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Consequences of No Child Left Behind

How Retention Impacts Learning Gains on the Florida Comprehensive Assessment Test in a Northwest Florida School District

by Lynn Marie Chester-d'Albertis ยท 2007

ISBN:  Unavailable

Category: Unavailable

Page count: 304

ABSTRACT: The validation of the American educational system through accountability called for rewards and sanctions, one of which is the action of mandatory student retention as an educational reform measures to ensure a knowledgeable citizenry. The state of America's educational system and the pursuit of a knowledgeable citizenry have a prominent focal point in conversations across the country. Concentration on high stakes standardized tests can often be in direct conflict with many educational philosophies, limiting possibilities in educational reform (Elmore, 2002). As the focus on high stakes standardized testing prevails, conversations on goals and instructional practices turn to high stakes standardized testing results, and the default philosophy places a superior value on high stakes standardized test scores achieved instead of the achievement represented in the scores (Gunzenhauser, 2003). When policymakers began to withhold promotion (retention) and graduation as sanctions, the stakes of testing were forevermore changed (Glaser & Silver, 1994). The problem of this study was succinct: Does the mandatory retention of middle school students increase learning gains as measured by the Florida Comprehensive Assessment Test Developmental Scale Scores(FCAT DSS) in Reading and Mathematics? This study measured learning gains of middle school students who were retained in eighth grade in a Northwest Florida school district. The mean differences in learning gains of retention cohorts, Mandatory Retention and Retained for Other Reasons, were analyzed by student demographics of academic ability, gender, ethnicity, and socioeconomic status. Statistically significant differences were present in student demographic variables. FCAT DSS in Reading learning gains' means differences were analyzed for category interactions using the analysis of variance (ANOVA) design and t-tests.