Predictive Value of Veterinary Student Application Data for Class Rank at End of Year 1
| Autor: | Scott A. Brown, Steven D. Holladay, Brandy A. Burgess, Parkerson C Moore, R. Cary Tuckfield, Robert M. Gogal |
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| Rok vydání: | 2020 |
| Předmět: |
Veterinary medicine
lcsh:Veterinary medicine 020205 medical informatics General Veterinary 040301 veterinary sciences predicting class rank student admissions undergraduate transcript data 04 agricultural and veterinary sciences 02 engineering and technology Verbal reasoning Predictive value Article 0403 veterinary science applicant file data 0202 electrical engineering electronic engineering information engineering lcsh:SF600-1100 Class rank Psychology Curriculum veterinary college |
| Zdroj: | Veterinary Sciences Volume 7 Issue 3 Veterinary Sciences, Vol 7, Iss 120, p 120 (2020) |
| ISSN: | 2306-7381 |
| Popis: | Student applications for admission to the University of Georgia College of Veterinary Medicine include the following information: undergraduate grade point average (GPA), GPA in science courses (GPAScience), GPA for the last 45 credit hours (GPALast45hrs), results for the Graduate Record Examination Quantitative and Verbal Reasoning Measures (GRE-QV), results for the GRE Analytical Writing Measure (GRE-AW), and grades received for 10 required prerequisite courses. In addition, three faculty members independently review and score subjective information in applicants&rsquo files (FileScore). The admissions committee determines a composite Admission Score (AdmScore), which is based on GPA, GPAScience, GPALast45hrs, GRE-QV, GRE-AW, and the FileScore. The AdmScore is generally perceived to be a good predictor of class rank at the end of year 1 (CREY1). However, this has not been verified, nor has it been determined which components of the AdmScore have the strongest correlation with CREY1. The present study therefore compared each component of the AdmScore for correlation with CREY1, for the three classes admitted in 2015, 2016 and 2017 (Class15, Class16, Class17). Results suggest that only a few components of the application file are needed to make strong predictive statements about the academic success of veterinary students during the first year of the curriculum. |
| Databáze: | OpenAIRE |
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