Neural Feedback Generation

Neural Feedback Generation is a computational method for automated feedback generation that leverages neural network architectures to synthesize personalized, context-aware feedback by modeling complex patterns in learner responses through learned representations.

16

Publications

525

Citations

60

Authors

25

Institutions

Publications per year

2017–2026

7

Authors

60

  1. MS
    Mehran Sahami

    Stanford University, United States

    2 publications · 146 citations

    2

    h-index

  2. AN
    Andy Nguyễn

    University of Southern Queensland, Australia

    2 publications · 146 citations

    2

    h-index

  3. AS
    Armando Solar-Lezama

    Massachusetts Institute of Technology, United States

    2 publications · 40 citations

    2

    h-index

  4. LG
    Leonidas Guibas

    Stanford University, United States

    2 publications · 146 citations

    2

    h-index

  5. XZ
    Xin Zhang

    Xiamen University, China

    2 publications · 40 citations

    2

    h-index

1–5 of 60

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 157 citations

    3

    h-index

  2. Journal of Applied Physiology

    1 publications · 61 citations

    1

    h-index

  3. Canadian Journal of Zoology

    1 publications · 10 citations

    1

    h-index

  4. Frontiers in Neuroscience

    1 publications · 30 citations

    1

    h-index

  5. 1

    h-index

1–5