Sparse Data Knowledge Tracing

Sparse Data Knowledge Tracing is a knowledge tracing framework designed to model student learning effectively under conditions of limited or sparsely available interaction data by leveraging specialized architectures, regularization techniques, or data augmentation strategies to mitigate the challenges of data insufficiency.

7

Publications

653

Citations

17

Authors

9

Institutions

Publications per year

2017–2026

7

Authors

17

  1. GK
    George Karypis

    University of Minnesota, United States

    2 publications · 201 citations

    2

    h-index

  2. JV
    Jill-Jênn Vie

    University of Michigan, United States

    2 publications · 201 citations

    2

    h-index

  3. HK
    Hisashi Kashima

    Kyoto University, Japan

    2 publications · 201 citations

    2

    h-index

  4. SP
    Shalini Pandey

    University of Minnesota System, United States

    2 publications · 201 citations

    2

    h-index

  5. CC
    Chaoran Cui

    Shandong University of Finance and Economics, China

    1 publications · 19 citations

    1

    h-index

1–5 of 17

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 233 citations

    3

    h-index

  2. IEEE/CAA Journal of Automatica Sinica

    1 publications · 19 citations

    1

    h-index

  3. International Journal of Intelligent Systems

    1 publications · 67 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 186 citations

    1

    h-index

  5. IEEE Transactions on Learning Technologies

    1 publications · 23 citations

1–5