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Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance.

Eugene Muratov1, Margaret Lewis1, Denis Fourches2

  • 1UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

American Journal of Pharmaceutical Education
|May 13, 2017
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New machine learning models predict pharmacy student success. These computational tools can help identify candidates likely to excel in the Doctor of Pharmacy (PharmD) program, improving admissions.

Keywords:
Pharmacy College Admission Testacademic performanceadmissionscomputer-mediated communicationevaluation methodologies

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Area of Science:

  • Computational modeling in pharmaceutical education
  • Machine learning applications in higher education

Background:

  • The Doctor of Pharmacy (PharmD) curriculum is academically rigorous.
  • Predicting student success is crucial for effective admissions.

Purpose of the Study:

  • To develop predictive computational models for student academic performance in the PharmD program.
  • To create admission-assisting tools for pharmacy school applicants.

Main Methods:

  • Utilized Random Forest machine learning for binary classification.
  • Analyzed 11 pre-admission parameters from PharmD candidates across three admission cycles.
  • Grouped candidates based on completing the program with a GPA ≥ 3 versus others.

Main Results:

  • Developed robust, externally predictive models with 77% accuracy for high/low academic performers.
  • Multivariate models demonstrated high accuracy in predicting academic success.
  • Performance comparable to models using only undergraduate GPA and PCAT scores.

Conclusions:

  • The developed models can enhance the pharmacy school admission process.
  • These tools can serve as preliminary filters to identify successful candidates.
  • Aims to improve the identification of students likely to thrive in the PharmD curriculum.