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Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – AERA Open, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Identification, Two Year College Students, Community Colleges
Yanagiura, Takeshi – Community College Research Center, Teachers College, Columbia University, 2020
Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as…
Descriptors: Community Colleges, Man Machine Systems, Artificial Intelligence, Prediction
Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Higher Education, Predictive Measurement, Models
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Yanagiura, Takeshi – Community College Review, 2023
Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…
Descriptors: Community Colleges, Community College Students, Educational Indicators, Outcomes of Education
Grogan, Rita D. – ProQuest LLC, 2017
Purpose: The purpose of this case study was to determine the impact of utilizing predictive modeling to improve successful course completion rates for at-risk students at California community colleges. A secondary purpose of the study was to identify factors of predictive modeling that have the most importance for improving successful course…
Descriptors: Community Colleges, Case Studies, Models, Academic Persistence
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Mertes, Scott J.; Hoover, Richard E. – Community College Journal of Research and Practice, 2014
Retention is a complex issue of great importance to community colleges. Several retention models have been developed to help explain this phenomenon. However, these models typically have used four-year college and university environments to build their foundations. Several researchers have attempted to identify predictor variables using…
Descriptors: Community Colleges, Predictor Variables, College Freshmen, Academic Persistence
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Kotamraju, Pradeep; Blackman, Orville – Community College Journal of Research and Practice, 2011
The paper uses the Integrated Postsecondary Education Data system (IPEDS) data to simulate the 2020 American Graduation Initiative (AGI) goal introduced by President Obama in the summer of 2009. We estimate community college graduation rates and completion numbers under different scenarios that include the following sets of variables: (a) internal…
Descriptors: Community Colleges, Graduation Rate, Educational Attainment, Predictor Variables
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Moosai, Susan; Walker, David A.; Floyd, Deborah L. – Community College Journal of Research and Practice, 2011
Prediction models using graduation rate as the performance indicator were obtained for community colleges in California, Florida, and Michigan. The results of this study indicated that institutional graduation rate could be predicted effectively from an aggregate of student and institutional characteristics. A performance measure was computed, the…
Descriptors: Community Colleges, Graduation Rate, Institutional Evaluation, Institutional Characteristics
Smith, Vernon C.; Lange, Adam; Huston, Daniel R. – Journal of Asynchronous Learning Networks, 2012
Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student…
Descriptors: Academic Achievement, At Risk Students, Prediction, Community Colleges
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Sanchez, Bonnie M. – Community/Junior College Research Quarterly, 1978
A selected and annotated bibliography of ERIC documents describing community college enrollment projection methods, including a demographic planning model, a modified Delphi technique, a cohort survival model, the age participation technique, and the bond program projection technique. (Author/AC)
Descriptors: Annotated Bibliographies, Community Colleges, Enrollment Projections, Mathematical Models
Lynch, Mary V. – 1972
A comprehensive guidance program aimed at predicting chances of success in a student's choice of programs in community college and occupational programs, this project was undertaken during the fall of 1971 at Wayne Community College. The subjects used were those seniors from the five high schools who were interested in one of the vocational…
Descriptors: Academic Achievement, Community Colleges, Guidance Programs, Models
Clagett, Craig A. – 1989
In forecasting its fall credit headcounts, the Office of Institutional Research and Analysis at Prince George's Community College (PGCC) utilizes the Component Yield Method (CYM), an enrollment projection model developed by the college's planning analyst in the early 1980's. By disaggregating enrolled students into multiple groups, each with an…
Descriptors: College Transfer Students, Community Colleges, Enrollment Influences, Enrollment Projections
McIntyre, Chuck – 1997
Comprehensive enrollment management (CEM) ensures that academic, student, and fiscal planning are done in concert in order to acknowledge the turbulence confronting an institution. A four-phase model of CEM has been developed that can be replicated at any college or university. In phase 1 of the model, the past 25 years of institutional enrollment…
Descriptors: College Planning, Community Colleges, Enrollment Influences, Enrollment Management
Hinkle, Dennis; Houston, Charles A. – 1977
The purpose of this study was to present and evaluate Bayesian-type models for estimating probabilities of program completion and for predicting first quarter grade point averages of community college students entering certain allied health fields. Two Bayesian models were tested. Bayesian Model 1--Estimating Probabilities of Program…
Descriptors: Academic Achievement, Admission Criteria, Admissions Counseling, Allied Health Occupations Education
Cowart, Susan Cooper – 1990
Background information is provided on the development and activities of Project Cooperation, a demonstration project to help institutions improve educational effectiveness by employing outcomes measures and assessment strategies. After highlighting the various activities of the project (e.g., a national survey, research on different plans and…
Descriptors: Academic Achievement, College Outcomes Assessment, Community Colleges, Cooperative Planning
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