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Soltys, Michael; Dang, Hung D.; Reyes Reilly, Ginger; Soltys, Katharine – Strategic Enrollment Management Quarterly, 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,…
Descriptors: Enrollment Management, Strategic Planning, Prediction, Computer Software
Perez-Vergara, Kelly – Strategic Enrollment Management Quarterly, 2020
Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…
Descriptors: Enrollment Projections, Enrollment Management, Enrollment Trends, Models
Beaver, William – College and University, 2014
Non-selective Division III institutions often face challenges in meeting their enrollment goals. To ensure their continued viability, these schools recruit large numbers of student athletes. As a result, when compared to FBS (Football Bowl Division) institutions these schools have a much higher percentage of student athletes on campus and a…
Descriptors: Enrollment Trends, Enrollment Rate, Enrollment Management, College Athletics
Bozick, Robert; Gonzalez, Gabriella; Engberg, John – Journal of Student Financial Aid, 2015
The Pittsburgh Promise is a scholarship program that provides $5,000 per year toward college tuition for public high school graduates in Pittsburgh, Pennsylvania who earned a 2.5 GPA and a 90% attendance record. This study used a difference-in-difference design to assess whether the introduction of the Promise scholarship program directly…
Descriptors: Merit Scholarships, College Bound Students, Enrollment Influences, Enrollment Management
Shapiro, Joel; Bray, Christopher – Continuing Higher Education Review, 2011
This article describes a model that can be used to analyze student enrollment data and can give insights for improving retention of part-time students and refining institutional budgeting and planning efforts. Adult higher-education programs are often challenged in that part-time students take courses less reliably than full-time students. For…
Descriptors: Higher Education, Adult Students, Part Time Students, Enrollment Trends
Chang, Lin – New Directions for Institutional Research, 2006
Data-mining technology's predictive modeling was applied to enhance the prediction of enrollment behaviors of admitted applicants at a large state university. (Contains 4 tables and 6 figures.)
Descriptors: College Admission, Data Collection, Data Analysis, Models
Campbell, John P.; DeBlois, Peter B.; Oblinger, Diana G. – EDUCAUSE Review, 2007
In responding to internal and external pressures for accountability in higher education, especially in the areas of improved learning outcomes and student success, IT leaders may soon become critical partners with academic and student affairs. IT can help answer this call for accountability through "academic analytics," which is emerging…
Descriptors: Accountability, Higher Education, Information Technology, Outcomes of Education