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Showing 1 to 15 of 18 results Save | Export
Kye, Anna – ProQuest LLC, 2023
Every year, the national high school graduation rate is declining and impacting the number of students applying to colleges. Moreover, the majority of students are applying to more than one college. This makes a lot of colleges to be highly competitive in student recruitment for enrollment and thus, the necessity for institutions to anticipate…
Descriptors: Comparative Analysis, Classification, College Enrollment, Prediction
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Hutton, Amy – Strategic Enrollment Management Quarterly, 2021
Strategy and research are essential parts of strategic enrollment management (SEM), yet little information exists regarding how to use research and predictive analytics for effective strategy. It is often easier to react to what is happening in the moment, rather than be proactive in predicting the future or developing long-term plans. This…
Descriptors: Enrollment Management, Strategic Planning, Educational Research, Prediction
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Tara Hornor; Christopher W. Tremblay – Strategic Enrollment Management Quarterly, 2025
This research study examines the establishment and evolution of the first enrollment management credential in the higher education field 25 years ago at the University of Miami. Interviews were conducted with the program's current program director and the two pioneering administrators who created the program. The study also employed an alumni…
Descriptors: Program Development, Enrollment Management, Graduate Study, Enrollment Trends
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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
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Aulck, Lovenoor; Nambi, Dev; West, Jevin – International Educational Data Mining Society, 2020
Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management…
Descriptors: Resource Allocation, Scholarships, Artificial Intelligence, Data Analysis
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Hansen, David M. – Strategic Enrollment Management Quarterly, 2020
In recent years we have developed a data analytics pipeline using artificial neural networks to predict prospective student matriculation for university admissions using very limited demographic data. Predictions are generated at the earliest stages of the admissions process and successfully inform recruiting and admissions staff about the…
Descriptors: Artificial Intelligence, Data Analysis, College Admission, Enrollment Management
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Phan, Vinhthuy; Wright, Laura; Decent, Bridgette – International Educational Data Mining Society, 2022
A strategy for allocating merit-based awards and need-based aid is critical to a university. Such a strategy, however, must address multiple, sometimes competing objectives. We introduce an approach that couples a gradient boosting classifier for predicting outcomes from an allocation strategy with a local search optimization algorithm, which…
Descriptors: Resource Allocation, Access to Education, Higher Education, Educational Finance
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Langston, Randall; Loreto, David – Strategic Enrollment Management Quarterly, 2017
The field of strategic enrollment management has become increasingly invested in data-informed practices. In 2015, The College at Brockport, State University of New York implemented a recruitment strategy that incorporated both predictive analytics and customer relationship management (CRM) technology. This effort both reduced budget expenditures…
Descriptors: Undergraduate Students, Student Recruitment, Marketing, Enrollment Management
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Attaran, Mohsen; Stark, John; Stotler, Derek – Industry and Higher Education, 2018
Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to compete effectively in a digitally driven world. They rely on data analytics to accelerate time to insight and to gain a better understanding of their customers' needs and wants. However, big data and data analytics solutions in…
Descriptors: Models, Higher Education, Data Collection, Program Implementation
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Gansemer-Topf, Ann M.; Compton, Jonathan; Wohlgemuth, Darin; Forbes, Greg; Ralston, Ekaterina – Strategic Enrollment Management Quarterly, 2015
Improving student success and degree completion is one of the core principles of strategic enrollment management. To address this principle, institutional data were used to develop a statistical model to identify academically at-risk students. The model employs multiple linear regression techniques to predict students at risk of earning below a…
Descriptors: At Risk Students, Academic Achievement, Multiple Regression Analysis, College Freshmen
Goodale, Brian D. – ProQuest LLC, 2013
Senior managers in public research universities monitor and anticipate the evolution of enrollment as part of a planning process that is linked to budget and staffing matters. While the tracking and planning of enrollment figures is important for all types of institutions, the position of public research universities and the non-resident students…
Descriptors: Undergraduate Students, Enrollment, Research Universities, Public Colleges
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Sawyer, Richard – Applied Measurement in Education, 2013
Correlational evidence suggests that high school GPA is better than admission test scores in predicting first-year college GPA, although test scores have incremental predictive validity. The usefulness of a selection variable in making admission decisions depends in part on its predictive validity, but also on institutions' selectivity and…
Descriptors: High Schools, Grade Point Average, College Entrance Examinations, College Admission
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Fallon, Mary A. C. – Journal of College Admission, 2011
Enrollment managers will be watching to see how recruitment strategies change when higher education's sleeping giant--net price calculators (NPCs)--wakes in the fall of 2011. Some predict yield projections may be more difficult and reputations will be challenged as prospective students, their families, high school counselors, and independent…
Descriptors: Enrollment Management, School Counselors, Paying for College, Student Recruitment
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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
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Eshghi, Abdoloreza; Haughton, Dominique; Li, Mingfei; Senne, Linda; Skaletsky, Maria; Woolford, Sam – Journal of Institutional Research, 2011
The increasing competition for graduate students among business schools has resulted in a greater emphasis on graduate business student retention. In an effort to address this issue, the current article uses survival analysis, decision trees and TreeNet® to identify factors that can be used to identify students who are at risk of dropping out of a…
Descriptors: Enrollment Management, Graduate Students, Business Administration Education, Prediction
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