Data-driven Feedback Generation

Data-Driven Feedback Generation is a computational approach to automated feedback generation that derives feedback through systematic analysis of empirical data, distinguished from rule-based or expert-defined methods by its reliance on observed patterns and statistical regularities extracted from large-scale datasets.

50

Publications

2.3K

Citations

229

Authors

83

Institutions

Publications per year

2017–2026

27

Authors

229

  1. TB
    Tiffany Barnes

    North Carolina State University, United States

    4 publications · 147 citations

    4

    h-index

  2. TP
    Thomas Price

    North Carolina State University, United States

    3 publications · 118 citations

    3

    h-index

  3. RS
    Rishabh Singh

    Microsoft (United States), United States

    3 publications · 125 citations

    3

    h-index

  4. TJ
    Thorsten Joachims

    Cornell University, United States

    2 publications · 28 citations

    2

    h-index

  5. TS
    Tobias Schnabel

    Cornell University, United States

    2 publications · 28 citations

    2

    h-index

1–5 of 229

Venues

  1. Educational Data Mining

    Conference · 2 publications · 84 citations

    2

    h-index

  2. Journal of Learning Analytics

    2 publications · 47 citations

    2

    h-index

  3. Computers in Human Behavior

    2 publications · 470 citations

    2

    h-index

  4. BMJ Open

    2 publications · 39 citations

    2

    h-index

  5. Prehospital Emergency Care

    1 publications · 20 citations

    1

    h-index

1–5