| Literature DB >> 30323397 |
Samuel C Karpen1, Steve C Ellis2.
Abstract
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.1 Likewise, the 2014-2015 Argus Commission Report discussed uses for big data analytics in the classroom, practice, and admissions.2 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.3.Keywords: admissions; decision tree; predictive analytics
Mesh:
Year: 2018 PMID: 30323397 PMCID: PMC6181165 DOI: 10.5688/ajpe6980
Source DB: PubMed Journal: Am J Pharm Educ ISSN: 0002-9459 Impact factor: 2.047