Learning Analytics Explainability

Learning Analytics Explainability is a scholarly construct within educational data science that denotes the capacity of learning analytics systems to provide transparent, interpretable, and human-understandable representations of analytical processes and outcomes, thereby enabling stakeholders to comprehend how data-driven insights about learner behavior and performance are generated and validated.

22

Publications

1K

Citations

80

Authors

28

Institutions

Publications per year

2017–2026

19

Authors

80

  1. AF
    Alyssa Friend Wise

    Simon Fraser University, Canada

    2 publications · 44 citations

    2

    h-index

  2. SK
    Simon Knight

    University of Technology Sydney, Australia

    2 publications · 44 citations

    2

    h-index

  3. XO
    Xavier Ochôa

    Escuela Superior Politecnica del Litoral, Ecuador

    2 publications · 44 citations

    2

    h-index

  4. ZZ
    Zdeněk Zdráhal

    The Open University, United Kingdom

    1 publications · 17 citations

    1

    h-index

  5. CB
    Carmel Bar

    Weizmann Institute of Science, Israel

    1 publications · 61 citations

    1

    h-index

1–5 of 80

Venues

  1. Journal of Learning Analytics

    3 publications · 64 citations

    3

    h-index

  2. IEEE Access

    1 publications · 35 citations

    1

    h-index

  3. Educational Technology archive

    1 publications · 15 citations

    1

    h-index

  4. Computers in Human Behavior

    1 publications · 96 citations

    1

    h-index

  5. Education and Information Technologies

    1 publications · 156 citations

    1

    h-index

1–5