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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

No Child Left Behind Leaves Behind English Language Learners

Canas Baena, Daniela A 01 January 2016 (has links)
Goals 2000 and No Child Left behind have developed a new timeline for language acquisition, have promoted the practice of teaching to the test rather than the understanding of content, and High School Exit Exams have led to the widening of the achievement gap between ELL students and their Non-ELL counterparts. In additions, the policies’ narrow definition of success leads to federal sanctions that penalize schools with ELL student populations further contributing to the widening of the gap.
2

A Comparison Of Eighth Grade Reading Scores By State And By The Four Census-defined Regions Identified By Naep

Gordon, William, II 01 January 2009 (has links)
This study provided information for policymakers and practitioners by comparing performance of eighth grade students in 2007 on state standardized reading assessments and by the four census-defined regions identified by NAEP. NCLB required states to set their own performance standards and to create their own data collection instruments resulting in increased transparency of student performance data and a lack of uniform accountability systems. The inability of educators, policy-makers, and the general public to make state-by-state comparisons in the area of reading was the catalyst for the study. NAEP data were collected from NCES and state performance data were collected from the USDOE SY 2006-2007 CSPR to determine if a relationship existed between eighth grade students' state scores and NAEP scores in the four census-defined regions. Data were further disaggregated by low socioeconomic students and by nonwhite students. A regression analysis was statistically significant in predicting: a) the state proficient and above scores from the NAEP proficient and above scores, b) the low socioeconomic state proficient and above scores from the NAEP proficient and above scores in the West census-defined region, and c) the nonwhite state proficient and above scores from the NAEP proficient and above scores in all regions. A regression analysis was not statistically significant in predicting low socioeconomic state proficient and above scores from the low socioeconomic NAEP proficient and above scores in the Midwest, South and Northeast regions.

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