{"id":{"repo_id":"ucf","oai_identifier":"oai:stars.library.ucf.edu:etd-1632"},"canonical_url":"https://search.dev.ndltd.org/etd/ucf/oai:stars.library.ucf.edu:etd-1632","repository":{"repo_id":"ucf","name":"Central Florida","base_url":"https://stars.library.ucf.edu/do/oai/"},"display":{"title":"The Unraveling Of America's Education System","abstract":"This research project takes a critical look at the data that drives educational policies. This research project looks at the data at the national level as well as the regional levels in order to see if the data is functioning differently at the different levels. All data has been collected from the National Assessment of Educational Progress (NAEP) through reports published by the American Legislative Exchange Council (ALEC), an independent committee assigned to collect and analyze educational data. The data was collected and then correlations were run between the expenditures per pupil, number of pupils per teacher, standardized test scores, such as average ACT, average SAT, average 8th grade Math and Reading tests, and average 4th grade Math and Reading tests. This research project also included the percentage of minority students in the classroom, a variable whose data has been collected over the years, but it has never been included in any prior analyses. What this research project found is that some of the data, such as the standardized test scores, have a different strength of relationship between variables at the different levels. For example, expenditures per pupil have strength in the relationship between the different standardized test scores at the national level, but once those numbers are broken down by region, the strength in the variables relationship is weakened. This research project also discovered that the make up of the classroom, specifically the percentage of minority students, is a vital factor in the performance of all students.","abstract_html":"This research project takes a critical look at the data that drives educational policies. This research project looks at the data at the national level as well as the regional levels in order to see if the data is functioning differently at the different levels. All data has been collected from the National Assessment of Educational Progress (NAEP) through reports published by the American Legislative Exchange Council (ALEC), an independent committee assigned to collect and analyze educational data. The data was collected and then correlations were run between the expenditures per pupil, number of pupils per teacher, standardized test scores, such as average ACT, average SAT, average 8th grade Math and Reading tests, and average 4th grade Math and Reading tests. This research project also included the percentage of minority students in the classroom, a variable whose data has been collected over the years, but it has never been included in any prior analyses. What this research project found is that some of the data, such as the standardized test scores, have a different strength of relationship between variables at the different levels. For example, expenditures per pupil have strength in the relationship between the different standardized test scores at the national level, but once those numbers are broken down by region, the strength in the variables relationship is weakened. This research project also discovered that the make up of the classroom, specifically the percentage of minority students, is a vital factor in the performance of all students.","abstract_has_math":false,"creators":["Wright, Amy"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kiel, Dwight"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-01-01T08:00:00Z","date_published":"2005-01-01T08:00:00Z","updated_at":"2026-07-24T05:09:09Z","subjects":["No Child Left Behind","education policy","educational policy","education reform","educational reform","standardized testing","standardized tests","Political Science"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["CFE0000766"],"render_values":[{"text":"CFE0000766","href":null,"code":true}]}]},"links":{"outbound_url":"https://stars.library.ucf.edu/etd/633","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kiel, Dwight"]},{"key":"dc:creator","label":"Author","values":["Wright, Amy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Masters Thesis (Open Access)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["No Child Left Behind","education policy","educational policy","education reform","educational reform","standardized testing","standardized tests","Political Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["CFE0000766"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://stars.library.ucf.edu/etd/633"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["<p>If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at <a href=\"mailto:STARS@ucf.edu\">STARS@ucf.edu</a></p>","Master of Arts (M.A.)","College of Arts and Sciences","Political Science"]},{"key":"dc:description.abstract","label":"Abstract","values":["This research project takes a critical look at the data that drives educational policies. 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What this research project found is that some of the data, such as the standardized test scores, have a different strength of relationship between variables at the different levels. For example, expenditures per pupil have strength in the relationship between the different standardized test scores at the national level, but once those numbers are broken down by region, the strength in the variables relationship is weakened. 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