Learner Affect Modeling

Learner Affect Modeling is a computational framework that identifies and interprets affective states of learners during educational interactions by integrating multimodal data to enable adaptive learning systems.

148

Publications

9.1K

Citations

375

Authors

155

Institutions

Publications per year

2017–2026

46

Variants

Affective Learner Modeling

Authors

375

  1. RS
    Ryan S. Baker

    Columbia University, United States

    11 publications · 829 citations

    11

    h-index

  2. SK
    Sidney K. D’Mello

    University of Notre Dame, United States

    9 publications · 1.2K citations

    9

    h-index

  3. JC
    James C. Lester

    North Carolina State University, United States

    6 publications · 253 citations

    6

    h-index

  4. CC
    Cristina Conati

    University of British Columbia, Canada

    6 publications · 720 citations

    6

    h-index

  5. JO
    Jaclyn Ocumpaugh

    Columbia University, United States

    5 publications · 352 citations

    5

    h-index

1–5 of 375

Venues

  1. Computers & Education

    4 publications · 581 citations

    4

    h-index

  2. Educational Data Mining

    Conference · 4 publications · 136 citations

    4

    h-index

  3. User Modeling and User-Adapted Interaction

    4 publications · 563 citations

    4

    h-index

  4. IEEE Transactions on Learning Technologies

    3 publications · 66 citations

    3

    h-index

  5. Education and Information Technologies

    3 publications · 220 citations

    3

    h-index

1–5