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The Application of Classification Trees to Pharmacy School Admissions
Samuel C Karpen1, Steve C Ellis2
1College of Veterinary Medicine, University of Georgia, Athens, Georgia.
American Journal of Pharmaceutical Education
|October 17, 2018
Summary
This commentary introduces classification trees as a big data analytic technique for pharmacy school admissions. These methods can help manage large applicant datasets, which is crucial given declining application numbers.
Area of Science:
- Pharmaceutical Education
- Data Science in Higher Education
Background:
- The American Association of Colleges of Pharmacy (AACP) advocates for big data analytics in pharmaceutical education.
- Previous reports highlighted the potential of learning analytics and big data in pharmacy education, practice, and admissions.
- However, specific analytic techniques were not detailed in prior AACP reports.
Purpose of the Study:
- To introduce classification trees as a specific big data analytic technique.
- To emphasize the application of classification trees in the admissions process for pharmacy programs.
Main Methods:
- The commentary focuses on classification trees, a type of machine learning algorithm.
- It discusses the application of these techniques to the large electronic applicant records now available to pharmacy schools.
Main Results:
- Classification trees offer a method to analyze detailed applicant data with thousands of observations.
- This technique can aid pharmacy admissions, especially important with declining application rates nationwide.
Conclusions:
- Classification trees represent a valuable, specific analytic technique for leveraging big data in pharmacy admissions.
- Admissions analytics using such methods may be increasingly vital for pharmacy schools facing application trends.
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