Affective Data Mining

Affective Data Mining is a computational methodology within affective computing that identifies, extracts, and analyzes emotional and cognitive states from multimodal educational data to infer learners' affective conditions during learning processes.

190

Publications

10.1K

Citations

638

Authors

284

Institutions

Publications per year

2017–2026

101

Authors

638

  1. EC
    Erik Cambria

    Nanyang Technological University, Singapore

    8 publications · 980 citations

    8

    h-index

  2. RS
    Ryan S. Baker

    Columbia University, United States

    7 publications · 522 citations

    7

    h-index

  3. AH
    Amir Hussain

    Edinburgh Napier University, United Kingdom

    6 publications · 699 citations

    6

    h-index

  4. SK
    Sidney K. D’Mello

    University of Notre Dame, United States

    6 publications · 430 citations

    6

    h-index

  5. JG
    Jesús G. Boticario

    Universidad Nacional de Educación a Distancia, Spain

    5 publications · 194 citations

    5

    h-index

1–5 of 638

Venues

  1. Educational Data Mining

    Conference · 4 publications · 117 citations

    4

    h-index

  2. Expert Systems with Applications

    4 publications · 479 citations

    4

    h-index

  3. IEEE Access

    4 publications · 107 citations

    4

    h-index

  4. Journal of Engineering Design

    4 publications · 193 citations

    4

    h-index

  5. Information Processing & Management

    4 publications · 400 citations

    4

    h-index

1–5