• YAKHAK HOEJI
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• v.58 no.4
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• pp.250-254
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• 2014

Background: A new admission procedure was introduced to Korea when Doctor of Pharmacy (Pharm. D.) program was first established in 2011. In the new procedure, applicants are reviewed based on quantitative qualifications, which include Pharmacy Education Eligibility Test (PEET) scores, Test of English for International Communication (TOEIC) scores, and grade point average (GPA), as well as qualitative qualifications such as essay writing, extracurricular activities including volunteer experiences. Purpose: This study aimed to find relationships between entrance qualifications and academic performance of pharmacy students. Method: Entrance qualifications and academic performance of 164 first-year students were analyzed from 2011 to 2013 at Catholic University of Daegu. Other characteristics such as age and gender were also considered in the study. Results: Only GPA and TOEIC scores showed positive Pearson correlation with academic performance, while PEET scores did not. Conclusion: GPA and TOEIC scores could potentially have predictive validity for academic performance in pharmacy school. PEET scores, however, should simply be considered as pre-entrance qualifications, without predictive validity for academic performance.

• The Journal of Korean Association of Computer Education
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• v.23 no.3
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• pp.49-58
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• 2020

Organizing entrance evaluation system for selecting students who can become expert in data science field according to need of the age and social demand is important. This study was conducted for the purpose of analyzing data science field graduate school entrance evaluation system and deriving implications after taking into account the importance of talents possessing convergence competency. For this aim, a total of 22 graduate schools in 7 countries have been selected targeting data scientist postgraduate program around the world. The selected graduate schools have been analyzed based on qualifications, necessary skills prior to entrance, entrance conditions, and selection methods. As a result of the analysis, 'graduate school which I can apply for regardless of possessing undergraduate degree or undergraduate major (63.6 percent)' in qualifications category, 'graduate school which mentioned skills required in completing master's degree prior to entrance (63.6 percent)' in skills required prior to entrance category, 'graduate school which does not mention separate entrance condition (81.8 percent)' in entrance conditions category, and 'graduate school selecting students merely based on document screening (68.2 percent)' in selection methods category took the highest portion. Based on the above, this study summarized the results of the data scientist process and suggested implications for objectifying admission evaluation.