Using Institutional Data to Identify Students at Risk for Leaving Community College: An Event History Approach

Literaturnachweis - Detailanzeige

Trefferliste

Autor/in	Bachler, Paul T.
Titel	Using Institutional Data to Identify Students at Risk for Leaving Community College: An Event History Approach
Quelle	(2013), (223 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, City University of New York
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3035-3646-5
Schlagwörter	Hochschulschrift; Dissertation; Community Colleges; At Risk Students; Dropouts; Graduation Rate; Dropout Rate; Transfer Rates (College); College Transfer Students; Higher Education; College Attendance; Two Year College Students; Student Attrition; Academic Achievement; Withdrawal (Education); Predictive Validity; Academic Failure; Regression (Statistics); New York + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Community college; Community College; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Hochschulwechsel; Schulwechsel; Studienortwechsel; Hochschulbildung; Hochschulsystem; Hochschulwesen; College; Colleges; Attendance; Hochschule; Fachhochschule; Anwesenheit; Schülerbeurlaubung; Schulleistung; Kursabbruch; Regression; Regressionsanalyse
Abstract	Community colleges have been criticized for having lower graduation rates than four year colleges, but few studies have looked at non-graduation transfer, in which a student leaves the community college for a four-year college without taking an associate degree. The current study utilizes institutional data and a discrete-time event history model to predict non-transfer attrition in community colleges. The data utilized include five years of institutional data from 21,724 first-time freshmen from the six community colleges of the City University of New York. The study includes students who resided in New York City and its two adjacent suburban counties and who matriculated in the fall of the 2004 and 2005 academic years. Multinomial logistic regression was employed in an event history model of student absence and transfer; models were developed for both the first and second spells. Data on students who transferred were obtained from the National Student Loan Clearinghouse (NSLC). Continuation or type of leaving following each semester constituted the dependent variable. Many of the risk factors for leaving were related to academic performance. Students who were writing proficient and who had higher GPAs and more credit completion were more likely to remain enrolled or to transfer; students who failed were more likely to leave. Notably, course withdrawal was a greater risk factor for leaving than course failure. Financial aid in the form of grants and loans was associated with a decreased risk for attrition, and weekly travel was associated with an increased risk for leaving as well as an increased risk for transfer. Smaller class size and time spent on campus and especially in class was associated with lower risks for attrition. Three models were employed, two of these modeled transfer as separate form of leaving; one included transfer together with graduation and continuation as a successful semester outcome. Parameters obtained from the 2004 cohort were applied to the 2005 cohort to assess each model's predictive validity in a naive dataset. The most successful model for the first spell correctly identified 34.6 percent of the leavers in the semester in which they left, with a 35 percent false positive rate. The most successful model for the second spell identified 49.6 percent of leavers with a 30.8 percent false positive rate. If a false positive rate of 50 percent is allowed, about 60 percent of leavers in the first spell and about 80 percent of the leavers in second spell can be detected. Remedial study does not present a risk, but the data suggest that remedial education may be using too much of a student's grant money. It is suggested that additional study may be needed to determine how to effectively remediate students in math and writing, and that a model for course withdrawal and failure using interim grades be developed. Since withdrawal and failure present acute risks, it is suggested that a student's fitness and prerequisite skills for courses be assessed prior to course enrollment. Since many of the risk factors are interrelated, it is suggested that a structural model may be needed to assess each predictor's relevance. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
Anmerkungen	ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01