College of Veterinary Medicine, University of Georgia, Athens, Georgia.
Bill Gatton College of Pharmacy, East Tennessee State University, Johnson City, Tennessee.
Am J Pharm Educ. 2018 Sep;82(7):6980. doi: 10.5688/ajpe6980.
In recent years, the American Association of Colleges of Pharmacy (AACP) has encouraged the application of big data analytic techniques to pharmaceutical education. Indeed, the 2013-2014 Academic Affairs Committee Report included a "Learning Analytics in Pharmacy Education" section that reviewed the potential benefits of adopting big data techniques. Likewise, the 2014-2015 Argus Commission Report discussed uses for big data analytics in the classroom, practice, and admissions. While both of these reports were thorough, neither discussed specific analytic techniques. Consequently, this commentary will introduce classification trees, with a particular emphasis on their use in admission. With electronic applications, pharmacy schools and colleges now have access to detailed applicant records containing thousands of observations. With declining applications nationwide, admissions analytics may be more important than ever..
近年来，美国药学院协会（AACP）鼓励将大数据分析技术应用于药学教育。事实上，2013-2014 年学术事务委员会报告中包含了“药学教育中的学习分析”部分，其中审查了采用大数据技术的潜在好处。同样，2014-2015 年 Argus 委员会报告讨论了大数据分析在课堂、实践和招生中的应用。尽管这两份报告都很全面，但都没有讨论具体的分析技术。因此，本评论将介绍分类树，特别强调其在招生中的应用。随着电子申请的出现，药学院校现在可以访问包含数千条观察结果的详细申请人记录。由于全国范围内的申请人数下降，招生分析可能比以往任何时候都更为重要。
