Educational Data Mining Explainability

Educational Data Mining Explainability is a scholarly framework within learning analytics that enables the interpretation of patterns and models derived from educational data by making the underlying data-driven inferences transparent, traceable, and comprehensible to stakeholders in educational settings.

9

Publications

610

Citations

21

Authors

9

Institutions

Publications per year

2017–2026

7

Variants

Edm Explainability

Authors

21

  1. AR
    Ashwin Rachha

    Virginia Tech, United States

    1 publications · 58 citations

    1

    h-index

  2. MS
    Mohammed Seyam

    Virginia Tech, United States

    1 publications · 58 citations

    1

    h-index

  3. IU
    Irfan Ullah Khan

    Imam Abdulrahman Bin Faisal University, Saudi Arabia

    1 publications · 59 citations

    1

    h-index

  4. PG
    Pratiyush Guleria

    Himachal Pradesh University, India

    1 publications · 156 citations

    1

    h-index

  5. MK
    Mounia Khelfaoui

    Université Djilali Bounaama Khemis Miliana, Algeria

    1 publications · 54 citations

    1

    h-index

1–5 of 21

Venues

  1. Education and Information Technologies

    1 publications · 156 citations

    1

    h-index

  2. Educational Data Mining

    Conference · 1 publications · 102 citations

    1

    h-index

  3. Information Discovery and Delivery

    1 publications · 54 citations

    1

    h-index

  4. Sustainability

    1 publications · 96 citations

    1

    h-index

  5. SSRN Electronic Journal

    Repository · 1 publications · 13 citations

    1

    h-index

1–5