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A Combinatorial Optimization Framework for Scoring Students in University Admissions.

Lucy Shao1, Richard A Levine2, Stefan Hyman3

  • 1Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, 7117University of California San Diego, San Diego, CA, USA.

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Machine learning enhances college admissions by predicting student enrollment and optimizing applicant selection. This data-driven approach aids universities in managing enrollment effectively and improving class composition.

Keywords:
SuperLearnerenrollment managementensemble learningsimulated annealingyield rate

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Area of Science:

  • Higher Education Administration
  • Data Science in Education
  • Machine Learning Applications

Background:

  • College admissions processing is labor-intensive and critical for university operations.
  • Effective enrollment management requires data-informed decision-making.
  • Accurate student yield prediction is essential to avoid over- or under-enrollment.

Purpose of the Study:

  • To leverage machine learning for improved college admissions.
  • To enhance enrollment management through data-driven insights.
  • To develop a framework for optimizing student selection.

Main Methods:

  • Ensemble learning using the SuperLearner algorithm for student yield rate prediction.
  • A combinatorial optimization framework employing simulated annealing for ranking and selection.
  • Efficacy study to evaluate the proposed framework's performance.

Main Results:

  • The SuperLearner algorithm demonstrated improved prediction accuracy for student yield rates.
  • The combinatorial optimization framework effectively weighed academic and experiential factors.
  • Illustrative examples showed optimized selection processes based on target metrics like graduation rate.

Conclusions:

  • Machine learning offers powerful tools for data-informed decision-making in college admissions.
  • The proposed framework provides a robust method for optimizing student selection and enrollment management.
  • R code is provided to facilitate the application of these methods by researchers and practitioners.