Knowledge Tracing Explainability

Knowledge Tracing Explainability is an academic construct within Explainable Artificial Intelligence in Education that refers to the transparency and interpretability of the internal mechanisms and decision-making processes used by knowledge tracing models to infer and predict a learner's evolving cognitive state over time.

5

Publications

235

Citations

19

Authors

17

Institutions

Publications per year

2017–2026

5

Authors

19

  1. ML
    Ming Li

    Zhejiang Normal University, China

    1 publications · 83 citations

    1

    h-index

  2. WZ
    Wei Zhang

    East China Normal University, China

    1 publications · 15 citations

    1

    h-index

  3. TZ
    Ting Zhang

    Nanjing University of Science and Technology, China

    1 publications · 15 citations

    1

    h-index

  4. KL
    Kwei-Jay Lin

    University of California, Irvine, United States

    1 publications · 83 citations

    1

    h-index

  5. BJ
    Bo Jiang

    Zhejiang University of Technology, China

    1 publications · 15 citations

    1

    h-index

1–5 of 19

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 76 citations

    1

    h-index

  2. Information Processing & Management

    1 publications · 15 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 43 citations

    1

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 83 citations

    1

    h-index

  5. 1

    h-index

1–5