Multimodal Feedback Generation

Multimodal Feedback Generation is a method for automatically producing instructional feedback that integrates and aligns information from multiple modalities—such as text, speech, gesture, or visual data—distinguished from unimodal approaches by its capacity to synthesize heterogeneous input sources into coherent, contextually grounded responses.

47

Publications

1.2K

Citations

168

Authors

90

Institutions

Publications per year

2017–2026

23

Authors

168

  1. JM
    John M. Carroll

    Pennsylvania State University, United States

    1 publications · 26 citations

    2

    h-index

  2. MB
    Mary Beth Rosson

    Pennsylvania State University, United States

    1 publications · 26 citations

    2

    h-index

  3. VN
    Vijaykrishnan Narayanan

    Pennsylvania State University, United States

    1 publications · 26 citations

    2

    h-index

  4. RM
    Roderick Murray‐Smith

    University of Glasgow, United Kingdom

    2 publications · 25 citations

    2

    h-index

  5. CL
    Chonghan Lee

    Pennsylvania State University, United States

    1 publications · 26 citations

    2

    h-index

1–5 of 168

Venues

  1. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 2 publications · 37 citations

    2

    h-index

  2. Multimodal Technologies and Interaction

    1 publications · 13 citations

    1

    h-index

  3. Computers & Electrical Engineering

    1 publications · 21 citations

    1

    h-index

  4. International Journal of Research in Marketing

    IJRM · 1 publications · 36 citations

    1

    h-index

  5. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 22 citations

    1

    h-index

1–5