Enrollment Predictions with Machine Learning

Happy to announce that our joint paper, Enrollment Predictions with Machine Learning, co-authored with Hung Dang, Ginger Reyes Reilly and Katharine Soltys, has appeared in Volume 9, number 2, of the Strategic Enrollment Management Quarterly (SEMQ).

In this paper 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 Scikit-Learn. 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 admission 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 Machine Learning model to help with predicting admission rates. Technical experts, on the other hand, will gain a blue-print for what is required from them.

This paper has been made possible in part by the AWS Pilot Program in Machine Learning where California State University Channel Islands was one if the participating institutions.

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