Create a data‐driven culture within your enrollment management operations

Langston, Randall

doi:10.1002/emt.30375

Cited by 2 publications

(1 citation statement)

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“…Institutions of higher education continue to lag behind businesses in embracing new technologies (e.g. Zhao and Luan, 2006;Amburgey and Yi, 2011;Norris and Baer, 2013;Langston, 2018). Often, this delay is caused by a lack of funding (Bichsel, 2012), difficulty in recruiting qualified personnel to perform analytics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Enrollment management analytics: a practical framework

Delcoure

Carmona

2019

JARHE

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Purpose The purpose of this paper is to examine the role of data analytics in addressing contemporary challenges and opportunities facing institutions of higher education, with a special emphasis on enrollment management for the example of a college of business administration in a Hispanic-serving four-year institution of higher education. Design/methodology/approach After reviewing previous research on this subject, the authors propose adding a new stratum to higher education analytics, enrollment management analytics, which uniquely identifies the actions associated with attracting and recruiting students, accounts for institutional differences and leads to students’ successful matriculation at institutions. Findings The authors demonstrate on the example of a professional college in a four-year institution of higher education how the utilization of modern analytical tools and techniques develop actionable decisions in the students’ recruitment process, which leads to their matriculation. Originality/value A definition and a new stratum in higher education analytics are proposed for enrollment management analytics.

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Section: Introductionmentioning

confidence: 99%

Enrollment management analytics: a practical framework

Delcoure

Carmona

2019

JARHE

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Predicting Medical School Enrollment Behavior: Comparing an Enrollment Management Model to Expert Human Judgment

Burkhardt¹,

DesJardins²,

Teener³

et al. 2018

Academic Medicine

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Purpose Medical school admissions committees are tasked with fulfilling the values of their institutions through careful recruitment. Making accurate predictions regarding enrollment behavior of admitted students is critical to intentionally formulating class composition and impacts long-term physician representation. The predictive accuracy and potential advantages of employing an enrollment predictive model in medical school admissions compared with expert human judgment have not been tested. Method The enrollment management-based predictive model previously generated using historical data was employed to provide a predicted enrollment percentage for each admitted student in the 2016–2017 application pool (N = 352). Concurrently, the human expert created a predicted enrollment percentage for each applicant while blinded to the values generated by the model. An absolute error for each applicant for both approaches was calculated. Statistical significance between approaches (expert vs. enrollment model) was assessed using t tests. Results The enrollment management approach was noninferior to expert prediction in all cases (P < .05) with a superior correct classification rate (77.7% vs. 71.2%). When considering subgroup analyses for specific populations of potential importance in recruiting (underrepresented in medicine, female, and in-state applicants), the enrollment management predictions were statistically more accurate (P < .05). Conclusions Examining a single admitted class, the enrollment predictions using the enrollment management model were at least as accurate as the expert human estimates, and in specific populations of interest more accurate. This information can be readily exported for a real-time dashboard system to drive recruitment behaviors.

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Create a data‐driven culture within your enrollment management operations

Cited by 2 publications

References 0 publications

Enrollment management analytics: a practical framework

Enrollment management analytics: a practical framework

Predicting Medical School Enrollment Behavior: Comparing an Enrollment Management Model to Expert Human Judgment

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