Tracing
    • 100
    • Highly Influential
    • PDF
    Going Deeper with Deep Knowledge Tracing
    • 60
    • PDF
    Augmenting Knowledge Tracing by Considering Forgetting Behavior
    • 17
    • PDF