Multi-modal Learner Modeling

Multi-Modal Learner Modeling is a learner modeling approach that integrates multiple types of data modalities—such as behavioral, physiological, linguistic, and interaction traces—to construct a comprehensive and dynamic representation of individual learners, thereby enabling more nuanced inferences about cognitive, affective, and metacognitive states than single-modality models.

14

Publications

809

Citations

73

Authors

42

Institutions

Publications per year

2017–2026

11

Authors

73

  1. MW
    Marcelo Worsley

    Stanford University, United States

    3 publications · 63 citations

    3

    h-index

  2. DS
    Daniel Spikol

    Malmö University, Sweden

    2 publications · 29 citations

    2

    h-index

  3. MC
    Mutlu Cukurova

    University College London, United Kingdom

    2 publications · 29 citations

    2

    h-index

  4. YL
    Yaoman Li

    Delft University of Technology, Netherlands

    1 publications · 84 citations

    1

    h-index

  5. LL
    Liang Liao

    Wuhan University, China

    1 publications · 69 citations

    1

    h-index

1–5 of 73

Venues

  1. Journal of Computer Assisted Learning

    2 publications · 51 citations

    2

    h-index

  2. IEEE Access

    2 publications · 66 citations

    2

    h-index

  3. Energy and Buildings

    1 publications · 209 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 84 citations

    1

    h-index

  5. Research in Science Education

    1 publications · 215 citations

    1

    h-index

1–5