Interpretable Dropout Prediction

Interpretable Dropout Prediction is a predictive modeling approach within educational data mining that enables the identification of students at risk of dropping out while providing transparent, human-understandable explanations of the model’s decision-making process.

22

Publications

4.1K

Citations

53

Authors

29

Institutions

Publications per year

2017–2026

11

Authors

53

  1. ET
    Ernest T. Pascarella

    University of Illinois Chicago, United States

    2 publications · 1.6K citations

    2

    h-index

  2. RM
    Roland Molontay

    Budapest University of Technology and Economics, Hungary

    2 publications · 171 citations

    2

    h-index

  3. PT
    Patrick T. Terenzini

    Pennsylvania State University, United States

    2 publications · 1.6K citations

    2

    h-index

  4. MN
    Marcell Nagy

    Budapest University of Technology and Economics, Hungary

    2 publications · 171 citations

    2

    h-index

  5. WF
    William F. Gross

    United States Department of Veterans Affairs, United States

    1 publications · 22 citations

    1

    h-index

1–5 of 53

Venues

  1. The Journal of Higher Education

    JHE · 2 publications · 1.6K citations

    2

    h-index

  2. The Personnel and Guidance Journal

    1 publications · 55 citations

    1

    h-index

  3. Behavior Therapy

    1 publications · 66 citations

    1

    h-index

  4. Interchange

    1 publications · 772 citations

    1

    h-index

  5. Quarterly Journal of Studies on Alcohol

    1 publications · 22 citations

    1

    h-index

1–5