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• W317942882 abstract "The value of persistence for degree completion has been widely recognized in the on student outcomes. But institutions can only hope to positively affect student persistence through understanding what influences it. Some of the devoted to that end has explored the role of performance in persistence, and the current study is intended to contribute to that research. Employing a classificationtree methodology, this study uses performance measures to develop a predictive model of fall-to-fall persistence of first-semester, degree-seeking students at a large, suburban community college. Results confirmed that first-semester performance is a powerful predictor of persistence and provided information useful for segmenting degree aspirants into subpopulations at greater risk for undesirable outcomes. Background It has been estimated that as many as one-half of community college students with plans to complete an associate degree or transfer to a four-year college or university to complete a bachelor's degree do not achieve their goals. This is particularly important in the midst of national concerns to increase the proportions of Americans with college degrees. It has been estimated that the number of jobs requiring at least an associate degree will grow twice as rapidly in the near future as the number of jobs requiring no college. The opportunity costs experienced by the students themselves who fail to complete a degree, as well as by the nation as a consequence of so many students failing to achieve their degree-related goals, are substantial. The students themselves are likely to experience lower earnings than would otherwise be the case, and the nation experiences the economic costs associated with the consequences of failing to produce adequate numbers of qualified individuals for the jobs that need to be filled. A recent initiative proposed by President Obama would provide $12 billion over a decade to help community colleges to educate and train more people. Recognizing the opportunity costs consequent upon the failure of so many community college students to achieve their educational goals, a feature of this initiative would target innovative strategies that promote degree completion. The initiative also envisages the creation of a research center to identify what works. While this initiative is clearly welcome and needed, we already know much about the factors influencing degree completion. One of the key factors that has identified as important for degree completion and time to completion is persistence - a pattern of continued enrollment (Adelman, 2006). But how can we help to ensure that degree seekers persist? Fortunately, we also know something about the factors influencing persistence. One of the key factors has identified as important for persistence is success, often conceived in the as having two components - academic integration and academic (Cofer «Sc Somers, 2000; Tinto, 1993; Bean & Metzner, 1985). Cofer and Somers (Cofer «Sc Somers, 2000), for example, examined the impact of background, achievement, college experiences, price, and debt load on within-year (fall to spring semester) persistence at two-year colleges using the National Postsecondary Student Aid Survey (NPSAS: 96). In addition to significant findings concerning cost and debt load, the authors also found that several background and intensity and success variables were significantly related to within-year persistence, such as age, dependency status, aspirations, class load, class level, and GPA. Students over age 30 were more likely to persist than students between ages 22 and 30. Dependent students were more likely to persist than independent students. Full-time students were more likely to persist than part-time students, and sophomores were more likely to persist than freshmen. Students with low or no GPA scores were less likely to persist than students with GPAs between 2. …" @default.
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• W317942882 date "2011-10-01" @default.
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• W317942882 title "First-Semester Academic Performance as a Predictor of Fall-to-Fall Persistence: An Application of Classification Tree Analysis" @default.
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