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ERIC Number: EJ1311204
Record Type: Journal
Publication Date: 2021-Jul
Pages: 8
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-2325-4750
EISSN: N/A
Enrollment Predictions with Machine Learning
Soltys, Michael; Dang, Hung D.; Reyes Reilly, Ginger; Soltys, Katharine
Strategic Enrollment Management Quarterly, v9 n2 p11-18 Jul 2021
A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history, a model is built to compute--individually or in batch mode--probabilities of enrollments for given applicants. These probabilities can then be used during the admissions period to target undecided students. The audience for this paper is both SEM practitioners and technical practitioners in the area of data analytics. Through reading this paper, enrollment management professionals will be able to understand what goes into the preparation of a Machine Learning model to help with predicting admission rates. Technical experts, on the other hand, will gain a blueprint for what is required from them.
American Association of Collegiate Registrars and Admissions Officers. One Dupont Circle NW Suite 520, Washington, DC 20036. Tel: 301-490-7651; e-mail: pubs@aacrao.org; Web site: https://www.aacrao.org/research-publications/quarterly-journals/sem-quarterly
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A