Peer Feedback Automation

Peer Feedback Automation is a methodological framework within automated feedback systems that enables the systematic orchestration and computational support of feedback exchanges among peers, distinguishing itself by integrating rule-based or machine learning–driven mechanisms to standardize, scale, and personalize peer-generated evaluations in academic or instructional settings.

10

Publications

459

Citations

34

Authors

15

Institutions

Publications per year

2017–2026

5

Authors

34

  1. KG
    Kai Guo

    University of Hong Kong, Hong Kong SAR China

    2 publications · 156 citations

    2

    h-index

  2. CM
    Christopher Mong

    Purdue University West Lafayette, United States

    1 publications · 40 citations

    1

    h-index

  3. TL
    Teresa López Pellisa

    Universitat de les Illes Balears, Spain

    1 publications · 74 citations

    1

    h-index

  4. GV
    Ginger V. Shultz

    University of Michigan, United States

    1 publications · 11 citations

    1

    h-index

  5. YH
    Yu-Chang Hsu

    Boise State University, United States

    1 publications · 101 citations

    1

    h-index

1–5 of 34

Venues

  1. Assessing Writing

    1 publications · 31 citations

    1

    h-index

  2. The Internet and Higher Education

    1 publications · 125 citations

    1

    h-index

  3. Journal of Educational Computing Research

    1 publications · 40 citations

    1

    h-index

  4. British Journal of Educational Psychology

    1 publications · 14 citations

    1

    h-index

  5. Journal of Biomedical Informatics

    1 publications · 17 citations

    1

    h-index

1–5