Fine-grained Knowledge State Modeling

Fine-Grained Knowledge State Modeling is a computational framework that belongs to the class of knowledge tracing models, distinguished by its ability to represent student knowledge at a highly detailed, granular level—such as individual misconceptions or sub-skills—rather than broad skill categories, enabling precise diagnosis and tracking of learning trajectories.

29

Publications

2.5K

Citations

116

Authors

32

Institutions

Publications per year

2017–2026

25

Authors

116

  1. ZH
    Zhenya Huang

    University of Science and Technology of China, China

    5 publications · 328 citations

    5

    h-index

  2. EC
    Enhong Chen

    University of Science and Technology of China, China

    5 publications · 328 citations

    5

    h-index

  3. QL
    Qi Liu

    University of Science and Technology of China, China

    5 publications · 328 citations

    5

    h-index

  4. SS
    Shuanghong Shen

    University of Science and Technology of China, China

    3 publications · 262 citations

    3

    h-index

  5. PC
    Penghe Chen

    Beijing Normal University, China

    2 publications · 204 citations

    2

    h-index

1–5 of 116

Venues

  1. IEEE Transactions on Learning Technologies

    5 publications · 275 citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 122 citations

    3

    h-index

  3. Educational Data Mining

    Conference · 1 publications · 23 citations

    1

    h-index

  4. IEEE Transactions on Cybernetics

    1 publications · 74 citations

    1

    h-index

  5. 1

    h-index

1–5