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

American Journal of Pharmaceutical Education
Eugene MuratovWendy C Cox

Abstract

Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

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Citations

Oct 17, 2018·American Journal of Pharmaceutical Education·Samuel C Karpen, Steve C Ellis
Oct 1, 2017·Bioscience Reports·Doo Nam Kim, Karissa Y Sanbonmatsu
Oct 13, 2019·American Journal of Health-system Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists·Kathryn A Morbitzer, Stephen F Eckel
May 2, 2020·Chemical Society Reviews·Eugene N MuratovAlexander Tropsha
Jul 21, 2021·American Journal of Pharmaceutical Education·Jamie L HallPaul Jungnickel
Aug 5, 2021·Academic Medicine : Journal of the Association of American Medical Colleges·Jesse Burk-RafelYindalon Aphinyanaphongs

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