Explainable Feedback Generation

Explainable Feedback Generation is a method within the domain of Automated Feedback Generation that provides transparent, human-interpretable justifications for generated feedback by revealing the underlying reasoning, rules, or evidence guiding its feedback decisions.

8

Publications

616

Citations

23

Authors

14

Institutions

Publications per year

2017–2026

3

Authors

23

  1. JR
    Julian Roelle

    Ruhr University Bochum, Germany

    1 publications · 15 citations

    1

    h-index

  2. KB
    Kirsten Berthold

    Bielefeld University, Germany

    1 publications · 15 citations

    1

    h-index

  3. MS
    Michael Striewe

    University of Duisburg-Essen, Germany

    1 publications · 26 citations

    1

    h-index

  4. PP
    Pat Pataranutaporn

    Massachusetts Institute of Technology, United States

    1 publications · 31 citations

    1

    h-index

  5. FK
    Franz Kümmert

    Bielefeld University, Germany

    1 publications · 55 citations

    1

    h-index

1–5 of 23

Venues

  1. Computers in Human Behavior

    1 publications · 430 citations

    1

    h-index

  2. Journal of Social Ontology

    1 publications · 22 citations

    1

    h-index

  3. Science of Computer Programming

    1 publications · 26 citations

    1

    h-index

  4. European Journal of Psychology of Education

    1 publications · 15 citations

    1

    h-index

  5. CHI Conference on Human Factors in Computing Systems

    Conference · 1 publications · 21 citations

    1

    h-index

1–5