Admissions Predictive Analytics

Admissions Predictive Analytics is a methodological framework within higher education data science that distinguishes itself by employing statistical modeling and machine learning techniques to analyze applicant-level data for the purpose of forecasting enrollment outcomes and institutional fit within the context of admissions decision-making.

87

Publications

3.5K

Citations

235

Authors

98

Institutions

Publications per year

2017–2026

42

Authors

235

  1. SK
    Sidney K. D’Mello

    University of Notre Dame, United States

    2 publications · 50 citations

    2

    h-index

  2. AR
    Antonius Rachmat Chrismanto

    Duta Wacana Christian University, Indonesia

    2 publications · 25 citations

    2

    h-index

  3. SH
    Stephen Hutt

    University of Colorado Boulder, United States

    2 publications · 50 citations

    2

    h-index

  4. DP
    Donald P. Schwab

    University of Wisconsin–Madison, United States

    2 publications · 28 citations

    2

    h-index

  5. AW
    Austin Waters

    Google (United States), United States

    2 publications · 103 citations

    2

    h-index

1–5 of 235

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 44 citations

    2

    h-index

  2. Education and Information Technologies

    2 publications · 137 citations

    2

    h-index

  3. Journal of Applied Psychology

    2 publications · 28 citations

    2

    h-index

  4. Journal of Dental Education

    2 publications · 32 citations

    2

    h-index

  5. Frontiers of Computer Science

    1 publications · 46 citations

    1

    h-index

1–5