Adaptive Learner Models

Adaptive Learner Models is a computational framework within learner profiling that dynamically captures and updates individual learners' knowledge states, cognitive traits, and learning behaviors over time by integrating real-time interaction data, thereby distinguishing itself from static learner models through its capacity for ongoing personalization and responsiveness to evolving learner needs.

162

Publications

8K

Citations

478

Authors

189

Institutions

Publications per year

2017–2026

72

Authors

478

  1. SB
    Susan Bull

    University of Birmingham, United Kingdom

    8 publications · 342 citations

    8

    h-index

  2. KK
    Kinshuk Kinshuk

    Athabasca University, Canada

    5 publications · 436 citations

    5

    h-index

  3. WB
    William B. Cutrer

    Vanderbilt University, United States

    4 publications · 490 citations

    4

    h-index

  4. HJ
    Hossein Jahankhani

    University of East London, United Kingdom

    3 publications · 98 citations

    3

    h-index

  5. MY
    Maryam Yarandi

    University of East London, United Kingdom

    3 publications · 98 citations

    3

    h-index

1–5 of 478

Venues

  1. IEEE Transactions on Learning Technologies

    5 publications · 441 citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 101 citations

    5

    h-index

  3. 5

    h-index

  4. 4

    h-index

  5. Medical Teacher

    4 publications · 151 citations

    4

    h-index

1–5